diff --git a/comfy/checkpoint_pickle.py b/comfy/checkpoint_pickle.py new file mode 100644 index 0000000000000000000000000000000000000000..206551d3c1cf0d654c907534629a800196ba138b --- /dev/null +++ b/comfy/checkpoint_pickle.py @@ -0,0 +1,13 @@ +import pickle + +load = pickle.load + +class Empty: + pass + +class Unpickler(pickle.Unpickler): + def find_class(self, module, name): + #TODO: safe unpickle + if module.startswith("pytorch_lightning"): + return Empty + return super().find_class(module, name) diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py new file mode 100644 index 0000000000000000000000000000000000000000..25148313117cfc3d11daee484170fd69d53641ed --- /dev/null +++ b/comfy/cldm/cldm.py @@ -0,0 +1,308 @@ +#taken from: https://github.com/lllyasviel/ControlNet +#and modified + +import torch +import torch as th +import torch.nn as nn + +from ..ldm.modules.diffusionmodules.util import ( + zero_module, + timestep_embedding, +) + +from ..ldm.modules.attention import SpatialTransformer +from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample +from ..ldm.util import exists +import comfy.ops + +class ControlledUnetModel(UNetModel): + #implemented in the ldm unet + pass + +class ControlNet(nn.Module): + def __init__( + self, + image_size, + in_channels, + model_channels, + hint_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + use_fp16=False, + use_bf16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + transformer_depth_middle=None, + device=None, + operations=comfy.ops, + ): + super().__init__() + assert use_spatial_transformer == True, "use_spatial_transformer has to be true" + if use_spatial_transformer: + assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + # from omegaconf.listconfig import ListConfig + # if type(context_dim) == ListConfig: + # context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.dims = dims + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + if isinstance(transformer_depth, int): + transformer_depth = len(channel_mult) * [transformer_depth] + if transformer_depth_middle is None: + transformer_depth_middle = transformer_depth[-1] + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError("provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult") + self.num_res_blocks = num_res_blocks + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) + print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " + f"This option has LESS priority than attention_resolutions {attention_resolutions}, " + f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " + f"attention will still not be set.") + + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.dtype = th.bfloat16 if use_bf16 else self.dtype + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + elif self.num_classes == "continuous": + print("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + ) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + ] + ) + self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations)]) + + self.input_hint_block = TimestepEmbedSequential( + operations.conv_nd(dims, hint_channels, 16, 3, padding=1), + nn.SiLU(), + operations.conv_nd(dims, 16, 16, 3, padding=1), + nn.SiLU(), + operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2), + nn.SiLU(), + operations.conv_nd(dims, 32, 32, 3, padding=1), + nn.SiLU(), + operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2), + nn.SiLU(), + operations.conv_nd(dims, 96, 96, 3, padding=1), + nn.SiLU(), + operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2), + nn.SiLU(), + zero_module(operations.conv_nd(dims, 256, model_channels, 3, padding=1)) + ) + + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + operations=operations + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append( + SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, operations=operations + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self.zero_convs.append(self.make_zero_conv(ch, operations=operations)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + operations=operations + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch, operations=operations + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + self.zero_convs.append(self.make_zero_conv(ch, operations=operations)) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + operations=operations + ), + SpatialTransformer( # always uses a self-attn + ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, operations=operations + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + operations=operations + ), + ) + self.middle_block_out = self.make_zero_conv(ch, operations=operations) + self._feature_size += ch + + def make_zero_conv(self, channels, operations=None): + return TimestepEmbedSequential(zero_module(operations.conv_nd(self.dims, channels, channels, 1, padding=0))) + + def forward(self, x, hint, timesteps, context, y=None, **kwargs): + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype) + emb = self.time_embed(t_emb) + + guided_hint = self.input_hint_block(hint, emb, context) + + outs = [] + + hs = [] + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x.type(self.dtype) + for module, zero_conv in zip(self.input_blocks, self.zero_convs): + if guided_hint is not None: + h = module(h, emb, context) + h += guided_hint + guided_hint = None + else: + h = module(h, emb, context) + outs.append(zero_conv(h, emb, context)) + + h = self.middle_block(h, emb, context) + outs.append(self.middle_block_out(h, emb, context)) + + return outs + diff --git a/comfy/cli_args.py b/comfy/cli_args.py new file mode 100644 index 0000000000000000000000000000000000000000..ffae81c49d1566a06247c7ab7d133d9c874d1255 --- /dev/null +++ b/comfy/cli_args.py @@ -0,0 +1,106 @@ +import argparse +import enum +import comfy.options + +class EnumAction(argparse.Action): + """ + Argparse action for handling Enums + """ + def __init__(self, **kwargs): + # Pop off the type value + enum_type = kwargs.pop("type", None) + + # Ensure an Enum subclass is provided + if enum_type is None: + raise ValueError("type must be assigned an Enum when using EnumAction") + if not issubclass(enum_type, enum.Enum): + raise TypeError("type must be an Enum when using EnumAction") + + # Generate choices from the Enum + choices = tuple(e.value for e in enum_type) + kwargs.setdefault("choices", choices) + kwargs.setdefault("metavar", f"[{','.join(list(choices))}]") + + super(EnumAction, self).__init__(**kwargs) + + self._enum = enum_type + + def __call__(self, parser, namespace, values, option_string=None): + # Convert value back into an Enum + value = self._enum(values) + setattr(namespace, self.dest, value) + + +parser = argparse.ArgumentParser() + +parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)") +parser.add_argument("--port", type=int, default=8188, help="Set the listen port.") +parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.") +parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.") +parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.") +parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).") +parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.") +parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.") +parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.") +cm_group = parser.add_mutually_exclusive_group() +cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).") +cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.") + +parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.") + +fp_group = parser.add_mutually_exclusive_group() +fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).") +fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.") + +fpvae_group = parser.add_mutually_exclusive_group() +fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.") +fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.") +fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.") + +parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") + +parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.") + +class LatentPreviewMethod(enum.Enum): + NoPreviews = "none" + Auto = "auto" + Latent2RGB = "latent2rgb" + TAESD = "taesd" + +parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction) + +attn_group = parser.add_mutually_exclusive_group() +attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.") +attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.") +attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.") + +parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.") + +vram_group = parser.add_mutually_exclusive_group() +vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).") +vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.") +vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.") +vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.") +vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.") +vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).") + + +parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.") + + +parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.") +parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.") +parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).") + +parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") + +if comfy.options.args_parsing: + args = parser.parse_args() +else: + args = parser.parse_args([]) + +if args.windows_standalone_build: + args.auto_launch = True + +if args.disable_auto_launch: + args.auto_launch = False diff --git a/comfy/clip_config_bigg.json b/comfy/clip_config_bigg.json new file mode 100644 index 0000000000000000000000000000000000000000..32d82ff39ba66ba0be15ec101993e1c46cc3f7ab --- /dev/null +++ b/comfy/clip_config_bigg.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_size": 1280, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 20, + "num_hidden_layers": 32, + "pad_token_id": 1, + "projection_dim": 1280, + "torch_dtype": "float32", + "vocab_size": 49408 +} diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py new file mode 100644 index 0000000000000000000000000000000000000000..1206c680d61495f92aaca3a6f8adfb9113b69f41 --- /dev/null +++ b/comfy/clip_vision.py @@ -0,0 +1,114 @@ +from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor, modeling_utils +from .utils import load_torch_file, transformers_convert +import os +import torch +import contextlib + +import comfy.ops +import comfy.model_patcher +import comfy.model_management + +class ClipVisionModel(): + def __init__(self, json_config): + config = CLIPVisionConfig.from_json_file(json_config) + self.load_device = comfy.model_management.text_encoder_device() + offload_device = comfy.model_management.text_encoder_offload_device() + self.dtype = torch.float32 + if comfy.model_management.should_use_fp16(self.load_device, prioritize_performance=False): + self.dtype = torch.float16 + + with comfy.ops.use_comfy_ops(offload_device, self.dtype): + with modeling_utils.no_init_weights(): + self.model = CLIPVisionModelWithProjection(config) + self.model.to(self.dtype) + + self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) + self.processor = CLIPImageProcessor(crop_size=224, + do_center_crop=True, + do_convert_rgb=True, + do_normalize=True, + do_resize=True, + image_mean=[ 0.48145466,0.4578275,0.40821073], + image_std=[0.26862954,0.26130258,0.27577711], + resample=3, #bicubic + size=224) + + def load_sd(self, sd): + return self.model.load_state_dict(sd, strict=False) + + def encode_image(self, image): + img = torch.clip((255. * image), 0, 255).round().int() + img = list(map(lambda a: a, img)) + inputs = self.processor(images=img, return_tensors="pt") + comfy.model_management.load_model_gpu(self.patcher) + pixel_values = inputs['pixel_values'].to(self.load_device) + + if self.dtype != torch.float32: + precision_scope = torch.autocast + else: + precision_scope = lambda a, b: contextlib.nullcontext(a) + + with precision_scope(comfy.model_management.get_autocast_device(self.load_device), torch.float32): + outputs = self.model(pixel_values=pixel_values, output_hidden_states=True) + + for k in outputs: + t = outputs[k] + if t is not None: + if k == 'hidden_states': + outputs["penultimate_hidden_states"] = t[-2].cpu() + outputs["hidden_states"] = None + else: + outputs[k] = t.cpu() + + return outputs + +def convert_to_transformers(sd, prefix): + sd_k = sd.keys() + if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k: + keys_to_replace = { + "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding", + "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight", + "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight", + "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias", + "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight", + "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias", + "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight", + } + + for x in keys_to_replace: + if x in sd_k: + sd[keys_to_replace[x]] = sd.pop(x) + + if "{}proj".format(prefix) in sd_k: + sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1) + + sd = transformers_convert(sd, prefix, "vision_model.", 48) + return sd + +def load_clipvision_from_sd(sd, prefix="", convert_keys=False): + if convert_keys: + sd = convert_to_transformers(sd, prefix) + if "vision_model.encoder.layers.47.layer_norm1.weight" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json") + elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") + else: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") + clip = ClipVisionModel(json_config) + m, u = clip.load_sd(sd) + if len(m) > 0: + print("missing clip vision:", m) + u = set(u) + keys = list(sd.keys()) + for k in keys: + if k not in u: + t = sd.pop(k) + del t + return clip + +def load(ckpt_path): + sd = load_torch_file(ckpt_path) + if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd: + return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True) + else: + return load_clipvision_from_sd(sd) diff --git a/comfy/clip_vision_config_g.json b/comfy/clip_vision_config_g.json new file mode 100644 index 0000000000000000000000000000000000000000..708e7e21ac3513a719d6a49e88e756f5ef7e2c8d --- /dev/null +++ b/comfy/clip_vision_config_g.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1664, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 8192, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 48, + "patch_size": 14, + "projection_dim": 1280, + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_config_h.json b/comfy/clip_vision_config_h.json new file mode 100644 index 0000000000000000000000000000000000000000..bb71be419a4be0ad5c8c157850de032a65593cb9 --- /dev/null +++ b/comfy/clip_vision_config_h.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1280, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 32, + "patch_size": 14, + "projection_dim": 1024, + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_config_vitl.json b/comfy/clip_vision_config_vitl.json new file mode 100644 index 0000000000000000000000000000000000000000..c59b8ed5a4c1f41fbcc9e6811d2c7dfe44273de7 --- /dev/null +++ b/comfy/clip_vision_config_vitl.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "torch_dtype": "float32" +} diff --git a/comfy/controlnet.py b/comfy/controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..ea219c7e560cf9f12bcc6a9328ae462f8c4e5dc9 --- /dev/null +++ b/comfy/controlnet.py @@ -0,0 +1,488 @@ +import torch +import math +import os +import comfy.utils +import comfy.model_management +import comfy.model_detection +import comfy.model_patcher + +import comfy.cldm.cldm +import comfy.t2i_adapter.adapter + + +def broadcast_image_to(tensor, target_batch_size, batched_number): + current_batch_size = tensor.shape[0] + #print(current_batch_size, target_batch_size) + if current_batch_size == 1: + return tensor + + per_batch = target_batch_size // batched_number + tensor = tensor[:per_batch] + + if per_batch > tensor.shape[0]: + tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0) + + current_batch_size = tensor.shape[0] + if current_batch_size == target_batch_size: + return tensor + else: + return torch.cat([tensor] * batched_number, dim=0) + +class ControlBase: + def __init__(self, device=None): + self.cond_hint_original = None + self.cond_hint = None + self.strength = 1.0 + self.timestep_percent_range = (1.0, 0.0) + self.timestep_range = None + + if device is None: + device = comfy.model_management.get_torch_device() + self.device = device + self.previous_controlnet = None + self.global_average_pooling = False + + def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(1.0, 0.0)): + self.cond_hint_original = cond_hint + self.strength = strength + self.timestep_percent_range = timestep_percent_range + return self + + def pre_run(self, model, percent_to_timestep_function): + self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1])) + if self.previous_controlnet is not None: + self.previous_controlnet.pre_run(model, percent_to_timestep_function) + + def set_previous_controlnet(self, controlnet): + self.previous_controlnet = controlnet + return self + + def cleanup(self): + if self.previous_controlnet is not None: + self.previous_controlnet.cleanup() + if self.cond_hint is not None: + del self.cond_hint + self.cond_hint = None + self.timestep_range = None + + def get_models(self): + out = [] + if self.previous_controlnet is not None: + out += self.previous_controlnet.get_models() + return out + + def copy_to(self, c): + c.cond_hint_original = self.cond_hint_original + c.strength = self.strength + c.timestep_percent_range = self.timestep_percent_range + + def inference_memory_requirements(self, dtype): + if self.previous_controlnet is not None: + return self.previous_controlnet.inference_memory_requirements(dtype) + return 0 + + def control_merge(self, control_input, control_output, control_prev, output_dtype): + out = {'input':[], 'middle':[], 'output': []} + + if control_input is not None: + for i in range(len(control_input)): + key = 'input' + x = control_input[i] + if x is not None: + x *= self.strength + if x.dtype != output_dtype: + x = x.to(output_dtype) + out[key].insert(0, x) + + if control_output is not None: + for i in range(len(control_output)): + if i == (len(control_output) - 1): + key = 'middle' + index = 0 + else: + key = 'output' + index = i + x = control_output[i] + if x is not None: + if self.global_average_pooling: + x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) + + x *= self.strength + if x.dtype != output_dtype: + x = x.to(output_dtype) + + out[key].append(x) + if control_prev is not None: + for x in ['input', 'middle', 'output']: + o = out[x] + for i in range(len(control_prev[x])): + prev_val = control_prev[x][i] + if i >= len(o): + o.append(prev_val) + elif prev_val is not None: + if o[i] is None: + o[i] = prev_val + else: + o[i] += prev_val + return out + +class ControlNet(ControlBase): + def __init__(self, control_model, global_average_pooling=False, device=None): + super().__init__(device) + self.control_model = control_model + self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device()) + self.global_average_pooling = global_average_pooling + + def get_control(self, x_noisy, t, cond, batched_number): + control_prev = None + if self.previous_controlnet is not None: + control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) + + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + if control_prev is not None: + return control_prev + else: + return None + + output_dtype = x_noisy.dtype + if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: + if self.cond_hint is not None: + del self.cond_hint + self.cond_hint = None + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device) + if x_noisy.shape[0] != self.cond_hint.shape[0]: + self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) + + + context = cond['c_crossattn'] + y = cond.get('c_adm', None) + if y is not None: + y = y.to(self.control_model.dtype) + control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=t, context=context.to(self.control_model.dtype), y=y) + return self.control_merge(None, control, control_prev, output_dtype) + + def copy(self): + c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling) + self.copy_to(c) + return c + + def get_models(self): + out = super().get_models() + out.append(self.control_model_wrapped) + return out + +class ControlLoraOps: + class Linear(torch.nn.Module): + def __init__(self, in_features: int, out_features: int, bias: bool = True, + device=None, dtype=None) -> None: + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.weight = None + self.up = None + self.down = None + self.bias = None + + def forward(self, input): + if self.up is not None: + return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias) + else: + return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias) + + class Conv2d(torch.nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + padding_mode='zeros', + device=None, + dtype=None + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.transposed = False + self.output_padding = 0 + self.groups = groups + self.padding_mode = padding_mode + + self.weight = None + self.bias = None + self.up = None + self.down = None + + + def forward(self, input): + if self.up is not None: + return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups) + else: + return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups) + + def conv_nd(self, dims, *args, **kwargs): + if dims == 2: + return self.Conv2d(*args, **kwargs) + else: + raise ValueError(f"unsupported dimensions: {dims}") + + +class ControlLora(ControlNet): + def __init__(self, control_weights, global_average_pooling=False, device=None): + ControlBase.__init__(self, device) + self.control_weights = control_weights + self.global_average_pooling = global_average_pooling + + def pre_run(self, model, percent_to_timestep_function): + super().pre_run(model, percent_to_timestep_function) + controlnet_config = model.model_config.unet_config.copy() + controlnet_config.pop("out_channels") + controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] + controlnet_config["operations"] = ControlLoraOps() + self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) + dtype = model.get_dtype() + self.control_model.to(dtype) + self.control_model.to(comfy.model_management.get_torch_device()) + diffusion_model = model.diffusion_model + sd = diffusion_model.state_dict() + cm = self.control_model.state_dict() + + for k in sd: + weight = comfy.model_management.resolve_lowvram_weight(sd[k], diffusion_model, k) + try: + comfy.utils.set_attr(self.control_model, k, weight) + except: + pass + + for k in self.control_weights: + if k not in {"lora_controlnet"}: + comfy.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) + + def copy(self): + c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) + self.copy_to(c) + return c + + def cleanup(self): + del self.control_model + self.control_model = None + super().cleanup() + + def get_models(self): + out = ControlBase.get_models(self) + return out + + def inference_memory_requirements(self, dtype): + return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) + +def load_controlnet(ckpt_path, model=None): + controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) + if "lora_controlnet" in controlnet_data: + return ControlLora(controlnet_data) + + controlnet_config = None + if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format + use_fp16 = comfy.model_management.should_use_fp16() + controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16) + diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config) + diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" + diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" + + count = 0 + loop = True + while loop: + suffix = [".weight", ".bias"] + for s in suffix: + k_in = "controlnet_down_blocks.{}{}".format(count, s) + k_out = "zero_convs.{}.0{}".format(count, s) + if k_in not in controlnet_data: + loop = False + break + diffusers_keys[k_in] = k_out + count += 1 + + count = 0 + loop = True + while loop: + suffix = [".weight", ".bias"] + for s in suffix: + if count == 0: + k_in = "controlnet_cond_embedding.conv_in{}".format(s) + else: + k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s) + k_out = "input_hint_block.{}{}".format(count * 2, s) + if k_in not in controlnet_data: + k_in = "controlnet_cond_embedding.conv_out{}".format(s) + loop = False + diffusers_keys[k_in] = k_out + count += 1 + + new_sd = {} + for k in diffusers_keys: + if k in controlnet_data: + new_sd[diffusers_keys[k]] = controlnet_data.pop(k) + + leftover_keys = controlnet_data.keys() + if len(leftover_keys) > 0: + print("leftover keys:", leftover_keys) + controlnet_data = new_sd + + pth_key = 'control_model.zero_convs.0.0.weight' + pth = False + key = 'zero_convs.0.0.weight' + if pth_key in controlnet_data: + pth = True + key = pth_key + prefix = "control_model." + elif key in controlnet_data: + prefix = "" + else: + net = load_t2i_adapter(controlnet_data) + if net is None: + print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path) + return net + + if controlnet_config is None: + use_fp16 = comfy.model_management.should_use_fp16() + controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16, True).unet_config + controlnet_config.pop("out_channels") + controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] + control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) + + if pth: + if 'difference' in controlnet_data: + if model is not None: + comfy.model_management.load_models_gpu([model]) + model_sd = model.model_state_dict() + for x in controlnet_data: + c_m = "control_model." + if x.startswith(c_m): + sd_key = "diffusion_model.{}".format(x[len(c_m):]) + if sd_key in model_sd: + cd = controlnet_data[x] + cd += model_sd[sd_key].type(cd.dtype).to(cd.device) + else: + print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") + + class WeightsLoader(torch.nn.Module): + pass + w = WeightsLoader() + w.control_model = control_model + missing, unexpected = w.load_state_dict(controlnet_data, strict=False) + else: + missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) + print(missing, unexpected) + + if use_fp16: + control_model = control_model.half() + + global_average_pooling = False + filename = os.path.splitext(ckpt_path)[0] + if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling + global_average_pooling = True + + control = ControlNet(control_model, global_average_pooling=global_average_pooling) + return control + +class T2IAdapter(ControlBase): + def __init__(self, t2i_model, channels_in, device=None): + super().__init__(device) + self.t2i_model = t2i_model + self.channels_in = channels_in + self.control_input = None + + def scale_image_to(self, width, height): + unshuffle_amount = self.t2i_model.unshuffle_amount + width = math.ceil(width / unshuffle_amount) * unshuffle_amount + height = math.ceil(height / unshuffle_amount) * unshuffle_amount + return width, height + + def get_control(self, x_noisy, t, cond, batched_number): + control_prev = None + if self.previous_controlnet is not None: + control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) + + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + if control_prev is not None: + return control_prev + else: + return {} + + if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: + if self.cond_hint is not None: + del self.cond_hint + self.control_input = None + self.cond_hint = None + width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8) + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device) + if self.channels_in == 1 and self.cond_hint.shape[1] > 1: + self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) + if x_noisy.shape[0] != self.cond_hint.shape[0]: + self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) + if self.control_input is None: + self.t2i_model.to(x_noisy.dtype) + self.t2i_model.to(self.device) + self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) + self.t2i_model.cpu() + + control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input)) + mid = None + if self.t2i_model.xl == True: + mid = control_input[-1:] + control_input = control_input[:-1] + return self.control_merge(control_input, mid, control_prev, x_noisy.dtype) + + def copy(self): + c = T2IAdapter(self.t2i_model, self.channels_in) + self.copy_to(c) + return c + +def load_t2i_adapter(t2i_data): + if 'adapter' in t2i_data: + t2i_data = t2i_data['adapter'] + if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format + prefix_replace = {} + for i in range(4): + for j in range(2): + prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j) + prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2) + prefix_replace["adapter."] = "" + t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace) + keys = t2i_data.keys() + + if "body.0.in_conv.weight" in keys: + cin = t2i_data['body.0.in_conv.weight'].shape[1] + model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) + elif 'conv_in.weight' in keys: + cin = t2i_data['conv_in.weight'].shape[1] + channel = t2i_data['conv_in.weight'].shape[0] + ksize = t2i_data['body.0.block2.weight'].shape[2] + use_conv = False + down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) + if len(down_opts) > 0: + use_conv = True + xl = False + if cin == 256 or cin == 768: + xl = True + model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) + else: + return None + missing, unexpected = model_ad.load_state_dict(t2i_data) + if len(missing) > 0: + print("t2i missing", missing) + + if len(unexpected) > 0: + print("t2i unexpected", unexpected) + + return T2IAdapter(model_ad, model_ad.input_channels) diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..a9eb9302f14f4fa2710c9652e0b58c6453c0cf7b --- /dev/null +++ b/comfy/diffusers_convert.py @@ -0,0 +1,261 @@ +import re +import torch + +# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py + +# =================# +# UNet Conversion # +# =================# + +unet_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), +] + +unet_conversion_map_resnet = [ + # (stable-diffusion, HF Diffusers) + ("in_layers.0", "norm1"), + ("in_layers.2", "conv1"), + ("out_layers.0", "norm2"), + ("out_layers.3", "conv2"), + ("emb_layers.1", "time_emb_proj"), + ("skip_connection", "conv_shortcut"), +] + +unet_conversion_map_layer = [] +# hardcoded number of downblocks and resnets/attentions... +# would need smarter logic for other networks. +for i in range(4): + # loop over downblocks/upblocks + + for j in range(2): + # loop over resnets/attentions for downblocks + hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." + sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i < 3: + # no attention layers in down_blocks.3 + hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + for j in range(3): + # loop over resnets/attentions for upblocks + hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." + sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." + unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) + + if i > 0: + # no attention layers in up_blocks.0 + hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." + sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." + unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) + + if i < 3: + # no downsample in down_blocks.3 + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." + sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." + unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) + + # no upsample in up_blocks.3 + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." + unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) + +hf_mid_atn_prefix = "mid_block.attentions.0." +sd_mid_atn_prefix = "middle_block.1." +unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) + +for j in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{j}." + sd_mid_res_prefix = f"middle_block.{2 * j}." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + +def convert_unet_state_dict(unet_state_dict): + # buyer beware: this is a *brittle* function, + # and correct output requires that all of these pieces interact in + # the exact order in which I have arranged them. + mapping = {k: k for k in unet_state_dict.keys()} + for sd_name, hf_name in unet_conversion_map: + mapping[hf_name] = sd_name + for k, v in mapping.items(): + if "resnets" in k: + for sd_part, hf_part in unet_conversion_map_resnet: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + for sd_part, hf_part in unet_conversion_map_layer: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} + return new_state_dict + + +# ================# +# VAE Conversion # +# ================# + +vae_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("nin_shortcut", "conv_shortcut"), + ("norm_out", "conv_norm_out"), + ("mid.attn_1.", "mid_block.attentions.0."), +] + +for i in range(4): + # down_blocks have two resnets + for j in range(2): + hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." + sd_down_prefix = f"encoder.down.{i}.block.{j}." + vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) + + if i < 3: + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." + sd_downsample_prefix = f"down.{i}.downsample." + vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) + + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"up.{3 - i}.upsample." + vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) + + # up_blocks have three resnets + # also, up blocks in hf are numbered in reverse from sd + for j in range(3): + hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." + sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." + vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) + +# this part accounts for mid blocks in both the encoder and the decoder +for i in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{i}." + sd_mid_res_prefix = f"mid.block_{i + 1}." + vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) + +vae_conversion_map_attn = [ + # (stable-diffusion, HF Diffusers) + ("norm.", "group_norm."), + ("q.", "query."), + ("k.", "key."), + ("v.", "value."), + ("q.", "to_q."), + ("k.", "to_k."), + ("v.", "to_v."), + ("proj_out.", "to_out.0."), + ("proj_out.", "proj_attn."), +] + + +def reshape_weight_for_sd(w): + # convert HF linear weights to SD conv2d weights + return w.reshape(*w.shape, 1, 1) + + +def convert_vae_state_dict(vae_state_dict): + mapping = {k: k for k in vae_state_dict.keys()} + for k, v in mapping.items(): + for sd_part, hf_part in vae_conversion_map: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + if "attentions" in k: + for sd_part, hf_part in vae_conversion_map_attn: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} + weights_to_convert = ["q", "k", "v", "proj_out"] + for k, v in new_state_dict.items(): + for weight_name in weights_to_convert: + if f"mid.attn_1.{weight_name}.weight" in k: + print(f"Reshaping {k} for SD format") + new_state_dict[k] = reshape_weight_for_sd(v) + return new_state_dict + + +# =========================# +# Text Encoder Conversion # +# =========================# + + +textenc_conversion_lst = [ + # (stable-diffusion, HF Diffusers) + ("resblocks.", "text_model.encoder.layers."), + ("ln_1", "layer_norm1"), + ("ln_2", "layer_norm2"), + (".c_fc.", ".fc1."), + (".c_proj.", ".fc2."), + (".attn", ".self_attn"), + ("ln_final.", "transformer.text_model.final_layer_norm."), + ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), + ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), +] +protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} +textenc_pattern = re.compile("|".join(protected.keys())) + +# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp +code2idx = {"q": 0, "k": 1, "v": 2} + + +def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): + new_state_dict = {} + capture_qkv_weight = {} + capture_qkv_bias = {} + for k, v in text_enc_dict.items(): + if not k.startswith(prefix): + continue + if ( + k.endswith(".self_attn.q_proj.weight") + or k.endswith(".self_attn.k_proj.weight") + or k.endswith(".self_attn.v_proj.weight") + ): + k_pre = k[: -len(".q_proj.weight")] + k_code = k[-len("q_proj.weight")] + if k_pre not in capture_qkv_weight: + capture_qkv_weight[k_pre] = [None, None, None] + capture_qkv_weight[k_pre][code2idx[k_code]] = v + continue + + if ( + k.endswith(".self_attn.q_proj.bias") + or k.endswith(".self_attn.k_proj.bias") + or k.endswith(".self_attn.v_proj.bias") + ): + k_pre = k[: -len(".q_proj.bias")] + k_code = k[-len("q_proj.bias")] + if k_pre not in capture_qkv_bias: + capture_qkv_bias[k_pre] = [None, None, None] + capture_qkv_bias[k_pre][code2idx[k_code]] = v + continue + + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) + new_state_dict[relabelled_key] = v + + for k_pre, tensors in capture_qkv_weight.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) + + for k_pre, tensors in capture_qkv_bias.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) + + return new_state_dict + + +def convert_text_enc_state_dict(text_enc_dict): + return text_enc_dict + + diff --git a/comfy/diffusers_load.py b/comfy/diffusers_load.py new file mode 100644 index 0000000000000000000000000000000000000000..a52e0102b7378016d5b9f0687f83826f2148187a --- /dev/null +++ b/comfy/diffusers_load.py @@ -0,0 +1,36 @@ +import json +import os + +import comfy.sd + +def first_file(path, filenames): + for f in filenames: + p = os.path.join(path, f) + if os.path.exists(p): + return p + return None + +def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None): + diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"] + unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names) + vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names) + + text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"] + text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names) + text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names) + + text_encoder_paths = [text_encoder1_path] + if text_encoder2_path is not None: + text_encoder_paths.append(text_encoder2_path) + + unet = comfy.sd.load_unet(unet_path) + + clip = None + if output_clip: + clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) + + vae = None + if output_vae: + vae = comfy.sd.VAE(ckpt_path=vae_path) + + return (unet, clip, vae) diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py new file mode 100644 index 0000000000000000000000000000000000000000..7eaf6ff62b6c983d2f28a83e58511ab99ac4e060 --- /dev/null +++ b/comfy/extra_samplers/uni_pc.py @@ -0,0 +1,883 @@ +#code taken from: https://github.com/wl-zhao/UniPC and modified + +import torch +import torch.nn.functional as F +import math + +from tqdm.auto import trange, tqdm + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + ): + """Create a wrapper class for the forward SDE (VP type). + + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + + t = self.inverse_lambda(lambda_t) + + =============================================================== + + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + + 1. For discrete-time DPMs: + + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + + Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + + + 2. For continuous-time DPMs: + + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + + =============================================================== + + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + + Example: + + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0**2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + + We support four types of the diffusion model by setting `model_type`: + + 1. "noise": noise prediction model. (Trained by predicting noise). + + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + + =============================================================== + + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * 1000. + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + output = model(x, t_input, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return -expand_dims(sigma_t, dims) * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class UniPC: + def __init__( + self, + model_fn, + noise_schedule, + predict_x0=True, + thresholding=False, + max_val=1., + variant='bh1', + noise_mask=None, + masked_image=None, + noise=None, + ): + """Construct a UniPC. + + We support both data_prediction and noise_prediction. + """ + self.model = model_fn + self.noise_schedule = noise_schedule + self.variant = variant + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + self.noise_mask = noise_mask + self.masked_image = masked_image + self.noise = noise + + def dynamic_thresholding_fn(self, x0, t=None): + """ + The dynamic thresholding method. + """ + dims = x0.dim() + p = self.dynamic_thresholding_ratio + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + if self.noise_mask is not None: + return self.model(x, t) * self.noise_mask + else: + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + if self.noise_mask is not None: + x0 = x0 * self.noise_mask + (1. - self.noise_mask) * self.masked_image + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3,] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3,] * (K - 1) + [1] + else: + orders = [3,] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2,] * K + else: + K = steps // 2 + 1 + orders = [2,] * (K - 1) + [1] + elif order == 1: + K = steps + orders = [1,] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): + if len(t.shape) == 0: + t = t.view(-1) + if 'bh' in self.variant: + return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + else: + assert self.variant == 'vary_coeff' + return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + + def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): + print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') + ns = self.noise_schedule + assert order <= len(model_prev_list) + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_t = ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = (lambda_prev_i - lambda_prev_0) / h + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + K = len(rks) + # build C matrix + C = [] + + col = torch.ones_like(rks) + for k in range(1, K + 1): + C.append(col) + col = col * rks / (k + 1) + C = torch.stack(C, dim=1) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + C_inv_p = torch.linalg.inv(C[:-1, :-1]) + A_p = C_inv_p + + if use_corrector: + print('using corrector') + C_inv = torch.linalg.inv(C) + A_c = C_inv + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) + h_phi_ks = [] + factorial_k = 1 + h_phi_k = h_phi_1 + for k in range(1, K + 2): + h_phi_ks.append(h_phi_k) + h_phi_k = h_phi_k / hh - 1 / factorial_k + factorial_k *= (k + 1) + + model_t = None + if self.predict_x0: + x_t_ = ( + sigma_t / sigma_prev_0 * x + - alpha_t * h_phi_1 * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + else: + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + x_t_ = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * h_phi_1) * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + return x_t, model_t + + def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): + # print(f'using unified predictor-corrector with order {order} (solver type: B(h))') + ns = self.noise_schedule + assert order <= len(model_prev_list) + dims = x.dim() + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = ((lambda_prev_i - lambda_prev_0) / h)[0] + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + R = [] + b = [] + + hh = -h[0] if self.predict_x0 else h[0] + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.variant == 'bh1': + B_h = hh + elif self.variant == 'bh2': + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= (i + 1) + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=x.device) + + # now predictor + use_predictor = len(D1s) > 0 and x_t is None + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + if x_t is None: + # for order 2, we use a simplified version + if order == 2: + rhos_p = torch.tensor([0.5], device=b.device) + else: + rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) + else: + D1s = None + + if use_corrector: + # print('using corrector') + # for order 1, we use a simplified version + if order == 1: + rhos_c = torch.tensor([0.5], device=b.device) + else: + rhos_c = torch.linalg.solve(R, b) + + model_t = None + if self.predict_x0: + x_t_ = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 + ) + + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + else: + x_t_ = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dimss) * x + - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 + ) + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + return x_t, model_t + + + def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', + method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', + atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False + ): + t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + t_T = self.noise_schedule.T if t_start is None else t_start + device = x.device + steps = len(timesteps) - 1 + if method == 'multistep': + assert steps >= order + # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + # with torch.no_grad(): + for step_index in trange(steps, disable=disable_pbar): + if self.noise_mask is not None: + x = x * self.noise_mask + (1. - self.noise_mask) * (self.masked_image * self.noise_schedule.marginal_alpha(timesteps[step_index]) + self.noise * self.noise_schedule.marginal_std(timesteps[step_index])) + if step_index == 0: + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + elif step_index < order: + init_order = step_index + # Init the first `order` values by lower order multistep DPM-Solver. + # for init_order in range(1, order): + vec_t = timesteps[init_order].expand(x.shape[0]) + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list.append(model_x) + t_prev_list.append(vec_t) + else: + extra_final_step = 0 + if step_index == (steps - 1): + extra_final_step = 1 + for step in range(step_index, step_index + 1 + extra_final_step): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final: + step_order = min(order, steps + 1 - step) + else: + step_order = order + # print('this step order:', step_order) + if step == steps: + # print('do not run corrector at the last step') + use_corrector = False + else: + use_corrector = True + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list[-1] = model_x + if callback is not None: + callback(step_index, model_prev_list[-1], x, steps) + else: + raise NotImplementedError() + if denoise_to_zero: + x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,)*(dims - 1)] + + + +def sample_unipc(model, noise, image, sigmas, sampling_function, max_denoise, extra_args=None, callback=None, disable=False, noise_mask=None, variant='bh1'): + to_zero = False + if sigmas[-1] == 0: + timesteps = torch.nn.functional.interpolate(sigmas[None,None,:-1], size=(len(sigmas),), mode='linear')[0][0] + to_zero = True + else: + timesteps = sigmas.clone() + + alphas_cumprod = model.inner_model.alphas_cumprod + + for s in range(timesteps.shape[0]): + timesteps[s] = (model.sigma_to_discrete_timestep(timesteps[s]) / 1000) + (1 / len(alphas_cumprod)) + + ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + + if image is not None: + img = image * ns.marginal_alpha(timesteps[0]) + if max_denoise: + noise_mult = 1.0 + else: + noise_mult = ns.marginal_std(timesteps[0]) + img += noise * noise_mult + else: + img = noise + + if to_zero: + timesteps[-1] = (1 / len(alphas_cumprod)) + + device = noise.device + + + model_type = "noise" + + model_fn = model_wrapper( + model.predict_eps_discrete_timestep, + ns, + model_type=model_type, + guidance_type="uncond", + model_kwargs=extra_args, + ) + + order = min(3, len(timesteps) - 1) + uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant) + x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) + if not to_zero: + x /= ns.marginal_alpha(timesteps[-1]) + return x diff --git a/comfy/gligen.py b/comfy/gligen.py new file mode 100644 index 0000000000000000000000000000000000000000..8d182839e05de490c55d7d9acbc00ba37f1acd43 --- /dev/null +++ b/comfy/gligen.py @@ -0,0 +1,341 @@ +import torch +from torch import nn, einsum +from .ldm.modules.attention import CrossAttention +from inspect import isfunction + + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * torch.nn.functional.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +class GatedCrossAttentionDense(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + self.attn = CrossAttention( + query_dim=query_dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = nn.LayerNorm(query_dim) + self.norm2 = nn.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + x = x + self.scale * \ + torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs) + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class GatedSelfAttentionDense(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + # we need a linear projection since we need cat visual feature and obj + # feature + self.linear = nn.Linear(context_dim, query_dim) + + self.attn = CrossAttention( + query_dim=query_dim, + context_dim=query_dim, + heads=n_heads, + dim_head=d_head) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = nn.LayerNorm(query_dim) + self.norm2 = nn.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + N_visual = x.shape[1] + objs = self.linear(objs) + + x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn( + self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :] + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class GatedSelfAttentionDense2(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + # we need a linear projection since we need cat visual feature and obj + # feature + self.linear = nn.Linear(context_dim, query_dim) + + self.attn = CrossAttention( + query_dim=query_dim, context_dim=query_dim, dim_head=d_head) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = nn.LayerNorm(query_dim) + self.norm2 = nn.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + B, N_visual, _ = x.shape + B, N_ground, _ = objs.shape + + objs = self.linear(objs) + + # sanity check + size_v = math.sqrt(N_visual) + size_g = math.sqrt(N_ground) + assert int(size_v) == size_v, "Visual tokens must be square rootable" + assert int(size_g) == size_g, "Grounding tokens must be square rootable" + size_v = int(size_v) + size_g = int(size_g) + + # select grounding token and resize it to visual token size as residual + out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[ + :, N_visual:, :] + out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g) + out = torch.nn.functional.interpolate( + out, (size_v, size_v), mode='bicubic') + residual = out.reshape(B, -1, N_visual).permute(0, 2, 1) + + # add residual to visual feature + x = x + self.scale * torch.tanh(self.alpha_attn) * residual + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class FourierEmbedder(): + def __init__(self, num_freqs=64, temperature=100): + + self.num_freqs = num_freqs + self.temperature = temperature + self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs) + + @torch.no_grad() + def __call__(self, x, cat_dim=-1): + "x: arbitrary shape of tensor. dim: cat dim" + out = [] + for freq in self.freq_bands: + out.append(torch.sin(freq * x)) + out.append(torch.cos(freq * x)) + return torch.cat(out, cat_dim) + + +class PositionNet(nn.Module): + def __init__(self, in_dim, out_dim, fourier_freqs=8): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs) + self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy + + self.linears = nn.Sequential( + nn.Linear(self.in_dim + self.position_dim, 512), + nn.SiLU(), + nn.Linear(512, 512), + nn.SiLU(), + nn.Linear(512, out_dim), + ) + + self.null_positive_feature = torch.nn.Parameter( + torch.zeros([self.in_dim])) + self.null_position_feature = torch.nn.Parameter( + torch.zeros([self.position_dim])) + + def forward(self, boxes, masks, positive_embeddings): + B, N, _ = boxes.shape + dtype = self.linears[0].weight.dtype + masks = masks.unsqueeze(-1).to(dtype) + positive_embeddings = positive_embeddings.to(dtype) + + # embedding position (it may includes padding as placeholder) + xyxy_embedding = self.fourier_embedder(boxes.to(dtype)) # B*N*4 --> B*N*C + + # learnable null embedding + positive_null = self.null_positive_feature.view(1, 1, -1) + xyxy_null = self.null_position_feature.view(1, 1, -1) + + # replace padding with learnable null embedding + positive_embeddings = positive_embeddings * \ + masks + (1 - masks) * positive_null + xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null + + objs = self.linears( + torch.cat([positive_embeddings, xyxy_embedding], dim=-1)) + assert objs.shape == torch.Size([B, N, self.out_dim]) + return objs + + +class Gligen(nn.Module): + def __init__(self, modules, position_net, key_dim): + super().__init__() + self.module_list = nn.ModuleList(modules) + self.position_net = position_net + self.key_dim = key_dim + self.max_objs = 30 + self.current_device = torch.device("cpu") + + def _set_position(self, boxes, masks, positive_embeddings): + objs = self.position_net(boxes, masks, positive_embeddings) + def func(x, extra_options): + key = extra_options["transformer_index"] + module = self.module_list[key] + return module(x, objs) + return func + + def set_position(self, latent_image_shape, position_params, device): + batch, c, h, w = latent_image_shape + masks = torch.zeros([self.max_objs], device="cpu") + boxes = [] + positive_embeddings = [] + for p in position_params: + x1 = (p[4]) / w + y1 = (p[3]) / h + x2 = (p[4] + p[2]) / w + y2 = (p[3] + p[1]) / h + masks[len(boxes)] = 1.0 + boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)] + positive_embeddings += [p[0]] + append_boxes = [] + append_conds = [] + if len(boxes) < self.max_objs: + append_boxes = [torch.zeros( + [self.max_objs - len(boxes), 4], device="cpu")] + append_conds = [torch.zeros( + [self.max_objs - len(boxes), self.key_dim], device="cpu")] + + box_out = torch.cat( + boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1) + masks = masks.unsqueeze(0).repeat(batch, 1) + conds = torch.cat(positive_embeddings + + append_conds).unsqueeze(0).repeat(batch, 1, 1) + return self._set_position( + box_out.to(device), + masks.to(device), + conds.to(device)) + + def set_empty(self, latent_image_shape, device): + batch, c, h, w = latent_image_shape + masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1) + box_out = torch.zeros([self.max_objs, 4], + device="cpu").repeat(batch, 1, 1) + conds = torch.zeros([self.max_objs, self.key_dim], + device="cpu").repeat(batch, 1, 1) + return self._set_position( + box_out.to(device), + masks.to(device), + conds.to(device)) + + +def load_gligen(sd): + sd_k = sd.keys() + output_list = [] + key_dim = 768 + for a in ["input_blocks", "middle_block", "output_blocks"]: + for b in range(20): + k_temp = filter(lambda k: "{}.{}.".format(a, b) + in k and ".fuser." in k, sd_k) + k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp) + + n_sd = {} + for k in k_temp: + n_sd[k[1]] = sd[k[0]] + if len(n_sd) > 0: + query_dim = n_sd["linear.weight"].shape[0] + key_dim = n_sd["linear.weight"].shape[1] + + if key_dim == 768: # SD1.x + n_heads = 8 + d_head = query_dim // n_heads + else: + d_head = 64 + n_heads = query_dim // d_head + + gated = GatedSelfAttentionDense( + query_dim, key_dim, n_heads, d_head) + gated.load_state_dict(n_sd, strict=False) + output_list.append(gated) + + if "position_net.null_positive_feature" in sd_k: + in_dim = sd["position_net.null_positive_feature"].shape[0] + out_dim = sd["position_net.linears.4.weight"].shape[0] + + class WeightsLoader(torch.nn.Module): + pass + w = WeightsLoader() + w.position_net = PositionNet(in_dim, out_dim) + w.load_state_dict(sd, strict=False) + + gligen = Gligen(output_list, w.position_net, key_dim) + return gligen diff --git a/comfy/k_diffusion/external.py b/comfy/k_diffusion/external.py new file mode 100644 index 0000000000000000000000000000000000000000..c1a137d9c0cbb239ef0990877bd6c32d824683aa --- /dev/null +++ b/comfy/k_diffusion/external.py @@ -0,0 +1,190 @@ +import math + +import torch +from torch import nn + +from . import sampling, utils + + +class VDenoiser(nn.Module): + """A v-diffusion-pytorch model wrapper for k-diffusion.""" + + def __init__(self, inner_model): + super().__init__() + self.inner_model = inner_model + self.sigma_data = 1. + + def get_scalings(self, sigma): + c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + return c_skip, c_out, c_in + + def sigma_to_t(self, sigma): + return sigma.atan() / math.pi * 2 + + def t_to_sigma(self, t): + return (t * math.pi / 2).tan() + + def loss(self, input, noise, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + noised_input = input + noise * utils.append_dims(sigma, input.ndim) + model_output = self.inner_model(noised_input * c_in, self.sigma_to_t(sigma), **kwargs) + target = (input - c_skip * noised_input) / c_out + return (model_output - target).pow(2).flatten(1).mean(1) + + def forward(self, input, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + return self.inner_model(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip + + +class DiscreteSchedule(nn.Module): + """A mapping between continuous noise levels (sigmas) and a list of discrete noise + levels.""" + + def __init__(self, sigmas, quantize): + super().__init__() + self.register_buffer('sigmas', sigmas) + self.register_buffer('log_sigmas', sigmas.log()) + self.quantize = quantize + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def get_sigmas(self, n=None): + if n is None: + return sampling.append_zero(self.sigmas.flip(0)) + t_max = len(self.sigmas) - 1 + t = torch.linspace(t_max, 0, n, device=self.sigmas.device) + return sampling.append_zero(self.t_to_sigma(t)) + + def sigma_to_discrete_timestep(self, sigma): + log_sigma = sigma.log() + dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] + return dists.abs().argmin(dim=0).view(sigma.shape) + + def sigma_to_t(self, sigma, quantize=None): + quantize = self.quantize if quantize is None else quantize + if quantize: + return self.sigma_to_discrete_timestep(sigma) + log_sigma = sigma.log() + dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] + low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2) + high_idx = low_idx + 1 + low, high = self.log_sigmas[low_idx], self.log_sigmas[high_idx] + w = (low - log_sigma) / (low - high) + w = w.clamp(0, 1) + t = (1 - w) * low_idx + w * high_idx + return t.view(sigma.shape) + + def t_to_sigma(self, t): + t = t.float() + low_idx = t.floor().long() + high_idx = t.ceil().long() + w = t-low_idx if t.device.type == 'mps' else t.frac() + log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] + return log_sigma.exp() + + def predict_eps_discrete_timestep(self, input, t, **kwargs): + if t.dtype != torch.int64 and t.dtype != torch.int32: + t = t.round() + sigma = self.t_to_sigma(t) + input = input * ((utils.append_dims(sigma, input.ndim) ** 2 + 1.0) ** 0.5) + return (input - self(input, sigma, **kwargs)) / utils.append_dims(sigma, input.ndim) + +class DiscreteEpsDDPMDenoiser(DiscreteSchedule): + """A wrapper for discrete schedule DDPM models that output eps (the predicted + noise).""" + + def __init__(self, model, alphas_cumprod, quantize): + super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize) + self.inner_model = model + self.sigma_data = 1. + + def get_scalings(self, sigma): + c_out = -sigma + c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + return c_out, c_in + + def get_eps(self, *args, **kwargs): + return self.inner_model(*args, **kwargs) + + def loss(self, input, noise, sigma, **kwargs): + c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + noised_input = input + noise * utils.append_dims(sigma, input.ndim) + eps = self.get_eps(noised_input * c_in, self.sigma_to_t(sigma), **kwargs) + return (eps - noise).pow(2).flatten(1).mean(1) + + def forward(self, input, sigma, **kwargs): + c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs) + return input + eps * c_out + + +class OpenAIDenoiser(DiscreteEpsDDPMDenoiser): + """A wrapper for OpenAI diffusion models.""" + + def __init__(self, model, diffusion, quantize=False, has_learned_sigmas=True, device='cpu'): + alphas_cumprod = torch.tensor(diffusion.alphas_cumprod, device=device, dtype=torch.float32) + super().__init__(model, alphas_cumprod, quantize=quantize) + self.has_learned_sigmas = has_learned_sigmas + + def get_eps(self, *args, **kwargs): + model_output = self.inner_model(*args, **kwargs) + if self.has_learned_sigmas: + return model_output.chunk(2, dim=1)[0] + return model_output + + +class CompVisDenoiser(DiscreteEpsDDPMDenoiser): + """A wrapper for CompVis diffusion models.""" + + def __init__(self, model, quantize=False, device='cpu'): + super().__init__(model, model.alphas_cumprod, quantize=quantize) + + def get_eps(self, *args, **kwargs): + return self.inner_model.apply_model(*args, **kwargs) + + +class DiscreteVDDPMDenoiser(DiscreteSchedule): + """A wrapper for discrete schedule DDPM models that output v.""" + + def __init__(self, model, alphas_cumprod, quantize): + super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize) + self.inner_model = model + self.sigma_data = 1. + + def get_scalings(self, sigma): + c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + return c_skip, c_out, c_in + + def get_v(self, *args, **kwargs): + return self.inner_model(*args, **kwargs) + + def loss(self, input, noise, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + noised_input = input + noise * utils.append_dims(sigma, input.ndim) + model_output = self.get_v(noised_input * c_in, self.sigma_to_t(sigma), **kwargs) + target = (input - c_skip * noised_input) / c_out + return (model_output - target).pow(2).flatten(1).mean(1) + + def forward(self, input, sigma, **kwargs): + c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + return self.get_v(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip + + +class CompVisVDenoiser(DiscreteVDDPMDenoiser): + """A wrapper for CompVis diffusion models that output v.""" + + def __init__(self, model, quantize=False, device='cpu'): + super().__init__(model, model.alphas_cumprod, quantize=quantize) + + def get_v(self, x, t, cond, **kwargs): + return self.inner_model.apply_model(x, t, cond) diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py new file mode 100644 index 0000000000000000000000000000000000000000..937c5a3881d25c0e3a5cd00cb577a6e320357156 --- /dev/null +++ b/comfy/k_diffusion/sampling.py @@ -0,0 +1,739 @@ +import math + +from scipy import integrate +import torch +from torch import nn +import torchsde +from tqdm.auto import trange, tqdm + +from . import utils + + +def append_zero(x): + return torch.cat([x, x.new_zeros([1])]) + + +def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): + """Constructs the noise schedule of Karras et al. (2022).""" + ramp = torch.linspace(0, 1, n, device=device) + min_inv_rho = sigma_min ** (1 / rho) + max_inv_rho = sigma_max ** (1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return append_zero(sigmas).to(device) + + +def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): + """Constructs an exponential noise schedule.""" + sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() + return append_zero(sigmas) + + +def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): + """Constructs an polynomial in log sigma noise schedule.""" + ramp = torch.linspace(1, 0, n, device=device) ** rho + sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) + return append_zero(sigmas) + + +def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): + """Constructs a continuous VP noise schedule.""" + t = torch.linspace(1, eps_s, n, device=device) + sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1) + return append_zero(sigmas) + + +def to_d(x, sigma, denoised): + """Converts a denoiser output to a Karras ODE derivative.""" + return (x - denoised) / utils.append_dims(sigma, x.ndim) + + +def get_ancestral_step(sigma_from, sigma_to, eta=1.): + """Calculates the noise level (sigma_down) to step down to and the amount + of noise to add (sigma_up) when doing an ancestral sampling step.""" + if not eta: + return sigma_to, 0. + sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) + sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 + return sigma_down, sigma_up + + +def default_noise_sampler(x): + return lambda sigma, sigma_next: torch.randn_like(x) + + +class BatchedBrownianTree: + """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" + + def __init__(self, x, t0, t1, seed=None, **kwargs): + self.cpu_tree = True + if "cpu" in kwargs: + self.cpu_tree = kwargs.pop("cpu") + t0, t1, self.sign = self.sort(t0, t1) + w0 = kwargs.get('w0', torch.zeros_like(x)) + if seed is None: + seed = torch.randint(0, 2 ** 63 - 1, []).item() + self.batched = True + try: + assert len(seed) == x.shape[0] + w0 = w0[0] + except TypeError: + seed = [seed] + self.batched = False + if self.cpu_tree: + self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed] + else: + self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] + + @staticmethod + def sort(a, b): + return (a, b, 1) if a < b else (b, a, -1) + + def __call__(self, t0, t1): + t0, t1, sign = self.sort(t0, t1) + if self.cpu_tree: + w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign) + else: + w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) + + return w if self.batched else w[0] + + +class BrownianTreeNoiseSampler: + """A noise sampler backed by a torchsde.BrownianTree. + + Args: + x (Tensor): The tensor whose shape, device and dtype to use to generate + random samples. + sigma_min (float): The low end of the valid interval. + sigma_max (float): The high end of the valid interval. + seed (int or List[int]): The random seed. If a list of seeds is + supplied instead of a single integer, then the noise sampler will + use one BrownianTree per batch item, each with its own seed. + transform (callable): A function that maps sigma to the sampler's + internal timestep. + """ + + def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): + self.transform = transform + t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) + self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) + + def __call__(self, sigma, sigma_next): + t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) + return self.tree(t0, t1) / (t1 - t0).abs().sqrt() + + +@torch.no_grad() +def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + # Euler method + x = x + d * dt + return x + + +@torch.no_grad() +def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +@torch.no_grad() +def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + if sigmas[i + 1] == 0: + # Euler method + x = x + d * dt + else: + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + d_prime = (d + d_2) / 2 + x = x + d_prime * dt + return x + + +@torch.no_grad() +def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + dt = sigmas[i + 1] - sigma_hat + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() + dt_1 = sigma_mid - sigma_hat + dt_2 = sigmas[i + 1] - sigma_hat + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + return x + + +@torch.no_grad() +def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + if sigma_down == 0: + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() + dt_1 = sigma_mid - sigmas[i] + dt_2 = sigma_down - sigmas[i] + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +def linear_multistep_coeff(order, t, i, j): + if order - 1 > i: + raise ValueError(f'Order {order} too high for step {i}') + def fn(tau): + prod = 1. + for k in range(order): + if j == k: + continue + prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) + return prod + return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] + + +@torch.no_grad() +def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigmas_cpu = sigmas.detach().cpu().numpy() + ds = [] + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + d = to_d(x, sigmas[i], denoised) + ds.append(d) + if len(ds) > order: + ds.pop(0) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + cur_order = min(i + 1, order) + coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] + x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) + return x + + +class PIDStepSizeController: + """A PID controller for ODE adaptive step size control.""" + def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): + self.h = h + self.b1 = (pcoeff + icoeff + dcoeff) / order + self.b2 = -(pcoeff + 2 * dcoeff) / order + self.b3 = dcoeff / order + self.accept_safety = accept_safety + self.eps = eps + self.errs = [] + + def limiter(self, x): + return 1 + math.atan(x - 1) + + def propose_step(self, error): + inv_error = 1 / (float(error) + self.eps) + if not self.errs: + self.errs = [inv_error, inv_error, inv_error] + self.errs[0] = inv_error + factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 + factor = self.limiter(factor) + accept = factor >= self.accept_safety + if accept: + self.errs[2] = self.errs[1] + self.errs[1] = self.errs[0] + self.h *= factor + return accept + + +class DPMSolver(nn.Module): + """DPM-Solver. See https://arxiv.org/abs/2206.00927.""" + + def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): + super().__init__() + self.model = model + self.extra_args = {} if extra_args is None else extra_args + self.eps_callback = eps_callback + self.info_callback = info_callback + + def t(self, sigma): + return -sigma.log() + + def sigma(self, t): + return t.neg().exp() + + def eps(self, eps_cache, key, x, t, *args, **kwargs): + if key in eps_cache: + return eps_cache[key], eps_cache + sigma = self.sigma(t) * x.new_ones([x.shape[0]]) + eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) + if self.eps_callback is not None: + self.eps_callback() + return eps, {key: eps, **eps_cache} + + def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + x_1 = x - self.sigma(t_next) * h.expm1() * eps + return x_1, eps_cache + + def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) + return x_2, eps_cache + + def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + s2 = t + r2 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) + eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) + x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) + return x_3, eps_cache + + def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + if not t_end > t_start and eta: + raise ValueError('eta must be 0 for reverse sampling') + + m = math.floor(nfe / 3) + 1 + ts = torch.linspace(t_start, t_end, m + 1, device=x.device) + + if nfe % 3 == 0: + orders = [3] * (m - 2) + [2, 1] + else: + orders = [3] * (m - 1) + [nfe % 3] + + for i in range(len(orders)): + eps_cache = {} + t, t_next = ts[i], ts[i + 1] + if eta: + sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) + t_next_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 + else: + t_next_, su = t_next, 0. + + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + denoised = x - self.sigma(t) * eps + if self.info_callback is not None: + self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) + + if orders[i] == 1: + x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) + elif orders[i] == 2: + x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) + else: + x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) + + x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) + + return x + + def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + if order not in {2, 3}: + raise ValueError('order should be 2 or 3') + forward = t_end > t_start + if not forward and eta: + raise ValueError('eta must be 0 for reverse sampling') + h_init = abs(h_init) * (1 if forward else -1) + atol = torch.tensor(atol) + rtol = torch.tensor(rtol) + s = t_start + x_prev = x + accept = True + pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) + info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} + + while s < t_end - 1e-5 if forward else s > t_end + 1e-5: + eps_cache = {} + t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) + if eta: + sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) + t_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 + else: + t_, su = t, 0. + + eps, eps_cache = self.eps(eps_cache, 'eps', x, s) + denoised = x - self.sigma(s) * eps + + if order == 2: + x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) + else: + x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) + delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) + error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 + accept = pid.propose_step(error) + if accept: + x_prev = x_low + x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) + s = t + info['n_accept'] += 1 + else: + info['n_reject'] += 1 + info['nfe'] += order + info['steps'] += 1 + + if self.info_callback is not None: + self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) + + return x, info + + +@torch.no_grad() +def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): + """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(total=n, disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) + return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) + + +@torch.no_grad() +def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False): + """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) + x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) + if return_info: + return x, info + return x + + +@torch.no_grad() +def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigma_down == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++(2S) + t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) + r = 1 / 2 + h = t_next - t + s = t + r * h + x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 + # Noise addition + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +@torch.no_grad() +def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): + """DPM-Solver++ (stochastic).""" + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + seed = extra_args.get("seed", None) + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigmas[i + 1] - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++ + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + s = t + h * r + fac = 1 / (2 * r) + + # Step 1 + sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) + s_ = t_fn(sd) + x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised + x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + + # Step 2 + sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta) + t_next_ = t_fn(sd) + denoised_d = (1 - fac) * denoised + fac * denoised_2 + x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d + x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su + return x + + +@torch.no_grad() +def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): + """DPM-Solver++(2M).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + old_denoised = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + if old_denoised is None or sigmas[i + 1] == 0: + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised + else: + h_last = t - t_fn(sigmas[i - 1]) + r = h_last / h + denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d + old_denoised = denoised + return x + +@torch.no_grad() +def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): + """DPM-Solver++(2M) SDE.""" + + if solver_type not in {'heun', 'midpoint'}: + raise ValueError('solver_type must be \'heun\' or \'midpoint\'') + + seed = extra_args.get("seed", None) + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + old_denoised = None + h_last = None + h = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + # DPM-Solver++(2M) SDE + t, s = -sigmas[i].log(), -sigmas[i + 1].log() + h = s - t + eta_h = eta * h + + x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised + + if old_denoised is not None: + r = h_last / h + if solver_type == 'heun': + x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised) + elif solver_type == 'midpoint': + x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised) + + if eta: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise + + old_denoised = denoised + h_last = h + return x + +@torch.no_grad() +def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """DPM-Solver++(3M) SDE.""" + + seed = extra_args.get("seed", None) + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + denoised_1, denoised_2 = None, None + h, h_1, h_2 = None, None, None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + t, s = -sigmas[i].log(), -sigmas[i + 1].log() + h = s - t + h_eta = h * (eta + 1) + + x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised + + if h_2 is not None: + r0 = h_1 / h + r1 = h_2 / h + d1_0 = (denoised - denoised_1) / r0 + d1_1 = (denoised_1 - denoised_2) / r1 + d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1) + d2 = (d1_0 - d1_1) / (r0 + r1) + phi_2 = h_eta.neg().expm1() / h_eta + 1 + phi_3 = phi_2 / h_eta - 0.5 + x = x + phi_2 * d1 - phi_3 * d2 + elif h_1 is not None: + r = h_1 / h + d = (denoised - denoised_1) / r + phi_2 = h_eta.neg().expm1() / h_eta + 1 + x = x + phi_2 * d + + if eta: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise + + denoised_1, denoised_2 = denoised, denoised_1 + h_1, h_2 = h, h_1 + return x + +@torch.no_grad() +def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler) + +@torch.no_grad() +def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) + +@torch.no_grad() +def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) + + +def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler): + alpha_cumprod = 1 / ((sigma * sigma) + 1) + alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1) + alpha = (alpha_cumprod / alpha_cumprod_prev) + + mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt()) + if sigma_prev > 0: + mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev) + return mu + + +def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None): + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler) + if sigmas[i + 1] != 0: + x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0) + return x + + +@torch.no_grad() +def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): + return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step) + diff --git a/comfy/k_diffusion/utils.py b/comfy/k_diffusion/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a644df2f3cf82b32ac6e9bf2cb7bfc70c95e05f9 --- /dev/null +++ b/comfy/k_diffusion/utils.py @@ -0,0 +1,313 @@ +from contextlib import contextmanager +import hashlib +import math +from pathlib import Path +import shutil +import urllib +import warnings + +from PIL import Image +import torch +from torch import nn, optim +from torch.utils import data + + +def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'): + """Apply passed in transforms for HuggingFace Datasets.""" + images = [transform(image.convert(mode)) for image in examples[image_key]] + return {image_key: images} + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') + expanded = x[(...,) + (None,) * dims_to_append] + # MPS will get inf values if it tries to index into the new axes, but detaching fixes this. + # https://github.com/pytorch/pytorch/issues/84364 + return expanded.detach().clone() if expanded.device.type == 'mps' else expanded + + +def n_params(module): + """Returns the number of trainable parameters in a module.""" + return sum(p.numel() for p in module.parameters()) + + +def download_file(path, url, digest=None): + """Downloads a file if it does not exist, optionally checking its SHA-256 hash.""" + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + if not path.exists(): + with urllib.request.urlopen(url) as response, open(path, 'wb') as f: + shutil.copyfileobj(response, f) + if digest is not None: + file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest() + if digest != file_digest: + raise OSError(f'hash of {path} (url: {url}) failed to validate') + return path + + +@contextmanager +def train_mode(model, mode=True): + """A context manager that places a model into training mode and restores + the previous mode on exit.""" + modes = [module.training for module in model.modules()] + try: + yield model.train(mode) + finally: + for i, module in enumerate(model.modules()): + module.training = modes[i] + + +def eval_mode(model): + """A context manager that places a model into evaluation mode and restores + the previous mode on exit.""" + return train_mode(model, False) + + +@torch.no_grad() +def ema_update(model, averaged_model, decay): + """Incorporates updated model parameters into an exponential moving averaged + version of a model. It should be called after each optimizer step.""" + model_params = dict(model.named_parameters()) + averaged_params = dict(averaged_model.named_parameters()) + assert model_params.keys() == averaged_params.keys() + + for name, param in model_params.items(): + averaged_params[name].mul_(decay).add_(param, alpha=1 - decay) + + model_buffers = dict(model.named_buffers()) + averaged_buffers = dict(averaged_model.named_buffers()) + assert model_buffers.keys() == averaged_buffers.keys() + + for name, buf in model_buffers.items(): + averaged_buffers[name].copy_(buf) + + +class EMAWarmup: + """Implements an EMA warmup using an inverse decay schedule. + If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are + good values for models you plan to train for a million or more steps (reaches decay + factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models + you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at + 215.4k steps). + Args: + inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. + power (float): Exponential factor of EMA warmup. Default: 1. + min_value (float): The minimum EMA decay rate. Default: 0. + max_value (float): The maximum EMA decay rate. Default: 1. + start_at (int): The epoch to start averaging at. Default: 0. + last_epoch (int): The index of last epoch. Default: 0. + """ + + def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0, + last_epoch=0): + self.inv_gamma = inv_gamma + self.power = power + self.min_value = min_value + self.max_value = max_value + self.start_at = start_at + self.last_epoch = last_epoch + + def state_dict(self): + """Returns the state of the class as a :class:`dict`.""" + return dict(self.__dict__.items()) + + def load_state_dict(self, state_dict): + """Loads the class's state. + Args: + state_dict (dict): scaler state. Should be an object returned + from a call to :meth:`state_dict`. + """ + self.__dict__.update(state_dict) + + def get_value(self): + """Gets the current EMA decay rate.""" + epoch = max(0, self.last_epoch - self.start_at) + value = 1 - (1 + epoch / self.inv_gamma) ** -self.power + return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value)) + + def step(self): + """Updates the step count.""" + self.last_epoch += 1 + + +class InverseLR(optim.lr_scheduler._LRScheduler): + """Implements an inverse decay learning rate schedule with an optional exponential + warmup. When last_epoch=-1, sets initial lr as lr. + inv_gamma is the number of steps/epochs required for the learning rate to decay to + (1 / 2)**power of its original value. + Args: + optimizer (Optimizer): Wrapped optimizer. + inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. + power (float): Exponential factor of learning rate decay. Default: 1. + warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) + Default: 0. + min_lr (float): The minimum learning rate. Default: 0. + last_epoch (int): The index of last epoch. Default: -1. + verbose (bool): If ``True``, prints a message to stdout for + each update. Default: ``False``. + """ + + def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0., + last_epoch=-1, verbose=False): + self.inv_gamma = inv_gamma + self.power = power + if not 0. <= warmup < 1: + raise ValueError('Invalid value for warmup') + self.warmup = warmup + self.min_lr = min_lr + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn("To get the last learning rate computed by the scheduler, " + "please use `get_last_lr()`.") + + return self._get_closed_form_lr() + + def _get_closed_form_lr(self): + warmup = 1 - self.warmup ** (self.last_epoch + 1) + lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power + return [warmup * max(self.min_lr, base_lr * lr_mult) + for base_lr in self.base_lrs] + + +class ExponentialLR(optim.lr_scheduler._LRScheduler): + """Implements an exponential learning rate schedule with an optional exponential + warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate + continuously by decay (default 0.5) every num_steps steps. + Args: + optimizer (Optimizer): Wrapped optimizer. + num_steps (float): The number of steps to decay the learning rate by decay in. + decay (float): The factor by which to decay the learning rate every num_steps + steps. Default: 0.5. + warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) + Default: 0. + min_lr (float): The minimum learning rate. Default: 0. + last_epoch (int): The index of last epoch. Default: -1. + verbose (bool): If ``True``, prints a message to stdout for + each update. Default: ``False``. + """ + + def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0., + last_epoch=-1, verbose=False): + self.num_steps = num_steps + self.decay = decay + if not 0. <= warmup < 1: + raise ValueError('Invalid value for warmup') + self.warmup = warmup + self.min_lr = min_lr + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn("To get the last learning rate computed by the scheduler, " + "please use `get_last_lr()`.") + + return self._get_closed_form_lr() + + def _get_closed_form_lr(self): + warmup = 1 - self.warmup ** (self.last_epoch + 1) + lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch + return [warmup * max(self.min_lr, base_lr * lr_mult) + for base_lr in self.base_lrs] + + +def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): + """Draws samples from an lognormal distribution.""" + return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp() + + +def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): + """Draws samples from an optionally truncated log-logistic distribution.""" + min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64) + max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64) + min_cdf = min_value.log().sub(loc).div(scale).sigmoid() + max_cdf = max_value.log().sub(loc).div(scale).sigmoid() + u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf + return u.logit().mul(scale).add(loc).exp().to(dtype) + + +def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32): + """Draws samples from an log-uniform distribution.""" + min_value = math.log(min_value) + max_value = math.log(max_value) + return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp() + + +def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): + """Draws samples from a truncated v-diffusion training timestep distribution.""" + min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi + max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi + u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf + return torch.tan(u * math.pi / 2) * sigma_data + + +def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32): + """Draws samples from a split lognormal distribution.""" + n = torch.randn(shape, device=device, dtype=dtype).abs() + u = torch.rand(shape, device=device, dtype=dtype) + n_left = n * -scale_1 + loc + n_right = n * scale_2 + loc + ratio = scale_1 / (scale_1 + scale_2) + return torch.where(u < ratio, n_left, n_right).exp() + + +class FolderOfImages(data.Dataset): + """Recursively finds all images in a directory. It does not support + classes/targets.""" + + IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} + + def __init__(self, root, transform=None): + super().__init__() + self.root = Path(root) + self.transform = nn.Identity() if transform is None else transform + self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS) + + def __repr__(self): + return f'FolderOfImages(root="{self.root}", len: {len(self)})' + + def __len__(self): + return len(self.paths) + + def __getitem__(self, key): + path = self.paths[key] + with open(path, 'rb') as f: + image = Image.open(f).convert('RGB') + image = self.transform(image) + return image, + + +class CSVLogger: + def __init__(self, filename, columns): + self.filename = Path(filename) + self.columns = columns + if self.filename.exists(): + self.file = open(self.filename, 'a') + else: + self.file = open(self.filename, 'w') + self.write(*self.columns) + + def write(self, *args): + print(*args, sep=',', file=self.file, flush=True) + + +@contextmanager +def tf32_mode(cudnn=None, matmul=None): + """A context manager that sets whether TF32 is allowed on cuDNN or matmul.""" + cudnn_old = torch.backends.cudnn.allow_tf32 + matmul_old = torch.backends.cuda.matmul.allow_tf32 + try: + if cudnn is not None: + torch.backends.cudnn.allow_tf32 = cudnn + if matmul is not None: + torch.backends.cuda.matmul.allow_tf32 = matmul + yield + finally: + if cudnn is not None: + torch.backends.cudnn.allow_tf32 = cudnn_old + if matmul is not None: + torch.backends.cuda.matmul.allow_tf32 = matmul_old diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..fadc0eec75233652b7905ad11a73fea64d9f0011 --- /dev/null +++ b/comfy/latent_formats.py @@ -0,0 +1,35 @@ + +class LatentFormat: + scale_factor = 1.0 + latent_rgb_factors = None + taesd_decoder_name = None + + def process_in(self, latent): + return latent * self.scale_factor + + def process_out(self, latent): + return latent / self.scale_factor + +class SD15(LatentFormat): + def __init__(self, scale_factor=0.18215): + self.scale_factor = scale_factor + self.latent_rgb_factors = [ + # R G B + [ 0.3512, 0.2297, 0.3227], + [ 0.3250, 0.4974, 0.2350], + [-0.2829, 0.1762, 0.2721], + [-0.2120, -0.2616, -0.7177] + ] + self.taesd_decoder_name = "taesd_decoder.pth" + +class SDXL(LatentFormat): + def __init__(self): + self.scale_factor = 0.13025 + self.latent_rgb_factors = [ + # R G B + [ 0.3920, 0.4054, 0.4549], + [-0.2634, -0.0196, 0.0653], + [ 0.0568, 0.1687, -0.0755], + [-0.3112, -0.2359, -0.2076] + ] + self.taesd_decoder_name = "taesdxl_decoder.pth" diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..1fb7ed879fcdc5bcd2aeaaee08f05b1bf6a3aceb --- /dev/null +++ b/comfy/ldm/models/autoencoder.py @@ -0,0 +1,223 @@ +import torch +# import pytorch_lightning as pl +import torch.nn.functional as F +from contextlib import contextmanager + +from comfy.ldm.modules.diffusionmodules.model import Encoder, Decoder +from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution + +from comfy.ldm.util import instantiate_from_config +from comfy.ldm.modules.ema import LitEma + +# class AutoencoderKL(pl.LightningModule): +class AutoencoderKL(torch.nn.Module): + def __init__(self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + ema_decay=None, + learn_logvar=False + ): + super().__init__() + self.learn_logvar = learn_logvar + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + + self.use_ema = ema_decay is not None + if self.use_ema: + self.ema_decay = ema_decay + assert 0. < ema_decay < 1. + self.model_ema = LitEma(self, decay=ema_decay) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list()): + if path.lower().endswith(".safetensors"): + import safetensors.torch + sd = safetensors.torch.load_file(path, device="cpu") + else: + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + print(f"Restored from {path}") + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self) + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z): + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return aeloss + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + + self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return discloss + + def validation_step(self, batch, batch_idx): + log_dict = self._validation_step(batch, batch_idx) + with self.ema_scope(): + log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema") + return log_dict + + def _validation_step(self, batch, batch_idx, postfix=""): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, + last_layer=self.get_last_layer(), split="val"+postfix) + + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, + last_layer=self.get_last_layer(), split="val"+postfix) + + self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"]) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr = self.learning_rate + ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list( + self.quant_conv.parameters()) + list(self.post_quant_conv.parameters()) + if self.learn_logvar: + print(f"{self.__class__.__name__}: Learning logvar") + ae_params_list.append(self.loss.logvar) + opt_ae = torch.optim.Adam(ae_params_list, + lr=lr, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr, betas=(0.5, 0.9)) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + if log_ema or self.use_ema: + with self.ema_scope(): + xrec_ema, posterior_ema = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec_ema.shape[1] > 3 + xrec_ema = self.to_rgb(xrec_ema) + log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample())) + log["reconstructions_ema"] = xrec_ema + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x + diff --git a/comfy/ldm/models/diffusion/__init__.py b/comfy/ldm/models/diffusion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy/ldm/models/diffusion/ddim.py b/comfy/ldm/models/diffusion/ddim.py new file mode 100644 index 0000000000000000000000000000000000000000..befab0075ca228c05f85792ae27f8e5c534258a2 --- /dev/null +++ b/comfy/ldm/models/diffusion/ddim.py @@ -0,0 +1,418 @@ +"""SAMPLING ONLY.""" + +import torch +import numpy as np +from tqdm import tqdm + +from comfy.ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor + + +class DDIMSampler(object): + def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + self.device = device + self.parameterization = kwargs.get("parameterization", "eps") + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != self.device: + attr = attr.float().to(self.device) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + self.make_schedule_timesteps(ddim_timesteps, ddim_eta=ddim_eta, verbose=verbose) + + def make_schedule_timesteps(self, ddim_timesteps, ddim_eta=0., verbose=True): + self.ddim_timesteps = torch.tensor(ddim_timesteps) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.device) + + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample_custom(self, + ddim_timesteps, + conditioning, + callback=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + dynamic_threshold=None, + ucg_schedule=None, + denoise_function=None, + extra_args=None, + to_zero=True, + end_step=None, + disable_pbar=False, + **kwargs + ): + self.make_schedule_timesteps(ddim_timesteps=ddim_timesteps, ddim_eta=eta, verbose=verbose) + samples, intermediates = self.ddim_sampling(conditioning, x_T.shape, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + dynamic_threshold=dynamic_threshold, + ucg_schedule=ucg_schedule, + denoise_function=denoise_function, + extra_args=extra_args, + to_zero=to_zero, + end_step=end_step, + disable_pbar=disable_pbar + ) + return samples, intermediates + + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + dynamic_threshold=None, + ucg_schedule=None, + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): ctmp = ctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + + elif isinstance(conditioning, list): + for ctmp in conditioning: + if ctmp.shape[0] != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for DDIM sampling is {size}, eta {eta}') + + samples, intermediates = self.ddim_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + dynamic_threshold=dynamic_threshold, + ucg_schedule=ucg_schedule, + denoise_function=None, + extra_args=None + ) + return samples, intermediates + + def q_sample(self, x_start, t, noise=None): + if noise is None: + noise = torch.randn_like(x_start) + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + + @torch.no_grad() + def ddim_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None, + ucg_schedule=None, denoise_function=None, extra_args=None, to_zero=True, end_step=None, disable_pbar=False): + device = self.model.alphas_cumprod.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else timesteps.flip(0) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + # print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range[:end_step], desc='DDIM Sampler', total=end_step, disable=disable_pbar) + + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + if mask is not None: + assert x0 is not None + img_orig = self.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img + + if ucg_schedule is not None: + assert len(ucg_schedule) == len(time_range) + unconditional_guidance_scale = ucg_schedule[i] + + outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + dynamic_threshold=dynamic_threshold, denoise_function=denoise_function, extra_args=extra_args) + img, pred_x0 = outs + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + if to_zero: + img = pred_x0 + else: + if ddim_use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + img /= sqrt_alphas_cumprod[index - 1] + + return img, intermediates + + @torch.no_grad() + def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, + dynamic_threshold=None, denoise_function=None, extra_args=None): + b, *_, device = *x.shape, x.device + + if denoise_function is not None: + model_output = denoise_function(x, t, **extra_args) + elif unconditional_conditioning is None or unconditional_guidance_scale == 1.: + model_output = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + if isinstance(c, dict): + assert isinstance(unconditional_conditioning, dict) + c_in = dict() + for k in c: + if isinstance(c[k], list): + c_in[k] = [torch.cat([ + unconditional_conditioning[k][i], + c[k][i]]) for i in range(len(c[k]))] + else: + c_in[k] = torch.cat([ + unconditional_conditioning[k], + c[k]]) + elif isinstance(c, list): + c_in = list() + assert isinstance(unconditional_conditioning, list) + for i in range(len(c)): + c_in.append(torch.cat([unconditional_conditioning[i], c[i]])) + else: + c_in = torch.cat([unconditional_conditioning, c]) + model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond) + + if self.parameterization == "v": + e_t = extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * model_output + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x + else: + e_t = model_output + + if score_corrector is not None: + assert self.parameterization == "eps", 'not implemented' + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + if self.parameterization != "v": + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + else: + pred_x0 = extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * x - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * model_output + + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + + if dynamic_threshold is not None: + raise NotImplementedError() + + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + @torch.no_grad() + def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None, + unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None): + num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0] + + assert t_enc <= num_reference_steps + num_steps = t_enc + + if use_original_steps: + alphas_next = self.alphas_cumprod[:num_steps] + alphas = self.alphas_cumprod_prev[:num_steps] + else: + alphas_next = self.ddim_alphas[:num_steps] + alphas = torch.tensor(self.ddim_alphas_prev[:num_steps]) + + x_next = x0 + intermediates = [] + inter_steps = [] + for i in tqdm(range(num_steps), desc='Encoding Image'): + t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long) + if unconditional_guidance_scale == 1.: + noise_pred = self.model.apply_model(x_next, t, c) + else: + assert unconditional_conditioning is not None + e_t_uncond, noise_pred = torch.chunk( + self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)), + torch.cat((unconditional_conditioning, c))), 2) + noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond) + + xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next + weighted_noise_pred = alphas_next[i].sqrt() * ( + (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred + x_next = xt_weighted + weighted_noise_pred + if return_intermediates and i % ( + num_steps // return_intermediates) == 0 and i < num_steps - 1: + intermediates.append(x_next) + inter_steps.append(i) + elif return_intermediates and i >= num_steps - 2: + intermediates.append(x_next) + inter_steps.append(i) + if callback: callback(i) + + out = {'x_encoded': x_next, 'intermediate_steps': inter_steps} + if return_intermediates: + out.update({'intermediates': intermediates}) + return x_next, out + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None, max_denoise=False): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + if max_denoise: + noise_multiplier = 1.0 + else: + noise_multiplier = extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) + + return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + noise_multiplier * noise) + + @torch.no_grad() + def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, + use_original_steps=False, callback=None): + + timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='Decoding image', total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) + x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + if callback: callback(i) + return x_dec \ No newline at end of file diff --git a/comfy/ldm/models/diffusion/dpm_solver/__init__.py b/comfy/ldm/models/diffusion/dpm_solver/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7427f38c07530afbab79154ea8aaf88c4bf70a08 --- /dev/null +++ b/comfy/ldm/models/diffusion/dpm_solver/__init__.py @@ -0,0 +1 @@ +from .sampler import DPMSolverSampler \ No newline at end of file diff --git a/comfy/ldm/models/diffusion/dpm_solver/dpm_solver.py b/comfy/ldm/models/diffusion/dpm_solver/dpm_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..da8d41f9c5e93d2f9e07a22aeef9aeb06d0b7dd3 --- /dev/null +++ b/comfy/ldm/models/diffusion/dpm_solver/dpm_solver.py @@ -0,0 +1,1163 @@ +import torch +import torch.nn.functional as F +import math +from tqdm import tqdm + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + ): + """Create a wrapper class for the forward SDE (VP type). + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + t = self.inverse_lambda(lambda_t) + =============================================================== + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + 1. For discrete-time DPMs: + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + 2. For continuous-time DPMs: + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + =============================================================== + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + Example: + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError( + "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format( + schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * ( + 1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), + self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0 ** 2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), + torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * ( + 1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + We support four types of the diffusion model by setting `model_type`: + 1. "noise": noise prediction model. (Trained by predicting noise). + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + =============================================================== + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * 1000. + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + if cond is None: + output = model(x, t_input, **model_kwargs) + else: + output = model(x, t_input, cond, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return -expand_dims(sigma_t, dims) * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + if isinstance(condition, dict): + assert isinstance(unconditional_condition, dict) + c_in = dict() + for k in condition: + if isinstance(condition[k], list): + c_in[k] = [torch.cat([unconditional_condition[k][i], condition[k][i]]) for i in range(len(condition[k]))] + else: + c_in[k] = torch.cat([unconditional_condition[k], condition[k]]) + else: + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class DPM_Solver: + def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.): + """Construct a DPM-Solver. + We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0"). + If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver). + If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++). + In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True. + The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales. + Args: + model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]): + `` + def model_fn(x, t_continuous): + return noise + `` + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model. + thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1]. + max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding. + + [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b. + """ + self.model = model_fn + self.noise_schedule = noise_schedule + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + Args: + skip_type: A `str`. The type for the spacing of the time steps. We support three types: + - 'logSNR': uniform logSNR for the time steps. + - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.) + - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.) + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + N: A `int`. The total number of the spacing of the time steps. + device: A torch device. + Returns: + A pytorch tensor of the time steps, with the shape (N + 1,). + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError( + "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast". + Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is: + - If order == 1: + We take `steps` of DPM-Solver-1 (i.e. DDIM). + - If order == 2: + - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling. + - If steps % 2 == 0, we use K steps of DPM-Solver-2. + - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1. + - If order == 3: + - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling. + - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1. + - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1. + - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2. + ============================================ + Args: + order: A `int`. The max order for the solver (2 or 3). + steps: A `int`. The total number of function evaluations (NFE). + skip_type: A `str`. The type for the spacing of the time steps. We support three types: + - 'logSNR': uniform logSNR for the time steps. + - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.) + - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.) + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + device: A torch device. + Returns: + orders: A list of the solver order of each step. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3, ] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3, ] * (K - 1) + [1] + else: + orders = [3, ] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2, ] * K + else: + K = steps // 2 + 1 + orders = [2, ] * (K - 1) + [1] + elif order == 1: + K = 1 + orders = [1, ] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[ + torch.cumsum(torch.tensor([0, ] + orders)).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False): + """ + DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s`. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + if self.predict_x0: + phi_1 = torch.expm1(-h) + if model_s is None: + model_s = self.model_fn(x, s) + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + ) + if return_intermediate: + return x_t, {'model_s': model_s} + else: + return x_t + else: + phi_1 = torch.expm1(h) + if model_s is None: + model_s = self.model_fn(x, s) + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + ) + if return_intermediate: + return x_t, {'model_s': model_s} + else: + return x_t + + def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, + solver_type='dpm_solver'): + """ + Singlestep solver DPM-Solver-2 from time `s` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + r1: A `float`. The hyperparameter of the second-order solver. + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpm_solver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) + if r1 is None: + r1 = 0.5 + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + lambda_s1 = lambda_s + r1 * h + s1 = ns.inverse_lambda(lambda_s1) + log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff( + s1), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t) + alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t) + + if self.predict_x0: + phi_11 = torch.expm1(-r1 * h) + phi_1 = torch.expm1(-h) + + if model_s is None: + model_s = self.model_fn(x, s) + x_s1 = ( + expand_dims(sigma_s1 / sigma_s, dims) * x + - expand_dims(alpha_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s) + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * ( + model_s1 - model_s) + ) + else: + phi_11 = torch.expm1(r1 * h) + phi_1 = torch.expm1(h) + + if model_s is None: + model_s = self.model_fn(x, s) + x_s1 = ( + expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x + - expand_dims(sigma_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s) + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s) + ) + if return_intermediate: + return x_t, {'model_s': model_s, 'model_s1': model_s1} + else: + return x_t + + def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None, + return_intermediate=False, solver_type='dpm_solver'): + """ + Singlestep solver DPM-Solver-3 from time `s` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + r1: A `float`. The hyperparameter of the third-order solver. + r2: A `float`. The hyperparameter of the third-order solver. + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`). + If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpm_solver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) + if r1 is None: + r1 = 1. / 3. + if r2 is None: + r2 = 2. / 3. + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + lambda_s1 = lambda_s + r1 * h + lambda_s2 = lambda_s + r2 * h + s1 = ns.inverse_lambda(lambda_s1) + s2 = ns.inverse_lambda(lambda_s2) + log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff( + s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std( + s2), ns.marginal_std(t) + alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t) + + if self.predict_x0: + phi_11 = torch.expm1(-r1 * h) + phi_12 = torch.expm1(-r2 * h) + phi_1 = torch.expm1(-h) + phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1. + phi_2 = phi_1 / h + 1. + phi_3 = phi_2 / h - 0.5 + + if model_s is None: + model_s = self.model_fn(x, s) + if model_s1 is None: + x_s1 = ( + expand_dims(sigma_s1 / sigma_s, dims) * x + - expand_dims(alpha_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + x_s2 = ( + expand_dims(sigma_s2 / sigma_s, dims) * x + - expand_dims(alpha_s2 * phi_12, dims) * model_s + + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s) + ) + model_s2 = self.model_fn(x_s2, s2) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s) + ) + elif solver_type == 'taylor': + D1_0 = (1. / r1) * (model_s1 - model_s) + D1_1 = (1. / r2) * (model_s2 - model_s) + D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1) + D2 = 2. * (D1_1 - D1_0) / (r2 - r1) + x_t = ( + expand_dims(sigma_t / sigma_s, dims) * x + - expand_dims(alpha_t * phi_1, dims) * model_s + + expand_dims(alpha_t * phi_2, dims) * D1 + - expand_dims(alpha_t * phi_3, dims) * D2 + ) + else: + phi_11 = torch.expm1(r1 * h) + phi_12 = torch.expm1(r2 * h) + phi_1 = torch.expm1(h) + phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1. + phi_2 = phi_1 / h - 1. + phi_3 = phi_2 / h - 0.5 + + if model_s is None: + model_s = self.model_fn(x, s) + if model_s1 is None: + x_s1 = ( + expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x + - expand_dims(sigma_s1 * phi_11, dims) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + x_s2 = ( + expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x + - expand_dims(sigma_s2 * phi_12, dims) * model_s + - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s) + ) + model_s2 = self.model_fn(x_s2, s2) + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s) + ) + elif solver_type == 'taylor': + D1_0 = (1. / r1) * (model_s1 - model_s) + D1_1 = (1. / r2) * (model_s2 - model_s) + D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1) + D2 = 2. * (D1_1 - D1_0) / (r2 - r1) + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x + - expand_dims(sigma_t * phi_1, dims) * model_s + - expand_dims(sigma_t * phi_2, dims) * D1 + - expand_dims(sigma_t * phi_3, dims) * D2 + ) + + if return_intermediate: + return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2} + else: + return x_t + + def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"): + """ + Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpm_solver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type)) + ns = self.noise_schedule + dims = x.dim() + model_prev_1, model_prev_0 = model_prev_list + t_prev_1, t_prev_0 = t_prev_list + lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda( + t_prev_0), ns.marginal_lambda(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + h_0 = lambda_prev_0 - lambda_prev_1 + h = lambda_t - lambda_prev_0 + r0 = h_0 / h + D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1) + if self.predict_x0: + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 + - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0 + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 + + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0 + ) + else: + if solver_type == 'dpm_solver': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 + - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0 + ) + elif solver_type == 'taylor': + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 + - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0 + ) + return x_t + + def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'): + """ + Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + ns = self.noise_schedule + dims = x.dim() + model_prev_2, model_prev_1, model_prev_0 = model_prev_list + t_prev_2, t_prev_1, t_prev_0 = t_prev_list + lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda( + t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + h_1 = lambda_prev_1 - lambda_prev_2 + h_0 = lambda_prev_0 - lambda_prev_1 + h = lambda_t - lambda_prev_0 + r0, r1 = h_0 / h, h_1 / h + D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1) + D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2) + D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1) + D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1) + if self.predict_x0: + x_t = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0 + + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1 + - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2 + ) + else: + x_t = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0 + - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1 + - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2 + ) + return x_t + + def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, + r2=None): + """ + Singlestep DPM-Solver with the order `order` from time `s` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (x.shape[0],). + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3. + return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times). + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + r1: A `float`. The hyperparameter of the second-order or third-order solver. + r2: A `float`. The hyperparameter of the third-order solver. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if order == 1: + return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate) + elif order == 2: + return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, + solver_type=solver_type, r1=r1) + elif order == 3: + return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, + solver_type=solver_type, r1=r1, r2=r2) + else: + raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) + + def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'): + """ + Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`. + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],) + t: A pytorch tensor. The ending time, with the shape (x.shape[0],). + order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3. + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if order == 1: + return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1]) + elif order == 2: + return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) + elif order == 3: + return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) + else: + raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) + + def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, + solver_type='dpm_solver'): + """ + The adaptive step size solver based on singlestep DPM-Solver. + Args: + x: A pytorch tensor. The initial value at time `t_T`. + order: A `int`. The (higher) order of the solver. We only support order == 2 or 3. + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + h_init: A `float`. The initial step size (for logSNR). + atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1]. + rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05. + theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1]. + t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the + current time and `t_0` is less than `t_err`. The default setting is 1e-5. + solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpm_solver' type. + Returns: + x_0: A pytorch tensor. The approximated solution at time `t_0`. + [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021. + """ + ns = self.noise_schedule + s = t_T * torch.ones((x.shape[0],)).to(x) + lambda_s = ns.marginal_lambda(s) + lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x)) + h = h_init * torch.ones_like(s).to(x) + x_prev = x + nfe = 0 + if order == 2: + r1 = 0.5 + lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True) + higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, + solver_type=solver_type, + **kwargs) + elif order == 3: + r1, r2 = 1. / 3., 2. / 3. + lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, + return_intermediate=True, + solver_type=solver_type) + higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, + solver_type=solver_type, + **kwargs) + else: + raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order)) + while torch.abs((s - t_0)).mean() > t_err: + t = ns.inverse_lambda(lambda_s + h) + x_lower, lower_noise_kwargs = lower_update(x, s, t) + x_higher = higher_update(x, s, t, **lower_noise_kwargs) + delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev))) + norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True)) + E = norm_fn((x_higher - x_lower) / delta).max() + if torch.all(E <= 1.): + x = x_higher + s = t + x_prev = x_lower + lambda_s = ns.marginal_lambda(s) + h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s) + nfe += order + print('adaptive solver nfe', nfe) + return x + + def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform', + method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', + atol=0.0078, rtol=0.05, + ): + """ + Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`. + ===================================================== + We support the following algorithms for both noise prediction model and data prediction model: + - 'singlestep': + Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver. + We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps). + The total number of function evaluations (NFE) == `steps`. + Given a fixed NFE == `steps`, the sampling procedure is: + - If `order` == 1: + - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM). + - If `order` == 2: + - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling. + - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2. + - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1. + - If `order` == 3: + - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling. + - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1. + - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1. + - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2. + - 'multistep': + Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`. + We initialize the first `order` values by lower order multistep solvers. + Given a fixed NFE == `steps`, the sampling procedure is: + Denote K = steps. + - If `order` == 1: + - We use K steps of DPM-Solver-1 (i.e. DDIM). + - If `order` == 2: + - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2. + - If `order` == 3: + - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3. + - 'singlestep_fixed': + Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3). + We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE. + - 'adaptive': + Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper). + We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`. + You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs + (NFE) and the sample quality. + - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2. + - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3. + ===================================================== + Some advices for choosing the algorithm: + - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs: + Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`. + e.g. + >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False) + >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3, + skip_type='time_uniform', method='singlestep') + - For **guided sampling with large guidance scale** by DPMs: + Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`. + e.g. + >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True) + >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2, + skip_type='time_uniform', method='multistep') + We support three types of `skip_type`: + - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images** + - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**. + - 'time_quadratic': quadratic time for the time steps. + ===================================================== + Args: + x: A pytorch tensor. The initial value at time `t_start` + e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution. + steps: A `int`. The total number of function evaluations (NFE). + t_start: A `float`. The starting time of the sampling. + If `T` is None, we use self.noise_schedule.T (default is 1.0). + t_end: A `float`. The ending time of the sampling. + If `t_end` is None, we use 1. / self.noise_schedule.total_N. + e.g. if total_N == 1000, we have `t_end` == 1e-3. + For discrete-time DPMs: + - We recommend `t_end` == 1. / self.noise_schedule.total_N. + For continuous-time DPMs: + - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15. + order: A `int`. The order of DPM-Solver. + skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'. + method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'. + denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step. + Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1). + This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and + score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID + for diffusion models sampling by diffusion SDEs for low-resolutional images + (such as CIFAR-10). However, we observed that such trick does not matter for + high-resolutional images. As it needs an additional NFE, we do not recommend + it for high-resolutional images. + lower_order_final: A `bool`. Whether to use lower order solvers at the final steps. + Only valid for `method=multistep` and `steps < 15`. We empirically find that + this trick is a key to stabilizing the sampling by DPM-Solver with very few steps + (especially for steps <= 10). So we recommend to set it to be `True`. + solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`. + atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'. + rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'. + Returns: + x_end: A pytorch tensor. The approximated solution at time `t_end`. + """ + t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + t_T = self.noise_schedule.T if t_start is None else t_start + device = x.device + if method == 'adaptive': + with torch.no_grad(): + x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, + solver_type=solver_type) + elif method == 'multistep': + assert steps >= order + timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + with torch.no_grad(): + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + # Init the first `order` values by lower order multistep DPM-Solver. + for init_order in tqdm(range(1, order), desc="DPM init order"): + vec_t = timesteps[init_order].expand(x.shape[0]) + x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, + solver_type=solver_type) + model_prev_list.append(self.model_fn(x, vec_t)) + t_prev_list.append(vec_t) + # Compute the remaining values by `order`-th order multistep DPM-Solver. + for step in tqdm(range(order, steps + 1), desc="DPM multistep"): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final and steps < 15: + step_order = min(order, steps + 1 - step) + else: + step_order = order + x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order, + solver_type=solver_type) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + model_prev_list[-1] = self.model_fn(x, vec_t) + elif method in ['singlestep', 'singlestep_fixed']: + if method == 'singlestep': + timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, + skip_type=skip_type, + t_T=t_T, t_0=t_0, + device=device) + elif method == 'singlestep_fixed': + K = steps // order + orders = [order, ] * K + timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device) + for i, order in enumerate(orders): + t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1] + timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), + N=order, device=device) + lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner) + vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0]) + h = lambda_inner[-1] - lambda_inner[0] + r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h + r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h + x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2) + if denoise_to_zero: + x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,) * (dims - 1)] \ No newline at end of file diff --git a/comfy/ldm/models/diffusion/dpm_solver/sampler.py b/comfy/ldm/models/diffusion/dpm_solver/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..e4d0d0a387548a280d872b60344d0a74dac5e1f0 --- /dev/null +++ b/comfy/ldm/models/diffusion/dpm_solver/sampler.py @@ -0,0 +1,96 @@ +"""SAMPLING ONLY.""" +import torch + +from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver + +MODEL_TYPES = { + "eps": "noise", + "v": "v" +} + + +class DPMSolverSampler(object): + def __init__(self, model, device=torch.device("cuda"), **kwargs): + super().__init__() + self.model = model + self.device = device + to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device) + self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod)) + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != self.device: + attr = attr.to(self.device) + setattr(self, name, attr) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): ctmp = ctmp[0] + if isinstance(ctmp, torch.Tensor): + cbs = ctmp.shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + elif isinstance(conditioning, list): + for ctmp in conditioning: + if ctmp.shape[0] != batch_size: + print(f"Warning: Got {ctmp.shape[0]} conditionings but batch-size is {batch_size}") + else: + if isinstance(conditioning, torch.Tensor): + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + + print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}') + + device = self.model.betas.device + if x_T is None: + img = torch.randn(size, device=device) + else: + img = x_T + + ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod) + + model_fn = model_wrapper( + lambda x, t, c: self.model.apply_model(x, t, c), + ns, + model_type=MODEL_TYPES[self.model.parameterization], + guidance_type="classifier-free", + condition=conditioning, + unconditional_condition=unconditional_conditioning, + guidance_scale=unconditional_guidance_scale, + ) + + dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False) + x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, + lower_order_final=True) + + return x.to(device), None diff --git a/comfy/ldm/models/diffusion/plms.py b/comfy/ldm/models/diffusion/plms.py new file mode 100644 index 0000000000000000000000000000000000000000..9d31b3994ed283e9d97ed0ae275d89046442cc89 --- /dev/null +++ b/comfy/ldm/models/diffusion/plms.py @@ -0,0 +1,245 @@ +"""SAMPLING ONLY.""" + +import torch +import numpy as np +from tqdm import tqdm +from functools import partial + +from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like +from ldm.models.diffusion.sampling_util import norm_thresholding + + +class PLMSSampler(object): + def __init__(self, model, schedule="linear", device=torch.device("cuda"), **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + self.device = device + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != self.device: + attr = attr.to(self.device) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + if ddim_eta != 0: + raise ValueError('ddim_eta must be 0 for PLMS') + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + dynamic_threshold=None, + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for PLMS sampling is {size}') + + samples, intermediates = self.plms_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + dynamic_threshold=dynamic_threshold, + ) + return samples, intermediates + + @torch.no_grad() + def plms_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, + dynamic_threshold=None): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + print(f"Running PLMS Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) + old_eps = [] + + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) + + if mask is not None: + assert x0 is not None + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img + + outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + old_eps=old_eps, t_next=ts_next, + dynamic_threshold=dynamic_threshold) + img, pred_x0, e_t = outs + old_eps.append(e_t) + if len(old_eps) >= 4: + old_eps.pop(0) + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, + dynamic_threshold=None): + b, *_, device = *x.shape, x.device + + def get_model_output(x, t): + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + c_in = torch.cat([unconditional_conditioning, c]) + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + return e_t + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + + def get_x_prev_and_pred_x0(e_t, index): + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + if dynamic_threshold is not None: + pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + e_t = get_model_output(x, t) + if len(old_eps) == 0: + # Pseudo Improved Euler (2nd order) + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) + e_t_next = get_model_output(x_prev, t_next) + e_t_prime = (e_t + e_t_next) / 2 + elif len(old_eps) == 1: + # 2nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (3 * e_t - old_eps[-1]) / 2 + elif len(old_eps) == 2: + # 3nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 + elif len(old_eps) >= 3: + # 4nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 + + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) + + return x_prev, pred_x0, e_t diff --git a/comfy/ldm/models/diffusion/sampling_util.py b/comfy/ldm/models/diffusion/sampling_util.py new file mode 100644 index 0000000000000000000000000000000000000000..7eff02be6d7c54d43ee6680636ac0698dd3b3f33 --- /dev/null +++ b/comfy/ldm/models/diffusion/sampling_util.py @@ -0,0 +1,22 @@ +import torch +import numpy as np + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions. + From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') + return x[(...,) + (None,) * dims_to_append] + + +def norm_thresholding(x0, value): + s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim) + return x0 * (value / s) + + +def spatial_norm_thresholding(x0, value): + # b c h w + s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value) + return x0 * (value / s) \ No newline at end of file diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..34484b288b49d60398795b549bf35e74fbf21be1 --- /dev/null +++ b/comfy/ldm/modules/attention.py @@ -0,0 +1,700 @@ +from inspect import isfunction +import math +import torch +import torch.nn.functional as F +from torch import nn, einsum +from einops import rearrange, repeat +from typing import Optional, Any + +from .diffusionmodules.util import checkpoint +from .sub_quadratic_attention import efficient_dot_product_attention + +from comfy import model_management + +if model_management.xformers_enabled(): + import xformers + import xformers.ops + +from comfy.cli_args import args +import comfy.ops + +# CrossAttn precision handling +if args.dont_upcast_attention: + print("disabling upcasting of attention") + _ATTN_PRECISION = "fp16" +else: + _ATTN_PRECISION = "fp32" + + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=comfy.ops): + super().__init__() + self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=comfy.ops): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + operations.Linear(dim, inner_dim, dtype=dtype, device=device), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) + ) + + def forward(self, x): + return self.net(x) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def Normalize(in_channels, dtype=None, device=None): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = rearrange(q, 'b c h w -> b (h w) c') + k = rearrange(k, 'b c h w -> b c (h w)') + w_ = torch.einsum('bij,bjk->bik', q, k) + + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, 'b c h w -> b c (h w)') + w_ = rearrange(w_, 'b i j -> b j i') + h_ = torch.einsum('bij,bjk->bik', v, w_) + h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) + h_ = self.proj_out(h_) + + return x+h_ + + +class CrossAttentionBirchSan(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + + self.to_out = nn.Sequential( + operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), + nn.Dropout(dropout) + ) + + def forward(self, x, context=None, value=None, mask=None): + h = self.heads + + query = self.to_q(x) + context = default(context, x) + key = self.to_k(context) + if value is not None: + value = self.to_v(value) + else: + value = self.to_v(context) + + del context, x + + query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1) + key_t = key.transpose(1,2).unflatten(1, (self.heads, -1)).flatten(end_dim=1) + del key + value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1) + + dtype = query.dtype + upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32 + if upcast_attention: + bytes_per_token = torch.finfo(torch.float32).bits//8 + else: + bytes_per_token = torch.finfo(query.dtype).bits//8 + batch_x_heads, q_tokens, _ = query.shape + _, _, k_tokens = key_t.shape + qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens + + mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True) + + chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD + + kv_chunk_size_min = None + + #not sure at all about the math here + #TODO: tweak this + if mem_free_total > 8192 * 1024 * 1024 * 1.3: + query_chunk_size_x = 1024 * 4 + elif mem_free_total > 4096 * 1024 * 1024 * 1.3: + query_chunk_size_x = 1024 * 2 + else: + query_chunk_size_x = 1024 + kv_chunk_size_min_x = None + kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024 + if kv_chunk_size_x < 1024: + kv_chunk_size_x = None + + if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes: + # the big matmul fits into our memory limit; do everything in 1 chunk, + # i.e. send it down the unchunked fast-path + query_chunk_size = q_tokens + kv_chunk_size = k_tokens + else: + query_chunk_size = query_chunk_size_x + kv_chunk_size = kv_chunk_size_x + kv_chunk_size_min = kv_chunk_size_min_x + + hidden_states = efficient_dot_product_attention( + query, + key_t, + value, + query_chunk_size=query_chunk_size, + kv_chunk_size=kv_chunk_size, + kv_chunk_size_min=kv_chunk_size_min, + use_checkpoint=self.training, + upcast_attention=upcast_attention, + ) + + hidden_states = hidden_states.to(dtype) + + hidden_states = hidden_states.unflatten(0, (-1, self.heads)).transpose(1,2).flatten(start_dim=2) + + out_proj, dropout = self.to_out + hidden_states = out_proj(hidden_states) + hidden_states = dropout(hidden_states) + + return hidden_states + + +class CrossAttentionDoggettx(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + + self.to_out = nn.Sequential( + operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), + nn.Dropout(dropout) + ) + + def forward(self, x, context=None, value=None, mask=None): + h = self.heads + + q_in = self.to_q(x) + context = default(context, x) + k_in = self.to_k(context) + if value is not None: + v_in = self.to_v(value) + del value + else: + v_in = self.to_v(context) + del context, x + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) + del q_in, k_in, v_in + + r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) + + mem_free_total = model_management.get_free_memory(q.device) + + gb = 1024 ** 3 + tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() + modifier = 3 if q.element_size() == 2 else 2.5 + mem_required = tensor_size * modifier + steps = 1 + + + if mem_required > mem_free_total: + steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) + # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " + # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") + + if steps > 64: + max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 + raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' + f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') + + # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) + first_op_done = False + cleared_cache = False + while True: + try: + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + if _ATTN_PRECISION =="fp32": + with torch.autocast(enabled=False, device_type = 'cuda'): + s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * self.scale + else: + s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale + first_op_done = True + + s2 = s1.softmax(dim=-1).to(v.dtype) + del s1 + + r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) + del s2 + break + except model_management.OOM_EXCEPTION as e: + if first_op_done == False: + model_management.soft_empty_cache(True) + if cleared_cache == False: + cleared_cache = True + print("out of memory error, emptying cache and trying again") + continue + steps *= 2 + if steps > 64: + raise e + print("out of memory error, increasing steps and trying again", steps) + else: + raise e + + del q, k, v + + r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h) + del r1 + + return self.to_out(r2) + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + + self.to_out = nn.Sequential( + operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), + nn.Dropout(dropout) + ) + + def forward(self, x, context=None, value=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + if value is not None: + v = self.to_v(value) + del value + else: + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + # force cast to fp32 to avoid overflowing + if _ATTN_PRECISION =="fp32": + with torch.autocast(enabled=False, device_type = 'cuda'): + q, k = q.float(), k.float() + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + else: + sim = einsum('b i d, b j d -> b i j', q, k) * self.scale + + del q, k + + if exists(mask): + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', sim, v) + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + return self.to_out(out) + +class MemoryEfficientCrossAttention(nn.Module): + # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=comfy.ops): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.heads = heads + self.dim_head = dim_head + + self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + + self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) + self.attention_op: Optional[Any] = None + + def forward(self, x, context=None, value=None, mask=None): + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + if value is not None: + v = self.to_v(value) + del value + else: + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + + # actually compute the attention, what we cannot get enough of + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) + + if exists(mask): + raise NotImplementedError + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + return self.to_out(out) + +class CrossAttentionPytorch(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.heads = heads + self.dim_head = dim_head + + self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + + self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) + self.attention_op: Optional[Any] = None + + def forward(self, x, context=None, value=None, mask=None): + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + if value is not None: + v = self.to_v(value) + del value + else: + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.view(b, -1, self.heads, self.dim_head).transpose(1, 2), + (q, k, v), + ) + + out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) + + if exists(mask): + raise NotImplementedError + out = ( + out.transpose(1, 2).reshape(b, -1, self.heads * self.dim_head) + ) + + return self.to_out(out) + +if model_management.xformers_enabled(): + print("Using xformers cross attention") + CrossAttention = MemoryEfficientCrossAttention +elif model_management.pytorch_attention_enabled(): + print("Using pytorch cross attention") + CrossAttention = CrossAttentionPytorch +else: + if args.use_split_cross_attention: + print("Using split optimization for cross attention") + CrossAttention = CrossAttentionDoggettx + else: + print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention") + CrossAttention = CrossAttentionBirchSan + + +class BasicTransformerBlock(nn.Module): + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, + disable_self_attn=False, dtype=None, device=None, operations=comfy.ops): + super().__init__() + self.disable_self_attn = disable_self_attn + self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) + self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, + heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim, dtype=dtype, device=device) + self.norm2 = nn.LayerNorm(dim, dtype=dtype, device=device) + self.norm3 = nn.LayerNorm(dim, dtype=dtype, device=device) + self.checkpoint = checkpoint + self.n_heads = n_heads + self.d_head = d_head + + def forward(self, x, context=None, transformer_options={}): + return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint) + + def _forward(self, x, context=None, transformer_options={}): + extra_options = {} + block = None + block_index = 0 + if "current_index" in transformer_options: + extra_options["transformer_index"] = transformer_options["current_index"] + if "block_index" in transformer_options: + block_index = transformer_options["block_index"] + extra_options["block_index"] = block_index + if "original_shape" in transformer_options: + extra_options["original_shape"] = transformer_options["original_shape"] + if "block" in transformer_options: + block = transformer_options["block"] + extra_options["block"] = block + if "patches" in transformer_options: + transformer_patches = transformer_options["patches"] + else: + transformer_patches = {} + + extra_options["n_heads"] = self.n_heads + extra_options["dim_head"] = self.d_head + + if "patches_replace" in transformer_options: + transformer_patches_replace = transformer_options["patches_replace"] + else: + transformer_patches_replace = {} + + n = self.norm1(x) + if self.disable_self_attn: + context_attn1 = context + else: + context_attn1 = None + value_attn1 = None + + if "attn1_patch" in transformer_patches: + patch = transformer_patches["attn1_patch"] + if context_attn1 is None: + context_attn1 = n + value_attn1 = context_attn1 + for p in patch: + n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options) + + if block is not None: + transformer_block = (block[0], block[1], block_index) + else: + transformer_block = None + attn1_replace_patch = transformer_patches_replace.get("attn1", {}) + block_attn1 = transformer_block + if block_attn1 not in attn1_replace_patch: + block_attn1 = block + + if block_attn1 in attn1_replace_patch: + if context_attn1 is None: + context_attn1 = n + value_attn1 = n + n = self.attn1.to_q(n) + context_attn1 = self.attn1.to_k(context_attn1) + value_attn1 = self.attn1.to_v(value_attn1) + n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options) + n = self.attn1.to_out(n) + else: + n = self.attn1(n, context=context_attn1, value=value_attn1) + + if "attn1_output_patch" in transformer_patches: + patch = transformer_patches["attn1_output_patch"] + for p in patch: + n = p(n, extra_options) + + x += n + if "middle_patch" in transformer_patches: + patch = transformer_patches["middle_patch"] + for p in patch: + x = p(x, extra_options) + + n = self.norm2(x) + + context_attn2 = context + value_attn2 = None + if "attn2_patch" in transformer_patches: + patch = transformer_patches["attn2_patch"] + value_attn2 = context_attn2 + for p in patch: + n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options) + + attn2_replace_patch = transformer_patches_replace.get("attn2", {}) + block_attn2 = transformer_block + if block_attn2 not in attn2_replace_patch: + block_attn2 = block + + if block_attn2 in attn2_replace_patch: + if value_attn2 is None: + value_attn2 = context_attn2 + n = self.attn2.to_q(n) + context_attn2 = self.attn2.to_k(context_attn2) + value_attn2 = self.attn2.to_v(value_attn2) + n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) + n = self.attn2.to_out(n) + else: + n = self.attn2(n, context=context_attn2, value=value_attn2) + + if "attn2_output_patch" in transformer_patches: + patch = transformer_patches["attn2_output_patch"] + for p in patch: + n = p(n, extra_options) + + x += n + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None, + disable_self_attn=False, use_linear=False, + use_checkpoint=True, dtype=None, device=None, operations=comfy.ops): + super().__init__() + if exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] * depth + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = Normalize(in_channels, dtype=dtype, device=device) + if not use_linear: + self.proj_in = operations.Conv2d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0, dtype=dtype, device=device) + else: + self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], + disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations) + for d in range(depth)] + ) + if not use_linear: + self.proj_out = operations.Conv2d(inner_dim,in_channels, + kernel_size=1, + stride=1, + padding=0, dtype=dtype, device=device) + else: + self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) + self.use_linear = use_linear + + def forward(self, x, context=None, transformer_options={}): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] * len(self.transformer_blocks) + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'b c h w -> b (h w) c').contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + transformer_options["block_index"] = i + x = block(x, context=context[i], transformer_options=transformer_options) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + diff --git a/comfy/ldm/modules/diffusionmodules/__init__.py b/comfy/ldm/modules/diffusionmodules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py new file mode 100644 index 0000000000000000000000000000000000000000..5f38640c3d8d1baf64dddc3b6fe7a45aabf4ab60 --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -0,0 +1,737 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange +from typing import Optional, Any + +from ..attention import MemoryEfficientCrossAttention +from comfy import model_management +import comfy.ops + +if model_management.xformers_enabled_vae(): + import xformers + import xformers.ops + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + try: + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + except: #operation not implemented for bf16 + b, c, h, w = x.shape + out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device) + split = 8 + l = out.shape[1] // split + for i in range(0, out.shape[1], l): + out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype) + del x + x = out + + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.swish = torch.nn.SiLU(inplace=True) + self.norm1 = Normalize(in_channels) + self.conv1 = comfy.ops.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = comfy.ops.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout, inplace=True) + self.conv2 = comfy.ops.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = comfy.ops.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = comfy.ops.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = self.swish(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(self.swish(temb))[:,:,None,None] + + h = self.norm2(h) + h = self.swish(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + +def slice_attention(q, k, v): + r1 = torch.zeros_like(k, device=q.device) + scale = (int(q.shape[-1])**(-0.5)) + + mem_free_total = model_management.get_free_memory(q.device) + + gb = 1024 ** 3 + tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() + modifier = 3 if q.element_size() == 2 else 2.5 + mem_required = tensor_size * modifier + steps = 1 + + if mem_required > mem_free_total: + steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) + + while True: + try: + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + s1 = torch.bmm(q[:, i:end], k) * scale + + s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1) + del s1 + + r1[:, :, i:end] = torch.bmm(v, s2) + del s2 + break + except model_management.OOM_EXCEPTION as e: + model_management.soft_empty_cache(True) + steps *= 2 + if steps > 128: + raise e + print("out of memory error, increasing steps and trying again", steps) + + return r1 + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + v = v.reshape(b,c,h*w) + + r1 = slice_attention(q, k, v) + h_ = r1.reshape(b,c,h,w) + del r1 + h_ = self.proj_out(h_) + + return x+h_ + +class MemoryEfficientAttnBlock(nn.Module): + """ + Uses xformers efficient implementation, + see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + Note: this is a single-head self-attention operation + """ + # + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.attention_op: Optional[Any] = None + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + B, C, H, W = q.shape + q, k, v = map( + lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), + (q, k, v), + ) + + try: + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) + out = out.transpose(1, 2).reshape(B, C, H, W) + except NotImplementedError as e: + out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) + + out = self.proj_out(out) + return x+out + +class MemoryEfficientAttnBlockPytorch(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = comfy.ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.attention_op: Optional[Any] = None + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + B, C, H, W = q.shape + q, k, v = map( + lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(), + (q, k, v), + ) + + try: + out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) + out = out.transpose(2, 3).reshape(B, C, H, W) + except model_management.OOM_EXCEPTION as e: + print("scaled_dot_product_attention OOMed: switched to slice attention") + out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) + + out = self.proj_out(out) + return x+out + +class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention): + def forward(self, x, context=None, mask=None): + b, c, h, w = x.shape + x = rearrange(x, 'b c h w -> b (h w) c') + out = super().forward(x, context=context, mask=mask) + out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c) + return x + out + + +def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): + assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown' + if model_management.xformers_enabled_vae() and attn_type == "vanilla": + attn_type = "vanilla-xformers" + if model_management.pytorch_attention_enabled() and attn_type == "vanilla": + attn_type = "vanilla-pytorch" + print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + assert attn_kwargs is None + return AttnBlock(in_channels) + elif attn_type == "vanilla-xformers": + print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...") + return MemoryEfficientAttnBlock(in_channels) + elif attn_type == "vanilla-pytorch": + return MemoryEfficientAttnBlockPytorch(in_channels) + elif type == "memory-efficient-cross-attn": + attn_kwargs["query_dim"] = in_channels + return MemoryEfficientCrossAttentionWrapper(**attn_kwargs) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + raise NotImplementedError() + + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + comfy.ops.Linear(self.ch, + self.temb_ch), + comfy.ops.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = comfy.ops.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = comfy.ops.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, t=None, context=None): + #assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = comfy.ops.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = comfy.ops.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # timestep embedding + temb = None + # downsampling + h = self.conv_in(x) + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + if i_level != self.num_resolutions-1: + h = self.down[i_level].downsample(h) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + attn_type="vanilla", **ignorekwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = comfy.ops.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = comfy.ops.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + if self.tanh_out: + h = torch.tanh(h) + return h diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py new file mode 100644 index 0000000000000000000000000000000000000000..b42637c821af38c8b3cb487a1564b8a098893d76 --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -0,0 +1,664 @@ +from abc import abstractmethod +import math + +import numpy as np +import torch as th +import torch.nn as nn +import torch.nn.functional as F + +from .util import ( + checkpoint, + avg_pool_nd, + zero_module, + normalization, + timestep_embedding, +) +from ..attention import SpatialTransformer +from comfy.ldm.util import exists +import comfy.ops + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None, transformer_options={}, output_shape=None): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context, transformer_options) + elif isinstance(layer, Upsample): + x = layer(x, output_shape=output_shape) + else: + x = layer(x) + return x + +#This is needed because accelerate makes a copy of transformer_options which breaks "current_index" +def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None): + for layer in ts: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context, transformer_options) + transformer_options["current_index"] += 1 + elif isinstance(layer, Upsample): + x = layer(x, output_shape=output_shape) + else: + x = layer(x) + return x + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device) + + def forward(self, x, output_shape=None): + assert x.shape[1] == self.channels + if self.dims == 3: + shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2] + if output_shape is not None: + shape[1] = output_shape[3] + shape[2] = output_shape[4] + else: + shape = [x.shape[2] * 2, x.shape[3] * 2] + if output_shape is not None: + shape[0] = output_shape[2] + shape[1] = output_shape[3] + + x = F.interpolate(x, size=shape, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = operations.conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + dtype=None, + device=None, + operations=comfy.ops + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + nn.GroupNorm(32, channels, dtype=dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device) + self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device) + elif down: + self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device) + self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + operations.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device + ), + ) + self.out_layers = nn.Sequential( + nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + operations.conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = operations.conv_nd( + dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device + ) + else: + self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = th.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + +class Timestep(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, t): + return timestep_embedding(t, self.dim) + + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + use_fp16=False, + use_bf16=False, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + transformer_depth_middle=None, + device=None, + operations=comfy.ops, + ): + super().__init__() + assert use_spatial_transformer == True, "use_spatial_transformer has to be true" + if use_spatial_transformer: + assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + # from omegaconf.listconfig import ListConfig + # if type(context_dim) == ListConfig: + # context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + if isinstance(transformer_depth, int): + transformer_depth = len(channel_mult) * [transformer_depth] + if transformer_depth_middle is None: + transformer_depth_middle = transformer_depth[-1] + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError("provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult") + self.num_res_blocks = num_res_blocks + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) + print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " + f"This option has LESS priority than attention_resolutions {attention_resolutions}, " + f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " + f"attention will still not be set.") + + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = th.float16 if use_fp16 else th.float32 + self.dtype = th.bfloat16 if use_bf16 else self.dtype + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + elif self.num_classes == "continuous": + print("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + ) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append(SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + ), + SpatialTransformer( # always uses a self-attn + ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or i < num_attention_blocks[level]: + layers.append( + SpatialTransformer( + ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + nn.GroupNorm(32, ch, dtype=self.dtype, device=device), + nn.SiLU(), + zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + nn.GroupNorm(32, ch, dtype=self.dtype, device=device), + operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), + #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + transformer_options["original_shape"] = list(x.shape) + transformer_options["current_index"] = 0 + transformer_patches = transformer_options.get("patches", {}) + + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype) + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x.type(self.dtype) + for id, module in enumerate(self.input_blocks): + transformer_options["block"] = ("input", id) + h = forward_timestep_embed(module, h, emb, context, transformer_options) + if control is not None and 'input' in control and len(control['input']) > 0: + ctrl = control['input'].pop() + if ctrl is not None: + h += ctrl + hs.append(h) + transformer_options["block"] = ("middle", 0) + h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options) + if control is not None and 'middle' in control and len(control['middle']) > 0: + ctrl = control['middle'].pop() + if ctrl is not None: + h += ctrl + + for id, module in enumerate(self.output_blocks): + transformer_options["block"] = ("output", id) + hsp = hs.pop() + if control is not None and 'output' in control and len(control['output']) > 0: + ctrl = control['output'].pop() + if ctrl is not None: + hsp += ctrl + + if "output_block_patch" in transformer_patches: + patch = transformer_patches["output_block_patch"] + for p in patch: + h, hsp = p(h, hsp, transformer_options) + + h = th.cat([h, hsp], dim=1) + del hsp + if len(hs) > 0: + output_shape = hs[-1].shape + else: + output_shape = None + h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) diff --git a/comfy/ldm/modules/diffusionmodules/upscaling.py b/comfy/ldm/modules/diffusionmodules/upscaling.py new file mode 100644 index 0000000000000000000000000000000000000000..709a7f52e0603fa5e31f3a1cc785eac93599a0ae --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/upscaling.py @@ -0,0 +1,81 @@ +import torch +import torch.nn as nn +import numpy as np +from functools import partial + +from .util import extract_into_tensor, make_beta_schedule +from comfy.ldm.util import default + + +class AbstractLowScaleModel(nn.Module): + # for concatenating a downsampled image to the latent representation + def __init__(self, noise_schedule_config=None): + super(AbstractLowScaleModel, self).__init__() + if noise_schedule_config is not None: + self.register_schedule(**noise_schedule_config) + + def register_schedule(self, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + + def forward(self, x): + return x, None + + def decode(self, x): + return x + + +class SimpleImageConcat(AbstractLowScaleModel): + # no noise level conditioning + def __init__(self): + super(SimpleImageConcat, self).__init__(noise_schedule_config=None) + self.max_noise_level = 0 + + def forward(self, x): + # fix to constant noise level + return x, torch.zeros(x.shape[0], device=x.device).long() + + +class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): + def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): + super().__init__(noise_schedule_config=noise_schedule_config) + self.max_noise_level = max_noise_level + + def forward(self, x, noise_level=None): + if noise_level is None: + noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() + else: + assert isinstance(noise_level, torch.Tensor) + z = self.q_sample(x, noise_level) + return z, noise_level + + + diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py new file mode 100644 index 0000000000000000000000000000000000000000..d890c8044aaa27e4e924485baad5232a589e9c7a --- /dev/null +++ b/comfy/ldm/modules/diffusionmodules/util.py @@ -0,0 +1,278 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + + +import os +import math +import torch +import torch.nn as nn +import numpy as np +from einops import repeat + +from comfy.ldm.util import instantiate_from_config +import comfy.ops + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = ( + torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "squaredcos_cap_v2": # used for karlo prior + # return early + return betas_for_alpha_bar( + n_timestep, + lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, + ) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + print(f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), + "dtype": torch.get_autocast_gpu_dtype(), + "cache_enabled": torch.is_autocast_cache_enabled()} + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(), \ + torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def normalization(channels, dtype=None): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNorm32(32, channels, dtype=dtype) + + +# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. +class SiLU(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return comfy.ops.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return comfy.ops.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class HybridConditioner(nn.Module): + + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() diff --git a/comfy/ldm/modules/distributions/__init__.py b/comfy/ldm/modules/distributions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy/ldm/modules/distributions/distributions.py b/comfy/ldm/modules/distributions/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..f2b8ef901130efc171aa69742ca0244d94d3f2e9 --- /dev/null +++ b/comfy/ldm/modules/distributions/distributions.py @@ -0,0 +1,92 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/comfy/ldm/modules/ema.py b/comfy/ldm/modules/ema.py new file mode 100644 index 0000000000000000000000000000000000000000..bded25019b9bcbcd0260f0b8185f8c7859ca58c4 --- /dev/null +++ b/comfy/ldm/modules/ema.py @@ -0,0 +1,80 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError('Decay must be between 0 and 1') + + self.m_name2s_name = {} + self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) + self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates + else torch.tensor(-1, dtype=torch.int)) + + for name, p in model.named_parameters(): + if p.requires_grad: + # remove as '.'-character is not allowed in buffers + s_name = name.replace('.', '') + self.m_name2s_name.update({name: s_name}) + self.register_buffer(s_name, p.clone().detach().data) + + self.collected_params = [] + + def reset_num_updates(self): + del self.num_updates + self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) + + def forward(self, model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) diff --git a/comfy/ldm/modules/encoders/__init__.py b/comfy/ldm/modules/encoders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy/ldm/modules/encoders/noise_aug_modules.py b/comfy/ldm/modules/encoders/noise_aug_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..b59bf204bc9fc625c122d23469e3574521d369e8 --- /dev/null +++ b/comfy/ldm/modules/encoders/noise_aug_modules.py @@ -0,0 +1,35 @@ +from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation +from ..diffusionmodules.openaimodel import Timestep +import torch + +class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): + def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs): + super().__init__(*args, **kwargs) + if clip_stats_path is None: + clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) + else: + clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu") + self.register_buffer("data_mean", clip_mean[None, :], persistent=False) + self.register_buffer("data_std", clip_std[None, :], persistent=False) + self.time_embed = Timestep(timestep_dim) + + def scale(self, x): + # re-normalize to centered mean and unit variance + x = (x - self.data_mean) * 1. / self.data_std + return x + + def unscale(self, x): + # back to original data stats + x = (x * self.data_std) + self.data_mean + return x + + def forward(self, x, noise_level=None): + if noise_level is None: + noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() + else: + assert isinstance(noise_level, torch.Tensor) + x = self.scale(x) + z = self.q_sample(x, noise_level) + z = self.unscale(z) + noise_level = self.time_embed(noise_level) + return z, noise_level diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..4d42059b5a87d7a4ee964a183b2a5372beb3229a --- /dev/null +++ b/comfy/ldm/modules/sub_quadratic_attention.py @@ -0,0 +1,250 @@ +# original source: +# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py +# license: +# MIT +# credit: +# Amin Rezaei (original author) +# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks) +# implementation of: +# Self-attention Does Not Need O(n2) Memory": +# https://arxiv.org/abs/2112.05682v2 + +from functools import partial +import torch +from torch import Tensor +from torch.utils.checkpoint import checkpoint +import math + +try: + from typing import Optional, NamedTuple, List, Protocol +except ImportError: + from typing import Optional, NamedTuple, List + from typing_extensions import Protocol + +from torch import Tensor +from typing import List + +from comfy import model_management + +def dynamic_slice( + x: Tensor, + starts: List[int], + sizes: List[int], +) -> Tensor: + slicing = [slice(start, start + size) for start, size in zip(starts, sizes)] + return x[slicing] + +class AttnChunk(NamedTuple): + exp_values: Tensor + exp_weights_sum: Tensor + max_score: Tensor + +class SummarizeChunk(Protocol): + @staticmethod + def __call__( + query: Tensor, + key_t: Tensor, + value: Tensor, + ) -> AttnChunk: ... + +class ComputeQueryChunkAttn(Protocol): + @staticmethod + def __call__( + query: Tensor, + key_t: Tensor, + value: Tensor, + ) -> Tensor: ... + +def _summarize_chunk( + query: Tensor, + key_t: Tensor, + value: Tensor, + scale: float, + upcast_attention: bool, +) -> AttnChunk: + if upcast_attention: + with torch.autocast(enabled=False, device_type = 'cuda'): + query = query.float() + key_t = key_t.float() + attn_weights = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + else: + attn_weights = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + max_score, _ = torch.max(attn_weights, -1, keepdim=True) + max_score = max_score.detach() + torch.exp(attn_weights - max_score, out=attn_weights) + exp_weights = attn_weights.to(value.dtype) + exp_values = torch.bmm(exp_weights, value) + max_score = max_score.squeeze(-1) + return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score) + +def _query_chunk_attention( + query: Tensor, + key_t: Tensor, + value: Tensor, + summarize_chunk: SummarizeChunk, + kv_chunk_size: int, +) -> Tensor: + batch_x_heads, k_channels_per_head, k_tokens = key_t.shape + _, _, v_channels_per_head = value.shape + + def chunk_scanner(chunk_idx: int) -> AttnChunk: + key_chunk = dynamic_slice( + key_t, + (0, 0, chunk_idx), + (batch_x_heads, k_channels_per_head, kv_chunk_size) + ) + value_chunk = dynamic_slice( + value, + (0, chunk_idx, 0), + (batch_x_heads, kv_chunk_size, v_channels_per_head) + ) + return summarize_chunk(query, key_chunk, value_chunk) + + chunks: List[AttnChunk] = [ + chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size) + ] + acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks))) + chunk_values, chunk_weights, chunk_max = acc_chunk + + global_max, _ = torch.max(chunk_max, 0, keepdim=True) + max_diffs = torch.exp(chunk_max - global_max) + chunk_values *= torch.unsqueeze(max_diffs, -1) + chunk_weights *= max_diffs + + all_values = chunk_values.sum(dim=0) + all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0) + return all_values / all_weights + +# TODO: refactor CrossAttention#get_attention_scores to share code with this +def _get_attention_scores_no_kv_chunking( + query: Tensor, + key_t: Tensor, + value: Tensor, + scale: float, + upcast_attention: bool, +) -> Tensor: + if upcast_attention: + with torch.autocast(enabled=False, device_type = 'cuda'): + query = query.float() + key_t = key_t.float() + attn_scores = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + else: + attn_scores = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + + try: + attn_probs = attn_scores.softmax(dim=-1) + del attn_scores + except model_management.OOM_EXCEPTION: + print("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead") + attn_scores -= attn_scores.max(dim=-1, keepdim=True).values + torch.exp(attn_scores, out=attn_scores) + summed = torch.sum(attn_scores, dim=-1, keepdim=True) + attn_scores /= summed + attn_probs = attn_scores + + hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value) + return hidden_states_slice + +class ScannedChunk(NamedTuple): + chunk_idx: int + attn_chunk: AttnChunk + +def efficient_dot_product_attention( + query: Tensor, + key_t: Tensor, + value: Tensor, + query_chunk_size=1024, + kv_chunk_size: Optional[int] = None, + kv_chunk_size_min: Optional[int] = None, + use_checkpoint=True, + upcast_attention=False, +): + """Computes efficient dot-product attention given query, transposed key, and value. + This is efficient version of attention presented in + https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements. + Args: + query: queries for calculating attention with shape of + `[batch * num_heads, tokens, channels_per_head]`. + key_t: keys for calculating attention with shape of + `[batch * num_heads, channels_per_head, tokens]`. + value: values to be used in attention with shape of + `[batch * num_heads, tokens, channels_per_head]`. + query_chunk_size: int: query chunks size + kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens) + kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done). + use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference) + Returns: + Output of shape `[batch * num_heads, query_tokens, channels_per_head]`. + """ + batch_x_heads, q_tokens, q_channels_per_head = query.shape + _, _, k_tokens = key_t.shape + scale = q_channels_per_head ** -0.5 + + kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens) + if kv_chunk_size_min is not None: + kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min) + + def get_query_chunk(chunk_idx: int) -> Tensor: + return dynamic_slice( + query, + (0, chunk_idx, 0), + (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head) + ) + + summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention) + summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk + compute_query_chunk_attn: ComputeQueryChunkAttn = partial( + _get_attention_scores_no_kv_chunking, + scale=scale, + upcast_attention=upcast_attention + ) if k_tokens <= kv_chunk_size else ( + # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw) + partial( + _query_chunk_attention, + kv_chunk_size=kv_chunk_size, + summarize_chunk=summarize_chunk, + ) + ) + + if q_tokens <= query_chunk_size: + # fast-path for when there's just 1 query chunk + return compute_query_chunk_attn( + query=query, + key_t=key_t, + value=value, + ) + + # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, + # and pass slices to be mutated, instead of torch.cat()ing the returned slices + res = torch.cat([ + compute_query_chunk_attn( + query=get_query_chunk(i * query_chunk_size), + key_t=key_t, + value=value, + ) for i in range(math.ceil(q_tokens / query_chunk_size)) + ], dim=1) + return res diff --git a/comfy/ldm/util.py b/comfy/ldm/util.py new file mode 100644 index 0000000000000000000000000000000000000000..8c09ca1c72f7ceb3f9d7f9546aae5561baf62b13 --- /dev/null +++ b/comfy/ldm/util.py @@ -0,0 +1,197 @@ +import importlib + +import torch +from torch import optim +import numpy as np + +from inspect import isfunction +from PIL import Image, ImageDraw, ImageFont + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) + + try: + draw.text((0, 0), lines, fill="black", font=font) + except UnicodeEncodeError: + print("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x,torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +class AdamWwithEMAandWings(optim.Optimizer): + # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 + def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using + weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code + ema_power=1., param_names=()): + """AdamW that saves EMA versions of the parameters.""" + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + if not 0.0 <= weight_decay: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + if not 0.0 <= ema_decay <= 1.0: + raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) + defaults = dict(lr=lr, betas=betas, eps=eps, + weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, + ema_power=ema_power, param_names=param_names) + super().__init__(params, defaults) + + def __setstate__(self, state): + super().__setstate__(state) + for group in self.param_groups: + group.setdefault('amsgrad', False) + + @torch.no_grad() + def step(self, closure=None): + """Performs a single optimization step. + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + params_with_grad = [] + grads = [] + exp_avgs = [] + exp_avg_sqs = [] + ema_params_with_grad = [] + state_sums = [] + max_exp_avg_sqs = [] + state_steps = [] + amsgrad = group['amsgrad'] + beta1, beta2 = group['betas'] + ema_decay = group['ema_decay'] + ema_power = group['ema_power'] + + for p in group['params']: + if p.grad is None: + continue + params_with_grad.append(p) + if p.grad.is_sparse: + raise RuntimeError('AdamW does not support sparse gradients') + grads.append(p.grad) + + state = self.state[p] + + # State initialization + if len(state) == 0: + state['step'] = 0 + # Exponential moving average of gradient values + state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) + # Exponential moving average of squared gradient values + state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) + if amsgrad: + # Maintains max of all exp. moving avg. of sq. grad. values + state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) + # Exponential moving average of parameter values + state['param_exp_avg'] = p.detach().float().clone() + + exp_avgs.append(state['exp_avg']) + exp_avg_sqs.append(state['exp_avg_sq']) + ema_params_with_grad.append(state['param_exp_avg']) + + if amsgrad: + max_exp_avg_sqs.append(state['max_exp_avg_sq']) + + # update the steps for each param group update + state['step'] += 1 + # record the step after step update + state_steps.append(state['step']) + + optim._functional.adamw(params_with_grad, + grads, + exp_avgs, + exp_avg_sqs, + max_exp_avg_sqs, + state_steps, + amsgrad=amsgrad, + beta1=beta1, + beta2=beta2, + lr=group['lr'], + weight_decay=group['weight_decay'], + eps=group['eps'], + maximize=False) + + cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) + for param, ema_param in zip(params_with_grad, ema_params_with_grad): + ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) + + return loss \ No newline at end of file diff --git a/comfy/lora.py b/comfy/lora.py new file mode 100644 index 0000000000000000000000000000000000000000..3009a1c9e0c24cb1b31111c097d89fb03c7bd3a8 --- /dev/null +++ b/comfy/lora.py @@ -0,0 +1,199 @@ +import comfy.utils + +LORA_CLIP_MAP = { + "mlp.fc1": "mlp_fc1", + "mlp.fc2": "mlp_fc2", + "self_attn.k_proj": "self_attn_k_proj", + "self_attn.q_proj": "self_attn_q_proj", + "self_attn.v_proj": "self_attn_v_proj", + "self_attn.out_proj": "self_attn_out_proj", +} + + +def load_lora(lora, to_load): + patch_dict = {} + loaded_keys = set() + for x in to_load: + alpha_name = "{}.alpha".format(x) + alpha = None + if alpha_name in lora.keys(): + alpha = lora[alpha_name].item() + loaded_keys.add(alpha_name) + + regular_lora = "{}.lora_up.weight".format(x) + diffusers_lora = "{}_lora.up.weight".format(x) + transformers_lora = "{}.lora_linear_layer.up.weight".format(x) + A_name = None + + if regular_lora in lora.keys(): + A_name = regular_lora + B_name = "{}.lora_down.weight".format(x) + mid_name = "{}.lora_mid.weight".format(x) + elif diffusers_lora in lora.keys(): + A_name = diffusers_lora + B_name = "{}_lora.down.weight".format(x) + mid_name = None + elif transformers_lora in lora.keys(): + A_name = transformers_lora + B_name ="{}.lora_linear_layer.down.weight".format(x) + mid_name = None + + if A_name is not None: + mid = None + if mid_name is not None and mid_name in lora.keys(): + mid = lora[mid_name] + loaded_keys.add(mid_name) + patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid) + loaded_keys.add(A_name) + loaded_keys.add(B_name) + + + ######## loha + hada_w1_a_name = "{}.hada_w1_a".format(x) + hada_w1_b_name = "{}.hada_w1_b".format(x) + hada_w2_a_name = "{}.hada_w2_a".format(x) + hada_w2_b_name = "{}.hada_w2_b".format(x) + hada_t1_name = "{}.hada_t1".format(x) + hada_t2_name = "{}.hada_t2".format(x) + if hada_w1_a_name in lora.keys(): + hada_t1 = None + hada_t2 = None + if hada_t1_name in lora.keys(): + hada_t1 = lora[hada_t1_name] + hada_t2 = lora[hada_t2_name] + loaded_keys.add(hada_t1_name) + loaded_keys.add(hada_t2_name) + + patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2) + loaded_keys.add(hada_w1_a_name) + loaded_keys.add(hada_w1_b_name) + loaded_keys.add(hada_w2_a_name) + loaded_keys.add(hada_w2_b_name) + + + ######## lokr + lokr_w1_name = "{}.lokr_w1".format(x) + lokr_w2_name = "{}.lokr_w2".format(x) + lokr_w1_a_name = "{}.lokr_w1_a".format(x) + lokr_w1_b_name = "{}.lokr_w1_b".format(x) + lokr_t2_name = "{}.lokr_t2".format(x) + lokr_w2_a_name = "{}.lokr_w2_a".format(x) + lokr_w2_b_name = "{}.lokr_w2_b".format(x) + + lokr_w1 = None + if lokr_w1_name in lora.keys(): + lokr_w1 = lora[lokr_w1_name] + loaded_keys.add(lokr_w1_name) + + lokr_w2 = None + if lokr_w2_name in lora.keys(): + lokr_w2 = lora[lokr_w2_name] + loaded_keys.add(lokr_w2_name) + + lokr_w1_a = None + if lokr_w1_a_name in lora.keys(): + lokr_w1_a = lora[lokr_w1_a_name] + loaded_keys.add(lokr_w1_a_name) + + lokr_w1_b = None + if lokr_w1_b_name in lora.keys(): + lokr_w1_b = lora[lokr_w1_b_name] + loaded_keys.add(lokr_w1_b_name) + + lokr_w2_a = None + if lokr_w2_a_name in lora.keys(): + lokr_w2_a = lora[lokr_w2_a_name] + loaded_keys.add(lokr_w2_a_name) + + lokr_w2_b = None + if lokr_w2_b_name in lora.keys(): + lokr_w2_b = lora[lokr_w2_b_name] + loaded_keys.add(lokr_w2_b_name) + + lokr_t2 = None + if lokr_t2_name in lora.keys(): + lokr_t2 = lora[lokr_t2_name] + loaded_keys.add(lokr_t2_name) + + if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): + patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2) + + + w_norm_name = "{}.w_norm".format(x) + b_norm_name = "{}.b_norm".format(x) + w_norm = lora.get(w_norm_name, None) + b_norm = lora.get(b_norm_name, None) + + if w_norm is not None: + loaded_keys.add(w_norm_name) + patch_dict[to_load[x]] = (w_norm,) + if b_norm is not None: + loaded_keys.add(b_norm_name) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = (b_norm,) + + for x in lora.keys(): + if x not in loaded_keys: + print("lora key not loaded", x) + return patch_dict + +def model_lora_keys_clip(model, key_map={}): + sdk = model.state_dict().keys() + + text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" + clip_l_present = False + for b in range(32): + for c in LORA_CLIP_MAP: + k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base + key_map[lora_key] = k + clip_l_present = True + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + if clip_l_present: + lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base + key_map[lora_key] = k + lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + else: + lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + return key_map + +def model_lora_keys_unet(model, key_map={}): + sdk = model.state_dict().keys() + + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = k + + diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config) + for k in diffusers_keys: + if k.endswith(".weight"): + unet_key = "diffusion_model.{}".format(diffusers_keys[k]) + key_lora = k[:-len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = unet_key + + diffusers_lora_prefix = ["", "unet."] + for p in diffusers_lora_prefix: + diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) + if diffusers_lora_key.endswith(".to_out.0"): + diffusers_lora_key = diffusers_lora_key[:-2] + key_map[diffusers_lora_key] = unet_key + return key_map diff --git a/comfy/model_base.py b/comfy/model_base.py new file mode 100644 index 0000000000000000000000000000000000000000..ed2dc83e4e097ae1a131204f5f34a474289d7131 --- /dev/null +++ b/comfy/model_base.py @@ -0,0 +1,210 @@ +import torch +from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel +from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation +from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule +from comfy.ldm.modules.diffusionmodules.openaimodel import Timestep +import comfy.model_management +import numpy as np +from enum import Enum +from . import utils + +class ModelType(Enum): + EPS = 1 + V_PREDICTION = 2 + +class BaseModel(torch.nn.Module): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__() + + unet_config = model_config.unet_config + self.latent_format = model_config.latent_format + self.model_config = model_config + self.register_schedule(given_betas=None, beta_schedule=model_config.beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3) + if not unet_config.get("disable_unet_model_creation", False): + self.diffusion_model = UNetModel(**unet_config, device=device) + self.model_type = model_type + self.adm_channels = unet_config.get("adm_in_channels", None) + if self.adm_channels is None: + self.adm_channels = 0 + print("model_type", model_type.name) + print("adm", self.adm_channels) + + def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if given_betas is not None: + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + + self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) + self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) + self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) + + def apply_model(self, x, t, c_concat=None, c_crossattn=None, c_adm=None, control=None, transformer_options={}): + if c_concat is not None: + xc = torch.cat([x] + [c_concat], dim=1) + else: + xc = x + context = c_crossattn + dtype = self.get_dtype() + xc = xc.to(dtype) + t = t.to(dtype) + context = context.to(dtype) + if c_adm is not None: + c_adm = c_adm.to(dtype) + return self.diffusion_model(xc, t, context=context, y=c_adm, control=control, transformer_options=transformer_options).float() + + def get_dtype(self): + return self.diffusion_model.dtype + + def is_adm(self): + return self.adm_channels > 0 + + def encode_adm(self, **kwargs): + return None + + def load_model_weights(self, sd, unet_prefix=""): + to_load = {} + keys = list(sd.keys()) + for k in keys: + if k.startswith(unet_prefix): + to_load[k[len(unet_prefix):]] = sd.pop(k) + + m, u = self.diffusion_model.load_state_dict(to_load, strict=False) + if len(m) > 0: + print("unet missing:", m) + + if len(u) > 0: + print("unet unexpected:", u) + del to_load + return self + + def process_latent_in(self, latent): + return self.latent_format.process_in(latent) + + def process_latent_out(self, latent): + return self.latent_format.process_out(latent) + + def state_dict_for_saving(self, clip_state_dict, vae_state_dict): + clip_state_dict = self.model_config.process_clip_state_dict_for_saving(clip_state_dict) + unet_sd = self.diffusion_model.state_dict() + unet_state_dict = {} + for k in unet_sd: + unet_state_dict[k] = comfy.model_management.resolve_lowvram_weight(unet_sd[k], self.diffusion_model, k) + + unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) + vae_state_dict = self.model_config.process_vae_state_dict_for_saving(vae_state_dict) + if self.get_dtype() == torch.float16: + clip_state_dict = utils.convert_sd_to(clip_state_dict, torch.float16) + vae_state_dict = utils.convert_sd_to(vae_state_dict, torch.float16) + + if self.model_type == ModelType.V_PREDICTION: + unet_state_dict["v_pred"] = torch.tensor([]) + + return {**unet_state_dict, **vae_state_dict, **clip_state_dict} + + def set_inpaint(self): + self.concat_keys = ("mask", "masked_image") + +def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0): + adm_inputs = [] + weights = [] + noise_aug = [] + for unclip_cond in unclip_conditioning: + for adm_cond in unclip_cond["clip_vision_output"].image_embeds: + weight = unclip_cond["strength"] + noise_augment = unclip_cond["noise_augmentation"] + noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) + c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device)) + adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight + weights.append(weight) + noise_aug.append(noise_augment) + adm_inputs.append(adm_out) + + if len(noise_aug) > 1: + adm_out = torch.stack(adm_inputs).sum(0) + noise_augment = noise_augment_merge + noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) + c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device)) + adm_out = torch.cat((c_adm, noise_level_emb), 1) + + return adm_out + +class SD21UNCLIP(BaseModel): + def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None): + super().__init__(model_config, model_type, device=device) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) + + def encode_adm(self, **kwargs): + unclip_conditioning = kwargs.get("unclip_conditioning", None) + device = kwargs["device"] + if unclip_conditioning is None: + return torch.zeros((1, self.adm_channels)) + else: + return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05)) + +def sdxl_pooled(args, noise_augmentor): + if "unclip_conditioning" in args: + return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor)[:,:1280] + else: + return args["pooled_output"] + +class SDXLRefiner(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) + + def encode_adm(self, **kwargs): + clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + crop_w = kwargs.get("crop_w", 0) + crop_h = kwargs.get("crop_h", 0) + + if kwargs.get("prompt_type", "") == "negative": + aesthetic_score = kwargs.get("aesthetic_score", 2.5) + else: + aesthetic_score = kwargs.get("aesthetic_score", 6) + + out = [] + out.append(self.embedder(torch.Tensor([height]))) + out.append(self.embedder(torch.Tensor([width]))) + out.append(self.embedder(torch.Tensor([crop_h]))) + out.append(self.embedder(torch.Tensor([crop_w]))) + out.append(self.embedder(torch.Tensor([aesthetic_score]))) + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return torch.cat((clip_pooled.to(flat.device), flat), dim=1) + +class SDXL(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) + + def encode_adm(self, **kwargs): + clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + crop_w = kwargs.get("crop_w", 0) + crop_h = kwargs.get("crop_h", 0) + target_width = kwargs.get("target_width", width) + target_height = kwargs.get("target_height", height) + + out = [] + out.append(self.embedder(torch.Tensor([height]))) + out.append(self.embedder(torch.Tensor([width]))) + out.append(self.embedder(torch.Tensor([crop_h]))) + out.append(self.embedder(torch.Tensor([crop_w]))) + out.append(self.embedder(torch.Tensor([target_height]))) + out.append(self.embedder(torch.Tensor([target_width]))) + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return torch.cat((clip_pooled.to(flat.device), flat), dim=1) diff --git a/comfy/model_detection.py b/comfy/model_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..787c78575aef7b16e3923f5d0fdc2f2771d34120 --- /dev/null +++ b/comfy/model_detection.py @@ -0,0 +1,210 @@ +import comfy.supported_models +import comfy.supported_models_base + +def count_blocks(state_dict_keys, prefix_string): + count = 0 + while True: + c = False + for k in state_dict_keys: + if k.startswith(prefix_string.format(count)): + c = True + break + if c == False: + break + count += 1 + return count + +def detect_unet_config(state_dict, key_prefix, use_fp16): + state_dict_keys = list(state_dict.keys()) + + unet_config = { + "use_checkpoint": False, + "image_size": 32, + "out_channels": 4, + "use_spatial_transformer": True, + "legacy": False + } + + y_input = '{}label_emb.0.0.weight'.format(key_prefix) + if y_input in state_dict_keys: + unet_config["num_classes"] = "sequential" + unet_config["adm_in_channels"] = state_dict[y_input].shape[1] + else: + unet_config["adm_in_channels"] = None + + unet_config["use_fp16"] = use_fp16 + model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0] + in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1] + + num_res_blocks = [] + channel_mult = [] + attention_resolutions = [] + transformer_depth = [] + context_dim = None + use_linear_in_transformer = False + + + current_res = 1 + count = 0 + + last_res_blocks = 0 + last_transformer_depth = 0 + last_channel_mult = 0 + + while True: + prefix = '{}input_blocks.{}.'.format(key_prefix, count) + block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys))) + if len(block_keys) == 0: + break + + if "{}0.op.weight".format(prefix) in block_keys: #new layer + if last_transformer_depth > 0: + attention_resolutions.append(current_res) + transformer_depth.append(last_transformer_depth) + num_res_blocks.append(last_res_blocks) + channel_mult.append(last_channel_mult) + + current_res *= 2 + last_res_blocks = 0 + last_transformer_depth = 0 + last_channel_mult = 0 + else: + res_block_prefix = "{}0.in_layers.0.weight".format(prefix) + if res_block_prefix in block_keys: + last_res_blocks += 1 + last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels + + transformer_prefix = prefix + "1.transformer_blocks." + transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys))) + if len(transformer_keys) > 0: + last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}') + if context_dim is None: + context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1] + use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2 + + count += 1 + + if last_transformer_depth > 0: + attention_resolutions.append(current_res) + transformer_depth.append(last_transformer_depth) + num_res_blocks.append(last_res_blocks) + channel_mult.append(last_channel_mult) + transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}') + + if len(set(num_res_blocks)) == 1: + num_res_blocks = num_res_blocks[0] + + if len(set(transformer_depth)) == 1: + transformer_depth = transformer_depth[0] + + unet_config["in_channels"] = in_channels + unet_config["model_channels"] = model_channels + unet_config["num_res_blocks"] = num_res_blocks + unet_config["attention_resolutions"] = attention_resolutions + unet_config["transformer_depth"] = transformer_depth + unet_config["channel_mult"] = channel_mult + unet_config["transformer_depth_middle"] = transformer_depth_middle + unet_config['use_linear_in_transformer'] = use_linear_in_transformer + unet_config["context_dim"] = context_dim + return unet_config + +def model_config_from_unet_config(unet_config): + for model_config in comfy.supported_models.models: + if model_config.matches(unet_config): + return model_config(unet_config) + + print("no match", unet_config) + return None + +def model_config_from_unet(state_dict, unet_key_prefix, use_fp16, use_base_if_no_match=False): + unet_config = detect_unet_config(state_dict, unet_key_prefix, use_fp16) + model_config = model_config_from_unet_config(unet_config) + if model_config is None and use_base_if_no_match: + return comfy.supported_models_base.BASE(unet_config) + else: + return model_config + +def unet_config_from_diffusers_unet(state_dict, use_fp16): + match = {} + attention_resolutions = [] + + attn_res = 1 + for i in range(5): + k = "down_blocks.{}.attentions.1.transformer_blocks.0.attn2.to_k.weight".format(i) + if k in state_dict: + match["context_dim"] = state_dict[k].shape[1] + attention_resolutions.append(attn_res) + attn_res *= 2 + + match["attention_resolutions"] = attention_resolutions + + match["model_channels"] = state_dict["conv_in.weight"].shape[0] + match["in_channels"] = state_dict["conv_in.weight"].shape[1] + match["adm_in_channels"] = None + if "class_embedding.linear_1.weight" in state_dict: + match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1] + elif "add_embedding.linear_1.weight" in state_dict: + match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1] + + SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64} + + SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2560, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 384, + 'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 4, 4, 0], 'channel_mult': [1, 2, 4, 4], + 'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280, "num_head_channels": 64} + + SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2, + 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4], + 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64} + + SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2048, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4], + 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64} + + SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 1536, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4], + 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024} + + SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2, + 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4], + 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, "num_heads": 8} + + SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': 2, 'attention_resolutions': [4], 'transformer_depth': [0, 0, 1], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64} + + SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': 2, 'attention_resolutions': [], 'transformer_depth': [0, 0, 0], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 0, 'use_linear_in_transformer': True, "num_head_channels": 64, 'context_dim': 1} + + SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 9, 'model_channels': 320, + 'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4], + 'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64} + + supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint] + + for unet_config in supported_models: + matches = True + for k in match: + if match[k] != unet_config[k]: + matches = False + break + if matches: + return unet_config + return None + +def model_config_from_diffusers_unet(state_dict, use_fp16): + unet_config = unet_config_from_diffusers_unet(state_dict, use_fp16) + if unet_config is not None: + return model_config_from_unet_config(unet_config) + return None diff --git a/comfy/model_management.py b/comfy/model_management.py new file mode 100644 index 0000000000000000000000000000000000000000..8b8963726870d44f07852960474fdb26f360357a --- /dev/null +++ b/comfy/model_management.py @@ -0,0 +1,711 @@ +import psutil +from enum import Enum +from comfy.cli_args import args +import comfy.utils +import torch +import sys + +class VRAMState(Enum): + DISABLED = 0 #No vram present: no need to move models to vram + NO_VRAM = 1 #Very low vram: enable all the options to save vram + LOW_VRAM = 2 + NORMAL_VRAM = 3 + HIGH_VRAM = 4 + SHARED = 5 #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both. + +class CPUState(Enum): + GPU = 0 + CPU = 1 + MPS = 2 + +# Determine VRAM State +vram_state = VRAMState.NORMAL_VRAM +set_vram_to = VRAMState.NORMAL_VRAM +cpu_state = CPUState.GPU + +total_vram = 0 + +lowvram_available = True +xpu_available = False + +directml_enabled = False +if args.directml is not None: + import torch_directml + directml_enabled = True + device_index = args.directml + if device_index < 0: + directml_device = torch_directml.device() + else: + directml_device = torch_directml.device(device_index) + print("Using directml with device:", torch_directml.device_name(device_index)) + # torch_directml.disable_tiled_resources(True) + lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. + +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + xpu_available = True +except: + pass + +try: + if torch.backends.mps.is_available(): + cpu_state = CPUState.MPS + import torch.mps +except: + pass + +if args.cpu: + cpu_state = CPUState.CPU + +def is_intel_xpu(): + global cpu_state + global xpu_available + if cpu_state == CPUState.GPU: + if xpu_available: + return True + return False + +def get_torch_device(): + global directml_enabled + global cpu_state + if directml_enabled: + global directml_device + return directml_device + if cpu_state == CPUState.MPS: + return torch.device("mps") + if cpu_state == CPUState.CPU: + return torch.device("cpu") + else: + if is_intel_xpu(): + return torch.device("xpu") + else: + return torch.device(torch.cuda.current_device()) + +def get_total_memory(dev=None, torch_total_too=False): + global directml_enabled + if dev is None: + dev = get_torch_device() + + if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): + mem_total = psutil.virtual_memory().total + mem_total_torch = mem_total + else: + if directml_enabled: + mem_total = 1024 * 1024 * 1024 #TODO + mem_total_torch = mem_total + elif is_intel_xpu(): + stats = torch.xpu.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + mem_total = torch.xpu.get_device_properties(dev).total_memory + mem_total_torch = mem_reserved + else: + stats = torch.cuda.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + _, mem_total_cuda = torch.cuda.mem_get_info(dev) + mem_total_torch = mem_reserved + mem_total = mem_total_cuda + + if torch_total_too: + return (mem_total, mem_total_torch) + else: + return mem_total + +total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) +total_ram = psutil.virtual_memory().total / (1024 * 1024) +print("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) +if not args.normalvram and not args.cpu: + if lowvram_available and total_vram <= 4096: + print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram") + set_vram_to = VRAMState.LOW_VRAM + +try: + OOM_EXCEPTION = torch.cuda.OutOfMemoryError +except: + OOM_EXCEPTION = Exception + +XFORMERS_VERSION = "" +XFORMERS_ENABLED_VAE = True +if args.disable_xformers: + XFORMERS_IS_AVAILABLE = False +else: + try: + import xformers + import xformers.ops + XFORMERS_IS_AVAILABLE = True + try: + XFORMERS_VERSION = xformers.version.__version__ + print("xformers version:", XFORMERS_VERSION) + if XFORMERS_VERSION.startswith("0.0.18"): + print() + print("WARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") + print("Please downgrade or upgrade xformers to a different version.") + print() + XFORMERS_ENABLED_VAE = False + except: + pass + except: + XFORMERS_IS_AVAILABLE = False + +def is_nvidia(): + global cpu_state + if cpu_state == CPUState.GPU: + if torch.version.cuda: + return True + return False + +ENABLE_PYTORCH_ATTENTION = args.use_pytorch_cross_attention +VAE_DTYPE = torch.float32 + +try: + if is_nvidia(): + torch_version = torch.version.__version__ + if int(torch_version[0]) >= 2: + if ENABLE_PYTORCH_ATTENTION == False and XFORMERS_IS_AVAILABLE == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: + ENABLE_PYTORCH_ATTENTION = True + if torch.cuda.is_bf16_supported(): + VAE_DTYPE = torch.bfloat16 + if is_intel_xpu(): + if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: + ENABLE_PYTORCH_ATTENTION = True +except: + pass + +if is_intel_xpu(): + VAE_DTYPE = torch.bfloat16 + +if args.fp16_vae: + VAE_DTYPE = torch.float16 +elif args.bf16_vae: + VAE_DTYPE = torch.bfloat16 +elif args.fp32_vae: + VAE_DTYPE = torch.float32 + + +if ENABLE_PYTORCH_ATTENTION: + torch.backends.cuda.enable_math_sdp(True) + torch.backends.cuda.enable_flash_sdp(True) + torch.backends.cuda.enable_mem_efficient_sdp(True) + XFORMERS_IS_AVAILABLE = False + +if args.lowvram: + set_vram_to = VRAMState.LOW_VRAM + lowvram_available = True +elif args.novram: + set_vram_to = VRAMState.NO_VRAM +elif args.highvram or args.gpu_only: + vram_state = VRAMState.HIGH_VRAM + +FORCE_FP32 = False +FORCE_FP16 = False +if args.force_fp32: + print("Forcing FP32, if this improves things please report it.") + FORCE_FP32 = True + +if args.force_fp16: + print("Forcing FP16.") + FORCE_FP16 = True + +if lowvram_available: + try: + import accelerate + if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): + vram_state = set_vram_to + except Exception as e: + import traceback + print(traceback.format_exc()) + print("ERROR: LOW VRAM MODE NEEDS accelerate.") + lowvram_available = False + + +if cpu_state != CPUState.GPU: + vram_state = VRAMState.DISABLED + +if cpu_state == CPUState.MPS: + vram_state = VRAMState.SHARED + +print(f"Set vram state to: {vram_state.name}") + +DISABLE_SMART_MEMORY = args.disable_smart_memory + +if DISABLE_SMART_MEMORY: + print("Disabling smart memory management") + +def get_torch_device_name(device): + if hasattr(device, 'type'): + if device.type == "cuda": + try: + allocator_backend = torch.cuda.get_allocator_backend() + except: + allocator_backend = "" + return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend) + else: + return "{}".format(device.type) + elif is_intel_xpu(): + return "{} {}".format(device, torch.xpu.get_device_name(device)) + else: + return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) + +try: + print("Device:", get_torch_device_name(get_torch_device())) +except: + print("Could not pick default device.") + +print("VAE dtype:", VAE_DTYPE) + +current_loaded_models = [] + +class LoadedModel: + def __init__(self, model): + self.model = model + self.model_accelerated = False + self.device = model.load_device + + def model_memory(self): + return self.model.model_size() + + def model_memory_required(self, device): + if device == self.model.current_device: + return 0 + else: + return self.model_memory() + + def model_load(self, lowvram_model_memory=0): + patch_model_to = None + if lowvram_model_memory == 0: + patch_model_to = self.device + + self.model.model_patches_to(self.device) + self.model.model_patches_to(self.model.model_dtype()) + + try: + self.real_model = self.model.patch_model(device_to=patch_model_to) #TODO: do something with loras and offloading to CPU + except Exception as e: + self.model.unpatch_model(self.model.offload_device) + self.model_unload() + raise e + + if lowvram_model_memory > 0: + print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024)) + device_map = accelerate.infer_auto_device_map(self.real_model, max_memory={0: "{}MiB".format(lowvram_model_memory // (1024 * 1024)), "cpu": "16GiB"}) + accelerate.dispatch_model(self.real_model, device_map=device_map, main_device=self.device) + self.model_accelerated = True + + if is_intel_xpu() and not args.disable_ipex_optimize: + self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True) + + return self.real_model + + def model_unload(self): + if self.model_accelerated: + accelerate.hooks.remove_hook_from_submodules(self.real_model) + self.model_accelerated = False + + self.model.unpatch_model(self.model.offload_device) + self.model.model_patches_to(self.model.offload_device) + + def __eq__(self, other): + return self.model is other.model + +def minimum_inference_memory(): + return (1024 * 1024 * 1024) + +def unload_model_clones(model): + to_unload = [] + for i in range(len(current_loaded_models)): + if model.is_clone(current_loaded_models[i].model): + to_unload = [i] + to_unload + + for i in to_unload: + print("unload clone", i) + current_loaded_models.pop(i).model_unload() + +def free_memory(memory_required, device, keep_loaded=[]): + unloaded_model = False + for i in range(len(current_loaded_models) -1, -1, -1): + if not DISABLE_SMART_MEMORY: + if get_free_memory(device) > memory_required: + break + shift_model = current_loaded_models[i] + if shift_model.device == device: + if shift_model not in keep_loaded: + m = current_loaded_models.pop(i) + m.model_unload() + del m + unloaded_model = True + + if unloaded_model: + soft_empty_cache() + + +def load_models_gpu(models, memory_required=0): + global vram_state + + inference_memory = minimum_inference_memory() + extra_mem = max(inference_memory, memory_required) + + models_to_load = [] + models_already_loaded = [] + for x in models: + loaded_model = LoadedModel(x) + + if loaded_model in current_loaded_models: + index = current_loaded_models.index(loaded_model) + current_loaded_models.insert(0, current_loaded_models.pop(index)) + models_already_loaded.append(loaded_model) + else: + models_to_load.append(loaded_model) + + if len(models_to_load) == 0: + devs = set(map(lambda a: a.device, models_already_loaded)) + for d in devs: + if d != torch.device("cpu"): + free_memory(extra_mem, d, models_already_loaded) + return + + print("loading new") + + total_memory_required = {} + for loaded_model in models_to_load: + unload_model_clones(loaded_model.model) + total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) + + for device in total_memory_required: + if device != torch.device("cpu"): + free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded) + + for loaded_model in models_to_load: + model = loaded_model.model + torch_dev = model.load_device + if is_device_cpu(torch_dev): + vram_set_state = VRAMState.DISABLED + else: + vram_set_state = vram_state + lowvram_model_memory = 0 + if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM): + model_size = loaded_model.model_memory_required(torch_dev) + current_free_mem = get_free_memory(torch_dev) + lowvram_model_memory = int(max(256 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 )) + if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary + vram_set_state = VRAMState.LOW_VRAM + else: + lowvram_model_memory = 0 + + if vram_set_state == VRAMState.NO_VRAM: + lowvram_model_memory = 256 * 1024 * 1024 + + cur_loaded_model = loaded_model.model_load(lowvram_model_memory) + current_loaded_models.insert(0, loaded_model) + return + + +def load_model_gpu(model): + return load_models_gpu([model]) + +def cleanup_models(): + to_delete = [] + for i in range(len(current_loaded_models)): + print(sys.getrefcount(current_loaded_models[i].model)) + if sys.getrefcount(current_loaded_models[i].model) <= 2: + to_delete = [i] + to_delete + + for i in to_delete: + x = current_loaded_models.pop(i) + x.model_unload() + del x + +def dtype_size(dtype): + dtype_size = 4 + if dtype == torch.float16 or dtype == torch.bfloat16: + dtype_size = 2 + return dtype_size + +def unet_offload_device(): + if vram_state == VRAMState.HIGH_VRAM: + return get_torch_device() + else: + return torch.device("cpu") + +def unet_inital_load_device(parameters, dtype): + torch_dev = get_torch_device() + if vram_state == VRAMState.HIGH_VRAM: + return torch_dev + + cpu_dev = torch.device("cpu") + if DISABLE_SMART_MEMORY: + return cpu_dev + + model_size = dtype_size(dtype) * parameters + + mem_dev = get_free_memory(torch_dev) + mem_cpu = get_free_memory(cpu_dev) + if mem_dev > mem_cpu and model_size < mem_dev: + return torch_dev + else: + return cpu_dev + +def text_encoder_offload_device(): + if args.gpu_only: + return get_torch_device() + else: + return torch.device("cpu") + +def text_encoder_device(): + if args.gpu_only: + return get_torch_device() + elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: + if is_intel_xpu(): + return torch.device("cpu") + if should_use_fp16(prioritize_performance=False): + return get_torch_device() + else: + return torch.device("cpu") + else: + return torch.device("cpu") + +def vae_device(): + return get_torch_device() + +def vae_offload_device(): + if args.gpu_only: + return get_torch_device() + else: + return torch.device("cpu") + +def vae_dtype(): + global VAE_DTYPE + return VAE_DTYPE + +def get_autocast_device(dev): + if hasattr(dev, 'type'): + return dev.type + return "cuda" + +def cast_to_device(tensor, device, dtype, copy=False): + device_supports_cast = False + if tensor.dtype == torch.float32 or tensor.dtype == torch.float16: + device_supports_cast = True + elif tensor.dtype == torch.bfloat16: + if hasattr(device, 'type') and device.type.startswith("cuda"): + device_supports_cast = True + elif is_intel_xpu(): + device_supports_cast = True + + if device_supports_cast: + if copy: + if tensor.device == device: + return tensor.to(dtype, copy=copy) + return tensor.to(device, copy=copy).to(dtype) + else: + return tensor.to(device).to(dtype) + else: + return tensor.to(dtype).to(device, copy=copy) + +def xformers_enabled(): + global directml_enabled + global cpu_state + if cpu_state != CPUState.GPU: + return False + if is_intel_xpu(): + return False + if directml_enabled: + return False + return XFORMERS_IS_AVAILABLE + + +def xformers_enabled_vae(): + enabled = xformers_enabled() + if not enabled: + return False + + return XFORMERS_ENABLED_VAE + +def pytorch_attention_enabled(): + global ENABLE_PYTORCH_ATTENTION + return ENABLE_PYTORCH_ATTENTION + +def pytorch_attention_flash_attention(): + global ENABLE_PYTORCH_ATTENTION + if ENABLE_PYTORCH_ATTENTION: + #TODO: more reliable way of checking for flash attention? + if is_nvidia(): #pytorch flash attention only works on Nvidia + return True + return False + +def get_free_memory(dev=None, torch_free_too=False): + global directml_enabled + if dev is None: + dev = get_torch_device() + + if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): + mem_free_total = psutil.virtual_memory().available + mem_free_torch = mem_free_total + else: + if directml_enabled: + mem_free_total = 1024 * 1024 * 1024 #TODO + mem_free_torch = mem_free_total + elif is_intel_xpu(): + stats = torch.xpu.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_allocated = stats['allocated_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_torch = mem_reserved - mem_active + mem_free_total = torch.xpu.get_device_properties(dev).total_memory - mem_allocated + else: + stats = torch.cuda.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_cuda, _ = torch.cuda.mem_get_info(dev) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_cuda + mem_free_torch + + if torch_free_too: + return (mem_free_total, mem_free_torch) + else: + return mem_free_total + +def batch_area_memory(area): + if xformers_enabled() or pytorch_attention_flash_attention(): + #TODO: these formulas are copied from maximum_batch_area below + return (area / 20) * (1024 * 1024) + else: + return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024) + +def maximum_batch_area(): + global vram_state + if vram_state == VRAMState.NO_VRAM: + return 0 + + memory_free = get_free_memory() / (1024 * 1024) + if xformers_enabled() or pytorch_attention_flash_attention(): + #TODO: this needs to be tweaked + area = 20 * memory_free + else: + #TODO: this formula is because AMD sucks and has memory management issues which might be fixed in the future + area = ((memory_free - 1024) * 0.9) / (0.6) + return int(max(area, 0)) + +def cpu_mode(): + global cpu_state + return cpu_state == CPUState.CPU + +def mps_mode(): + global cpu_state + return cpu_state == CPUState.MPS + +def is_device_cpu(device): + if hasattr(device, 'type'): + if (device.type == 'cpu'): + return True + return False + +def is_device_mps(device): + if hasattr(device, 'type'): + if (device.type == 'mps'): + return True + return False + +def should_use_fp16(device=None, model_params=0, prioritize_performance=True): + global directml_enabled + + if device is not None: + if is_device_cpu(device): + return False + + if FORCE_FP16: + return True + + if device is not None: #TODO + if is_device_mps(device): + return False + + if FORCE_FP32: + return False + + if directml_enabled: + return False + + if cpu_mode() or mps_mode(): + return False #TODO ? + + if is_intel_xpu(): + return True + + if torch.cuda.is_bf16_supported(): + return True + + props = torch.cuda.get_device_properties("cuda") + if props.major < 6: + return False + + fp16_works = False + #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled + #when the model doesn't actually fit on the card + #TODO: actually test if GP106 and others have the same type of behavior + nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050"] + for x in nvidia_10_series: + if x in props.name.lower(): + fp16_works = True + + if fp16_works: + free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory()) + if (not prioritize_performance) or model_params * 4 > free_model_memory: + return True + + if props.major < 7: + return False + + #FP16 is just broken on these cards + nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX"] + for x in nvidia_16_series: + if x in props.name: + return False + + return True + +def soft_empty_cache(force=False): + global cpu_state + if cpu_state == CPUState.MPS: + torch.mps.empty_cache() + elif is_intel_xpu(): + torch.xpu.empty_cache() + elif torch.cuda.is_available(): + if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + +def resolve_lowvram_weight(weight, model, key): + if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break. + key_split = key.split('.') # I have no idea why they don't just leave the weight there instead of using the meta device. + op = comfy.utils.get_attr(model, '.'.join(key_split[:-1])) + weight = op._hf_hook.weights_map[key_split[-1]] + return weight + +#TODO: might be cleaner to put this somewhere else +import threading + +class InterruptProcessingException(Exception): + pass + +interrupt_processing_mutex = threading.RLock() + +interrupt_processing = False +def interrupt_current_processing(value=True): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + interrupt_processing = value + +def processing_interrupted(): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + return interrupt_processing + +def throw_exception_if_processing_interrupted(): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + if interrupt_processing: + interrupt_processing = False + raise InterruptProcessingException() diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py new file mode 100644 index 0000000000000000000000000000000000000000..ba505221e777808e70c382b1dafeb49f903cbeb9 --- /dev/null +++ b/comfy/model_patcher.py @@ -0,0 +1,288 @@ +import torch +import copy +import inspect + +import comfy.utils +import comfy.model_management + +class ModelPatcher: + def __init__(self, model, load_device, offload_device, size=0, current_device=None): + self.size = size + self.model = model + self.patches = {} + self.backup = {} + self.model_options = {"transformer_options":{}} + self.model_size() + self.load_device = load_device + self.offload_device = offload_device + if current_device is None: + self.current_device = self.offload_device + else: + self.current_device = current_device + + def model_size(self): + if self.size > 0: + return self.size + model_sd = self.model.state_dict() + size = 0 + for k in model_sd: + t = model_sd[k] + size += t.nelement() * t.element_size() + self.size = size + self.model_keys = set(model_sd.keys()) + return size + + def clone(self): + n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device) + n.patches = {} + for k in self.patches: + n.patches[k] = self.patches[k][:] + + n.model_options = copy.deepcopy(self.model_options) + n.model_keys = self.model_keys + return n + + def is_clone(self, other): + if hasattr(other, 'model') and self.model is other.model: + return True + return False + + def set_model_sampler_cfg_function(self, sampler_cfg_function): + if len(inspect.signature(sampler_cfg_function).parameters) == 3: + self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way + else: + self.model_options["sampler_cfg_function"] = sampler_cfg_function + + def set_model_unet_function_wrapper(self, unet_wrapper_function): + self.model_options["model_function_wrapper"] = unet_wrapper_function + + def set_model_patch(self, patch, name): + to = self.model_options["transformer_options"] + if "patches" not in to: + to["patches"] = {} + to["patches"][name] = to["patches"].get(name, []) + [patch] + + def set_model_patch_replace(self, patch, name, block_name, number): + to = self.model_options["transformer_options"] + if "patches_replace" not in to: + to["patches_replace"] = {} + if name not in to["patches_replace"]: + to["patches_replace"][name] = {} + to["patches_replace"][name][(block_name, number)] = patch + + def set_model_attn1_patch(self, patch): + self.set_model_patch(patch, "attn1_patch") + + def set_model_attn2_patch(self, patch): + self.set_model_patch(patch, "attn2_patch") + + def set_model_attn1_replace(self, patch, block_name, number): + self.set_model_patch_replace(patch, "attn1", block_name, number) + + def set_model_attn2_replace(self, patch, block_name, number): + self.set_model_patch_replace(patch, "attn2", block_name, number) + + def set_model_attn1_output_patch(self, patch): + self.set_model_patch(patch, "attn1_output_patch") + + def set_model_attn2_output_patch(self, patch): + self.set_model_patch(patch, "attn2_output_patch") + + def set_model_output_block_patch(self, patch): + self.set_model_patch(patch, "output_block_patch") + + def model_patches_to(self, device): + to = self.model_options["transformer_options"] + if "patches" in to: + patches = to["patches"] + for name in patches: + patch_list = patches[name] + for i in range(len(patch_list)): + if hasattr(patch_list[i], "to"): + patch_list[i] = patch_list[i].to(device) + if "patches_replace" in to: + patches = to["patches_replace"] + for name in patches: + patch_list = patches[name] + for k in patch_list: + if hasattr(patch_list[k], "to"): + patch_list[k] = patch_list[k].to(device) + + def model_dtype(self): + if hasattr(self.model, "get_dtype"): + return self.model.get_dtype() + + def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): + p = set() + for k in patches: + if k in self.model_keys: + p.add(k) + current_patches = self.patches.get(k, []) + current_patches.append((strength_patch, patches[k], strength_model)) + self.patches[k] = current_patches + + return list(p) + + def get_key_patches(self, filter_prefix=None): + model_sd = self.model_state_dict() + p = {} + for k in model_sd: + if filter_prefix is not None: + if not k.startswith(filter_prefix): + continue + if k in self.patches: + p[k] = [model_sd[k]] + self.patches[k] + else: + p[k] = (model_sd[k],) + return p + + def model_state_dict(self, filter_prefix=None): + sd = self.model.state_dict() + keys = list(sd.keys()) + if filter_prefix is not None: + for k in keys: + if not k.startswith(filter_prefix): + sd.pop(k) + return sd + + def patch_model(self, device_to=None): + model_sd = self.model_state_dict() + for key in self.patches: + if key not in model_sd: + print("could not patch. key doesn't exist in model:", key) + continue + + weight = model_sd[key] + + if key not in self.backup: + self.backup[key] = weight.to(self.offload_device) + + if device_to is not None: + temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) + else: + temp_weight = weight.to(torch.float32, copy=True) + out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) + comfy.utils.set_attr(self.model, key, out_weight) + del temp_weight + + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to + + return self.model + + def calculate_weight(self, patches, weight, key): + for p in patches: + alpha = p[0] + v = p[1] + strength_model = p[2] + + if strength_model != 1.0: + weight *= strength_model + + if isinstance(v, list): + v = (self.calculate_weight(v[1:], v[0].clone(), key), ) + + if len(v) == 1: + w1 = v[0] + if alpha != 0.0: + if w1.shape != weight.shape: + print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) + else: + weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype) + elif len(v) == 4: #lora/locon + mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32) + mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32) + if v[2] is not None: + alpha *= v[2] / mat2.shape[0] + if v[3] is not None: + #locon mid weights, hopefully the math is fine because I didn't properly test it + mat3 = comfy.model_management.cast_to_device(v[3], weight.device, torch.float32) + final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] + mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) + try: + weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif len(v) == 8: #lokr + w1 = v[0] + w2 = v[1] + w1_a = v[3] + w1_b = v[4] + w2_a = v[5] + w2_b = v[6] + t2 = v[7] + dim = None + + if w1 is None: + dim = w1_b.shape[0] + w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, torch.float32), + comfy.model_management.cast_to_device(w1_b, weight.device, torch.float32)) + else: + w1 = comfy.model_management.cast_to_device(w1, weight.device, torch.float32) + + if w2 is None: + dim = w2_b.shape[0] + if t2 is None: + w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32), + comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32)) + else: + w2 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t2, weight.device, torch.float32), + comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32), + comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32)) + else: + w2 = comfy.model_management.cast_to_device(w2, weight.device, torch.float32) + + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + if v[2] is not None and dim is not None: + alpha *= v[2] / dim + + try: + weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + else: #loha + w1a = v[0] + w1b = v[1] + if v[2] is not None: + alpha *= v[2] / w1b.shape[0] + w2a = v[3] + w2b = v[4] + if v[5] is not None: #cp decomposition + t1 = v[5] + t2 = v[6] + m1 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t1, weight.device, torch.float32), + comfy.model_management.cast_to_device(w1b, weight.device, torch.float32), + comfy.model_management.cast_to_device(w1a, weight.device, torch.float32)) + + m2 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t2, weight.device, torch.float32), + comfy.model_management.cast_to_device(w2b, weight.device, torch.float32), + comfy.model_management.cast_to_device(w2a, weight.device, torch.float32)) + else: + m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, torch.float32), + comfy.model_management.cast_to_device(w1b, weight.device, torch.float32)) + m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, torch.float32), + comfy.model_management.cast_to_device(w2b, weight.device, torch.float32)) + + try: + weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + + return weight + + def unpatch_model(self, device_to=None): + keys = list(self.backup.keys()) + + for k in keys: + comfy.utils.set_attr(self.model, k, self.backup[k]) + + self.backup = {} + + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to diff --git a/comfy/ops.py b/comfy/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..610d54584fa8ed070293e475ae17711f762996ef --- /dev/null +++ b/comfy/ops.py @@ -0,0 +1,46 @@ +import torch +from contextlib import contextmanager + +class Linear(torch.nn.Module): + def __init__(self, in_features: int, out_features: int, bias: bool = True, + device=None, dtype=None) -> None: + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.weight = torch.nn.Parameter(torch.empty((out_features, in_features), **factory_kwargs)) + if bias: + self.bias = torch.nn.Parameter(torch.empty(out_features, **factory_kwargs)) + else: + self.register_parameter('bias', None) + + def forward(self, input): + return torch.nn.functional.linear(input, self.weight, self.bias) + +class Conv2d(torch.nn.Conv2d): + def reset_parameters(self): + return None + +def conv_nd(dims, *args, **kwargs): + if dims == 2: + return Conv2d(*args, **kwargs) + else: + raise ValueError(f"unsupported dimensions: {dims}") + +@contextmanager +def use_comfy_ops(device=None, dtype=None): # Kind of an ugly hack but I can't think of a better way + old_torch_nn_linear = torch.nn.Linear + force_device = device + force_dtype = dtype + def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None): + if force_device is not None: + device = force_device + if force_dtype is not None: + dtype = force_dtype + return Linear(in_features, out_features, bias=bias, device=device, dtype=dtype) + + torch.nn.Linear = linear_with_dtype + try: + yield + finally: + torch.nn.Linear = old_torch_nn_linear diff --git a/comfy/options.py b/comfy/options.py new file mode 100644 index 0000000000000000000000000000000000000000..f7f8af41ebd8b9669ef0ef21827ea6195bcb4752 --- /dev/null +++ b/comfy/options.py @@ -0,0 +1,6 @@ + +args_parsing = False + +def enable_args_parsing(enable=True): + global args_parsing + args_parsing = enable diff --git a/comfy/sample.py b/comfy/sample.py new file mode 100644 index 0000000000000000000000000000000000000000..e4730b189adee2160a5c48fea5128cb903013e34 --- /dev/null +++ b/comfy/sample.py @@ -0,0 +1,97 @@ +import torch +import comfy.model_management +import comfy.samplers +import comfy.utils +import math +import numpy as np + +def prepare_noise(latent_image, seed, noise_inds=None): + """ + creates random noise given a latent image and a seed. + optional arg skip can be used to skip and discard x number of noise generations for a given seed + """ + generator = torch.manual_seed(seed) + if noise_inds is None: + return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") + + unique_inds, inverse = np.unique(noise_inds, return_inverse=True) + noises = [] + for i in range(unique_inds[-1]+1): + noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") + if i in unique_inds: + noises.append(noise) + noises = [noises[i] for i in inverse] + noises = torch.cat(noises, axis=0) + return noises + +def prepare_mask(noise_mask, shape, device): + """ensures noise mask is of proper dimensions""" + noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear") + noise_mask = noise_mask.round() + noise_mask = torch.cat([noise_mask] * shape[1], dim=1) + noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0]) + noise_mask = noise_mask.to(device) + return noise_mask + +def broadcast_cond(cond, batch, device): + """broadcasts conditioning to the batch size""" + copy = [] + for p in cond: + t = comfy.utils.repeat_to_batch_size(p[0], batch) + t = t.to(device) + copy += [[t] + p[1:]] + return copy + +def get_models_from_cond(cond, model_type): + models = [] + for c in cond: + if model_type in c[1]: + models += [c[1][model_type]] + return models + +def get_additional_models(positive, negative, dtype): + """loads additional models in positive and negative conditioning""" + control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")) + + inference_memory = 0 + control_models = [] + for m in control_nets: + control_models += m.get_models() + inference_memory += m.inference_memory_requirements(dtype) + + gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen") + gligen = [x[1] for x in gligen] + models = control_models + gligen + return models, inference_memory + +def cleanup_additional_models(models): + """cleanup additional models that were loaded""" + for m in models: + if hasattr(m, 'cleanup'): + m.cleanup() + +def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): + device = comfy.model_management.get_torch_device() + + if noise_mask is not None: + noise_mask = prepare_mask(noise_mask, noise.shape, device) + + real_model = None + models, inference_memory = get_additional_models(positive, negative, model.model_dtype()) + comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]) + inference_memory) + real_model = model.model + + noise = noise.to(device) + latent_image = latent_image.to(device) + + positive_copy = broadcast_cond(positive, noise.shape[0], device) + negative_copy = broadcast_cond(negative, noise.shape[0], device) + + + sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) + + samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = samples.cpu() + + cleanup_additional_models(models) + return samples diff --git a/comfy/samplers.py b/comfy/samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..e3192ca58f4800eb62a83a681077f3f15ae0b179 --- /dev/null +++ b/comfy/samplers.py @@ -0,0 +1,744 @@ +from .k_diffusion import sampling as k_diffusion_sampling +from .k_diffusion import external as k_diffusion_external +from .extra_samplers import uni_pc +import torch +from comfy import model_management +from .ldm.models.diffusion.ddim import DDIMSampler +from .ldm.modules.diffusionmodules.util import make_ddim_timesteps +import math +from comfy import model_base +import comfy.utils + +def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9) + return abs(a*b) // math.gcd(a, b) + +#The main sampling function shared by all the samplers +#Returns predicted noise +def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}, seed=None): + def get_area_and_mult(cond, x_in, cond_concat_in, timestep_in): + area = (x_in.shape[2], x_in.shape[3], 0, 0) + strength = 1.0 + if 'timestep_start' in cond[1]: + timestep_start = cond[1]['timestep_start'] + if timestep_in[0] > timestep_start: + return None + if 'timestep_end' in cond[1]: + timestep_end = cond[1]['timestep_end'] + if timestep_in[0] < timestep_end: + return None + if 'area' in cond[1]: + area = cond[1]['area'] + if 'strength' in cond[1]: + strength = cond[1]['strength'] + + adm_cond = None + if 'adm_encoded' in cond[1]: + adm_cond = cond[1]['adm_encoded'] + + input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] + if 'mask' in cond[1]: + # Scale the mask to the size of the input + # The mask should have been resized as we began the sampling process + mask_strength = 1.0 + if "mask_strength" in cond[1]: + mask_strength = cond[1]["mask_strength"] + mask = cond[1]['mask'] + assert(mask.shape[1] == x_in.shape[2]) + assert(mask.shape[2] == x_in.shape[3]) + mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength + mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) + else: + mask = torch.ones_like(input_x) + mult = mask * strength + + if 'mask' not in cond[1]: + rr = 8 + if area[2] != 0: + for t in range(rr): + mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1)) + if (area[0] + area[2]) < x_in.shape[2]: + for t in range(rr): + mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1)) + if area[3] != 0: + for t in range(rr): + mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1)) + if (area[1] + area[3]) < x_in.shape[3]: + for t in range(rr): + mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1)) + + conditionning = {} + conditionning['c_crossattn'] = cond[0] + if cond_concat_in is not None and len(cond_concat_in) > 0: + cropped = [] + for x in cond_concat_in: + cr = x[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] + cropped.append(cr) + conditionning['c_concat'] = torch.cat(cropped, dim=1) + + if adm_cond is not None: + conditionning['c_adm'] = adm_cond + + control = None + if 'control' in cond[1]: + control = cond[1]['control'] + + patches = None + if 'gligen' in cond[1]: + gligen = cond[1]['gligen'] + patches = {} + gligen_type = gligen[0] + gligen_model = gligen[1] + if gligen_type == "position": + gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) + else: + gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) + + patches['middle_patch'] = [gligen_patch] + + return (input_x, mult, conditionning, area, control, patches) + + def cond_equal_size(c1, c2): + if c1 is c2: + return True + if c1.keys() != c2.keys(): + return False + if 'c_crossattn' in c1: + s1 = c1['c_crossattn'].shape + s2 = c2['c_crossattn'].shape + if s1 != s2: + if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen + return False + + mult_min = lcm(s1[1], s2[1]) + diff = mult_min // min(s1[1], s2[1]) + if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much + return False + if 'c_concat' in c1: + if c1['c_concat'].shape != c2['c_concat'].shape: + return False + if 'c_adm' in c1: + if c1['c_adm'].shape != c2['c_adm'].shape: + return False + return True + + def can_concat_cond(c1, c2): + if c1[0].shape != c2[0].shape: + return False + + #control + if (c1[4] is None) != (c2[4] is None): + return False + if c1[4] is not None: + if c1[4] is not c2[4]: + return False + + #patches + if (c1[5] is None) != (c2[5] is None): + return False + if (c1[5] is not None): + if c1[5] is not c2[5]: + return False + + return cond_equal_size(c1[2], c2[2]) + + def cond_cat(c_list): + c_crossattn = [] + c_concat = [] + c_adm = [] + crossattn_max_len = 0 + for x in c_list: + if 'c_crossattn' in x: + c = x['c_crossattn'] + if crossattn_max_len == 0: + crossattn_max_len = c.shape[1] + else: + crossattn_max_len = lcm(crossattn_max_len, c.shape[1]) + c_crossattn.append(c) + if 'c_concat' in x: + c_concat.append(x['c_concat']) + if 'c_adm' in x: + c_adm.append(x['c_adm']) + out = {} + c_crossattn_out = [] + for c in c_crossattn: + if c.shape[1] < crossattn_max_len: + c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result + c_crossattn_out.append(c) + + if len(c_crossattn_out) > 0: + out['c_crossattn'] = torch.cat(c_crossattn_out) + if len(c_concat) > 0: + out['c_concat'] = torch.cat(c_concat) + if len(c_adm) > 0: + out['c_adm'] = torch.cat(c_adm) + return out + + def calc_cond_uncond_batch(model_function, cond, uncond, x_in, timestep, max_total_area, cond_concat_in, model_options): + out_cond = torch.zeros_like(x_in) + out_count = torch.ones_like(x_in)/100000.0 + + out_uncond = torch.zeros_like(x_in) + out_uncond_count = torch.ones_like(x_in)/100000.0 + + COND = 0 + UNCOND = 1 + + to_run = [] + for x in cond: + p = get_area_and_mult(x, x_in, cond_concat_in, timestep) + if p is None: + continue + + to_run += [(p, COND)] + if uncond is not None: + for x in uncond: + p = get_area_and_mult(x, x_in, cond_concat_in, timestep) + if p is None: + continue + + to_run += [(p, UNCOND)] + + while len(to_run) > 0: + first = to_run[0] + first_shape = first[0][0].shape + to_batch_temp = [] + for x in range(len(to_run)): + if can_concat_cond(to_run[x][0], first[0]): + to_batch_temp += [x] + + to_batch_temp.reverse() + to_batch = to_batch_temp[:1] + + for i in range(1, len(to_batch_temp) + 1): + batch_amount = to_batch_temp[:len(to_batch_temp)//i] + if (len(batch_amount) * first_shape[0] * first_shape[2] * first_shape[3] < max_total_area): + to_batch = batch_amount + break + + input_x = [] + mult = [] + c = [] + cond_or_uncond = [] + area = [] + control = None + patches = None + for x in to_batch: + o = to_run.pop(x) + p = o[0] + input_x += [p[0]] + mult += [p[1]] + c += [p[2]] + area += [p[3]] + cond_or_uncond += [o[1]] + control = p[4] + patches = p[5] + + batch_chunks = len(cond_or_uncond) + input_x = torch.cat(input_x) + c = cond_cat(c) + timestep_ = torch.cat([timestep] * batch_chunks) + + if control is not None: + c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond)) + + transformer_options = {} + if 'transformer_options' in model_options: + transformer_options = model_options['transformer_options'].copy() + + if patches is not None: + if "patches" in transformer_options: + cur_patches = transformer_options["patches"].copy() + for p in patches: + if p in cur_patches: + cur_patches[p] = cur_patches[p] + patches[p] + else: + cur_patches[p] = patches[p] + else: + transformer_options["patches"] = patches + + transformer_options["cond_or_uncond"] = cond_or_uncond[:] + c['transformer_options'] = transformer_options + + if 'model_function_wrapper' in model_options: + output = model_options['model_function_wrapper'](model_function, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) + else: + output = model_function(input_x, timestep_, **c).chunk(batch_chunks) + del input_x + + for o in range(batch_chunks): + if cond_or_uncond[o] == COND: + out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] + out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] + else: + out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] + out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] + del mult + + out_cond /= out_count + del out_count + out_uncond /= out_uncond_count + del out_uncond_count + + return out_cond, out_uncond + + + max_total_area = model_management.maximum_batch_area() + if math.isclose(cond_scale, 1.0): + uncond = None + + cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat, model_options) + if "sampler_cfg_function" in model_options: + args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep} + return model_options["sampler_cfg_function"](args) + else: + return uncond + (cond - uncond) * cond_scale + + +class CompVisVDenoiser(k_diffusion_external.DiscreteVDDPMDenoiser): + def __init__(self, model, quantize=False, device='cpu'): + super().__init__(model, model.alphas_cumprod, quantize=quantize) + + def get_v(self, x, t, cond, **kwargs): + return self.inner_model.apply_model(x, t, cond, **kwargs) + + +class CFGNoisePredictor(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.inner_model = model + self.alphas_cumprod = model.alphas_cumprod + def apply_model(self, x, timestep, cond, uncond, cond_scale, cond_concat=None, model_options={}, seed=None): + out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, cond_concat, model_options=model_options, seed=seed) + return out + + +class KSamplerX0Inpaint(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.inner_model = model + def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None, model_options={}, seed=None): + if denoise_mask is not None: + latent_mask = 1. - denoise_mask + x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask + out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat, model_options=model_options, seed=seed) + if denoise_mask is not None: + out *= denoise_mask + + if denoise_mask is not None: + out += self.latent_image * latent_mask + return out + +def simple_scheduler(model, steps): + sigs = [] + ss = len(model.sigmas) / steps + for x in range(steps): + sigs += [float(model.sigmas[-(1 + int(x * ss))])] + sigs += [0.0] + return torch.FloatTensor(sigs) + +def ddim_scheduler(model, steps): + sigs = [] + ddim_timesteps = make_ddim_timesteps(ddim_discr_method="uniform", num_ddim_timesteps=steps, num_ddpm_timesteps=model.inner_model.inner_model.num_timesteps, verbose=False) + for x in range(len(ddim_timesteps) - 1, -1, -1): + ts = ddim_timesteps[x] + if ts > 999: + ts = 999 + sigs.append(model.t_to_sigma(torch.tensor(ts))) + sigs += [0.0] + return torch.FloatTensor(sigs) + +def sgm_scheduler(model, steps): + sigs = [] + timesteps = torch.linspace(model.inner_model.inner_model.num_timesteps - 1, 0, steps + 1)[:-1].type(torch.int) + for x in range(len(timesteps)): + ts = timesteps[x] + if ts > 999: + ts = 999 + sigs.append(model.t_to_sigma(torch.tensor(ts))) + sigs += [0.0] + return torch.FloatTensor(sigs) + +def blank_inpaint_image_like(latent_image): + blank_image = torch.ones_like(latent_image) + # these are the values for "zero" in pixel space translated to latent space + blank_image[:,0] *= 0.8223 + blank_image[:,1] *= -0.6876 + blank_image[:,2] *= 0.6364 + blank_image[:,3] *= 0.1380 + return blank_image + +def get_mask_aabb(masks): + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device, dtype=torch.int) + + b = masks.shape[0] + + bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int) + is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool) + for i in range(b): + mask = masks[i] + if mask.numel() == 0: + continue + if torch.max(mask != 0) == False: + is_empty[i] = True + continue + y, x = torch.where(mask) + bounding_boxes[i, 0] = torch.min(x) + bounding_boxes[i, 1] = torch.min(y) + bounding_boxes[i, 2] = torch.max(x) + bounding_boxes[i, 3] = torch.max(y) + + return bounding_boxes, is_empty + +def resolve_areas_and_cond_masks(conditions, h, w, device): + # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. + # While we're doing this, we can also resolve the mask device and scaling for performance reasons + for i in range(len(conditions)): + c = conditions[i] + if 'area' in c[1]: + area = c[1]['area'] + if area[0] == "percentage": + modified = c[1].copy() + area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w)) + modified['area'] = area + c = [c[0], modified] + conditions[i] = c + + if 'mask' in c[1]: + mask = c[1]['mask'] + mask = mask.to(device=device) + modified = c[1].copy() + if len(mask.shape) == 2: + mask = mask.unsqueeze(0) + if mask.shape[1] != h or mask.shape[2] != w: + mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1) + + if modified.get("set_area_to_bounds", False): + bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) + boxes, is_empty = get_mask_aabb(bounds) + if is_empty[0]: + # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway) + modified['area'] = (8, 8, 0, 0) + else: + box = boxes[0] + H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0]) + H = max(8, H) + W = max(8, W) + area = (int(H), int(W), int(Y), int(X)) + modified['area'] = area + + modified['mask'] = mask + conditions[i] = [c[0], modified] + +def create_cond_with_same_area_if_none(conds, c): + if 'area' not in c[1]: + return + + c_area = c[1]['area'] + smallest = None + for x in conds: + if 'area' in x[1]: + a = x[1]['area'] + if c_area[2] >= a[2] and c_area[3] >= a[3]: + if a[0] + a[2] >= c_area[0] + c_area[2]: + if a[1] + a[3] >= c_area[1] + c_area[3]: + if smallest is None: + smallest = x + elif 'area' not in smallest[1]: + smallest = x + else: + if smallest[1]['area'][0] * smallest[1]['area'][1] > a[0] * a[1]: + smallest = x + else: + if smallest is None: + smallest = x + if smallest is None: + return + if 'area' in smallest[1]: + if smallest[1]['area'] == c_area: + return + n = c[1].copy() + conds += [[smallest[0], n]] + +def calculate_start_end_timesteps(model, conds): + for t in range(len(conds)): + x = conds[t] + + timestep_start = None + timestep_end = None + if 'start_percent' in x[1]: + timestep_start = model.sigma_to_t(model.t_to_sigma(torch.tensor(x[1]['start_percent'] * 999.0))) + if 'end_percent' in x[1]: + timestep_end = model.sigma_to_t(model.t_to_sigma(torch.tensor(x[1]['end_percent'] * 999.0))) + + if (timestep_start is not None) or (timestep_end is not None): + n = x[1].copy() + if (timestep_start is not None): + n['timestep_start'] = timestep_start + if (timestep_end is not None): + n['timestep_end'] = timestep_end + conds[t] = [x[0], n] + +def pre_run_control(model, conds): + for t in range(len(conds)): + x = conds[t] + + timestep_start = None + timestep_end = None + percent_to_timestep_function = lambda a: model.sigma_to_t(model.t_to_sigma(torch.tensor(a) * 999.0)) + if 'control' in x[1]: + x[1]['control'].pre_run(model.inner_model.inner_model, percent_to_timestep_function) + +def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func): + cond_cnets = [] + cond_other = [] + uncond_cnets = [] + uncond_other = [] + for t in range(len(conds)): + x = conds[t] + if 'area' not in x[1]: + if name in x[1] and x[1][name] is not None: + cond_cnets.append(x[1][name]) + else: + cond_other.append((x, t)) + for t in range(len(uncond)): + x = uncond[t] + if 'area' not in x[1]: + if name in x[1] and x[1][name] is not None: + uncond_cnets.append(x[1][name]) + else: + uncond_other.append((x, t)) + + if len(uncond_cnets) > 0: + return + + for x in range(len(cond_cnets)): + temp = uncond_other[x % len(uncond_other)] + o = temp[0] + if name in o[1] and o[1][name] is not None: + n = o[1].copy() + n[name] = uncond_fill_func(cond_cnets, x) + uncond += [[o[0], n]] + else: + n = o[1].copy() + n[name] = uncond_fill_func(cond_cnets, x) + uncond[temp[1]] = [o[0], n] + +def encode_adm(model, conds, batch_size, width, height, device, prompt_type): + for t in range(len(conds)): + x = conds[t] + adm_out = None + if 'adm' in x[1]: + adm_out = x[1]["adm"] + else: + params = x[1].copy() + params["width"] = params.get("width", width * 8) + params["height"] = params.get("height", height * 8) + params["prompt_type"] = params.get("prompt_type", prompt_type) + adm_out = model.encode_adm(device=device, **params) + + if adm_out is not None: + x[1] = x[1].copy() + x[1]["adm_encoded"] = comfy.utils.repeat_to_batch_size(adm_out, batch_size).to(device) + + return conds + + +class KSampler: + SCHEDULERS = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"] + SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral", + "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", + "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "ddim", "uni_pc", "uni_pc_bh2"] + + def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}): + self.model = model + self.model_denoise = CFGNoisePredictor(self.model) + if self.model.model_type == model_base.ModelType.V_PREDICTION: + self.model_wrap = CompVisVDenoiser(self.model_denoise, quantize=True) + else: + self.model_wrap = k_diffusion_external.CompVisDenoiser(self.model_denoise, quantize=True) + + self.model_k = KSamplerX0Inpaint(self.model_wrap) + self.device = device + if scheduler not in self.SCHEDULERS: + scheduler = self.SCHEDULERS[0] + if sampler not in self.SAMPLERS: + sampler = self.SAMPLERS[0] + self.scheduler = scheduler + self.sampler = sampler + self.sigma_min=float(self.model_wrap.sigma_min) + self.sigma_max=float(self.model_wrap.sigma_max) + self.set_steps(steps, denoise) + self.denoise = denoise + self.model_options = model_options + + def calculate_sigmas(self, steps): + sigmas = None + + discard_penultimate_sigma = False + if self.sampler in ['dpm_2', 'dpm_2_ancestral']: + steps += 1 + discard_penultimate_sigma = True + + if self.scheduler == "karras": + sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max) + elif self.scheduler == "exponential": + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max) + elif self.scheduler == "normal": + sigmas = self.model_wrap.get_sigmas(steps) + elif self.scheduler == "simple": + sigmas = simple_scheduler(self.model_wrap, steps) + elif self.scheduler == "ddim_uniform": + sigmas = ddim_scheduler(self.model_wrap, steps) + elif self.scheduler == "sgm_uniform": + sigmas = sgm_scheduler(self.model_wrap, steps) + else: + print("error invalid scheduler", self.scheduler) + + if discard_penultimate_sigma: + sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) + return sigmas + + def set_steps(self, steps, denoise=None): + self.steps = steps + if denoise is None or denoise > 0.9999: + self.sigmas = self.calculate_sigmas(steps).to(self.device) + else: + new_steps = int(steps/denoise) + sigmas = self.calculate_sigmas(new_steps).to(self.device) + self.sigmas = sigmas[-(steps + 1):] + + def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): + if sigmas is None: + sigmas = self.sigmas + sigma_min = self.sigma_min + + if last_step is not None and last_step < (len(sigmas) - 1): + sigma_min = sigmas[last_step] + sigmas = sigmas[:last_step + 1] + if force_full_denoise: + sigmas[-1] = 0 + + if start_step is not None: + if start_step < (len(sigmas) - 1): + sigmas = sigmas[start_step:] + else: + if latent_image is not None: + return latent_image + else: + return torch.zeros_like(noise) + + positive = positive[:] + negative = negative[:] + + resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], self.device) + resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], self.device) + + calculate_start_end_timesteps(self.model_wrap, negative) + calculate_start_end_timesteps(self.model_wrap, positive) + + #make sure each cond area has an opposite one with the same area + for c in positive: + create_cond_with_same_area_if_none(negative, c) + for c in negative: + create_cond_with_same_area_if_none(positive, c) + + pre_run_control(self.model_wrap, negative + positive) + + apply_empty_x_to_equal_area(list(filter(lambda c: c[1].get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x]) + apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x]) + + if self.model.is_adm(): + positive = encode_adm(self.model, positive, noise.shape[0], noise.shape[3], noise.shape[2], self.device, "positive") + negative = encode_adm(self.model, negative, noise.shape[0], noise.shape[3], noise.shape[2], self.device, "negative") + + if latent_image is not None: + latent_image = self.model.process_latent_in(latent_image) + + extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": self.model_options, "seed":seed} + + cond_concat = None + if hasattr(self.model, 'concat_keys'): #inpaint + cond_concat = [] + for ck in self.model.concat_keys: + if denoise_mask is not None: + if ck == "mask": + cond_concat.append(denoise_mask[:,:1]) + elif ck == "masked_image": + cond_concat.append(latent_image) #NOTE: the latent_image should be masked by the mask in pixel space + else: + if ck == "mask": + cond_concat.append(torch.ones_like(noise)[:,:1]) + elif ck == "masked_image": + cond_concat.append(blank_inpaint_image_like(noise)) + extra_args["cond_concat"] = cond_concat + + if sigmas[0] != self.sigmas[0] or (self.denoise is not None and self.denoise < 1.0): + max_denoise = False + else: + max_denoise = True + + + if self.sampler == "uni_pc": + samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar) + elif self.sampler == "uni_pc_bh2": + samples = uni_pc.sample_unipc(self.model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=max_denoise, extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar) + elif self.sampler == "ddim": + timesteps = [] + for s in range(sigmas.shape[0]): + timesteps.insert(0, self.model_wrap.sigma_to_discrete_timestep(sigmas[s])) + noise_mask = None + if denoise_mask is not None: + noise_mask = 1.0 - denoise_mask + + ddim_callback = None + if callback is not None: + total_steps = len(timesteps) - 1 + ddim_callback = lambda pred_x0, i: callback(i, pred_x0, None, total_steps) + + sampler = DDIMSampler(self.model, device=self.device) + sampler.make_schedule_timesteps(ddim_timesteps=timesteps, verbose=False) + z_enc = sampler.stochastic_encode(latent_image, torch.tensor([len(timesteps) - 1] * noise.shape[0]).to(self.device), noise=noise, max_denoise=max_denoise) + samples, _ = sampler.sample_custom(ddim_timesteps=timesteps, + conditioning=positive, + batch_size=noise.shape[0], + shape=noise.shape[1:], + verbose=False, + unconditional_guidance_scale=cfg, + unconditional_conditioning=negative, + eta=0.0, + x_T=z_enc, + x0=latent_image, + img_callback=ddim_callback, + denoise_function=self.model_wrap.predict_eps_discrete_timestep, + extra_args=extra_args, + mask=noise_mask, + to_zero=sigmas[-1]==0, + end_step=sigmas.shape[0] - 1, + disable_pbar=disable_pbar) + + else: + extra_args["denoise_mask"] = denoise_mask + self.model_k.latent_image = latent_image + self.model_k.noise = noise + + if max_denoise: + noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0) + else: + noise = noise * sigmas[0] + + k_callback = None + total_steps = len(sigmas) - 1 + if callback is not None: + k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) + + if latent_image is not None: + noise += latent_image + if self.sampler == "dpm_fast": + samples = k_diffusion_sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=k_callback, disable=disable_pbar) + elif self.sampler == "dpm_adaptive": + samples = k_diffusion_sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=k_callback, disable=disable_pbar) + else: + samples = getattr(k_diffusion_sampling, "sample_{}".format(self.sampler))(self.model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar) + + return self.model.process_latent_out(samples.to(torch.float32)) diff --git a/comfy/sd.py b/comfy/sd.py new file mode 100644 index 0000000000000000000000000000000000000000..9bdb2ad64ff6850e8cb58cf5ed6edfe2d55d3b9e --- /dev/null +++ b/comfy/sd.py @@ -0,0 +1,486 @@ +import torch +import contextlib +import math + +from comfy import model_management +from .ldm.util import instantiate_from_config +from .ldm.models.autoencoder import AutoencoderKL +import yaml + +import comfy.utils + +from . import clip_vision +from . import gligen +from . import diffusers_convert +from . import model_base +from . import model_detection + +from . import sd1_clip +from . import sd2_clip +from . import sdxl_clip + +import comfy.model_patcher +import comfy.lora +import comfy.t2i_adapter.adapter +import comfy.supported_models_base + +def load_model_weights(model, sd): + m, u = model.load_state_dict(sd, strict=False) + m = set(m) + unexpected_keys = set(u) + + k = list(sd.keys()) + for x in k: + if x not in unexpected_keys: + w = sd.pop(x) + del w + if len(m) > 0: + print("missing", m) + return model + +def load_clip_weights(model, sd): + k = list(sd.keys()) + for x in k: + if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): + y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") + sd[y] = sd.pop(x) + + if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd: + ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] + if ids.dtype == torch.float32: + sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() + + sd = comfy.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) + return load_model_weights(model, sd) + + +def load_lora_for_models(model, clip, lora, strength_model, strength_clip): + key_map = comfy.lora.model_lora_keys_unet(model.model) + key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) + loaded = comfy.lora.load_lora(lora, key_map) + new_modelpatcher = model.clone() + k = new_modelpatcher.add_patches(loaded, strength_model) + new_clip = clip.clone() + k1 = new_clip.add_patches(loaded, strength_clip) + k = set(k) + k1 = set(k1) + for x in loaded: + if (x not in k) and (x not in k1): + print("NOT LOADED", x) + + return (new_modelpatcher, new_clip) + + +class CLIP: + def __init__(self, target=None, embedding_directory=None, no_init=False): + if no_init: + return + params = target.params.copy() + clip = target.clip + tokenizer = target.tokenizer + + load_device = model_management.text_encoder_device() + offload_device = model_management.text_encoder_offload_device() + params['device'] = offload_device + if model_management.should_use_fp16(load_device, prioritize_performance=False): + params['dtype'] = torch.float16 + else: + params['dtype'] = torch.float32 + + self.cond_stage_model = clip(**(params)) + + self.tokenizer = tokenizer(embedding_directory=embedding_directory) + self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) + self.layer_idx = None + + def clone(self): + n = CLIP(no_init=True) + n.patcher = self.patcher.clone() + n.cond_stage_model = self.cond_stage_model + n.tokenizer = self.tokenizer + n.layer_idx = self.layer_idx + return n + + def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): + return self.patcher.add_patches(patches, strength_patch, strength_model) + + def clip_layer(self, layer_idx): + self.layer_idx = layer_idx + + def tokenize(self, text, return_word_ids=False): + return self.tokenizer.tokenize_with_weights(text, return_word_ids) + + def encode_from_tokens(self, tokens, return_pooled=False): + if self.layer_idx is not None: + self.cond_stage_model.clip_layer(self.layer_idx) + else: + self.cond_stage_model.reset_clip_layer() + + self.load_model() + cond, pooled = self.cond_stage_model.encode_token_weights(tokens) + if return_pooled: + return cond, pooled + return cond + + def encode(self, text): + tokens = self.tokenize(text) + return self.encode_from_tokens(tokens) + + def load_sd(self, sd): + return self.cond_stage_model.load_sd(sd) + + def get_sd(self): + return self.cond_stage_model.state_dict() + + def load_model(self): + model_management.load_model_gpu(self.patcher) + return self.patcher + + def get_key_patches(self): + return self.patcher.get_key_patches() + +class VAE: + def __init__(self, ckpt_path=None, device=None, config=None): + if config is None: + #default SD1.x/SD2.x VAE parameters + ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + self.first_stage_model = AutoencoderKL(ddconfig, {'target': 'torch.nn.Identity'}, 4, monitor="val/rec_loss") + else: + self.first_stage_model = AutoencoderKL(**(config['params'])) + self.first_stage_model = self.first_stage_model.eval() + if ckpt_path is not None: + sd = comfy.utils.load_torch_file(ckpt_path) + if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format + sd = diffusers_convert.convert_vae_state_dict(sd) + self.first_stage_model.load_state_dict(sd, strict=False) + + if device is None: + device = model_management.vae_device() + self.device = device + self.offload_device = model_management.vae_offload_device() + self.vae_dtype = model_management.vae_dtype() + self.first_stage_model.to(self.vae_dtype) + + def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): + steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) + steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = comfy.utils.ProgressBar(steps) + + decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float() + output = torch.clamp(( + (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) + + comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) + + comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar)) + / 3.0) / 2.0, min=0.0, max=1.0) + return output + + def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): + steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) + steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = comfy.utils.ProgressBar(steps) + + encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.vae_dtype).to(self.device) - 1.).sample().float() + samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) + samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) + samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) + samples /= 3.0 + return samples + + def decode(self, samples_in): + self.first_stage_model = self.first_stage_model.to(self.device) + try: + memory_used = (2562 * samples_in.shape[2] * samples_in.shape[3] * 64) * 1.7 + model_management.free_memory(memory_used, self.device) + free_memory = model_management.get_free_memory(self.device) + batch_number = int(free_memory / memory_used) + batch_number = max(1, batch_number) + + pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu") + for x in range(0, samples_in.shape[0], batch_number): + samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) + pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu().float() + except model_management.OOM_EXCEPTION as e: + print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") + pixel_samples = self.decode_tiled_(samples_in) + + self.first_stage_model = self.first_stage_model.to(self.offload_device) + pixel_samples = pixel_samples.cpu().movedim(1,-1) + return pixel_samples + + def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16): + self.first_stage_model = self.first_stage_model.to(self.device) + output = self.decode_tiled_(samples, tile_x, tile_y, overlap) + self.first_stage_model = self.first_stage_model.to(self.offload_device) + return output.movedim(1,-1) + + def encode(self, pixel_samples): + self.first_stage_model = self.first_stage_model.to(self.device) + pixel_samples = pixel_samples.movedim(-1,1) + try: + memory_used = (2078 * pixel_samples.shape[2] * pixel_samples.shape[3]) * 1.7 #NOTE: this constant along with the one in the decode above are estimated from the mem usage for the VAE and could change. + model_management.free_memory(memory_used, self.device) + free_memory = model_management.get_free_memory(self.device) + batch_number = int(free_memory / memory_used) + batch_number = max(1, batch_number) + samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device="cpu") + for x in range(0, pixel_samples.shape[0], batch_number): + pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device) + samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).sample().cpu().float() + + except model_management.OOM_EXCEPTION as e: + print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") + samples = self.encode_tiled_(pixel_samples) + + self.first_stage_model = self.first_stage_model.to(self.offload_device) + return samples + + def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): + self.first_stage_model = self.first_stage_model.to(self.device) + pixel_samples = pixel_samples.movedim(-1,1) + samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap) + self.first_stage_model = self.first_stage_model.to(self.offload_device) + return samples + + def get_sd(self): + return self.first_stage_model.state_dict() + +class StyleModel: + def __init__(self, model, device="cpu"): + self.model = model + + def get_cond(self, input): + return self.model(input.last_hidden_state) + + +def load_style_model(ckpt_path): + model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) + keys = model_data.keys() + if "style_embedding" in keys: + model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) + else: + raise Exception("invalid style model {}".format(ckpt_path)) + model.load_state_dict(model_data) + return StyleModel(model) + + +def load_clip(ckpt_paths, embedding_directory=None): + clip_data = [] + for p in ckpt_paths: + clip_data.append(comfy.utils.load_torch_file(p, safe_load=True)) + + class EmptyClass: + pass + + for i in range(len(clip_data)): + if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: + clip_data[i] = comfy.utils.transformers_convert(clip_data[i], "", "text_model.", 32) + + clip_target = EmptyClass() + clip_target.params = {} + if len(clip_data) == 1: + if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]: + clip_target.clip = sdxl_clip.SDXLRefinerClipModel + clip_target.tokenizer = sdxl_clip.SDXLTokenizer + elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]: + clip_target.clip = sd2_clip.SD2ClipModel + clip_target.tokenizer = sd2_clip.SD2Tokenizer + else: + clip_target.clip = sd1_clip.SD1ClipModel + clip_target.tokenizer = sd1_clip.SD1Tokenizer + else: + clip_target.clip = sdxl_clip.SDXLClipModel + clip_target.tokenizer = sdxl_clip.SDXLTokenizer + + clip = CLIP(clip_target, embedding_directory=embedding_directory) + for c in clip_data: + m, u = clip.load_sd(c) + if len(m) > 0: + print("clip missing:", m) + + if len(u) > 0: + print("clip unexpected:", u) + return clip + +def load_gligen(ckpt_path): + data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) + model = gligen.load_gligen(data) + if model_management.should_use_fp16(): + model = model.half() + return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) + +def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): + #TODO: this function is a mess and should be removed eventually + if config is None: + with open(config_path, 'r') as stream: + config = yaml.safe_load(stream) + model_config_params = config['model']['params'] + clip_config = model_config_params['cond_stage_config'] + scale_factor = model_config_params['scale_factor'] + vae_config = model_config_params['first_stage_config'] + + fp16 = False + if "unet_config" in model_config_params: + if "params" in model_config_params["unet_config"]: + unet_config = model_config_params["unet_config"]["params"] + if "use_fp16" in unet_config: + fp16 = unet_config["use_fp16"] + + noise_aug_config = None + if "noise_aug_config" in model_config_params: + noise_aug_config = model_config_params["noise_aug_config"] + + model_type = model_base.ModelType.EPS + + if "parameterization" in model_config_params: + if model_config_params["parameterization"] == "v": + model_type = model_base.ModelType.V_PREDICTION + + clip = None + vae = None + + class WeightsLoader(torch.nn.Module): + pass + + if state_dict is None: + state_dict = comfy.utils.load_torch_file(ckpt_path) + + class EmptyClass: + pass + + model_config = comfy.supported_models_base.BASE({}) + + from . import latent_formats + model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor) + model_config.unet_config = unet_config + + if config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"): + model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], model_type=model_type) + else: + model = model_base.BaseModel(model_config, model_type=model_type) + + if config['model']["target"].endswith("LatentInpaintDiffusion"): + model.set_inpaint() + + if fp16: + model = model.half() + + offload_device = model_management.unet_offload_device() + model = model.to(offload_device) + model.load_model_weights(state_dict, "model.diffusion_model.") + + if output_vae: + w = WeightsLoader() + vae = VAE(config=vae_config) + w.first_stage_model = vae.first_stage_model + load_model_weights(w, state_dict) + + if output_clip: + w = WeightsLoader() + clip_target = EmptyClass() + clip_target.params = clip_config.get("params", {}) + if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"): + clip_target.clip = sd2_clip.SD2ClipModel + clip_target.tokenizer = sd2_clip.SD2Tokenizer + elif clip_config["target"].endswith("FrozenCLIPEmbedder"): + clip_target.clip = sd1_clip.SD1ClipModel + clip_target.tokenizer = sd1_clip.SD1Tokenizer + clip = CLIP(clip_target, embedding_directory=embedding_directory) + w.cond_stage_model = clip.cond_stage_model + load_clip_weights(w, state_dict) + + return (comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) + +def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None): + sd = comfy.utils.load_torch_file(ckpt_path) + sd_keys = sd.keys() + clip = None + clipvision = None + vae = None + model = None + clip_target = None + + parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.") + fp16 = model_management.should_use_fp16(model_params=parameters) + + class WeightsLoader(torch.nn.Module): + pass + + model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", fp16) + if model_config is None: + raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) + + if model_config.clip_vision_prefix is not None: + if output_clipvision: + clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) + + dtype = torch.float32 + if fp16: + dtype = torch.float16 + + inital_load_device = model_management.unet_inital_load_device(parameters, dtype) + offload_device = model_management.unet_offload_device() + model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device) + model.load_model_weights(sd, "model.diffusion_model.") + + if output_vae: + vae = VAE() + w = WeightsLoader() + w.first_stage_model = vae.first_stage_model + load_model_weights(w, sd) + + if output_clip: + w = WeightsLoader() + clip_target = model_config.clip_target() + clip = CLIP(clip_target, embedding_directory=embedding_directory) + w.cond_stage_model = clip.cond_stage_model + sd = model_config.process_clip_state_dict(sd) + load_model_weights(w, sd) + + left_over = sd.keys() + if len(left_over) > 0: + print("left over keys:", left_over) + + model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device(), current_device=inital_load_device) + if inital_load_device != torch.device("cpu"): + print("loaded straight to GPU") + model_management.load_model_gpu(model_patcher) + + return (model_patcher, clip, vae, clipvision) + + +def load_unet(unet_path): #load unet in diffusers format + sd = comfy.utils.load_torch_file(unet_path) + parameters = comfy.utils.calculate_parameters(sd) + fp16 = model_management.should_use_fp16(model_params=parameters) + if "input_blocks.0.0.weight" in sd: #ldm + model_config = model_detection.model_config_from_unet(sd, "", fp16) + if model_config is None: + raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) + new_sd = sd + + else: #diffusers + model_config = model_detection.model_config_from_diffusers_unet(sd, fp16) + if model_config is None: + print("ERROR UNSUPPORTED UNET", unet_path) + return None + + diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config) + + new_sd = {} + for k in diffusers_keys: + if k in sd: + new_sd[diffusers_keys[k]] = sd.pop(k) + else: + print(diffusers_keys[k], k) + offload_device = model_management.unet_offload_device() + model = model_config.get_model(new_sd, "") + model = model.to(offload_device) + model.load_model_weights(new_sd, "") + return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device) + +def save_checkpoint(output_path, model, clip, vae, metadata=None): + model_management.load_models_gpu([model, clip.load_model()]) + sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd()) + comfy.utils.save_torch_file(sd, output_path, metadata=metadata) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..9978b6c35c66e755a94e3748d40356f908eadc5c --- /dev/null +++ b/comfy/sd1_clip.py @@ -0,0 +1,450 @@ +import os + +from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextConfig, modeling_utils +import comfy.ops +import torch +import traceback +import zipfile +from . import model_management +import contextlib + +class ClipTokenWeightEncoder: + def encode_token_weights(self, token_weight_pairs): + to_encode = list(self.empty_tokens) + for x in token_weight_pairs: + tokens = list(map(lambda a: a[0], x)) + to_encode.append(tokens) + + out, pooled = self.encode(to_encode) + z_empty = out[0:1] + if pooled.shape[0] > 1: + first_pooled = pooled[1:2] + else: + first_pooled = pooled[0:1] + + output = [] + for k in range(1, out.shape[0]): + z = out[k:k+1] + for i in range(len(z)): + for j in range(len(z[i])): + weight = token_weight_pairs[k - 1][j][1] + z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j] + output.append(z) + + if (len(output) == 0): + return z_empty.cpu(), first_pooled.cpu() + return torch.cat(output, dim=-2).cpu(), first_pooled.cpu() + +class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder): + """Uses the CLIP transformer encoder for text (from huggingface)""" + LAYERS = [ + "last", + "pooled", + "hidden" + ] + def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, + freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, textmodel_path=None, dtype=None): # clip-vit-base-patch32 + super().__init__() + assert layer in self.LAYERS + self.num_layers = 12 + if textmodel_path is not None: + self.transformer = CLIPTextModel.from_pretrained(textmodel_path) + else: + if textmodel_json_config is None: + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") + config = CLIPTextConfig.from_json_file(textmodel_json_config) + self.num_layers = config.num_hidden_layers + with comfy.ops.use_comfy_ops(device, dtype): + with modeling_utils.no_init_weights(): + self.transformer = CLIPTextModel(config) + + if dtype is not None: + self.transformer.to(dtype) + self.transformer.text_model.embeddings.token_embedding.to(torch.float32) + self.transformer.text_model.embeddings.position_embedding.to(torch.float32) + + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = None + self.empty_tokens = [[49406] + [49407] * 76] + self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1])) + self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) + self.enable_attention_masks = False + + self.layer_norm_hidden_state = True + if layer == "hidden": + assert layer_idx is not None + assert abs(layer_idx) <= self.num_layers + self.clip_layer(layer_idx) + self.layer_default = (self.layer, self.layer_idx) + + def freeze(self): + self.transformer = self.transformer.eval() + #self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def clip_layer(self, layer_idx): + if abs(layer_idx) >= self.num_layers: + self.layer = "last" + else: + self.layer = "hidden" + self.layer_idx = layer_idx + + def reset_clip_layer(self): + self.layer = self.layer_default[0] + self.layer_idx = self.layer_default[1] + + def set_up_textual_embeddings(self, tokens, current_embeds): + out_tokens = [] + next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1 + embedding_weights = [] + + for x in tokens: + tokens_temp = [] + for y in x: + if isinstance(y, int): + if y == token_dict_size: #EOS token + y = -1 + tokens_temp += [y] + else: + if y.shape[0] == current_embeds.weight.shape[1]: + embedding_weights += [y] + tokens_temp += [next_new_token] + next_new_token += 1 + else: + print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored", y.shape[0], current_embeds.weight.shape[1]) + while len(tokens_temp) < len(x): + tokens_temp += [self.empty_tokens[0][-1]] + out_tokens += [tokens_temp] + + n = token_dict_size + if len(embedding_weights) > 0: + new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype) + new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1] + for x in embedding_weights: + new_embedding.weight[n] = x + n += 1 + new_embedding.weight[n] = current_embeds.weight[-1] #EOS embedding + self.transformer.set_input_embeddings(new_embedding) + + processed_tokens = [] + for x in out_tokens: + processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one + + return processed_tokens + + def forward(self, tokens): + backup_embeds = self.transformer.get_input_embeddings() + device = backup_embeds.weight.device + tokens = self.set_up_textual_embeddings(tokens, backup_embeds) + tokens = torch.LongTensor(tokens).to(device) + + if self.transformer.text_model.final_layer_norm.weight.dtype != torch.float32: + precision_scope = torch.autocast + else: + precision_scope = lambda a, b: contextlib.nullcontext(a) + + with precision_scope(model_management.get_autocast_device(device), torch.float32): + attention_mask = None + if self.enable_attention_masks: + attention_mask = torch.zeros_like(tokens) + max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 + for x in range(attention_mask.shape[0]): + for y in range(attention_mask.shape[1]): + attention_mask[x, y] = 1 + if tokens[x, y] == max_token: + break + + outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask, output_hidden_states=self.layer=="hidden") + self.transformer.set_input_embeddings(backup_embeds) + + if self.layer == "last": + z = outputs.last_hidden_state + elif self.layer == "pooled": + z = outputs.pooler_output[:, None, :] + else: + z = outputs.hidden_states[self.layer_idx] + if self.layer_norm_hidden_state: + z = self.transformer.text_model.final_layer_norm(z) + + pooled_output = outputs.pooler_output + if self.text_projection is not None: + pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float() + return z.float(), pooled_output.float() + + def encode(self, tokens): + return self(tokens) + + def load_sd(self, sd): + if "text_projection" in sd: + self.text_projection[:] = sd.pop("text_projection") + if "text_projection.weight" in sd: + self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1) + return self.transformer.load_state_dict(sd, strict=False) + +def parse_parentheses(string): + result = [] + current_item = "" + nesting_level = 0 + for char in string: + if char == "(": + if nesting_level == 0: + if current_item: + result.append(current_item) + current_item = "(" + else: + current_item = "(" + else: + current_item += char + nesting_level += 1 + elif char == ")": + nesting_level -= 1 + if nesting_level == 0: + result.append(current_item + ")") + current_item = "" + else: + current_item += char + else: + current_item += char + if current_item: + result.append(current_item) + return result + +def token_weights(string, current_weight): + a = parse_parentheses(string) + out = [] + for x in a: + weight = current_weight + if len(x) >= 2 and x[-1] == ')' and x[0] == '(': + x = x[1:-1] + xx = x.rfind(":") + weight *= 1.1 + if xx > 0: + try: + weight = float(x[xx+1:]) + x = x[:xx] + except: + pass + out += token_weights(x, weight) + else: + out += [(x, current_weight)] + return out + +def escape_important(text): + text = text.replace("\\)", "\0\1") + text = text.replace("\\(", "\0\2") + return text + +def unescape_important(text): + text = text.replace("\0\1", ")") + text = text.replace("\0\2", "(") + return text + +def safe_load_embed_zip(embed_path): + with zipfile.ZipFile(embed_path) as myzip: + names = list(filter(lambda a: "data/" in a, myzip.namelist())) + names.reverse() + for n in names: + with myzip.open(n) as myfile: + data = myfile.read() + number = len(data) // 4 + length_embed = 1024 #sd2.x + if number < 768: + continue + if number % 768 == 0: + length_embed = 768 #sd1.x + num_embeds = number // length_embed + embed = torch.frombuffer(data, dtype=torch.float) + out = embed.reshape((num_embeds, length_embed)).clone() + del embed + return out + +def expand_directory_list(directories): + dirs = set() + for x in directories: + dirs.add(x) + for root, subdir, file in os.walk(x, followlinks=True): + dirs.add(root) + return list(dirs) + +def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None): + if isinstance(embedding_directory, str): + embedding_directory = [embedding_directory] + + embedding_directory = expand_directory_list(embedding_directory) + + valid_file = None + for embed_dir in embedding_directory: + embed_path = os.path.join(embed_dir, embedding_name) + if not os.path.isfile(embed_path): + extensions = ['.safetensors', '.pt', '.bin'] + for x in extensions: + t = embed_path + x + if os.path.isfile(t): + valid_file = t + break + else: + valid_file = embed_path + if valid_file is not None: + break + + if valid_file is None: + return None + + embed_path = valid_file + + embed_out = None + + try: + if embed_path.lower().endswith(".safetensors"): + import safetensors.torch + embed = safetensors.torch.load_file(embed_path, device="cpu") + else: + if 'weights_only' in torch.load.__code__.co_varnames: + try: + embed = torch.load(embed_path, weights_only=True, map_location="cpu") + except: + embed_out = safe_load_embed_zip(embed_path) + else: + embed = torch.load(embed_path, map_location="cpu") + except Exception as e: + print(traceback.format_exc()) + print() + print("error loading embedding, skipping loading:", embedding_name) + return None + + if embed_out is None: + if 'string_to_param' in embed: + values = embed['string_to_param'].values() + embed_out = next(iter(values)) + elif isinstance(embed, list): + out_list = [] + for x in range(len(embed)): + for k in embed[x]: + t = embed[x][k] + if t.shape[-1] != embedding_size: + continue + out_list.append(t.reshape(-1, t.shape[-1])) + embed_out = torch.cat(out_list, dim=0) + elif embed_key is not None and embed_key in embed: + embed_out = embed[embed_key] + else: + values = embed.values() + embed_out = next(iter(values)) + return embed_out + +class SD1Tokenizer: + def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l'): + if tokenizer_path is None: + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + self.max_length = max_length + self.max_tokens_per_section = self.max_length - 2 + + empty = self.tokenizer('')["input_ids"] + self.start_token = empty[0] + self.end_token = empty[1] + self.pad_with_end = pad_with_end + vocab = self.tokenizer.get_vocab() + self.inv_vocab = {v: k for k, v in vocab.items()} + self.embedding_directory = embedding_directory + self.max_word_length = 8 + self.embedding_identifier = "embedding:" + self.embedding_size = embedding_size + self.embedding_key = embedding_key + + def _try_get_embedding(self, embedding_name:str): + ''' + Takes a potential embedding name and tries to retrieve it. + Returns a Tuple consisting of the embedding and any leftover string, embedding can be None. + ''' + embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key) + if embed is None: + stripped = embedding_name.strip(',') + if len(stripped) < len(embedding_name): + embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key) + return (embed, embedding_name[len(stripped):]) + return (embed, "") + + + def tokenize_with_weights(self, text:str, return_word_ids=False): + ''' + Takes a prompt and converts it to a list of (token, weight, word id) elements. + Tokens can both be integer tokens and pre computed CLIP tensors. + Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. + Returned list has the dimensions NxM where M is the input size of CLIP + ''' + if self.pad_with_end: + pad_token = self.end_token + else: + pad_token = 0 + + text = escape_important(text) + parsed_weights = token_weights(text, 1.0) + + #tokenize words + tokens = [] + for weighted_segment, weight in parsed_weights: + to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ') + to_tokenize = [x for x in to_tokenize if x != ""] + for word in to_tokenize: + #if we find an embedding, deal with the embedding + if word.startswith(self.embedding_identifier) and self.embedding_directory is not None: + embedding_name = word[len(self.embedding_identifier):].strip('\n') + embed, leftover = self._try_get_embedding(embedding_name) + if embed is None: + print(f"warning, embedding:{embedding_name} does not exist, ignoring") + else: + if len(embed.shape) == 1: + tokens.append([(embed, weight)]) + else: + tokens.append([(embed[x], weight) for x in range(embed.shape[0])]) + #if we accidentally have leftover text, continue parsing using leftover, else move on to next word + if leftover != "": + word = leftover + else: + continue + #parse word + tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][1:-1]]) + + #reshape token array to CLIP input size + batched_tokens = [] + batch = [(self.start_token, 1.0, 0)] + batched_tokens.append(batch) + for i, t_group in enumerate(tokens): + #determine if we're going to try and keep the tokens in a single batch + is_large = len(t_group) >= self.max_word_length + + while len(t_group) > 0: + if len(t_group) + len(batch) > self.max_length - 1: + remaining_length = self.max_length - len(batch) - 1 + #break word in two and add end token + if is_large: + batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]]) + batch.append((self.end_token, 1.0, 0)) + t_group = t_group[remaining_length:] + #add end token and pad + else: + batch.append((self.end_token, 1.0, 0)) + batch.extend([(pad_token, 1.0, 0)] * (remaining_length)) + #start new batch + batch = [(self.start_token, 1.0, 0)] + batched_tokens.append(batch) + else: + batch.extend([(t,w,i+1) for t,w in t_group]) + t_group = [] + + #fill last batch + batch.extend([(self.end_token, 1.0, 0)] + [(pad_token, 1.0, 0)] * (self.max_length - len(batch) - 1)) + + if not return_word_ids: + batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] + + return batched_tokens + + + def untokenize(self, token_weight_pair): + return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) diff --git a/comfy/sd1_clip_config.json b/comfy/sd1_clip_config.json new file mode 100644 index 0000000000000000000000000000000000000000..0158a1fd52727adf22359238285afafb150f66f2 --- /dev/null +++ b/comfy/sd1_clip_config.json @@ -0,0 +1,25 @@ +{ + "_name_or_path": "openai/clip-vit-large-patch14", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "projection_dim": 768, + "torch_dtype": "float32", + "transformers_version": "4.24.0", + "vocab_size": 49408 +} diff --git a/comfy/sd1_tokenizer/merges.txt b/comfy/sd1_tokenizer/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..76e821f1b6f0a9709293c3b6b51ed90980b3166b --- /dev/null +++ b/comfy/sd1_tokenizer/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or +n e +ð Ł +r a +h a +d e +o l +v e +s i +u r +a l +s e +' s +u n +d i +b e +l a +w h +o o +d ay +e n +m a +n o +l e +t o +ou r +i r +g h +w it +i t +y o +a s +s p +th is +t s +at i +yo u +wit h +a d +i s +a b +l y +w e +th e +t e +a s +a g +v i +p p +s u +h o +m y +. . +b u +c om +s e +er s +m e +m e +al l +c on +m o +k e +g e +ou t +en t +c o +f e +v er +a r +f ro +a u +p o +c e +gh 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+import torch +import os + +class SD2ClipModel(sd1_clip.SD1ClipModel): + def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, textmodel_path=None, dtype=None): + if layer == "penultimate": + layer="hidden" + layer_idx=23 + + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json") + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype) + self.empty_tokens = [[49406] + [49407] + [0] * 75] + +class SD2Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None): + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024) diff --git a/comfy/sd2_clip_config.json b/comfy/sd2_clip_config.json new file mode 100644 index 0000000000000000000000000000000000000000..85cec832be9a1d0957245a8d125af398829f247e --- /dev/null +++ b/comfy/sd2_clip_config.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_size": 1024, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "pad_token_id": 1, + "projection_dim": 1024, + "torch_dtype": "float32", + "vocab_size": 49408 +} diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..e3ac2ee0b4a956219c95b62aa83872d6cdacd0f7 --- /dev/null +++ b/comfy/sdxl_clip.py @@ -0,0 +1,83 @@ +from comfy import sd1_clip +import torch +import os + +class SDXLClipG(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, textmodel_path=None, dtype=None): + if layer == "penultimate": + layer="hidden" + layer_idx=-2 + + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype) + self.empty_tokens = [[49406] + [49407] + [0] * 75] + self.layer_norm_hidden_state = False + + def load_sd(self, sd): + return super().load_sd(sd) + +class SDXLClipGTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None): + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') + + +class SDXLTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None): + self.clip_l = sd1_clip.SD1Tokenizer(embedding_directory=embedding_directory) + self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory) + + def tokenize_with_weights(self, text:str, return_word_ids=False): + out = {} + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + return out + + def untokenize(self, token_weight_pair): + return self.clip_g.untokenize(token_weight_pair) + +class SDXLClipModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None): + super().__init__() + self.clip_l = sd1_clip.SD1ClipModel(layer="hidden", layer_idx=11, device=device, dtype=dtype) + self.clip_l.layer_norm_hidden_state = False + self.clip_g = SDXLClipG(device=device, dtype=dtype) + + def clip_layer(self, layer_idx): + self.clip_l.clip_layer(layer_idx) + self.clip_g.clip_layer(layer_idx) + + def reset_clip_layer(self): + self.clip_g.reset_clip_layer() + self.clip_l.reset_clip_layer() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_g = token_weight_pairs["g"] + token_weight_pairs_l = token_weight_pairs["l"] + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + return torch.cat([l_out, g_out], dim=-1), g_pooled + + def load_sd(self, sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return self.clip_g.load_sd(sd) + else: + return self.clip_l.load_sd(sd) + +class SDXLRefinerClipModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None): + super().__init__() + self.clip_g = SDXLClipG(device=device, dtype=dtype) + + def clip_layer(self, layer_idx): + self.clip_g.clip_layer(layer_idx) + + def reset_clip_layer(self): + self.clip_g.reset_clip_layer() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_g = token_weight_pairs["g"] + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + return g_out, g_pooled + + def load_sd(self, sd): + return self.clip_g.load_sd(sd) diff --git a/comfy/supported_models.py b/comfy/supported_models.py new file mode 100644 index 0000000000000000000000000000000000000000..bb8ae2148fd1af3d3064b0082d2ab4f227e365cf --- /dev/null +++ b/comfy/supported_models.py @@ -0,0 +1,193 @@ +import torch +from . import model_base +from . import utils + +from . import sd1_clip +from . import sd2_clip +from . import sdxl_clip + +from . import supported_models_base +from . import latent_formats + +from . import diffusers_convert + +class SD15(supported_models_base.BASE): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + } + + unet_extra_config = { + "num_heads": 8, + "num_head_channels": -1, + } + + latent_format = latent_formats.SD15 + + def process_clip_state_dict(self, state_dict): + k = list(state_dict.keys()) + for x in k: + if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): + y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") + state_dict[y] = state_dict.pop(x) + + if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict: + ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] + if ids.dtype == torch.float32: + state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() + + return state_dict + + def clip_target(self): + return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel) + +class SD20(supported_models_base.BASE): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": None, + } + + latent_format = latent_formats.SD15 + + def model_type(self, state_dict, prefix=""): + if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction + k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) + out = state_dict[k] + if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out. + return model_base.ModelType.V_PREDICTION + return model_base.ModelType.EPS + + def process_clip_state_dict(self, state_dict): + state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + replace_prefix[""] = "cond_stage_model.model." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) + return state_dict + + def clip_target(self): + return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel) + +class SD21UnclipL(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": 1536, + } + + clip_vision_prefix = "embedder.model.visual." + noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768} + + +class SD21UnclipH(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": 2048, + } + + clip_vision_prefix = "embedder.model.visual." + noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024} + +class SDXLRefiner(supported_models_base.BASE): + unet_config = { + "model_channels": 384, + "use_linear_in_transformer": True, + "context_dim": 1280, + "adm_in_channels": 2560, + "transformer_depth": [0, 4, 4, 0], + } + + latent_format = latent_formats.SDXL + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SDXLRefiner(self, device=device) + + def process_clip_state_dict(self, state_dict): + keys_to_replace = {} + replace_prefix = {} + + state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) + keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection" + keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" + + state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") + if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: + state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") + replace_prefix["clip_g"] = "conditioner.embedders.0.model" + state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) + return state_dict_g + + def clip_target(self): + return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel) + +class SDXL(supported_models_base.BASE): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 2, 10], + "context_dim": 2048, + "adm_in_channels": 2816 + } + + latent_format = latent_formats.SDXL + + def model_type(self, state_dict, prefix=""): + if "v_pred" in state_dict: + return model_base.ModelType.V_PREDICTION + else: + return model_base.ModelType.EPS + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) + if self.inpaint_model(): + out.set_inpaint() + return out + + def process_clip_state_dict(self, state_dict): + keys_to_replace = {} + replace_prefix = {} + + replace_prefix["conditioner.embedders.0.transformer.text_model"] = "cond_stage_model.clip_l.transformer.text_model" + state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) + keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection" + keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" + + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + keys_to_replace = {} + state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") + if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: + state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") + for k in state_dict: + if k.startswith("clip_l"): + state_dict_g[k] = state_dict[k] + + replace_prefix["clip_g"] = "conditioner.embedders.1.model" + replace_prefix["clip_l"] = "conditioner.embedders.0" + state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) + return state_dict_g + + def clip_target(self): + return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel) + + +models = [SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL] diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py new file mode 100644 index 0000000000000000000000000000000000000000..88a1d7fde49902a975726de93c422ed4f1295df4 --- /dev/null +++ b/comfy/supported_models_base.py @@ -0,0 +1,67 @@ +import torch +from . import model_base +from . import utils +from . import latent_formats + +class ClipTarget: + def __init__(self, tokenizer, clip): + self.clip = clip + self.tokenizer = tokenizer + self.params = {} + +class BASE: + unet_config = {} + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + } + + clip_prefix = [] + clip_vision_prefix = None + noise_aug_config = None + beta_schedule = "linear" + latent_format = latent_formats.LatentFormat + + @classmethod + def matches(s, unet_config): + for k in s.unet_config: + if s.unet_config[k] != unet_config[k]: + return False + return True + + def model_type(self, state_dict, prefix=""): + return model_base.ModelType.EPS + + def inpaint_model(self): + return self.unet_config["in_channels"] > 4 + + def __init__(self, unet_config): + self.unet_config = unet_config + self.latent_format = self.latent_format() + for x in self.unet_extra_config: + self.unet_config[x] = self.unet_extra_config[x] + + def get_model(self, state_dict, prefix="", device=None): + if self.noise_aug_config is not None: + out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) + else: + out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) + if self.inpaint_model(): + out.set_inpaint() + return out + + def process_clip_state_dict(self, state_dict): + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "cond_stage_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_unet_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "model.diffusion_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_vae_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "first_stage_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + diff --git a/comfy/t2i_adapter/adapter.py b/comfy/t2i_adapter/adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..e9a606b1cd67fd9a955a0ea0a86d1bd5498d85e5 --- /dev/null +++ b/comfy/t2i_adapter/adapter.py @@ -0,0 +1,293 @@ +#taken from https://github.com/TencentARC/T2I-Adapter +import torch +import torch.nn as nn +from collections import OrderedDict + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + if not self.use_conv: + padding = [x.shape[2] % 2, x.shape[3] % 2] + self.op.padding = padding + + x = self.op(x) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): + super().__init__() + ps = ksize // 2 + if in_c != out_c or sk == False: + self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + # print('n_in') + self.in_conv = None + self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) + if sk == False: + self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + self.skep = None + + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=use_conv) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + if self.in_conv is not None: # edit + x = self.in_conv(x) + + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + if self.skep is not None: + return h + self.skep(x) + else: + return h + x + + +class Adapter(nn.Module): + def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True): + super(Adapter, self).__init__() + self.unshuffle_amount = 8 + resblock_no_downsample = [] + resblock_downsample = [3, 2, 1] + self.xl = xl + if self.xl: + self.unshuffle_amount = 16 + resblock_no_downsample = [1] + resblock_downsample = [2] + + self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) + self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) + self.channels = channels + self.nums_rb = nums_rb + self.body = [] + for i in range(len(channels)): + for j in range(nums_rb): + if (i in resblock_downsample) and (j == 0): + self.body.append( + ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv)) + elif (i in resblock_no_downsample) and (j == 0): + self.body.append( + ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) + else: + self.body.append( + ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) + self.body = nn.ModuleList(self.body) + self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) + + def forward(self, x): + # unshuffle + x = self.unshuffle(x) + # extract features + features = [] + x = self.conv_in(x) + for i in range(len(self.channels)): + for j in range(self.nums_rb): + idx = i * self.nums_rb + j + x = self.body[idx](x) + if self.xl: + features.append(None) + if i == 0: + features.append(None) + features.append(None) + if i == 2: + features.append(None) + else: + features.append(None) + features.append(None) + features.append(x) + + return features + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential( + OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model))])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class StyleAdapter(nn.Module): + + def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4): + super().__init__() + + scale = width ** -0.5 + self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)]) + self.num_token = num_token + self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale) + self.ln_post = LayerNorm(width) + self.ln_pre = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, context_dim)) + + def forward(self, x): + # x shape [N, HW+1, C] + style_embedding = self.style_embedding + torch.zeros( + (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device) + x = torch.cat([x, style_embedding], dim=1) + x = self.ln_pre(x) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer_layes(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_post(x[:, -self.num_token:, :]) + x = x @ self.proj + + return x + + +class ResnetBlock_light(nn.Module): + def __init__(self, in_c): + super().__init__() + self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1) + + def forward(self, x): + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + + return h + x + + +class extractor(nn.Module): + def __init__(self, in_c, inter_c, out_c, nums_rb, down=False): + super().__init__() + self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0) + self.body = [] + for _ in range(nums_rb): + self.body.append(ResnetBlock_light(inter_c)) + self.body = nn.Sequential(*self.body) + self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0) + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=False) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + x = self.in_conv(x) + x = self.body(x) + x = self.out_conv(x) + + return x + + +class Adapter_light(nn.Module): + def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64): + super(Adapter_light, self).__init__() + self.unshuffle_amount = 8 + self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) + self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) + self.channels = channels + self.nums_rb = nums_rb + self.body = [] + self.xl = False + + for i in range(len(channels)): + if i == 0: + self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False)) + else: + self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True)) + self.body = nn.ModuleList(self.body) + + def forward(self, x): + # unshuffle + x = self.unshuffle(x) + # extract features + features = [] + for i in range(len(self.channels)): + x = self.body[i](x) + features.append(None) + features.append(None) + features.append(x) + + return features diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py new file mode 100644 index 0000000000000000000000000000000000000000..1549345ae5312f0f51664ee0b96fc883b1f3796a --- /dev/null +++ b/comfy/taesd/taesd.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python3 +""" +Tiny AutoEncoder for Stable Diffusion +(DNN for encoding / decoding SD's latent space) +""" +import torch +import torch.nn as nn + +def conv(n_in, n_out, **kwargs): + return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs) + +class Clamp(nn.Module): + def forward(self, x): + return torch.tanh(x / 3) * 3 + +class Block(nn.Module): + def __init__(self, n_in, n_out): + super().__init__() + self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) + self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() + self.fuse = nn.ReLU() + def forward(self, x): + return self.fuse(self.conv(x) + self.skip(x)) + +def Encoder(): + return nn.Sequential( + conv(3, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 4), + ) + +def Decoder(): + return nn.Sequential( + Clamp(), conv(4, 64), nn.ReLU(), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), conv(64, 3), + ) + +class TAESD(nn.Module): + latent_magnitude = 3 + latent_shift = 0.5 + + def __init__(self, encoder_path="taesd_encoder.pth", decoder_path="taesd_decoder.pth"): + """Initialize pretrained TAESD on the given device from the given checkpoints.""" + super().__init__() + self.encoder = Encoder() + self.decoder = Decoder() + if encoder_path is not None: + self.encoder.load_state_dict(torch.load(encoder_path, map_location="cpu", weights_only=True)) + if decoder_path is not None: + self.decoder.load_state_dict(torch.load(decoder_path, map_location="cpu", weights_only=True)) + + @staticmethod + def scale_latents(x): + """raw latents -> [0, 1]""" + return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) + + @staticmethod + def unscale_latents(x): + """[0, 1] -> raw latents""" + return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) diff --git a/comfy/utils.py b/comfy/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7843b58ccade76d6d867f6305b6113c80f392264 --- /dev/null +++ b/comfy/utils.py @@ -0,0 +1,434 @@ +import torch +import math +import struct +import comfy.checkpoint_pickle +import safetensors.torch +import numpy as np +from PIL import Image + +def load_torch_file(ckpt, safe_load=False, device=None): + if device is None: + device = torch.device("cpu") + if ckpt.lower().endswith(".safetensors"): + sd = safetensors.torch.load_file(ckpt, device=device.type) + else: + if safe_load: + if not 'weights_only' in torch.load.__code__.co_varnames: + print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.") + safe_load = False + if safe_load: + pl_sd = torch.load(ckpt, map_location=device, weights_only=True) + else: + pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle) + if "global_step" in pl_sd: + print(f"Global Step: {pl_sd['global_step']}") + if "state_dict" in pl_sd: + sd = pl_sd["state_dict"] + else: + sd = pl_sd + return sd + +def save_torch_file(sd, ckpt, metadata=None): + if metadata is not None: + safetensors.torch.save_file(sd, ckpt, metadata=metadata) + else: + safetensors.torch.save_file(sd, ckpt) + +def calculate_parameters(sd, prefix=""): + params = 0 + for k in sd.keys(): + if k.startswith(prefix): + params += sd[k].nelement() + return params + +def state_dict_key_replace(state_dict, keys_to_replace): + for x in keys_to_replace: + if x in state_dict: + state_dict[keys_to_replace[x]] = state_dict.pop(x) + return state_dict + +def state_dict_prefix_replace(state_dict, replace_prefix): + for rp in replace_prefix: + replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) + for x in replace: + state_dict[x[1]] = state_dict.pop(x[0]) + return state_dict + + +def transformers_convert(sd, prefix_from, prefix_to, number): + keys_to_replace = { + "{}positional_embedding": "{}embeddings.position_embedding.weight", + "{}token_embedding.weight": "{}embeddings.token_embedding.weight", + "{}ln_final.weight": "{}final_layer_norm.weight", + "{}ln_final.bias": "{}final_layer_norm.bias", + } + + for k in keys_to_replace: + x = k.format(prefix_from) + if x in sd: + sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x) + + resblock_to_replace = { + "ln_1": "layer_norm1", + "ln_2": "layer_norm2", + "mlp.c_fc": "mlp.fc1", + "mlp.c_proj": "mlp.fc2", + "attn.out_proj": "self_attn.out_proj", + } + + for resblock in range(number): + for x in resblock_to_replace: + for y in ["weight", "bias"]: + k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y) + k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y) + if k in sd: + sd[k_to] = sd.pop(k) + + for y in ["weight", "bias"]: + k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y) + if k_from in sd: + weights = sd.pop(k_from) + shape_from = weights.shape[0] // 3 + for x in range(3): + p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] + k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) + sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] + return sd + +UNET_MAP_ATTENTIONS = { + "proj_in.weight", + "proj_in.bias", + "proj_out.weight", + "proj_out.bias", + "norm.weight", + "norm.bias", +} + +TRANSFORMER_BLOCKS = { + "norm1.weight", + "norm1.bias", + "norm2.weight", + "norm2.bias", + "norm3.weight", + "norm3.bias", + "attn1.to_q.weight", + "attn1.to_k.weight", + "attn1.to_v.weight", + "attn1.to_out.0.weight", + "attn1.to_out.0.bias", + "attn2.to_q.weight", + "attn2.to_k.weight", + "attn2.to_v.weight", + "attn2.to_out.0.weight", + "attn2.to_out.0.bias", + "ff.net.0.proj.weight", + "ff.net.0.proj.bias", + "ff.net.2.weight", + "ff.net.2.bias", +} + +UNET_MAP_RESNET = { + "in_layers.2.weight": "conv1.weight", + "in_layers.2.bias": "conv1.bias", + "emb_layers.1.weight": "time_emb_proj.weight", + "emb_layers.1.bias": "time_emb_proj.bias", + "out_layers.3.weight": "conv2.weight", + "out_layers.3.bias": "conv2.bias", + "skip_connection.weight": "conv_shortcut.weight", + "skip_connection.bias": "conv_shortcut.bias", + "in_layers.0.weight": "norm1.weight", + "in_layers.0.bias": "norm1.bias", + "out_layers.0.weight": "norm2.weight", + "out_layers.0.bias": "norm2.bias", +} + +UNET_MAP_BASIC = { + ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), + ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias") +} + +def unet_to_diffusers(unet_config): + num_res_blocks = unet_config["num_res_blocks"] + attention_resolutions = unet_config["attention_resolutions"] + channel_mult = unet_config["channel_mult"] + transformer_depth = unet_config["transformer_depth"] + num_blocks = len(channel_mult) + if isinstance(num_res_blocks, int): + num_res_blocks = [num_res_blocks] * num_blocks + if isinstance(transformer_depth, int): + transformer_depth = [transformer_depth] * num_blocks + + transformers_per_layer = [] + res = 1 + for i in range(num_blocks): + transformers = 0 + if res in attention_resolutions: + transformers = transformer_depth[i] + transformers_per_layer.append(transformers) + res *= 2 + + transformers_mid = unet_config.get("transformer_depth_middle", transformer_depth[-1]) + + diffusers_unet_map = {} + for x in range(num_blocks): + n = 1 + (num_res_blocks[x] + 1) * x + for i in range(num_res_blocks[x]): + for b in UNET_MAP_RESNET: + diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) + if transformers_per_layer[x] > 0: + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) + for t in range(transformers_per_layer[x]): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) + n += 1 + for k in ["weight", "bias"]: + diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) + + i = 0 + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) + for t in range(transformers_mid): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) + + for i, n in enumerate([0, 2]): + for b in UNET_MAP_RESNET: + diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) + + num_res_blocks = list(reversed(num_res_blocks)) + transformers_per_layer = list(reversed(transformers_per_layer)) + for x in range(num_blocks): + n = (num_res_blocks[x] + 1) * x + l = num_res_blocks[x] + 1 + for i in range(l): + c = 0 + for b in UNET_MAP_RESNET: + diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) + c += 1 + if transformers_per_layer[x] > 0: + c += 1 + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) + for t in range(transformers_per_layer[x]): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) + if i == l - 1: + for k in ["weight", "bias"]: + diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) + n += 1 + + for k in UNET_MAP_BASIC: + diffusers_unet_map[k[1]] = k[0] + + return diffusers_unet_map + +def repeat_to_batch_size(tensor, batch_size): + if tensor.shape[0] > batch_size: + return tensor[:batch_size] + elif tensor.shape[0] < batch_size: + return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size] + return tensor + +def convert_sd_to(state_dict, dtype): + keys = list(state_dict.keys()) + for k in keys: + state_dict[k] = state_dict[k].to(dtype) + return state_dict + +def safetensors_header(safetensors_path, max_size=100*1024*1024): + with open(safetensors_path, "rb") as f: + header = f.read(8) + length_of_header = struct.unpack(' max_size: + return None + return f.read(length_of_header) + +def set_attr(obj, attr, value): + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + setattr(obj, attrs[-1], torch.nn.Parameter(value)) + del prev + +def get_attr(obj, attr): + attrs = attr.split(".") + for name in attrs: + obj = getattr(obj, name) + return obj + +def bislerp(samples, width, height): + def slerp(b1, b2, r): + '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' + + c = b1.shape[-1] + + #norms + b1_norms = torch.norm(b1, dim=-1, keepdim=True) + b2_norms = torch.norm(b2, dim=-1, keepdim=True) + + #normalize + b1_normalized = b1 / b1_norms + b2_normalized = b2 / b2_norms + + #zero when norms are zero + b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0 + b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0 + + #slerp + dot = (b1_normalized*b2_normalized).sum(1) + omega = torch.acos(dot) + so = torch.sin(omega) + + #technically not mathematically correct, but more pleasing? + res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized + res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c) + + #edge cases for same or polar opposites + res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] + res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] + return res + + def generate_bilinear_data(length_old, length_new): + coords_1 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32) + coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") + ratios = coords_1 - coords_1.floor() + coords_1 = coords_1.to(torch.int64) + + coords_2 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32) + 1 + coords_2[:,:,:,-1] -= 1 + coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") + coords_2 = coords_2.to(torch.int64) + return ratios, coords_1, coords_2 + + n,c,h,w = samples.shape + h_new, w_new = (height, width) + + #linear w + ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new) + coords_1 = coords_1.expand((n, c, h, -1)) + coords_2 = coords_2.expand((n, c, h, -1)) + ratios = ratios.expand((n, 1, h, -1)) + + pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c)) + pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c)) + ratios = ratios.movedim(1, -1).reshape((-1,1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h, w_new, c).movedim(-1, 1) + + #linear h + ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new) + coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) + coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) + ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) + + pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c)) + pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c)) + ratios = ratios.movedim(1, -1).reshape((-1,1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) + return result + +def lanczos(samples, width, height): + images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples] + images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images] + images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images] + result = torch.stack(images) + return result + +def common_upscale(samples, width, height, upscale_method, crop): + if crop == "center": + old_width = samples.shape[3] + old_height = samples.shape[2] + old_aspect = old_width / old_height + new_aspect = width / height + x = 0 + y = 0 + if old_aspect > new_aspect: + x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) + elif old_aspect < new_aspect: + y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) + s = samples[:,:,y:old_height-y,x:old_width-x] + else: + s = samples + + if upscale_method == "bislerp": + return bislerp(s, width, height) + elif upscale_method == "lanczos": + return lanczos(s, width, height) + else: + return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) + +def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): + return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap))) + +@torch.inference_mode() +def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, pbar = None): + output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device="cpu") + for b in range(samples.shape[0]): + s = samples[b:b+1] + out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu") + out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu") + for y in range(0, s.shape[2], tile_y - overlap): + for x in range(0, s.shape[3], tile_x - overlap): + s_in = s[:,:,y:y+tile_y,x:x+tile_x] + + ps = function(s_in).cpu() + mask = torch.ones_like(ps) + feather = round(overlap * upscale_amount) + for t in range(feather): + mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) + mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) + mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) + mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) + out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask + out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask + if pbar is not None: + pbar.update(1) + + output[b:b+1] = out/out_div + return output + + +PROGRESS_BAR_HOOK = None +def set_progress_bar_global_hook(function): + global PROGRESS_BAR_HOOK + PROGRESS_BAR_HOOK = function + +class ProgressBar: + def __init__(self, total): + global PROGRESS_BAR_HOOK + self.total = total + self.current = 0 + self.hook = PROGRESS_BAR_HOOK + + def update_absolute(self, value, total=None, preview=None): + if total is not None: + self.total = total + if value > self.total: + value = self.total + self.current = value + if self.hook is not None: + self.hook(self.current, self.total, preview) + + def update(self, value): + self.update_absolute(self.current + value) diff --git a/comfy_extras/chainner_models/__init__.py b/comfy_extras/chainner_models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy_extras/chainner_models/architecture/DAT.py b/comfy_extras/chainner_models/architecture/DAT.py new file mode 100644 index 0000000000000000000000000000000000000000..0bcc26ef422b73cef41744e2203901a3d290c2f0 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/DAT.py @@ -0,0 +1,1182 @@ +# pylint: skip-file +import math +import re + +import numpy as np +import torch +import torch.nn as nn +import torch.utils.checkpoint as checkpoint +from einops import rearrange +from einops.layers.torch import Rearrange +from torch import Tensor +from torch.nn import functional as F + +from .timm.drop import DropPath +from .timm.weight_init import trunc_normal_ + + +def img2windows(img, H_sp, W_sp): + """ + Input: Image (B, C, H, W) + Output: Window Partition (B', N, C) + """ + B, C, H, W = img.shape + img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp) + img_perm = ( + img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp * W_sp, C) + ) + return img_perm + + +def windows2img(img_splits_hw, H_sp, W_sp, H, W): + """ + Input: Window Partition (B', N, C) + Output: Image (B, H, W, C) + """ + B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp)) + + img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1) + img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return img + + +class SpatialGate(nn.Module): + """Spatial-Gate. + Args: + dim (int): Half of input channels. + """ + + def __init__(self, dim): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.conv = nn.Conv2d( + dim, dim, kernel_size=3, stride=1, padding=1, groups=dim + ) # DW Conv + + def forward(self, x, H, W): + # Split + x1, x2 = x.chunk(2, dim=-1) + B, N, C = x.shape + x2 = ( + self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C // 2, H, W)) + .flatten(2) + .transpose(-1, -2) + .contiguous() + ) + + return x1 * x2 + + +class SGFN(nn.Module): + """Spatial-Gate Feed-Forward Network. + Args: + in_features (int): Number of input channels. + hidden_features (int | None): Number of hidden channels. Default: None + out_features (int | None): Number of output channels. Default: None + act_layer (nn.Module): Activation layer. Default: nn.GELU + drop (float): Dropout rate. Default: 0.0 + """ + + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.sg = SpatialGate(hidden_features // 2) + self.fc2 = nn.Linear(hidden_features // 2, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + + x = self.sg(x, H, W) + x = self.drop(x) + + x = self.fc2(x) + x = self.drop(x) + return x + + +class DynamicPosBias(nn.Module): + # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py + """Dynamic Relative Position Bias. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + residual (bool): If True, use residual strage to connect conv. + """ + + def __init__(self, dim, num_heads, residual): + super().__init__() + self.residual = residual + self.num_heads = num_heads + self.pos_dim = dim // 4 + self.pos_proj = nn.Linear(2, self.pos_dim) + self.pos1 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.pos_dim), + ) + self.pos2 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.pos_dim), + ) + self.pos3 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.num_heads), + ) + + def forward(self, biases): + if self.residual: + pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads + pos = pos + self.pos1(pos) + pos = pos + self.pos2(pos) + pos = self.pos3(pos) + else: + pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases)))) + return pos + + +class Spatial_Attention(nn.Module): + """Spatial Window Self-Attention. + It supports rectangle window (containing square window). + Args: + dim (int): Number of input channels. + idx (int): The indentix of window. (0/1) + split_size (tuple(int)): Height and Width of spatial window. + dim_out (int | None): The dimension of the attention output. Default: None + num_heads (int): Number of attention heads. Default: 6 + attn_drop (float): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float): Dropout ratio of output. Default: 0.0 + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set + position_bias (bool): The dynamic relative position bias. Default: True + """ + + def __init__( + self, + dim, + idx, + split_size=[8, 8], + dim_out=None, + num_heads=6, + attn_drop=0.0, + proj_drop=0.0, + qk_scale=None, + position_bias=True, + ): + super().__init__() + self.dim = dim + self.dim_out = dim_out or dim + self.split_size = split_size + self.num_heads = num_heads + self.idx = idx + self.position_bias = position_bias + + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + if idx == 0: + H_sp, W_sp = self.split_size[0], self.split_size[1] + elif idx == 1: + W_sp, H_sp = self.split_size[0], self.split_size[1] + else: + print("ERROR MODE", idx) + exit(0) + self.H_sp = H_sp + self.W_sp = W_sp + + if self.position_bias: + self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False) + # generate mother-set + position_bias_h = torch.arange(1 - self.H_sp, self.H_sp) + position_bias_w = torch.arange(1 - self.W_sp, self.W_sp) + biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w])) + biases = biases.flatten(1).transpose(0, 1).contiguous().float() + self.register_buffer("rpe_biases", biases) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.H_sp) + coords_w = torch.arange(self.W_sp) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) + coords_flatten = torch.flatten(coords, 1) + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] + relative_coords = relative_coords.permute(1, 2, 0).contiguous() + relative_coords[:, :, 0] += self.H_sp - 1 + relative_coords[:, :, 1] += self.W_sp - 1 + relative_coords[:, :, 0] *= 2 * self.W_sp - 1 + relative_position_index = relative_coords.sum(-1) + self.register_buffer("relative_position_index", relative_position_index) + + self.attn_drop = nn.Dropout(attn_drop) + + def im2win(self, x, H, W): + B, N, C = x.shape + x = x.transpose(-2, -1).contiguous().view(B, C, H, W) + x = img2windows(x, self.H_sp, self.W_sp) + x = ( + x.reshape(-1, self.H_sp * self.W_sp, self.num_heads, C // self.num_heads) + .permute(0, 2, 1, 3) + .contiguous() + ) + return x + + def forward(self, qkv, H, W, mask=None): + """ + Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size + Output: x (B, H, W, C) + """ + q, k, v = qkv[0], qkv[1], qkv[2] + + B, L, C = q.shape + assert L == H * W, "flatten img_tokens has wrong size" + + # partition the q,k,v, image to window + q = self.im2win(q, H, W) + k = self.im2win(k, H, W) + v = self.im2win(v, H, W) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) # B head N C @ B head C N --> B head N N + + # calculate drpe + if self.position_bias: + pos = self.pos(self.rpe_biases) + # select position bias + relative_position_bias = pos[self.relative_position_index.view(-1)].view( + self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1 + ) + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() + attn = attn + relative_position_bias.unsqueeze(0) + + N = attn.shape[3] + + # use mask for shift window + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze( + 0 + ) + attn = attn.view(-1, self.num_heads, N, N) + + attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype) + attn = self.attn_drop(attn) + + x = attn @ v + x = x.transpose(1, 2).reshape( + -1, self.H_sp * self.W_sp, C + ) # B head N N @ B head N C + + # merge the window, window to image + x = windows2img(x, self.H_sp, self.W_sp, H, W) # B H' W' C + + return x + + +class Adaptive_Spatial_Attention(nn.Module): + # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT + """Adaptive Spatial Self-Attention + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. Default: 6 + split_size (tuple(int)): Height and Width of spatial window. + shift_size (tuple(int)): Shift size for spatial window. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. + drop (float): Dropout rate. Default: 0.0 + attn_drop (float): Attention dropout rate. Default: 0.0 + rg_idx (int): The indentix of Residual Group (RG) + b_idx (int): The indentix of Block in each RG + """ + + def __init__( + self, + dim, + num_heads, + reso=64, + split_size=[8, 8], + shift_size=[1, 2], + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + rg_idx=0, + b_idx=0, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.split_size = split_size + self.shift_size = shift_size + self.b_idx = b_idx + self.rg_idx = rg_idx + self.patches_resolution = reso + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + + assert ( + 0 <= self.shift_size[0] < self.split_size[0] + ), "shift_size must in 0-split_size0" + assert ( + 0 <= self.shift_size[1] < self.split_size[1] + ), "shift_size must in 0-split_size1" + + self.branch_num = 2 + + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(drop) + + self.attns = nn.ModuleList( + [ + Spatial_Attention( + dim // 2, + idx=i, + split_size=split_size, + num_heads=num_heads // 2, + dim_out=dim // 2, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + position_bias=True, + ) + for i in range(self.branch_num) + ] + ) + + if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( + self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 + ): + attn_mask = self.calculate_mask( + self.patches_resolution, self.patches_resolution + ) + self.register_buffer("attn_mask_0", attn_mask[0]) + self.register_buffer("attn_mask_1", attn_mask[1]) + else: + attn_mask = None + self.register_buffer("attn_mask_0", None) + self.register_buffer("attn_mask_1", None) + + self.dwconv = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), + nn.BatchNorm2d(dim), + nn.GELU(), + ) + self.channel_interaction = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(dim, dim // 8, kernel_size=1), + nn.BatchNorm2d(dim // 8), + nn.GELU(), + nn.Conv2d(dim // 8, dim, kernel_size=1), + ) + self.spatial_interaction = nn.Sequential( + nn.Conv2d(dim, dim // 16, kernel_size=1), + nn.BatchNorm2d(dim // 16), + nn.GELU(), + nn.Conv2d(dim // 16, 1, kernel_size=1), + ) + + def calculate_mask(self, H, W): + # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py + # calculate attention mask for shift window + img_mask_0 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=0 + img_mask_1 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=1 + h_slices_0 = ( + slice(0, -self.split_size[0]), + slice(-self.split_size[0], -self.shift_size[0]), + slice(-self.shift_size[0], None), + ) + w_slices_0 = ( + slice(0, -self.split_size[1]), + slice(-self.split_size[1], -self.shift_size[1]), + slice(-self.shift_size[1], None), + ) + + h_slices_1 = ( + slice(0, -self.split_size[1]), + slice(-self.split_size[1], -self.shift_size[1]), + slice(-self.shift_size[1], None), + ) + w_slices_1 = ( + slice(0, -self.split_size[0]), + slice(-self.split_size[0], -self.shift_size[0]), + slice(-self.shift_size[0], None), + ) + cnt = 0 + for h in h_slices_0: + for w in w_slices_0: + img_mask_0[:, h, w, :] = cnt + cnt += 1 + cnt = 0 + for h in h_slices_1: + for w in w_slices_1: + img_mask_1[:, h, w, :] = cnt + cnt += 1 + + # calculate mask for window-0 + img_mask_0 = img_mask_0.view( + 1, + H // self.split_size[0], + self.split_size[0], + W // self.split_size[1], + self.split_size[1], + 1, + ) + img_mask_0 = ( + img_mask_0.permute(0, 1, 3, 2, 4, 5) + .contiguous() + .view(-1, self.split_size[0], self.split_size[1], 1) + ) # nW, sw[0], sw[1], 1 + mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1]) + attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2) + attn_mask_0 = attn_mask_0.masked_fill( + attn_mask_0 != 0, float(-100.0) + ).masked_fill(attn_mask_0 == 0, float(0.0)) + + # calculate mask for window-1 + img_mask_1 = img_mask_1.view( + 1, + H // self.split_size[1], + self.split_size[1], + W // self.split_size[0], + self.split_size[0], + 1, + ) + img_mask_1 = ( + img_mask_1.permute(0, 1, 3, 2, 4, 5) + .contiguous() + .view(-1, self.split_size[1], self.split_size[0], 1) + ) # nW, sw[1], sw[0], 1 + mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0]) + attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2) + attn_mask_1 = attn_mask_1.masked_fill( + attn_mask_1 != 0, float(-100.0) + ).masked_fill(attn_mask_1 == 0, float(0.0)) + + return attn_mask_0, attn_mask_1 + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + B, L, C = x.shape + assert L == H * W, "flatten img_tokens has wrong size" + + qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C + # V without partition + v = qkv[2].transpose(-2, -1).contiguous().view(B, C, H, W) + + # image padding + max_split_size = max(self.split_size[0], self.split_size[1]) + pad_l = pad_t = 0 + pad_r = (max_split_size - W % max_split_size) % max_split_size + pad_b = (max_split_size - H % max_split_size) % max_split_size + + qkv = qkv.reshape(3 * B, H, W, C).permute(0, 3, 1, 2) # 3B C H W + qkv = ( + F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)) + .reshape(3, B, C, -1) + .transpose(-2, -1) + ) # l r t b + _H = pad_b + H + _W = pad_r + W + _L = _H * _W + + # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged + # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ... + if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( + self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 + ): + qkv = qkv.view(3, B, _H, _W, C) + qkv_0 = torch.roll( + qkv[:, :, :, :, : C // 2], + shifts=(-self.shift_size[0], -self.shift_size[1]), + dims=(2, 3), + ) + qkv_0 = qkv_0.view(3, B, _L, C // 2) + qkv_1 = torch.roll( + qkv[:, :, :, :, C // 2 :], + shifts=(-self.shift_size[1], -self.shift_size[0]), + dims=(2, 3), + ) + qkv_1 = qkv_1.view(3, B, _L, C // 2) + + if self.patches_resolution != _H or self.patches_resolution != _W: + mask_tmp = self.calculate_mask(_H, _W) + x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device)) + x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device)) + else: + x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0) + x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1) + + x1 = torch.roll( + x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2) + ) + x2 = torch.roll( + x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2) + ) + x1 = x1[:, :H, :W, :].reshape(B, L, C // 2) + x2 = x2[:, :H, :W, :].reshape(B, L, C // 2) + # attention output + attened_x = torch.cat([x1, x2], dim=2) + + else: + x1 = self.attns[0](qkv[:, :, :, : C // 2], _H, _W)[:, :H, :W, :].reshape( + B, L, C // 2 + ) + x2 = self.attns[1](qkv[:, :, :, C // 2 :], _H, _W)[:, :H, :W, :].reshape( + B, L, C // 2 + ) + # attention output + attened_x = torch.cat([x1, x2], dim=2) + + # convolution output + conv_x = self.dwconv(v) + + # Adaptive Interaction Module (AIM) + # C-Map (before sigmoid) + channel_map = ( + self.channel_interaction(conv_x) + .permute(0, 2, 3, 1) + .contiguous() + .view(B, 1, C) + ) + # S-Map (before sigmoid) + attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) + spatial_map = self.spatial_interaction(attention_reshape) + + # C-I + attened_x = attened_x * torch.sigmoid(channel_map) + # S-I + conv_x = torch.sigmoid(spatial_map) * conv_x + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C) + + x = attened_x + conv_x + + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class Adaptive_Channel_Attention(nn.Module): + # The implementation builds on XCiT code https://github.com/facebookresearch/xcit + """Adaptive Channel Self-Attention + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. Default: 6 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. + attn_drop (float): Attention dropout rate. Default: 0.0 + drop_path (float): Stochastic depth rate. Default: 0.0 + """ + + def __init__( + self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.num_heads = num_heads + self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.dwconv = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), + nn.BatchNorm2d(dim), + nn.GELU(), + ) + self.channel_interaction = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(dim, dim // 8, kernel_size=1), + nn.BatchNorm2d(dim // 8), + nn.GELU(), + nn.Conv2d(dim // 8, dim, kernel_size=1), + ) + self.spatial_interaction = nn.Sequential( + nn.Conv2d(dim, dim // 16, kernel_size=1), + nn.BatchNorm2d(dim // 16), + nn.GELU(), + nn.Conv2d(dim // 16, 1, kernel_size=1), + ) + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + qkv = qkv.permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + q = q.transpose(-2, -1) + k = k.transpose(-2, -1) + v = v.transpose(-2, -1) + + v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W) + + q = torch.nn.functional.normalize(q, dim=-1) + k = torch.nn.functional.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + # attention output + attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C) + + # convolution output + conv_x = self.dwconv(v_) + + # Adaptive Interaction Module (AIM) + # C-Map (before sigmoid) + attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) + channel_map = self.channel_interaction(attention_reshape) + # S-Map (before sigmoid) + spatial_map = ( + self.spatial_interaction(conv_x) + .permute(0, 2, 3, 1) + .contiguous() + .view(B, N, 1) + ) + + # S-I + attened_x = attened_x * torch.sigmoid(spatial_map) + # C-I + conv_x = conv_x * torch.sigmoid(channel_map) + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C) + + x = attened_x + conv_x + + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class DATB(nn.Module): + def __init__( + self, + dim, + num_heads, + reso=64, + split_size=[2, 4], + shift_size=[1, 2], + expansion_factor=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + rg_idx=0, + b_idx=0, + ): + super().__init__() + + self.norm1 = norm_layer(dim) + + if b_idx % 2 == 0: + # DSTB + self.attn = Adaptive_Spatial_Attention( + dim, + num_heads=num_heads, + reso=reso, + split_size=split_size, + shift_size=shift_size, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + rg_idx=rg_idx, + b_idx=b_idx, + ) + else: + # DCTB + self.attn = Adaptive_Channel_Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + ffn_hidden_dim = int(dim * expansion_factor) + self.ffn = SGFN( + in_features=dim, + hidden_features=ffn_hidden_dim, + out_features=dim, + act_layer=act_layer, + ) + self.norm2 = norm_layer(dim) + + def forward(self, x, x_size): + """ + Input: x: (B, H*W, C), x_size: (H, W) + Output: x: (B, H*W, C) + """ + H, W = x_size + x = x + self.drop_path(self.attn(self.norm1(x), H, W)) + x = x + self.drop_path(self.ffn(self.norm2(x), H, W)) + + return x + + +class ResidualGroup(nn.Module): + """ResidualGroup + Args: + dim (int): Number of input channels. + reso (int): Input resolution. + num_heads (int): Number of attention heads. + split_size (tuple(int)): Height and Width of spatial window. + expansion_factor (float): Ratio of ffn hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop (float): Dropout rate. Default: 0 + attn_drop(float): Attention dropout rate. Default: 0 + drop_paths (float | None): Stochastic depth rate. + act_layer (nn.Module): Activation layer. Default: nn.GELU + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm + depth (int): Number of dual aggregation Transformer blocks in residual group. + use_chk (bool): Whether to use checkpointing to save memory. + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + dim, + reso, + num_heads, + split_size=[2, 4], + expansion_factor=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_paths=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + depth=2, + use_chk=False, + resi_connection="1conv", + rg_idx=0, + ): + super().__init__() + self.use_chk = use_chk + self.reso = reso + + self.blocks = nn.ModuleList( + [ + DATB( + dim=dim, + num_heads=num_heads, + reso=reso, + split_size=split_size, + shift_size=[split_size[0] // 2, split_size[1] // 2], + expansion_factor=expansion_factor, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_paths[i], + act_layer=act_layer, + norm_layer=norm_layer, + rg_idx=rg_idx, + b_idx=i, + ) + for i in range(depth) + ] + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + def forward(self, x, x_size): + """ + Input: x: (B, H*W, C), x_size: (H, W) + Output: x: (B, H*W, C) + """ + H, W = x_size + res = x + for blk in self.blocks: + if self.use_chk: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) + x = self.conv(x) + x = rearrange(x, "b c h w -> b (h w) c") + x = res + x + + return x + + +class Upsample(nn.Sequential): + """Upsample module. + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + h, w = self.input_resolution + flops = h * w * self.num_feat * 3 * 9 + return flops + + +class DAT(nn.Module): + """Dual Aggregation Transformer + Args: + img_size (int): Input image size. Default: 64 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 180 + depths (tuple(int)): Depth of each residual group (number of DATB in each RG). + split_size (tuple(int)): Height and Width of spatial window. + num_heads (tuple(int)): Number of attention heads in different residual groups. + expansion_factor (float): Ratio of ffn hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + act_layer (nn.Module): Activation layer. Default: nn.GELU + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm + use_chk (bool): Whether to use checkpointing to save memory. + upscale: Upscale factor. 2/3/4 for image SR + img_range: Image range. 1. or 255. + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__(self, state_dict): + super().__init__() + + # defaults + img_size = 64 + in_chans = 3 + embed_dim = 180 + split_size = [2, 4] + depth = [2, 2, 2, 2] + num_heads = [2, 2, 2, 2] + expansion_factor = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + act_layer = nn.GELU + norm_layer = nn.LayerNorm + use_chk = False + upscale = 2 + img_range = 1.0 + resi_connection = "1conv" + upsampler = "pixelshuffle" + + self.model_arch = "DAT" + self.sub_type = "SR" + self.state = state_dict + + state_keys = state_dict.keys() + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + state_dict.get("conv_before_upsample.0.weight", None).shape[1] + if state_dict.get("conv_before_upsample.weight", None) + else 64 + ) + + num_in_ch = state_dict["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = state_dict["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = state_dict[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(state_dict["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match(r"layers.(\d*).blocks.(\d*).norm1.weight", key) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depth = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if "layers.0.blocks.1.attn.temperature" in state_keys: + num_heads_num = state_dict["layers.0.blocks.1.attn.temperature"].shape[0] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depth + + embed_dim = state_dict["conv_first.weight"].shape[0] + expansion_factor = float( + state_dict["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + if "layers.0.blocks.2.attn.attn_mask_0" in state_keys: + attn_mask_0_x, attn_mask_0_y, attn_mask_0_z = state_dict[ + "layers.0.blocks.2.attn.attn_mask_0" + ].shape + + img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y)) + + if "layers.0.blocks.0.attn.attns.0.rpe_biases" in state_keys: + split_sizes = ( + state_dict["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1 + ) + split_size = [int(x) for x in split_sizes] + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depth = depth + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.expansion_factor = expansion_factor + self.resi_connection = resi_connection + self.split_size = split_size + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + + # ------------------------- 1, Shallow Feature Extraction ------------------------- # + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + # ------------------------- 2, Deep Feature Extraction ------------------------- # + self.num_layers = len(depth) + self.use_chk = use_chk + self.num_features = ( + self.embed_dim + ) = embed_dim # num_features for consistency with other models + heads = num_heads + + self.before_RG = nn.Sequential( + Rearrange("b c h w -> b (h w) c"), nn.LayerNorm(embed_dim) + ) + + curr_dim = embed_dim + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth)) + ] # stochastic depth decay rule + + self.layers = nn.ModuleList() + for i in range(self.num_layers): + layer = ResidualGroup( + dim=embed_dim, + num_heads=heads[i], + reso=img_size, + split_size=split_size, + expansion_factor=expansion_factor, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_paths=dpr[sum(depth[:i]) : sum(depth[: i + 1])], + act_layer=act_layer, + norm_layer=norm_layer, + depth=depth[i], + use_chk=use_chk, + resi_connection=resi_connection, + rg_idx=i, + ) + self.layers.append(layer) + + self.norm = norm_layer(curr_dim) + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + # ------------------------- 3, Reconstruction ------------------------- # + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, embed_dim, num_out_ch, (img_size, img_size) + ) + + self.apply(self._init_weights) + self.load_state_dict(state_dict, strict=True) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance( + m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d) + ): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward_features(self, x): + _, _, H, W = x.shape + x_size = [H, W] + x = self.before_RG(x) + for layer in self.layers: + x = layer(x, x_size) + x = self.norm(x) + x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) + + return x + + def forward(self, x): + """ + Input: x: (B, C, H, W) + """ + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == "pixelshuffle": + # for image SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + + x = x / self.img_range + self.mean + return x diff --git a/comfy_extras/chainner_models/architecture/HAT.py b/comfy_extras/chainner_models/architecture/HAT.py new file mode 100644 index 0000000000000000000000000000000000000000..6694742199bcbdb34ca197b941804dc68af353e7 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/HAT.py @@ -0,0 +1,1277 @@ +# pylint: skip-file +# HAT from https://github.com/XPixelGroup/HAT/blob/main/hat/archs/hat_arch.py +import math +import re + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from .timm.helpers import to_2tuple +from .timm.weight_init import trunc_normal_ + + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py + """ + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) # type: ignore + + +class ChannelAttention(nn.Module): + """Channel attention used in RCAN. + Args: + num_feat (int): Channel number of intermediate features. + squeeze_factor (int): Channel squeeze factor. Default: 16. + """ + + def __init__(self, num_feat, squeeze_factor=16): + super(ChannelAttention, self).__init__() + self.attention = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0), + nn.ReLU(inplace=True), + nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), + nn.Sigmoid(), + ) + + def forward(self, x): + y = self.attention(x) + return x * y + + +class CAB(nn.Module): + def __init__(self, num_feat, compress_ratio=3, squeeze_factor=30): + super(CAB, self).__init__() + + self.cab = nn.Sequential( + nn.Conv2d(num_feat, num_feat // compress_ratio, 3, 1, 1), + nn.GELU(), + nn.Conv2d(num_feat // compress_ratio, num_feat, 3, 1, 1), + ChannelAttention(num_feat, squeeze_factor), + ) + + def forward(self, x): + return self.cab(x) + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (b, h, w, c) + window_size (int): window size + Returns: + windows: (num_windows*b, window_size, window_size, c) + """ + b, h, w, c = x.shape + x = x.view(b, h // window_size, window_size, w // window_size, window_size, c) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c) + ) + return windows + + +def window_reverse(windows, window_size, h, w): + """ + Args: + windows: (num_windows*b, window_size, window_size, c) + window_size (int): Window size + h (int): Height of image + w (int): Width of image + Returns: + x: (b, h, w, c) + """ + b = int(windows.shape[0] / (h * w / window_size / window_size)) + x = windows.view( + b, h // window_size, w // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1) + return x + + +class WindowAttention(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( # type: ignore + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, rpi, mask=None): + """ + Args: + x: input features with shape of (num_windows*b, n, c) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + b_, n, c = x.shape + qkv = ( + self.qkv(x) + .reshape(b_, n, 3, self.num_heads, c // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view( + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nw = mask.shape[0] + attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze( + 1 + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, n, n) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(b_, n, c) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class HAB(nn.Module): + r"""Hybrid Attention Block. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + compress_ratio=3, + squeeze_factor=30, + conv_scale=0.01, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.conv_scale = conv_scale + self.conv_block = CAB( + num_feat=dim, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + def forward(self, x, x_size, rpi_sa, attn_mask): + h, w = x_size + b, _, c = x.shape + # assert seq_len == h * w, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(b, h, w, c) + + # Conv_X + conv_x = self.conv_block(x.permute(0, 3, 1, 2)) + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(b, h * w, c) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) + ) + attn_mask = attn_mask + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nw*b, window_size, window_size, c + x_windows = x_windows.view( + -1, self.window_size * self.window_size, c + ) # nw*b, window_size*window_size, c + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + attn_windows = self.attn(x_windows, rpi=rpi_sa, mask=attn_mask) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c) + shifted_x = window_reverse(attn_windows, self.window_size, h, w) # b h' w' c + + # reverse cyclic shift + if self.shift_size > 0: + attn_x = torch.roll( + shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) + ) + else: + attn_x = shifted_x + attn_x = attn_x.view(b, h * w, c) + + # FFN + x = shortcut + self.drop_path(attn_x) + conv_x * self.conv_scale + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: b, h*w, c + """ + h, w = self.input_resolution + b, seq_len, c = x.shape + assert seq_len == h * w, "input feature has wrong size" + assert h % 2 == 0 and w % 2 == 0, f"x size ({h}*{w}) are not even." + + x = x.view(b, h, w, c) + + x0 = x[:, 0::2, 0::2, :] # b h/2 w/2 c + x1 = x[:, 1::2, 0::2, :] # b h/2 w/2 c + x2 = x[:, 0::2, 1::2, :] # b h/2 w/2 c + x3 = x[:, 1::2, 1::2, :] # b h/2 w/2 c + x = torch.cat([x0, x1, x2, x3], -1) # b h/2 w/2 4*c + x = x.view(b, -1, 4 * c) # b h/2*w/2 4*c + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class OCAB(nn.Module): + # overlapping cross-attention block + + def __init__( + self, + dim, + input_resolution, + window_size, + overlap_ratio, + num_heads, + qkv_bias=True, + qk_scale=None, + mlp_ratio=2, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.window_size = window_size + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + self.overlap_win_size = int(window_size * overlap_ratio) + window_size + + self.norm1 = norm_layer(dim) + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.unfold = nn.Unfold( + kernel_size=(self.overlap_win_size, self.overlap_win_size), + stride=window_size, + padding=(self.overlap_win_size - window_size) // 2, + ) + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( # type: ignore + torch.zeros( + (window_size + self.overlap_win_size - 1) + * (window_size + self.overlap_win_size - 1), + num_heads, + ) + ) # 2*Wh-1 * 2*Ww-1, nH + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + self.proj = nn.Linear(dim, dim) + + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, hidden_features=mlp_hidden_dim, act_layer=nn.GELU + ) + + def forward(self, x, x_size, rpi): + h, w = x_size + b, _, c = x.shape + + shortcut = x + x = self.norm1(x) + x = x.view(b, h, w, c) + + qkv = self.qkv(x).reshape(b, h, w, 3, c).permute(3, 0, 4, 1, 2) # 3, b, c, h, w + q = qkv[0].permute(0, 2, 3, 1) # b, h, w, c + kv = torch.cat((qkv[1], qkv[2]), dim=1) # b, 2*c, h, w + + # partition windows + q_windows = window_partition( + q, self.window_size + ) # nw*b, window_size, window_size, c + q_windows = q_windows.view( + -1, self.window_size * self.window_size, c + ) # nw*b, window_size*window_size, c + + kv_windows = self.unfold(kv) # b, c*w*w, nw + kv_windows = rearrange( + kv_windows, + "b (nc ch owh oww) nw -> nc (b nw) (owh oww) ch", + nc=2, + ch=c, + owh=self.overlap_win_size, + oww=self.overlap_win_size, + ).contiguous() # 2, nw*b, ow*ow, c + # Do the above rearrangement without the rearrange function + # kv_windows = kv_windows.view( + # 2, b, self.overlap_win_size, self.overlap_win_size, c, -1 + # ) + # kv_windows = kv_windows.permute(0, 5, 1, 2, 3, 4).contiguous() + # kv_windows = kv_windows.view( + # 2, -1, self.overlap_win_size * self.overlap_win_size, c + # ) + + k_windows, v_windows = kv_windows[0], kv_windows[1] # nw*b, ow*ow, c + + b_, nq, _ = q_windows.shape + _, n, _ = k_windows.shape + d = self.dim // self.num_heads + q = q_windows.reshape(b_, nq, self.num_heads, d).permute( + 0, 2, 1, 3 + ) # nw*b, nH, nq, d + k = k_windows.reshape(b_, n, self.num_heads, d).permute( + 0, 2, 1, 3 + ) # nw*b, nH, n, d + v = v_windows.reshape(b_, n, self.num_heads, d).permute( + 0, 2, 1, 3 + ) # nw*b, nH, n, d + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view( + self.window_size * self.window_size, + self.overlap_win_size * self.overlap_win_size, + -1, + ) # ws*ws, wse*wse, nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, ws*ws, wse*wse + attn = attn + relative_position_bias.unsqueeze(0) + + attn = self.softmax(attn) + attn_windows = (attn @ v).transpose(1, 2).reshape(b_, nq, self.dim) + + # merge windows + attn_windows = attn_windows.view( + -1, self.window_size, self.window_size, self.dim + ) + x = window_reverse(attn_windows, self.window_size, h, w) # b h w c + x = x.view(b, h * w, self.dim) + + x = self.proj(x) + shortcut + + x = x + self.mlp(self.norm2(x)) + return x + + +class AttenBlocks(nn.Module): + """A series of attention blocks for one RHAG. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + compress_ratio, + squeeze_factor, + conv_scale, + overlap_ratio, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + HAB( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + compress_ratio=compress_ratio, + squeeze_factor=squeeze_factor, + conv_scale=conv_scale, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # OCAB + self.overlap_attn = OCAB( + dim=dim, + input_resolution=input_resolution, + window_size=window_size, + overlap_ratio=overlap_ratio, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + mlp_ratio=mlp_ratio, # type: ignore + norm_layer=norm_layer, + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, x_size, params): + for blk in self.blocks: + x = blk(x, x_size, params["rpi_sa"], params["attn_mask"]) + + x = self.overlap_attn(x, x_size, params["rpi_oca"]) + + if self.downsample is not None: + x = self.downsample(x) + return x + + +class RHAG(nn.Module): + """Residual Hybrid Attention Group (RHAG). + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + compress_ratio, + squeeze_factor, + conv_scale, + overlap_ratio, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection="1conv", + ): + super(RHAG, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = AttenBlocks( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + compress_ratio=compress_ratio, + squeeze_factor=squeeze_factor, + conv_scale=conv_scale, + overlap_ratio=overlap_ratio, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint, + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "identity": + self.conv = nn.Identity() + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + def forward(self, x, x_size, params): + return ( + self.patch_embed( + self.conv( + self.patch_unembed(self.residual_group(x, x_size, params), x_size) + ) + ) + + x + ) + + +class PatchEmbed(nn.Module): + r"""Image to Patch Embedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + x = x.flatten(2).transpose(1, 2) # b Ph*Pw c + if self.norm is not None: + x = self.norm(x) + return x + + +class PatchUnEmbed(nn.Module): + r"""Image to Patch Unembedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + x = ( + x.transpose(1, 2) + .contiguous() + .view(x.shape[0], self.embed_dim, x_size[0], x_size[1]) + ) # b Ph*Pw c + return x + + +class Upsample(nn.Sequential): + """Upsample module. + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class HAT(nn.Module): + r"""Hybrid Attention Transformer + A PyTorch implementation of : `Activating More Pixels in Image Super-Resolution Transformer`. + Some codes are based on SwinIR. + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + state_dict, + **kwargs, + ): + super(HAT, self).__init__() + + # Defaults + img_size = 64 + patch_size = 1 + in_chans = 3 + embed_dim = 96 + depths = (6, 6, 6, 6) + num_heads = (6, 6, 6, 6) + window_size = 7 + compress_ratio = 3 + squeeze_factor = 30 + conv_scale = 0.01 + overlap_ratio = 0.5 + mlp_ratio = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + norm_layer = nn.LayerNorm + ape = False + patch_norm = True + use_checkpoint = False + upscale = 2 + img_range = 1.0 + upsampler = "" + resi_connection = "1conv" + + self.state = state_dict + self.model_arch = "HAT" + self.sub_type = "SR" + self.supports_fp16 = False + self.support_bf16 = True + self.min_size_restriction = 16 + + state_keys = list(state_dict.keys()) + + num_feat = state_dict["conv_last.weight"].shape[1] + in_chans = state_dict["conv_first.weight"].shape[1] + num_out_ch = state_dict["conv_last.weight"].shape[0] + embed_dim = state_dict["conv_first.weight"].shape[0] + + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = self.state[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match( + r"layers.(\d*).residual_group.blocks.(\d*).conv_block.cab.0.weight", key + ) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if ( + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + in state_keys + ): + num_heads_num = self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + ].shape[-1] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depths + + mlp_ratio = float( + self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] + / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + window_size = int(math.sqrt(self.state["relative_position_index_SA"].shape[0])) + + # Not sure if this is needed or used at all anywhere in HAT's config + if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: + img_size = int( + math.sqrt( + self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] + ) + * window_size + ) + + self.window_size = window_size + self.shift_size = window_size // 2 + self.overlap_ratio = overlap_ratio + + self.in_nc = in_chans + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depths = depths + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.resi_connection = resi_connection + + num_in_ch = in_chans + # num_out_ch = in_chans + # num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + + # relative position index + relative_position_index_SA = self.calculate_rpi_sa() + relative_position_index_OCA = self.calculate_rpi_oca() + self.register_buffer("relative_position_index_SA", relative_position_index_SA) + self.register_buffer("relative_position_index_OCA", relative_position_index_OCA) + + # ------------------------- 1, shallow feature extraction ------------------------- # + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + # ------------------------- 2, deep feature extraction ------------------------- # + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter( # type: ignore[arg-type] + torch.zeros(1, num_patches, embed_dim) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build Residual Hybrid Attention Groups (RHAG) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RHAG( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + compress_ratio=compress_ratio, + squeeze_factor=squeeze_factor, + conv_scale=conv_scale, + overlap_ratio=overlap_ratio, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[ + sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) # type: ignore + ], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers.append(layer) + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "identity": + self.conv_after_body = nn.Identity() + + # ------------------------- 3, high quality image reconstruction ------------------------- # + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + self.load_state_dict(self.state, strict=False) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def calculate_rpi_sa(self): + # calculate relative position index for SA + coords_h = torch.arange(self.window_size) + coords_w = torch.arange(self.window_size) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size - 1 + relative_coords[:, :, 0] *= 2 * self.window_size - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + return relative_position_index + + def calculate_rpi_oca(self): + # calculate relative position index for OCA + window_size_ori = self.window_size + window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size) + + coords_h = torch.arange(window_size_ori) + coords_w = torch.arange(window_size_ori) + coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, ws, ws + coords_ori_flatten = torch.flatten(coords_ori, 1) # 2, ws*ws + + coords_h = torch.arange(window_size_ext) + coords_w = torch.arange(window_size_ext) + coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, wse, wse + coords_ext_flatten = torch.flatten(coords_ext, 1) # 2, wse*wse + + relative_coords = ( + coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None] + ) # 2, ws*ws, wse*wse + + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # ws*ws, wse*wse, 2 + relative_coords[:, :, 0] += ( + window_size_ori - window_size_ext + 1 + ) # shift to start from 0 + relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1 + + relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1 + relative_position_index = relative_coords.sum(-1) + return relative_position_index + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + h, w = x_size + img_mask = torch.zeros((1, h, w, 1)) # 1 h w 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nw, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + return attn_mask + + @torch.jit.ignore # type: ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore # type: ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + + # Calculate attention mask and relative position index in advance to speed up inference. + # The original code is very time-cosuming for large window size. + attn_mask = self.calculate_mask(x_size).to(x.device) + params = { + "attn_mask": attn_mask, + "rpi_sa": self.relative_position_index_SA, + "rpi_oca": self.relative_position_index_OCA, + } + + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size, params) + + x = self.norm(x) # b seq_len c + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + x = self.check_image_size(x) + + if self.upsampler == "pixelshuffle": + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + + x = x / self.img_range + self.mean + + return x[:, :, : H * self.upscale, : W * self.upscale] diff --git a/comfy_extras/chainner_models/architecture/LICENSE-DAT b/comfy_extras/chainner_models/architecture/LICENSE-DAT new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/LICENSE-DAT @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN b/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-HAT b/comfy_extras/chainner_models/architecture/LICENSE-HAT new file mode 100644 index 0000000000000000000000000000000000000000..003e97e96cbed07d07b5ff15831711181607edb3 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/LICENSE-HAT @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Xiangyu Chen + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN b/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN new file mode 100644 index 0000000000000000000000000000000000000000..552a1eeaf01f4e7077013ed3496600c608f35202 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2021, Xintao Wang +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SCUNet b/comfy_extras/chainner_models/architecture/LICENSE-SCUNet new file mode 100644 index 0000000000000000000000000000000000000000..ff75c988f3482ab21da41f0d10068108be54ad88 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/LICENSE-SCUNet @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2022 Kai Zhang (cskaizhang@gmail.com, https://cszn.github.io/). All rights reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SPSR b/comfy_extras/chainner_models/architecture/LICENSE-SPSR new file mode 100644 index 0000000000000000000000000000000000000000..3245f3f9e4f476ee3a283f41dd0d9db65544c222 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/LICENSE-SPSR @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. 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Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2021] [SwinIR Authors] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SwinIR b/comfy_extras/chainner_models/architecture/LICENSE-SwinIR new file mode 100644 index 0000000000000000000000000000000000000000..e5e4ee061a3f3fbad64bc837425716af7fb108f5 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/LICENSE-SwinIR @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2021] [SwinIR Authors] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LICENSE-lama b/comfy_extras/chainner_models/architecture/LICENSE-lama new file mode 100644 index 0000000000000000000000000000000000000000..ca822bb5f62a37a5a73f56a2d563b16dab46c03f --- /dev/null +++ b/comfy_extras/chainner_models/architecture/LICENSE-lama @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2021] Samsung Research + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/LaMa.py b/comfy_extras/chainner_models/architecture/LaMa.py new file mode 100644 index 0000000000000000000000000000000000000000..a781f3e4dda789c06493fcf35a9803ee61efce73 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/LaMa.py @@ -0,0 +1,694 @@ +# pylint: skip-file +""" +Model adapted from advimman's lama project: https://github.com/advimman/lama +""" + +# Fast Fourier Convolution NeurIPS 2020 +# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py +# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf + +from typing import List + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision.transforms.functional import InterpolationMode, rotate + + +class LearnableSpatialTransformWrapper(nn.Module): + def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True): + super().__init__() + self.impl = impl + self.angle = torch.rand(1) * angle_init_range + if train_angle: + self.angle = nn.Parameter(self.angle, requires_grad=True) + self.pad_coef = pad_coef + + def forward(self, x): + if torch.is_tensor(x): + return self.inverse_transform(self.impl(self.transform(x)), x) + elif isinstance(x, tuple): + x_trans = tuple(self.transform(elem) for elem in x) + y_trans = self.impl(x_trans) + return tuple( + self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x) + ) + else: + raise ValueError(f"Unexpected input type {type(x)}") + + def transform(self, x): + height, width = x.shape[2:] + pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) + x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode="reflect") + x_padded_rotated = rotate( + x_padded, self.angle.to(x_padded), InterpolationMode.BILINEAR, fill=0 + ) + + return x_padded_rotated + + def inverse_transform(self, y_padded_rotated, orig_x): + height, width = orig_x.shape[2:] + pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) + + y_padded = rotate( + y_padded_rotated, + -self.angle.to(y_padded_rotated), + InterpolationMode.BILINEAR, + fill=0, + ) + y_height, y_width = y_padded.shape[2:] + y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w] + return y + + +class SELayer(nn.Module): + def __init__(self, channel, reduction=16): + super(SELayer, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid(), + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + res = x * y.expand_as(x) + return res + + +class FourierUnit(nn.Module): + def __init__( + self, + in_channels, + out_channels, + groups=1, + spatial_scale_factor=None, + spatial_scale_mode="bilinear", + spectral_pos_encoding=False, + use_se=False, + se_kwargs=None, + ffc3d=False, + fft_norm="ortho", + ): + # bn_layer not used + super(FourierUnit, self).__init__() + self.groups = groups + + self.conv_layer = torch.nn.Conv2d( + in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0), + out_channels=out_channels * 2, + kernel_size=1, + stride=1, + padding=0, + groups=self.groups, + bias=False, + ) + self.bn = torch.nn.BatchNorm2d(out_channels * 2) + self.relu = torch.nn.ReLU(inplace=True) + + # squeeze and excitation block + self.use_se = use_se + if use_se: + if se_kwargs is None: + se_kwargs = {} + self.se = SELayer(self.conv_layer.in_channels, **se_kwargs) + + self.spatial_scale_factor = spatial_scale_factor + self.spatial_scale_mode = spatial_scale_mode + self.spectral_pos_encoding = spectral_pos_encoding + self.ffc3d = ffc3d + self.fft_norm = fft_norm + + def forward(self, x): + half_check = False + if x.type() == "torch.cuda.HalfTensor": + # half only works on gpu anyway + half_check = True + + batch = x.shape[0] + + if self.spatial_scale_factor is not None: + orig_size = x.shape[-2:] + x = F.interpolate( + x, + scale_factor=self.spatial_scale_factor, + mode=self.spatial_scale_mode, + align_corners=False, + ) + + # (batch, c, h, w/2+1, 2) + fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1) + if half_check == True: + ffted = torch.fft.rfftn( + x.float(), dim=fft_dim, norm=self.fft_norm + ) # .type(torch.cuda.HalfTensor) + else: + ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm) + + ffted = torch.stack((ffted.real, ffted.imag), dim=-1) + ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1) + ffted = ffted.view( + ( + batch, + -1, + ) + + ffted.size()[3:] + ) + + if self.spectral_pos_encoding: + height, width = ffted.shape[-2:] + coords_vert = ( + torch.linspace(0, 1, height)[None, None, :, None] + .expand(batch, 1, height, width) + .to(ffted) + ) + coords_hor = ( + torch.linspace(0, 1, width)[None, None, None, :] + .expand(batch, 1, height, width) + .to(ffted) + ) + ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1) + + if self.use_se: + ffted = self.se(ffted) + + if half_check == True: + ffted = self.conv_layer(ffted.half()) # (batch, c*2, h, w/2+1) + else: + ffted = self.conv_layer( + ffted + ) # .type(torch.cuda.FloatTensor) # (batch, c*2, h, w/2+1) + + ffted = self.relu(self.bn(ffted)) + # forcing to be always float + ffted = ffted.float() + + ffted = ( + ffted.view( + ( + batch, + -1, + 2, + ) + + ffted.size()[2:] + ) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) # (batch,c, t, h, w/2+1, 2) + + ffted = torch.complex(ffted[..., 0], ffted[..., 1]) + + ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:] + output = torch.fft.irfftn( + ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm + ) + + if half_check == True: + output = output.half() + + if self.spatial_scale_factor is not None: + output = F.interpolate( + output, + size=orig_size, + mode=self.spatial_scale_mode, + align_corners=False, + ) + + return output + + +class SpectralTransform(nn.Module): + def __init__( + self, + in_channels, + out_channels, + stride=1, + groups=1, + enable_lfu=True, + separable_fu=False, + **fu_kwargs, + ): + # bn_layer not used + super(SpectralTransform, self).__init__() + self.enable_lfu = enable_lfu + if stride == 2: + self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2) + else: + self.downsample = nn.Identity() + + self.stride = stride + self.conv1 = nn.Sequential( + nn.Conv2d( + in_channels, out_channels // 2, kernel_size=1, groups=groups, bias=False + ), + nn.BatchNorm2d(out_channels // 2), + nn.ReLU(inplace=True), + ) + fu_class = FourierUnit + self.fu = fu_class(out_channels // 2, out_channels // 2, groups, **fu_kwargs) + if self.enable_lfu: + self.lfu = fu_class(out_channels // 2, out_channels // 2, groups) + self.conv2 = torch.nn.Conv2d( + out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False + ) + + def forward(self, x): + x = self.downsample(x) + x = self.conv1(x) + output = self.fu(x) + + if self.enable_lfu: + _, c, h, _ = x.shape + split_no = 2 + split_s = h // split_no + xs = torch.cat( + torch.split(x[:, : c // 4], split_s, dim=-2), dim=1 + ).contiguous() + xs = torch.cat(torch.split(xs, split_s, dim=-1), dim=1).contiguous() + xs = self.lfu(xs) + xs = xs.repeat(1, 1, split_no, split_no).contiguous() + else: + xs = 0 + + output = self.conv2(x + output + xs) + + return output + + +class FFC(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + ratio_gin, + ratio_gout, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=False, + enable_lfu=True, + padding_type="reflect", + gated=False, + **spectral_kwargs, + ): + super(FFC, self).__init__() + + assert stride == 1 or stride == 2, "Stride should be 1 or 2." + self.stride = stride + + in_cg = int(in_channels * ratio_gin) + in_cl = in_channels - in_cg + out_cg = int(out_channels * ratio_gout) + out_cl = out_channels - out_cg + # groups_g = 1 if groups == 1 else int(groups * ratio_gout) + # groups_l = 1 if groups == 1 else groups - groups_g + + self.ratio_gin = ratio_gin + self.ratio_gout = ratio_gout + self.global_in_num = in_cg + + module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d + self.convl2l = module( + in_cl, + out_cl, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode=padding_type, + ) + module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d + self.convl2g = module( + in_cl, + out_cg, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode=padding_type, + ) + module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d + self.convg2l = module( + in_cg, + out_cl, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode=padding_type, + ) + module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform + self.convg2g = module( + in_cg, + out_cg, + stride, + 1 if groups == 1 else groups // 2, + enable_lfu, + **spectral_kwargs, + ) + + self.gated = gated + module = ( + nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d + ) + self.gate = module(in_channels, 2, 1) + + def forward(self, x): + x_l, x_g = x if type(x) is tuple else (x, 0) + out_xl, out_xg = 0, 0 + + if self.gated: + total_input_parts = [x_l] + if torch.is_tensor(x_g): + total_input_parts.append(x_g) + total_input = torch.cat(total_input_parts, dim=1) + + gates = torch.sigmoid(self.gate(total_input)) + g2l_gate, l2g_gate = gates.chunk(2, dim=1) + else: + g2l_gate, l2g_gate = 1, 1 + + if self.ratio_gout != 1: + out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate + if self.ratio_gout != 0: + out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g) + + return out_xl, out_xg + + +class FFC_BN_ACT(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + ratio_gin, + ratio_gout, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=False, + norm_layer=nn.BatchNorm2d, + activation_layer=nn.Identity, + padding_type="reflect", + enable_lfu=True, + **kwargs, + ): + super(FFC_BN_ACT, self).__init__() + self.ffc = FFC( + in_channels, + out_channels, + kernel_size, + ratio_gin, + ratio_gout, + stride, + padding, + dilation, + groups, + bias, + enable_lfu, + padding_type=padding_type, + **kwargs, + ) + lnorm = nn.Identity if ratio_gout == 1 else norm_layer + gnorm = nn.Identity if ratio_gout == 0 else norm_layer + global_channels = int(out_channels * ratio_gout) + self.bn_l = lnorm(out_channels - global_channels) + self.bn_g = gnorm(global_channels) + + lact = nn.Identity if ratio_gout == 1 else activation_layer + gact = nn.Identity if ratio_gout == 0 else activation_layer + self.act_l = lact(inplace=True) + self.act_g = gact(inplace=True) + + def forward(self, x): + x_l, x_g = self.ffc(x) + x_l = self.act_l(self.bn_l(x_l)) + x_g = self.act_g(self.bn_g(x_g)) + return x_l, x_g + + +class FFCResnetBlock(nn.Module): + def __init__( + self, + dim, + padding_type, + norm_layer, + activation_layer=nn.ReLU, + dilation=1, + spatial_transform_kwargs=None, + inline=False, + **conv_kwargs, + ): + super().__init__() + self.conv1 = FFC_BN_ACT( + dim, + dim, + kernel_size=3, + padding=dilation, + dilation=dilation, + norm_layer=norm_layer, + activation_layer=activation_layer, + padding_type=padding_type, + **conv_kwargs, + ) + self.conv2 = FFC_BN_ACT( + dim, + dim, + kernel_size=3, + padding=dilation, + dilation=dilation, + norm_layer=norm_layer, + activation_layer=activation_layer, + padding_type=padding_type, + **conv_kwargs, + ) + if spatial_transform_kwargs is not None: + self.conv1 = LearnableSpatialTransformWrapper( + self.conv1, **spatial_transform_kwargs + ) + self.conv2 = LearnableSpatialTransformWrapper( + self.conv2, **spatial_transform_kwargs + ) + self.inline = inline + + def forward(self, x): + if self.inline: + x_l, x_g = ( + x[:, : -self.conv1.ffc.global_in_num], + x[:, -self.conv1.ffc.global_in_num :], + ) + else: + x_l, x_g = x if type(x) is tuple else (x, 0) + + id_l, id_g = x_l, x_g + + x_l, x_g = self.conv1((x_l, x_g)) + x_l, x_g = self.conv2((x_l, x_g)) + + x_l, x_g = id_l + x_l, id_g + x_g + out = x_l, x_g + if self.inline: + out = torch.cat(out, dim=1) + return out + + +class ConcatTupleLayer(nn.Module): + def forward(self, x): + assert isinstance(x, tuple) + x_l, x_g = x + assert torch.is_tensor(x_l) or torch.is_tensor(x_g) + if not torch.is_tensor(x_g): + return x_l + return torch.cat(x, dim=1) + + +class FFCResNetGenerator(nn.Module): + def __init__( + self, + input_nc, + output_nc, + ngf=64, + n_downsampling=3, + n_blocks=18, + norm_layer=nn.BatchNorm2d, + padding_type="reflect", + activation_layer=nn.ReLU, + up_norm_layer=nn.BatchNorm2d, + up_activation=nn.ReLU(True), + init_conv_kwargs={}, + downsample_conv_kwargs={}, + resnet_conv_kwargs={}, + spatial_transform_layers=None, + spatial_transform_kwargs={}, + max_features=1024, + out_ffc=False, + out_ffc_kwargs={}, + ): + assert n_blocks >= 0 + super().__init__() + """ + init_conv_kwargs = {'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False} + downsample_conv_kwargs = {'ratio_gin': '${generator.init_conv_kwargs.ratio_gout}', 'ratio_gout': '${generator.downsample_conv_kwargs.ratio_gin}', 'enable_lfu': False} + resnet_conv_kwargs = {'ratio_gin': 0.75, 'ratio_gout': '${generator.resnet_conv_kwargs.ratio_gin}', 'enable_lfu': False} + spatial_transform_kwargs = {} + out_ffc_kwargs = {} + """ + """ + print(input_nc, output_nc, ngf, n_downsampling, n_blocks, norm_layer, + padding_type, activation_layer, + up_norm_layer, up_activation, + spatial_transform_layers, + add_out_act, max_features, out_ffc, file=sys.stderr) + + 4 3 64 3 18 + reflect + + ReLU(inplace=True) + None sigmoid 1024 False + """ + init_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False} + downsample_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False} + resnet_conv_kwargs = { + "ratio_gin": 0.75, + "ratio_gout": 0.75, + "enable_lfu": False, + } + spatial_transform_kwargs = {} + out_ffc_kwargs = {} + + model = [ + nn.ReflectionPad2d(3), + FFC_BN_ACT( + input_nc, + ngf, + kernel_size=7, + padding=0, + norm_layer=norm_layer, + activation_layer=activation_layer, + **init_conv_kwargs, + ), + ] + + ### downsample + for i in range(n_downsampling): + mult = 2**i + if i == n_downsampling - 1: + cur_conv_kwargs = dict(downsample_conv_kwargs) + cur_conv_kwargs["ratio_gout"] = resnet_conv_kwargs.get("ratio_gin", 0) + else: + cur_conv_kwargs = downsample_conv_kwargs + model += [ + FFC_BN_ACT( + min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, + stride=2, + padding=1, + norm_layer=norm_layer, + activation_layer=activation_layer, + **cur_conv_kwargs, + ) + ] + + mult = 2**n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + ### resnet blocks + for i in range(n_blocks): + cur_resblock = FFCResnetBlock( + feats_num_bottleneck, + padding_type=padding_type, + activation_layer=activation_layer, + norm_layer=norm_layer, + **resnet_conv_kwargs, + ) + if spatial_transform_layers is not None and i in spatial_transform_layers: + cur_resblock = LearnableSpatialTransformWrapper( + cur_resblock, **spatial_transform_kwargs + ) + model += [cur_resblock] + + model += [ConcatTupleLayer()] + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += [ + nn.ConvTranspose2d( + min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, + stride=2, + padding=1, + output_padding=1, + ), + up_norm_layer(min(max_features, int(ngf * mult / 2))), + up_activation, + ] + + if out_ffc: + model += [ + FFCResnetBlock( + ngf, + padding_type=padding_type, + activation_layer=activation_layer, + norm_layer=norm_layer, + inline=True, + **out_ffc_kwargs, + ) + ] + + model += [ + nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), + ] + model.append(nn.Sigmoid()) + self.model = nn.Sequential(*model) + + def forward(self, image, mask): + return self.model(torch.cat([image, mask], dim=1)) + + +class LaMa(nn.Module): + def __init__(self, state_dict) -> None: + super(LaMa, self).__init__() + self.model_arch = "LaMa" + self.sub_type = "Inpaint" + self.in_nc = 4 + self.out_nc = 3 + self.scale = 1 + + self.min_size = None + self.pad_mod = 8 + self.pad_to_square = False + + self.model = FFCResNetGenerator(self.in_nc, self.out_nc) + self.state = { + k.replace("generator.model", "model.model"): v + for k, v in state_dict.items() + } + + self.supports_fp16 = False + self.support_bf16 = True + + self.load_state_dict(self.state, strict=False) + + def forward(self, img, mask): + masked_img = img * (1 - mask) + inpainted_mask = mask * self.model.forward(masked_img, mask) + result = inpainted_mask + (1 - mask) * img + return result diff --git a/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py b/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py new file mode 100644 index 0000000000000000000000000000000000000000..f4d52aa1e063d274b7aec7bd1ace77b19eb2ca61 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py @@ -0,0 +1,110 @@ +import math + +import torch.nn as nn + + +class CA_layer(nn.Module): + def __init__(self, channel, reduction=16): + super(CA_layer, self).__init__() + # global average pooling + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False), + nn.GELU(), + nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False), + # nn.Sigmoid() + ) + + def forward(self, x): + y = self.fc(self.gap(x)) + return x * y.expand_as(x) + + +class Simple_CA_layer(nn.Module): + def __init__(self, channel): + super(Simple_CA_layer, self).__init__() + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Conv2d( + in_channels=channel, + out_channels=channel, + kernel_size=1, + padding=0, + stride=1, + groups=1, + bias=True, + ) + + def forward(self, x): + return x * self.fc(self.gap(x)) + + +class ECA_layer(nn.Module): + """Constructs a ECA module. + Args: + channel: Number of channels of the input feature map + k_size: Adaptive selection of kernel size + """ + + def __init__(self, channel): + super(ECA_layer, self).__init__() + + b = 1 + gamma = 2 + k_size = int(abs(math.log(channel, 2) + b) / gamma) + k_size = k_size if k_size % 2 else k_size + 1 + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv = nn.Conv1d( + 1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False + ) + # self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # x: input features with shape [b, c, h, w] + # b, c, h, w = x.size() + + # feature descriptor on the global spatial information + y = self.avg_pool(x) + + # Two different branches of ECA module + y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) + + # Multi-scale information fusion + # y = self.sigmoid(y) + + return x * y.expand_as(x) + + +class ECA_MaxPool_layer(nn.Module): + """Constructs a ECA module. + Args: + channel: Number of channels of the input feature map + k_size: Adaptive selection of kernel size + """ + + def __init__(self, channel): + super(ECA_MaxPool_layer, self).__init__() + + b = 1 + gamma = 2 + k_size = int(abs(math.log(channel, 2) + b) / gamma) + k_size = k_size if k_size % 2 else k_size + 1 + self.max_pool = nn.AdaptiveMaxPool2d(1) + self.conv = nn.Conv1d( + 1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False + ) + # self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # x: input features with shape [b, c, h, w] + # b, c, h, w = x.size() + + # feature descriptor on the global spatial information + y = self.max_pool(x) + + # Two different branches of ECA module + y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) + + # Multi-scale information fusion + # y = self.sigmoid(y) + + return x * y.expand_as(x) diff --git a/comfy_extras/chainner_models/architecture/OmniSR/LICENSE b/comfy_extras/chainner_models/architecture/OmniSR/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/OmniSR/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OSA.py b/comfy_extras/chainner_models/architecture/OmniSR/OSA.py new file mode 100644 index 0000000000000000000000000000000000000000..d7a129696b254b022fa6fc54dc85befcc19ffc2c --- /dev/null +++ b/comfy_extras/chainner_models/architecture/OmniSR/OSA.py @@ -0,0 +1,577 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: OSA.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Sunday, 23rd April 2023 3:07:42 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import torch +import torch.nn.functional as F +from einops import rearrange, repeat +from einops.layers.torch import Rearrange, Reduce +from torch import einsum, nn + +from .layernorm import LayerNorm2d + +# helpers + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +def cast_tuple(val, length=1): + return val if isinstance(val, tuple) else ((val,) * length) + + +# helper classes + + +class PreNormResidual(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + + def forward(self, x): + return self.fn(self.norm(x)) + x + + +class Conv_PreNormResidual(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = LayerNorm2d(dim) + self.fn = fn + + def forward(self, x): + return self.fn(self.norm(x)) + x + + +class FeedForward(nn.Module): + def __init__(self, dim, mult=2, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + self.net = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(inner_dim, dim), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + + +class Conv_FeedForward(nn.Module): + def __init__(self, dim, mult=2, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + self.net = nn.Sequential( + nn.Conv2d(dim, inner_dim, 1, 1, 0), + nn.GELU(), + nn.Dropout(dropout), + nn.Conv2d(inner_dim, dim, 1, 1, 0), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + + +class Gated_Conv_FeedForward(nn.Module): + def __init__(self, dim, mult=1, bias=False, dropout=0.0): + super().__init__() + + hidden_features = int(dim * mult) + + self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias) + + self.dwconv = nn.Conv2d( + hidden_features * 2, + hidden_features * 2, + kernel_size=3, + stride=1, + padding=1, + groups=hidden_features * 2, + bias=bias, + ) + + self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias) + + def forward(self, x): + x = self.project_in(x) + x1, x2 = self.dwconv(x).chunk(2, dim=1) + x = F.gelu(x1) * x2 + x = self.project_out(x) + return x + + +# MBConv + + +class SqueezeExcitation(nn.Module): + def __init__(self, dim, shrinkage_rate=0.25): + super().__init__() + hidden_dim = int(dim * shrinkage_rate) + + self.gate = nn.Sequential( + Reduce("b c h w -> b c", "mean"), + nn.Linear(dim, hidden_dim, bias=False), + nn.SiLU(), + nn.Linear(hidden_dim, dim, bias=False), + nn.Sigmoid(), + Rearrange("b c -> b c 1 1"), + ) + + def forward(self, x): + return x * self.gate(x) + + +class MBConvResidual(nn.Module): + def __init__(self, fn, dropout=0.0): + super().__init__() + self.fn = fn + self.dropsample = Dropsample(dropout) + + def forward(self, x): + out = self.fn(x) + out = self.dropsample(out) + return out + x + + +class Dropsample(nn.Module): + def __init__(self, prob=0): + super().__init__() + self.prob = prob + + def forward(self, x): + device = x.device + + if self.prob == 0.0 or (not self.training): + return x + + keep_mask = ( + torch.FloatTensor((x.shape[0], 1, 1, 1), device=device).uniform_() + > self.prob + ) + return x * keep_mask / (1 - self.prob) + + +def MBConv( + dim_in, dim_out, *, downsample, expansion_rate=4, shrinkage_rate=0.25, dropout=0.0 +): + hidden_dim = int(expansion_rate * dim_out) + stride = 2 if downsample else 1 + + net = nn.Sequential( + nn.Conv2d(dim_in, hidden_dim, 1), + # nn.BatchNorm2d(hidden_dim), + nn.GELU(), + nn.Conv2d( + hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim + ), + # nn.BatchNorm2d(hidden_dim), + nn.GELU(), + SqueezeExcitation(hidden_dim, shrinkage_rate=shrinkage_rate), + nn.Conv2d(hidden_dim, dim_out, 1), + # nn.BatchNorm2d(dim_out) + ) + + if dim_in == dim_out and not downsample: + net = MBConvResidual(net, dropout=dropout) + + return net + + +# attention related classes +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=32, + dropout=0.0, + window_size=7, + with_pe=True, + ): + super().__init__() + assert ( + dim % dim_head + ) == 0, "dimension should be divisible by dimension per head" + + self.heads = dim // dim_head + self.scale = dim_head**-0.5 + self.with_pe = with_pe + + self.to_qkv = nn.Linear(dim, dim * 3, bias=False) + + self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout)) + + self.to_out = nn.Sequential( + nn.Linear(dim, dim, bias=False), nn.Dropout(dropout) + ) + + # relative positional bias + if self.with_pe: + self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads) + + pos = torch.arange(window_size) + grid = torch.stack(torch.meshgrid(pos, pos)) + grid = rearrange(grid, "c i j -> (i j) c") + rel_pos = rearrange(grid, "i ... -> i 1 ...") - rearrange( + grid, "j ... -> 1 j ..." + ) + rel_pos += window_size - 1 + rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum( + dim=-1 + ) + + self.register_buffer("rel_pos_indices", rel_pos_indices, persistent=False) + + def forward(self, x): + batch, height, width, window_height, window_width, _, device, h = ( + *x.shape, + x.device, + self.heads, + ) + + # flatten + + x = rearrange(x, "b x y w1 w2 d -> (b x y) (w1 w2) d") + + # project for queries, keys, values + + q, k, v = self.to_qkv(x).chunk(3, dim=-1) + + # split heads + + q, k, v = map(lambda t: rearrange(t, "b n (h d ) -> b h n d", h=h), (q, k, v)) + + # scale + + q = q * self.scale + + # sim + + sim = einsum("b h i d, b h j d -> b h i j", q, k) + + # add positional bias + if self.with_pe: + bias = self.rel_pos_bias(self.rel_pos_indices) + sim = sim + rearrange(bias, "i j h -> h i j") + + # attention + + attn = self.attend(sim) + + # aggregate + + out = einsum("b h i j, b h j d -> b h i d", attn, v) + + # merge heads + + out = rearrange( + out, "b h (w1 w2) d -> b w1 w2 (h d)", w1=window_height, w2=window_width + ) + + # combine heads out + + out = self.to_out(out) + return rearrange(out, "(b x y) ... -> b x y ...", x=height, y=width) + + +class Block_Attention(nn.Module): + def __init__( + self, + dim, + dim_head=32, + bias=False, + dropout=0.0, + window_size=7, + with_pe=True, + ): + super().__init__() + assert ( + dim % dim_head + ) == 0, "dimension should be divisible by dimension per head" + + self.heads = dim // dim_head + self.ps = window_size + self.scale = dim_head**-0.5 + self.with_pe = with_pe + + self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + dim * 3, + dim * 3, + kernel_size=3, + stride=1, + padding=1, + groups=dim * 3, + bias=bias, + ) + + self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout)) + + self.to_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + + def forward(self, x): + # project for queries, keys, values + b, c, h, w = x.shape + + qkv = self.qkv_dwconv(self.qkv(x)) + q, k, v = qkv.chunk(3, dim=1) + + # split heads + + q, k, v = map( + lambda t: rearrange( + t, + "b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d", + h=self.heads, + w1=self.ps, + w2=self.ps, + ), + (q, k, v), + ) + + # scale + + q = q * self.scale + + # sim + + sim = einsum("b h i d, b h j d -> b h i j", q, k) + + # attention + attn = self.attend(sim) + + # aggregate + + out = einsum("b h i j, b h j d -> b h i d", attn, v) + + # merge heads + out = rearrange( + out, + "(b x y) head (w1 w2) d -> b (head d) (x w1) (y w2)", + x=h // self.ps, + y=w // self.ps, + head=self.heads, + w1=self.ps, + w2=self.ps, + ) + + out = self.to_out(out) + return out + + +class Channel_Attention(nn.Module): + def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7): + super(Channel_Attention, self).__init__() + self.heads = heads + + self.temperature = nn.Parameter(torch.ones(heads, 1, 1)) + + self.ps = window_size + + self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + dim * 3, + dim * 3, + kernel_size=3, + stride=1, + padding=1, + groups=dim * 3, + bias=bias, + ) + self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + + def forward(self, x): + b, c, h, w = x.shape + + qkv = self.qkv_dwconv(self.qkv(x)) + qkv = qkv.chunk(3, dim=1) + + q, k, v = map( + lambda t: rearrange( + t, + "b (head d) (h ph) (w pw) -> b (h w) head d (ph pw)", + ph=self.ps, + pw=self.ps, + head=self.heads, + ), + qkv, + ) + + q = F.normalize(q, dim=-1) + k = F.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + out = attn @ v + + out = rearrange( + out, + "b (h w) head d (ph pw) -> b (head d) (h ph) (w pw)", + h=h // self.ps, + w=w // self.ps, + ph=self.ps, + pw=self.ps, + head=self.heads, + ) + + out = self.project_out(out) + + return out + + +class Channel_Attention_grid(nn.Module): + def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7): + super(Channel_Attention_grid, self).__init__() + self.heads = heads + + self.temperature = nn.Parameter(torch.ones(heads, 1, 1)) + + self.ps = window_size + + self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + dim * 3, + dim * 3, + kernel_size=3, + stride=1, + padding=1, + groups=dim * 3, + bias=bias, + ) + self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + + def forward(self, x): + b, c, h, w = x.shape + + qkv = self.qkv_dwconv(self.qkv(x)) + qkv = qkv.chunk(3, dim=1) + + q, k, v = map( + lambda t: rearrange( + t, + "b (head d) (h ph) (w pw) -> b (ph pw) head d (h w)", + ph=self.ps, + pw=self.ps, + head=self.heads, + ), + qkv, + ) + + q = F.normalize(q, dim=-1) + k = F.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + out = attn @ v + + out = rearrange( + out, + "b (ph pw) head d (h w) -> b (head d) (h ph) (w pw)", + h=h // self.ps, + w=w // self.ps, + ph=self.ps, + pw=self.ps, + head=self.heads, + ) + + out = self.project_out(out) + + return out + + +class OSA_Block(nn.Module): + def __init__( + self, + channel_num=64, + bias=True, + ffn_bias=True, + window_size=8, + with_pe=False, + dropout=0.0, + ): + super(OSA_Block, self).__init__() + + w = window_size + + self.layer = nn.Sequential( + MBConv( + channel_num, + channel_num, + downsample=False, + expansion_rate=1, + shrinkage_rate=0.25, + ), + Rearrange( + "b d (x w1) (y w2) -> b x y w1 w2 d", w1=w, w2=w + ), # block-like attention + PreNormResidual( + channel_num, + Attention( + dim=channel_num, + dim_head=channel_num // 4, + dropout=dropout, + window_size=window_size, + with_pe=with_pe, + ), + ), + Rearrange("b x y w1 w2 d -> b d (x w1) (y w2)"), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + # channel-like attention + Conv_PreNormResidual( + channel_num, + Channel_Attention( + dim=channel_num, heads=4, dropout=dropout, window_size=window_size + ), + ), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + Rearrange( + "b d (w1 x) (w2 y) -> b x y w1 w2 d", w1=w, w2=w + ), # grid-like attention + PreNormResidual( + channel_num, + Attention( + dim=channel_num, + dim_head=channel_num // 4, + dropout=dropout, + window_size=window_size, + with_pe=with_pe, + ), + ), + Rearrange("b x y w1 w2 d -> b d (w1 x) (w2 y)"), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + # channel-like attention + Conv_PreNormResidual( + channel_num, + Channel_Attention_grid( + dim=channel_num, heads=4, dropout=dropout, window_size=window_size + ), + ), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + ) + + def forward(self, x): + out = self.layer(x) + return out diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py b/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py new file mode 100644 index 0000000000000000000000000000000000000000..477e81f9da4eb1db9b5ec418549d75dd591209ec --- /dev/null +++ b/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: OSAG.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Sunday, 23rd April 2023 3:08:49 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + + +import torch.nn as nn + +from .esa import ESA +from .OSA import OSA_Block + + +class OSAG(nn.Module): + def __init__( + self, + channel_num=64, + bias=True, + block_num=4, + ffn_bias=False, + window_size=0, + pe=False, + ): + super(OSAG, self).__init__() + + # print("window_size: %d" % (window_size)) + # print("with_pe", pe) + # print("ffn_bias: %d" % (ffn_bias)) + + # block_script_name = kwargs.get("block_script_name", "OSA") + # block_class_name = kwargs.get("block_class_name", "OSA_Block") + + # script_name = "." + block_script_name + # package = __import__(script_name, fromlist=True) + block_class = OSA_Block # getattr(package, block_class_name) + group_list = [] + for _ in range(block_num): + temp_res = block_class( + channel_num, + bias, + ffn_bias=ffn_bias, + window_size=window_size, + with_pe=pe, + ) + group_list.append(temp_res) + group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias)) + self.residual_layer = nn.Sequential(*group_list) + esa_channel = max(channel_num // 4, 16) + self.esa = ESA(esa_channel, channel_num) + + def forward(self, x): + out = self.residual_layer(x) + out = out + x + return self.esa(out) diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py b/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py new file mode 100644 index 0000000000000000000000000000000000000000..1e1c3f35e657fb972d4209456719a61163831385 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: OmniSR.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Sunday, 23rd April 2023 3:06:36 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .OSAG import OSAG +from .pixelshuffle import pixelshuffle_block + + +class OmniSR(nn.Module): + def __init__( + self, + state_dict, + **kwargs, + ): + super(OmniSR, self).__init__() + self.state = state_dict + + bias = True # Fine to assume this for now + block_num = 1 # Fine to assume this for now + ffn_bias = True + pe = True + + num_feat = state_dict["input.weight"].shape[0] or 64 + num_in_ch = state_dict["input.weight"].shape[1] or 3 + num_out_ch = num_in_ch # we can just assume this for now. pixelshuffle smh + + pixelshuffle_shape = state_dict["up.0.weight"].shape[0] + up_scale = math.sqrt(pixelshuffle_shape / num_out_ch) + if up_scale - int(up_scale) > 0: + print( + "out_nc is probably different than in_nc, scale calculation might be wrong" + ) + up_scale = int(up_scale) + res_num = 0 + for key in state_dict.keys(): + if "residual_layer" in key: + temp_res_num = int(key.split(".")[1]) + if temp_res_num > res_num: + res_num = temp_res_num + res_num = res_num + 1 # zero-indexed + + residual_layer = [] + self.res_num = res_num + + if ( + "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" + in state_dict.keys() + ): + rel_pos_bias_weight = state_dict[ + "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" + ].shape[0] + self.window_size = int((math.sqrt(rel_pos_bias_weight) + 1) / 2) + else: + self.window_size = 8 + + self.up_scale = up_scale + + for _ in range(res_num): + temp_res = OSAG( + channel_num=num_feat, + bias=bias, + block_num=block_num, + ffn_bias=ffn_bias, + window_size=self.window_size, + pe=pe, + ) + residual_layer.append(temp_res) + self.residual_layer = nn.Sequential(*residual_layer) + self.input = nn.Conv2d( + in_channels=num_in_ch, + out_channels=num_feat, + kernel_size=3, + stride=1, + padding=1, + bias=bias, + ) + self.output = nn.Conv2d( + in_channels=num_feat, + out_channels=num_feat, + kernel_size=3, + stride=1, + padding=1, + bias=bias, + ) + self.up = pixelshuffle_block(num_feat, num_out_ch, up_scale, bias=bias) + + # self.tail = pixelshuffle_block(num_feat,num_out_ch,up_scale,bias=bias) + + # for m in self.modules(): + # if isinstance(m, nn.Conv2d): + # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + # m.weight.data.normal_(0, sqrt(2. / n)) + + # chaiNNer specific stuff + self.model_arch = "OmniSR" + self.sub_type = "SR" + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.scale = up_scale + + self.supports_fp16 = True # TODO: Test this + self.supports_bfp16 = True + self.min_size_restriction = 16 + + self.load_state_dict(state_dict, strict=False) + + def check_image_size(self, x): + _, _, h, w = x.size() + # import pdb; pdb.set_trace() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + # x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "constant", 0) + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + residual = self.input(x) + out = self.residual_layer(residual) + + # origin + out = torch.add(self.output(out), residual) + out = self.up(out) + + out = out[:, :, : H * self.up_scale, : W * self.up_scale] + return out diff --git a/comfy_extras/chainner_models/architecture/OmniSR/esa.py b/comfy_extras/chainner_models/architecture/OmniSR/esa.py new file mode 100644 index 0000000000000000000000000000000000000000..f9ce7f7a60bfe20b3737eaa2e3110fd460a2d104 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/OmniSR/esa.py @@ -0,0 +1,294 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: esa.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Thursday, 20th April 2023 9:28:06 am +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .layernorm import LayerNorm2d + + +def moment(x, dim=(2, 3), k=2): + assert len(x.size()) == 4 + mean = torch.mean(x, dim=dim).unsqueeze(-1).unsqueeze(-1) + mk = (1 / (x.size(2) * x.size(3))) * torch.sum(torch.pow(x - mean, k), dim=dim) + return mk + + +class ESA(nn.Module): + """ + Modification of Enhanced Spatial Attention (ESA), which is proposed by + `Residual Feature Aggregation Network for Image Super-Resolution` + Note: `conv_max` and `conv3_` are NOT used here, so the corresponding codes + are deleted. + """ + + def __init__(self, esa_channels, n_feats, conv=nn.Conv2d): + super(ESA, self).__init__() + f = esa_channels + self.conv1 = conv(n_feats, f, kernel_size=1) + self.conv_f = conv(f, f, kernel_size=1) + self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0) + self.conv3 = conv(f, f, kernel_size=3, padding=1) + self.conv4 = conv(f, n_feats, kernel_size=1) + self.sigmoid = nn.Sigmoid() + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + c1_ = self.conv1(x) + c1 = self.conv2(c1_) + v_max = F.max_pool2d(c1, kernel_size=7, stride=3) + c3 = self.conv3(v_max) + c3 = F.interpolate( + c3, (x.size(2), x.size(3)), mode="bilinear", align_corners=False + ) + cf = self.conv_f(c1_) + c4 = self.conv4(c3 + cf) + m = self.sigmoid(c4) + return x * m + + +class LK_ESA(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(LK_ESA, self).__init__() + f = esa_channels + self.conv1 = conv(n_feats, f, kernel_size=1) + self.conv_f = conv(f, f, kernel_size=1) + + kernel_size = 17 + kernel_expand = kernel_expand + padding = kernel_size // 2 + + self.vec_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, kernel_size), + padding=(0, padding), + groups=2, + bias=bias, + ) + self.vec_conv3x1 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, 3), + padding=(0, 1), + groups=2, + bias=bias, + ) + + self.hor_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(kernel_size, 1), + padding=(padding, 0), + groups=2, + bias=bias, + ) + self.hor_conv1x3 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(3, 1), + padding=(1, 0), + groups=2, + bias=bias, + ) + + self.conv4 = conv(f, n_feats, kernel_size=1) + self.sigmoid = nn.Sigmoid() + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + c1_ = self.conv1(x) + + res = self.vec_conv(c1_) + self.vec_conv3x1(c1_) + res = self.hor_conv(res) + self.hor_conv1x3(res) + + cf = self.conv_f(c1_) + c4 = self.conv4(res + cf) + m = self.sigmoid(c4) + return x * m + + +class LK_ESA_LN(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(LK_ESA_LN, self).__init__() + f = esa_channels + self.conv1 = conv(n_feats, f, kernel_size=1) + self.conv_f = conv(f, f, kernel_size=1) + + kernel_size = 17 + kernel_expand = kernel_expand + padding = kernel_size // 2 + + self.norm = LayerNorm2d(n_feats) + + self.vec_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, kernel_size), + padding=(0, padding), + groups=2, + bias=bias, + ) + self.vec_conv3x1 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, 3), + padding=(0, 1), + groups=2, + bias=bias, + ) + + self.hor_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(kernel_size, 1), + padding=(padding, 0), + groups=2, + bias=bias, + ) + self.hor_conv1x3 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(3, 1), + padding=(1, 0), + groups=2, + bias=bias, + ) + + self.conv4 = conv(f, n_feats, kernel_size=1) + self.sigmoid = nn.Sigmoid() + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + c1_ = self.norm(x) + c1_ = self.conv1(c1_) + + res = self.vec_conv(c1_) + self.vec_conv3x1(c1_) + res = self.hor_conv(res) + self.hor_conv1x3(res) + + cf = self.conv_f(c1_) + c4 = self.conv4(res + cf) + m = self.sigmoid(c4) + return x * m + + +class AdaGuidedFilter(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(AdaGuidedFilter, self).__init__() + + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Conv2d( + in_channels=n_feats, + out_channels=1, + kernel_size=1, + padding=0, + stride=1, + groups=1, + bias=True, + ) + + self.r = 5 + + def box_filter(self, x, r): + channel = x.shape[1] + kernel_size = 2 * r + 1 + weight = 1.0 / (kernel_size**2) + box_kernel = weight * torch.ones( + (channel, 1, kernel_size, kernel_size), dtype=torch.float32, device=x.device + ) + output = F.conv2d(x, weight=box_kernel, stride=1, padding=r, groups=channel) + return output + + def forward(self, x): + _, _, H, W = x.shape + N = self.box_filter( + torch.ones((1, 1, H, W), dtype=x.dtype, device=x.device), self.r + ) + + # epsilon = self.fc(self.gap(x)) + # epsilon = torch.pow(epsilon, 2) + epsilon = 1e-2 + + mean_x = self.box_filter(x, self.r) / N + var_x = self.box_filter(x * x, self.r) / N - mean_x * mean_x + + A = var_x / (var_x + epsilon) + b = (1 - A) * mean_x + m = A * x + b + + # mean_A = self.box_filter(A, self.r) / N + # mean_b = self.box_filter(b, self.r) / N + # m = mean_A * x + mean_b + return x * m + + +class AdaConvGuidedFilter(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(AdaConvGuidedFilter, self).__init__() + f = esa_channels + + self.conv_f = conv(f, f, kernel_size=1) + + kernel_size = 17 + kernel_expand = kernel_expand + padding = kernel_size // 2 + + self.vec_conv = nn.Conv2d( + in_channels=f, + out_channels=f, + kernel_size=(1, kernel_size), + padding=(0, padding), + groups=f, + bias=bias, + ) + + self.hor_conv = nn.Conv2d( + in_channels=f, + out_channels=f, + kernel_size=(kernel_size, 1), + padding=(padding, 0), + groups=f, + bias=bias, + ) + + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Conv2d( + in_channels=f, + out_channels=f, + kernel_size=1, + padding=0, + stride=1, + groups=1, + bias=True, + ) + + def forward(self, x): + y = self.vec_conv(x) + y = self.hor_conv(y) + + sigma = torch.pow(y, 2) + epsilon = self.fc(self.gap(y)) + + weight = sigma / (sigma + epsilon) + + m = weight * x + (1 - weight) + + return x * m diff --git a/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py b/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py new file mode 100644 index 0000000000000000000000000000000000000000..731a25f7542d45757a284648055d7c6ffad4c3fd --- /dev/null +++ b/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: layernorm.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Thursday, 20th April 2023 9:28:20 am +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import torch +import torch.nn as nn + + +class LayerNormFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, x, weight, bias, eps): + ctx.eps = eps + N, C, H, W = x.size() + mu = x.mean(1, keepdim=True) + var = (x - mu).pow(2).mean(1, keepdim=True) + y = (x - mu) / (var + eps).sqrt() + ctx.save_for_backward(y, var, weight) + y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1) + return y + + @staticmethod + def backward(ctx, grad_output): + eps = ctx.eps + + N, C, H, W = grad_output.size() + y, var, weight = ctx.saved_variables + g = grad_output * weight.view(1, C, 1, 1) + mean_g = g.mean(dim=1, keepdim=True) + + mean_gy = (g * y).mean(dim=1, keepdim=True) + gx = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g) + return ( + gx, + (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), + grad_output.sum(dim=3).sum(dim=2).sum(dim=0), + None, + ) + + +class LayerNorm2d(nn.Module): + def __init__(self, channels, eps=1e-6): + super(LayerNorm2d, self).__init__() + self.register_parameter("weight", nn.Parameter(torch.ones(channels))) + self.register_parameter("bias", nn.Parameter(torch.zeros(channels))) + self.eps = eps + + def forward(self, x): + return LayerNormFunction.apply(x, self.weight, self.bias, self.eps) + + +class GRN(nn.Module): + """GRN (Global Response Normalization) layer""" + + def __init__(self, dim): + super().__init__() + self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1)) + self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True) + Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6) + return self.gamma * (x * Nx) + self.beta + x diff --git a/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py b/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py new file mode 100644 index 0000000000000000000000000000000000000000..4260fb7c9d8d912e34899ce7877595b617f9bb02 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: pixelshuffle.py +# Created Date: Friday July 1st 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Friday, 1st July 2022 10:18:39 am +# Modified By: Chen Xuanhong +# Copyright (c) 2022 Shanghai Jiao Tong University +############################################################# + +import torch.nn as nn + + +def pixelshuffle_block( + in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False +): + """ + Upsample features according to `upscale_factor`. + """ + padding = kernel_size // 2 + conv = nn.Conv2d( + in_channels, + out_channels * (upscale_factor**2), + kernel_size, + padding=1, + bias=bias, + ) + pixel_shuffle = nn.PixelShuffle(upscale_factor) + return nn.Sequential(*[conv, pixel_shuffle]) diff --git a/comfy_extras/chainner_models/architecture/RRDB.py b/comfy_extras/chainner_models/architecture/RRDB.py new file mode 100644 index 0000000000000000000000000000000000000000..b50db7c24a8e6edc9154168a3d807c9219cb8cea --- /dev/null +++ b/comfy_extras/chainner_models/architecture/RRDB.py @@ -0,0 +1,296 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import functools +import math +import re +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from . import block as B + + +# Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/ESRGAN.py +# Which enhanced stuff that was already here +class RRDBNet(nn.Module): + def __init__( + self, + state_dict, + norm=None, + act: str = "leakyrelu", + upsampler: str = "upconv", + mode: B.ConvMode = "CNA", + ) -> None: + """ + ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks. + By Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, + and Chen Change Loy. + This is old-arch Residual in Residual Dense Block Network and is not + the newest revision that's available at github.com/xinntao/ESRGAN. + This is on purpose, the newest Network has severely limited the + potential use of the Network with no benefits. + This network supports model files from both new and old-arch. + Args: + norm: Normalization layer + act: Activation layer + upsampler: Upsample layer. upconv, pixel_shuffle + mode: Convolution mode + """ + super(RRDBNet, self).__init__() + self.model_arch = "ESRGAN" + self.sub_type = "SR" + + self.state = state_dict + self.norm = norm + self.act = act + self.upsampler = upsampler + self.mode = mode + + self.state_map = { + # currently supports old, new, and newer RRDBNet arch models + # ESRGAN, BSRGAN/RealSR, Real-ESRGAN + "model.0.weight": ("conv_first.weight",), + "model.0.bias": ("conv_first.bias",), + "model.1.sub./NB/.weight": ("trunk_conv.weight", "conv_body.weight"), + "model.1.sub./NB/.bias": ("trunk_conv.bias", "conv_body.bias"), + r"model.1.sub.\1.RDB\2.conv\3.0.\4": ( + r"RRDB_trunk\.(\d+)\.RDB(\d)\.conv(\d+)\.(weight|bias)", + r"body\.(\d+)\.rdb(\d)\.conv(\d+)\.(weight|bias)", + ), + } + if "params_ema" in self.state: + self.state = self.state["params_ema"] + # self.model_arch = "RealESRGAN" + self.num_blocks = self.get_num_blocks() + self.plus = any("conv1x1" in k for k in self.state.keys()) + if self.plus: + self.model_arch = "ESRGAN+" + + self.state = self.new_to_old_arch(self.state) + + self.key_arr = list(self.state.keys()) + + self.in_nc: int = self.state[self.key_arr[0]].shape[1] + self.out_nc: int = self.state[self.key_arr[-1]].shape[0] + + self.scale: int = self.get_scale() + self.num_filters: int = self.state[self.key_arr[0]].shape[0] + + c2x2 = False + if self.state["model.0.weight"].shape[-2] == 2: + c2x2 = True + self.scale = round(math.sqrt(self.scale / 4)) + self.model_arch = "ESRGAN-2c2" + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + # Detect if pixelunshuffle was used (Real-ESRGAN) + if self.in_nc in (self.out_nc * 4, self.out_nc * 16) and self.out_nc in ( + self.in_nc / 4, + self.in_nc / 16, + ): + self.shuffle_factor = int(math.sqrt(self.in_nc / self.out_nc)) + else: + self.shuffle_factor = None + + upsample_block = { + "upconv": B.upconv_block, + "pixel_shuffle": B.pixelshuffle_block, + }.get(self.upsampler) + if upsample_block is None: + raise NotImplementedError(f"Upsample mode [{self.upsampler}] is not found") + + if self.scale == 3: + upsample_blocks = upsample_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + upscale_factor=3, + act_type=self.act, + c2x2=c2x2, + ) + else: + upsample_blocks = [ + upsample_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + act_type=self.act, + c2x2=c2x2, + ) + for _ in range(int(math.log(self.scale, 2))) + ] + + self.model = B.sequential( + # fea conv + B.conv_block( + in_nc=self.in_nc, + out_nc=self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + c2x2=c2x2, + ), + B.ShortcutBlock( + B.sequential( + # rrdb blocks + *[ + B.RRDB( + nf=self.num_filters, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=self.norm, + act_type=self.act, + mode="CNA", + plus=self.plus, + c2x2=c2x2, + ) + for _ in range(self.num_blocks) + ], + # lr conv + B.conv_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + kernel_size=3, + norm_type=self.norm, + act_type=None, + mode=self.mode, + c2x2=c2x2, + ), + ) + ), + *upsample_blocks, + # hr_conv0 + B.conv_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + kernel_size=3, + norm_type=None, + act_type=self.act, + c2x2=c2x2, + ), + # hr_conv1 + B.conv_block( + in_nc=self.num_filters, + out_nc=self.out_nc, + kernel_size=3, + norm_type=None, + act_type=None, + c2x2=c2x2, + ), + ) + + # Adjust these properties for calculations outside of the model + if self.shuffle_factor: + self.in_nc //= self.shuffle_factor**2 + self.scale //= self.shuffle_factor + + self.load_state_dict(self.state, strict=False) + + def new_to_old_arch(self, state): + """Convert a new-arch model state dictionary to an old-arch dictionary.""" + if "params_ema" in state: + state = state["params_ema"] + + if "conv_first.weight" not in state: + # model is already old arch, this is a loose check, but should be sufficient + return state + + # add nb to state keys + for kind in ("weight", "bias"): + self.state_map[f"model.1.sub.{self.num_blocks}.{kind}"] = self.state_map[ + f"model.1.sub./NB/.{kind}" + ] + del self.state_map[f"model.1.sub./NB/.{kind}"] + + old_state = OrderedDict() + for old_key, new_keys in self.state_map.items(): + for new_key in new_keys: + if r"\1" in old_key: + for k, v in state.items(): + sub = re.sub(new_key, old_key, k) + if sub != k: + old_state[sub] = v + else: + if new_key in state: + old_state[old_key] = state[new_key] + + # upconv layers + max_upconv = 0 + for key in state.keys(): + match = re.match(r"(upconv|conv_up)(\d)\.(weight|bias)", key) + if match is not None: + _, key_num, key_type = match.groups() + old_state[f"model.{int(key_num) * 3}.{key_type}"] = state[key] + max_upconv = max(max_upconv, int(key_num) * 3) + + # final layers + for key in state.keys(): + if key in ("HRconv.weight", "conv_hr.weight"): + old_state[f"model.{max_upconv + 2}.weight"] = state[key] + elif key in ("HRconv.bias", "conv_hr.bias"): + old_state[f"model.{max_upconv + 2}.bias"] = state[key] + elif key in ("conv_last.weight",): + old_state[f"model.{max_upconv + 4}.weight"] = state[key] + elif key in ("conv_last.bias",): + old_state[f"model.{max_upconv + 4}.bias"] = state[key] + + # Sort by first numeric value of each layer + def compare(item1, item2): + parts1 = item1.split(".") + parts2 = item2.split(".") + int1 = int(parts1[1]) + int2 = int(parts2[1]) + return int1 - int2 + + sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare)) + + # Rebuild the output dict in the right order + out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys) + + return out_dict + + def get_scale(self, min_part: int = 6) -> int: + n = 0 + for part in list(self.state): + parts = part.split(".")[1:] + if len(parts) == 2: + part_num = int(parts[0]) + if part_num > min_part and parts[1] == "weight": + n += 1 + return 2**n + + def get_num_blocks(self) -> int: + nbs = [] + state_keys = self.state_map[r"model.1.sub.\1.RDB\2.conv\3.0.\4"] + ( + r"model\.\d+\.sub\.(\d+)\.RDB(\d+)\.conv(\d+)\.0\.(weight|bias)", + ) + for state_key in state_keys: + for k in self.state: + m = re.search(state_key, k) + if m: + nbs.append(int(m.group(1))) + if nbs: + break + return max(*nbs) + 1 + + def forward(self, x): + if self.shuffle_factor: + _, _, h, w = x.size() + mod_pad_h = ( + self.shuffle_factor - h % self.shuffle_factor + ) % self.shuffle_factor + mod_pad_w = ( + self.shuffle_factor - w % self.shuffle_factor + ) % self.shuffle_factor + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + x = torch.pixel_unshuffle(x, downscale_factor=self.shuffle_factor) + x = self.model(x) + return x[:, :, : h * self.scale, : w * self.scale] + return self.model(x) diff --git a/comfy_extras/chainner_models/architecture/SCUNet.py b/comfy_extras/chainner_models/architecture/SCUNet.py new file mode 100644 index 0000000000000000000000000000000000000000..b8354a873085140e9ff7d582c43ba9818ed9524e --- /dev/null +++ b/comfy_extras/chainner_models/architecture/SCUNet.py @@ -0,0 +1,455 @@ +# pylint: skip-file +# ----------------------------------------------------------------------------------- +# SCUNet: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, https://arxiv.org/abs/2203.13278 +# Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Timofte, Radu and Van Gool, Luc +# ----------------------------------------------------------------------------------- + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from einops.layers.torch import Rearrange + +from .timm.drop import DropPath +from .timm.weight_init import trunc_normal_ + + +# Borrowed from https://github.com/cszn/SCUNet/blob/main/models/network_scunet.py +class WMSA(nn.Module): + """Self-attention module in Swin Transformer""" + + def __init__(self, input_dim, output_dim, head_dim, window_size, type): + super(WMSA, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.head_dim = head_dim + self.scale = self.head_dim**-0.5 + self.n_heads = input_dim // head_dim + self.window_size = window_size + self.type = type + self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True) + + self.relative_position_params = nn.Parameter( + torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) + ) + # TODO recover + # self.relative_position_params = nn.Parameter(torch.zeros(self.n_heads, 2 * window_size - 1, 2 * window_size -1)) + self.relative_position_params = nn.Parameter( + torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) + ) + + self.linear = nn.Linear(self.input_dim, self.output_dim) + + trunc_normal_(self.relative_position_params, std=0.02) + self.relative_position_params = torch.nn.Parameter( + self.relative_position_params.view( + 2 * window_size - 1, 2 * window_size - 1, self.n_heads + ) + .transpose(1, 2) + .transpose(0, 1) + ) + + def generate_mask(self, h, w, p, shift): + """generating the mask of SW-MSA + Args: + shift: shift parameters in CyclicShift. + Returns: + attn_mask: should be (1 1 w p p), + """ + # supporting square. + attn_mask = torch.zeros( + h, + w, + p, + p, + p, + p, + dtype=torch.bool, + device=self.relative_position_params.device, + ) + if self.type == "W": + return attn_mask + + s = p - shift + attn_mask[-1, :, :s, :, s:, :] = True + attn_mask[-1, :, s:, :, :s, :] = True + attn_mask[:, -1, :, :s, :, s:] = True + attn_mask[:, -1, :, s:, :, :s] = True + attn_mask = rearrange( + attn_mask, "w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)" + ) + return attn_mask + + def forward(self, x): + """Forward pass of Window Multi-head Self-attention module. + Args: + x: input tensor with shape of [b h w c]; + attn_mask: attention mask, fill -inf where the value is True; + Returns: + output: tensor shape [b h w c] + """ + if self.type != "W": + x = torch.roll( + x, + shifts=(-(self.window_size // 2), -(self.window_size // 2)), + dims=(1, 2), + ) + + x = rearrange( + x, + "b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c", + p1=self.window_size, + p2=self.window_size, + ) + h_windows = x.size(1) + w_windows = x.size(2) + # square validation + # assert h_windows == w_windows + + x = rearrange( + x, + "b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c", + p1=self.window_size, + p2=self.window_size, + ) + qkv = self.embedding_layer(x) + q, k, v = rearrange( + qkv, "b nw np (threeh c) -> threeh b nw np c", c=self.head_dim + ).chunk(3, dim=0) + sim = torch.einsum("hbwpc,hbwqc->hbwpq", q, k) * self.scale + # Adding learnable relative embedding + sim = sim + rearrange(self.relative_embedding(), "h p q -> h 1 1 p q") + # Using Attn Mask to distinguish different subwindows. + if self.type != "W": + attn_mask = self.generate_mask( + h_windows, w_windows, self.window_size, shift=self.window_size // 2 + ) + sim = sim.masked_fill_(attn_mask, float("-inf")) + + probs = nn.functional.softmax(sim, dim=-1) + output = torch.einsum("hbwij,hbwjc->hbwic", probs, v) + output = rearrange(output, "h b w p c -> b w p (h c)") + output = self.linear(output) + output = rearrange( + output, + "b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c", + w1=h_windows, + p1=self.window_size, + ) + + if self.type != "W": + output = torch.roll( + output, + shifts=(self.window_size // 2, self.window_size // 2), + dims=(1, 2), + ) + + return output + + def relative_embedding(self): + cord = torch.tensor( + np.array( + [ + [i, j] + for i in range(self.window_size) + for j in range(self.window_size) + ] + ) + ) + relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1 + # negative is allowed + return self.relative_position_params[ + :, relation[:, :, 0].long(), relation[:, :, 1].long() + ] + + +class Block(nn.Module): + def __init__( + self, + input_dim, + output_dim, + head_dim, + window_size, + drop_path, + type="W", + input_resolution=None, + ): + """SwinTransformer Block""" + super(Block, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + assert type in ["W", "SW"] + self.type = type + if input_resolution <= window_size: + self.type = "W" + + self.ln1 = nn.LayerNorm(input_dim) + self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.ln2 = nn.LayerNorm(input_dim) + self.mlp = nn.Sequential( + nn.Linear(input_dim, 4 * input_dim), + nn.GELU(), + nn.Linear(4 * input_dim, output_dim), + ) + + def forward(self, x): + x = x + self.drop_path(self.msa(self.ln1(x))) + x = x + self.drop_path(self.mlp(self.ln2(x))) + return x + + +class ConvTransBlock(nn.Module): + def __init__( + self, + conv_dim, + trans_dim, + head_dim, + window_size, + drop_path, + type="W", + input_resolution=None, + ): + """SwinTransformer and Conv Block""" + super(ConvTransBlock, self).__init__() + self.conv_dim = conv_dim + self.trans_dim = trans_dim + self.head_dim = head_dim + self.window_size = window_size + self.drop_path = drop_path + self.type = type + self.input_resolution = input_resolution + + assert self.type in ["W", "SW"] + if self.input_resolution <= self.window_size: + self.type = "W" + + self.trans_block = Block( + self.trans_dim, + self.trans_dim, + self.head_dim, + self.window_size, + self.drop_path, + self.type, + self.input_resolution, + ) + self.conv1_1 = nn.Conv2d( + self.conv_dim + self.trans_dim, + self.conv_dim + self.trans_dim, + 1, + 1, + 0, + bias=True, + ) + self.conv1_2 = nn.Conv2d( + self.conv_dim + self.trans_dim, + self.conv_dim + self.trans_dim, + 1, + 1, + 0, + bias=True, + ) + + self.conv_block = nn.Sequential( + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), + nn.ReLU(True), + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), + ) + + def forward(self, x): + conv_x, trans_x = torch.split( + self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1 + ) + conv_x = self.conv_block(conv_x) + conv_x + trans_x = Rearrange("b c h w -> b h w c")(trans_x) + trans_x = self.trans_block(trans_x) + trans_x = Rearrange("b h w c -> b c h w")(trans_x) + res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1)) + x = x + res + + return x + + +class SCUNet(nn.Module): + def __init__( + self, + state_dict, + in_nc=3, + config=[4, 4, 4, 4, 4, 4, 4], + dim=64, + drop_path_rate=0.0, + input_resolution=256, + ): + super(SCUNet, self).__init__() + self.model_arch = "SCUNet" + self.sub_type = "SR" + + self.num_filters: int = 0 + + self.state = state_dict + self.config = config + self.dim = dim + self.head_dim = 32 + self.window_size = 8 + + self.in_nc = in_nc + self.out_nc = self.in_nc + self.scale = 1 + self.supports_fp16 = True + + # drop path rate for each layer + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))] + + self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)] + + begin = 0 + self.m_down1 = [ + ConvTransBlock( + dim // 2, + dim // 2, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution, + ) + for i in range(config[0]) + ] + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)] + + begin += config[0] + self.m_down2 = [ + ConvTransBlock( + dim, + dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 2, + ) + for i in range(config[1]) + ] + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)] + + begin += config[1] + self.m_down3 = [ + ConvTransBlock( + 2 * dim, + 2 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 4, + ) + for i in range(config[2]) + ] + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)] + + begin += config[2] + self.m_body = [ + ConvTransBlock( + 4 * dim, + 4 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 8, + ) + for i in range(config[3]) + ] + + begin += config[3] + self.m_up3 = [ + nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + 2 * dim, + 2 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 4, + ) + for i in range(config[4]) + ] + + begin += config[4] + self.m_up2 = [ + nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + dim, + dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 2, + ) + for i in range(config[5]) + ] + + begin += config[5] + self.m_up1 = [ + nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + dim // 2, + dim // 2, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution, + ) + for i in range(config[6]) + ] + + self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)] + + self.m_head = nn.Sequential(*self.m_head) + self.m_down1 = nn.Sequential(*self.m_down1) + self.m_down2 = nn.Sequential(*self.m_down2) + self.m_down3 = nn.Sequential(*self.m_down3) + self.m_body = nn.Sequential(*self.m_body) + self.m_up3 = nn.Sequential(*self.m_up3) + self.m_up2 = nn.Sequential(*self.m_up2) + self.m_up1 = nn.Sequential(*self.m_up1) + self.m_tail = nn.Sequential(*self.m_tail) + # self.apply(self._init_weights) + self.load_state_dict(state_dict, strict=True) + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (64 - h % 64) % 64 + mod_pad_w = (64 - w % 64) % 64 + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward(self, x0): + h, w = x0.size()[-2:] + x0 = self.check_image_size(x0) + + x1 = self.m_head(x0) + x2 = self.m_down1(x1) + x3 = self.m_down2(x2) + x4 = self.m_down3(x3) + x = self.m_body(x4) + x = self.m_up3(x + x4) + x = self.m_up2(x + x3) + x = self.m_up1(x + x2) + x = self.m_tail(x + x1) + + x = x[:, :, :h, :w] + return x + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) diff --git a/comfy_extras/chainner_models/architecture/SPSR.py b/comfy_extras/chainner_models/architecture/SPSR.py new file mode 100644 index 0000000000000000000000000000000000000000..c3cefff190292a63cf61fe3fa9c28131dac4f369 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/SPSR.py @@ -0,0 +1,383 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from . import block as B + + +class Get_gradient_nopadding(nn.Module): + def __init__(self): + super(Get_gradient_nopadding, self).__init__() + kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]] + kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]] + kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0) + kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0) + self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False) # type: ignore + + self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False) # type: ignore + + def forward(self, x): + x_list = [] + for i in range(x.shape[1]): + x_i = x[:, i] + x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1) + x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1) + x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6) + x_list.append(x_i) + + x = torch.cat(x_list, dim=1) + + return x + + +class SPSRNet(nn.Module): + def __init__( + self, + state_dict, + norm=None, + act: str = "leakyrelu", + upsampler: str = "upconv", + mode: B.ConvMode = "CNA", + ): + super(SPSRNet, self).__init__() + self.model_arch = "SPSR" + self.sub_type = "SR" + + self.state = state_dict + self.norm = norm + self.act = act + self.upsampler = upsampler + self.mode = mode + + self.num_blocks = self.get_num_blocks() + + self.in_nc: int = self.state["model.0.weight"].shape[1] + self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0] + + self.scale = self.get_scale(4) + self.num_filters: int = self.state["model.0.weight"].shape[0] + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + n_upscale = int(math.log(self.scale, 2)) + if self.scale == 3: + n_upscale = 1 + + fea_conv = B.conv_block( + self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None + ) + rb_blocks = [ + B.RRDB( + self.num_filters, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + for _ in range(self.num_blocks) + ] + LR_conv = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=norm, + act_type=None, + mode=mode, + ) + + if upsampler == "upconv": + upsample_block = B.upconv_block + elif upsampler == "pixelshuffle": + upsample_block = B.pixelshuffle_block + else: + raise NotImplementedError(f"upsample mode [{upsampler}] is not found") + if self.scale == 3: + a_upsampler = upsample_block( + self.num_filters, self.num_filters, 3, act_type=act + ) + else: + a_upsampler = [ + upsample_block(self.num_filters, self.num_filters, act_type=act) + for _ in range(n_upscale) + ] + self.HR_conv0_new = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=act, + ) + self.HR_conv1_new = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + + self.model = B.sequential( + fea_conv, + B.ShortcutBlockSPSR(B.sequential(*rb_blocks, LR_conv)), + *a_upsampler, + self.HR_conv0_new, + ) + + self.get_g_nopadding = Get_gradient_nopadding() + + self.b_fea_conv = B.conv_block( + self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None + ) + + self.b_concat_1 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_1 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_concat_2 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_2 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_concat_3 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_3 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_concat_4 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_4 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_LR_conv = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=norm, + act_type=None, + mode=mode, + ) + + if upsampler == "upconv": + upsample_block = B.upconv_block + elif upsampler == "pixelshuffle": + upsample_block = B.pixelshuffle_block + else: + raise NotImplementedError(f"upsample mode [{upsampler}] is not found") + if self.scale == 3: + b_upsampler = upsample_block( + self.num_filters, self.num_filters, 3, act_type=act + ) + else: + b_upsampler = [ + upsample_block(self.num_filters, self.num_filters, act_type=act) + for _ in range(n_upscale) + ] + + b_HR_conv0 = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=act, + ) + b_HR_conv1 = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + + self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1) + + self.conv_w = B.conv_block( + self.num_filters, self.out_nc, kernel_size=1, norm_type=None, act_type=None + ) + + self.f_concat = B.conv_block( + self.num_filters * 2, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + + self.f_block = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.f_HR_conv0 = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=act, + ) + self.f_HR_conv1 = B.conv_block( + self.num_filters, self.out_nc, kernel_size=3, norm_type=None, act_type=None + ) + + self.load_state_dict(self.state, strict=False) + + def get_scale(self, min_part: int = 4) -> int: + n = 0 + for part in list(self.state): + parts = part.split(".") + if len(parts) == 3: + part_num = int(parts[1]) + if part_num > min_part and parts[0] == "model" and parts[2] == "weight": + n += 1 + return 2**n + + def get_num_blocks(self) -> int: + nb = 0 + for part in list(self.state): + parts = part.split(".") + n_parts = len(parts) + if n_parts == 5 and parts[2] == "sub": + nb = int(parts[3]) + return nb + + def forward(self, x): + x_grad = self.get_g_nopadding(x) + x = self.model[0](x) + + x, block_list = self.model[1](x) + + x_ori = x + for i in range(5): + x = block_list[i](x) + x_fea1 = x + + for i in range(5): + x = block_list[i + 5](x) + x_fea2 = x + + for i in range(5): + x = block_list[i + 10](x) + x_fea3 = x + + for i in range(5): + x = block_list[i + 15](x) + x_fea4 = x + + x = block_list[20:](x) + # short cut + x = x_ori + x + x = self.model[2:](x) + x = self.HR_conv1_new(x) + + x_b_fea = self.b_fea_conv(x_grad) + x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1) + + x_cat_1 = self.b_block_1(x_cat_1) + x_cat_1 = self.b_concat_1(x_cat_1) + + x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1) + + x_cat_2 = self.b_block_2(x_cat_2) + x_cat_2 = self.b_concat_2(x_cat_2) + + x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1) + + x_cat_3 = self.b_block_3(x_cat_3) + x_cat_3 = self.b_concat_3(x_cat_3) + + x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1) + + x_cat_4 = self.b_block_4(x_cat_4) + x_cat_4 = self.b_concat_4(x_cat_4) + + x_cat_4 = self.b_LR_conv(x_cat_4) + + # short cut + x_cat_4 = x_cat_4 + x_b_fea + x_branch = self.b_module(x_cat_4) + + # x_out_branch = self.conv_w(x_branch) + ######## + x_branch_d = x_branch + x_f_cat = torch.cat([x_branch_d, x], dim=1) + x_f_cat = self.f_block(x_f_cat) + x_out = self.f_concat(x_f_cat) + x_out = self.f_HR_conv0(x_out) + x_out = self.f_HR_conv1(x_out) + + ######### + # return x_out_branch, x_out, x_grad + return x_out diff --git a/comfy_extras/chainner_models/architecture/SRVGG.py b/comfy_extras/chainner_models/architecture/SRVGG.py new file mode 100644 index 0000000000000000000000000000000000000000..7a8ec37ae5dc4effd0ba688cf4c3a51801e1f2c9 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/SRVGG.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import math + +import torch.nn as nn +import torch.nn.functional as F + + +class SRVGGNetCompact(nn.Module): + """A compact VGG-style network structure for super-resolution. + It is a compact network structure, which performs upsampling in the last layer and no convolution is + conducted on the HR feature space. + Args: + num_in_ch (int): Channel number of inputs. Default: 3. + num_out_ch (int): Channel number of outputs. Default: 3. + num_feat (int): Channel number of intermediate features. Default: 64. + num_conv (int): Number of convolution layers in the body network. Default: 16. + upscale (int): Upsampling factor. Default: 4. + act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu. + """ + + def __init__( + self, + state_dict, + act_type: str = "prelu", + ): + super(SRVGGNetCompact, self).__init__() + self.model_arch = "SRVGG (RealESRGAN)" + self.sub_type = "SR" + + self.act_type = act_type + + self.state = state_dict + + if "params" in self.state: + self.state = self.state["params"] + + self.key_arr = list(self.state.keys()) + + self.in_nc = self.get_in_nc() + self.num_feat = self.get_num_feats() + self.num_conv = self.get_num_conv() + self.out_nc = self.in_nc # :( + self.pixelshuffle_shape = None # Defined in get_scale() + self.scale = self.get_scale() + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + self.body = nn.ModuleList() + # the first conv + self.body.append(nn.Conv2d(self.in_nc, self.num_feat, 3, 1, 1)) + # the first activation + if act_type == "relu": + activation = nn.ReLU(inplace=True) + elif act_type == "prelu": + activation = nn.PReLU(num_parameters=self.num_feat) + elif act_type == "leakyrelu": + activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.body.append(activation) # type: ignore + + # the body structure + for _ in range(self.num_conv): + self.body.append(nn.Conv2d(self.num_feat, self.num_feat, 3, 1, 1)) + # activation + if act_type == "relu": + activation = nn.ReLU(inplace=True) + elif act_type == "prelu": + activation = nn.PReLU(num_parameters=self.num_feat) + elif act_type == "leakyrelu": + activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.body.append(activation) # type: ignore + + # the last conv + self.body.append(nn.Conv2d(self.num_feat, self.pixelshuffle_shape, 3, 1, 1)) # type: ignore + # upsample + self.upsampler = nn.PixelShuffle(self.scale) + + self.load_state_dict(self.state, strict=False) + + def get_num_conv(self) -> int: + return (int(self.key_arr[-1].split(".")[1]) - 2) // 2 + + def get_num_feats(self) -> int: + return self.state[self.key_arr[0]].shape[0] + + def get_in_nc(self) -> int: + return self.state[self.key_arr[0]].shape[1] + + def get_scale(self) -> int: + self.pixelshuffle_shape = self.state[self.key_arr[-1]].shape[0] + # Assume out_nc is the same as in_nc + # I cant think of a better way to do that + self.out_nc = self.in_nc + scale = math.sqrt(self.pixelshuffle_shape / self.out_nc) + if scale - int(scale) > 0: + print( + "out_nc is probably different than in_nc, scale calculation might be wrong" + ) + scale = int(scale) + return scale + + def forward(self, x): + out = x + for i in range(0, len(self.body)): + out = self.body[i](out) + + out = self.upsampler(out) + # add the nearest upsampled image, so that the network learns the residual + base = F.interpolate(x, scale_factor=self.scale, mode="nearest") + out += base + return out diff --git a/comfy_extras/chainner_models/architecture/SwiftSRGAN.py b/comfy_extras/chainner_models/architecture/SwiftSRGAN.py new file mode 100644 index 0000000000000000000000000000000000000000..dbb7725b08dc2462661b7ba45db605a06fadacb9 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/SwiftSRGAN.py @@ -0,0 +1,161 @@ +# From https://github.com/Koushik0901/Swift-SRGAN/blob/master/swift-srgan/models.py + +import torch +from torch import nn + + +class SeperableConv2d(nn.Module): + def __init__( + self, in_channels, out_channels, kernel_size, stride=1, padding=1, bias=True + ): + super(SeperableConv2d, self).__init__() + self.depthwise = nn.Conv2d( + in_channels, + in_channels, + kernel_size=kernel_size, + stride=stride, + groups=in_channels, + bias=bias, + padding=padding, + ) + self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias) + + def forward(self, x): + return self.pointwise(self.depthwise(x)) + + +class ConvBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + use_act=True, + use_bn=True, + discriminator=False, + **kwargs, + ): + super(ConvBlock, self).__init__() + + self.use_act = use_act + self.cnn = SeperableConv2d(in_channels, out_channels, **kwargs, bias=not use_bn) + self.bn = nn.BatchNorm2d(out_channels) if use_bn else nn.Identity() + self.act = ( + nn.LeakyReLU(0.2, inplace=True) + if discriminator + else nn.PReLU(num_parameters=out_channels) + ) + + def forward(self, x): + return self.act(self.bn(self.cnn(x))) if self.use_act else self.bn(self.cnn(x)) + + +class UpsampleBlock(nn.Module): + def __init__(self, in_channels, scale_factor): + super(UpsampleBlock, self).__init__() + + self.conv = SeperableConv2d( + in_channels, + in_channels * scale_factor**2, + kernel_size=3, + stride=1, + padding=1, + ) + self.ps = nn.PixelShuffle( + scale_factor + ) # (in_channels * 4, H, W) -> (in_channels, H*2, W*2) + self.act = nn.PReLU(num_parameters=in_channels) + + def forward(self, x): + return self.act(self.ps(self.conv(x))) + + +class ResidualBlock(nn.Module): + def __init__(self, in_channels): + super(ResidualBlock, self).__init__() + + self.block1 = ConvBlock( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + self.block2 = ConvBlock( + in_channels, in_channels, kernel_size=3, stride=1, padding=1, use_act=False + ) + + def forward(self, x): + out = self.block1(x) + out = self.block2(out) + return out + x + + +class Generator(nn.Module): + """Swift-SRGAN Generator + Args: + in_channels (int): number of input image channels. + num_channels (int): number of hidden channels. + num_blocks (int): number of residual blocks. + upscale_factor (int): factor to upscale the image [2x, 4x, 8x]. + Returns: + torch.Tensor: super resolution image + """ + + def __init__( + self, + state_dict, + ): + super(Generator, self).__init__() + self.model_arch = "Swift-SRGAN" + self.sub_type = "SR" + self.state = state_dict + if "model" in self.state: + self.state = self.state["model"] + + self.in_nc: int = self.state["initial.cnn.depthwise.weight"].shape[0] + self.out_nc: int = self.state["final_conv.pointwise.weight"].shape[0] + self.num_filters: int = self.state["initial.cnn.pointwise.weight"].shape[0] + self.num_blocks = len( + set([x.split(".")[1] for x in self.state.keys() if "residual" in x]) + ) + self.scale: int = 2 ** len( + set([x.split(".")[1] for x in self.state.keys() if "upsampler" in x]) + ) + + in_channels = self.in_nc + num_channels = self.num_filters + num_blocks = self.num_blocks + upscale_factor = self.scale + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + self.initial = ConvBlock( + in_channels, num_channels, kernel_size=9, stride=1, padding=4, use_bn=False + ) + self.residual = nn.Sequential( + *[ResidualBlock(num_channels) for _ in range(num_blocks)] + ) + self.convblock = ConvBlock( + num_channels, + num_channels, + kernel_size=3, + stride=1, + padding=1, + use_act=False, + ) + self.upsampler = nn.Sequential( + *[ + UpsampleBlock(num_channels, scale_factor=2) + for _ in range(upscale_factor // 2) + ] + ) + self.final_conv = SeperableConv2d( + num_channels, in_channels, kernel_size=9, stride=1, padding=4 + ) + + self.load_state_dict(self.state, strict=False) + + def forward(self, x): + initial = self.initial(x) + x = self.residual(initial) + x = self.convblock(x) + initial + x = self.upsampler(x) + return (torch.tanh(self.final_conv(x)) + 1) / 2 diff --git a/comfy_extras/chainner_models/architecture/Swin2SR.py b/comfy_extras/chainner_models/architecture/Swin2SR.py new file mode 100644 index 0000000000000000000000000000000000000000..cb57ecfc4ada45a6b087247017732437b1af0fcc --- /dev/null +++ b/comfy_extras/chainner_models/architecture/Swin2SR.py @@ -0,0 +1,1377 @@ +# pylint: skip-file +# ----------------------------------------------------------------------------------- +# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/2209.11345 +# Written by Conde and Choi et al. +# From: https://raw.githubusercontent.com/mv-lab/swin2sr/main/models/network_swin2sr.py +# ----------------------------------------------------------------------------------- + +import math +import re + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +# Originally from the timm package +from .timm.drop import DropPath +from .timm.helpers import to_2tuple +from .timm.weight_init import trunc_normal_ + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + ) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view( + B, H // window_size, W // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + pretrained_window_size (tuple[int]): The height and width of the window in pre-training. + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + attn_drop=0.0, + proj_drop=0.0, + pretrained_window_size=[0, 0], + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.pretrained_window_size = pretrained_window_size + self.num_heads = num_heads + + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) # type: ignore + + # mlp to generate continuous relative position bias + self.cpb_mlp = nn.Sequential( + nn.Linear(2, 512, bias=True), + nn.ReLU(inplace=True), + nn.Linear(512, num_heads, bias=False), + ) + + # get relative_coords_table + relative_coords_h = torch.arange( + -(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32 + ) + relative_coords_w = torch.arange( + -(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32 + ) + relative_coords_table = ( + torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) + .permute(1, 2, 0) + .contiguous() + .unsqueeze(0) + ) # 1, 2*Wh-1, 2*Ww-1, 2 + if pretrained_window_size[0] > 0: + relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1 + else: + relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1 + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = ( + torch.sign(relative_coords_table) + * torch.log2(torch.abs(relative_coords_table) + 1.0) + / np.log2(8) + ) + + self.register_buffer("relative_coords_table", relative_coords_table) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(dim)) # type: ignore + self.v_bias = nn.Parameter(torch.zeros(dim)) # type: ignore + else: + self.q_bias = None + self.v_bias = None + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) # type: ignore + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + # cosine attention + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + logit_scale = torch.clamp( + self.logit_scale, + max=torch.log(torch.tensor(1.0 / 0.01)).to(self.logit_scale.device), + ).exp() + attn = attn * logit_scale + + relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view( + -1, self.num_heads + ) + relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( # type: ignore + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( + 1 + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, window_size={self.window_size}, " + f"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}" + ) + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + +class SwinTransformerBlock(nn.Module): + r"""Swin Transformer Block. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + pretrained_window_size (int): Window size in pre-training. + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + pretrained_window_size=0, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop, + pretrained_window_size=to_2tuple(pretrained_window_size), + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + H, W = x_size + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + return attn_mask + + def forward(self, x, x_size): + H, W = x_size + B, L, C = x.shape + # assert L == H * W, "input feature has wrong size" + + shortcut = x + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) + ) + else: + shifted_x = x + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, self.window_size * self.window_size, C + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn( + x_windows, mask=self.attn_mask + ) # nW*B, window_size*window_size, C + else: + attn_windows = self.attn( + x_windows, mask=self.calculate_mask(x_size).to(x.device) + ) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll( + shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) + ) + else: + x = shifted_x + x = x.view(B, H * W, C) + x = shortcut + self.drop_path(self.norm1(x)) + + # FFN + x = x + self.drop_path(self.norm2(self.mlp(x))) + + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + ) + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(2 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.reduction(x) + x = self.norm(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + flops += H * W * self.dim // 2 + return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + pretrained_window_size (int): Local window size in pre-training. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + pretrained_window_size=0, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + norm_layer=norm_layer, + pretrained_window_size=pretrained_window_size, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() # type: ignore + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + def _init_respostnorm(self): + for blk in self.blocks: + nn.init.constant_(blk.norm1.bias, 0) # type: ignore + nn.init.constant_(blk.norm1.weight, 0) # type: ignore + nn.init.constant_(blk.norm2.bias, 0) # type: ignore + nn.init.constant_(blk.norm2.weight, 0) # type: ignore + + +class PatchEmbed(nn.Module): + r"""Image to Patch Embedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] # type: ignore + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=patch_size, stride=patch_size # type: ignore + ) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + # assert H == self.img_size[0] and W == self.img_size[1], + # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) # type: ignore + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + + +class RSTB(nn.Module): + """Residual Swin Transformer Block (RSTB). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection="1conv", + ): + super(RSTB, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint, + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=dim, + embed_dim=dim, + norm_layer=None, + ) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=dim, + embed_dim=dim, + norm_layer=None, + ) + + def forward(self, x, x_size): + return ( + self.patch_embed( + self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size)) + ) + + x + ) + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + H, W = self.input_resolution + flops += H * W * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + + +class PatchUnEmbed(nn.Module): + r"""Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] # type: ignore + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + B, HW, C = x.shape + x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class Upsample_hf(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample_hf, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + H, W = self.input_resolution # type: ignore + flops = H * W * self.num_feat * 3 * 9 + return flops + + +class Swin2SR(nn.Module): + r"""Swin2SR + A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + state_dict, + **kwargs, + ): + super(Swin2SR, self).__init__() + + # Defaults + img_size = 128 + patch_size = 1 + in_chans = 3 + embed_dim = 96 + depths = [6, 6, 6, 6] + num_heads = [6, 6, 6, 6] + window_size = 7 + mlp_ratio = 4.0 + qkv_bias = True + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + norm_layer = nn.LayerNorm + ape = False + patch_norm = True + use_checkpoint = False + upscale = 2 + img_range = 1.0 + upsampler = "" + resi_connection = "1conv" + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + + self.model_arch = "Swin2SR" + self.sub_type = "SR" + self.state = state_dict + if "params_ema" in self.state: + self.state = self.state["params_ema"] + elif "params" in self.state: + self.state = self.state["params"] + + state_keys = self.state.keys() + + if "conv_before_upsample.0.weight" in state_keys: + if "conv_aux.weight" in state_keys: + upsampler = "pixelshuffle_aux" + elif "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + self.state.get("conv_before_upsample.0.weight", None).shape[1] + if self.state.get("conv_before_upsample.weight", None) + else 64 + ) + + num_in_ch = self.state["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = self.state["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle" or upsampler == "pixelshuffle_aux": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = self.state[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match( + r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key + ) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if ( + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + in state_keys + ): + num_heads_num = self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + ].shape[-1] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depths + + embed_dim = self.state["conv_first.weight"].shape[0] + + mlp_ratio = float( + self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] + / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + window_size = int( + math.sqrt( + self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_index" + ].shape[0] + ) + ) + + if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: + img_size = int( + math.sqrt( + self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] + ) + * window_size + ) + + # The JPEG models are the only ones with window-size 7, and they also use this range + img_range = 255.0 if window_size == 7 else 1.0 + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depths = depths + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.resi_connection = resi_connection + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + ## END AUTO DETECTION + + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + self.window_size = window_size + + ##################################################################################################### + ################################### 1, shallow feature extraction ################################### + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + ##################################################################################################### + ################################### 2, deep feature extraction ###################################### + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) # type: ignore + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build Residual Swin Transformer blocks (RSTB) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], # type: ignore # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers.append(layer) + + if self.upsampler == "pixelshuffle_hf": + self.layers_hf = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], # type: ignore # no impact on SR results # type: ignore + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers_hf.append(layer) + + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + ##################################################################################################### + ################################ 3, high quality image reconstruction ################################ + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffle_aux": + self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.conv_after_aux = nn.Sequential( + nn.Conv2d(3, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + elif self.upsampler == "pixelshuffle_hf": + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.upsample_hf = Upsample_hf(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.conv_first_hf = nn.Sequential( + nn.Conv2d(num_feat, embed_dim, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + self.conv_before_upsample_hf = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, + embed_dim, + num_out_ch, + (patches_resolution[0], patches_resolution[1]), + ) + elif self.upsampler == "nearest+conv": + # for real-world SR (less artifacts) + assert self.upscale == 4, "only support x4 now." + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + + self.load_state_dict(state_dict) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore # type: ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore # type: ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward_features_hf(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers_hf: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == "pixelshuffle": + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffle_aux": + bicubic = F.interpolate( + x, + size=(H * self.upscale, W * self.upscale), + mode="bicubic", + align_corners=False, + ) + bicubic = self.conv_bicubic(bicubic) + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + aux = self.conv_aux(x) # b, 3, LR_H, LR_W + x = self.conv_after_aux(aux) + x = ( + self.upsample(x)[:, :, : H * self.upscale, : W * self.upscale] + + bicubic[:, :, : H * self.upscale, : W * self.upscale] + ) + x = self.conv_last(x) + aux = aux / self.img_range + self.mean + elif self.upsampler == "pixelshuffle_hf": + # for classical SR with HF + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x_before = self.conv_before_upsample(x) + x_out = self.conv_last(self.upsample(x_before)) + + x_hf = self.conv_first_hf(x_before) + x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf + x_hf = self.conv_before_upsample_hf(x_hf) + x_hf = self.conv_last_hf(self.upsample_hf(x_hf)) + x = x_out + x_hf + x_hf = x_hf / self.img_range + self.mean + + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == "nearest+conv": + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu( + self.conv_up1( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") + ) + ) + x = self.lrelu( + self.conv_up2( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") + ) + ) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + if self.upsampler == "pixelshuffle_aux": + # NOTE: I removed an "aux" output here. not sure what that was for + return x[:, :, : H * self.upscale, : W * self.upscale] # type: ignore + + elif self.upsampler == "pixelshuffle_hf": + x_out = x_out / self.img_range + self.mean # type: ignore + return x_out[:, :, : H * self.upscale, : W * self.upscale], x[:, :, : H * self.upscale, : W * self.upscale], x_hf[:, :, : H * self.upscale, : W * self.upscale] # type: ignore + + else: + return x[:, :, : H * self.upscale, : W * self.upscale] + + def flops(self): + flops = 0 + H, W = self.patches_resolution + flops += H * W * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for i, layer in enumerate(self.layers): + flops += layer.flops() # type: ignore + flops += H * W * 3 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() # type: ignore + return flops diff --git a/comfy_extras/chainner_models/architecture/SwinIR.py b/comfy_extras/chainner_models/architecture/SwinIR.py new file mode 100644 index 0000000000000000000000000000000000000000..439dcbcb2b12f7ff27a01490f4c2ae7b6e4eab9e --- /dev/null +++ b/comfy_extras/chainner_models/architecture/SwinIR.py @@ -0,0 +1,1224 @@ +# pylint: skip-file +# ----------------------------------------------------------------------------------- +# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 +# Originally Written by Ze Liu, Modified by Jingyun Liang. +# ----------------------------------------------------------------------------------- + +import math +import re + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +# Originally from the timm package +from .timm.drop import DropPath +from .timm.helpers import to_2tuple +from .timm.weight_init import trunc_normal_ + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + ) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view( + B, H // window_size, W // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( # type: ignore + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B_, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) # type: ignore + ].view( + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( + 1 + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}" + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + +class SwinTransformerBlock(nn.Module): + r"""Swin Transformer Block. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + H, W = x_size + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + return attn_mask + + def forward(self, x, x_size): + H, W = x_size + B, L, C = x.shape + # assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) + ) + else: + shifted_x = x + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, self.window_size * self.window_size, C + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn( + x_windows, mask=self.attn_mask + ) # nW*B, window_size*window_size, C + else: + attn_windows = self.attn( + x_windows, mask=self.calculate_mask(x_size).to(x.device) + ) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll( + shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) + ) + else: + x = shifted_x + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + ) + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = H * W * self.dim + flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() # type: ignore + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + +class RSTB(nn.Module): + """Residual Swin Transformer Block (RSTB). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection="1conv", + ): + super(RSTB, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint, + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + def forward(self, x, x_size): + return ( + self.patch_embed( + self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size)) + ) + + x + ) + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + H, W = self.input_resolution + flops += H * W * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + + +class PatchEmbed(nn.Module): + r"""Image to Patch Embedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + x = x.flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + flops = 0 + H, W = self.img_size + if self.norm is not None: + flops += H * W * self.embed_dim # type: ignore + return flops + + +class PatchUnEmbed(nn.Module): + r"""Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + B, HW, C = x.shape + x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + H, W = self.input_resolution # type: ignore + flops = H * W * self.num_feat * 3 * 9 + return flops + + +class SwinIR(nn.Module): + r"""SwinIR + A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + state_dict, + **kwargs, + ): + super(SwinIR, self).__init__() + + # Defaults + img_size = 64 + patch_size = 1 + in_chans = 3 + embed_dim = 96 + depths = [6, 6, 6, 6] + num_heads = [6, 6, 6, 6] + window_size = 7 + mlp_ratio = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + norm_layer = nn.LayerNorm + ape = False + patch_norm = True + use_checkpoint = False + upscale = 2 + img_range = 1.0 + upsampler = "" + resi_connection = "1conv" + num_feat = 64 + num_in_ch = in_chans + num_out_ch = in_chans + supports_fp16 = True + self.start_unshuffle = 1 + + self.model_arch = "SwinIR" + self.sub_type = "SR" + self.state = state_dict + if "params_ema" in self.state: + self.state = self.state["params_ema"] + elif "params" in self.state: + self.state = self.state["params"] + + state_keys = self.state.keys() + + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + self.state.get("conv_before_upsample.0.weight", None).shape[1] + if self.state.get("conv_before_upsample.weight", None) + else 64 + ) + + if "conv_first.1.weight" in self.state: + self.state["conv_first.weight"] = self.state.pop("conv_first.1.weight") + self.state["conv_first.bias"] = self.state.pop("conv_first.1.bias") + self.start_unshuffle = round(math.sqrt(self.state["conv_first.weight"].shape[1] // 3)) + + num_in_ch = self.state["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = self.state["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = self.state[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match( + r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key + ) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if ( + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + in state_keys + ): + num_heads_num = self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + ].shape[-1] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depths + + embed_dim = self.state["conv_first.weight"].shape[0] + + mlp_ratio = float( + self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] + / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + window_size = int( + math.sqrt( + self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_index" + ].shape[0] + ) + ) + + if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: + img_size = int( + math.sqrt( + self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] + ) + * window_size + ) + + # The JPEG models are the only ones with window-size 7, and they also use this range + img_range = 255.0 if window_size == 7 else 1.0 + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depths = depths + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.scale = upscale / self.start_unshuffle + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.resi_connection = resi_connection + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + self.window_size = window_size + + ##################################################################################################### + ################################### 1, shallow feature extraction ################################### + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + ##################################################################################################### + ################################### 2, deep feature extraction ###################################### + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter( # type: ignore + torch.zeros(1, num_patches, embed_dim) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build Residual Swin Transformer blocks (RSTB) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[ + sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) # type: ignore + ], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers.append(layer) + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + ##################################################################################################### + ################################ 3, high quality image reconstruction ################################ + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, + embed_dim, + num_out_ch, + (patches_resolution[0], patches_resolution[1]), + ) + elif self.upsampler == "nearest+conv": + # for real-world SR (less artifacts) + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + if self.upscale == 4: + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + elif self.upscale == 8: + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + self.load_state_dict(self.state, strict=False) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore # type: ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore # type: ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.start_unshuffle > 1: + x = torch.nn.functional.pixel_unshuffle(x, self.start_unshuffle) + + if self.upsampler == "pixelshuffle": + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == "nearest+conv": + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu( + self.conv_up1( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") # type: ignore + ) + ) + if self.upscale == 4: + x = self.lrelu( + self.conv_up2( + torch.nn.functional.interpolate( # type: ignore + x, scale_factor=2, mode="nearest" + ) + ) + ) + elif self.upscale == 8: + x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.lrelu(self.conv_up3(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + + return x[:, :, : H * self.upscale, : W * self.upscale] + + def flops(self): + flops = 0 + H, W = self.patches_resolution + flops += H * W * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for i, layer in enumerate(self.layers): + flops += layer.flops() # type: ignore + flops += H * W * 3 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() # type: ignore + return flops diff --git a/comfy_extras/chainner_models/architecture/__init__.py b/comfy_extras/chainner_models/architecture/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/comfy_extras/chainner_models/architecture/block.py b/comfy_extras/chainner_models/architecture/block.py new file mode 100644 index 0000000000000000000000000000000000000000..d7bc5d227008a73c40f9087da1ee3ae2ca25a896 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/block.py @@ -0,0 +1,546 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from __future__ import annotations + +from collections import OrderedDict +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal + +import torch +import torch.nn as nn + +#################### +# Basic blocks +#################### + + +def act(act_type: str, inplace=True, neg_slope=0.2, n_prelu=1): + # helper selecting activation + # neg_slope: for leakyrelu and init of prelu + # n_prelu: for p_relu num_parameters + act_type = act_type.lower() + if act_type == "relu": + layer = nn.ReLU(inplace) + elif act_type == "leakyrelu": + layer = nn.LeakyReLU(neg_slope, inplace) + elif act_type == "prelu": + layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope) + else: + raise NotImplementedError( + "activation layer [{:s}] is not found".format(act_type) + ) + return layer + + +def norm(norm_type: str, nc: int): + # helper selecting normalization layer + norm_type = norm_type.lower() + if norm_type == "batch": + layer = nn.BatchNorm2d(nc, affine=True) + elif norm_type == "instance": + layer = nn.InstanceNorm2d(nc, affine=False) + else: + raise NotImplementedError( + "normalization layer [{:s}] is not found".format(norm_type) + ) + return layer + + +def pad(pad_type: str, padding): + # helper selecting padding layer + # if padding is 'zero', do by conv layers + pad_type = pad_type.lower() + if padding == 0: + return None + if pad_type == "reflect": + layer = nn.ReflectionPad2d(padding) + elif pad_type == "replicate": + layer = nn.ReplicationPad2d(padding) + else: + raise NotImplementedError( + "padding layer [{:s}] is not implemented".format(pad_type) + ) + return layer + + +def get_valid_padding(kernel_size, dilation): + kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) + padding = (kernel_size - 1) // 2 + return padding + + +class ConcatBlock(nn.Module): + # Concat the output of a submodule to its input + def __init__(self, submodule): + super(ConcatBlock, self).__init__() + self.sub = submodule + + def forward(self, x): + output = torch.cat((x, self.sub(x)), dim=1) + return output + + def __repr__(self): + tmpstr = "Identity .. \n|" + modstr = self.sub.__repr__().replace("\n", "\n|") + tmpstr = tmpstr + modstr + return tmpstr + + +class ShortcutBlock(nn.Module): + # Elementwise sum the output of a submodule to its input + def __init__(self, submodule): + super(ShortcutBlock, self).__init__() + self.sub = submodule + + def forward(self, x): + output = x + self.sub(x) + return output + + def __repr__(self): + tmpstr = "Identity + \n|" + modstr = self.sub.__repr__().replace("\n", "\n|") + tmpstr = tmpstr + modstr + return tmpstr + + +class ShortcutBlockSPSR(nn.Module): + # Elementwise sum the output of a submodule to its input + def __init__(self, submodule): + super(ShortcutBlockSPSR, self).__init__() + self.sub = submodule + + def forward(self, x): + return x, self.sub + + def __repr__(self): + tmpstr = "Identity + \n|" + modstr = self.sub.__repr__().replace("\n", "\n|") + tmpstr = tmpstr + modstr + return tmpstr + + +def sequential(*args): + # Flatten Sequential. It unwraps nn.Sequential. + if len(args) == 1: + if isinstance(args[0], OrderedDict): + raise NotImplementedError("sequential does not support OrderedDict input.") + return args[0] # No sequential is needed. + modules = [] + for module in args: + if isinstance(module, nn.Sequential): + for submodule in module.children(): + modules.append(submodule) + elif isinstance(module, nn.Module): + modules.append(module) + return nn.Sequential(*modules) + + +ConvMode = Literal["CNA", "NAC", "CNAC"] + + +# 2x2x2 Conv Block +def conv_block_2c2( + in_nc, + out_nc, + act_type="relu", +): + return sequential( + nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1), + nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0), + act(act_type) if act_type else None, + ) + + +def conv_block( + in_nc: int, + out_nc: int, + kernel_size, + stride=1, + dilation=1, + groups=1, + bias=True, + pad_type="zero", + norm_type: str | None = None, + act_type: str | None = "relu", + mode: ConvMode = "CNA", + c2x2=False, +): + """ + Conv layer with padding, normalization, activation + mode: CNA --> Conv -> Norm -> Act + NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16) + """ + + if c2x2: + return conv_block_2c2(in_nc, out_nc, act_type=act_type) + + assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode) + padding = get_valid_padding(kernel_size, dilation) + p = pad(pad_type, padding) if pad_type and pad_type != "zero" else None + padding = padding if pad_type == "zero" else 0 + + c = nn.Conv2d( + in_nc, + out_nc, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias, + groups=groups, + ) + a = act(act_type) if act_type else None + if mode in ("CNA", "CNAC"): + n = norm(norm_type, out_nc) if norm_type else None + return sequential(p, c, n, a) + elif mode == "NAC": + if norm_type is None and act_type is not None: + a = act(act_type, inplace=False) + # Important! + # input----ReLU(inplace)----Conv--+----output + # |________________________| + # inplace ReLU will modify the input, therefore wrong output + n = norm(norm_type, in_nc) if norm_type else None + return sequential(n, a, p, c) + else: + assert False, f"Invalid conv mode {mode}" + + +#################### +# Useful blocks +#################### + + +class ResNetBlock(nn.Module): + """ + ResNet Block, 3-3 style + with extra residual scaling used in EDSR + (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17) + """ + + def __init__( + self, + in_nc, + mid_nc, + out_nc, + kernel_size=3, + stride=1, + dilation=1, + groups=1, + bias=True, + pad_type="zero", + norm_type=None, + act_type="relu", + mode: ConvMode = "CNA", + res_scale=1, + ): + super(ResNetBlock, self).__init__() + conv0 = conv_block( + in_nc, + mid_nc, + kernel_size, + stride, + dilation, + groups, + bias, + pad_type, + norm_type, + act_type, + mode, + ) + if mode == "CNA": + act_type = None + if mode == "CNAC": # Residual path: |-CNAC-| + act_type = None + norm_type = None + conv1 = conv_block( + mid_nc, + out_nc, + kernel_size, + stride, + dilation, + groups, + bias, + pad_type, + norm_type, + act_type, + mode, + ) + # if in_nc != out_nc: + # self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \ + # None, None) + # print('Need a projecter in ResNetBlock.') + # else: + # self.project = lambda x:x + self.res = sequential(conv0, conv1) + self.res_scale = res_scale + + def forward(self, x): + res = self.res(x).mul(self.res_scale) + return x + res + + +class RRDB(nn.Module): + """ + Residual in Residual Dense Block + (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks) + """ + + def __init__( + self, + nf, + kernel_size=3, + gc=32, + stride=1, + bias: bool = True, + pad_type="zero", + norm_type=None, + act_type="leakyrelu", + mode: ConvMode = "CNA", + _convtype="Conv2D", + _spectral_norm=False, + plus=False, + c2x2=False, + ): + super(RRDB, self).__init__() + self.RDB1 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + self.RDB2 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + self.RDB3 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + + def forward(self, x): + out = self.RDB1(x) + out = self.RDB2(out) + out = self.RDB3(out) + return out * 0.2 + x + + +class ResidualDenseBlock_5C(nn.Module): + """ + Residual Dense Block + style: 5 convs + The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18) + Modified options that can be used: + - "Partial Convolution based Padding" arXiv:1811.11718 + - "Spectral normalization" arXiv:1802.05957 + - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. + {Rakotonirina} and A. {Rasoanaivo} + + Args: + nf (int): Channel number of intermediate features (num_feat). + gc (int): Channels for each growth (num_grow_ch: growth channel, + i.e. intermediate channels). + convtype (str): the type of convolution to use. Default: 'Conv2D' + gaussian_noise (bool): enable the ESRGAN+ gaussian noise (no new + trainable parameters) + plus (bool): enable the additional residual paths from ESRGAN+ + (adds trainable parameters) + """ + + def __init__( + self, + nf=64, + kernel_size=3, + gc=32, + stride=1, + bias: bool = True, + pad_type="zero", + norm_type=None, + act_type="leakyrelu", + mode: ConvMode = "CNA", + plus=False, + c2x2=False, + ): + super(ResidualDenseBlock_5C, self).__init__() + + ## + + self.conv1x1 = conv1x1(nf, gc) if plus else None + ## + + + self.conv1 = conv_block( + nf, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv2 = conv_block( + nf + gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv3 = conv_block( + nf + 2 * gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv4 = conv_block( + nf + 3 * gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + if mode == "CNA": + last_act = None + else: + last_act = act_type + self.conv5 = conv_block( + nf + 4 * gc, + nf, + 3, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=last_act, + mode=mode, + c2x2=c2x2, + ) + + def forward(self, x): + x1 = self.conv1(x) + x2 = self.conv2(torch.cat((x, x1), 1)) + if self.conv1x1: + # pylint: disable=not-callable + x2 = x2 + self.conv1x1(x) # + + x3 = self.conv3(torch.cat((x, x1, x2), 1)) + x4 = self.conv4(torch.cat((x, x1, x2, x3), 1)) + if self.conv1x1: + x4 = x4 + x2 # + + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + return x5 * 0.2 + x + + +def conv1x1(in_planes, out_planes, stride=1): + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +#################### +# Upsampler +#################### + + +def pixelshuffle_block( + in_nc: int, + out_nc: int, + upscale_factor=2, + kernel_size=3, + stride=1, + bias=True, + pad_type="zero", + norm_type: str | None = None, + act_type="relu", +): + """ + Pixel shuffle layer + (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional + Neural Network, CVPR17) + """ + conv = conv_block( + in_nc, + out_nc * (upscale_factor**2), + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=None, + act_type=None, + ) + pixel_shuffle = nn.PixelShuffle(upscale_factor) + + n = norm(norm_type, out_nc) if norm_type else None + a = act(act_type) if act_type else None + return sequential(conv, pixel_shuffle, n, a) + + +def upconv_block( + in_nc: int, + out_nc: int, + upscale_factor=2, + kernel_size=3, + stride=1, + bias=True, + pad_type="zero", + norm_type: str | None = None, + act_type="relu", + mode="nearest", + c2x2=False, +): + # Up conv + # described in https://distill.pub/2016/deconv-checkerboard/ + upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode) + conv = conv_block( + in_nc, + out_nc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + c2x2=c2x2, + ) + return sequential(upsample, conv) diff --git a/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN b/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN new file mode 100644 index 0000000000000000000000000000000000000000..5ac273fd509e328f396e6e4444673a3b051a4968 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN @@ -0,0 +1,351 @@ +Tencent is pleased to support the open source community by making GFPGAN available. + +Copyright (C) 2021 THL A29 Limited, a Tencent company. 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We list these here. + +Name: Numpydoc +Files: doc/sphinxext/numpydoc/* +License: BSD-2-Clause + For details, see doc/sphinxext/LICENSE.txt + +Name: scipy-sphinx-theme +Files: doc/scipy-sphinx-theme/* +License: BSD-3-Clause AND PSF-2.0 AND Apache-2.0 + For details, see doc/scipy-sphinx-theme/LICENSE.txt + +Name: lapack-lite +Files: numpy/linalg/lapack_lite/* +License: BSD-3-Clause + For details, see numpy/linalg/lapack_lite/LICENSE.txt + +Name: tempita +Files: tools/npy_tempita/* +License: MIT + For details, see tools/npy_tempita/license.txt + +Name: dragon4 +Files: numpy/core/src/multiarray/dragon4.c +License: MIT + For license text, see numpy/core/src/multiarray/dragon4.c + + + +Open Source Software licensed under the MIT license: +--------------------------------------------- +1. facexlib +Copyright (c) 2020 Xintao Wang + +2. opencv-python +Copyright (c) Olli-Pekka Heinisuo +Please note that only files in cv2 package are used. + + +Terms of the MIT License: +--------------------------------------------- +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + + +Open Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein: +--------------------------------------------- +1. tqdm +Copyright (c) 2013 noamraph + +`tqdm` is a product of collaborative work. +Unless otherwise stated, all authors (see commit logs) retain copyright +for their respective work, and release the work under the MIT licence +(text below). + +Exceptions or notable authors are listed below +in reverse chronological order: + +* files: * + MPLv2.0 2015-2020 (c) Casper da Costa-Luis + [casperdcl](https://github.com/casperdcl). +* files: tqdm/_tqdm.py + MIT 2016 (c) [PR #96] on behalf of Google Inc. +* files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore + MIT 2013 (c) Noam Yorav-Raphael, original author. + +[PR #96]: https://github.com/tqdm/tqdm/pull/96 + + +Mozilla Public Licence (MPL) v. 2.0 - Exhibit A +----------------------------------------------- + +This Source Code Form is subject to the terms of the +Mozilla Public License, v. 2.0. +If a copy of the MPL was not distributed with this file, +You can obtain one at https://mozilla.org/MPL/2.0/. + + +MIT License (MIT) +----------------- + +Copyright (c) 2013 noamraph + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file diff --git a/comfy_extras/chainner_models/architecture/face/LICENSE-codeformer b/comfy_extras/chainner_models/architecture/face/LICENSE-codeformer new file mode 100644 index 0000000000000000000000000000000000000000..be6c4ed8048a7cb436376bbea84cb0bd726ab721 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/LICENSE-codeformer @@ -0,0 +1,35 @@ +S-Lab License 1.0 + +Copyright 2022 S-Lab + +Redistribution and use for non-commercial purpose in source and +binary forms, with or without modification, are permitted provided +that the following conditions are met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +In the event that redistribution and/or use for commercial purpose in +source or binary forms, with or without modification is required, +please contact the contributor(s) of the work. diff --git a/comfy_extras/chainner_models/architecture/face/arcface_arch.py b/comfy_extras/chainner_models/architecture/face/arcface_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..b548af059a71b38c6c18cd35cbfed7bae7e55441 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/arcface_arch.py @@ -0,0 +1,265 @@ +import torch.nn as nn + + +def conv3x3(inplanes, outplanes, stride=1): + """A simple wrapper for 3x3 convolution with padding. + + Args: + inplanes (int): Channel number of inputs. + outplanes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + """ + return nn.Conv2d( + inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False + ) + + +class BasicBlock(nn.Module): + """Basic residual block used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + """ + + expansion = 1 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class IRBlock(nn.Module): + """Improved residual block (IR Block) used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. + """ + + expansion = 1 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): + super(IRBlock, self).__init__() + self.bn0 = nn.BatchNorm2d(inplanes) + self.conv1 = conv3x3(inplanes, inplanes) + self.bn1 = nn.BatchNorm2d(inplanes) + self.prelu = nn.PReLU() + self.conv2 = conv3x3(inplanes, planes, stride) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + self.use_se = use_se + if self.use_se: + self.se = SEBlock(planes) + + def forward(self, x): + residual = x + out = self.bn0(x) + out = self.conv1(out) + out = self.bn1(out) + out = self.prelu(out) + + out = self.conv2(out) + out = self.bn2(out) + if self.use_se: + out = self.se(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.prelu(out) + + return out + + +class Bottleneck(nn.Module): + """Bottleneck block used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + """ + + expansion = 4 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d( + planes, planes, kernel_size=3, stride=stride, padding=1, bias=False + ) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d( + planes, planes * self.expansion, kernel_size=1, bias=False + ) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class SEBlock(nn.Module): + """The squeeze-and-excitation block (SEBlock) used in the IRBlock. + + Args: + channel (int): Channel number of inputs. + reduction (int): Channel reduction ration. Default: 16. + """ + + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d( + 1 + ) # pool to 1x1 without spatial information + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction), + nn.PReLU(), + nn.Linear(channel // reduction, channel), + nn.Sigmoid(), + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +class ResNetArcFace(nn.Module): + """ArcFace with ResNet architectures. + + Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition. + + Args: + block (str): Block used in the ArcFace architecture. + layers (tuple(int)): Block numbers in each layer. + use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. + """ + + def __init__(self, block, layers, use_se=True): + if block == "IRBlock": + block = IRBlock + self.inplanes = 64 + self.use_se = use_se + super(ResNetArcFace, self).__init__() + + self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.prelu = nn.PReLU() + self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.bn4 = nn.BatchNorm2d(512) + self.dropout = nn.Dropout() + self.fc5 = nn.Linear(512 * 8 * 8, 512) + self.bn5 = nn.BatchNorm1d(512) + + # initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.xavier_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, planes, num_blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(planes * block.expansion), + ) + layers = [] + layers.append( + block(self.inplanes, planes, stride, downsample, use_se=self.use_se) + ) + self.inplanes = planes + for _ in range(1, num_blocks): + layers.append(block(self.inplanes, planes, use_se=self.use_se)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.prelu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.bn4(x) + x = self.dropout(x) + x = x.view(x.size(0), -1) + x = self.fc5(x) + x = self.bn5(x) + + return x diff --git a/comfy_extras/chainner_models/architecture/face/codeformer.py b/comfy_extras/chainner_models/architecture/face/codeformer.py new file mode 100644 index 0000000000000000000000000000000000000000..066140078643d2274259283163cd392bb692b409 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/codeformer.py @@ -0,0 +1,790 @@ +""" +Modified from https://github.com/sczhou/CodeFormer +VQGAN code, adapted from the original created by the Unleashing Transformers authors: +https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py +This verison of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me. +""" +import math +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +import logging as logger +from torch import Tensor + + +class VectorQuantizer(nn.Module): + def __init__(self, codebook_size, emb_dim, beta): + super(VectorQuantizer, self).__init__() + self.codebook_size = codebook_size # number of embeddings + self.emb_dim = emb_dim # dimension of embedding + self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) + self.embedding.weight.data.uniform_( + -1.0 / self.codebook_size, 1.0 / self.codebook_size + ) + + def forward(self, z): + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.emb_dim) + + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + d = ( + (z_flattened**2).sum(dim=1, keepdim=True) + + (self.embedding.weight**2).sum(1) + - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) + ) + + mean_distance = torch.mean(d) + # find closest encodings + # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) + min_encoding_scores, min_encoding_indices = torch.topk( + d, 1, dim=1, largest=False + ) + # [0-1], higher score, higher confidence + min_encoding_scores = torch.exp(-min_encoding_scores / 10) + + min_encodings = torch.zeros( + min_encoding_indices.shape[0], self.codebook_size + ).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + # compute loss for embedding + loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean( + (z_q - z.detach()) ** 2 + ) + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return ( + z_q, + loss, + { + "perplexity": perplexity, + "min_encodings": min_encodings, + "min_encoding_indices": min_encoding_indices, + "min_encoding_scores": min_encoding_scores, + "mean_distance": mean_distance, + }, + ) + + def get_codebook_feat(self, indices, shape): + # input indices: batch*token_num -> (batch*token_num)*1 + # shape: batch, height, width, channel + indices = indices.view(-1, 1) + min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) + min_encodings.scatter_(1, indices, 1) + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: # reshape back to match original input shape + z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() + + return z_q + + +class GumbelQuantizer(nn.Module): + def __init__( + self, + codebook_size, + emb_dim, + num_hiddens, + straight_through=False, + kl_weight=5e-4, + temp_init=1.0, + ): + super().__init__() + self.codebook_size = codebook_size # number of embeddings + self.emb_dim = emb_dim # dimension of embedding + self.straight_through = straight_through + self.temperature = temp_init + self.kl_weight = kl_weight + self.proj = nn.Conv2d( + num_hiddens, codebook_size, 1 + ) # projects last encoder layer to quantized logits + self.embed = nn.Embedding(codebook_size, emb_dim) + + def forward(self, z): + hard = self.straight_through if self.training else True + + logits = self.proj(z) + + soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) + + z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) + + # + kl divergence to the prior loss + qy = F.softmax(logits, dim=1) + diff = ( + self.kl_weight + * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() + ) + min_encoding_indices = soft_one_hot.argmax(dim=1) + + return z_q, diff, {"min_encoding_indices": min_encoding_indices} + + +class Downsample(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def forward(self, x): + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x + + +class Upsample(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.conv = nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + x = F.interpolate(x, scale_factor=2.0, mode="nearest") + x = self.conv(x) + + return x + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, c, h * w) + q = q.permute(0, 2, 1) + k = k.reshape(b, c, h * w) + w_ = torch.bmm(q, k) + w_ = w_ * (int(c) ** (-0.5)) + w_ = F.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b, c, h * w) + w_ = w_.permute(0, 2, 1) + h_ = torch.bmm(v, w_) + h_ = h_.reshape(b, c, h, w) + + h_ = self.proj_out(h_) + + return x + h_ + + +class Encoder(nn.Module): + def __init__( + self, + in_channels, + nf, + out_channels, + ch_mult, + num_res_blocks, + resolution, + attn_resolutions, + ): + super().__init__() + self.nf = nf + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.attn_resolutions = attn_resolutions + + curr_res = self.resolution + in_ch_mult = (1,) + tuple(ch_mult) + + blocks = [] + # initial convultion + blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) + + # residual and downsampling blocks, with attention on smaller res (16x16) + for i in range(self.num_resolutions): + block_in_ch = nf * in_ch_mult[i] + block_out_ch = nf * ch_mult[i] + for _ in range(self.num_res_blocks): + blocks.append(ResBlock(block_in_ch, block_out_ch)) + block_in_ch = block_out_ch + if curr_res in attn_resolutions: + blocks.append(AttnBlock(block_in_ch)) + + if i != self.num_resolutions - 1: + blocks.append(Downsample(block_in_ch)) + curr_res = curr_res // 2 + + # non-local attention block + blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore + blocks.append(AttnBlock(block_in_ch)) # type: ignore + blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore + + # normalise and convert to latent size + blocks.append(normalize(block_in_ch)) # type: ignore + blocks.append( + nn.Conv2d(block_in_ch, out_channels, kernel_size=3, stride=1, padding=1) # type: ignore + ) + self.blocks = nn.ModuleList(blocks) + + def forward(self, x): + for block in self.blocks: + x = block(x) + + return x + + +class Generator(nn.Module): + def __init__(self, nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim): + super().__init__() + self.nf = nf + self.ch_mult = ch_mult + self.num_resolutions = len(self.ch_mult) + self.num_res_blocks = res_blocks + self.resolution = img_size + self.attn_resolutions = attn_resolutions + self.in_channels = emb_dim + self.out_channels = 3 + block_in_ch = self.nf * self.ch_mult[-1] + curr_res = self.resolution // 2 ** (self.num_resolutions - 1) + + blocks = [] + # initial conv + blocks.append( + nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1) + ) + + # non-local attention block + blocks.append(ResBlock(block_in_ch, block_in_ch)) + blocks.append(AttnBlock(block_in_ch)) + blocks.append(ResBlock(block_in_ch, block_in_ch)) + + for i in reversed(range(self.num_resolutions)): + block_out_ch = self.nf * self.ch_mult[i] + + for _ in range(self.num_res_blocks): + blocks.append(ResBlock(block_in_ch, block_out_ch)) + block_in_ch = block_out_ch + + if curr_res in self.attn_resolutions: + blocks.append(AttnBlock(block_in_ch)) + + if i != 0: + blocks.append(Upsample(block_in_ch)) + curr_res = curr_res * 2 + + blocks.append(normalize(block_in_ch)) + blocks.append( + nn.Conv2d( + block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1 + ) + ) + + self.blocks = nn.ModuleList(blocks) + + def forward(self, x): + for block in self.blocks: + x = block(x) + + return x + + +class VQAutoEncoder(nn.Module): + def __init__( + self, + img_size, + nf, + ch_mult, + quantizer="nearest", + res_blocks=2, + attn_resolutions=[16], + codebook_size=1024, + emb_dim=256, + beta=0.25, + gumbel_straight_through=False, + gumbel_kl_weight=1e-8, + model_path=None, + ): + super().__init__() + self.in_channels = 3 + self.nf = nf + self.n_blocks = res_blocks + self.codebook_size = codebook_size + self.embed_dim = emb_dim + self.ch_mult = ch_mult + self.resolution = img_size + self.attn_resolutions = attn_resolutions + self.quantizer_type = quantizer + self.encoder = Encoder( + self.in_channels, + self.nf, + self.embed_dim, + self.ch_mult, + self.n_blocks, + self.resolution, + self.attn_resolutions, + ) + if self.quantizer_type == "nearest": + self.beta = beta # 0.25 + self.quantize = VectorQuantizer( + self.codebook_size, self.embed_dim, self.beta + ) + elif self.quantizer_type == "gumbel": + self.gumbel_num_hiddens = emb_dim + self.straight_through = gumbel_straight_through + self.kl_weight = gumbel_kl_weight + self.quantize = GumbelQuantizer( + self.codebook_size, + self.embed_dim, + self.gumbel_num_hiddens, + self.straight_through, + self.kl_weight, + ) + self.generator = Generator( + nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim + ) + + if model_path is not None: + chkpt = torch.load(model_path, map_location="cpu") + if "params_ema" in chkpt: + self.load_state_dict( + torch.load(model_path, map_location="cpu")["params_ema"] + ) + logger.info(f"vqgan is loaded from: {model_path} [params_ema]") + elif "params" in chkpt: + self.load_state_dict( + torch.load(model_path, map_location="cpu")["params"] + ) + logger.info(f"vqgan is loaded from: {model_path} [params]") + else: + raise ValueError("Wrong params!") + + def forward(self, x): + x = self.encoder(x) + quant, codebook_loss, quant_stats = self.quantize(x) + x = self.generator(quant) + return x, codebook_loss, quant_stats + + +def calc_mean_std(feat, eps=1e-5): + """Calculate mean and std for adaptive_instance_normalization. + Args: + feat (Tensor): 4D tensor. + eps (float): A small value added to the variance to avoid + divide-by-zero. Default: 1e-5. + """ + size = feat.size() + assert len(size) == 4, "The input feature should be 4D tensor." + b, c = size[:2] + feat_var = feat.view(b, c, -1).var(dim=2) + eps + feat_std = feat_var.sqrt().view(b, c, 1, 1) + feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) + return feat_mean, feat_std + + +def adaptive_instance_normalization(content_feat, style_feat): + """Adaptive instance normalization. + Adjust the reference features to have the similar color and illuminations + as those in the degradate features. + Args: + content_feat (Tensor): The reference feature. + style_feat (Tensor): The degradate features. + """ + size = content_feat.size() + style_mean, style_std = calc_mean_std(style_feat) + content_mean, content_std = calc_mean_std(content_feat) + normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand( + size + ) + return normalized_feat * style_std.expand(size) + style_mean.expand(size) + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__( + self, num_pos_feats=64, temperature=10000, normalize=False, scale=None + ): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + if mask is None: + mask = torch.zeros( + (x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool + ) + not_mask = ~mask # pylint: disable=invalid-unary-operand-type + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") + + +class TransformerSALayer(nn.Module): + def __init__( + self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu" + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) + # Implementation of Feedforward model - MLP + self.linear1 = nn.Linear(embed_dim, dim_mlp) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_mlp, embed_dim) + + self.norm1 = nn.LayerNorm(embed_dim) + self.norm2 = nn.LayerNorm(embed_dim) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward( + self, + tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + # self attention + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn( + q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask + )[0] + tgt = tgt + self.dropout1(tgt2) + + # ffn + tgt2 = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout2(tgt2) + return tgt + + +def normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +@torch.jit.script # type: ignore +def swish(x): + return x * torch.sigmoid(x) + + +class ResBlock(nn.Module): + def __init__(self, in_channels, out_channels=None): + super(ResBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = in_channels if out_channels is None else out_channels + self.norm1 = normalize(in_channels) + self.conv1 = nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore + ) + self.norm2 = normalize(out_channels) + self.conv2 = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore + ) + if self.in_channels != self.out_channels: + self.conv_out = nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 # type: ignore + ) + + def forward(self, x_in): + x = x_in + x = self.norm1(x) + x = swish(x) + x = self.conv1(x) + x = self.norm2(x) + x = swish(x) + x = self.conv2(x) + if self.in_channels != self.out_channels: + x_in = self.conv_out(x_in) + + return x + x_in + + +class Fuse_sft_block(nn.Module): + def __init__(self, in_ch, out_ch): + super().__init__() + self.encode_enc = ResBlock(2 * in_ch, out_ch) + + self.scale = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), + ) + + self.shift = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), + ) + + def forward(self, enc_feat, dec_feat, w=1): + enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) + scale = self.scale(enc_feat) + shift = self.shift(enc_feat) + residual = w * (dec_feat * scale + shift) + out = dec_feat + residual + return out + + +class CodeFormer(VQAutoEncoder): + def __init__(self, state_dict): + dim_embd = 512 + n_head = 8 + n_layers = 9 + codebook_size = 1024 + latent_size = 256 + connect_list = ["32", "64", "128", "256"] + fix_modules = ["quantize", "generator"] + + # This is just a guess as I only have one model to look at + position_emb = state_dict["position_emb"] + dim_embd = position_emb.shape[1] + latent_size = position_emb.shape[0] + + try: + n_layers = len( + set([x.split(".")[1] for x in state_dict.keys() if "ft_layers" in x]) + ) + except: + pass + + codebook_size = state_dict["quantize.embedding.weight"].shape[0] + + # This is also just another guess + n_head_exp = ( + state_dict["ft_layers.0.self_attn.in_proj_weight"].shape[0] // dim_embd + ) + n_head = 2**n_head_exp + + in_nc = state_dict["encoder.blocks.0.weight"].shape[1] + + self.model_arch = "CodeFormer" + self.sub_type = "Face SR" + self.scale = 8 + self.in_nc = in_nc + self.out_nc = in_nc + + self.state = state_dict + + self.supports_fp16 = False + self.supports_bf16 = True + self.min_size_restriction = 16 + + super(CodeFormer, self).__init__( + 512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size + ) + + if fix_modules is not None: + for module in fix_modules: + for param in getattr(self, module).parameters(): + param.requires_grad = False + + self.connect_list = connect_list + self.n_layers = n_layers + self.dim_embd = dim_embd + self.dim_mlp = dim_embd * 2 + + self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) # type: ignore + self.feat_emb = nn.Linear(256, self.dim_embd) + + # transformer + self.ft_layers = nn.Sequential( + *[ + TransformerSALayer( + embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0 + ) + for _ in range(self.n_layers) + ] + ) + + # logits_predict head + self.idx_pred_layer = nn.Sequential( + nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False) + ) + + self.channels = { + "16": 512, + "32": 256, + "64": 256, + "128": 128, + "256": 128, + "512": 64, + } + + # after second residual block for > 16, before attn layer for ==16 + self.fuse_encoder_block = { + "512": 2, + "256": 5, + "128": 8, + "64": 11, + "32": 14, + "16": 18, + } + # after first residual block for > 16, before attn layer for ==16 + self.fuse_generator_block = { + "16": 6, + "32": 9, + "64": 12, + "128": 15, + "256": 18, + "512": 21, + } + + # fuse_convs_dict + self.fuse_convs_dict = nn.ModuleDict() + for f_size in self.connect_list: + in_ch = self.channels[f_size] + self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) + + self.load_state_dict(state_dict) + + def _init_weights(self, module): + if isinstance(module, (nn.Linear, nn.Embedding)): + module.weight.data.normal_(mean=0.0, std=0.02) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def forward(self, x, weight=0.5, **kwargs): + detach_16 = True + code_only = False + adain = True + # ################### Encoder ##################### + enc_feat_dict = {} + out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] + for i, block in enumerate(self.encoder.blocks): + x = block(x) + if i in out_list: + enc_feat_dict[str(x.shape[-1])] = x.clone() + + lq_feat = x + # ################# Transformer ################### + # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) + pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1) + # BCHW -> BC(HW) -> (HW)BC + feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1)) + query_emb = feat_emb + # Transformer encoder + for layer in self.ft_layers: + query_emb = layer(query_emb, query_pos=pos_emb) + + # output logits + logits = self.idx_pred_layer(query_emb) # (hw)bn + logits = logits.permute(1, 0, 2) # (hw)bn -> b(hw)n + + if code_only: # for training stage II + # logits doesn't need softmax before cross_entropy loss + return logits, lq_feat + + # ################# Quantization ################### + # if self.training: + # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) + # # b(hw)c -> bc(hw) -> bchw + # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) + # ------------ + soft_one_hot = F.softmax(logits, dim=2) + _, top_idx = torch.topk(soft_one_hot, 1, dim=2) + quant_feat = self.quantize.get_codebook_feat( + top_idx, shape=[x.shape[0], 16, 16, 256] # type: ignore + ) + # preserve gradients + # quant_feat = lq_feat + (quant_feat - lq_feat).detach() + + if detach_16: + quant_feat = quant_feat.detach() # for training stage III + if adain: + quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) + + # ################## Generator #################### + x = quant_feat + fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] + + for i, block in enumerate(self.generator.blocks): + x = block(x) + if i in fuse_list: # fuse after i-th block + f_size = str(x.shape[-1]) + if weight > 0: + x = self.fuse_convs_dict[f_size]( + enc_feat_dict[f_size].detach(), x, weight + ) + out = x + # logits doesn't need softmax before cross_entropy loss + # return out, logits, lq_feat + return out, logits diff --git a/comfy_extras/chainner_models/architecture/face/fused_act.py b/comfy_extras/chainner_models/architecture/face/fused_act.py new file mode 100644 index 0000000000000000000000000000000000000000..7ed526547b4644ac6341947a801b76d9ed798f26 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/fused_act.py @@ -0,0 +1,81 @@ +# pylint: skip-file +# type: ignore +# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501 + +import torch +from torch import nn +from torch.autograd import Function + +fused_act_ext = None + + +class FusedLeakyReLUFunctionBackward(Function): + @staticmethod + def forward(ctx, grad_output, out, negative_slope, scale): + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + empty = grad_output.new_empty(0) + + grad_input = fused_act_ext.fused_bias_act( + grad_output, empty, out, 3, 1, negative_slope, scale + ) + + dim = [0] + + if grad_input.ndim > 2: + dim += list(range(2, grad_input.ndim)) + + grad_bias = grad_input.sum(dim).detach() + + return grad_input, grad_bias + + @staticmethod + def backward(ctx, gradgrad_input, gradgrad_bias): + (out,) = ctx.saved_tensors + gradgrad_out = fused_act_ext.fused_bias_act( + gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale + ) + + return gradgrad_out, None, None, None + + +class FusedLeakyReLUFunction(Function): + @staticmethod + def forward(ctx, input, bias, negative_slope, scale): + empty = input.new_empty(0) + out = fused_act_ext.fused_bias_act( + input, bias, empty, 3, 0, negative_slope, scale + ) + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + return out + + @staticmethod + def backward(ctx, grad_output): + (out,) = ctx.saved_tensors + + grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( + grad_output, out, ctx.negative_slope, ctx.scale + ) + + return grad_input, grad_bias, None, None + + +class FusedLeakyReLU(nn.Module): + def __init__(self, channel, negative_slope=0.2, scale=2**0.5): + super().__init__() + + self.bias = nn.Parameter(torch.zeros(channel)) + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, input): + return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) + + +def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5): + return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py b/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e820e006f52936c3399d3d37fdf571f2385dcb --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py @@ -0,0 +1,389 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn + +from .gfpganv1_arch import ResUpBlock +from .stylegan2_bilinear_arch import ( + ConvLayer, + EqualConv2d, + EqualLinear, + ResBlock, + ScaledLeakyReLU, + StyleGAN2GeneratorBilinear, +) + + +class StyleGAN2GeneratorBilinearSFT(StyleGAN2GeneratorBilinear): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + It is the bilinear version. It does not use the complicated UpFirDnSmooth function that is not friendly for + deployment. It can be easily converted to the clean version: StyleGAN2GeneratorCSFT. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + lr_mlp=0.01, + narrow=1, + sft_half=False, + ): + super(StyleGAN2GeneratorBilinearSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + lr_mlp=lr_mlp, + narrow=narrow, + ) + self.sft_half = sft_half + + def forward( + self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorBilinearSFT. + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class GFPGANBilinear(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + It is the bilinear version and it does not use the complicated UpFirDnSmooth function that is not friendly for + deployment. It can be easily converted to the clean version: GFPGANv1Clean. + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + num_mlp (int): Layer number of MLP style layers. Default: 8. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False, + ): + super(GFPGANBilinear, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + self.min_size_restriction = 512 + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + "4": int(512 * unet_narrow), + "8": int(512 * unet_narrow), + "16": int(512 * unet_narrow), + "32": int(512 * unet_narrow), + "64": int(256 * channel_multiplier * unet_narrow), + "128": int(128 * channel_multiplier * unet_narrow), + "256": int(64 * channel_multiplier * unet_narrow), + "512": int(32 * channel_multiplier * unet_narrow), + "1024": int(16 * channel_multiplier * unet_narrow), + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2 ** (int(math.log(out_size, 2))) + + self.conv_body_first = ConvLayer( + 3, channels[f"{first_out_size}"], 1, bias=True, activate=True + ) + + # downsample + in_channels = channels[f"{first_out_size}"] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f"{2**(i - 1)}"] + self.conv_body_down.append(ResBlock(in_channels, out_channels)) + in_channels = out_channels + + self.final_conv = ConvLayer( + in_channels, channels["4"], 3, bias=True, activate=True + ) + + # upsample + in_channels = channels["4"] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append( + EqualConv2d( + channels[f"{2**i}"], + 3, + 1, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ) + ) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = EqualLinear( + channels["4"] * 4 * 4, + linear_out_channel, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorBilinearSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + lr_mlp=lr_mlp, + narrow=narrow, + sft_half=sft_half, + ) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load( + decoder_load_path, map_location=lambda storage, loc: storage + )["params_ema"] + ) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=1, + ), + ) + ) + self.condition_shift.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ) + ) + + def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): + """Forward function for GFPGANBilinear. + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = self.conv_body_first(x) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + + feat = self.final_conv(feat) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder( + [style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise, + ) + + return image, out_rgbs diff --git a/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py b/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..72d72fc865ec35b2ccd23f13b3d8ef0be5dbaf7a --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py @@ -0,0 +1,566 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .fused_act import FusedLeakyReLU +from .stylegan2_arch import ( + ConvLayer, + EqualConv2d, + EqualLinear, + ResBlock, + ScaledLeakyReLU, + StyleGAN2Generator, +) + + +class StyleGAN2GeneratorSFT(StyleGAN2Generator): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be + applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1, + sft_half=False, + ): + super(StyleGAN2GeneratorSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + resample_kernel=resample_kernel, + lr_mlp=lr_mlp, + narrow=narrow, + ) + self.sft_half = sft_half + + def forward( + self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorSFT. + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ConvUpLayer(nn.Module): + """Convolutional upsampling layer. It uses bilinear upsampler + Conv. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + activate=True, + ): + super(ConvUpLayer, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + # self.scale is used to scale the convolution weights, which is related to the common initializations. + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter( + torch.randn(out_channels, in_channels, kernel_size, kernel_size) + ) + + if bias and not activate: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + # activation + if activate: + if bias: + self.activation = FusedLeakyReLU(out_channels) + else: + self.activation = ScaledLeakyReLU(0.2) + else: + self.activation = None + + def forward(self, x): + # bilinear upsample + out = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False) + # conv + out = F.conv2d( + out, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + # activation + if self.activation is not None: + out = self.activation(out) + return out + + +class ResUpBlock(nn.Module): + """Residual block with upsampling. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + """ + + def __init__(self, in_channels, out_channels): + super(ResUpBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvUpLayer( + in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True + ) + self.skip = ConvUpLayer( + in_channels, out_channels, 1, bias=False, activate=False + ) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out + + +class GFPGANv1(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be + applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + num_mlp (int): Layer number of MLP style layers. Default: 8. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False, + ): + super(GFPGANv1, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + "4": int(512 * unet_narrow), + "8": int(512 * unet_narrow), + "16": int(512 * unet_narrow), + "32": int(512 * unet_narrow), + "64": int(256 * channel_multiplier * unet_narrow), + "128": int(128 * channel_multiplier * unet_narrow), + "256": int(64 * channel_multiplier * unet_narrow), + "512": int(32 * channel_multiplier * unet_narrow), + "1024": int(16 * channel_multiplier * unet_narrow), + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2 ** (int(math.log(out_size, 2))) + + self.conv_body_first = ConvLayer( + 3, channels[f"{first_out_size}"], 1, bias=True, activate=True + ) + + # downsample + in_channels = channels[f"{first_out_size}"] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f"{2**(i - 1)}"] + self.conv_body_down.append( + ResBlock(in_channels, out_channels, resample_kernel) + ) + in_channels = out_channels + + self.final_conv = ConvLayer( + in_channels, channels["4"], 3, bias=True, activate=True + ) + + # upsample + in_channels = channels["4"] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append( + EqualConv2d( + channels[f"{2**i}"], + 3, + 1, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ) + ) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = EqualLinear( + channels["4"] * 4 * 4, + linear_out_channel, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + resample_kernel=resample_kernel, + lr_mlp=lr_mlp, + narrow=narrow, + sft_half=sft_half, + ) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load( + decoder_load_path, map_location=lambda storage, loc: storage + )["params_ema"] + ) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=1, + ), + ) + ) + self.condition_shift.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ) + ) + + def forward( + self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs + ): + """Forward function for GFPGANv1. + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = self.conv_body_first(x) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + + feat = self.final_conv(feat) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder( + [style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise, + ) + + return image, out_rgbs + + +class FacialComponentDiscriminator(nn.Module): + """Facial component (eyes, mouth, noise) discriminator used in GFPGAN.""" + + def __init__(self): + super(FacialComponentDiscriminator, self).__init__() + # It now uses a VGG-style architectrue with fixed model size + self.conv1 = ConvLayer( + 3, + 64, + 3, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv2 = ConvLayer( + 64, + 128, + 3, + downsample=True, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv3 = ConvLayer( + 128, + 128, + 3, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv4 = ConvLayer( + 128, + 256, + 3, + downsample=True, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv5 = ConvLayer( + 256, + 256, + 3, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False) + + def forward(self, x, return_feats=False, **kwargs): + """Forward function for FacialComponentDiscriminator. + Args: + x (Tensor): Input images. + return_feats (bool): Whether to return intermediate features. Default: False. + """ + feat = self.conv1(x) + feat = self.conv3(self.conv2(feat)) + rlt_feats = [] + if return_feats: + rlt_feats.append(feat.clone()) + feat = self.conv5(self.conv4(feat)) + if return_feats: + rlt_feats.append(feat.clone()) + out = self.final_conv(feat) + + if return_feats: + return out, rlt_feats + else: + return out, None diff --git a/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py b/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..16470d6345f71ed1517ff26f65b9cd125d80d99e --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py @@ -0,0 +1,370 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .stylegan2_clean_arch import StyleGAN2GeneratorClean + + +class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + It is the clean version without custom compiled CUDA extensions used in StyleGAN2. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + narrow=1, + sft_half=False, + ): + super(StyleGAN2GeneratorCSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + narrow=narrow, + ) + self.sft_half = sft_half + + def forward( + self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorCSFT. + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ResBlock(nn.Module): + """Residual block with bilinear upsampling/downsampling. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + mode (str): Upsampling/downsampling mode. Options: down | up. Default: down. + """ + + def __init__(self, in_channels, out_channels, mode="down"): + super(ResBlock, self).__init__() + + self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1) + self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1) + self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False) + if mode == "down": + self.scale_factor = 0.5 + elif mode == "up": + self.scale_factor = 2 + + def forward(self, x): + out = F.leaky_relu_(self.conv1(x), negative_slope=0.2) + # upsample/downsample + out = F.interpolate( + out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False + ) + out = F.leaky_relu_(self.conv2(out), negative_slope=0.2) + # skip + x = F.interpolate( + x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False + ) + skip = self.skip(x) + out = out + skip + return out + + +class GFPGANv1Clean(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + It is the clean version without custom compiled CUDA extensions used in StyleGAN2. + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + num_mlp (int): Layer number of MLP style layers. Default: 8. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + state_dict, + ): + super(GFPGANv1Clean, self).__init__() + + out_size = 512 + num_style_feat = 512 + channel_multiplier = 2 + decoder_load_path = None + fix_decoder = False + num_mlp = 8 + input_is_latent = True + different_w = True + narrow = 1 + sft_half = True + + self.model_arch = "GFPGAN" + self.sub_type = "Face SR" + self.scale = 8 + self.in_nc = 3 + self.out_nc = 3 + self.state = state_dict + + self.supports_fp16 = False + self.supports_bf16 = True + self.min_size_restriction = 512 + + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + "4": int(512 * unet_narrow), + "8": int(512 * unet_narrow), + "16": int(512 * unet_narrow), + "32": int(512 * unet_narrow), + "64": int(256 * channel_multiplier * unet_narrow), + "128": int(128 * channel_multiplier * unet_narrow), + "256": int(64 * channel_multiplier * unet_narrow), + "512": int(32 * channel_multiplier * unet_narrow), + "1024": int(16 * channel_multiplier * unet_narrow), + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2 ** (int(math.log(out_size, 2))) + + self.conv_body_first = nn.Conv2d(3, channels[f"{first_out_size}"], 1) + + # downsample + in_channels = channels[f"{first_out_size}"] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f"{2**(i - 1)}"] + self.conv_body_down.append(ResBlock(in_channels, out_channels, mode="down")) + in_channels = out_channels + + self.final_conv = nn.Conv2d(in_channels, channels["4"], 3, 1, 1) + + # upsample + in_channels = channels["4"] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.conv_body_up.append(ResBlock(in_channels, out_channels, mode="up")) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append(nn.Conv2d(channels[f"{2**i}"], 3, 1)) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = nn.Linear(channels["4"] * 4 * 4, linear_out_channel) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorCSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + narrow=narrow, + sft_half=sft_half, + ) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load( + decoder_load_path, map_location=lambda storage, loc: storage + )["params_ema"] + ) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1), + ) + ) + self.condition_shift.append( + nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1), + ) + ) + self.load_state_dict(state_dict) + + def forward( + self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs + ): + """Forward function for GFPGANv1Clean. + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder( + [style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise, + ) + + return image, out_rgbs diff --git a/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py b/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..4492260291d6d74b2c0d38130f7aa8b50ba2fc11 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py @@ -0,0 +1,776 @@ +# pylint: skip-file +# type: ignore +"""Modified from https://github.com/wzhouxiff/RestoreFormer +""" +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class VectorQuantizer(nn.Module): + """ + see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py + ____________________________________________ + Discretization bottleneck part of the VQ-VAE. + Inputs: + - n_e : number of embeddings + - e_dim : dimension of embedding + - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + _____________________________________________ + """ + + def __init__(self, n_e, e_dim, beta): + super(VectorQuantizer, self).__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + def forward(self, z): + """ + Inputs the output of the encoder network z and maps it to a discrete + one-hot vector that is the index of the closest embedding vector e_j + z (continuous) -> z_q (discrete) + z.shape = (batch, channel, height, width) + quantization pipeline: + 1. get encoder input (B,C,H,W) + 2. flatten input to (B*H*W,C) + """ + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = ( + torch.sum(z_flattened**2, dim=1, keepdim=True) + + torch.sum(self.embedding.weight**2, dim=1) + - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) + ) + + # could possible replace this here + # #\start... + # find closest encodings + + min_value, min_encoding_indices = torch.min(d, dim=1) + + min_encoding_indices = min_encoding_indices.unsqueeze(1) + + min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # dtype min encodings: torch.float32 + # min_encodings shape: torch.Size([2048, 512]) + # min_encoding_indices.shape: torch.Size([2048, 1]) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + # .........\end + + # with: + # .........\start + # min_encoding_indices = torch.argmin(d, dim=1) + # z_q = self.embedding(min_encoding_indices) + # ......\end......... (TODO) + + # compute loss for embedding + loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean( + (z_q - z.detach()) ** 2 + ) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices, d) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + # TODO: check for more easy handling with nn.Embedding + min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) + min_encodings.scatter_(1, indices[:, None], 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: + z_q = z_q.view(shape) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + + +# pytorch_diffusion + derived encoder decoder +def nonlinearity(x): + # swish + return x * torch.sigmoid(x) + + +def Normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def forward(self, x): + if self.with_conv: + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout, + temb_channels=512 + ): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + else: + self.nin_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x + h + + +class MultiHeadAttnBlock(nn.Module): + def __init__(self, in_channels, head_size=1): + super().__init__() + self.in_channels = in_channels + self.head_size = head_size + self.att_size = in_channels // head_size + assert ( + in_channels % head_size == 0 + ), "The size of head should be divided by the number of channels." + + self.norm1 = Normalize(in_channels) + self.norm2 = Normalize(in_channels) + + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.num = 0 + + def forward(self, x, y=None): + h_ = x + h_ = self.norm1(h_) + if y is None: + y = h_ + else: + y = self.norm2(y) + + q = self.q(y) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, self.head_size, self.att_size, h * w) + q = q.permute(0, 3, 1, 2) # b, hw, head, att + + k = k.reshape(b, self.head_size, self.att_size, h * w) + k = k.permute(0, 3, 1, 2) + + v = v.reshape(b, self.head_size, self.att_size, h * w) + v = v.permute(0, 3, 1, 2) + + q = q.transpose(1, 2) + v = v.transpose(1, 2) + k = k.transpose(1, 2).transpose(2, 3) + + scale = int(self.att_size) ** (-0.5) + q.mul_(scale) + w_ = torch.matmul(q, k) + w_ = F.softmax(w_, dim=3) + + w_ = w_.matmul(v) + + w_ = w_.transpose(1, 2).contiguous() # [b, h*w, head, att] + w_ = w_.view(b, h, w, -1) + w_ = w_.permute(0, 3, 1, 2) + + w_ = self.proj_out(w_) + + return x + w_ + + +class MultiHeadEncoder(nn.Module): + def __init__( + self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16,), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + double_z=True, + enable_mid=True, + head_size=1, + **ignore_kwargs + ): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.enable_mid = enable_mid + + # downsampling + self.conv_in = torch.nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, + 2 * z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1, + ) + + def forward(self, x): + hs = {} + # timestep embedding + temb = None + + # downsampling + h = self.conv_in(x) + hs["in"] = h + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + + if i_level != self.num_resolutions - 1: + # hs.append(h) + hs["block_" + str(i_level)] = h + h = self.down[i_level].downsample(h) + + # middle + # h = hs[-1] + if self.enable_mid: + h = self.mid.block_1(h, temb) + hs["block_" + str(i_level) + "_atten"] = h + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + hs["mid_atten"] = h + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + # hs.append(h) + hs["out"] = h + + return hs + + +class MultiHeadDecoder(nn.Module): + def __init__( + self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16,), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + give_pre_end=False, + enable_mid=True, + head_size=1, + **ignorekwargs + ): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.enable_mid = enable_mid + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z): + # assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + if self.enable_mid: + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class MultiHeadDecoderTransformer(nn.Module): + def __init__( + self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16,), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + give_pre_end=False, + enable_mid=True, + head_size=1, + **ignorekwargs + ): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.enable_mid = enable_mid + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z, hs): + # assert z.shape[1:] == self.z_shape[1:] + # self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + if self.enable_mid: + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h, hs["mid_atten"]) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block]( + h, hs["block_" + str(i_level) + "_atten"] + ) + # hfeature = h.clone() + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class RestoreFormer(nn.Module): + def __init__( + self, + state_dict, + ): + super(RestoreFormer, self).__init__() + + n_embed = 1024 + embed_dim = 256 + ch = 64 + out_ch = 3 + ch_mult = (1, 2, 2, 4, 4, 8) + num_res_blocks = 2 + attn_resolutions = (16,) + dropout = 0.0 + in_channels = 3 + resolution = 512 + z_channels = 256 + double_z = False + enable_mid = True + fix_decoder = False + fix_codebook = True + fix_encoder = False + head_size = 8 + + self.model_arch = "RestoreFormer" + self.sub_type = "Face SR" + self.scale = 8 + self.in_nc = 3 + self.out_nc = out_ch + self.state = state_dict + + self.supports_fp16 = False + self.supports_bf16 = True + self.min_size_restriction = 16 + + self.encoder = MultiHeadEncoder( + ch=ch, + out_ch=out_ch, + ch_mult=ch_mult, + num_res_blocks=num_res_blocks, + attn_resolutions=attn_resolutions, + dropout=dropout, + in_channels=in_channels, + resolution=resolution, + z_channels=z_channels, + double_z=double_z, + enable_mid=enable_mid, + head_size=head_size, + ) + self.decoder = MultiHeadDecoderTransformer( + ch=ch, + out_ch=out_ch, + ch_mult=ch_mult, + num_res_blocks=num_res_blocks, + attn_resolutions=attn_resolutions, + dropout=dropout, + in_channels=in_channels, + resolution=resolution, + z_channels=z_channels, + enable_mid=enable_mid, + head_size=head_size, + ) + + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25) + + self.quant_conv = torch.nn.Conv2d(z_channels, embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, z_channels, 1) + + if fix_decoder: + for _, param in self.decoder.named_parameters(): + param.requires_grad = False + for _, param in self.post_quant_conv.named_parameters(): + param.requires_grad = False + for _, param in self.quantize.named_parameters(): + param.requires_grad = False + elif fix_codebook: + for _, param in self.quantize.named_parameters(): + param.requires_grad = False + + if fix_encoder: + for _, param in self.encoder.named_parameters(): + param.requires_grad = False + + self.load_state_dict(state_dict) + + def encode(self, x): + hs = self.encoder(x) + h = self.quant_conv(hs["out"]) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info, hs + + def decode(self, quant, hs): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant, hs) + + return dec + + def forward(self, input, **kwargs): + quant, diff, info, hs = self.encode(input) + dec = self.decode(quant, hs) + + return dec, None diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..1eb0e9f15f706e2b9759bde4d0244d424c3ae76f --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py @@ -0,0 +1,865 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .fused_act import FusedLeakyReLU, fused_leaky_relu +from .upfirdn2d import upfirdn2d + + +class NormStyleCode(nn.Module): + def forward(self, x): + """Normalize the style codes. + + Args: + x (Tensor): Style codes with shape (b, c). + + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +def make_resample_kernel(k): + """Make resampling kernel for UpFirDn. + + Args: + k (list[int]): A list indicating the 1D resample kernel magnitude. + + Returns: + Tensor: 2D resampled kernel. + """ + k = torch.tensor(k, dtype=torch.float32) + if k.ndim == 1: + k = k[None, :] * k[:, None] # to 2D kernel, outer product + # normalize + k /= k.sum() + return k + + +class UpFirDnUpsample(nn.Module): + """Upsample, FIR filter, and downsample (upsampole version). + + References: + 1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html # noqa: E501 + 2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html # noqa: E501 + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + factor (int): Upsampling scale factor. Default: 2. + """ + + def __init__(self, resample_kernel, factor=2): + super(UpFirDnUpsample, self).__init__() + self.kernel = make_resample_kernel(resample_kernel) * (factor**2) + self.factor = factor + + pad = self.kernel.shape[0] - factor + self.pad = ((pad + 1) // 2 + factor - 1, pad // 2) + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad) + return out + + def __repr__(self): + return f"{self.__class__.__name__}(factor={self.factor})" + + +class UpFirDnDownsample(nn.Module): + """Upsample, FIR filter, and downsample (downsampole version). + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + factor (int): Downsampling scale factor. Default: 2. + """ + + def __init__(self, resample_kernel, factor=2): + super(UpFirDnDownsample, self).__init__() + self.kernel = make_resample_kernel(resample_kernel) + self.factor = factor + + pad = self.kernel.shape[0] - factor + self.pad = ((pad + 1) // 2, pad // 2) + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad) + return out + + def __repr__(self): + return f"{self.__class__.__name__}(factor={self.factor})" + + +class UpFirDnSmooth(nn.Module): + """Upsample, FIR filter, and downsample (smooth version). + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + upsample_factor (int): Upsampling scale factor. Default: 1. + downsample_factor (int): Downsampling scale factor. Default: 1. + kernel_size (int): Kernel size: Default: 1. + """ + + def __init__( + self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1 + ): + super(UpFirDnSmooth, self).__init__() + self.upsample_factor = upsample_factor + self.downsample_factor = downsample_factor + self.kernel = make_resample_kernel(resample_kernel) + if upsample_factor > 1: + self.kernel = self.kernel * (upsample_factor**2) + + if upsample_factor > 1: + pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1) + self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1) + elif downsample_factor > 1: + pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1) + self.pad = ((pad + 1) // 2, pad // 2) + else: + raise NotImplementedError + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad) + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(upsample_factor={self.upsample_factor}" + f", downsample_factor={self.downsample_factor})" + ) + + +class EqualLinear(nn.Module): + """Equalized Linear as StyleGAN2. + + Args: + in_channels (int): Size of each sample. + out_channels (int): Size of each output sample. + bias (bool): If set to ``False``, the layer will not learn an additive + bias. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + lr_mul (float): Learning rate multiplier. Default: 1. + activation (None | str): The activation after ``linear`` operation. + Supported: 'fused_lrelu', None. Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ): + super(EqualLinear, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.lr_mul = lr_mul + self.activation = activation + if self.activation not in ["fused_lrelu", None]: + raise ValueError( + f"Wrong activation value in EqualLinear: {activation}" + "Supported ones are: ['fused_lrelu', None]." + ) + self.scale = (1 / math.sqrt(in_channels)) * lr_mul + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + if self.bias is None: + bias = None + else: + bias = self.bias * self.lr_mul + if self.activation == "fused_lrelu": + out = F.linear(x, self.weight * self.scale) + out = fused_leaky_relu(out, bias) + else: + out = F.linear(x, self.weight * self.scale, bias=bias) + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, bias={self.bias is not None})" + ) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + + There is no bias in ModulatedConv2d. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. + Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + eps (float): A value added to the denominator for numerical stability. + Default: 1e-8. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=(1, 3, 3, 1), + eps=1e-8, + ): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + + if self.sample_mode == "upsample": + self.smooth = UpFirDnSmooth( + resample_kernel, + upsample_factor=2, + downsample_factor=1, + kernel_size=kernel_size, + ) + elif self.sample_mode == "downsample": + self.smooth = UpFirDnSmooth( + resample_kernel, + upsample_factor=1, + downsample_factor=2, + kernel_size=kernel_size, + ) + elif self.sample_mode is None: + pass + else: + raise ValueError( + f"Wrong sample mode {self.sample_mode}, " + "supported ones are ['upsample', 'downsample', None]." + ) + + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + # modulation inside each modulated conv + self.modulation = EqualLinear( + num_style_feat, + in_channels, + bias=True, + bias_init_val=1, + lr_mul=1, + activation=None, + ) + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) + ) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view( + b * self.out_channels, c, self.kernel_size, self.kernel_size + ) + + if self.sample_mode == "upsample": + x = x.view(1, b * c, h, w) + weight = weight.view( + b, self.out_channels, c, self.kernel_size, self.kernel_size + ) + weight = weight.transpose(1, 2).reshape( + b * c, self.out_channels, self.kernel_size, self.kernel_size + ) + out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + out = self.smooth(out) + elif self.sample_mode == "downsample": + x = self.smooth(x) + x = x.view(1, b * c, *x.shape[2:4]) + out = F.conv2d(x, weight, padding=0, stride=2, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + else: + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, " + f"demodulate={self.demodulate}, sample_mode={self.sample_mode})" + ) + + +class StyleConv(nn.Module): + """Style conv. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=(1, 3, 3, 1), + ): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + resample_kernel=resample_kernel, + ) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.activate = FusedLeakyReLU(out_channels) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # activation (with bias) + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB from features. + + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + """ + + def __init__( + self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1) + ): + super(ToRGB, self).__init__() + if upsample: + self.upsample = UpFirDnUpsample(resample_kernel, factor=2) + else: + self.upsample = None + self.modulated_conv = ModulatedConv2d( + in_channels, + 3, + kernel_size=1, + num_style_feat=num_style_feat, + demodulate=False, + sample_mode=None, + ) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = self.upsample(skip) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +class StyleGAN2Generator(nn.Module): + """StyleGAN2 Generator. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of + StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. A cross production will be applied to extent 1D resample + kernel to 2D resample kernel. Default: (1, 3, 3, 1). + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1, + ): + super(StyleGAN2Generator, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.append( + EqualLinear( + num_style_feat, + num_style_feat, + bias=True, + bias_init_val=0, + lr_mul=lr_mlp, + activation="fused_lrelu", + ) + ) + self.style_mlp = nn.Sequential(*style_mlp_layers) + + channels = { + "4": int(512 * narrow), + "8": int(512 * narrow), + "16": int(512 * narrow), + "32": int(512 * narrow), + "64": int(256 * channel_multiplier * narrow), + "128": int(128 * channel_multiplier * narrow), + "256": int(64 * channel_multiplier * narrow), + "512": int(32 * channel_multiplier * narrow), + "1024": int(16 * channel_multiplier * narrow), + } + self.channels = channels + + self.constant_input = ConstantInput(channels["4"], size=4) + self.style_conv1 = StyleConv( + channels["4"], + channels["4"], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=resample_kernel, + ) + self.to_rgb1 = ToRGB( + channels["4"], + num_style_feat, + upsample=False, + resample_kernel=resample_kernel, + ) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels["4"] + # noise + for layer_idx in range(self.num_layers): + resolution = 2 ** ((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode="upsample", + resample_kernel=resample_kernel, + ) + ) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=resample_kernel, + ) + ) + self.to_rgbs.append( + ToRGB( + out_channels, + num_style_feat, + upsample=True, + resample_kernel=resample_kernel, + ) + ) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn( + num_latent, self.num_style_feat, device=self.constant_input.weight.device + ) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward( + self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2Generator. + + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. + Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is + False. Default: True. + truncation (float): TODO. Default: 1. + truncation_latent (Tensor | None): TODO. Default: None. + inject_index (int | None): The injection index for mixing noise. + Default: None. + return_latents (bool): Whether to return style latents. + Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latent with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ScaledLeakyReLU(nn.Module): + """Scaled LeakyReLU. + + Args: + negative_slope (float): Negative slope. Default: 0.2. + """ + + def __init__(self, negative_slope=0.2): + super(ScaledLeakyReLU, self).__init__() + self.negative_slope = negative_slope + + def forward(self, x): + out = F.leaky_relu(x, negative_slope=self.negative_slope) + return out * math.sqrt(2) + + +class EqualConv2d(nn.Module): + """Equalized Linear as StyleGAN2. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. + Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. + Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ): + super(EqualConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter( + torch.randn(out_channels, in_channels, kernel_size, kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + out = F.conv2d( + x, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}," + f" stride={self.stride}, padding={self.padding}, " + f"bias={self.bias is not None})" + ) + + +class ConvLayer(nn.Sequential): + """Conv Layer used in StyleGAN2 Discriminator. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Kernel size. + downsample (bool): Whether downsample by a factor of 2. + Default: False. + resample_kernel (list[int]): A list indicating the 1D resample + kernel magnitude. A cross production will be applied to + extent 1D resample kernel to 2D resample kernel. + Default: (1, 3, 3, 1). + bias (bool): Whether with bias. Default: True. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ): + layers = [] + # downsample + if downsample: + layers.append( + UpFirDnSmooth( + resample_kernel, + upsample_factor=1, + downsample_factor=2, + kernel_size=kernel_size, + ) + ) + stride = 2 + self.padding = 0 + else: + stride = 1 + self.padding = kernel_size // 2 + # conv + layers.append( + EqualConv2d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=self.padding, + bias=bias and not activate, + ) + ) + # activation + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channels)) + else: + layers.append(ScaledLeakyReLU(0.2)) + + super(ConvLayer, self).__init__(*layers) + + +class ResBlock(nn.Module): + """Residual block used in StyleGAN2 Discriminator. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + resample_kernel (list[int]): A list indicating the 1D resample + kernel magnitude. A cross production will be applied to + extent 1D resample kernel to 2D resample kernel. + Default: (1, 3, 3, 1). + """ + + def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)): + super(ResBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvLayer( + in_channels, + out_channels, + 3, + downsample=True, + resample_kernel=resample_kernel, + bias=True, + activate=True, + ) + self.skip = ConvLayer( + in_channels, + out_channels, + 1, + downsample=True, + resample_kernel=resample_kernel, + bias=False, + activate=False, + ) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..601f8cc4b33bdbb371d710a2bb0656e8ce102e26 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py @@ -0,0 +1,709 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .fused_act import FusedLeakyReLU, fused_leaky_relu + + +class NormStyleCode(nn.Module): + def forward(self, x): + """Normalize the style codes. + Args: + x (Tensor): Style codes with shape (b, c). + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +class EqualLinear(nn.Module): + """Equalized Linear as StyleGAN2. + Args: + in_channels (int): Size of each sample. + out_channels (int): Size of each output sample. + bias (bool): If set to ``False``, the layer will not learn an additive + bias. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + lr_mul (float): Learning rate multiplier. Default: 1. + activation (None | str): The activation after ``linear`` operation. + Supported: 'fused_lrelu', None. Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ): + super(EqualLinear, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.lr_mul = lr_mul + self.activation = activation + if self.activation not in ["fused_lrelu", None]: + raise ValueError( + f"Wrong activation value in EqualLinear: {activation}" + "Supported ones are: ['fused_lrelu', None]." + ) + self.scale = (1 / math.sqrt(in_channels)) * lr_mul + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + if self.bias is None: + bias = None + else: + bias = self.bias * self.lr_mul + if self.activation == "fused_lrelu": + out = F.linear(x, self.weight * self.scale) + out = fused_leaky_relu(out, bias) + else: + out = F.linear(x, self.weight * self.scale, bias=bias) + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, bias={self.bias is not None})" + ) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + There is no bias in ModulatedConv2d. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. + Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + eps (float): A value added to the denominator for numerical stability. + Default: 1e-8. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + eps=1e-8, + interpolation_mode="bilinear", + ): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + self.interpolation_mode = interpolation_mode + if self.interpolation_mode == "nearest": + self.align_corners = None + else: + self.align_corners = False + + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + # modulation inside each modulated conv + self.modulation = EqualLinear( + num_style_feat, + in_channels, + bias=True, + bias_init_val=1, + lr_mul=1, + activation=None, + ) + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) + ) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view( + b * self.out_channels, c, self.kernel_size, self.kernel_size + ) + + if self.sample_mode == "upsample": + x = F.interpolate( + x, + scale_factor=2, + mode=self.interpolation_mode, + align_corners=self.align_corners, + ) + elif self.sample_mode == "downsample": + x = F.interpolate( + x, + scale_factor=0.5, + mode=self.interpolation_mode, + align_corners=self.align_corners, + ) + + b, c, h, w = x.shape + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, " + f"demodulate={self.demodulate}, sample_mode={self.sample_mode})" + ) + + +class StyleConv(nn.Module): + """Style conv. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode="bilinear", + ): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + interpolation_mode=interpolation_mode, + ) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.activate = FusedLeakyReLU(out_channels) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # activation (with bias) + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB from features. + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + """ + + def __init__( + self, in_channels, num_style_feat, upsample=True, interpolation_mode="bilinear" + ): + super(ToRGB, self).__init__() + self.upsample = upsample + self.interpolation_mode = interpolation_mode + if self.interpolation_mode == "nearest": + self.align_corners = None + else: + self.align_corners = False + self.modulated_conv = ModulatedConv2d( + in_channels, + 3, + kernel_size=1, + num_style_feat=num_style_feat, + demodulate=False, + sample_mode=None, + interpolation_mode=interpolation_mode, + ) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = F.interpolate( + skip, + scale_factor=2, + mode=self.interpolation_mode, + align_corners=self.align_corners, + ) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +class StyleGAN2GeneratorBilinear(nn.Module): + """StyleGAN2 Generator. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of + StyleGAN2. Default: 2. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + lr_mlp=0.01, + narrow=1, + interpolation_mode="bilinear", + ): + super(StyleGAN2GeneratorBilinear, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.append( + EqualLinear( + num_style_feat, + num_style_feat, + bias=True, + bias_init_val=0, + lr_mul=lr_mlp, + activation="fused_lrelu", + ) + ) + self.style_mlp = nn.Sequential(*style_mlp_layers) + + channels = { + "4": int(512 * narrow), + "8": int(512 * narrow), + "16": int(512 * narrow), + "32": int(512 * narrow), + "64": int(256 * channel_multiplier * narrow), + "128": int(128 * channel_multiplier * narrow), + "256": int(64 * channel_multiplier * narrow), + "512": int(32 * channel_multiplier * narrow), + "1024": int(16 * channel_multiplier * narrow), + } + self.channels = channels + + self.constant_input = ConstantInput(channels["4"], size=4) + self.style_conv1 = StyleConv( + channels["4"], + channels["4"], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode=interpolation_mode, + ) + self.to_rgb1 = ToRGB( + channels["4"], + num_style_feat, + upsample=False, + interpolation_mode=interpolation_mode, + ) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels["4"] + # noise + for layer_idx in range(self.num_layers): + resolution = 2 ** ((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode="upsample", + interpolation_mode=interpolation_mode, + ) + ) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode=interpolation_mode, + ) + ) + self.to_rgbs.append( + ToRGB( + out_channels, + num_style_feat, + upsample=True, + interpolation_mode=interpolation_mode, + ) + ) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn( + num_latent, self.num_style_feat, device=self.constant_input.weight.device + ) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward( + self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2Generator. + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. + Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is + False. Default: True. + truncation (float): TODO. Default: 1. + truncation_latent (Tensor | None): TODO. Default: None. + inject_index (int | None): The injection index for mixing noise. + Default: None. + return_latents (bool): Whether to return style latents. + Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latent with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ScaledLeakyReLU(nn.Module): + """Scaled LeakyReLU. + Args: + negative_slope (float): Negative slope. Default: 0.2. + """ + + def __init__(self, negative_slope=0.2): + super(ScaledLeakyReLU, self).__init__() + self.negative_slope = negative_slope + + def forward(self, x): + out = F.leaky_relu(x, negative_slope=self.negative_slope) + return out * math.sqrt(2) + + +class EqualConv2d(nn.Module): + """Equalized Linear as StyleGAN2. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. + Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. + Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ): + super(EqualConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter( + torch.randn(out_channels, in_channels, kernel_size, kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + out = F.conv2d( + x, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}," + f" stride={self.stride}, padding={self.padding}, " + f"bias={self.bias is not None})" + ) + + +class ConvLayer(nn.Sequential): + """Conv Layer used in StyleGAN2 Discriminator. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Kernel size. + downsample (bool): Whether downsample by a factor of 2. + Default: False. + bias (bool): Whether with bias. Default: True. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + downsample=False, + bias=True, + activate=True, + interpolation_mode="bilinear", + ): + layers = [] + self.interpolation_mode = interpolation_mode + # downsample + if downsample: + if self.interpolation_mode == "nearest": + self.align_corners = None + else: + self.align_corners = False + + layers.append( + torch.nn.Upsample( + scale_factor=0.5, + mode=interpolation_mode, + align_corners=self.align_corners, + ) + ) + stride = 1 + self.padding = kernel_size // 2 + # conv + layers.append( + EqualConv2d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=self.padding, + bias=bias and not activate, + ) + ) + # activation + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channels)) + else: + layers.append(ScaledLeakyReLU(0.2)) + + super(ConvLayer, self).__init__(*layers) + + +class ResBlock(nn.Module): + """Residual block used in StyleGAN2 Discriminator. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + """ + + def __init__(self, in_channels, out_channels, interpolation_mode="bilinear"): + super(ResBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvLayer( + in_channels, + out_channels, + 3, + downsample=True, + interpolation_mode=interpolation_mode, + bias=True, + activate=True, + ) + self.skip = ConvLayer( + in_channels, + out_channels, + 1, + downsample=True, + interpolation_mode=interpolation_mode, + bias=False, + activate=False, + ) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..c48de9af6904b8d1891a84efa8e4d76104d5d710 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py @@ -0,0 +1,453 @@ +# pylint: skip-file +# type: ignore +import math + +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn import init +from torch.nn.modules.batchnorm import _BatchNorm + + +@torch.no_grad() +def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): + """Initialize network weights. + Args: + module_list (list[nn.Module] | nn.Module): Modules to be initialized. + scale (float): Scale initialized weights, especially for residual + blocks. Default: 1. + bias_fill (float): The value to fill bias. Default: 0 + kwargs (dict): Other arguments for initialization function. + """ + if not isinstance(module_list, list): + module_list = [module_list] + for module in module_list: + for m in module.modules(): + if isinstance(m, nn.Conv2d): + init.kaiming_normal_(m.weight, **kwargs) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.fill_(bias_fill) + elif isinstance(m, nn.Linear): + init.kaiming_normal_(m.weight, **kwargs) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.fill_(bias_fill) + elif isinstance(m, _BatchNorm): + init.constant_(m.weight, 1) + if m.bias is not None: + m.bias.data.fill_(bias_fill) + + +class NormStyleCode(nn.Module): + def forward(self, x): + """Normalize the style codes. + Args: + x (Tensor): Style codes with shape (b, c). + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + There is no bias in ModulatedConv2d. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. + eps (float): A value added to the denominator for numerical stability. Default: 1e-8. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + eps=1e-8, + ): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + + # modulation inside each modulated conv + self.modulation = nn.Linear(num_style_feat, in_channels, bias=True) + # initialization + default_init_weights( + self.modulation, + scale=1, + bias_fill=1, + a=0, + mode="fan_in", + nonlinearity="linear", + ) + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) + / math.sqrt(in_channels * kernel_size**2) + ) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view( + b * self.out_channels, c, self.kernel_size, self.kernel_size + ) + + # upsample or downsample if necessary + if self.sample_mode == "upsample": + x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False) + elif self.sample_mode == "downsample": + x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False) + + b, c, h, w = x.shape + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})" + ) + + +class StyleConv(nn.Module): + """Style conv used in StyleGAN2. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + ): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + ) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1)) + self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) * 2**0.5 # for conversion + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # add bias + out = out + self.bias + # activation + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB (image space) from features. + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + """ + + def __init__(self, in_channels, num_style_feat, upsample=True): + super(ToRGB, self).__init__() + self.upsample = upsample + self.modulated_conv = ModulatedConv2d( + in_channels, + 3, + kernel_size=1, + num_style_feat=num_style_feat, + demodulate=False, + sample_mode=None, + ) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = F.interpolate( + skip, scale_factor=2, mode="bilinear", align_corners=False + ) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +class StyleGAN2GeneratorClean(nn.Module): + """Clean version of StyleGAN2 Generator. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__( + self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1 + ): + super(StyleGAN2GeneratorClean, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.extend( + [ + nn.Linear(num_style_feat, num_style_feat, bias=True), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + ] + ) + self.style_mlp = nn.Sequential(*style_mlp_layers) + # initialization + default_init_weights( + self.style_mlp, + scale=1, + bias_fill=0, + a=0.2, + mode="fan_in", + nonlinearity="leaky_relu", + ) + + # channel list + channels = { + "4": int(512 * narrow), + "8": int(512 * narrow), + "16": int(512 * narrow), + "32": int(512 * narrow), + "64": int(256 * channel_multiplier * narrow), + "128": int(128 * channel_multiplier * narrow), + "256": int(64 * channel_multiplier * narrow), + "512": int(32 * channel_multiplier * narrow), + "1024": int(16 * channel_multiplier * narrow), + } + self.channels = channels + + self.constant_input = ConstantInput(channels["4"], size=4) + self.style_conv1 = StyleConv( + channels["4"], + channels["4"], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + ) + self.to_rgb1 = ToRGB(channels["4"], num_style_feat, upsample=False) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels["4"] + # noise + for layer_idx in range(self.num_layers): + resolution = 2 ** ((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode="upsample", + ) + ) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + ) + ) + self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True)) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn( + num_latent, self.num_style_feat, device=self.constant_input.weight.device + ) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward( + self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorClean. + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None diff --git a/comfy_extras/chainner_models/architecture/face/upfirdn2d.py b/comfy_extras/chainner_models/architecture/face/upfirdn2d.py new file mode 100644 index 0000000000000000000000000000000000000000..4ea4541513f27e3c9dddcee864cfeb87efddadb7 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/face/upfirdn2d.py @@ -0,0 +1,194 @@ +# pylint: skip-file +# type: ignore +# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 + +import os + +import torch +from torch.autograd import Function +from torch.nn import functional as F + +upfirdn2d_ext = None + + +class UpFirDn2dBackward(Function): + @staticmethod + def forward( + ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size + ): + up_x, up_y = up + down_x, down_y = down + g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad + + grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) + + grad_input = upfirdn2d_ext.upfirdn2d( + grad_output, + grad_kernel, + down_x, + down_y, + up_x, + up_y, + g_pad_x0, + g_pad_x1, + g_pad_y0, + g_pad_y1, + ) + grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) + + ctx.save_for_backward(kernel) + + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + ctx.up_x = up_x + ctx.up_y = up_y + ctx.down_x = down_x + ctx.down_y = down_y + ctx.pad_x0 = pad_x0 + ctx.pad_x1 = pad_x1 + ctx.pad_y0 = pad_y0 + ctx.pad_y1 = pad_y1 + ctx.in_size = in_size + ctx.out_size = out_size + + return grad_input + + @staticmethod + def backward(ctx, gradgrad_input): + (kernel,) = ctx.saved_tensors + + gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) + + gradgrad_out = upfirdn2d_ext.upfirdn2d( + gradgrad_input, + kernel, + ctx.up_x, + ctx.up_y, + ctx.down_x, + ctx.down_y, + ctx.pad_x0, + ctx.pad_x1, + ctx.pad_y0, + ctx.pad_y1, + ) + # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], + # ctx.out_size[1], ctx.in_size[3]) + gradgrad_out = gradgrad_out.view( + ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] + ) + + return gradgrad_out, None, None, None, None, None, None, None, None + + +class UpFirDn2d(Function): + @staticmethod + def forward(ctx, input, kernel, up, down, pad): + up_x, up_y = up + down_x, down_y = down + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + kernel_h, kernel_w = kernel.shape + _, channel, in_h, in_w = input.shape + ctx.in_size = input.shape + + input = input.reshape(-1, in_h, in_w, 1) + + ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + ctx.out_size = (out_h, out_w) + + ctx.up = (up_x, up_y) + ctx.down = (down_x, down_y) + ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) + + g_pad_x0 = kernel_w - pad_x0 - 1 + g_pad_y0 = kernel_h - pad_y0 - 1 + g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 + g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 + + ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) + + out = upfirdn2d_ext.upfirdn2d( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 + ) + # out = out.view(major, out_h, out_w, minor) + out = out.view(-1, channel, out_h, out_w) + + return out + + @staticmethod + def backward(ctx, grad_output): + kernel, grad_kernel = ctx.saved_tensors + + grad_input = UpFirDn2dBackward.apply( + grad_output, + kernel, + grad_kernel, + ctx.up, + ctx.down, + ctx.pad, + ctx.g_pad, + ctx.in_size, + ctx.out_size, + ) + + return grad_input, None, None, None, None + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + if input.device.type == "cpu": + out = upfirdn2d_native( + input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] + ) + else: + out = UpFirDn2d.apply( + input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) + ) + + return out + + +def upfirdn2d_native( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 +): + _, channel, in_h, in_w = input.shape + input = input.reshape(-1, in_h, in_w, 1) + + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + ) + out = out[ + :, + max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), + :, + ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] + ) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + out = out[:, ::down_y, ::down_x, :] + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + + return out.view(-1, channel, out_h, out_w) diff --git a/comfy_extras/chainner_models/architecture/timm/LICENSE b/comfy_extras/chainner_models/architecture/timm/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..b4e9438bd1e07e17abf58cfd86e536ec880348a3 --- /dev/null +++ b/comfy_extras/chainner_models/architecture/timm/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2019 Ross Wightman + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/comfy_extras/chainner_models/architecture/timm/drop.py b/comfy_extras/chainner_models/architecture/timm/drop.py new file mode 100644 index 0000000000000000000000000000000000000000..14f0da914b2a198af7e6124cd90bad6adaf8a84e --- /dev/null +++ b/comfy_extras/chainner_models/architecture/timm/drop.py @@ -0,0 +1,223 @@ +""" DropBlock, DropPath + +PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers. + +Papers: +DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890) + +Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382) + +Code: +DropBlock impl inspired by two Tensorflow impl that I liked: + - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74 + - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py + +Hacked together by / Copyright 2020 Ross Wightman +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def drop_block_2d( + x, + drop_prob: float = 0.1, + block_size: int = 7, + gamma_scale: float = 1.0, + with_noise: bool = False, + inplace: bool = False, + batchwise: bool = False, +): + """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf + + DropBlock with an experimental gaussian noise option. This layer has been tested on a few training + runs with success, but needs further validation and possibly optimization for lower runtime impact. + """ + _, C, H, W = x.shape + total_size = W * H + clipped_block_size = min(block_size, min(W, H)) + # seed_drop_rate, the gamma parameter + gamma = ( + gamma_scale + * drop_prob + * total_size + / clipped_block_size**2 + / ((W - block_size + 1) * (H - block_size + 1)) + ) + + # Forces the block to be inside the feature map. + w_i, h_i = torch.meshgrid( + torch.arange(W).to(x.device), torch.arange(H).to(x.device) + ) + valid_block = ( + (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2) + ) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2)) + valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype) + + if batchwise: + # one mask for whole batch, quite a bit faster + uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) + else: + uniform_noise = torch.rand_like(x) + block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype) + block_mask = -F.max_pool2d( + -block_mask, + kernel_size=clipped_block_size, # block_size, + stride=1, + padding=clipped_block_size // 2, + ) + + if with_noise: + normal_noise = ( + torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) + if batchwise + else torch.randn_like(x) + ) + if inplace: + x.mul_(block_mask).add_(normal_noise * (1 - block_mask)) + else: + x = x * block_mask + normal_noise * (1 - block_mask) + else: + normalize_scale = ( + block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7) + ).to(x.dtype) + if inplace: + x.mul_(block_mask * normalize_scale) + else: + x = x * block_mask * normalize_scale + return x + + +def drop_block_fast_2d( + x: torch.Tensor, + drop_prob: float = 0.1, + block_size: int = 7, + gamma_scale: float = 1.0, + with_noise: bool = False, + inplace: bool = False, +): + """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf + + DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid + block mask at edges. + """ + _, _, H, W = x.shape + total_size = W * H + clipped_block_size = min(block_size, min(W, H)) + gamma = ( + gamma_scale + * drop_prob + * total_size + / clipped_block_size**2 + / ((W - block_size + 1) * (H - block_size + 1)) + ) + + block_mask = torch.empty_like(x).bernoulli_(gamma) + block_mask = F.max_pool2d( + block_mask.to(x.dtype), + kernel_size=clipped_block_size, + stride=1, + padding=clipped_block_size // 2, + ) + + if with_noise: + normal_noise = torch.empty_like(x).normal_() + if inplace: + x.mul_(1.0 - block_mask).add_(normal_noise * block_mask) + else: + x = x * (1.0 - block_mask) + normal_noise * block_mask + else: + block_mask = 1 - block_mask + normalize_scale = ( + block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6) + ).to(dtype=x.dtype) + if inplace: + x.mul_(block_mask * normalize_scale) + else: + x = x * block_mask * normalize_scale + return x + + +class DropBlock2d(nn.Module): + """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf""" + + def __init__( + self, + drop_prob: float = 0.1, + block_size: int = 7, + gamma_scale: float = 1.0, + with_noise: bool = False, + inplace: bool = False, + batchwise: bool = False, + fast: bool = True, + ): + super(DropBlock2d, self).__init__() + self.drop_prob = drop_prob + self.gamma_scale = gamma_scale + self.block_size = block_size + self.with_noise = with_noise + self.inplace = inplace + self.batchwise = batchwise + self.fast = fast # FIXME finish comparisons of fast vs not + + def forward(self, x): + if not self.training or not self.drop_prob: + return x + if self.fast: + return drop_block_fast_2d( + x, + self.drop_prob, + self.block_size, + self.gamma_scale, + self.with_noise, + self.inplace, + ) + else: + return drop_block_2d( + x, + self.drop_prob, + self.block_size, + self.gamma_scale, + self.with_noise, + self.inplace, + self.batchwise, + ) + + +def drop_path( + x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True +): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + def extra_repr(self): + return f"drop_prob={round(self.drop_prob,3):0.3f}" diff --git a/comfy_extras/chainner_models/architecture/timm/helpers.py b/comfy_extras/chainner_models/architecture/timm/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..cdafee0709165dd992118e3b09b8d26f70ea8a2a --- /dev/null +++ b/comfy_extras/chainner_models/architecture/timm/helpers.py @@ -0,0 +1,31 @@ +""" Layer/Module Helpers +Hacked together by / Copyright 2020 Ross Wightman +""" +import collections.abc +from itertools import repeat + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return x + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple + + +def make_divisible(v, divisor=8, min_value=None, round_limit=0.9): + min_value = min_value or divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < round_limit * v: + new_v += divisor + return new_v diff --git a/comfy_extras/chainner_models/architecture/timm/weight_init.py b/comfy_extras/chainner_models/architecture/timm/weight_init.py new file mode 100644 index 0000000000000000000000000000000000000000..b0169774657d86c1946008e746f2f4f7e833a44c --- /dev/null +++ b/comfy_extras/chainner_models/architecture/timm/weight_init.py @@ -0,0 +1,128 @@ +import math +import warnings + +import torch +from torch.nn.init import _calculate_fan_in_and_fan_out + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_( + tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0 +) -> torch.Tensor: + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + + NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are + applied while sampling the normal with mean/std applied, therefore a, b args + should be adjusted to match the range of mean, std args. + + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def trunc_normal_tf_( + tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0 +) -> torch.Tensor: + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + + NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the + bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 + and the result is subsquently scaled and shifted by the mean and std args. + + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + _no_grad_trunc_normal_(tensor, 0, 1.0, a, b) + with torch.no_grad(): + tensor.mul_(std).add_(mean) + return tensor + + +def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + if mode == "fan_in": + denom = fan_in + elif mode == "fan_out": + denom = fan_out + elif mode == "fan_avg": + denom = (fan_in + fan_out) / 2 + + variance = scale / denom # type: ignore + + if distribution == "truncated_normal": + # constant is stddev of standard normal truncated to (-2, 2) + trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) + elif distribution == "normal": + tensor.normal_(std=math.sqrt(variance)) + elif distribution == "uniform": + bound = math.sqrt(3 * variance) + # pylint: disable=invalid-unary-operand-type + tensor.uniform_(-bound, bound) + else: + raise ValueError(f"invalid distribution {distribution}") + + +def lecun_normal_(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..e000871c1bfe66a07dc13b51ad709cb0de092a41 --- /dev/null +++ b/comfy_extras/chainner_models/model_loading.py @@ -0,0 +1,99 @@ +import logging as logger + +from .architecture.DAT import DAT +from .architecture.face.codeformer import CodeFormer +from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean +from .architecture.face.restoreformer_arch import RestoreFormer +from .architecture.HAT import HAT +from .architecture.LaMa import LaMa +from .architecture.OmniSR.OmniSR import OmniSR +from .architecture.RRDB import RRDBNet as ESRGAN +from .architecture.SCUNet import SCUNet +from .architecture.SPSR import SPSRNet as SPSR +from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 +from .architecture.SwiftSRGAN import Generator as SwiftSRGAN +from .architecture.Swin2SR import Swin2SR +from .architecture.SwinIR import SwinIR +from .types import PyTorchModel + + +class UnsupportedModel(Exception): + pass + + +def load_state_dict(state_dict) -> PyTorchModel: + logger.debug(f"Loading state dict into pytorch model arch") + + state_dict_keys = list(state_dict.keys()) + + if "params_ema" in state_dict_keys: + state_dict = state_dict["params_ema"] + elif "params-ema" in state_dict_keys: + state_dict = state_dict["params-ema"] + elif "params" in state_dict_keys: + state_dict = state_dict["params"] + + state_dict_keys = list(state_dict.keys()) + # SRVGGNet Real-ESRGAN (v2) + if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys: + model = RealESRGANv2(state_dict) + # SPSR (ESRGAN with lots of extra layers) + elif "f_HR_conv1.0.weight" in state_dict: + model = SPSR(state_dict) + # Swift-SRGAN + elif ( + "model" in state_dict_keys + and "initial.cnn.depthwise.weight" in state_dict["model"].keys() + ): + model = SwiftSRGAN(state_dict) + # SwinIR, Swin2SR, HAT + elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys: + if ( + "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" + in state_dict_keys + ): + model = HAT(state_dict) + elif "patch_embed.proj.weight" in state_dict_keys: + model = Swin2SR(state_dict) + else: + model = SwinIR(state_dict) + # GFPGAN + elif ( + "toRGB.0.weight" in state_dict_keys + and "stylegan_decoder.style_mlp.1.weight" in state_dict_keys + ): + model = GFPGANv1Clean(state_dict) + # RestoreFormer + elif ( + "encoder.conv_in.weight" in state_dict_keys + and "encoder.down.0.block.0.norm1.weight" in state_dict_keys + ): + model = RestoreFormer(state_dict) + elif ( + "encoder.blocks.0.weight" in state_dict_keys + and "quantize.embedding.weight" in state_dict_keys + ): + model = CodeFormer(state_dict) + # LaMa + elif ( + "model.model.1.bn_l.running_mean" in state_dict_keys + or "generator.model.1.bn_l.running_mean" in state_dict_keys + ): + model = LaMa(state_dict) + # Omni-SR + elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys: + model = OmniSR(state_dict) + # SCUNet + elif "m_head.0.weight" in state_dict_keys and "m_tail.0.weight" in state_dict_keys: + model = SCUNet(state_dict) + # DAT + elif "layers.0.blocks.2.attn.attn_mask_0" in state_dict_keys: + model = DAT(state_dict) + # Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1 + else: + try: + model = ESRGAN(state_dict) + except: + # pylint: disable=raise-missing-from + raise UnsupportedModel + return model diff --git a/comfy_extras/chainner_models/types.py b/comfy_extras/chainner_models/types.py new file mode 100644 index 0000000000000000000000000000000000000000..193333b9e8049d9558ca2ea253d41ee44b0b294b --- /dev/null +++ b/comfy_extras/chainner_models/types.py @@ -0,0 +1,69 @@ +from typing import Union + +from .architecture.DAT import DAT +from .architecture.face.codeformer import CodeFormer +from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean +from .architecture.face.restoreformer_arch import RestoreFormer +from .architecture.HAT import HAT +from .architecture.LaMa import LaMa +from .architecture.OmniSR.OmniSR import OmniSR +from .architecture.RRDB import RRDBNet as ESRGAN +from .architecture.SCUNet import SCUNet +from .architecture.SPSR import SPSRNet as SPSR +from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 +from .architecture.SwiftSRGAN import Generator as SwiftSRGAN +from .architecture.Swin2SR import Swin2SR +from .architecture.SwinIR import SwinIR + +PyTorchSRModels = ( + RealESRGANv2, + SPSR, + SwiftSRGAN, + ESRGAN, + SwinIR, + Swin2SR, + HAT, + OmniSR, + SCUNet, + DAT, +) +PyTorchSRModel = Union[ + RealESRGANv2, + SPSR, + SwiftSRGAN, + ESRGAN, + SwinIR, + Swin2SR, + HAT, + OmniSR, + SCUNet, + DAT, +] + + +def is_pytorch_sr_model(model: object): + return isinstance(model, PyTorchSRModels) + + +PyTorchFaceModels = (GFPGANv1Clean, RestoreFormer, CodeFormer) +PyTorchFaceModel = Union[GFPGANv1Clean, RestoreFormer, CodeFormer] + + +def is_pytorch_face_model(model: object): + return isinstance(model, PyTorchFaceModels) + + +PyTorchInpaintModels = (LaMa,) +PyTorchInpaintModel = Union[LaMa] + + +def is_pytorch_inpaint_model(model: object): + return isinstance(model, PyTorchInpaintModels) + + +PyTorchModels = (*PyTorchSRModels, *PyTorchFaceModels, *PyTorchInpaintModels) +PyTorchModel = Union[PyTorchSRModel, PyTorchFaceModel, PyTorchInpaintModel] + + +def is_pytorch_model(model: object): + return isinstance(model, PyTorchModels) diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py new file mode 100644 index 0000000000000000000000000000000000000000..94d453f2ca5ca75639c2b441ab5ec108df041f2e --- /dev/null +++ b/comfy_extras/nodes_canny.py @@ -0,0 +1,299 @@ +#From https://github.com/kornia/kornia +import math + +import torch +import torch.nn.functional as F +import comfy.model_management + +def get_canny_nms_kernel(device=None, dtype=None): + """Utility function that returns 3x3 kernels for the Canny Non-maximal suppression.""" + return torch.tensor( + [ + [[[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]]], + [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]], + [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]]], + [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]]], + [[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], + [[[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], + [[[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], + [[[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], + ], + device=device, + dtype=dtype, + ) + + +def get_hysteresis_kernel(device=None, dtype=None): + """Utility function that returns the 3x3 kernels for the Canny hysteresis.""" + return torch.tensor( + [ + [[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]]], + [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]], + [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]], + [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]]], + [[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], + [[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], + [[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], + [[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], + ], + device=device, + dtype=dtype, + ) + +def gaussian_blur_2d(img, kernel_size, sigma): + ksize_half = (kernel_size - 1) * 0.5 + + x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) + + pdf = torch.exp(-0.5 * (x / sigma).pow(2)) + + x_kernel = pdf / pdf.sum() + x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) + + kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) + kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) + + padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] + + img = torch.nn.functional.pad(img, padding, mode="reflect") + img = torch.nn.functional.conv2d(img, kernel2d, groups=img.shape[-3]) + + return img + +def get_sobel_kernel2d(device=None, dtype=None): + kernel_x = torch.tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]], device=device, dtype=dtype) + kernel_y = kernel_x.transpose(0, 1) + return torch.stack([kernel_x, kernel_y]) + +def spatial_gradient(input, normalized: bool = True): + r"""Compute the first order image derivative in both x and y using a Sobel operator. + .. image:: _static/img/spatial_gradient.png + Args: + input: input image tensor with shape :math:`(B, C, H, W)`. + mode: derivatives modality, can be: `sobel` or `diff`. + order: the order of the derivatives. + normalized: whether the output is normalized. + Return: + the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`. + .. note:: + See a working example `here `__. + Examples: + >>> input = torch.rand(1, 3, 4, 4) + >>> output = spatial_gradient(input) # 1x3x2x4x4 + >>> output.shape + torch.Size([1, 3, 2, 4, 4]) + """ + # KORNIA_CHECK_IS_TENSOR(input) + # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W']) + + # allocate kernel + kernel = get_sobel_kernel2d(device=input.device, dtype=input.dtype) + if normalized: + kernel = normalize_kernel2d(kernel) + + # prepare kernel + b, c, h, w = input.shape + tmp_kernel = kernel[:, None, ...] + + # Pad with "replicate for spatial dims, but with zeros for channel + spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2] + out_channels: int = 2 + padded_inp = torch.nn.functional.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate') + out = F.conv2d(padded_inp, tmp_kernel, groups=1, padding=0, stride=1) + return out.reshape(b, c, out_channels, h, w) + +def rgb_to_grayscale(image, rgb_weights = None): + r"""Convert a RGB image to grayscale version of image. + + .. image:: _static/img/rgb_to_grayscale.png + + The image data is assumed to be in the range of (0, 1). + + Args: + image: RGB image to be converted to grayscale with shape :math:`(*,3,H,W)`. + rgb_weights: Weights that will be applied on each channel (RGB). + The sum of the weights should add up to one. + Returns: + grayscale version of the image with shape :math:`(*,1,H,W)`. + + .. note:: + See a working example `here `__. + + Example: + >>> input = torch.rand(2, 3, 4, 5) + >>> gray = rgb_to_grayscale(input) # 2x1x4x5 + """ + + if len(image.shape) < 3 or image.shape[-3] != 3: + raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}") + + if rgb_weights is None: + # 8 bit images + if image.dtype == torch.uint8: + rgb_weights = torch.tensor([76, 150, 29], device=image.device, dtype=torch.uint8) + # floating point images + elif image.dtype in (torch.float16, torch.float32, torch.float64): + rgb_weights = torch.tensor([0.299, 0.587, 0.114], device=image.device, dtype=image.dtype) + else: + raise TypeError(f"Unknown data type: {image.dtype}") + else: + # is tensor that we make sure is in the same device/dtype + rgb_weights = rgb_weights.to(image) + + # unpack the color image channels with RGB order + r: Tensor = image[..., 0:1, :, :] + g: Tensor = image[..., 1:2, :, :] + b: Tensor = image[..., 2:3, :, :] + + w_r, w_g, w_b = rgb_weights.unbind() + return w_r * r + w_g * g + w_b * b + +def canny( + input, + low_threshold = 0.1, + high_threshold = 0.2, + kernel_size = 5, + sigma = 1, + hysteresis = True, + eps = 1e-6, +): + r"""Find edges of the input image and filters them using the Canny algorithm. + .. image:: _static/img/canny.png + Args: + input: input image tensor with shape :math:`(B,C,H,W)`. + low_threshold: lower threshold for the hysteresis procedure. + high_threshold: upper threshold for the hysteresis procedure. + kernel_size: the size of the kernel for the gaussian blur. + sigma: the standard deviation of the kernel for the gaussian blur. + hysteresis: if True, applies the hysteresis edge tracking. + Otherwise, the edges are divided between weak (0.5) and strong (1) edges. + eps: regularization number to avoid NaN during backprop. + Returns: + - the canny edge magnitudes map, shape of :math:`(B,1,H,W)`. + - the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`. + .. note:: + See a working example `here `__. + Example: + >>> input = torch.rand(5, 3, 4, 4) + >>> magnitude, edges = canny(input) # 5x3x4x4 + >>> magnitude.shape + torch.Size([5, 1, 4, 4]) + >>> edges.shape + torch.Size([5, 1, 4, 4]) + """ + # KORNIA_CHECK_IS_TENSOR(input) + # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W']) + # KORNIA_CHECK( + # low_threshold <= high_threshold, + # "Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: " + # f"{low_threshold}>{high_threshold}", + # ) + # KORNIA_CHECK(0 < low_threshold < 1, f'Invalid low threshold. Should be in range (0, 1). Got: {low_threshold}') + # KORNIA_CHECK(0 < high_threshold < 1, f'Invalid high threshold. Should be in range (0, 1). Got: {high_threshold}') + + device = input.device + dtype = input.dtype + + # To Grayscale + if input.shape[1] == 3: + input = rgb_to_grayscale(input) + + # Gaussian filter + blurred: Tensor = gaussian_blur_2d(input, kernel_size, sigma) + + # Compute the gradients + gradients: Tensor = spatial_gradient(blurred, normalized=False) + + # Unpack the edges + gx: Tensor = gradients[:, :, 0] + gy: Tensor = gradients[:, :, 1] + + # Compute gradient magnitude and angle + magnitude: Tensor = torch.sqrt(gx * gx + gy * gy + eps) + angle: Tensor = torch.atan2(gy, gx) + + # Radians to Degrees + angle = 180.0 * angle / math.pi + + # Round angle to the nearest 45 degree + angle = torch.round(angle / 45) * 45 + + # Non-maximal suppression + nms_kernels: Tensor = get_canny_nms_kernel(device, dtype) + nms_magnitude: Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2) + + # Get the indices for both directions + positive_idx: Tensor = (angle / 45) % 8 + positive_idx = positive_idx.long() + + negative_idx: Tensor = ((angle / 45) + 4) % 8 + negative_idx = negative_idx.long() + + # Apply the non-maximum suppression to the different directions + channel_select_filtered_positive: Tensor = torch.gather(nms_magnitude, 1, positive_idx) + channel_select_filtered_negative: Tensor = torch.gather(nms_magnitude, 1, negative_idx) + + channel_select_filtered: Tensor = torch.stack( + [channel_select_filtered_positive, channel_select_filtered_negative], 1 + ) + + is_max: Tensor = channel_select_filtered.min(dim=1)[0] > 0.0 + + magnitude = magnitude * is_max + + # Threshold + edges: Tensor = F.threshold(magnitude, low_threshold, 0.0) + + low: Tensor = magnitude > low_threshold + high: Tensor = magnitude > high_threshold + + edges = low * 0.5 + high * 0.5 + edges = edges.to(dtype) + + # Hysteresis + if hysteresis: + edges_old: Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype) + hysteresis_kernels: Tensor = get_hysteresis_kernel(device, dtype) + + while ((edges_old - edges).abs() != 0).any(): + weak: Tensor = (edges == 0.5).float() + strong: Tensor = (edges == 1).float() + + hysteresis_magnitude: Tensor = F.conv2d( + edges, hysteresis_kernels, padding=hysteresis_kernels.shape[-1] // 2 + ) + hysteresis_magnitude = (hysteresis_magnitude == 1).any(1, keepdim=True).to(dtype) + hysteresis_magnitude = hysteresis_magnitude * weak + strong + + edges_old = edges.clone() + edges = hysteresis_magnitude + (hysteresis_magnitude == 0) * weak * 0.5 + + edges = hysteresis_magnitude + + return magnitude, edges + + +class Canny: + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE",), + "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}), + "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01}) + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "detect_edge" + + CATEGORY = "image/preprocessors" + + def detect_edge(self, image, low_threshold, high_threshold): + output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) + img_out = output[1].cpu().repeat(1, 3, 1, 1).movedim(1, -1) + return (img_out,) + +NODE_CLASS_MAPPINGS = { + "Canny": Canny, +} diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py new file mode 100644 index 0000000000000000000000000000000000000000..dcf8859fa0c23231abbbfca0a9b01aaa7145a5e4 --- /dev/null +++ b/comfy_extras/nodes_clip_sdxl.py @@ -0,0 +1,56 @@ +import torch +from nodes import MAX_RESOLUTION + +class CLIPTextEncodeSDXLRefiner: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "text": ("STRING", {"multiline": True}), "clip": ("CLIP", ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, ascore, width, height, text): + tokens = clip.tokenize(text) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled, "aesthetic_score": ascore, "width": width,"height": height}]], ) + +class CLIPTextEncodeSDXL: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), + "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), + "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "text_g": ("STRING", {"multiline": True, "default": "CLIP_G"}), "clip": ("CLIP", ), + "text_l": ("STRING", {"multiline": True, "default": "CLIP_L"}), "clip": ("CLIP", ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l): + tokens = clip.tokenize(text_g) + tokens["l"] = clip.tokenize(text_l)["l"] + if len(tokens["l"]) != len(tokens["g"]): + empty = clip.tokenize("") + while len(tokens["l"]) < len(tokens["g"]): + tokens["l"] += empty["l"] + while len(tokens["l"]) > len(tokens["g"]): + tokens["g"] += empty["g"] + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]], ) + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner, + "CLIPTextEncodeSDXL": CLIPTextEncodeSDXL, +} diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py new file mode 100644 index 0000000000000000000000000000000000000000..c3542a7a4312e61794f2ba5d9f3873c623271ce4 --- /dev/null +++ b/comfy_extras/nodes_freelunch.py @@ -0,0 +1,56 @@ +#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License) + +import torch + + +def Fourier_filter(x, threshold, scale): + # FFT + x_freq = torch.fft.fftn(x.float(), dim=(-2, -1)) + x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1)) + + B, C, H, W = x_freq.shape + mask = torch.ones((B, C, H, W), device=x.device) + + crow, ccol = H // 2, W //2 + mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale + x_freq = x_freq * mask + + # IFFT + x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1)) + x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real + + return x_filtered.to(x.dtype) + + +class FreeU: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}), + "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}), + "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), + "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + + def patch(self, model, b1, b2, s1, s2): + model_channels = model.model.model_config.unet_config["model_channels"] + scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} + def output_block_patch(h, hsp, transformer_options): + scale = scale_dict.get(h.shape[1], None) + if scale is not None: + h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0] + hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) + return h, hsp + + m = model.clone() + m.set_model_output_block_patch(output_block_patch) + return (m, ) + + +NODE_CLASS_MAPPINGS = { + "FreeU": FreeU, +} diff --git a/comfy_extras/nodes_hypernetwork.py b/comfy_extras/nodes_hypernetwork.py new file mode 100644 index 0000000000000000000000000000000000000000..d16c49aeb24fcbfdaa8771b51148f8d6c0a403f9 --- /dev/null +++ b/comfy_extras/nodes_hypernetwork.py @@ -0,0 +1,110 @@ +import comfy.utils +import folder_paths +import torch + +def load_hypernetwork_patch(path, strength): + sd = comfy.utils.load_torch_file(path, safe_load=True) + activation_func = sd.get('activation_func', 'linear') + is_layer_norm = sd.get('is_layer_norm', False) + use_dropout = sd.get('use_dropout', False) + activate_output = sd.get('activate_output', False) + last_layer_dropout = sd.get('last_layer_dropout', False) + + valid_activation = { + "linear": torch.nn.Identity, + "relu": torch.nn.ReLU, + "leakyrelu": torch.nn.LeakyReLU, + "elu": torch.nn.ELU, + "swish": torch.nn.Hardswish, + "tanh": torch.nn.Tanh, + "sigmoid": torch.nn.Sigmoid, + "softsign": torch.nn.Softsign, + } + + if activation_func not in valid_activation: + print("Unsupported Hypernetwork format, if you report it I might implement it.", path, " ", activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout) + return None + + out = {} + + for d in sd: + try: + dim = int(d) + except: + continue + + output = [] + for index in [0, 1]: + attn_weights = sd[dim][index] + keys = attn_weights.keys() + + linears = filter(lambda a: a.endswith(".weight"), keys) + linears = list(map(lambda a: a[:-len(".weight")], linears)) + layers = [] + + for i in range(len(linears)): + lin_name = linears[i] + last_layer = (i == (len(linears) - 1)) + penultimate_layer = (i == (len(linears) - 2)) + + lin_weight = attn_weights['{}.weight'.format(lin_name)] + lin_bias = attn_weights['{}.bias'.format(lin_name)] + layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0]) + layer.load_state_dict({"weight": lin_weight, "bias": lin_bias}) + layers.append(layer) + if activation_func != "linear": + if (not last_layer) or (activate_output): + layers.append(valid_activation[activation_func]()) + if is_layer_norm: + layers.append(torch.nn.LayerNorm(lin_weight.shape[0])) + if use_dropout: + if (not last_layer) and (not penultimate_layer or last_layer_dropout): + layers.append(torch.nn.Dropout(p=0.3)) + + output.append(torch.nn.Sequential(*layers)) + out[dim] = torch.nn.ModuleList(output) + + class hypernetwork_patch: + def __init__(self, hypernet, strength): + self.hypernet = hypernet + self.strength = strength + def __call__(self, q, k, v, extra_options): + dim = k.shape[-1] + if dim in self.hypernet: + hn = self.hypernet[dim] + k = k + hn[0](k) * self.strength + v = v + hn[1](v) * self.strength + + return q, k, v + + def to(self, device): + for d in self.hypernet.keys(): + self.hypernet[d] = self.hypernet[d].to(device) + return self + + return hypernetwork_patch(out, strength) + +class HypernetworkLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "hypernetwork_name": (folder_paths.get_filename_list("hypernetworks"), ), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_hypernetwork" + + CATEGORY = "loaders" + + def load_hypernetwork(self, model, hypernetwork_name, strength): + hypernetwork_path = folder_paths.get_full_path("hypernetworks", hypernetwork_name) + model_hypernetwork = model.clone() + patch = load_hypernetwork_patch(hypernetwork_path, strength) + if patch is not None: + model_hypernetwork.set_model_attn1_patch(patch) + model_hypernetwork.set_model_attn2_patch(patch) + return (model_hypernetwork,) + +NODE_CLASS_MAPPINGS = { + "HypernetworkLoader": HypernetworkLoader +} diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py new file mode 100644 index 0000000000000000000000000000000000000000..001de39fceb76920a974e1c86c73239ed27c350e --- /dev/null +++ b/comfy_extras/nodes_latent.py @@ -0,0 +1,74 @@ +import comfy.utils + +def reshape_latent_to(target_shape, latent): + if latent.shape[1:] != target_shape[1:]: + latent.movedim(1, -1) + latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center") + latent.movedim(-1, 1) + return comfy.utils.repeat_to_batch_size(latent, target_shape[0]) + + +class LatentAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + samples_out["samples"] = s1 + s2 + return (samples_out,) + +class LatentSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + samples_out["samples"] = s1 - s2 + return (samples_out,) + +class LatentMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples, multiplier): + samples_out = samples.copy() + + s1 = samples["samples"] + samples_out["samples"] = s1 * multiplier + return (samples_out,) + +NODE_CLASS_MAPPINGS = { + "LatentAdd": LatentAdd, + "LatentSubtract": LatentSubtract, + "LatentMultiply": LatentMultiply, +} diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..43f623a62d2097be506cee41d9b8f6a62efd25b4 --- /dev/null +++ b/comfy_extras/nodes_mask.py @@ -0,0 +1,351 @@ +import numpy as np +from scipy.ndimage import grey_dilation +import torch + +from nodes import MAX_RESOLUTION + +def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): + if resize_source: + source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") + + x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) + y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) + + left, top = (x // multiplier, y // multiplier) + right, bottom = (left + source.shape[3], top + source.shape[2],) + + if mask is None: + mask = torch.ones_like(source) + else: + mask = mask.clone() + mask = torch.nn.functional.interpolate(mask[None, None], size=(source.shape[2], source.shape[3]), mode="bilinear") + mask = mask.repeat((source.shape[0], source.shape[1], 1, 1)) + + # calculate the bounds of the source that will be overlapping the destination + # this prevents the source trying to overwrite latent pixels that are out of bounds + # of the destination + visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) + + mask = mask[:, :, :visible_height, :visible_width] + inverse_mask = torch.ones_like(mask) - mask + + source_portion = mask * source[:, :, :visible_height, :visible_width] + destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] + + destination[:, :, top:bottom, left:right] = source_portion + destination_portion + return destination + +class LatentCompositeMasked: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "destination": ("LATENT",), + "source": ("LATENT",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "resize_source": ("BOOLEAN", {"default": False}), + }, + "optional": { + "mask": ("MASK",), + } + } + RETURN_TYPES = ("LATENT",) + FUNCTION = "composite" + + CATEGORY = "latent" + + def composite(self, destination, source, x, y, resize_source, mask = None): + output = destination.copy() + destination = destination["samples"].clone() + source = source["samples"] + output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) + return (output,) + +class ImageCompositeMasked: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "destination": ("IMAGE",), + "source": ("IMAGE",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "resize_source": ("BOOLEAN", {"default": False}), + }, + "optional": { + "mask": ("MASK",), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "composite" + + CATEGORY = "image" + + def composite(self, destination, source, x, y, resize_source, mask = None): + destination = destination.clone().movedim(-1, 1) + output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) + return (output,) + +class MaskToImage: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "mask_to_image" + + def mask_to_image(self, mask): + result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + return (result,) + +class ImageToMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "channel": (["red", "green", "blue"],), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, image, channel): + channels = ["red", "green", "blue"] + mask = image[0, :, :, channels.index(channel)] + return (mask,) + +class ImageColorToMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, image, color): + temp = (torch.clamp(image[0], 0, 1.0) * 255.0).round().to(torch.int) + temp = torch.bitwise_left_shift(temp[:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,1], 8) + temp[:,:,2] + mask = torch.where(temp == color, 255, 0).float() + return (mask,) + +class SolidMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "solid" + + def solid(self, value, width, height): + out = torch.full((height, width), value, dtype=torch.float32, device="cpu") + return (out,) + +class InvertMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "invert" + + def invert(self, mask): + out = 1.0 - mask + return (out,) + +class CropMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "crop" + + def crop(self, mask, x, y, width, height): + out = mask[y:y + height, x:x + width] + return (out,) + +class MaskComposite: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "destination": ("MASK",), + "source": ("MASK",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "operation": (["multiply", "add", "subtract", "and", "or", "xor"],), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "combine" + + def combine(self, destination, source, x, y, operation): + output = destination.clone() + + left, top = (x, y,) + right, bottom = (min(left + source.shape[1], destination.shape[1]), min(top + source.shape[0], destination.shape[0])) + visible_width, visible_height = (right - left, bottom - top,) + + source_portion = source[:visible_height, :visible_width] + destination_portion = destination[top:bottom, left:right] + + if operation == "multiply": + output[top:bottom, left:right] = destination_portion * source_portion + elif operation == "add": + output[top:bottom, left:right] = destination_portion + source_portion + elif operation == "subtract": + output[top:bottom, left:right] = destination_portion - source_portion + elif operation == "and": + output[top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float() + elif operation == "or": + output[top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float() + elif operation == "xor": + output[top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float() + + output = torch.clamp(output, 0.0, 1.0) + + return (output,) + +class FeatherMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "feather" + + def feather(self, mask, left, top, right, bottom): + output = mask.clone() + + left = min(left, output.shape[1]) + right = min(right, output.shape[1]) + top = min(top, output.shape[0]) + bottom = min(bottom, output.shape[0]) + + for x in range(left): + feather_rate = (x + 1.0) / left + output[:, x] *= feather_rate + + for x in range(right): + feather_rate = (x + 1) / right + output[:, -x] *= feather_rate + + for y in range(top): + feather_rate = (y + 1) / top + output[y, :] *= feather_rate + + for y in range(bottom): + feather_rate = (y + 1) / bottom + output[-y, :] *= feather_rate + + return (output,) + +class GrowMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "expand": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "tapered_corners": ("BOOLEAN", {"default": True}), + }, + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "expand_mask" + + def expand_mask(self, mask, expand, tapered_corners): + c = 0 if tapered_corners else 1 + kernel = np.array([[c, 1, c], + [1, 1, 1], + [c, 1, c]]) + output = mask.numpy().copy() + while expand > 0: + output = grey_dilation(output, footprint=kernel) + expand -= 1 + output = torch.from_numpy(output) + return (output,) + + + +NODE_CLASS_MAPPINGS = { + "LatentCompositeMasked": LatentCompositeMasked, + "ImageCompositeMasked": ImageCompositeMasked, + "MaskToImage": MaskToImage, + "ImageToMask": ImageToMask, + "ImageColorToMask": ImageColorToMask, + "SolidMask": SolidMask, + "InvertMask": InvertMask, + "CropMask": CropMask, + "MaskComposite": MaskComposite, + "FeatherMask": FeatherMask, + "GrowMask": GrowMask, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ImageToMask": "Convert Image to Mask", + "MaskToImage": "Convert Mask to Image", +} diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py new file mode 100644 index 0000000000000000000000000000000000000000..3d42d78067c593e1e7e25c0f5af4da2fbaca2201 --- /dev/null +++ b/comfy_extras/nodes_model_merging.py @@ -0,0 +1,189 @@ +import comfy.sd +import comfy.utils +import comfy.model_base + +import folder_paths +import json +import os + +from comfy.cli_args import args + +class ModelMergeSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, ratio): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + +class ModelSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, multiplier): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, - multiplier, multiplier) + return (m, ) + +class ModelAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, 1.0, 1.0) + return (m, ) + + +class CLIPMergeSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip1": ("CLIP",), + "clip2": ("CLIP",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, clip1, clip2, ratio): + m = clip1.clone() + kp = clip2.get_key_patches() + for k in kp: + if k.endswith(".position_ids") or k.endswith(".logit_scale"): + continue + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + +class ModelMergeBlocks: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, **kwargs): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + default_ratio = next(iter(kwargs.values())) + + for k in kp: + ratio = default_ratio + k_unet = k[len("diffusion_model."):] + + last_arg_size = 0 + for arg in kwargs: + if k_unet.startswith(arg) and last_arg_size < len(arg): + ratio = kwargs[arg] + last_arg_size = len(arg) + + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + +class CheckpointSave: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + + enable_modelspec = True + if isinstance(model.model, comfy.model_base.SDXL): + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" + elif isinstance(model.model, comfy.model_base.SDXLRefiner): + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" + else: + enable_modelspec = False + + if enable_modelspec: + metadata["modelspec.sai_model_spec"] = "1.0.0" + metadata["modelspec.implementation"] = "sgm" + metadata["modelspec.title"] = "{} {}".format(filename, counter) + + #TODO: + # "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512", + # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h", + # "v2-inpainting" + + if model.model.model_type == comfy.model_base.ModelType.EPS: + metadata["modelspec.predict_key"] = "epsilon" + elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION: + metadata["modelspec.predict_key"] = "v" + + if not args.disable_metadata: + metadata["prompt"] = prompt_info + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, metadata=metadata) + return {} + + +NODE_CLASS_MAPPINGS = { + "ModelMergeSimple": ModelMergeSimple, + "ModelMergeBlocks": ModelMergeBlocks, + "ModelMergeSubtract": ModelSubtract, + "ModelMergeAdd": ModelAdd, + "CheckpointSave": CheckpointSave, + "CLIPMergeSimple": CLIPMergeSimple, +} diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..3f651e5945671470b36e02057fdc12b89e465500 --- /dev/null +++ b/comfy_extras/nodes_post_processing.py @@ -0,0 +1,245 @@ +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image +import math + +import comfy.utils + + +class Blend: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image1": ("IMAGE",), + "image2": ("IMAGE",), + "blend_factor": ("FLOAT", { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01 + }), + "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "blend_images" + + CATEGORY = "image/postprocessing" + + def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): + if image1.shape != image2.shape: + image2 = image2.permute(0, 3, 1, 2) + image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') + image2 = image2.permute(0, 2, 3, 1) + + blended_image = self.blend_mode(image1, image2, blend_mode) + blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor + blended_image = torch.clamp(blended_image, 0, 1) + return (blended_image,) + + def blend_mode(self, img1, img2, mode): + if mode == "normal": + return img2 + elif mode == "multiply": + return img1 * img2 + elif mode == "screen": + return 1 - (1 - img1) * (1 - img2) + elif mode == "overlay": + return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) + elif mode == "soft_light": + return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1)) + else: + raise ValueError(f"Unsupported blend mode: {mode}") + + def g(self, x): + return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) + +def gaussian_kernel(kernel_size: int, sigma: float, device=None): + x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij") + d = torch.sqrt(x * x + y * y) + g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) + return g / g.sum() + +class Blur: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "blur_radius": ("INT", { + "default": 1, + "min": 1, + "max": 31, + "step": 1 + }), + "sigma": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 10.0, + "step": 0.1 + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "blur" + + CATEGORY = "image/postprocessing" + + def blur(self, image: torch.Tensor, blur_radius: int, sigma: float): + if blur_radius == 0: + return (image,) + + batch_size, height, width, channels = image.shape + + kernel_size = blur_radius * 2 + 1 + kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1) + + image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) + padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect') + blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius] + blurred = blurred.permute(0, 2, 3, 1) + + return (blurred,) + +class Quantize: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "colors": ("INT", { + "default": 256, + "min": 1, + "max": 256, + "step": 1 + }), + "dither": (["none", "floyd-steinberg"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "quantize" + + CATEGORY = "image/postprocessing" + + def quantize(self, image: torch.Tensor, colors: int = 256, dither: str = "FLOYDSTEINBERG"): + batch_size, height, width, _ = image.shape + result = torch.zeros_like(image) + + dither_option = Image.Dither.FLOYDSTEINBERG if dither == "floyd-steinberg" else Image.Dither.NONE + + for b in range(batch_size): + tensor_image = image[b] + img = (tensor_image * 255).to(torch.uint8).numpy() + pil_image = Image.fromarray(img, mode='RGB') + + palette = pil_image.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836 + quantized_image = pil_image.quantize(colors=colors, palette=palette, dither=dither_option) + + quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255 + result[b] = quantized_array + + return (result,) + +class Sharpen: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "sharpen_radius": ("INT", { + "default": 1, + "min": 1, + "max": 31, + "step": 1 + }), + "sigma": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 10.0, + "step": 0.1 + }), + "alpha": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 5.0, + "step": 0.1 + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "sharpen" + + CATEGORY = "image/postprocessing" + + def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float): + if sharpen_radius == 0: + return (image,) + + batch_size, height, width, channels = image.shape + + kernel_size = sharpen_radius * 2 + 1 + kernel = gaussian_kernel(kernel_size, sigma) * -(alpha*10) + center = kernel_size // 2 + kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 + kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) + + tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) + tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') + sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] + sharpened = sharpened.permute(0, 2, 3, 1) + + result = torch.clamp(sharpened, 0, 1) + + return (result,) + +class ImageScaleToTotalPixels: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, megapixels): + samples = image.movedim(-1,1) + total = int(megapixels * 1024 * 1024) + + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = s.movedim(1,-1) + return (s,) + +NODE_CLASS_MAPPINGS = { + "ImageBlend": Blend, + "ImageBlur": Blur, + "ImageQuantize": Quantize, + "ImageSharpen": Sharpen, + "ImageScaleToTotalPixels": ImageScaleToTotalPixels, +} diff --git a/comfy_extras/nodes_rebatch.py b/comfy_extras/nodes_rebatch.py new file mode 100644 index 0000000000000000000000000000000000000000..0a9daf2727618e3dc4bae63a7f7ca4f0e10a7ad4 --- /dev/null +++ b/comfy_extras/nodes_rebatch.py @@ -0,0 +1,108 @@ +import torch + +class LatentRebatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "latents": ("LATENT",), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, ) + + FUNCTION = "rebatch" + + CATEGORY = "latent/batch" + + @staticmethod + def get_batch(latents, list_ind, offset): + '''prepare a batch out of the list of latents''' + samples = latents[list_ind]['samples'] + shape = samples.shape + mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu') + if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]: + torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear") + if mask.shape[0] < samples.shape[0]: + mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]] + if 'batch_index' in latents[list_ind]: + batch_inds = latents[list_ind]['batch_index'] + else: + batch_inds = [x+offset for x in range(shape[0])] + return samples, mask, batch_inds + + @staticmethod + def get_slices(indexable, num, batch_size): + '''divides an indexable object into num slices of length batch_size, and a remainder''' + slices = [] + for i in range(num): + slices.append(indexable[i*batch_size:(i+1)*batch_size]) + if num * batch_size < len(indexable): + return slices, indexable[num * batch_size:] + else: + return slices, None + + @staticmethod + def slice_batch(batch, num, batch_size): + result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch] + return list(zip(*result)) + + @staticmethod + def cat_batch(batch1, batch2): + if batch1[0] is None: + return batch2 + result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)] + return result + + def rebatch(self, latents, batch_size): + batch_size = batch_size[0] + + output_list = [] + current_batch = (None, None, None) + processed = 0 + + for i in range(len(latents)): + # fetch new entry of list + #samples, masks, indices = self.get_batch(latents, i) + next_batch = self.get_batch(latents, i, processed) + processed += len(next_batch[2]) + # set to current if current is None + if current_batch[0] is None: + current_batch = next_batch + # add previous to list if dimensions do not match + elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]: + sliced, _ = self.slice_batch(current_batch, 1, batch_size) + output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) + current_batch = next_batch + # cat if everything checks out + else: + current_batch = self.cat_batch(current_batch, next_batch) + + # add to list if dimensions gone above target batch size + if current_batch[0].shape[0] > batch_size: + num = current_batch[0].shape[0] // batch_size + sliced, remainder = self.slice_batch(current_batch, num, batch_size) + + for i in range(num): + output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]}) + + current_batch = remainder + + #add remainder + if current_batch[0] is not None: + sliced, _ = self.slice_batch(current_batch, 1, batch_size) + output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) + + #get rid of empty masks + for s in output_list: + if s['noise_mask'].mean() == 1.0: + del s['noise_mask'] + + return (output_list,) + +NODE_CLASS_MAPPINGS = { + "RebatchLatents": LatentRebatch, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RebatchLatents": "Rebatch Latents", +} \ No newline at end of file diff --git a/comfy_extras/nodes_tomesd.py b/comfy_extras/nodes_tomesd.py new file mode 100644 index 0000000000000000000000000000000000000000..df0485063e68d3a38eac4755600748e096426231 --- /dev/null +++ b/comfy_extras/nodes_tomesd.py @@ -0,0 +1,177 @@ +#Taken from: https://github.com/dbolya/tomesd + +import torch +from typing import Tuple, Callable +import math + +def do_nothing(x: torch.Tensor, mode:str=None): + return x + + +def mps_gather_workaround(input, dim, index): + if input.shape[-1] == 1: + return torch.gather( + input.unsqueeze(-1), + dim - 1 if dim < 0 else dim, + index.unsqueeze(-1) + ).squeeze(-1) + else: + return torch.gather(input, dim, index) + + +def bipartite_soft_matching_random2d(metric: torch.Tensor, + w: int, h: int, sx: int, sy: int, r: int, + no_rand: bool = False) -> Tuple[Callable, Callable]: + """ + Partitions the tokens into src and dst and merges r tokens from src to dst. + Dst tokens are partitioned by choosing one randomy in each (sx, sy) region. + Args: + - metric [B, N, C]: metric to use for similarity + - w: image width in tokens + - h: image height in tokens + - sx: stride in the x dimension for dst, must divide w + - sy: stride in the y dimension for dst, must divide h + - r: number of tokens to remove (by merging) + - no_rand: if true, disable randomness (use top left corner only) + """ + B, N, _ = metric.shape + + if r <= 0 or w == 1 or h == 1: + return do_nothing, do_nothing + + gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather + + with torch.no_grad(): + + hsy, wsx = h // sy, w // sx + + # For each sy by sx kernel, randomly assign one token to be dst and the rest src + if no_rand: + rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64) + else: + rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device) + + # The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead + idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64) + idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) + idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx) + + # Image is not divisible by sx or sy so we need to move it into a new buffer + if (hsy * sy) < h or (wsx * sx) < w: + idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64) + idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view + else: + idx_buffer = idx_buffer_view + + # We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices + rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1) + + # We're finished with these + del idx_buffer, idx_buffer_view + + # rand_idx is currently dst|src, so split them + num_dst = hsy * wsx + a_idx = rand_idx[:, num_dst:, :] # src + b_idx = rand_idx[:, :num_dst, :] # dst + + def split(x): + C = x.shape[-1] + src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C)) + dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C)) + return src, dst + + # Cosine similarity between A and B + metric = metric / metric.norm(dim=-1, keepdim=True) + a, b = split(metric) + scores = a @ b.transpose(-1, -2) + + # Can't reduce more than the # tokens in src + r = min(a.shape[1], r) + + # Find the most similar greedily + node_max, node_idx = scores.max(dim=-1) + edge_idx = node_max.argsort(dim=-1, descending=True)[..., None] + + unm_idx = edge_idx[..., r:, :] # Unmerged Tokens + src_idx = edge_idx[..., :r, :] # Merged Tokens + dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx) + + def merge(x: torch.Tensor, mode="mean") -> torch.Tensor: + src, dst = split(x) + n, t1, c = src.shape + + unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c)) + src = gather(src, dim=-2, index=src_idx.expand(n, r, c)) + dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode) + + return torch.cat([unm, dst], dim=1) + + def unmerge(x: torch.Tensor) -> torch.Tensor: + unm_len = unm_idx.shape[1] + unm, dst = x[..., :unm_len, :], x[..., unm_len:, :] + _, _, c = unm.shape + + src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c)) + + # Combine back to the original shape + out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype) + out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst) + out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm) + out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src) + + return out + + return merge, unmerge + + +def get_functions(x, ratio, original_shape): + b, c, original_h, original_w = original_shape + original_tokens = original_h * original_w + downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1]))) + stride_x = 2 + stride_y = 2 + max_downsample = 1 + + if downsample <= max_downsample: + w = int(math.ceil(original_w / downsample)) + h = int(math.ceil(original_h / downsample)) + r = int(x.shape[1] * ratio) + no_rand = False + m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand) + return m, u + + nothing = lambda y: y + return nothing, nothing + + + +class TomePatchModel: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + + def patch(self, model, ratio): + self.u = None + def tomesd_m(q, k, v, extra_options): + #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q + #however from my basic testing it seems that using q instead gives better results + m, self.u = get_functions(q, ratio, extra_options["original_shape"]) + return m(q), k, v + def tomesd_u(n, extra_options): + return self.u(n) + + m = model.clone() + m.set_model_attn1_patch(tomesd_m) + m.set_model_attn1_output_patch(tomesd_u) + return (m, ) + + +NODE_CLASS_MAPPINGS = { + "TomePatchModel": TomePatchModel, +} diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py new file mode 100644 index 0000000000000000000000000000000000000000..2b5e49a55c2ecd64efea3cab9d1751b25912d898 --- /dev/null +++ b/comfy_extras/nodes_upscale_model.py @@ -0,0 +1,66 @@ +import os +from comfy_extras.chainner_models import model_loading +from comfy import model_management +import torch +import comfy.utils +import folder_paths + +class UpscaleModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ), + }} + RETURN_TYPES = ("UPSCALE_MODEL",) + FUNCTION = "load_model" + + CATEGORY = "loaders" + + def load_model(self, model_name): + model_path = folder_paths.get_full_path("upscale_models", model_name) + sd = comfy.utils.load_torch_file(model_path, safe_load=True) + if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: + sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""}) + out = model_loading.load_state_dict(sd).eval() + return (out, ) + + +class ImageUpscaleWithModel: + @classmethod + def INPUT_TYPES(s): + return {"required": { "upscale_model": ("UPSCALE_MODEL",), + "image": ("IMAGE",), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, upscale_model, image): + device = model_management.get_torch_device() + upscale_model.to(device) + in_img = image.movedim(-1,-3).to(device) + free_memory = model_management.get_free_memory(device) + + tile = 512 + overlap = 32 + + oom = True + while oom: + try: + steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) + pbar = comfy.utils.ProgressBar(steps) + s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) + oom = False + except model_management.OOM_EXCEPTION as e: + tile //= 2 + if tile < 128: + raise e + + upscale_model.cpu() + s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) + return (s,) + +NODE_CLASS_MAPPINGS = { + "UpscaleModelLoader": UpscaleModelLoader, + "ImageUpscaleWithModel": ImageUpscaleWithModel +} diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example new file mode 100644 index 0000000000000000000000000000000000000000..733014f3c7d393a81130b41810e3e1d574dd256b --- /dev/null +++ b/custom_nodes/example_node.py.example @@ -0,0 +1,102 @@ +class Example: + """ + A example node + + Class methods + ------------- + INPUT_TYPES (dict): + Tell the main program input parameters of nodes. + + Attributes + ---------- + RETURN_TYPES (`tuple`): + The type of each element in the output tulple. + RETURN_NAMES (`tuple`): + Optional: The name of each output in the output tulple. + FUNCTION (`str`): + The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute() + OUTPUT_NODE ([`bool`]): + If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example. + The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected. + Assumed to be False if not present. + CATEGORY (`str`): + The category the node should appear in the UI. + execute(s) -> tuple || None: + The entry point method. The name of this method must be the same as the value of property `FUNCTION`. + For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`. + """ + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + """ + Return a dictionary which contains config for all input fields. + Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT". + Input types "INT", "STRING" or "FLOAT" are special values for fields on the node. + The type can be a list for selection. + + Returns: `dict`: + - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required` + - Value input_fields (`dict`): Contains input fields config: + * Key field_name (`string`): Name of a entry-point method's argument + * Value field_config (`tuple`): + + First value is a string indicate the type of field or a list for selection. + + Secound value is a config for type "INT", "STRING" or "FLOAT". + """ + return { + "required": { + "image": ("IMAGE",), + "int_field": ("INT", { + "default": 0, + "min": 0, #Minimum value + "max": 4096, #Maximum value + "step": 64, #Slider's step + "display": "number" # Cosmetic only: display as "number" or "slider" + }), + "float_field": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 10.0, + "step": 0.01, + "round": 0.001, #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. + "display": "number"}), + "print_to_screen": (["enable", "disable"],), + "string_field": ("STRING", { + "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node + "default": "Hello World!" + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + #RETURN_NAMES = ("image_output_name",) + + FUNCTION = "test" + + #OUTPUT_NODE = False + + CATEGORY = "Example" + + def test(self, image, string_field, int_field, float_field, print_to_screen): + if print_to_screen == "enable": + print(f"""Your input contains: + string_field aka input text: {string_field} + int_field: {int_field} + float_field: {float_field} + """) + #do some processing on the image, in this example I just invert it + image = 1.0 - image + return (image,) + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "Example": Example +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "Example": "Example Node" +} diff --git a/input/example.png b/input/example.png new file mode 100644 index 0000000000000000000000000000000000000000..7b7f3c9cbbe6d8750c4a9eaf65d6ae4d2f108f79 Binary files /dev/null and b/input/example.png differ diff --git a/models/checkpoints/put_checkpoints_here b/models/checkpoints/put_checkpoints_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/clip/put_clip_or_text_encoder_models_here b/models/clip/put_clip_or_text_encoder_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/clip_vision/put_clip_vision_models_here b/models/clip_vision/put_clip_vision_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/configs/anything_v3.yaml b/models/configs/anything_v3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bcfe584ae73d60e2c7a6f89b3f7befbd487ea34 --- /dev/null +++ b/models/configs/anything_v3.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference.yaml b/models/configs/v1-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4effe569e897369918625f9d8be5603a0e6a0d6 --- /dev/null +++ b/models/configs/v1-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inference_clip_skip_2.yaml b/models/configs/v1-inference_clip_skip_2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bcfe584ae73d60e2c7a6f89b3f7befbd487ea34 --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference_clip_skip_2_fp16.yaml b/models/configs/v1-inference_clip_skip_2_fp16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7eca31c7b5e571c2b1348e94ed9d69978ebd2d52 --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2_fp16.yaml @@ -0,0 +1,74 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference_fp16.yaml b/models/configs/v1-inference_fp16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..147f42b17b835cc839338156f99e8f971df5c1aa --- /dev/null +++ b/models/configs/v1-inference_fp16.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inpainting-inference.yaml b/models/configs/v1-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..45f3f82d461cd8c6109f26ec3b1da75366eda0b0 --- /dev/null +++ b/models/configs/v1-inpainting-inference.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 7.5e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid # important + monitor: val/loss_simple_ema + scale_factor: 0.18215 + finetune_keys: null + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 9 # 4 data + 4 downscaled image + 1 mask + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + diff --git a/models/configs/v2-inference-v.yaml b/models/configs/v2-inference-v.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ec8dfbfefe94ae8522c93017668fea78d580acf --- /dev/null +++ b/models/configs/v2-inference-v.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference-v_fp32.yaml b/models/configs/v2-inference-v_fp32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d5c9b9cb29ca162ade44a7c922f59e75d7d57813 --- /dev/null +++ b/models/configs/v2-inference-v_fp32.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference.yaml b/models/configs/v2-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..152c4f3c2b36c3b246a9cb10eb8166134b0d2e1c --- /dev/null +++ b/models/configs/v2-inference.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference_fp32.yaml b/models/configs/v2-inference_fp32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0d03231f3f2c2e8ef8fbe0d781e5f3d65409ef3a --- /dev/null +++ b/models/configs/v2-inference_fp32.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inpainting-inference.yaml b/models/configs/v2-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..32a9471d71b828c51bcbbabfe34c5f6c8282c803 --- /dev/null +++ b/models/configs/v2-inpainting-inference.yaml @@ -0,0 +1,158 @@ +model: + base_learning_rate: 5.0e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: hybrid + scale_factor: 0.18215 + monitor: val/loss_simple_ema + finetune_keys: null + use_ema: False + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + image_size: 32 # unused + in_channels: 9 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + + +data: + target: ldm.data.laion.WebDataModuleFromConfig + params: + tar_base: null # for concat as in LAION-A + p_unsafe_threshold: 0.1 + filter_word_list: "data/filters.yaml" + max_pwatermark: 0.45 + batch_size: 8 + num_workers: 6 + multinode: True + min_size: 512 + train: + shards: + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" + shuffle: 10000 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + # NOTE use enough shards to avoid empty validation loops in workers + validation: + shards: + - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " + shuffle: 0 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.CenterCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + +lightning: + find_unused_parameters: True + modelcheckpoint: + params: + every_n_train_steps: 5000 + + callbacks: + metrics_over_trainsteps_checkpoint: + params: + every_n_train_steps: 10000 + + image_logger: + target: main.ImageLogger + params: + enable_autocast: False + disabled: False + batch_frequency: 1000 + max_images: 4 + increase_log_steps: False + log_first_step: False + log_images_kwargs: + use_ema_scope: False + inpaint: False + plot_progressive_rows: False + plot_diffusion_rows: False + N: 4 + unconditional_guidance_scale: 5.0 + unconditional_guidance_label: [""] + ddim_steps: 50 # todo check these out for depth2img, + ddim_eta: 0.0 # todo check these out for depth2img, + + trainer: + benchmark: True + val_check_interval: 5000000 + num_sanity_val_steps: 0 + accumulate_grad_batches: 1 diff --git a/models/controlnet/put_controlnets_and_t2i_here b/models/controlnet/put_controlnets_and_t2i_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/diffusers/put_diffusers_models_here b/models/diffusers/put_diffusers_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/embeddings/put_embeddings_or_textual_inversion_concepts_here b/models/embeddings/put_embeddings_or_textual_inversion_concepts_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/gligen/put_gligen_models_here b/models/gligen/put_gligen_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/hypernetworks/put_hypernetworks_here b/models/hypernetworks/put_hypernetworks_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/loras/put_loras_here b/models/loras/put_loras_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/style_models/put_t2i_style_model_here b/models/style_models/put_t2i_style_model_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/unet/put_unet_files_here b/models/unet/put_unet_files_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/upscale_models/put_esrgan_and_other_upscale_models_here b/models/upscale_models/put_esrgan_and_other_upscale_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vae/put_vae_here b/models/vae/put_vae_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here b/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vae_approx/taesd_decoder.pth b/models/vae_approx/taesd_decoder.pth new file mode 100644 index 0000000000000000000000000000000000000000..922b35877dc289718e400fe6e047c2045b13d902 --- /dev/null +++ b/models/vae_approx/taesd_decoder.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a575a762b1e2bea349f13a3528cdb2c3b2ead1093751ecc02ceaa35f6a8d3895 +size 4913525 diff --git a/models/vae_approx/taesd_encoder.pth b/models/vae_approx/taesd_encoder.pth new file mode 100644 index 0000000000000000000000000000000000000000..7f2446f9b9fb74f56c7ea2bf3000fef0934b2c35 --- /dev/null +++ b/models/vae_approx/taesd_encoder.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8ffa2d4957c3952d251484f7a34b8d9609ea0bb5cc362bae31939b0bd98e2bff +size 4913333 diff --git a/models/vae_approx/taesdxl_decoder.pth b/models/vae_approx/taesdxl_decoder.pth new file mode 100644 index 0000000000000000000000000000000000000000..0e1c3f95050785c1060545f3e9b735e186229706 --- /dev/null +++ b/models/vae_approx/taesdxl_decoder.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:be0310a2d0c8df4fe6903b8f71293f0437f50877f208be8df1fee809db57f3d9 +size 4913525 diff --git a/models/vae_approx/taesdxl_encoder.pth b/models/vae_approx/taesdxl_encoder.pth new file mode 100644 index 0000000000000000000000000000000000000000..72e4f59e917eee7d45a9de1879dde6c728681858 --- /dev/null +++ b/models/vae_approx/taesdxl_encoder.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66e36539d5fc79cf8b2485ee5846fb17a09807652a8eb50f5d11136edd272cbb +size 4913333 diff --git a/notebooks/comfyui_colab.ipynb b/notebooks/comfyui_colab.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4fdccaace4496a39903c20913624ec9f00d214ae --- /dev/null +++ b/notebooks/comfyui_colab.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "aaaaaaaaaa" + }, + "source": [ + "Git clone the repo and install the requirements. (ignore the pip errors about protobuf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbbbbbbbbb" + }, + "outputs": [], + "source": [ + "#@title Environment Setup\n", + "\n", + "from pathlib import Path\n", + "\n", + "OPTIONS = {}\n", + "\n", + "USE_GOOGLE_DRIVE = False #@param {type:\"boolean\"}\n", + "UPDATE_COMFY_UI = True #@param {type:\"boolean\"}\n", + "WORKSPACE = 'ComfyUI'\n", + "OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n", + "OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n", + "\n", + "if OPTIONS['USE_GOOGLE_DRIVE']:\n", + " !echo \"Mounting Google Drive...\"\n", + " %cd /\n", + " \n", + " from google.colab import drive\n", + " drive.mount('/content/drive')\n", + "\n", + " WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n", + " %cd /content/drive/MyDrive\n", + "\n", + "![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI\n", + "%cd $WORKSPACE\n", + "\n", + "if OPTIONS['UPDATE_COMFY_UI']:\n", + " !echo -= Updating ComfyUI =-\n", + " !git pull\n", + "\n", + "!echo -= Install dependencies =-\n", + "!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cccccccccc" + }, + "source": [ + "Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dddddddddd" + }, + "outputs": [], + "source": [ + "# Checkpoints\n", + "\n", + "### SDXL\n", + "### I recommend these workflow examples: https://comfyanonymous.github.io/ComfyUI_examples/sdxl/\n", + "\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors -P ./models/checkpoints/\n", + "\n", + "# SDXL ReVision\n", + "#!wget -c https://huggingface.co/comfyanonymous/clip_vision_g/resolve/main/clip_vision_g.safetensors -P ./models/clip_vision/\n", + "\n", + "# SD1.5\n", + "!wget -c https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -P ./models/checkpoints/\n", + "\n", + "# SD2\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Some SD1.5 anime style\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n", + "\n", + "# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n", + "#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# unCLIP models\n", + "#!wget -c https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "#!wget -c https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", + "\n", + "\n", + "# VAE\n", + "!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n", + "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n", + "#!wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P ./models/vae/\n", + "\n", + "\n", + "# Loras\n", + "#!wget -c https://civitai.com/api/download/models/10350 -O ./models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v\n", + "#!wget -c https://civitai.com/api/download/models/10638 -O ./models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x\n", + "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors -P ./models/loras/ #SDXL offset noise lora\n", + "\n", + "\n", + "# T2I-Adapter\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/controlnet/\n", + "\n", + "# T2I Styles Model\n", + "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P ./models/style_models/\n", + "\n", + "# CLIPVision model (needed for styles model)\n", + "#!wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O ./models/clip_vision/clip_vit14.bin\n", + "\n", + "\n", + "# ControlNet\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors -P ./models/controlnet/\n", + "\n", + "# ControlNet SDXL\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-canny-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-depth-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors -P ./models/controlnet/\n", + "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors -P ./models/controlnet/\n", + "\n", + "# Controlnet Preprocessor nodes by Fannovel16\n", + "#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n", + "\n", + "\n", + "# GLIGEN\n", + "#!wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/\n", + "\n", + "\n", + "# ESRGAN upscale model\n", + "#!wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n", + "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with cloudflared (Recommended Way)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n", + "!dpkg -i cloudflared-linux-amd64.deb\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch cloudflared (if it gets stuck here cloudflared is having issues)\\n\")\n", + "\n", + " p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", + " for line in p.stderr:\n", + " l = line.decode()\n", + " if \"trycloudflare.com \" in l:\n", + " print(\"This is the URL to access ComfyUI:\", l[l.find(\"http\"):], end='')\n", + " #print(l, end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kkkkkkkkkkkkkk" + }, + "source": [ + "### Run ComfyUI with localtunnel\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jjjjjjjjjjjjj" + }, + "outputs": [], + "source": [ + "!npm install -g localtunnel\n", + "\n", + "import subprocess\n", + "import threading\n", + "import time\n", + "import socket\n", + "import urllib.request\n", + "\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n", + "\n", + " print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n", + " p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n", + " for line in p.stdout:\n", + " print(line.decode(), end='')\n", + "\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gggggggggg" + }, + "source": [ + "### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n", + "\n", + "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n", + "\n", + "If you want to open it in another window use the link.\n", + "\n", + "Note that some UI features like live image previews won't work because the colab iframe blocks websockets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hhhhhhhhhh" + }, + "outputs": [], + "source": [ + "import threading\n", + "import time\n", + "import socket\n", + "def iframe_thread(port):\n", + " while True:\n", + " time.sleep(0.5)\n", + " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + " result = sock.connect_ex(('127.0.0.1', port))\n", + " if result == 0:\n", + " break\n", + " sock.close()\n", + " from google.colab import output\n", + " output.serve_kernel_port_as_iframe(port, height=1024)\n", + " print(\"to open it in a window you can open this link here:\")\n", + " output.serve_kernel_port_as_window(port)\n", + "\n", + "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", + "\n", + "!python main.py --dont-print-server" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/output/_output_images_will_be_put_here b/output/_output_images_will_be_put_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/script_examples/basic_api_example.py b/script_examples/basic_api_example.py new file mode 100644 index 0000000000000000000000000000000000000000..242d3175f2eecceeae70ed54d7b0d4f02ae5cfb7 --- /dev/null +++ b/script_examples/basic_api_example.py @@ -0,0 +1,120 @@ +import json +from urllib import request, parse +import random + +#This is the ComfyUI api prompt format. + +#If you want it for a specific workflow you can "enable dev mode options" +#in the settings of the UI (gear beside the "Queue Size: ") this will enable +#a button on the UI to save workflows in api format. + +#keep in mind ComfyUI is pre alpha software so this format will change a bit. + +#this is the one for the default workflow +prompt_text = """ +{ + "3": { + "class_type": "KSampler", + "inputs": { + "cfg": 8, + "denoise": 1, + "latent_image": [ + "5", + 0 + ], + "model": [ + "4", + 0 + ], + "negative": [ + "7", + 0 + ], + "positive": [ + "6", + 0 + ], + "sampler_name": "euler", + "scheduler": "normal", + "seed": 8566257, + "steps": 20 + } + }, + "4": { + "class_type": "CheckpointLoaderSimple", + "inputs": { + "ckpt_name": "v1-5-pruned-emaonly.ckpt" + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "batch_size": 1, + "height": 512, + "width": 512 + } + }, + "6": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "masterpiece best quality girl" + } + }, + "7": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "bad hands" + } + }, + "8": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "3", + 0 + ], + "vae": [ + "4", + 2 + ] + } + }, + "9": { + "class_type": "SaveImage", + "inputs": { + "filename_prefix": "ComfyUI", + "images": [ + "8", + 0 + ] + } + } +} +""" + +def queue_prompt(prompt): + p = {"prompt": prompt} + data = json.dumps(p).encode('utf-8') + req = request.Request("http://127.0.0.1:8188/prompt", data=data) + request.urlopen(req) + + +prompt = json.loads(prompt_text) +#set the text prompt for our positive CLIPTextEncode +prompt["6"]["inputs"]["text"] = "masterpiece best quality man" + +#set the seed for our KSampler node +prompt["3"]["inputs"]["seed"] = 5 + + +queue_prompt(prompt) + + diff --git a/script_examples/websockets_api_example.py b/script_examples/websockets_api_example.py new file mode 100644 index 0000000000000000000000000000000000000000..57a6cbd9bad181e547146e159a7969235dc9a945 --- /dev/null +++ b/script_examples/websockets_api_example.py @@ -0,0 +1,164 @@ +#This is an example that uses the websockets api to know when a prompt execution is done +#Once the prompt execution is done it downloads the images using the /history endpoint + +import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) +import uuid +import json +import urllib.request +import urllib.parse + +server_address = "127.0.0.1:8188" +client_id = str(uuid.uuid4()) + +def queue_prompt(prompt): + p = {"prompt": prompt, "client_id": client_id} + data = json.dumps(p).encode('utf-8') + req = urllib.request.Request("http://{}/prompt".format(server_address), data=data) + return json.loads(urllib.request.urlopen(req).read()) + +def get_image(filename, subfolder, folder_type): + data = {"filename": filename, "subfolder": subfolder, "type": folder_type} + url_values = urllib.parse.urlencode(data) + with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response: + return response.read() + +def get_history(prompt_id): + with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response: + return json.loads(response.read()) + +def get_images(ws, prompt): + prompt_id = queue_prompt(prompt)['prompt_id'] + output_images = {} + while True: + out = ws.recv() + if isinstance(out, str): + message = json.loads(out) + if message['type'] == 'executing': + data = message['data'] + if data['node'] is None and data['prompt_id'] == prompt_id: + break #Execution is done + else: + continue #previews are binary data + + history = get_history(prompt_id)[prompt_id] + for o in history['outputs']: + for node_id in history['outputs']: + node_output = history['outputs'][node_id] + if 'images' in node_output: + images_output = [] + for image in node_output['images']: + image_data = get_image(image['filename'], image['subfolder'], image['type']) + images_output.append(image_data) + output_images[node_id] = images_output + + return output_images + +prompt_text = """ +{ + "3": { + "class_type": "KSampler", + "inputs": { + "cfg": 8, + "denoise": 1, + "latent_image": [ + "5", + 0 + ], + "model": [ + "4", + 0 + ], + "negative": [ + "7", + 0 + ], + "positive": [ + "6", + 0 + ], + "sampler_name": "euler", + "scheduler": "normal", + "seed": 8566257, + "steps": 20 + } + }, + "4": { + "class_type": "CheckpointLoaderSimple", + "inputs": { + "ckpt_name": "v1-5-pruned-emaonly.ckpt" + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "batch_size": 1, + "height": 512, + "width": 512 + } + }, + "6": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "masterpiece best quality girl" + } + }, + "7": { + "class_type": "CLIPTextEncode", + "inputs": { + "clip": [ + "4", + 1 + ], + "text": "bad hands" + } + }, + "8": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "3", + 0 + ], + "vae": [ + "4", + 2 + ] + } + }, + "9": { + "class_type": "SaveImage", + "inputs": { + "filename_prefix": "ComfyUI", + "images": [ + "8", + 0 + ] + } + } +} +""" + +prompt = json.loads(prompt_text) +#set the text prompt for our positive CLIPTextEncode +prompt["6"]["inputs"]["text"] = "masterpiece best quality man" + +#set the seed for our KSampler node +prompt["3"]["inputs"]["seed"] = 5 + +ws = websocket.WebSocket() +ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id)) +images = get_images(ws, prompt) + +#Commented out code to display the output images: + +# for node_id in images: +# for image_data in images[node_id]: +# from PIL import Image +# import io +# image = Image.open(io.BytesIO(image_data)) +# image.show() + diff --git a/tests/README.md b/tests/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2005fd45b2bbd249fb7f1dfff789b2ae236568ba --- /dev/null +++ b/tests/README.md @@ -0,0 +1,29 @@ +# Automated Testing + +## Running tests locally + +Additional requirements for running tests: +``` +pip install pytest +pip install websocket-client==1.6.1 +opencv-python==4.6.0.66 +scikit-image==0.21.0 +``` +Run inference tests: +``` +pytest tests/inference +``` + +## Quality regression test +Compares images in 2 directories to ensure they are the same + +1) Run an inference test to save a directory of "ground truth" images +``` + pytest tests/inference --output_dir tests/inference/baseline +``` +2) Make code edits + +3) Run inference and quality comparison tests +``` +pytest +``` \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/compare/conftest.py b/tests/compare/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..dd5078c9e6e432c7de2462c5641068bfa8e0aaee --- /dev/null +++ b/tests/compare/conftest.py @@ -0,0 +1,41 @@ +import os +import pytest + +# Command line arguments for pytest +def pytest_addoption(parser): + parser.addoption('--baseline_dir', action="store", default='tests/inference/baseline', help='Directory for ground-truth images') + parser.addoption('--test_dir', action="store", default='tests/inference/samples', help='Directory for images to test') + parser.addoption('--metrics_file', action="store", default='tests/metrics.md', help='Output file for metrics') + parser.addoption('--img_output_dir', action="store", default='tests/compare/samples', help='Output directory for diff metric images') + +# This initializes args at the beginning of the test session +@pytest.fixture(scope="session", autouse=True) +def args_pytest(pytestconfig): + args = {} + args['baseline_dir'] = pytestconfig.getoption('baseline_dir') + args['test_dir'] = pytestconfig.getoption('test_dir') + args['metrics_file'] = pytestconfig.getoption('metrics_file') + args['img_output_dir'] = pytestconfig.getoption('img_output_dir') + + # Initialize metrics file + with open(args['metrics_file'], 'a') as f: + # if file is empty, write header + if os.stat(args['metrics_file']).st_size == 0: + f.write("| date | run | file | status | value | \n") + f.write("| --- | --- | --- | --- | --- | \n") + + return args + + +def gather_file_basenames(directory: str): + files = [] + for file in os.listdir(directory): + if file.endswith(".png"): + files.append(file) + return files + +# Creates the list of baseline file names to use as a fixture +def pytest_generate_tests(metafunc): + if "baseline_fname" in metafunc.fixturenames: + baseline_fnames = gather_file_basenames(metafunc.config.getoption("baseline_dir")) + metafunc.parametrize("baseline_fname", baseline_fnames) diff --git a/tests/compare/test_quality.py b/tests/compare/test_quality.py new file mode 100644 index 0000000000000000000000000000000000000000..92a2d5a8b021e98c6d24343033ac01020a5016bd --- /dev/null +++ b/tests/compare/test_quality.py @@ -0,0 +1,195 @@ +import datetime +import numpy as np +import os +from PIL import Image +import pytest +from pytest import fixture +from typing import Tuple, List + +from cv2 import imread, cvtColor, COLOR_BGR2RGB +from skimage.metrics import structural_similarity as ssim + + +""" +This test suite compares images in 2 directories by file name +The directories are specified by the command line arguments --baseline_dir and --test_dir + +""" +# ssim: Structural Similarity Index +# Returns a tuple of (ssim, diff_image) +def ssim_score(img0: np.ndarray, img1: np.ndarray) -> Tuple[float, np.ndarray]: + score, diff = ssim(img0, img1, channel_axis=-1, full=True) + # rescale the difference image to 0-255 range + diff = (diff * 255).astype("uint8") + return score, diff + +# Metrics must return a tuple of (score, diff_image) +METRICS = {"ssim": ssim_score} +METRICS_PASS_THRESHOLD = {"ssim": 0.95} + + +class TestCompareImageMetrics: + @fixture(scope="class") + def test_file_names(self, args_pytest): + test_dir = args_pytest['test_dir'] + fnames = self.gather_file_basenames(test_dir) + yield fnames + del fnames + + @fixture(scope="class", autouse=True) + def teardown(self, args_pytest): + yield + # Runs after all tests are complete + # Aggregate output files into a grid of images + baseline_dir = args_pytest['baseline_dir'] + test_dir = args_pytest['test_dir'] + img_output_dir = args_pytest['img_output_dir'] + metrics_file = args_pytest['metrics_file'] + + grid_dir = os.path.join(img_output_dir, "grid") + os.makedirs(grid_dir, exist_ok=True) + + for metric_dir in METRICS.keys(): + metric_path = os.path.join(img_output_dir, metric_dir) + for file in os.listdir(metric_path): + if file.endswith(".png"): + score = self.lookup_score_from_fname(file, metrics_file) + image_file_list = [] + image_file_list.append([ + os.path.join(baseline_dir, file), + os.path.join(test_dir, file), + os.path.join(metric_path, file) + ]) + # Create grid + image_list = [[Image.open(file) for file in files] for files in image_file_list] + grid = self.image_grid(image_list) + grid.save(os.path.join(grid_dir, f"{metric_dir}_{score:.3f}_{file}")) + + # Tests run for each baseline file name + @fixture() + def fname(self, baseline_fname): + yield baseline_fname + del baseline_fname + + def test_directories_not_empty(self, args_pytest): + baseline_dir = args_pytest['baseline_dir'] + test_dir = args_pytest['test_dir'] + assert len(os.listdir(baseline_dir)) != 0, f"Baseline directory {baseline_dir} is empty" + assert len(os.listdir(test_dir)) != 0, f"Test directory {test_dir} is empty" + + def test_dir_has_all_matching_metadata(self, fname, test_file_names, args_pytest): + # Check that all files in baseline_dir have a file in test_dir with matching metadata + baseline_file_path = os.path.join(args_pytest['baseline_dir'], fname) + file_paths = [os.path.join(args_pytest['test_dir'], f) for f in test_file_names] + file_match = self.find_file_match(baseline_file_path, file_paths) + assert file_match is not None, f"Could not find a file in {args_pytest['test_dir']} with matching metadata to {baseline_file_path}" + + # For a baseline image file, finds the corresponding file name in test_dir and + # compares the images using the metrics in METRICS + @pytest.mark.parametrize("metric", METRICS.keys()) + def test_pipeline_compare( + self, + args_pytest, + fname, + test_file_names, + metric, + ): + baseline_dir = args_pytest['baseline_dir'] + test_dir = args_pytest['test_dir'] + metrics_output_file = args_pytest['metrics_file'] + img_output_dir = args_pytest['img_output_dir'] + + baseline_file_path = os.path.join(baseline_dir, fname) + + # Find file match + file_paths = [os.path.join(test_dir, f) for f in test_file_names] + test_file = self.find_file_match(baseline_file_path, file_paths) + + # Run metrics + sample_baseline = self.read_img(baseline_file_path) + sample_secondary = self.read_img(test_file) + + score, metric_img = METRICS[metric](sample_baseline, sample_secondary) + metric_status = score > METRICS_PASS_THRESHOLD[metric] + + # Save metric values + with open(metrics_output_file, 'a') as f: + run_info = os.path.splitext(fname)[0] + metric_status_str = "PASS ✅" if metric_status else "FAIL ❌" + date_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + f.write(f"| {date_str} | {run_info} | {metric} | {metric_status_str} | {score} | \n") + + # Save metric image + metric_img_dir = os.path.join(img_output_dir, metric) + os.makedirs(metric_img_dir, exist_ok=True) + output_filename = f'{fname}' + Image.fromarray(metric_img).save(os.path.join(metric_img_dir, output_filename)) + + assert score > METRICS_PASS_THRESHOLD[metric] + + def read_img(self, filename: str) -> np.ndarray: + cvImg = imread(filename) + cvImg = cvtColor(cvImg, COLOR_BGR2RGB) + return cvImg + + def image_grid(self, img_list: list[list[Image.Image]]): + # imgs is a 2D list of images + # Assumes the input images are a rectangular grid of equal sized images + rows = len(img_list) + cols = len(img_list[0]) + + w, h = img_list[0][0].size + grid = Image.new('RGB', size=(cols*w, rows*h)) + + for i, row in enumerate(img_list): + for j, img in enumerate(row): + grid.paste(img, box=(j*w, i*h)) + return grid + + def lookup_score_from_fname(self, + fname: str, + metrics_output_file: str + ) -> float: + fname_basestr = os.path.splitext(fname)[0] + with open(metrics_output_file, 'r') as f: + for line in f: + if fname_basestr in line: + score = float(line.split('|')[5]) + return score + raise ValueError(f"Could not find score for {fname} in {metrics_output_file}") + + def gather_file_basenames(self, directory: str): + files = [] + for file in os.listdir(directory): + if file.endswith(".png"): + files.append(file) + return files + + def read_file_prompt(self, fname:str) -> str: + # Read prompt from image file metadata + img = Image.open(fname) + img.load() + return img.info['prompt'] + + def find_file_match(self, baseline_file: str, file_paths: List[str]): + # Find a file in file_paths with matching metadata to baseline_file + baseline_prompt = self.read_file_prompt(baseline_file) + + # Do not match empty prompts + if baseline_prompt is None or baseline_prompt == "": + return None + + # Find file match + # Reorder test_file_names so that the file with matching name is first + # This is an optimization because matching file names are more likely + # to have matching metadata if they were generated with the same script + basename = os.path.basename(baseline_file) + file_path_basenames = [os.path.basename(f) for f in file_paths] + if basename in file_path_basenames: + match_index = file_path_basenames.index(basename) + file_paths.insert(0, file_paths.pop(match_index)) + + for f in file_paths: + test_file_prompt = self.read_file_prompt(f) + if baseline_prompt == test_file_prompt: + return f \ No newline at end of file diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..1a35880af5bf86f4a1680b6835b1dcf2e4ef59c8 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,36 @@ +import os +import pytest + +# Command line arguments for pytest +def pytest_addoption(parser): + parser.addoption('--output_dir', action="store", default='tests/inference/samples', help='Output directory for generated images') + parser.addoption("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)") + parser.addoption("--port", type=int, default=8188, help="Set the listen port.") + +# This initializes args at the beginning of the test session +@pytest.fixture(scope="session", autouse=True) +def args_pytest(pytestconfig): + args = {} + args['output_dir'] = pytestconfig.getoption('output_dir') + args['listen'] = pytestconfig.getoption('listen') + args['port'] = pytestconfig.getoption('port') + + os.makedirs(args['output_dir'], exist_ok=True) + + return args + +def pytest_collection_modifyitems(items): + # Modifies items so tests run in the correct order + + LAST_TESTS = ['test_quality'] + + # Move the last items to the end + last_items = [] + for test_name in LAST_TESTS: + for item in items.copy(): + print(item.module.__name__, item) + if item.module.__name__ == test_name: + last_items.append(item) + items.remove(item) + + items.extend(last_items) diff --git a/tests/inference/__init__.py b/tests/inference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/inference/graphs/default_graph_sdxl1_0.json b/tests/inference/graphs/default_graph_sdxl1_0.json new file mode 100644 index 0000000000000000000000000000000000000000..c06c6829c6253cc71982f3714620bd10dd41bd73 --- /dev/null +++ b/tests/inference/graphs/default_graph_sdxl1_0.json @@ -0,0 +1,144 @@ +{ + "4": { + "inputs": { + "ckpt_name": "sd_xl_base_1.0.safetensors" + }, + "class_type": "CheckpointLoaderSimple" + }, + "5": { + "inputs": { + "width": 1024, + "height": 1024, + "batch_size": 1 + }, + "class_type": "EmptyLatentImage" + }, + "6": { + "inputs": { + "text": "a photo of a cat", + "clip": [ + "4", + 1 + ] + }, + "class_type": "CLIPTextEncode" + }, + "10": { + "inputs": { + "add_noise": "enable", + "noise_seed": 42, + "steps": 20, + "cfg": 7.5, + "sampler_name": "euler", + "scheduler": "normal", + "start_at_step": 0, + "end_at_step": 32, + "return_with_leftover_noise": "enable", + "model": [ + "4", + 0 + ], + "positive": [ + "6", + 0 + ], + "negative": [ + "15", + 0 + ], + "latent_image": [ + "5", + 0 + ] + }, + "class_type": "KSamplerAdvanced" + }, + "12": { + "inputs": { + "samples": [ + "14", + 0 + ], + "vae": [ + "4", + 2 + ] + }, + "class_type": "VAEDecode" + }, + "13": { + "inputs": { + "filename_prefix": "test_inference", + "images": [ + "12", + 0 + ] + }, + "class_type": "SaveImage" + }, + "14": { + "inputs": { + "add_noise": "disable", + "noise_seed": 42, + "steps": 20, + "cfg": 7.5, + "sampler_name": "euler", + "scheduler": "normal", + "start_at_step": 32, + "end_at_step": 10000, + "return_with_leftover_noise": "disable", + "model": [ + "16", + 0 + ], + "positive": [ + "17", + 0 + ], + "negative": [ + "20", + 0 + ], + "latent_image": [ + "10", + 0 + ] + }, + "class_type": "KSamplerAdvanced" + }, + "15": { + "inputs": { + "conditioning": [ + "6", + 0 + ] + }, + "class_type": "ConditioningZeroOut" + }, + "16": { + "inputs": { + "ckpt_name": "sd_xl_refiner_1.0.safetensors" + }, + "class_type": "CheckpointLoaderSimple" + }, + "17": { + "inputs": { + "text": "a photo of a cat", + "clip": [ + "16", + 1 + ] + }, + "class_type": "CLIPTextEncode" + }, + "20": { + "inputs": { + "text": "", + "clip": [ + "16", + 1 + ] + }, + "class_type": "CLIPTextEncode" + } + } \ No newline at end of file diff --git a/tests/inference/test_inference.py b/tests/inference/test_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..141cc5c7eac02e5fe98394f7fc8d76cf820138bb --- /dev/null +++ b/tests/inference/test_inference.py @@ -0,0 +1,239 @@ +from copy import deepcopy +from io import BytesIO +from urllib import request +import numpy +import os +from PIL import Image +import pytest +from pytest import fixture +import time +import torch +from typing import Union +import json +import subprocess +import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) +import uuid +import urllib.request +import urllib.parse + + +from comfy.samplers import KSampler + +""" +These tests generate and save images through a range of parameters +""" + +class ComfyGraph: + def __init__(self, + graph: dict, + sampler_nodes: list[str], + ): + self.graph = graph + self.sampler_nodes = sampler_nodes + + def set_prompt(self, prompt, negative_prompt=None): + # Sets the prompt for the sampler nodes (eg. base and refiner) + for node in self.sampler_nodes: + prompt_node = self.graph[node]['inputs']['positive'][0] + self.graph[prompt_node]['inputs']['text'] = prompt + if negative_prompt: + negative_prompt_node = self.graph[node]['inputs']['negative'][0] + self.graph[negative_prompt_node]['inputs']['text'] = negative_prompt + + def set_sampler_name(self, sampler_name:str, ): + # sets the sampler name for the sampler nodes (eg. base and refiner) + for node in self.sampler_nodes: + self.graph[node]['inputs']['sampler_name'] = sampler_name + + def set_scheduler(self, scheduler:str): + # sets the sampler name for the sampler nodes (eg. base and refiner) + for node in self.sampler_nodes: + self.graph[node]['inputs']['scheduler'] = scheduler + + def set_filename_prefix(self, prefix:str): + # sets the filename prefix for the save nodes + for node in self.graph: + if self.graph[node]['class_type'] == 'SaveImage': + self.graph[node]['inputs']['filename_prefix'] = prefix + + +class ComfyClient: + # From examples/websockets_api_example.py + + def connect(self, + listen:str = '127.0.0.1', + port:Union[str,int] = 8188, + client_id: str = str(uuid.uuid4()) + ): + self.client_id = client_id + self.server_address = f"{listen}:{port}" + ws = websocket.WebSocket() + ws.connect("ws://{}/ws?clientId={}".format(self.server_address, self.client_id)) + self.ws = ws + + def queue_prompt(self, prompt): + p = {"prompt": prompt, "client_id": self.client_id} + data = json.dumps(p).encode('utf-8') + req = urllib.request.Request("http://{}/prompt".format(self.server_address), data=data) + return json.loads(urllib.request.urlopen(req).read()) + + def get_image(self, filename, subfolder, folder_type): + data = {"filename": filename, "subfolder": subfolder, "type": folder_type} + url_values = urllib.parse.urlencode(data) + with urllib.request.urlopen("http://{}/view?{}".format(self.server_address, url_values)) as response: + return response.read() + + def get_history(self, prompt_id): + with urllib.request.urlopen("http://{}/history/{}".format(self.server_address, prompt_id)) as response: + return json.loads(response.read()) + + def get_images(self, graph, save=True): + prompt = graph + if not save: + # Replace save nodes with preview nodes + prompt_str = json.dumps(prompt) + prompt_str = prompt_str.replace('SaveImage', 'PreviewImage') + prompt = json.loads(prompt_str) + + prompt_id = self.queue_prompt(prompt)['prompt_id'] + output_images = {} + while True: + out = self.ws.recv() + if isinstance(out, str): + message = json.loads(out) + if message['type'] == 'executing': + data = message['data'] + if data['node'] is None and data['prompt_id'] == prompt_id: + break #Execution is done + else: + continue #previews are binary data + + history = self.get_history(prompt_id)[prompt_id] + for o in history['outputs']: + for node_id in history['outputs']: + node_output = history['outputs'][node_id] + if 'images' in node_output: + images_output = [] + for image in node_output['images']: + image_data = self.get_image(image['filename'], image['subfolder'], image['type']) + images_output.append(image_data) + output_images[node_id] = images_output + + return output_images + +# +# Initialize graphs +# +default_graph_file = 'tests/inference/graphs/default_graph_sdxl1_0.json' +with open(default_graph_file, 'r') as file: + default_graph = json.loads(file.read()) +DEFAULT_COMFY_GRAPH = ComfyGraph(graph=default_graph, sampler_nodes=['10','14']) +DEFAULT_COMFY_GRAPH_ID = os.path.splitext(os.path.basename(default_graph_file))[0] + +# +# Loop through these variables +# +comfy_graph_list = [DEFAULT_COMFY_GRAPH] +comfy_graph_ids = [DEFAULT_COMFY_GRAPH_ID] +prompt_list = [ + 'a painting of a cat', +] + +sampler_list = KSampler.SAMPLERS +scheduler_list = KSampler.SCHEDULERS + +@pytest.mark.inference +@pytest.mark.parametrize("sampler", sampler_list) +@pytest.mark.parametrize("scheduler", scheduler_list) +@pytest.mark.parametrize("prompt", prompt_list) +class TestInference: + # + # Initialize server and client + # + @fixture(scope="class", autouse=True) + def _server(self, args_pytest): + # Start server + p = subprocess.Popen([ + 'python','main.py', + '--output-directory', args_pytest["output_dir"], + '--listen', args_pytest["listen"], + '--port', str(args_pytest["port"]), + ]) + yield + p.kill() + torch.cuda.empty_cache() + + def start_client(self, listen:str, port:int): + # Start client + comfy_client = ComfyClient() + # Connect to server (with retries) + n_tries = 5 + for i in range(n_tries): + time.sleep(4) + try: + comfy_client.connect(listen=listen, port=port) + except ConnectionRefusedError as e: + print(e) + print(f"({i+1}/{n_tries}) Retrying...") + else: + break + return comfy_client + + # + # Client and graph fixtures with server warmup + # + # Returns a "_client_graph", which is client-graph pair corresponding to an initialized server + # The "graph" is the default graph + @fixture(scope="class", params=comfy_graph_list, ids=comfy_graph_ids, autouse=True) + def _client_graph(self, request, args_pytest, _server) -> (ComfyClient, ComfyGraph): + comfy_graph = request.param + + # Start client + comfy_client = self.start_client(args_pytest["listen"], args_pytest["port"]) + + # Warm up pipeline + comfy_client.get_images(graph=comfy_graph.graph, save=False) + + yield comfy_client, comfy_graph + del comfy_client + del comfy_graph + torch.cuda.empty_cache() + + @fixture + def client(self, _client_graph): + client = _client_graph[0] + yield client + + @fixture + def comfy_graph(self, _client_graph): + # avoid mutating the graph + graph = deepcopy(_client_graph[1]) + yield graph + + def test_comfy( + self, + client, + comfy_graph, + sampler, + scheduler, + prompt, + request + ): + test_info = request.node.name + comfy_graph.set_filename_prefix(test_info) + # Settings for comfy graph + comfy_graph.set_sampler_name(sampler) + comfy_graph.set_scheduler(scheduler) + comfy_graph.set_prompt(prompt) + + # Generate + images = client.get_images(comfy_graph.graph) + + assert len(images) != 0, "No images generated" + # assert all images are not blank + for images_output in images.values(): + for image_data in images_output: + pil_image = Image.open(BytesIO(image_data)) + assert numpy.array(pil_image).any() != 0, "Image is blank" + + diff --git a/web/extensions/core/clipspace.js b/web/extensions/core/clipspace.js new file mode 100644 index 0000000000000000000000000000000000000000..e376a02f70db855e056cb3ccc0c57b6627a190f5 --- /dev/null +++ b/web/extensions/core/clipspace.js @@ -0,0 +1,166 @@ +import { app } from "../../scripts/app.js"; +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { ComfyApp } from "../../scripts/app.js"; + +export class ClipspaceDialog extends ComfyDialog { + static items = []; + static instance = null; + + static registerButton(name, contextPredicate, callback) { + const item = + $el("button", { + type: "button", + textContent: name, + contextPredicate: contextPredicate, + onclick: callback + }) + + ClipspaceDialog.items.push(item); + } + + static invalidatePreview() { + if(ComfyApp.clipspace && ComfyApp.clipspace.imgs && ComfyApp.clipspace.imgs.length > 0) { + const img_preview = document.getElementById("clipspace_preview"); + if(img_preview) { + img_preview.src = ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src; + img_preview.style.maxHeight = "100%"; + img_preview.style.maxWidth = "100%"; + } + } + } + + static invalidate() { + if(ClipspaceDialog.instance) { + const self = ClipspaceDialog.instance; + // allow reconstruct controls when copying from non-image to image content. + const children = $el("div.comfy-modal-content", [ self.createImgSettings(), ...self.createButtons() ]); + + if(self.element) { + // update + self.element.removeChild(self.element.firstChild); + self.element.appendChild(children); + } + else { + // new + self.element = $el("div.comfy-modal", { parent: document.body }, [children,]); + } + + if(self.element.children[0].children.length <= 1) { + self.element.children[0].appendChild($el("p", {}, ["Unable to find the features to edit content of a format stored in the current Clipspace."])); + } + + ClipspaceDialog.invalidatePreview(); + } + } + + constructor() { + super(); + } + + createButtons(self) { + const buttons = []; + + for(let idx in ClipspaceDialog.items) { + const item = ClipspaceDialog.items[idx]; + if(!item.contextPredicate || item.contextPredicate()) + buttons.push(ClipspaceDialog.items[idx]); + } + + buttons.push( + $el("button", { + type: "button", + textContent: "Close", + onclick: () => { this.close(); } + }) + ); + + return buttons; + } + + createImgSettings() { + if(ComfyApp.clipspace.imgs) { + const combo_items = []; + const imgs = ComfyApp.clipspace.imgs; + + for(let i=0; i < imgs.length; i++) { + combo_items.push($el("option", {value:i}, [`${i}`])); + } + + const combo1 = $el("select", + {id:"clipspace_img_selector", onchange:(event) => { + ComfyApp.clipspace['selectedIndex'] = event.target.selectedIndex; + ClipspaceDialog.invalidatePreview(); + } }, combo_items); + + const row1 = + $el("tr", {}, + [ + $el("td", {}, [$el("font", {color:"white"}, ["Select Image"])]), + $el("td", {}, [combo1]) + ]); + + + const combo2 = $el("select", + {id:"clipspace_img_paste_mode", onchange:(event) => { + ComfyApp.clipspace['img_paste_mode'] = event.target.value; + } }, + [ + $el("option", {value:'selected'}, 'selected'), + $el("option", {value:'all'}, 'all') + ]); + combo2.value = ComfyApp.clipspace['img_paste_mode']; + + const row2 = + $el("tr", {}, + [ + $el("td", {}, [$el("font", {color:"white"}, ["Paste Mode"])]), + $el("td", {}, [combo2]) + ]); + + const td = $el("td", {align:'center', width:'100px', height:'100px', colSpan:'2'}, + [ $el("img",{id:"clipspace_preview", ondragstart:() => false},[]) ]); + + const row3 = + $el("tr", {}, [td]); + + return $el("table", {}, [row1, row2, row3]); + } + else { + return []; + } + } + + createImgPreview() { + if(ComfyApp.clipspace.imgs) { + return $el("img",{id:"clipspace_preview", ondragstart:() => false}); + } + else + return []; + } + + show() { + const img_preview = document.getElementById("clipspace_preview"); + ClipspaceDialog.invalidate(); + + this.element.style.display = "block"; + } +} + +app.registerExtension({ + name: "Comfy.Clipspace", + init(app) { + app.openClipspace = + function () { + if(!ClipspaceDialog.instance) { + ClipspaceDialog.instance = new ClipspaceDialog(app); + ComfyApp.clipspace_invalidate_handler = ClipspaceDialog.invalidate; + } + + if(ComfyApp.clipspace) { + ClipspaceDialog.instance.show(); + } + else + app.ui.dialog.show("Clipspace is Empty!"); + }; + } +}); \ No newline at end of file diff --git a/web/extensions/core/colorPalette.js b/web/extensions/core/colorPalette.js new file mode 100644 index 0000000000000000000000000000000000000000..3695b08e27f426ce7cad6e18755ad1d35c3eb356 --- /dev/null +++ b/web/extensions/core/colorPalette.js @@ -0,0 +1,550 @@ +import {app} from "../../scripts/app.js"; +import {$el} from "../../scripts/ui.js"; + +// Manage color palettes + +const colorPalettes = { + "dark": { + "id": "dark", + "name": "Dark (Default)", + "colors": { + "node_slot": { + "CLIP": "#FFD500", // bright yellow + "CLIP_VISION": "#A8DADC", // light blue-gray + "CLIP_VISION_OUTPUT": "#ad7452", // rusty brown-orange + "CONDITIONING": "#FFA931", // vibrant orange-yellow + "CONTROL_NET": "#6EE7B7", // soft mint green + "IMAGE": "#64B5F6", // bright sky blue + "LATENT": "#FF9CF9", // light pink-purple + "MASK": "#81C784", // muted green + "MODEL": "#B39DDB", // light lavender-purple + "STYLE_MODEL": "#C2FFAE", // light green-yellow + "VAE": "#FF6E6E", // bright red + "TAESD": "#DCC274", // cheesecake + }, + "litegraph_base": { + "BACKGROUND_IMAGE": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAQBJREFUeNrs1rEKwjAUhlETUkj3vP9rdmr1Ysammk2w5wdxuLgcMHyptfawuZX4pJSWZTnfnu/lnIe/jNNxHHGNn//HNbbv+4dr6V+11uF527arU7+u63qfa/bnmh8sWLBgwYJlqRf8MEptXPBXJXa37BSl3ixYsGDBMliwFLyCV/DeLIMFCxYsWLBMwSt4Be/NggXLYMGCBUvBK3iNruC9WbBgwYJlsGApeAWv4L1ZBgsWLFiwYJmCV/AK3psFC5bBggULloJX8BpdwXuzYMGCBctgwVLwCl7Be7MMFixYsGDBsu8FH1FaSmExVfAxBa/gvVmwYMGCZbBg/W4vAQYA5tRF9QYlv/QAAAAASUVORK5CYII=", + "CLEAR_BACKGROUND_COLOR": "#222", + "NODE_TITLE_COLOR": "#999", + "NODE_SELECTED_TITLE_COLOR": "#FFF", + "NODE_TEXT_SIZE": 14, + "NODE_TEXT_COLOR": "#AAA", + "NODE_SUBTEXT_SIZE": 12, + "NODE_DEFAULT_COLOR": "#333", + "NODE_DEFAULT_BGCOLOR": "#353535", + "NODE_DEFAULT_BOXCOLOR": "#666", + "NODE_DEFAULT_SHAPE": "box", + "NODE_BOX_OUTLINE_COLOR": "#FFF", + "DEFAULT_SHADOW_COLOR": "rgba(0,0,0,0.5)", + "DEFAULT_GROUP_FONT": 24, + + "WIDGET_BGCOLOR": "#222", + "WIDGET_OUTLINE_COLOR": "#666", + "WIDGET_TEXT_COLOR": "#DDD", + "WIDGET_SECONDARY_TEXT_COLOR": "#999", + + "LINK_COLOR": "#9A9", + "EVENT_LINK_COLOR": "#A86", + "CONNECTING_LINK_COLOR": "#AFA", + }, + "comfy_base": { + "fg-color": "#fff", + "bg-color": "#202020", + "comfy-menu-bg": "#353535", + "comfy-input-bg": "#222", + "input-text": "#ddd", + "descrip-text": "#999", + "drag-text": "#ccc", + "error-text": "#ff4444", + "border-color": "#4e4e4e", + "tr-even-bg-color": "#222", + "tr-odd-bg-color": "#353535", + } + }, + }, + "light": { + "id": "light", + "name": "Light", + "colors": { + "node_slot": { + "CLIP": "#FFA726", // orange + "CLIP_VISION": "#5C6BC0", // indigo + "CLIP_VISION_OUTPUT": "#8D6E63", // brown + "CONDITIONING": "#EF5350", // red + "CONTROL_NET": "#66BB6A", // green + "IMAGE": "#42A5F5", // blue + "LATENT": "#AB47BC", // purple + "MASK": "#9CCC65", // light green + "MODEL": "#7E57C2", // deep purple + "STYLE_MODEL": "#D4E157", // lime + "VAE": "#FF7043", // deep orange + }, + "litegraph_base": { + "BACKGROUND_IMAGE": "data:image/gif;base64,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", + "CLEAR_BACKGROUND_COLOR": "lightgray", + "NODE_TITLE_COLOR": "#222", + "NODE_SELECTED_TITLE_COLOR": "#000", + "NODE_TEXT_SIZE": 14, + "NODE_TEXT_COLOR": "#444", + "NODE_SUBTEXT_SIZE": 12, + "NODE_DEFAULT_COLOR": "#F7F7F7", + "NODE_DEFAULT_BGCOLOR": "#F5F5F5", + "NODE_DEFAULT_BOXCOLOR": "#CCC", + "NODE_DEFAULT_SHAPE": "box", + "NODE_BOX_OUTLINE_COLOR": "#000", + "DEFAULT_SHADOW_COLOR": "rgba(0,0,0,0.1)", + "DEFAULT_GROUP_FONT": 24, + + "WIDGET_BGCOLOR": "#D4D4D4", + "WIDGET_OUTLINE_COLOR": "#999", + "WIDGET_TEXT_COLOR": "#222", + "WIDGET_SECONDARY_TEXT_COLOR": "#555", + + "LINK_COLOR": "#4CAF50", + "EVENT_LINK_COLOR": "#FF9800", + "CONNECTING_LINK_COLOR": "#2196F3", + }, + "comfy_base": { + "fg-color": "#222", + "bg-color": "#DDD", + "comfy-menu-bg": "#F5F5F5", + "comfy-input-bg": "#C9C9C9", + "input-text": "#222", + "descrip-text": "#444", + "drag-text": "#555", + "error-text": "#F44336", + "border-color": "#888", + "tr-even-bg-color": "#f9f9f9", + "tr-odd-bg-color": "#fff", + } + }, + }, + "solarized": { + "id": "solarized", + "name": "Solarized", + "colors": { + "node_slot": { + "CLIP": "#2AB7CA", // light blue + "CLIP_VISION": "#6c71c4", // blue violet + "CLIP_VISION_OUTPUT": "#859900", // olive green + "CONDITIONING": "#d33682", // magenta + "CONTROL_NET": "#d1ffd7", // light mint green + "IMAGE": "#5940bb", // deep blue violet + "LATENT": "#268bd2", // blue + "MASK": "#CCC9E7", // light purple-gray + "MODEL": "#dc322f", // red + "STYLE_MODEL": "#1a998a", // teal + "UPSCALE_MODEL": "#054A29", // dark green + "VAE": "#facfad", // light pink-orange + }, + "litegraph_base": { + "NODE_TITLE_COLOR": "#fdf6e3", // Base3 + "NODE_SELECTED_TITLE_COLOR": "#A9D400", + "NODE_TEXT_SIZE": 14, + "NODE_TEXT_COLOR": "#657b83", // Base00 + "NODE_SUBTEXT_SIZE": 12, + "NODE_DEFAULT_COLOR": "#094656", + "NODE_DEFAULT_BGCOLOR": "#073642", // Base02 + "NODE_DEFAULT_BOXCOLOR": "#839496", // Base0 + "NODE_DEFAULT_SHAPE": "box", + "NODE_BOX_OUTLINE_COLOR": "#fdf6e3", // Base3 + "DEFAULT_SHADOW_COLOR": "rgba(0,0,0,0.5)", + "DEFAULT_GROUP_FONT": 24, + + "WIDGET_BGCOLOR": "#002b36", // Base03 + "WIDGET_OUTLINE_COLOR": "#839496", // Base0 + "WIDGET_TEXT_COLOR": "#fdf6e3", // Base3 + "WIDGET_SECONDARY_TEXT_COLOR": "#93a1a1", // Base1 + + "LINK_COLOR": "#2aa198", // Solarized Cyan + "EVENT_LINK_COLOR": "#268bd2", // Solarized Blue + "CONNECTING_LINK_COLOR": "#859900", // Solarized Green + }, + "comfy_base": { + "fg-color": "#fdf6e3", // Base3 + "bg-color": "#002b36", // Base03 + "comfy-menu-bg": "#073642", // Base02 + "comfy-input-bg": "#002b36", // Base03 + "input-text": "#93a1a1", // Base1 + "descrip-text": "#586e75", // Base01 + "drag-text": "#839496", // Base0 + "error-text": "#dc322f", // Solarized Red + "border-color": "#657b83", // Base00 + "tr-even-bg-color": "#002b36", + "tr-odd-bg-color": "#073642", + } + }, + } +}; + +const id = "Comfy.ColorPalette"; +const idCustomColorPalettes = "Comfy.CustomColorPalettes"; +const defaultColorPaletteId = "dark"; +const els = {} +// const ctxMenu = LiteGraph.ContextMenu; +app.registerExtension({ + name: id, + addCustomNodeDefs(node_defs) { + const sortObjectKeys = (unordered) => { + return Object.keys(unordered).sort().reduce((obj, key) => { + obj[key] = unordered[key]; + return obj; + }, {}); + }; + + function getSlotTypes() { + var types = []; + + const defs = node_defs; + for (const nodeId in defs) { + const nodeData = defs[nodeId]; + + var inputs = nodeData["input"]["required"]; + if (nodeData["input"]["optional"] !== undefined) { + inputs = Object.assign({}, nodeData["input"]["required"], nodeData["input"]["optional"]) + } + + for (const inputName in inputs) { + const inputData = inputs[inputName]; + const type = inputData[0]; + + if (!Array.isArray(type)) { + types.push(type); + } + } + + for (const o in nodeData["output"]) { + const output = nodeData["output"][o]; + types.push(output); + } + } + + return types; + } + + function completeColorPalette(colorPalette) { + var types = getSlotTypes(); + + for (const type of types) { + if (!colorPalette.colors.node_slot[type]) { + colorPalette.colors.node_slot[type] = ""; + } + } + + colorPalette.colors.node_slot = sortObjectKeys(colorPalette.colors.node_slot); + + return colorPalette; + } + + const getColorPaletteTemplate = async () => { + let colorPalette = { + "id": "my_color_palette_unique_id", + "name": "My Color Palette", + "colors": { + "node_slot": {}, + "litegraph_base": {}, + "comfy_base": {} + } + }; + + // Copy over missing keys from default color palette + const defaultColorPalette = colorPalettes[defaultColorPaletteId]; + for (const key in defaultColorPalette.colors.litegraph_base) { + if (!colorPalette.colors.litegraph_base[key]) { + colorPalette.colors.litegraph_base[key] = ""; + } + } + for (const key in defaultColorPalette.colors.comfy_base) { + if (!colorPalette.colors.comfy_base[key]) { + colorPalette.colors.comfy_base[key] = ""; + } + } + + return completeColorPalette(colorPalette); + }; + + const getCustomColorPalettes = () => { + return app.ui.settings.getSettingValue(idCustomColorPalettes, {}); + }; + + const setCustomColorPalettes = (customColorPalettes) => { + return app.ui.settings.setSettingValue(idCustomColorPalettes, customColorPalettes); + }; + + const addCustomColorPalette = async (colorPalette) => { + if (typeof (colorPalette) !== "object") { + alert("Invalid color palette."); + return; + } + + if (!colorPalette.id) { + alert("Color palette missing id."); + return; + } + + if (!colorPalette.name) { + alert("Color palette missing name."); + return; + } + + if (!colorPalette.colors) { + alert("Color palette missing colors."); + return; + } + + if (colorPalette.colors.node_slot && typeof (colorPalette.colors.node_slot) !== "object") { + alert("Invalid color palette colors.node_slot."); + return; + } + + const customColorPalettes = getCustomColorPalettes(); + customColorPalettes[colorPalette.id] = colorPalette; + setCustomColorPalettes(customColorPalettes); + + for (const option of els.select.childNodes) { + if (option.value === "custom_" + colorPalette.id) { + els.select.removeChild(option); + } + } + + els.select.append($el("option", { + textContent: colorPalette.name + " (custom)", + value: "custom_" + colorPalette.id, + selected: true + })); + + setColorPalette("custom_" + colorPalette.id); + await loadColorPalette(colorPalette); + }; + + const deleteCustomColorPalette = async (colorPaletteId) => { + const customColorPalettes = getCustomColorPalettes(); + delete customColorPalettes[colorPaletteId]; + setCustomColorPalettes(customColorPalettes); + + for (const option of els.select.childNodes) { + if (option.value === defaultColorPaletteId) { + option.selected = true; + } + + if (option.value === "custom_" + colorPaletteId) { + els.select.removeChild(option); + } + } + + setColorPalette(defaultColorPaletteId); + await loadColorPalette(getColorPalette()); + }; + + const loadColorPalette = async (colorPalette) => { + colorPalette = await completeColorPalette(colorPalette); + if (colorPalette.colors) { + // Sets the colors of node slots and links + if (colorPalette.colors.node_slot) { + Object.assign(app.canvas.default_connection_color_byType, colorPalette.colors.node_slot); + Object.assign(LGraphCanvas.link_type_colors, colorPalette.colors.node_slot); + } + // Sets the colors of the LiteGraph objects + if (colorPalette.colors.litegraph_base) { + // Everything updates correctly in the loop, except the Node Title and Link Color for some reason + app.canvas.node_title_color = colorPalette.colors.litegraph_base.NODE_TITLE_COLOR; + app.canvas.default_link_color = colorPalette.colors.litegraph_base.LINK_COLOR; + + for (const key in colorPalette.colors.litegraph_base) { + if (colorPalette.colors.litegraph_base.hasOwnProperty(key) && LiteGraph.hasOwnProperty(key)) { + LiteGraph[key] = colorPalette.colors.litegraph_base[key]; + } + } + } + // Sets the color of ComfyUI elements + if (colorPalette.colors.comfy_base) { + const rootStyle = document.documentElement.style; + for (const key in colorPalette.colors.comfy_base) { + rootStyle.setProperty('--' + key, colorPalette.colors.comfy_base[key]); + } + } + app.canvas.draw(true, true); + } + }; + + const getColorPalette = (colorPaletteId) => { + if (!colorPaletteId) { + colorPaletteId = app.ui.settings.getSettingValue(id, defaultColorPaletteId); + } + + if (colorPaletteId.startsWith("custom_")) { + colorPaletteId = colorPaletteId.substr(7); + let customColorPalettes = getCustomColorPalettes(); + if (customColorPalettes[colorPaletteId]) { + return customColorPalettes[colorPaletteId]; + } + } + + return colorPalettes[colorPaletteId]; + }; + + const setColorPalette = (colorPaletteId) => { + app.ui.settings.setSettingValue(id, colorPaletteId); + }; + + const fileInput = $el("input", { + type: "file", + accept: ".json", + style: {display: "none"}, + parent: document.body, + onchange: () => { + const file = fileInput.files[0]; + if (file.type === "application/json" || file.name.endsWith(".json")) { + const reader = new FileReader(); + reader.onload = async () => { + await addCustomColorPalette(JSON.parse(reader.result)); + }; + reader.readAsText(file); + } + }, + }); + + app.ui.settings.addSetting({ + id, + name: "Color Palette", + type: (name, setter, value) => { + const options = [ + ...Object.values(colorPalettes).map(c=> $el("option", { + textContent: c.name, + value: c.id, + selected: c.id === value + })), + ...Object.values(getCustomColorPalettes()).map(c=>$el("option", { + textContent: `${c.name} (custom)`, + value: `custom_${c.id}`, + selected: `custom_${c.id}` === value + })) , + ]; + + els.select = $el("select", { + style: { + marginBottom: "0.15rem", + width: "100%", + }, + onchange: (e) => { + setter(e.target.value); + } + }, options) + + return $el("tr", [ + $el("td", [ + $el("label", { + for: id.replaceAll(".", "-"), + textContent: "Color palette", + }), + ]), + $el("td", [ + els.select, + $el("div", { + style: { + display: "grid", + gap: "4px", + gridAutoFlow: "column", + }, + }, [ + $el("input", { + type: "button", + value: "Export", + onclick: async () => { + const colorPaletteId = app.ui.settings.getSettingValue(id, defaultColorPaletteId); + const colorPalette = await completeColorPalette(getColorPalette(colorPaletteId)); + const json = JSON.stringify(colorPalette, null, 2); // convert the data to a JSON string + const blob = new Blob([json], {type: "application/json"}); + const url = URL.createObjectURL(blob); + const a = $el("a", { + href: url, + download: colorPaletteId + ".json", + style: {display: "none"}, + parent: document.body, + }); + a.click(); + setTimeout(function () { + a.remove(); + window.URL.revokeObjectURL(url); + }, 0); + }, + }), + $el("input", { + type: "button", + value: "Import", + onclick: () => { + fileInput.click(); + } + }), + $el("input", { + type: "button", + value: "Template", + onclick: async () => { + const colorPalette = await getColorPaletteTemplate(); + const json = JSON.stringify(colorPalette, null, 2); // convert the data to a JSON string + const blob = new Blob([json], {type: "application/json"}); + const url = URL.createObjectURL(blob); + const a = $el("a", { + href: url, + download: "color_palette.json", + style: {display: "none"}, + parent: document.body, + }); + a.click(); + setTimeout(function () { + a.remove(); + window.URL.revokeObjectURL(url); + }, 0); + } + }), + $el("input", { + type: "button", + value: "Delete", + onclick: async () => { + let colorPaletteId = app.ui.settings.getSettingValue(id, defaultColorPaletteId); + + if (colorPalettes[colorPaletteId]) { + alert("You cannot delete a built-in color palette."); + return; + } + + if (colorPaletteId.startsWith("custom_")) { + colorPaletteId = colorPaletteId.substr(7); + } + + await deleteCustomColorPalette(colorPaletteId); + } + }), + ]), + ]), + ]) + }, + defaultValue: defaultColorPaletteId, + async onChange(value) { + if (!value) { + return; + } + + let palette = colorPalettes[value]; + if (palette) { + await loadColorPalette(palette); + } else if (value.startsWith("custom_")) { + value = value.substr(7); + let customColorPalettes = getCustomColorPalettes(); + if (customColorPalettes[value]) { + palette = customColorPalettes[value]; + await loadColorPalette(customColorPalettes[value]); + } + } + + let {BACKGROUND_IMAGE, CLEAR_BACKGROUND_COLOR} = palette.colors.litegraph_base; + if (BACKGROUND_IMAGE === undefined || CLEAR_BACKGROUND_COLOR === undefined) { + const base = colorPalettes["dark"].colors.litegraph_base; + BACKGROUND_IMAGE = base.BACKGROUND_IMAGE; + CLEAR_BACKGROUND_COLOR = base.CLEAR_BACKGROUND_COLOR; + } + app.canvas.updateBackground(BACKGROUND_IMAGE, CLEAR_BACKGROUND_COLOR); + }, + }); + }, +}); diff --git a/web/extensions/core/contextMenuFilter.js b/web/extensions/core/contextMenuFilter.js new file mode 100644 index 0000000000000000000000000000000000000000..152cd7043de757e37850981af7a8ac8e972e00bc --- /dev/null +++ b/web/extensions/core/contextMenuFilter.js @@ -0,0 +1,148 @@ +import {app} from "../../scripts/app.js"; + +// Adds filtering to combo context menus + +const ext = { + name: "Comfy.ContextMenuFilter", + init() { + const ctxMenu = LiteGraph.ContextMenu; + + LiteGraph.ContextMenu = function (values, options) { + const ctx = ctxMenu.call(this, values, options); + + // If we are a dark menu (only used for combo boxes) then add a filter input + if (options?.className === "dark" && values?.length > 10) { + const filter = document.createElement("input"); + filter.classList.add("comfy-context-menu-filter"); + filter.placeholder = "Filter list"; + this.root.prepend(filter); + + const items = Array.from(this.root.querySelectorAll(".litemenu-entry")); + let displayedItems = [...items]; + let itemCount = displayedItems.length; + + // We must request an animation frame for the current node of the active canvas to update. + requestAnimationFrame(() => { + const currentNode = LGraphCanvas.active_canvas.current_node; + const clickedComboValue = currentNode.widgets + .filter(w => w.type === "combo" && w.options.values.length === values.length) + .find(w => w.options.values.every((v, i) => v === values[i])) + ?.value; + + let selectedIndex = clickedComboValue ? values.findIndex(v => v === clickedComboValue) : 0; + if (selectedIndex < 0) { + selectedIndex = 0; + } + let selectedItem = displayedItems[selectedIndex]; + updateSelected(); + + // Apply highlighting to the selected item + function updateSelected() { + selectedItem?.style.setProperty("background-color", ""); + selectedItem?.style.setProperty("color", ""); + selectedItem = displayedItems[selectedIndex]; + selectedItem?.style.setProperty("background-color", "#ccc", "important"); + selectedItem?.style.setProperty("color", "#000", "important"); + } + + const positionList = () => { + const rect = this.root.getBoundingClientRect(); + + // If the top is off-screen then shift the element with scaling applied + if (rect.top < 0) { + const scale = 1 - this.root.getBoundingClientRect().height / this.root.clientHeight; + const shift = (this.root.clientHeight * scale) / 2; + this.root.style.top = -shift + "px"; + } + } + + // Arrow up/down to select items + filter.addEventListener("keydown", (event) => { + switch (event.key) { + case "ArrowUp": + event.preventDefault(); + if (selectedIndex === 0) { + selectedIndex = itemCount - 1; + } else { + selectedIndex--; + } + updateSelected(); + break; + case "ArrowRight": + event.preventDefault(); + selectedIndex = itemCount - 1; + updateSelected(); + break; + case "ArrowDown": + event.preventDefault(); + if (selectedIndex === itemCount - 1) { + selectedIndex = 0; + } else { + selectedIndex++; + } + updateSelected(); + break; + case "ArrowLeft": + event.preventDefault(); + selectedIndex = 0; + updateSelected(); + break; + case "Enter": + selectedItem?.click(); + break; + case "Escape": + this.close(); + break; + } + }); + + filter.addEventListener("input", () => { + // Hide all items that don't match our filter + const term = filter.value.toLocaleLowerCase(); + // When filtering, recompute which items are visible for arrow up/down and maintain selection. + displayedItems = items.filter(item => { + const isVisible = !term || item.textContent.toLocaleLowerCase().includes(term); + item.style.display = isVisible ? "block" : "none"; + return isVisible; + }); + + selectedIndex = 0; + if (displayedItems.includes(selectedItem)) { + selectedIndex = displayedItems.findIndex(d => d === selectedItem); + } + itemCount = displayedItems.length; + + updateSelected(); + + // If we have an event then we can try and position the list under the source + if (options.event) { + let top = options.event.clientY - 10; + + const bodyRect = document.body.getBoundingClientRect(); + const rootRect = this.root.getBoundingClientRect(); + if (bodyRect.height && top > bodyRect.height - rootRect.height - 10) { + top = Math.max(0, bodyRect.height - rootRect.height - 10); + } + + this.root.style.top = top + "px"; + positionList(); + } + }); + + requestAnimationFrame(() => { + // Focus the filter box when opening + filter.focus(); + + positionList(); + }); + }) + } + + return ctx; + }; + + LiteGraph.ContextMenu.prototype = ctxMenu.prototype; + }, +} + +app.registerExtension(ext); diff --git a/web/extensions/core/dynamicPrompts.js b/web/extensions/core/dynamicPrompts.js new file mode 100644 index 0000000000000000000000000000000000000000..599a9e685893dafbcdebb149bdfb68ab85db142d --- /dev/null +++ b/web/extensions/core/dynamicPrompts.js @@ -0,0 +1,48 @@ +import { app } from "../../scripts/app.js"; + +// Allows for simple dynamic prompt replacement +// Inputs in the format {a|b} will have a random value of a or b chosen when the prompt is queued. + +/* + * Strips C-style line and block comments from a string + */ +function stripComments(str) { + return str.replace(/\/\*[\s\S]*?\*\/|\/\/.*/g,''); +} + +app.registerExtension({ + name: "Comfy.DynamicPrompts", + nodeCreated(node) { + if (node.widgets) { + // Locate dynamic prompt text widgets + // Include any widgets with dynamicPrompts set to true, and customtext + const widgets = node.widgets.filter( + (n) => (n.type === "customtext" && n.dynamicPrompts !== false) || n.dynamicPrompts + ); + for (const widget of widgets) { + // Override the serialization of the value to resolve dynamic prompts for all widgets supporting it in this node + widget.serializeValue = (workflowNode, widgetIndex) => { + let prompt = stripComments(widget.value); + while (prompt.replace("\\{", "").includes("{") && prompt.replace("\\}", "").includes("}")) { + const startIndex = prompt.replace("\\{", "00").indexOf("{"); + const endIndex = prompt.replace("\\}", "00").indexOf("}"); + + const optionsString = prompt.substring(startIndex + 1, endIndex); + const options = optionsString.split("|"); + + const randomIndex = Math.floor(Math.random() * options.length); + const randomOption = options[randomIndex]; + + prompt = prompt.substring(0, startIndex) + randomOption + prompt.substring(endIndex + 1); + } + + // Overwrite the value in the serialized workflow pnginfo + if (workflowNode?.widgets_values) + workflowNode.widgets_values[widgetIndex] = prompt; + + return prompt; + }; + } + } + }, +}); diff --git a/web/extensions/core/editAttention.js b/web/extensions/core/editAttention.js new file mode 100644 index 0000000000000000000000000000000000000000..6792b235720115c4bc5c29694b25c4a66cd4a3bf --- /dev/null +++ b/web/extensions/core/editAttention.js @@ -0,0 +1,144 @@ +import { app } from "../../scripts/app.js"; + +// Allows you to edit the attention weight by holding ctrl (or cmd) and using the up/down arrow keys + +app.registerExtension({ + name: "Comfy.EditAttention", + init() { + const editAttentionDelta = app.ui.settings.addSetting({ + id: "Comfy.EditAttention.Delta", + name: "Ctrl+up/down precision", + type: "slider", + attrs: { + min: 0.01, + max: 0.5, + step: 0.01, + }, + defaultValue: 0.05, + }); + + function incrementWeight(weight, delta) { + const floatWeight = parseFloat(weight); + if (isNaN(floatWeight)) return weight; + const newWeight = floatWeight + delta; + if (newWeight < 0) return "0"; + return String(Number(newWeight.toFixed(10))); + } + + function findNearestEnclosure(text, cursorPos) { + let start = cursorPos, end = cursorPos; + let openCount = 0, closeCount = 0; + + // Find opening parenthesis before cursor + while (start >= 0) { + start--; + if (text[start] === "(" && openCount === closeCount) break; + if (text[start] === "(") openCount++; + if (text[start] === ")") closeCount++; + } + if (start < 0) return false; + + openCount = 0; + closeCount = 0; + + // Find closing parenthesis after cursor + while (end < text.length) { + if (text[end] === ")" && openCount === closeCount) break; + if (text[end] === "(") openCount++; + if (text[end] === ")") closeCount++; + end++; + } + if (end === text.length) return false; + + return { start: start + 1, end: end }; + } + + function addWeightToParentheses(text) { + const parenRegex = /^\((.*)\)$/; + const parenMatch = text.match(parenRegex); + + const floatRegex = /:([+-]?(\d*\.)?\d+([eE][+-]?\d+)?)/; + const floatMatch = text.match(floatRegex); + + if (parenMatch && !floatMatch) { + return `(${parenMatch[1]}:1.0)`; + } else { + return text; + } + }; + + function editAttention(event) { + const inputField = event.composedPath()[0]; + const delta = parseFloat(editAttentionDelta.value); + + if (inputField.tagName !== "TEXTAREA") return; + if (!(event.key === "ArrowUp" || event.key === "ArrowDown")) return; + if (!event.ctrlKey && !event.metaKey) return; + + event.preventDefault(); + + let start = inputField.selectionStart; + let end = inputField.selectionEnd; + let selectedText = inputField.value.substring(start, end); + + // If there is no selection, attempt to find the nearest enclosure, or select the current word + if (!selectedText) { + const nearestEnclosure = findNearestEnclosure(inputField.value, start); + if (nearestEnclosure) { + start = nearestEnclosure.start; + end = nearestEnclosure.end; + selectedText = inputField.value.substring(start, end); + } else { + // Select the current word, find the start and end of the word + const delimiters = " .,\\/!?%^*;:{}=-_`~()\r\n\t"; + + while (!delimiters.includes(inputField.value[start - 1]) && start > 0) { + start--; + } + + while (!delimiters.includes(inputField.value[end]) && end < inputField.value.length) { + end++; + } + + selectedText = inputField.value.substring(start, end); + if (!selectedText) return; + } + } + + // If the selection ends with a space, remove it + if (selectedText[selectedText.length - 1] === " ") { + selectedText = selectedText.substring(0, selectedText.length - 1); + end -= 1; + } + + // If there are parentheses left and right of the selection, select them + if (inputField.value[start - 1] === "(" && inputField.value[end] === ")") { + start -= 1; + end += 1; + selectedText = inputField.value.substring(start, end); + } + + // If the selection is not enclosed in parentheses, add them + if (selectedText[0] !== "(" || selectedText[selectedText.length - 1] !== ")") { + selectedText = `(${selectedText})`; + } + + // If the selection does not have a weight, add a weight of 1.0 + selectedText = addWeightToParentheses(selectedText); + + // Increment the weight + const weightDelta = event.key === "ArrowUp" ? delta : -delta; + const updatedText = selectedText.replace(/\((.*):(\d+(?:\.\d+)?)\)/, (match, text, weight) => { + weight = incrementWeight(weight, weightDelta); + if (weight == 1) { + return text; + } else { + return `(${text}:${weight})`; + } + }); + + inputField.setRangeText(updatedText, start, end, "select"); + } + window.addEventListener("keydown", editAttention); + }, +}); diff --git a/web/extensions/core/groupOptions.js b/web/extensions/core/groupOptions.js new file mode 100644 index 0000000000000000000000000000000000000000..1d935e90aefa4158e662675d77924135012f4373 --- /dev/null +++ b/web/extensions/core/groupOptions.js @@ -0,0 +1,167 @@ +import {app} from "../../scripts/app.js"; + +function setNodeMode(node, mode) { + node.mode = mode; + node.graph.change(); +} + +app.registerExtension({ + name: "Comfy.GroupOptions", + setup() { + const orig = LGraphCanvas.prototype.getCanvasMenuOptions; + // graph_mouse + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + const options = orig.apply(this, arguments); + const group = this.graph.getGroupOnPos(this.graph_mouse[0], this.graph_mouse[1]); + if (!group) { + return options; + } + + // Group nodes aren't recomputed until the group is moved, this ensures the nodes are up-to-date + group.recomputeInsideNodes(); + const nodesInGroup = group._nodes; + + // No nodes in group, return default options + if (nodesInGroup.length === 0) { + return options; + } else { + // Add a separator between the default options and the group options + options.push(null); + } + + // Check if all nodes are the same mode + let allNodesAreSameMode = true; + for (let i = 1; i < nodesInGroup.length; i++) { + if (nodesInGroup[i].mode !== nodesInGroup[0].mode) { + allNodesAreSameMode = false; + break; + } + } + + // Modes + // 0: Always + // 1: On Event + // 2: Never + // 3: On Trigger + // 4: Bypass + // If all nodes are the same mode, add a menu option to change the mode + if (allNodesAreSameMode) { + const mode = nodesInGroup[0].mode; + switch (mode) { + case 0: + // All nodes are always, option to disable, and bypass + options.push({ + content: "Set Group Nodes to Never", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 2); + } + } + }); + options.push({ + content: "Bypass Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 4); + } + } + }); + break; + case 2: + // All nodes are never, option to enable, and bypass + options.push({ + content: "Set Group Nodes to Always", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 0); + } + } + }); + options.push({ + content: "Bypass Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 4); + } + } + }); + break; + case 4: + // All nodes are bypass, option to enable, and disable + options.push({ + content: "Set Group Nodes to Always", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 0); + } + } + }); + options.push({ + content: "Set Group Nodes to Never", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 2); + } + } + }); + break; + default: + // All nodes are On Trigger or On Event(Or other?), option to disable, set to always, or bypass + options.push({ + content: "Set Group Nodes to Always", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 0); + } + } + }); + options.push({ + content: "Set Group Nodes to Never", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 2); + } + } + }); + options.push({ + content: "Bypass Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 4); + } + } + }); + break; + } + } else { + // Nodes are not all the same mode, add a menu option to change the mode to always, never, or bypass + options.push({ + content: "Set Group Nodes to Always", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 0); + } + } + }); + options.push({ + content: "Set Group Nodes to Never", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 2); + } + } + }); + options.push({ + content: "Bypass Group Nodes", + callback: () => { + for (const node of nodesInGroup) { + setNodeMode(node, 4); + } + } + }); + } + + return options + } + } +}); diff --git a/web/extensions/core/invertMenuScrolling.js b/web/extensions/core/invertMenuScrolling.js new file mode 100644 index 0000000000000000000000000000000000000000..98a1786ab48972ad3a92f4f7cda8fa4273e0bde6 --- /dev/null +++ b/web/extensions/core/invertMenuScrolling.js @@ -0,0 +1,36 @@ +import { app } from "../../scripts/app.js"; + +// Inverts the scrolling of context menus + +const id = "Comfy.InvertMenuScrolling"; +app.registerExtension({ + name: id, + init() { + const ctxMenu = LiteGraph.ContextMenu; + const replace = () => { + LiteGraph.ContextMenu = function (values, options) { + options = options || {}; + if (options.scroll_speed) { + options.scroll_speed *= -1; + } else { + options.scroll_speed = -0.1; + } + return ctxMenu.call(this, values, options); + }; + LiteGraph.ContextMenu.prototype = ctxMenu.prototype; + }; + app.ui.settings.addSetting({ + id, + name: "Invert Menu Scrolling", + type: "boolean", + defaultValue: false, + onChange(value) { + if (value) { + replace(); + } else { + LiteGraph.ContextMenu = ctxMenu; + } + }, + }); + }, +}); diff --git a/web/extensions/core/keybinds.js b/web/extensions/core/keybinds.js new file mode 100644 index 0000000000000000000000000000000000000000..cf698ea5a66cebfb3c8e9192d4d192503b461697 --- /dev/null +++ b/web/extensions/core/keybinds.js @@ -0,0 +1,70 @@ +import {app} from "../../scripts/app.js"; + +app.registerExtension({ + name: "Comfy.Keybinds", + init() { + const keybindListener = function (event) { + const modifierPressed = event.ctrlKey || event.metaKey; + + // Queue prompt using ctrl or command + enter + if (modifierPressed && event.key === "Enter") { + app.queuePrompt(event.shiftKey ? -1 : 0).then(); + return; + } + + const target = event.composedPath()[0]; + if (["INPUT", "TEXTAREA"].includes(target.tagName)) { + return; + } + + const modifierKeyIdMap = { + s: "#comfy-save-button", + o: "#comfy-file-input", + Backspace: "#comfy-clear-button", + Delete: "#comfy-clear-button", + d: "#comfy-load-default-button", + }; + + const modifierKeybindId = modifierKeyIdMap[event.key]; + if (modifierPressed && modifierKeybindId) { + event.preventDefault(); + + const elem = document.querySelector(modifierKeybindId); + elem.click(); + return; + } + + // Finished Handling all modifier keybinds, now handle the rest + if (event.ctrlKey || event.altKey || event.metaKey) { + return; + } + + // Close out of modals using escape + if (event.key === "Escape") { + const modals = document.querySelectorAll(".comfy-modal"); + const modal = Array.from(modals).find(modal => window.getComputedStyle(modal).getPropertyValue("display") !== "none"); + if (modal) { + modal.style.display = "none"; + } + + [...document.querySelectorAll("dialog")].forEach(d => { + d.close(); + }); + } + + const keyIdMap = { + q: "#comfy-view-queue-button", + h: "#comfy-view-history-button", + r: "#comfy-refresh-button", + }; + + const buttonId = keyIdMap[event.key]; + if (buttonId) { + const button = document.querySelector(buttonId); + button.click(); + } + } + + window.addEventListener("keydown", keybindListener, true); + } +}); diff --git a/web/extensions/core/linkRenderMode.js b/web/extensions/core/linkRenderMode.js new file mode 100644 index 0000000000000000000000000000000000000000..fb4df4234e587817c150be71059113f10443b01a --- /dev/null +++ b/web/extensions/core/linkRenderMode.js @@ -0,0 +1,25 @@ +import { app } from "../../scripts/app.js"; + +const id = "Comfy.LinkRenderMode"; +const ext = { + name: id, + async setup(app) { + app.ui.settings.addSetting({ + id, + name: "Link Render Mode", + defaultValue: 2, + type: "combo", + options: [...LiteGraph.LINK_RENDER_MODES, "Hidden"].map((m, i) => ({ + value: i, + text: m, + selected: i == app.canvas.links_render_mode, + })), + onChange(value) { + app.canvas.links_render_mode = +value; + app.graph.setDirtyCanvas(true); + }, + }); + }, +}; + +app.registerExtension(ext); diff --git a/web/extensions/core/maskeditor.js b/web/extensions/core/maskeditor.js new file mode 100644 index 0000000000000000000000000000000000000000..f6292b9e3782f95ec057108bf013f7b6e6b945e3 --- /dev/null +++ b/web/extensions/core/maskeditor.js @@ -0,0 +1,660 @@ +import { app } from "../../scripts/app.js"; +import { ComfyDialog, $el } from "../../scripts/ui.js"; +import { ComfyApp } from "../../scripts/app.js"; +import { api } from "../../scripts/api.js" +import { ClipspaceDialog } from "./clipspace.js"; + +// Helper function to convert a data URL to a Blob object +function dataURLToBlob(dataURL) { + const parts = dataURL.split(';base64,'); + const contentType = parts[0].split(':')[1]; + const byteString = atob(parts[1]); + const arrayBuffer = new ArrayBuffer(byteString.length); + const uint8Array = new Uint8Array(arrayBuffer); + for (let i = 0; i < byteString.length; i++) { + uint8Array[i] = byteString.charCodeAt(i); + } + return new Blob([arrayBuffer], { type: contentType }); +} + +function loadedImageToBlob(image) { + const canvas = document.createElement('canvas'); + + canvas.width = image.width; + canvas.height = image.height; + + const ctx = canvas.getContext('2d'); + + ctx.drawImage(image, 0, 0); + + const dataURL = canvas.toDataURL('image/png', 1); + const blob = dataURLToBlob(dataURL); + + return blob; +} + +async function uploadMask(filepath, formData) { + await api.fetchApi('/upload/mask', { + method: 'POST', + body: formData + }).then(response => {}).catch(error => { + console.error('Error:', error); + }); + + ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']] = new Image(); + ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src = api.apiURL("/view?" + new URLSearchParams(filepath).toString() + app.getPreviewFormatParam()); + + if(ComfyApp.clipspace.images) + ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']] = filepath; + + ClipspaceDialog.invalidatePreview(); +} + +function prepareRGB(image, backupCanvas, backupCtx) { + // paste mask data into alpha channel + backupCtx.drawImage(image, 0, 0, backupCanvas.width, backupCanvas.height); + const backupData = backupCtx.getImageData(0, 0, backupCanvas.width, backupCanvas.height); + + // refine mask image + for (let i = 0; i < backupData.data.length; i += 4) { + if(backupData.data[i+3] == 255) + backupData.data[i+3] = 0; + else + backupData.data[i+3] = 255; + + backupData.data[i] = 0; + backupData.data[i+1] = 0; + backupData.data[i+2] = 0; + } + + backupCtx.globalCompositeOperation = 'source-over'; + backupCtx.putImageData(backupData, 0, 0); +} + +class MaskEditorDialog extends ComfyDialog { + static instance = null; + + static getInstance() { + if(!MaskEditorDialog.instance) { + MaskEditorDialog.instance = new MaskEditorDialog(app); + } + + return MaskEditorDialog.instance; + } + + is_layout_created = false; + + constructor() { + super(); + this.element = $el("div.comfy-modal", { parent: document.body }, + [ $el("div.comfy-modal-content", + [...this.createButtons()]), + ]); + } + + createButtons() { + return []; + } + + createButton(name, callback) { + var button = document.createElement("button"); + button.innerText = name; + button.addEventListener("click", callback); + return button; + } + + createLeftButton(name, callback) { + var button = this.createButton(name, callback); + button.style.cssFloat = "left"; + button.style.marginRight = "4px"; + return button; + } + + createRightButton(name, callback) { + var button = this.createButton(name, callback); + button.style.cssFloat = "right"; + button.style.marginLeft = "4px"; + return button; + } + + createLeftSlider(self, name, callback) { + const divElement = document.createElement('div'); + divElement.id = "maskeditor-slider"; + divElement.style.cssFloat = "left"; + divElement.style.fontFamily = "sans-serif"; + divElement.style.marginRight = "4px"; + divElement.style.color = "var(--input-text)"; + divElement.style.backgroundColor = "var(--comfy-input-bg)"; + divElement.style.borderRadius = "8px"; + divElement.style.borderColor = "var(--border-color)"; + divElement.style.borderStyle = "solid"; + divElement.style.fontSize = "15px"; + divElement.style.height = "21px"; + divElement.style.padding = "1px 6px"; + divElement.style.display = "flex"; + divElement.style.position = "relative"; + divElement.style.top = "2px"; + self.brush_slider_input = document.createElement('input'); + self.brush_slider_input.setAttribute('type', 'range'); + self.brush_slider_input.setAttribute('min', '1'); + self.brush_slider_input.setAttribute('max', '100'); + self.brush_slider_input.setAttribute('value', '10'); + const labelElement = document.createElement("label"); + labelElement.textContent = name; + + divElement.appendChild(labelElement); + divElement.appendChild(self.brush_slider_input); + + self.brush_slider_input.addEventListener("change", callback); + + return divElement; + } + + setlayout(imgCanvas, maskCanvas) { + const self = this; + + // If it is specified as relative, using it only as a hidden placeholder for padding is recommended + // to prevent anomalies where it exceeds a certain size and goes outside of the window. + var placeholder = document.createElement("div"); + placeholder.style.position = "relative"; + placeholder.style.height = "50px"; + + var bottom_panel = document.createElement("div"); + bottom_panel.style.position = "absolute"; + bottom_panel.style.bottom = "0px"; + bottom_panel.style.left = "20px"; + bottom_panel.style.right = "20px"; + bottom_panel.style.height = "50px"; + + var brush = document.createElement("div"); + brush.id = "brush"; + brush.style.backgroundColor = "transparent"; + brush.style.outline = "1px dashed black"; + brush.style.boxShadow = "0 0 0 1px white"; + brush.style.borderRadius = "50%"; + brush.style.MozBorderRadius = "50%"; + brush.style.WebkitBorderRadius = "50%"; + brush.style.position = "absolute"; + brush.style.zIndex = 8889; + brush.style.pointerEvents = "none"; + this.brush = brush; + this.element.appendChild(imgCanvas); + this.element.appendChild(maskCanvas); + this.element.appendChild(placeholder); // must below z-index than bottom_panel to avoid covering button + this.element.appendChild(bottom_panel); + document.body.appendChild(brush); + + var brush_size_slider = this.createLeftSlider(self, "Thickness", (event) => { + self.brush_size = event.target.value; + self.updateBrushPreview(self, null, null); + }); + var clearButton = this.createLeftButton("Clear", + () => { + self.maskCtx.clearRect(0, 0, self.maskCanvas.width, self.maskCanvas.height); + self.backupCtx.clearRect(0, 0, self.backupCanvas.width, self.backupCanvas.height); + }); + var cancelButton = this.createRightButton("Cancel", () => { + document.removeEventListener("mouseup", MaskEditorDialog.handleMouseUp); + document.removeEventListener("keydown", MaskEditorDialog.handleKeyDown); + self.close(); + }); + + this.saveButton = this.createRightButton("Save", () => { + document.removeEventListener("mouseup", MaskEditorDialog.handleMouseUp); + document.removeEventListener("keydown", MaskEditorDialog.handleKeyDown); + self.save(); + }); + + this.element.appendChild(imgCanvas); + this.element.appendChild(maskCanvas); + this.element.appendChild(placeholder); // must below z-index than bottom_panel to avoid covering button + this.element.appendChild(bottom_panel); + + bottom_panel.appendChild(clearButton); + bottom_panel.appendChild(this.saveButton); + bottom_panel.appendChild(cancelButton); + bottom_panel.appendChild(brush_size_slider); + + imgCanvas.style.position = "relative"; + imgCanvas.style.top = "200"; + imgCanvas.style.left = "0"; + + maskCanvas.style.position = "absolute"; + } + + show() { + if(!this.is_layout_created) { + // layout + const imgCanvas = document.createElement('canvas'); + const maskCanvas = document.createElement('canvas'); + const backupCanvas = document.createElement('canvas'); + + imgCanvas.id = "imageCanvas"; + maskCanvas.id = "maskCanvas"; + backupCanvas.id = "backupCanvas"; + + this.setlayout(imgCanvas, maskCanvas); + + // prepare content + this.imgCanvas = imgCanvas; + this.maskCanvas = maskCanvas; + this.backupCanvas = backupCanvas; + this.maskCtx = maskCanvas.getContext('2d'); + this.backupCtx = backupCanvas.getContext('2d'); + + this.setEventHandler(maskCanvas); + + this.is_layout_created = true; + + // replacement of onClose hook since close is not real close + const self = this; + const observer = new MutationObserver(function(mutations) { + mutations.forEach(function(mutation) { + if (mutation.type === 'attributes' && mutation.attributeName === 'style') { + if(self.last_display_style && self.last_display_style != 'none' && self.element.style.display == 'none') { + ComfyApp.onClipspaceEditorClosed(); + } + + self.last_display_style = self.element.style.display; + } + }); + }); + + const config = { attributes: true }; + observer.observe(this.element, config); + } + + this.setImages(this.imgCanvas, this.backupCanvas); + + if(ComfyApp.clipspace_return_node) { + this.saveButton.innerText = "Save to node"; + } + else { + this.saveButton.innerText = "Save"; + } + this.saveButton.disabled = false; + + this.element.style.display = "block"; + this.element.style.zIndex = 8888; // NOTE: alert dialog must be high priority. + } + + isOpened() { + return this.element.style.display == "block"; + } + + setImages(imgCanvas, backupCanvas) { + const imgCtx = imgCanvas.getContext('2d'); + const backupCtx = backupCanvas.getContext('2d'); + const maskCtx = this.maskCtx; + const maskCanvas = this.maskCanvas; + + backupCtx.clearRect(0,0,this.backupCanvas.width,this.backupCanvas.height); + imgCtx.clearRect(0,0,this.imgCanvas.width,this.imgCanvas.height); + maskCtx.clearRect(0,0,this.maskCanvas.width,this.maskCanvas.height); + + // image load + const orig_image = new Image(); + window.addEventListener("resize", () => { + // repositioning + imgCanvas.width = window.innerWidth - 250; + imgCanvas.height = window.innerHeight - 200; + + // redraw image + let drawWidth = orig_image.width; + let drawHeight = orig_image.height; + if (orig_image.width > imgCanvas.width) { + drawWidth = imgCanvas.width; + drawHeight = (drawWidth / orig_image.width) * orig_image.height; + } + + if (drawHeight > imgCanvas.height) { + drawHeight = imgCanvas.height; + drawWidth = (drawHeight / orig_image.height) * orig_image.width; + } + + imgCtx.drawImage(orig_image, 0, 0, drawWidth, drawHeight); + + // update mask + maskCanvas.width = drawWidth; + maskCanvas.height = drawHeight; + maskCanvas.style.top = imgCanvas.offsetTop + "px"; + maskCanvas.style.left = imgCanvas.offsetLeft + "px"; + backupCtx.drawImage(maskCanvas, 0, 0, maskCanvas.width, maskCanvas.height, 0, 0, backupCanvas.width, backupCanvas.height); + maskCtx.drawImage(backupCanvas, 0, 0, backupCanvas.width, backupCanvas.height, 0, 0, maskCanvas.width, maskCanvas.height); + }); + + const filepath = ComfyApp.clipspace.images; + + const touched_image = new Image(); + + touched_image.onload = function() { + backupCanvas.width = touched_image.width; + backupCanvas.height = touched_image.height; + + prepareRGB(touched_image, backupCanvas, backupCtx); + }; + + const alpha_url = new URL(ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src) + alpha_url.searchParams.delete('channel'); + alpha_url.searchParams.delete('preview'); + alpha_url.searchParams.set('channel', 'a'); + touched_image.src = alpha_url; + + // original image load + orig_image.onload = function() { + window.dispatchEvent(new Event('resize')); + }; + + const rgb_url = new URL(ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src); + rgb_url.searchParams.delete('channel'); + rgb_url.searchParams.set('channel', 'rgb'); + orig_image.src = rgb_url; + this.image = orig_image; + } + + setEventHandler(maskCanvas) { + maskCanvas.addEventListener("contextmenu", (event) => { + event.preventDefault(); + }); + + const self = this; + maskCanvas.addEventListener('wheel', (event) => this.handleWheelEvent(self,event)); + maskCanvas.addEventListener('pointerdown', (event) => this.handlePointerDown(self,event)); + document.addEventListener('pointerup', MaskEditorDialog.handlePointerUp); + maskCanvas.addEventListener('pointermove', (event) => this.draw_move(self,event)); + maskCanvas.addEventListener('touchmove', (event) => this.draw_move(self,event)); + maskCanvas.addEventListener('pointerover', (event) => { this.brush.style.display = "block"; }); + maskCanvas.addEventListener('pointerleave', (event) => { this.brush.style.display = "none"; }); + document.addEventListener('keydown', MaskEditorDialog.handleKeyDown); + } + + brush_size = 10; + drawing_mode = false; + lastx = -1; + lasty = -1; + lasttime = 0; + + static handleKeyDown(event) { + const self = MaskEditorDialog.instance; + if (event.key === ']') { + self.brush_size = Math.min(self.brush_size+2, 100); + } else if (event.key === '[') { + self.brush_size = Math.max(self.brush_size-2, 1); + } else if(event.key === 'Enter') { + self.save(); + } + + self.updateBrushPreview(self); + } + + static handlePointerUp(event) { + event.preventDefault(); + MaskEditorDialog.instance.drawing_mode = false; + } + + updateBrushPreview(self) { + const brush = self.brush; + + var centerX = self.cursorX; + var centerY = self.cursorY; + + brush.style.width = self.brush_size * 2 + "px"; + brush.style.height = self.brush_size * 2 + "px"; + brush.style.left = (centerX - self.brush_size) + "px"; + brush.style.top = (centerY - self.brush_size) + "px"; + } + + handleWheelEvent(self, event) { + if(event.deltaY < 0) + self.brush_size = Math.min(self.brush_size+2, 100); + else + self.brush_size = Math.max(self.brush_size-2, 1); + + self.brush_slider_input.value = self.brush_size; + + self.updateBrushPreview(self); + } + + draw_move(self, event) { + event.preventDefault(); + + this.cursorX = event.pageX; + this.cursorY = event.pageY; + + self.updateBrushPreview(self); + + if (window.TouchEvent && event instanceof TouchEvent || event.buttons == 1) { + var diff = performance.now() - self.lasttime; + + const maskRect = self.maskCanvas.getBoundingClientRect(); + + var x = event.offsetX; + var y = event.offsetY + + if(event.offsetX == null) { + x = event.targetTouches[0].clientX - maskRect.left; + } + + if(event.offsetY == null) { + y = event.targetTouches[0].clientY - maskRect.top; + } + + var brush_size = this.brush_size; + if(event instanceof PointerEvent && event.pointerType == 'pen') { + brush_size *= event.pressure; + this.last_pressure = event.pressure; + } + else if(window.TouchEvent && event instanceof TouchEvent && diff < 20){ + // The firing interval of PointerEvents in Pen is unreliable, so it is supplemented by TouchEvents. + brush_size *= this.last_pressure; + } + else { + brush_size = this.brush_size; + } + + if(diff > 20 && !this.drawing_mode) + requestAnimationFrame(() => { + self.maskCtx.beginPath(); + self.maskCtx.fillStyle = "rgb(0,0,0)"; + self.maskCtx.globalCompositeOperation = "source-over"; + self.maskCtx.arc(x, y, brush_size, 0, Math.PI * 2, false); + self.maskCtx.fill(); + self.lastx = x; + self.lasty = y; + }); + else + requestAnimationFrame(() => { + self.maskCtx.beginPath(); + self.maskCtx.fillStyle = "rgb(0,0,0)"; + self.maskCtx.globalCompositeOperation = "source-over"; + + var dx = x - self.lastx; + var dy = y - self.lasty; + + var distance = Math.sqrt(dx * dx + dy * dy); + var directionX = dx / distance; + var directionY = dy / distance; + + for (var i = 0; i < distance; i+=5) { + var px = self.lastx + (directionX * i); + var py = self.lasty + (directionY * i); + self.maskCtx.arc(px, py, brush_size, 0, Math.PI * 2, false); + self.maskCtx.fill(); + } + self.lastx = x; + self.lasty = y; + }); + + self.lasttime = performance.now(); + } + else if(event.buttons == 2 || event.buttons == 5 || event.buttons == 32) { + const maskRect = self.maskCanvas.getBoundingClientRect(); + const x = event.offsetX || event.targetTouches[0].clientX - maskRect.left; + const y = event.offsetY || event.targetTouches[0].clientY - maskRect.top; + + var brush_size = this.brush_size; + if(event instanceof PointerEvent && event.pointerType == 'pen') { + brush_size *= event.pressure; + this.last_pressure = event.pressure; + } + else if(window.TouchEvent && event instanceof TouchEvent && diff < 20){ + brush_size *= this.last_pressure; + } + else { + brush_size = this.brush_size; + } + + if(diff > 20 && !drawing_mode) // cannot tracking drawing_mode for touch event + requestAnimationFrame(() => { + self.maskCtx.beginPath(); + self.maskCtx.globalCompositeOperation = "destination-out"; + self.maskCtx.arc(x, y, brush_size, 0, Math.PI * 2, false); + self.maskCtx.fill(); + self.lastx = x; + self.lasty = y; + }); + else + requestAnimationFrame(() => { + self.maskCtx.beginPath(); + self.maskCtx.globalCompositeOperation = "destination-out"; + + var dx = x - self.lastx; + var dy = y - self.lasty; + + var distance = Math.sqrt(dx * dx + dy * dy); + var directionX = dx / distance; + var directionY = dy / distance; + + for (var i = 0; i < distance; i+=5) { + var px = self.lastx + (directionX * i); + var py = self.lasty + (directionY * i); + self.maskCtx.arc(px, py, brush_size, 0, Math.PI * 2, false); + self.maskCtx.fill(); + } + self.lastx = x; + self.lasty = y; + }); + + self.lasttime = performance.now(); + } + } + + handlePointerDown(self, event) { + var brush_size = this.brush_size; + if(event instanceof PointerEvent && event.pointerType == 'pen') { + brush_size *= event.pressure; + this.last_pressure = event.pressure; + } + + if ([0, 2, 5].includes(event.button)) { + self.drawing_mode = true; + + event.preventDefault(); + const maskRect = self.maskCanvas.getBoundingClientRect(); + const x = event.offsetX || event.targetTouches[0].clientX - maskRect.left; + const y = event.offsetY || event.targetTouches[0].clientY - maskRect.top; + + self.maskCtx.beginPath(); + if (event.button == 0) { + self.maskCtx.fillStyle = "rgb(0,0,0)"; + self.maskCtx.globalCompositeOperation = "source-over"; + } else { + self.maskCtx.globalCompositeOperation = "destination-out"; + } + self.maskCtx.arc(x, y, brush_size, 0, Math.PI * 2, false); + self.maskCtx.fill(); + self.lastx = x; + self.lasty = y; + self.lasttime = performance.now(); + } + } + + async save() { + const backupCtx = this.backupCanvas.getContext('2d', {willReadFrequently:true}); + + backupCtx.clearRect(0,0,this.backupCanvas.width,this.backupCanvas.height); + backupCtx.drawImage(this.maskCanvas, + 0, 0, this.maskCanvas.width, this.maskCanvas.height, + 0, 0, this.backupCanvas.width, this.backupCanvas.height); + + // paste mask data into alpha channel + const backupData = backupCtx.getImageData(0, 0, this.backupCanvas.width, this.backupCanvas.height); + + // refine mask image + for (let i = 0; i < backupData.data.length; i += 4) { + if(backupData.data[i+3] == 255) + backupData.data[i+3] = 0; + else + backupData.data[i+3] = 255; + + backupData.data[i] = 0; + backupData.data[i+1] = 0; + backupData.data[i+2] = 0; + } + + backupCtx.globalCompositeOperation = 'source-over'; + backupCtx.putImageData(backupData, 0, 0); + + const formData = new FormData(); + const filename = "clipspace-mask-" + performance.now() + ".png"; + + const item = + { + "filename": filename, + "subfolder": "clipspace", + "type": "input", + }; + + if(ComfyApp.clipspace.images) + ComfyApp.clipspace.images[0] = item; + + if(ComfyApp.clipspace.widgets) { + const index = ComfyApp.clipspace.widgets.findIndex(obj => obj.name === 'image'); + + if(index >= 0) + ComfyApp.clipspace.widgets[index].value = item; + } + + const dataURL = this.backupCanvas.toDataURL(); + const blob = dataURLToBlob(dataURL); + + let original_url = new URL(this.image.src); + + const original_ref = { filename: original_url.searchParams.get('filename') }; + + let original_subfolder = original_url.searchParams.get("subfolder"); + if(original_subfolder) + original_ref.subfolder = original_subfolder; + + let original_type = original_url.searchParams.get("type"); + if(original_type) + original_ref.type = original_type; + + formData.append('image', blob, filename); + formData.append('original_ref', JSON.stringify(original_ref)); + formData.append('type', "input"); + formData.append('subfolder', "clipspace"); + + this.saveButton.innerText = "Saving..."; + this.saveButton.disabled = true; + await uploadMask(item, formData); + ComfyApp.onClipspaceEditorSave(); + this.close(); + } +} + +app.registerExtension({ + name: "Comfy.MaskEditor", + init(app) { + ComfyApp.open_maskeditor = + function () { + const dlg = MaskEditorDialog.getInstance(); + if(!dlg.isOpened()) { + dlg.show(); + } + }; + + const context_predicate = () => ComfyApp.clipspace && ComfyApp.clipspace.imgs && ComfyApp.clipspace.imgs.length > 0 + ClipspaceDialog.registerButton("MaskEditor", context_predicate, ComfyApp.open_maskeditor); + } +}); \ No newline at end of file diff --git a/web/extensions/core/nodeTemplates.js b/web/extensions/core/nodeTemplates.js new file mode 100644 index 0000000000000000000000000000000000000000..7059f826d74ce3ad4c66f2f39df42ac3cadf178d --- /dev/null +++ b/web/extensions/core/nodeTemplates.js @@ -0,0 +1,184 @@ +import { app } from "../../scripts/app.js"; +import { ComfyDialog, $el } from "../../scripts/ui.js"; + +// Adds the ability to save and add multiple nodes as a template +// To save: +// Select multiple nodes (ctrl + drag to select a region or ctrl+click individual nodes) +// Right click the canvas +// Save Node Template -> give it a name +// +// To add: +// Right click the canvas +// Node templates -> click the one to add +// +// To delete/rename: +// Right click the canvas +// Node templates -> Manage + +const id = "Comfy.NodeTemplates"; + +class ManageTemplates extends ComfyDialog { + constructor() { + super(); + this.element.classList.add("comfy-manage-templates"); + this.templates = this.load(); + } + + createButtons() { + const btns = super.createButtons(); + btns[0].textContent = "Cancel"; + btns.unshift( + $el("button", { + type: "button", + textContent: "Save", + onclick: () => this.save(), + }) + ); + return btns; + } + + load() { + const templates = localStorage.getItem(id); + if (templates) { + return JSON.parse(templates); + } else { + return []; + } + } + + save() { + // Find all visible inputs and save them as our new list + const inputs = this.element.querySelectorAll("input"); + const updated = []; + + for (let i = 0; i < inputs.length; i++) { + const input = inputs[i]; + if (input.parentElement.style.display !== "none") { + const t = this.templates[i]; + t.name = input.value.trim() || input.getAttribute("data-name"); + updated.push(t); + } + } + + this.templates = updated; + this.store(); + this.close(); + } + + store() { + localStorage.setItem(id, JSON.stringify(this.templates)); + } + + show() { + // Show list of template names + delete button + super.show( + $el( + "div", + { + style: { + display: "grid", + gridTemplateColumns: "1fr auto", + gap: "5px", + }, + }, + this.templates.flatMap((t) => { + let nameInput; + return [ + $el( + "label", + { + textContent: "Name: ", + }, + [ + $el("input", { + value: t.name, + dataset: { name: t.name }, + $: (el) => (nameInput = el), + }), + ] + ), + $el("button", { + textContent: "Delete", + style: { + fontSize: "12px", + color: "red", + fontWeight: "normal", + }, + onclick: (e) => { + nameInput.value = ""; + e.target.style.display = "none"; + e.target.previousElementSibling.style.display = "none"; + }, + }), + ]; + }) + ) + ); + } +} + +app.registerExtension({ + name: id, + setup() { + const manage = new ManageTemplates(); + + const clipboardAction = (cb) => { + // We use the clipboard functions but dont want to overwrite the current user clipboard + // Restore it after we've run our callback + const old = localStorage.getItem("litegrapheditor_clipboard"); + cb(); + localStorage.setItem("litegrapheditor_clipboard", old); + }; + + const orig = LGraphCanvas.prototype.getCanvasMenuOptions; + LGraphCanvas.prototype.getCanvasMenuOptions = function () { + const options = orig.apply(this, arguments); + + options.push(null); + options.push({ + content: `Save Selected as Template`, + disabled: !Object.keys(app.canvas.selected_nodes || {}).length, + callback: () => { + const name = prompt("Enter name"); + if (!name || !name.trim()) return; + + clipboardAction(() => { + app.canvas.copyToClipboard(); + manage.templates.push({ + name, + data: localStorage.getItem("litegrapheditor_clipboard"), + }); + manage.store(); + }); + }, + }); + + // Map each template to a menu item + const subItems = manage.templates.map((t) => ({ + content: t.name, + callback: () => { + clipboardAction(() => { + localStorage.setItem("litegrapheditor_clipboard", t.data); + app.canvas.pasteFromClipboard(); + }); + }, + })); + + if (subItems.length) { + subItems.push(null, { + content: "Manage", + callback: () => manage.show(), + }); + + options.push({ + content: "Node Templates", + submenu: { + options: subItems, + }, + }); + } + + return options; + }; + }, +}); diff --git a/web/extensions/core/noteNode.js b/web/extensions/core/noteNode.js new file mode 100644 index 0000000000000000000000000000000000000000..8d89054e9f6465e467acd72b609f53b292b87d70 --- /dev/null +++ b/web/extensions/core/noteNode.js @@ -0,0 +1,41 @@ +import {app} from "../../scripts/app.js"; +import {ComfyWidgets} from "../../scripts/widgets.js"; +// Node that add notes to your project + +app.registerExtension({ + name: "Comfy.NoteNode", + registerCustomNodes() { + class NoteNode { + color=LGraphCanvas.node_colors.yellow.color; + bgcolor=LGraphCanvas.node_colors.yellow.bgcolor; + groupcolor = LGraphCanvas.node_colors.yellow.groupcolor; + constructor() { + if (!this.properties) { + this.properties = {}; + this.properties.text=""; + } + + ComfyWidgets.STRING(this, "", ["", {default:this.properties.text, multiline: true}], app) + + this.serialize_widgets = true; + this.isVirtualNode = true; + + } + + + } + + // Load default visibility + + LiteGraph.registerNodeType( + "Note", + Object.assign(NoteNode, { + title_mode: LiteGraph.NORMAL_TITLE, + title: "Note", + collapsable: true, + }) + ); + + NoteNode.category = "utils"; + }, +}); diff --git a/web/extensions/core/rerouteNode.js b/web/extensions/core/rerouteNode.js new file mode 100644 index 0000000000000000000000000000000000000000..499a171da164bfb6369204964f22bbaca86b3e70 --- /dev/null +++ b/web/extensions/core/rerouteNode.js @@ -0,0 +1,233 @@ +import { app } from "../../scripts/app.js"; + +// Node that allows you to redirect connections for cleaner graphs + +app.registerExtension({ + name: "Comfy.RerouteNode", + registerCustomNodes() { + class RerouteNode { + constructor() { + if (!this.properties) { + this.properties = {}; + } + this.properties.showOutputText = RerouteNode.defaultVisibility; + this.properties.horizontal = false; + + this.addInput("", "*"); + this.addOutput(this.properties.showOutputText ? "*" : "", "*"); + + this.onConnectionsChange = function (type, index, connected, link_info) { + this.applyOrientation(); + + // Prevent multiple connections to different types when we have no input + if (connected && type === LiteGraph.OUTPUT) { + // Ignore wildcard nodes as these will be updated to real types + const types = new Set(this.outputs[0].links.map((l) => app.graph.links[l].type).filter((t) => t !== "*")); + if (types.size > 1) { + const linksToDisconnect = []; + for (let i = 0; i < this.outputs[0].links.length - 1; i++) { + const linkId = this.outputs[0].links[i]; + const link = app.graph.links[linkId]; + linksToDisconnect.push(link); + } + for (const link of linksToDisconnect) { + const node = app.graph.getNodeById(link.target_id); + node.disconnectInput(link.target_slot); + } + } + } + + // Find root input + let currentNode = this; + let updateNodes = []; + let inputType = null; + let inputNode = null; + while (currentNode) { + updateNodes.unshift(currentNode); + const linkId = currentNode.inputs[0].link; + if (linkId !== null) { + const link = app.graph.links[linkId]; + const node = app.graph.getNodeById(link.origin_id); + const type = node.constructor.type; + if (type === "Reroute") { + if (node === this) { + // We've found a circle + currentNode.disconnectInput(link.target_slot); + currentNode = null; + } + else { + // Move the previous node + currentNode = node; + } + } else { + // We've found the end + inputNode = currentNode; + inputType = node.outputs[link.origin_slot]?.type ?? null; + break; + } + } else { + // This path has no input node + currentNode = null; + break; + } + } + + // Find all outputs + const nodes = [this]; + let outputType = null; + while (nodes.length) { + currentNode = nodes.pop(); + const outputs = (currentNode.outputs ? currentNode.outputs[0].links : []) || []; + if (outputs.length) { + for (const linkId of outputs) { + const link = app.graph.links[linkId]; + + // When disconnecting sometimes the link is still registered + if (!link) continue; + + const node = app.graph.getNodeById(link.target_id); + const type = node.constructor.type; + + if (type === "Reroute") { + // Follow reroute nodes + nodes.push(node); + updateNodes.push(node); + } else { + // We've found an output + const nodeOutType = node.inputs && node.inputs[link?.target_slot] && node.inputs[link.target_slot].type ? node.inputs[link.target_slot].type : null; + if (inputType && nodeOutType !== inputType) { + // The output doesnt match our input so disconnect it + node.disconnectInput(link.target_slot); + } else { + outputType = nodeOutType; + } + } + } + } else { + // No more outputs for this path + } + } + + const displayType = inputType || outputType || "*"; + const color = LGraphCanvas.link_type_colors[displayType]; + + // Update the types of each node + for (const node of updateNodes) { + // If we dont have an input type we are always wildcard but we'll show the output type + // This lets you change the output link to a different type and all nodes will update + node.outputs[0].type = inputType || "*"; + node.__outputType = displayType; + node.outputs[0].name = node.properties.showOutputText ? displayType : ""; + node.size = node.computeSize(); + node.applyOrientation(); + + for (const l of node.outputs[0].links || []) { + const link = app.graph.links[l]; + if (link) { + link.color = color; + } + } + } + + if (inputNode) { + const link = app.graph.links[inputNode.inputs[0].link]; + if (link) { + link.color = color; + } + } + }; + + this.clone = function () { + const cloned = RerouteNode.prototype.clone.apply(this); + cloned.removeOutput(0); + cloned.addOutput(this.properties.showOutputText ? "*" : "", "*"); + cloned.size = cloned.computeSize(); + return cloned; + }; + + // This node is purely frontend and does not impact the resulting prompt so should not be serialized + this.isVirtualNode = true; + } + + getExtraMenuOptions(_, options) { + options.unshift( + { + content: (this.properties.showOutputText ? "Hide" : "Show") + " Type", + callback: () => { + this.properties.showOutputText = !this.properties.showOutputText; + if (this.properties.showOutputText) { + this.outputs[0].name = this.__outputType || this.outputs[0].type; + } else { + this.outputs[0].name = ""; + } + this.size = this.computeSize(); + this.applyOrientation(); + app.graph.setDirtyCanvas(true, true); + }, + }, + { + content: (RerouteNode.defaultVisibility ? "Hide" : "Show") + " Type By Default", + callback: () => { + RerouteNode.setDefaultTextVisibility(!RerouteNode.defaultVisibility); + }, + }, + { + // naming is inverted with respect to LiteGraphNode.horizontal + // LiteGraphNode.horizontal == true means that + // each slot in the inputs and outputs are layed out horizontally, + // which is the opposite of the visual orientation of the inputs and outputs as a node + content: "Set " + (this.properties.horizontal ? "Horizontal" : "Vertical"), + callback: () => { + this.properties.horizontal = !this.properties.horizontal; + this.applyOrientation(); + }, + } + ); + } + applyOrientation() { + this.horizontal = this.properties.horizontal; + if (this.horizontal) { + // we correct the input position, because LiteGraphNode.horizontal + // doesn't account for title presence + // which reroute nodes don't have + this.inputs[0].pos = [this.size[0] / 2, 0]; + } else { + delete this.inputs[0].pos; + } + app.graph.setDirtyCanvas(true, true); + } + + computeSize() { + return [ + this.properties.showOutputText && this.outputs && this.outputs.length + ? Math.max(75, LiteGraph.NODE_TEXT_SIZE * this.outputs[0].name.length * 0.6 + 40) + : 75, + 26, + ]; + } + + static setDefaultTextVisibility(visible) { + RerouteNode.defaultVisibility = visible; + if (visible) { + localStorage["Comfy.RerouteNode.DefaultVisibility"] = "true"; + } else { + delete localStorage["Comfy.RerouteNode.DefaultVisibility"]; + } + } + } + + // Load default visibility + RerouteNode.setDefaultTextVisibility(!!localStorage["Comfy.RerouteNode.DefaultVisibility"]); + + LiteGraph.registerNodeType( + "Reroute", + Object.assign(RerouteNode, { + title_mode: LiteGraph.NO_TITLE, + title: "Reroute", + collapsable: false, + }) + ); + + RerouteNode.category = "utils"; + }, +}); diff --git a/web/extensions/core/saveImageExtraOutput.js b/web/extensions/core/saveImageExtraOutput.js new file mode 100644 index 0000000000000000000000000000000000000000..99e2213bfee207f203f8177a3189bb84fdc13995 --- /dev/null +++ b/web/extensions/core/saveImageExtraOutput.js @@ -0,0 +1,100 @@ +import { app } from "../../scripts/app.js"; + +// Use widget values and dates in output filenames + +app.registerExtension({ + name: "Comfy.SaveImageExtraOutput", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData.name === "SaveImage") { + const onNodeCreated = nodeType.prototype.onNodeCreated; + + // Simple date formatter + const parts = { + d: (d) => d.getDate(), + M: (d) => d.getMonth() + 1, + h: (d) => d.getHours(), + m: (d) => d.getMinutes(), + s: (d) => d.getSeconds(), + }; + const format = + Object.keys(parts) + .map((k) => k + k + "?") + .join("|") + "|yyy?y?"; + + function formatDate(text, date) { + return text.replace(new RegExp(format, "g"), function (text) { + if (text === "yy") return (date.getFullYear() + "").substring(2); + if (text === "yyyy") return date.getFullYear(); + if (text[0] in parts) { + const p = parts[text[0]](date); + return (p + "").padStart(text.length, "0"); + } + return text; + }); + } + + // When the SaveImage node is created we want to override the serialization of the output name widget to run our S&R + nodeType.prototype.onNodeCreated = function () { + const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined; + + const widget = this.widgets.find((w) => w.name === "filename_prefix"); + widget.serializeValue = () => { + return widget.value.replace(/%([^%]+)%/g, function (match, text) { + const split = text.split("."); + if (split.length !== 2) { + // Special handling for dates + if (split[0].startsWith("date:")) { + return formatDate(split[0].substring(5), new Date()); + } + + if (text !== "width" && text !== "height") { + // Dont warn on standard replacements + console.warn("Invalid replacement pattern", text); + } + return match; + } + + // Find node with matching S&R property name + let nodes = app.graph._nodes.filter((n) => n.properties?.["Node name for S&R"] === split[0]); + // If we cant, see if there is a node with that title + if (!nodes.length) { + nodes = app.graph._nodes.filter((n) => n.title === split[0]); + } + if (!nodes.length) { + console.warn("Unable to find node", split[0]); + return match; + } + + if (nodes.length > 1) { + console.warn("Multiple nodes matched", split[0], "using first match"); + } + + const node = nodes[0]; + + const widget = node.widgets?.find((w) => w.name === split[1]); + if (!widget) { + console.warn("Unable to find widget", split[1], "on node", split[0], node); + return match; + } + + return ((widget.value ?? "") + "").replaceAll(/\/|\\/g, "_"); + }); + }; + + return r; + }; + } else { + // When any other node is created add a property to alias the node + const onNodeCreated = nodeType.prototype.onNodeCreated; + nodeType.prototype.onNodeCreated = function () { + const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined; + + if (!this.properties || !("Node name for S&R" in this.properties)) { + this.addProperty("Node name for S&R", this.constructor.type, "string"); + } + + return r; + }; + } + }, +}); diff --git a/web/extensions/core/slotDefaults.js b/web/extensions/core/slotDefaults.js new file mode 100644 index 0000000000000000000000000000000000000000..718d25405713ba2d6c341424f113f9a58c5d965f --- /dev/null +++ b/web/extensions/core/slotDefaults.js @@ -0,0 +1,91 @@ +import { app } from "../../scripts/app.js"; +import { ComfyWidgets } from "../../scripts/widgets.js"; +// Adds defaults for quickly adding nodes with middle click on the input/output + +app.registerExtension({ + name: "Comfy.SlotDefaults", + suggestionsNumber: null, + init() { + LiteGraph.search_filter_enabled = true; + LiteGraph.middle_click_slot_add_default_node = true; + this.suggestionsNumber = app.ui.settings.addSetting({ + id: "Comfy.NodeSuggestions.number", + name: "Number of nodes suggestions", + type: "slider", + attrs: { + min: 1, + max: 100, + step: 1, + }, + defaultValue: 5, + onChange: (newVal, oldVal) => { + this.setDefaults(newVal); + } + }); + }, + slot_types_default_out: {}, + slot_types_default_in: {}, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + var nodeId = nodeData.name; + var inputs = []; + inputs = nodeData["input"]["required"]; //only show required inputs to reduce the mess also not logical to create node with optional inputs + for (const inputKey in inputs) { + var input = (inputs[inputKey]); + if (typeof input[0] !== "string") continue; + + var type = input[0] + if (type in ComfyWidgets) { + var customProperties = input[1] + if (!(customProperties?.forceInput)) continue; //ignore widgets that don't force input + } + + if (!(type in this.slot_types_default_out)) { + this.slot_types_default_out[type] = ["Reroute"]; + } + if (this.slot_types_default_out[type].includes(nodeId)) continue; + this.slot_types_default_out[type].push(nodeId); + + // Input types have to be stored as lower case + // Store each node that can handle this input type + const lowerType = type.toLocaleLowerCase(); + if (!(lowerType in LiteGraph.registered_slot_in_types)) { + LiteGraph.registered_slot_in_types[lowerType] = { nodes: [] }; + } + LiteGraph.registered_slot_in_types[lowerType].nodes.push(nodeType.comfyClass); + } + + var outputs = nodeData["output"]; + for (const key in outputs) { + var type = outputs[key]; + if (!(type in this.slot_types_default_in)) { + this.slot_types_default_in[type] = ["Reroute"];// ["Reroute", "Primitive"]; primitive doesn't always work :'() + } + + this.slot_types_default_in[type].push(nodeId); + + // Store each node that can handle this output type + if (!(type in LiteGraph.registered_slot_out_types)) { + LiteGraph.registered_slot_out_types[type] = { nodes: [] }; + } + LiteGraph.registered_slot_out_types[type].nodes.push(nodeType.comfyClass); + + if(!LiteGraph.slot_types_out.includes(type)) { + LiteGraph.slot_types_out.push(type); + } + } + var maxNum = this.suggestionsNumber.value; + this.setDefaults(maxNum); + }, + setDefaults(maxNum) { + + LiteGraph.slot_types_default_out = {}; + LiteGraph.slot_types_default_in = {}; + + for (const type in this.slot_types_default_out) { + LiteGraph.slot_types_default_out[type] = this.slot_types_default_out[type].slice(0, maxNum); + } + for (const type in this.slot_types_default_in) { + LiteGraph.slot_types_default_in[type] = this.slot_types_default_in[type].slice(0, maxNum); + } + } +}); diff --git a/web/extensions/core/snapToGrid.js b/web/extensions/core/snapToGrid.js new file mode 100644 index 0000000000000000000000000000000000000000..dc534d6edf97a3d20a51b7ca5dc6d5fde770ef5a --- /dev/null +++ b/web/extensions/core/snapToGrid.js @@ -0,0 +1,89 @@ +import { app } from "../../scripts/app.js"; + +// Shift + drag/resize to snap to grid + +app.registerExtension({ + name: "Comfy.SnapToGrid", + init() { + // Add setting to control grid size + app.ui.settings.addSetting({ + id: "Comfy.SnapToGrid.GridSize", + name: "Grid Size", + type: "slider", + attrs: { + min: 1, + max: 500, + }, + tooltip: + "When dragging and resizing nodes while holding shift they will be aligned to the grid, this controls the size of that grid.", + defaultValue: LiteGraph.CANVAS_GRID_SIZE, + onChange(value) { + LiteGraph.CANVAS_GRID_SIZE = +value; + }, + }); + + // After moving a node, if the shift key is down align it to grid + const onNodeMoved = app.canvas.onNodeMoved; + app.canvas.onNodeMoved = function (node) { + const r = onNodeMoved?.apply(this, arguments); + + if (app.shiftDown) { + // Ensure all selected nodes are realigned + for (const id in this.selected_nodes) { + this.selected_nodes[id].alignToGrid(); + } + } + + return r; + }; + + // When a node is added, add a resize handler to it so we can fix align the size with the grid + const onNodeAdded = app.graph.onNodeAdded; + app.graph.onNodeAdded = function (node) { + const onResize = node.onResize; + node.onResize = function () { + if (app.shiftDown) { + const w = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.size[0] / LiteGraph.CANVAS_GRID_SIZE); + const h = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.size[1] / LiteGraph.CANVAS_GRID_SIZE); + node.size[0] = w; + node.size[1] = h; + } + return onResize?.apply(this, arguments); + }; + return onNodeAdded?.apply(this, arguments); + }; + + // Draw a preview of where the node will go if holding shift and the node is selected + const origDrawNode = LGraphCanvas.prototype.drawNode; + LGraphCanvas.prototype.drawNode = function (node, ctx) { + if (app.shiftDown && this.node_dragged && node.id in this.selected_nodes) { + const x = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[0] / LiteGraph.CANVAS_GRID_SIZE); + const y = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[1] / LiteGraph.CANVAS_GRID_SIZE); + + const shiftX = x - node.pos[0]; + let shiftY = y - node.pos[1]; + + let w, h; + if (node.flags.collapsed) { + w = node._collapsed_width; + h = LiteGraph.NODE_TITLE_HEIGHT; + shiftY -= LiteGraph.NODE_TITLE_HEIGHT; + } else { + w = node.size[0]; + h = node.size[1]; + let titleMode = node.constructor.title_mode; + if (titleMode !== LiteGraph.TRANSPARENT_TITLE && titleMode !== LiteGraph.NO_TITLE) { + h += LiteGraph.NODE_TITLE_HEIGHT; + shiftY -= LiteGraph.NODE_TITLE_HEIGHT; + } + } + const f = ctx.fillStyle; + ctx.fillStyle = "rgba(100, 100, 100, 0.5)"; + ctx.fillRect(shiftX, shiftY, w, h); + ctx.fillStyle = f; + } + + return origDrawNode.apply(this, arguments); + }; + }, +}); diff --git a/web/extensions/core/uploadImage.js b/web/extensions/core/uploadImage.js new file mode 100644 index 0000000000000000000000000000000000000000..530c4599e7990eb619af3b3dabacecec0a0e4334 --- /dev/null +++ b/web/extensions/core/uploadImage.js @@ -0,0 +1,12 @@ +import { app } from "../../scripts/app.js"; + +// Adds an upload button to the nodes + +app.registerExtension({ + name: "Comfy.UploadImage", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData?.input?.required?.image?.[1]?.image_upload === true) { + nodeData.input.required.upload = ["IMAGEUPLOAD"]; + } + }, +}); diff --git a/web/extensions/core/widgetInputs.js b/web/extensions/core/widgetInputs.js new file mode 100644 index 0000000000000000000000000000000000000000..606605f0a96cb7d0bcb46118bd7cf843ef97cc42 --- /dev/null +++ b/web/extensions/core/widgetInputs.js @@ -0,0 +1,435 @@ +import { ComfyWidgets, addValueControlWidget } from "../../scripts/widgets.js"; +import { app } from "../../scripts/app.js"; + +const CONVERTED_TYPE = "converted-widget"; +const VALID_TYPES = ["STRING", "combo", "number", "BOOLEAN"]; + +function isConvertableWidget(widget, config) { + return (VALID_TYPES.includes(widget.type) || VALID_TYPES.includes(config[0])) && !widget.options?.forceInput; +} + +function hideWidget(node, widget, suffix = "") { + widget.origType = widget.type; + widget.origComputeSize = widget.computeSize; + widget.origSerializeValue = widget.serializeValue; + widget.computeSize = () => [0, -4]; // -4 is due to the gap litegraph adds between widgets automatically + widget.type = CONVERTED_TYPE + suffix; + widget.serializeValue = () => { + // Prevent serializing the widget if we have no input linked + if (!node.inputs) { + return undefined; + } + let node_input = node.inputs.find((i) => i.widget?.name === widget.name); + + if (!node_input || !node_input.link) { + return undefined; + } + return widget.origSerializeValue ? widget.origSerializeValue() : widget.value; + }; + + // Hide any linked widgets, e.g. seed+seedControl + if (widget.linkedWidgets) { + for (const w of widget.linkedWidgets) { + hideWidget(node, w, ":" + widget.name); + } + } +} + +function showWidget(widget) { + widget.type = widget.origType; + widget.computeSize = widget.origComputeSize; + widget.serializeValue = widget.origSerializeValue; + + delete widget.origType; + delete widget.origComputeSize; + delete widget.origSerializeValue; + + // Hide any linked widgets, e.g. seed+seedControl + if (widget.linkedWidgets) { + for (const w of widget.linkedWidgets) { + showWidget(w); + } + } +} + +function convertToInput(node, widget, config) { + hideWidget(node, widget); + + const { linkType } = getWidgetType(config); + + // Add input and store widget config for creating on primitive node + const sz = node.size; + node.addInput(widget.name, linkType, { + widget: { name: widget.name, config }, + }); + + for (const widget of node.widgets) { + widget.last_y += LiteGraph.NODE_SLOT_HEIGHT; + } + + // Restore original size but grow if needed + node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])]); +} + +function convertToWidget(node, widget) { + showWidget(widget); + const sz = node.size; + node.removeInput(node.inputs.findIndex((i) => i.widget?.name === widget.name)); + + for (const widget of node.widgets) { + widget.last_y -= LiteGraph.NODE_SLOT_HEIGHT; + } + + // Restore original size but grow if needed + node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])]); +} + +function getWidgetType(config) { + // Special handling for COMBO so we restrict links based on the entries + let type = config[0]; + let linkType = type; + if (type instanceof Array) { + type = "COMBO"; + linkType = linkType.join(","); + } + return { type, linkType }; +} + +app.registerExtension({ + name: "Comfy.WidgetInputs", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + // Add menu options to conver to/from widgets + const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; + nodeType.prototype.getExtraMenuOptions = function (_, options) { + const r = origGetExtraMenuOptions ? origGetExtraMenuOptions.apply(this, arguments) : undefined; + + if (this.widgets) { + let toInput = []; + let toWidget = []; + for (const w of this.widgets) { + if (w.options?.forceInput) { + continue; + } + if (w.type === CONVERTED_TYPE) { + toWidget.push({ + content: `Convert ${w.name} to widget`, + callback: () => convertToWidget(this, w), + }); + } else { + const config = nodeData?.input?.required[w.name] || nodeData?.input?.optional?.[w.name] || [w.type, w.options || {}]; + if (isConvertableWidget(w, config)) { + toInput.push({ + content: `Convert ${w.name} to input`, + callback: () => convertToInput(this, w, config), + }); + } + } + } + if (toInput.length) { + options.push(...toInput, null); + } + + if (toWidget.length) { + options.push(...toWidget, null); + } + } + + return r; + }; + + const origOnNodeCreated = nodeType.prototype.onNodeCreated + nodeType.prototype.onNodeCreated = function () { + const r = origOnNodeCreated ? origOnNodeCreated.apply(this) : undefined; + if (this.widgets) { + for (const w of this.widgets) { + if (w?.options?.forceInput || w?.options?.defaultInput) { + const config = nodeData?.input?.required[w.name] || nodeData?.input?.optional?.[w.name] || [w.type, w.options || {}]; + convertToInput(this, w, config); + } + } + } + return r; + } + + // On initial configure of nodes hide all converted widgets + const origOnConfigure = nodeType.prototype.onConfigure; + nodeType.prototype.onConfigure = function () { + const r = origOnConfigure ? origOnConfigure.apply(this, arguments) : undefined; + + if (this.inputs) { + for (const input of this.inputs) { + if (input.widget && !input.widget.config[1]?.forceInput) { + const w = this.widgets.find((w) => w.name === input.widget.name); + if (w) { + hideWidget(this, w); + } else { + convertToWidget(this, input) + } + } + } + } + + return r; + }; + + function isNodeAtPos(pos) { + for (const n of app.graph._nodes) { + if (n.pos[0] === pos[0] && n.pos[1] === pos[1]) { + return true; + } + } + return false; + } + + // Double click a widget input to automatically attach a primitive + const origOnInputDblClick = nodeType.prototype.onInputDblClick; + const ignoreDblClick = Symbol(); + nodeType.prototype.onInputDblClick = function (slot) { + const r = origOnInputDblClick ? origOnInputDblClick.apply(this, arguments) : undefined; + + const input = this.inputs[slot]; + if (!input.widget || !input[ignoreDblClick]) { + // Not a widget input or already handled input + if (!(input.type in ComfyWidgets) && !(input.widget.config?.[0] instanceof Array)) { + return r; //also Not a ComfyWidgets input or combo (do nothing) + } + } + + // Create a primitive node + const node = LiteGraph.createNode("PrimitiveNode"); + app.graph.add(node); + + // Calculate a position that wont directly overlap another node + const pos = [this.pos[0] - node.size[0] - 30, this.pos[1]]; + while (isNodeAtPos(pos)) { + pos[1] += LiteGraph.NODE_TITLE_HEIGHT; + } + + node.pos = pos; + node.connect(0, this, slot); + node.title = input.name; + + // Prevent adding duplicates due to triple clicking + input[ignoreDblClick] = true; + setTimeout(() => { + delete input[ignoreDblClick]; + }, 300); + + return r; + }; + }, + registerCustomNodes() { + class PrimitiveNode { + constructor() { + this.addOutput("connect to widget input", "*"); + this.serialize_widgets = true; + this.isVirtualNode = true; + } + + applyToGraph() { + if (!this.outputs[0].links?.length) return; + + function get_links(node) { + let links = []; + for (const l of node.outputs[0].links) { + const linkInfo = app.graph.links[l]; + const n = node.graph.getNodeById(linkInfo.target_id); + if (n.type == "Reroute") { + links = links.concat(get_links(n)); + } else { + links.push(l); + } + } + return links; + } + + let links = get_links(this); + // For each output link copy our value over the original widget value + for (const l of links) { + const linkInfo = app.graph.links[l]; + const node = this.graph.getNodeById(linkInfo.target_id); + const input = node.inputs[linkInfo.target_slot]; + const widgetName = input.widget.name; + if (widgetName) { + const widget = node.widgets.find((w) => w.name === widgetName); + if (widget) { + widget.value = this.widgets[0].value; + if (widget.callback) { + widget.callback(widget.value, app.canvas, node, app.canvas.graph_mouse, {}); + } + } + } + } + } + + onConnectionsChange(_, index, connected) { + if (connected) { + if (this.outputs[0].links?.length) { + if (!this.widgets?.length) { + this.#onFirstConnection(); + } + if (!this.widgets?.length && this.outputs[0].widget) { + // On first load it often cant recreate the widget as the other node doesnt exist yet + // Manually recreate it from the output info + this.#createWidget(this.outputs[0].widget.config); + } + } + } else if (!this.outputs[0].links?.length) { + this.#onLastDisconnect(); + } + } + + onConnectOutput(slot, type, input, target_node, target_slot) { + // Fires before the link is made allowing us to reject it if it isn't valid + + // No widget, we cant connect + if (!input.widget) { + if (!(input.type in ComfyWidgets)) return false; + } + + if (this.outputs[slot].links?.length) { + return this.#isValidConnection(input); + } + } + + #onFirstConnection() { + // First connection can fire before the graph is ready on initial load so random things can be missing + const linkId = this.outputs[0].links[0]; + const link = this.graph.links[linkId]; + if (!link) return; + + const theirNode = this.graph.getNodeById(link.target_id); + if (!theirNode || !theirNode.inputs) return; + + const input = theirNode.inputs[link.target_slot]; + if (!input) return; + + + var _widget; + if (!input.widget) { + if (!(input.type in ComfyWidgets)) return; + _widget = { "name": input.name, "config": [input.type, {}] }//fake widget + } else { + _widget = input.widget; + } + + const widget = _widget; + const { type, linkType } = getWidgetType(widget.config); + // Update our output to restrict to the widget type + this.outputs[0].type = linkType; + this.outputs[0].name = type; + this.outputs[0].widget = widget; + + this.#createWidget(widget.config, theirNode, widget.name); + } + + #createWidget(inputData, node, widgetName) { + let type = inputData[0]; + + if (type instanceof Array) { + type = "COMBO"; + } + + let widget; + if (type in ComfyWidgets) { + widget = (ComfyWidgets[type](this, "value", inputData, app) || {}).widget; + } else { + widget = this.addWidget(type, "value", null, () => { }, {}); + } + + if (node?.widgets && widget) { + const theirWidget = node.widgets.find((w) => w.name === widgetName); + if (theirWidget) { + widget.value = theirWidget.value; + } + } + + if (widget.type === "number" || widget.type === "combo") { + addValueControlWidget(this, widget, "fixed"); + } + + // When our value changes, update other widgets to reflect our changes + // e.g. so LoadImage shows correct image + const callback = widget.callback; + const self = this; + widget.callback = function () { + const r = callback ? callback.apply(this, arguments) : undefined; + self.applyToGraph(); + return r; + }; + + // Grow our node if required + const sz = this.computeSize(); + if (this.size[0] < sz[0]) { + this.size[0] = sz[0]; + } + if (this.size[1] < sz[1]) { + this.size[1] = sz[1]; + } + + requestAnimationFrame(() => { + if (this.onResize) { + this.onResize(this.size); + } + }); + } + + #isValidConnection(input) { + // Only allow connections where the configs match + const config1 = this.outputs[0].widget.config; + const config2 = input.widget.config; + + if (config1[0] instanceof Array) { + // These checks shouldnt actually be necessary as the types should match + // but double checking doesn't hurt + + // New input isnt a combo + if (!(config2[0] instanceof Array)) return false; + // New imput combo has a different size + if (config1[0].length !== config2[0].length) return false; + // New input combo has different elements + if (config1[0].find((v, i) => config2[0][i] !== v)) return false; + } else if (config1[0] !== config2[0]) { + // Configs dont match + return false; + } + + for (const k in config1[1]) { + if (k !== "default" && k !== 'forceInput') { + if (config1[1][k] !== config2[1][k]) { + return false; + } + } + } + + return true; + } + + #onLastDisconnect() { + // We cant remove + re-add the output here as if you drag a link over the same link + // it removes, then re-adds, causing it to break + this.outputs[0].type = "*"; + this.outputs[0].name = "connect to widget input"; + delete this.outputs[0].widget; + + if (this.widgets) { + // Allow widgets to cleanup + for (const w of this.widgets) { + if (w.onRemove) { + w.onRemove(); + } + } + this.widgets.length = 0; + } + } + } + + LiteGraph.registerNodeType( + "PrimitiveNode", + Object.assign(PrimitiveNode, { + title: "Primitive", + }) + ); + PrimitiveNode.category = "utils"; + }, +}); diff --git a/web/extensions/logging.js.example b/web/extensions/logging.js.example new file mode 100644 index 0000000000000000000000000000000000000000..d015096a29f2732135b827a0efb513c6bf387bcf --- /dev/null +++ b/web/extensions/logging.js.example @@ -0,0 +1,55 @@ +import { app } from "../scripts/app.js"; + +const ext = { + // Unique name for the extension + name: "Example.LoggingExtension", + async init(app) { + // Any initial setup to run as soon as the page loads + console.log("[logging]", "extension init"); + }, + async setup(app) { + // Any setup to run after the app is created + console.log("[logging]", "extension setup"); + }, + async addCustomNodeDefs(defs, app) { + // Add custom node definitions + // These definitions will be configured and registered automatically + // defs is a lookup core nodes, add yours into this + console.log("[logging]", "add custom node definitions", "current nodes:", Object.keys(defs)); + }, + async getCustomWidgets(app) { + // Return custom widget types + // See ComfyWidgets for widget examples + console.log("[logging]", "provide custom widgets"); + }, + async beforeRegisterNodeDef(nodeType, nodeData, app) { + // Run custom logic before a node definition is registered with the graph + console.log("[logging]", "before register node: ", nodeType, nodeData); + + // This fires for every node definition so only log once + delete ext.beforeRegisterNodeDef; + }, + async registerCustomNodes(app) { + // Register any custom node implementations here allowing for more flexability than a custom node def + console.log("[logging]", "register custom nodes"); + }, + loadedGraphNode(node, app) { + // Fires for each node when loading/dragging/etc a workflow json or png + // If you break something in the backend and want to patch workflows in the frontend + // This is the place to do this + console.log("[logging]", "loaded graph node: ", node); + + // This fires for every node on each load so only log once + delete ext.loadedGraphNode; + }, + nodeCreated(node, app) { + // Fires every time a node is constructed + // You can modify widgets/add handlers/etc here + console.log("[logging]", "node created: ", node); + + // This fires for every node so only log once + delete ext.nodeCreated; + } +}; + +app.registerExtension(ext); diff --git a/web/index.html b/web/index.html new file mode 100644 index 0000000000000000000000000000000000000000..41bc246c090e51824212f70796e508645affe9c8 --- /dev/null +++ b/web/index.html @@ -0,0 +1,20 @@ + + + + + ComfyUI + + + + + + + + + + diff --git a/web/jsconfig.json b/web/jsconfig.json new file mode 100644 index 0000000000000000000000000000000000000000..57403d8cf2b5ca7b5d2bf2a4345c2a031e97516f --- /dev/null +++ b/web/jsconfig.json @@ -0,0 +1,9 @@ +{ + "compilerOptions": { + "baseUrl": ".", + "paths": { + "/*": ["./*"] + } + }, + "include": ["."] +} diff --git a/web/lib/litegraph.core.js b/web/lib/litegraph.core.js new file mode 100644 index 0000000000000000000000000000000000000000..f81c83a8a4c618ca106aa88bbcfc753a24855081 --- /dev/null +++ b/web/lib/litegraph.core.js @@ -0,0 +1,14385 @@ +//packer version + + +(function(global) { + // ************************************************************* + // LiteGraph CLASS ******* + // ************************************************************* + + /** + * The Global Scope. It contains all the registered node classes. + * + * @class LiteGraph + * @constructor + */ + + var LiteGraph = (global.LiteGraph = { + VERSION: 0.4, + + CANVAS_GRID_SIZE: 10, + + NODE_TITLE_HEIGHT: 30, + NODE_TITLE_TEXT_Y: 20, + NODE_SLOT_HEIGHT: 20, + NODE_WIDGET_HEIGHT: 20, + NODE_WIDTH: 140, + NODE_MIN_WIDTH: 50, + NODE_COLLAPSED_RADIUS: 10, + NODE_COLLAPSED_WIDTH: 80, + NODE_TITLE_COLOR: "#999", + NODE_SELECTED_TITLE_COLOR: "#FFF", + NODE_TEXT_SIZE: 14, + NODE_TEXT_COLOR: "#AAA", + NODE_SUBTEXT_SIZE: 12, + NODE_DEFAULT_COLOR: "#333", + NODE_DEFAULT_BGCOLOR: "#353535", + NODE_DEFAULT_BOXCOLOR: "#666", + NODE_DEFAULT_SHAPE: "box", + NODE_BOX_OUTLINE_COLOR: "#FFF", + DEFAULT_SHADOW_COLOR: "rgba(0,0,0,0.5)", + DEFAULT_GROUP_FONT: 24, + + WIDGET_BGCOLOR: "#222", + WIDGET_OUTLINE_COLOR: "#666", + WIDGET_TEXT_COLOR: "#DDD", + WIDGET_SECONDARY_TEXT_COLOR: "#999", + + LINK_COLOR: "#9A9", + EVENT_LINK_COLOR: "#A86", + CONNECTING_LINK_COLOR: "#AFA", + + MAX_NUMBER_OF_NODES: 1000, //avoid infinite loops + DEFAULT_POSITION: [100, 100], //default node position + VALID_SHAPES: ["default", "box", "round", "card"], //,"circle" + + //shapes are used for nodes but also for slots + BOX_SHAPE: 1, + ROUND_SHAPE: 2, + CIRCLE_SHAPE: 3, + CARD_SHAPE: 4, + ARROW_SHAPE: 5, + GRID_SHAPE: 6, // intended for slot arrays + + //enums + INPUT: 1, + OUTPUT: 2, + + EVENT: -1, //for outputs + ACTION: -1, //for inputs + + NODE_MODES: ["Always", "On Event", "Never", "On Trigger"], // helper, will add "On Request" and more in the future + NODE_MODES_COLORS:["#666","#422","#333","#224","#626"], // use with node_box_coloured_by_mode + ALWAYS: 0, + ON_EVENT: 1, + NEVER: 2, + ON_TRIGGER: 3, + + UP: 1, + DOWN: 2, + LEFT: 3, + RIGHT: 4, + CENTER: 5, + + LINK_RENDER_MODES: ["Straight", "Linear", "Spline"], // helper + STRAIGHT_LINK: 0, + LINEAR_LINK: 1, + SPLINE_LINK: 2, + + NORMAL_TITLE: 0, + NO_TITLE: 1, + TRANSPARENT_TITLE: 2, + AUTOHIDE_TITLE: 3, + VERTICAL_LAYOUT: "vertical", // arrange nodes vertically + + proxy: null, //used to redirect calls + node_images_path: "", + + debug: false, + catch_exceptions: true, + throw_errors: true, + allow_scripts: false, //if set to true some nodes like Formula would be allowed to evaluate code that comes from unsafe sources (like node configuration), which could lead to exploits + registered_node_types: {}, //nodetypes by string + node_types_by_file_extension: {}, //used for dropping files in the canvas + Nodes: {}, //node types by classname + Globals: {}, //used to store vars between graphs + + searchbox_extras: {}, //used to add extra features to the search box + auto_sort_node_types: false, // [true!] If set to true, will automatically sort node types / categories in the context menus + + node_box_coloured_when_on: false, // [true!] this make the nodes box (top left circle) coloured when triggered (execute/action), visual feedback + node_box_coloured_by_mode: false, // [true!] nodebox based on node mode, visual feedback + + dialog_close_on_mouse_leave: false, // [false on mobile] better true if not touch device, TODO add an helper/listener to close if false + dialog_close_on_mouse_leave_delay: 500, + + shift_click_do_break_link_from: false, // [false!] prefer false if results too easy to break links - implement with ALT or TODO custom keys + click_do_break_link_to: false, // [false!]prefer false, way too easy to break links + + search_hide_on_mouse_leave: true, // [false on mobile] better true if not touch device, TODO add an helper/listener to close if false + search_filter_enabled: false, // [true!] enable filtering slots type in the search widget, !requires auto_load_slot_types or manual set registered_slot_[in/out]_types and slot_types_[in/out] + search_show_all_on_open: true, // [true!] opens the results list when opening the search widget + + auto_load_slot_types: false, // [if want false, use true, run, get vars values to be statically set, than disable] nodes types and nodeclass association with node types need to be calculated, if dont want this, calculate once and set registered_slot_[in/out]_types and slot_types_[in/out] + + // set these values if not using auto_load_slot_types + registered_slot_in_types: {}, // slot types for nodeclass + registered_slot_out_types: {}, // slot types for nodeclass + slot_types_in: [], // slot types IN + slot_types_out: [], // slot types OUT + slot_types_default_in: [], // specify for each IN slot type a(/many) default node(s), use single string, array, or object (with node, title, parameters, ..) like for search + slot_types_default_out: [], // specify for each OUT slot type a(/many) default node(s), use single string, array, or object (with node, title, parameters, ..) like for search + + alt_drag_do_clone_nodes: false, // [true!] very handy, ALT click to clone and drag the new node + + do_add_triggers_slots: false, // [true!] will create and connect event slots when using action/events connections, !WILL CHANGE node mode when using onTrigger (enable mode colors), onExecuted does not need this + + allow_multi_output_for_events: true, // [false!] being events, it is strongly reccomended to use them sequentially, one by one + + middle_click_slot_add_default_node: false, //[true!] allows to create and connect a ndoe clicking with the third button (wheel) + + release_link_on_empty_shows_menu: false, //[true!] dragging a link to empty space will open a menu, add from list, search or defaults + + pointerevents_method: "pointer", // "mouse"|"pointer" use mouse for retrocompatibility issues? (none found @ now) + // TODO implement pointercancel, gotpointercapture, lostpointercapture, (pointerover, pointerout if necessary) + + ctrl_shift_v_paste_connect_unselected_outputs: true, //[true!] allows ctrl + shift + v to paste nodes with the outputs of the unselected nodes connected with the inputs of the newly pasted nodes + + // if true, all newly created nodes/links will use string UUIDs for their id fields instead of integers. + // use this if you must have node IDs that are unique across all graphs and subgraphs. + use_uuids: false, + + /** + * Register a node class so it can be listed when the user wants to create a new one + * @method registerNodeType + * @param {String} type name of the node and path + * @param {Class} base_class class containing the structure of a node + */ + + registerNodeType: function(type, base_class) { + if (!base_class.prototype) { + throw "Cannot register a simple object, it must be a class with a prototype"; + } + base_class.type = type; + + if (LiteGraph.debug) { + console.log("Node registered: " + type); + } + + const classname = base_class.name; + + const pos = type.lastIndexOf("/"); + base_class.category = type.substring(0, pos); + + if (!base_class.title) { + base_class.title = classname; + } + + //extend class + for (var i in LGraphNode.prototype) { + if (!base_class.prototype[i]) { + base_class.prototype[i] = LGraphNode.prototype[i]; + } + } + + const prev = this.registered_node_types[type]; + if(prev) { + console.log("replacing node type: " + type); + } + if( !Object.prototype.hasOwnProperty.call( base_class.prototype, "shape") ) { + Object.defineProperty(base_class.prototype, "shape", { + set: function(v) { + switch (v) { + case "default": + delete this._shape; + break; + case "box": + this._shape = LiteGraph.BOX_SHAPE; + break; + case "round": + this._shape = LiteGraph.ROUND_SHAPE; + break; + case "circle": + this._shape = LiteGraph.CIRCLE_SHAPE; + break; + case "card": + this._shape = LiteGraph.CARD_SHAPE; + break; + default: + this._shape = v; + } + }, + get: function() { + return this._shape; + }, + enumerable: true, + configurable: true + }); + + + //used to know which nodes to create when dragging files to the canvas + if (base_class.supported_extensions) { + for (let i in base_class.supported_extensions) { + const ext = base_class.supported_extensions[i]; + if(ext && ext.constructor === String) { + this.node_types_by_file_extension[ ext.toLowerCase() ] = base_class; + } + } + } + } + + this.registered_node_types[type] = base_class; + if (base_class.constructor.name) { + this.Nodes[classname] = base_class; + } + if (LiteGraph.onNodeTypeRegistered) { + LiteGraph.onNodeTypeRegistered(type, base_class); + } + if (prev && LiteGraph.onNodeTypeReplaced) { + LiteGraph.onNodeTypeReplaced(type, base_class, prev); + } + + //warnings + if (base_class.prototype.onPropertyChange) { + console.warn( + "LiteGraph node class " + + type + + " has onPropertyChange method, it must be called onPropertyChanged with d at the end" + ); + } + + // TODO one would want to know input and ouput :: this would allow through registerNodeAndSlotType to get all the slots types + if (this.auto_load_slot_types) { + new base_class(base_class.title || "tmpnode"); + } + }, + + /** + * removes a node type from the system + * @method unregisterNodeType + * @param {String|Object} type name of the node or the node constructor itself + */ + unregisterNodeType: function(type) { + const base_class = + type.constructor === String + ? this.registered_node_types[type] + : type; + if (!base_class) { + throw "node type not found: " + type; + } + delete this.registered_node_types[base_class.type]; + if (base_class.constructor.name) { + delete this.Nodes[base_class.constructor.name]; + } + }, + + /** + * Save a slot type and his node + * @method registerSlotType + * @param {String|Object} type name of the node or the node constructor itself + * @param {String} slot_type name of the slot type (variable type), eg. string, number, array, boolean, .. + */ + registerNodeAndSlotType: function(type, slot_type, out){ + out = out || false; + const base_class = + type.constructor === String && + this.registered_node_types[type] !== "anonymous" + ? this.registered_node_types[type] + : type; + + const class_type = base_class.constructor.type; + + let allTypes = []; + if (typeof slot_type === "string") { + allTypes = slot_type.split(","); + } else if (slot_type == this.EVENT || slot_type == this.ACTION) { + allTypes = ["_event_"]; + } else { + allTypes = ["*"]; + } + + for (let i = 0; i < allTypes.length; ++i) { + let slotType = allTypes[i]; + if (slotType === "") { + slotType = "*"; + } + const registerTo = out + ? "registered_slot_out_types" + : "registered_slot_in_types"; + if (this[registerTo][slotType] === undefined) { + this[registerTo][slotType] = { nodes: [] }; + } + if (!this[registerTo][slotType].nodes.includes(class_type)) { + this[registerTo][slotType].nodes.push(class_type); + } + + // check if is a new type + if (!out) { + if (!this.slot_types_in.includes(slotType.toLowerCase())) { + this.slot_types_in.push(slotType.toLowerCase()); + this.slot_types_in.sort(); + } + } else { + if (!this.slot_types_out.includes(slotType.toLowerCase())) { + this.slot_types_out.push(slotType.toLowerCase()); + this.slot_types_out.sort(); + } + } + } + }, + + /** + * Create a new nodetype by passing a function, it wraps it with a proper class and generates inputs according to the parameters of the function. + * Useful to wrap simple methods that do not require properties, and that only process some input to generate an output. + * @method wrapFunctionAsNode + * @param {String} name node name with namespace (p.e.: 'math/sum') + * @param {Function} func + * @param {Array} param_types [optional] an array containing the type of every parameter, otherwise parameters will accept any type + * @param {String} return_type [optional] string with the return type, otherwise it will be generic + * @param {Object} properties [optional] properties to be configurable + */ + wrapFunctionAsNode: function( + name, + func, + param_types, + return_type, + properties + ) { + var params = Array(func.length); + var code = ""; + var names = LiteGraph.getParameterNames(func); + for (var i = 0; i < names.length; ++i) { + code += + "this.addInput('" + + names[i] + + "'," + + (param_types && param_types[i] + ? "'" + param_types[i] + "'" + : "0") + + ");\n"; + } + code += + "this.addOutput('out'," + + (return_type ? "'" + return_type + "'" : 0) + + ");\n"; + if (properties) { + code += + "this.properties = " + JSON.stringify(properties) + ";\n"; + } + var classobj = Function(code); + classobj.title = name.split("/").pop(); + classobj.desc = "Generated from " + func.name; + classobj.prototype.onExecute = function onExecute() { + for (var i = 0; i < params.length; ++i) { + params[i] = this.getInputData(i); + } + var r = func.apply(this, params); + this.setOutputData(0, r); + }; + this.registerNodeType(name, classobj); + }, + + /** + * Removes all previously registered node's types + */ + clearRegisteredTypes: function() { + this.registered_node_types = {}; + this.node_types_by_file_extension = {}; + this.Nodes = {}; + this.searchbox_extras = {}; + }, + + /** + * Adds this method to all nodetypes, existing and to be created + * (You can add it to LGraphNode.prototype but then existing node types wont have it) + * @method addNodeMethod + * @param {Function} func + */ + addNodeMethod: function(name, func) { + LGraphNode.prototype[name] = func; + for (var i in this.registered_node_types) { + var type = this.registered_node_types[i]; + if (type.prototype[name]) { + type.prototype["_" + name] = type.prototype[name]; + } //keep old in case of replacing + type.prototype[name] = func; + } + }, + + /** + * Create a node of a given type with a name. The node is not attached to any graph yet. + * @method createNode + * @param {String} type full name of the node class. p.e. "math/sin" + * @param {String} name a name to distinguish from other nodes + * @param {Object} options to set options + */ + + createNode: function(type, title, options) { + var base_class = this.registered_node_types[type]; + if (!base_class) { + if (LiteGraph.debug) { + console.log( + 'GraphNode type "' + type + '" not registered.' + ); + } + return null; + } + + var prototype = base_class.prototype || base_class; + + title = title || base_class.title || type; + + var node = null; + + if (LiteGraph.catch_exceptions) { + try { + node = new base_class(title); + } catch (err) { + console.error(err); + return null; + } + } else { + node = new base_class(title); + } + + node.type = type; + + if (!node.title && title) { + node.title = title; + } + if (!node.properties) { + node.properties = {}; + } + if (!node.properties_info) { + node.properties_info = []; + } + if (!node.flags) { + node.flags = {}; + } + if (!node.size) { + node.size = node.computeSize(); + //call onresize? + } + if (!node.pos) { + node.pos = LiteGraph.DEFAULT_POSITION.concat(); + } + if (!node.mode) { + node.mode = LiteGraph.ALWAYS; + } + + //extra options + if (options) { + for (var i in options) { + node[i] = options[i]; + } + } + + // callback + if ( node.onNodeCreated ) { + node.onNodeCreated(); + } + + return node; + }, + + /** + * Returns a registered node type with a given name + * @method getNodeType + * @param {String} type full name of the node class. p.e. "math/sin" + * @return {Class} the node class + */ + getNodeType: function(type) { + return this.registered_node_types[type]; + }, + + /** + * Returns a list of node types matching one category + * @method getNodeType + * @param {String} category category name + * @return {Array} array with all the node classes + */ + + getNodeTypesInCategory: function(category, filter) { + var r = []; + for (var i in this.registered_node_types) { + var type = this.registered_node_types[i]; + if (type.filter != filter) { + continue; + } + + if (category == "") { + if (type.category == null) { + r.push(type); + } + } else if (type.category == category) { + r.push(type); + } + } + + if (this.auto_sort_node_types) { + r.sort(function(a,b){return a.title.localeCompare(b.title)}); + } + + return r; + }, + + /** + * Returns a list with all the node type categories + * @method getNodeTypesCategories + * @param {String} filter only nodes with ctor.filter equal can be shown + * @return {Array} array with all the names of the categories + */ + getNodeTypesCategories: function( filter ) { + var categories = { "": 1 }; + for (var i in this.registered_node_types) { + var type = this.registered_node_types[i]; + if ( type.category && !type.skip_list ) + { + if(type.filter != filter) + continue; + categories[type.category] = 1; + } + } + var result = []; + for (var i in categories) { + result.push(i); + } + return this.auto_sort_node_types ? result.sort() : result; + }, + + //debug purposes: reloads all the js scripts that matches a wildcard + reloadNodes: function(folder_wildcard) { + var tmp = document.getElementsByTagName("script"); + //weird, this array changes by its own, so we use a copy + var script_files = []; + for (var i=0; i < tmp.length; i++) { + script_files.push(tmp[i]); + } + + var docHeadObj = document.getElementsByTagName("head")[0]; + folder_wildcard = document.location.href + folder_wildcard; + + for (var i=0; i < script_files.length; i++) { + var src = script_files[i].src; + if ( + !src || + src.substr(0, folder_wildcard.length) != folder_wildcard + ) { + continue; + } + + try { + if (LiteGraph.debug) { + console.log("Reloading: " + src); + } + var dynamicScript = document.createElement("script"); + dynamicScript.type = "text/javascript"; + dynamicScript.src = src; + docHeadObj.appendChild(dynamicScript); + docHeadObj.removeChild(script_files[i]); + } catch (err) { + if (LiteGraph.throw_errors) { + throw err; + } + if (LiteGraph.debug) { + console.log("Error while reloading " + src); + } + } + } + + if (LiteGraph.debug) { + console.log("Nodes reloaded"); + } + }, + + //separated just to improve if it doesn't work + cloneObject: function(obj, target) { + if (obj == null) { + return null; + } + var r = JSON.parse(JSON.stringify(obj)); + if (!target) { + return r; + } + + for (var i in r) { + target[i] = r[i]; + } + return target; + }, + + /* + * https://gist.github.com/jed/982883?permalink_comment_id=852670#gistcomment-852670 + */ + uuidv4: function() { + return ([1e7]+-1e3+-4e3+-8e3+-1e11).replace(/[018]/g,a=>(a^Math.random()*16>>a/4).toString(16)); + }, + + /** + * Returns if the types of two slots are compatible (taking into account wildcards, etc) + * @method isValidConnection + * @param {String} type_a + * @param {String} type_b + * @return {Boolean} true if they can be connected + */ + isValidConnection: function(type_a, type_b) { + if (type_a=="" || type_a==="*") type_a = 0; + if (type_b=="" || type_b==="*") type_b = 0; + if ( + !type_a //generic output + || !type_b // generic input + || type_a == type_b //same type (is valid for triggers) + || (type_a == LiteGraph.EVENT && type_b == LiteGraph.ACTION) + ) { + return true; + } + + // Enforce string type to handle toLowerCase call (-1 number not ok) + type_a = String(type_a); + type_b = String(type_b); + type_a = type_a.toLowerCase(); + type_b = type_b.toLowerCase(); + + // For nodes supporting multiple connection types + if (type_a.indexOf(",") == -1 && type_b.indexOf(",") == -1) { + return type_a == type_b; + } + + // Check all permutations to see if one is valid + var supported_types_a = type_a.split(","); + var supported_types_b = type_b.split(","); + for (var i = 0; i < supported_types_a.length; ++i) { + for (var j = 0; j < supported_types_b.length; ++j) { + if(this.isValidConnection(supported_types_a[i],supported_types_b[j])){ + //if (supported_types_a[i] == supported_types_b[j]) { + return true; + } + } + } + + return false; + }, + + /** + * Register a string in the search box so when the user types it it will recommend this node + * @method registerSearchboxExtra + * @param {String} node_type the node recommended + * @param {String} description text to show next to it + * @param {Object} data it could contain info of how the node should be configured + * @return {Boolean} true if they can be connected + */ + registerSearchboxExtra: function(node_type, description, data) { + this.searchbox_extras[description.toLowerCase()] = { + type: node_type, + desc: description, + data: data + }; + }, + + /** + * Wrapper to load files (from url using fetch or from file using FileReader) + * @method fetchFile + * @param {String|File|Blob} url the url of the file (or the file itself) + * @param {String} type an string to know how to fetch it: "text","arraybuffer","json","blob" + * @param {Function} on_complete callback(data) + * @param {Function} on_error in case of an error + * @return {FileReader|Promise} returns the object used to + */ + fetchFile: function( url, type, on_complete, on_error ) { + var that = this; + if(!url) + return null; + + type = type || "text"; + if( url.constructor === String ) + { + if (url.substr(0, 4) == "http" && LiteGraph.proxy) { + url = LiteGraph.proxy + url.substr(url.indexOf(":") + 3); + } + return fetch(url) + .then(function(response) { + if(!response.ok) + throw new Error("File not found"); //it will be catch below + if(type == "arraybuffer") + return response.arrayBuffer(); + else if(type == "text" || type == "string") + return response.text(); + else if(type == "json") + return response.json(); + else if(type == "blob") + return response.blob(); + }) + .then(function(data) { + if(on_complete) + on_complete(data); + }) + .catch(function(error) { + console.error("error fetching file:",url); + if(on_error) + on_error(error); + }); + } + else if( url.constructor === File || url.constructor === Blob) + { + var reader = new FileReader(); + reader.onload = function(e) + { + var v = e.target.result; + if( type == "json" ) + v = JSON.parse(v); + if(on_complete) + on_complete(v); + } + if(type == "arraybuffer") + return reader.readAsArrayBuffer(url); + else if(type == "text" || type == "json") + return reader.readAsText(url); + else if(type == "blob") + return reader.readAsBinaryString(url); + } + return null; + } + }); + + //timer that works everywhere + if (typeof performance != "undefined") { + LiteGraph.getTime = performance.now.bind(performance); + } else if (typeof Date != "undefined" && Date.now) { + LiteGraph.getTime = Date.now.bind(Date); + } else if (typeof process != "undefined") { + LiteGraph.getTime = function() { + var t = process.hrtime(); + return t[0] * 0.001 + t[1] * 1e-6; + }; + } else { + LiteGraph.getTime = function getTime() { + return new Date().getTime(); + }; + } + + //********************************************************************************* + // LGraph CLASS + //********************************************************************************* + + /** + * LGraph is the class that contain a full graph. We instantiate one and add nodes to it, and then we can run the execution loop. + * supported callbacks: + + onNodeAdded: when a new node is added to the graph + + onNodeRemoved: when a node inside this graph is removed + + onNodeConnectionChange: some connection has changed in the graph (connected or disconnected) + * + * @class LGraph + * @constructor + * @param {Object} o data from previous serialization [optional] + */ + + function LGraph(o) { + if (LiteGraph.debug) { + console.log("Graph created"); + } + this.list_of_graphcanvas = null; + this.clear(); + + if (o) { + this.configure(o); + } + } + + global.LGraph = LiteGraph.LGraph = LGraph; + + //default supported types + LGraph.supported_types = ["number", "string", "boolean"]; + + //used to know which types of connections support this graph (some graphs do not allow certain types) + LGraph.prototype.getSupportedTypes = function() { + return this.supported_types || LGraph.supported_types; + }; + + LGraph.STATUS_STOPPED = 1; + LGraph.STATUS_RUNNING = 2; + + /** + * Removes all nodes from this graph + * @method clear + */ + + LGraph.prototype.clear = function() { + this.stop(); + this.status = LGraph.STATUS_STOPPED; + + this.last_node_id = 0; + this.last_link_id = 0; + + this._version = -1; //used to detect changes + + //safe clear + if (this._nodes) { + for (var i = 0; i < this._nodes.length; ++i) { + var node = this._nodes[i]; + if (node.onRemoved) { + node.onRemoved(); + } + } + } + + //nodes + this._nodes = []; + this._nodes_by_id = {}; + this._nodes_in_order = []; //nodes sorted in execution order + this._nodes_executable = null; //nodes that contain onExecute sorted in execution order + + //other scene stuff + this._groups = []; + + //links + this.links = {}; //container with all the links + + //iterations + this.iteration = 0; + + //custom data + this.config = {}; + this.vars = {}; + this.extra = {}; //to store custom data + + //timing + this.globaltime = 0; + this.runningtime = 0; + this.fixedtime = 0; + this.fixedtime_lapse = 0.01; + this.elapsed_time = 0.01; + this.last_update_time = 0; + this.starttime = 0; + + this.catch_errors = true; + + this.nodes_executing = []; + this.nodes_actioning = []; + this.nodes_executedAction = []; + + //subgraph_data + this.inputs = {}; + this.outputs = {}; + + //notify canvas to redraw + this.change(); + + this.sendActionToCanvas("clear"); + }; + + /** + * Attach Canvas to this graph + * @method attachCanvas + * @param {GraphCanvas} graph_canvas + */ + + LGraph.prototype.attachCanvas = function(graphcanvas) { + if (graphcanvas.constructor != LGraphCanvas) { + throw "attachCanvas expects a LGraphCanvas instance"; + } + if (graphcanvas.graph && graphcanvas.graph != this) { + graphcanvas.graph.detachCanvas(graphcanvas); + } + + graphcanvas.graph = this; + + if (!this.list_of_graphcanvas) { + this.list_of_graphcanvas = []; + } + this.list_of_graphcanvas.push(graphcanvas); + }; + + /** + * Detach Canvas from this graph + * @method detachCanvas + * @param {GraphCanvas} graph_canvas + */ + LGraph.prototype.detachCanvas = function(graphcanvas) { + if (!this.list_of_graphcanvas) { + return; + } + + var pos = this.list_of_graphcanvas.indexOf(graphcanvas); + if (pos == -1) { + return; + } + graphcanvas.graph = null; + this.list_of_graphcanvas.splice(pos, 1); + }; + + /** + * Starts running this graph every interval milliseconds. + * @method start + * @param {number} interval amount of milliseconds between executions, if 0 then it renders to the monitor refresh rate + */ + + LGraph.prototype.start = function(interval) { + if (this.status == LGraph.STATUS_RUNNING) { + return; + } + this.status = LGraph.STATUS_RUNNING; + + if (this.onPlayEvent) { + this.onPlayEvent(); + } + + this.sendEventToAllNodes("onStart"); + + //launch + this.starttime = LiteGraph.getTime(); + this.last_update_time = this.starttime; + interval = interval || 0; + var that = this; + + //execute once per frame + if ( interval == 0 && typeof window != "undefined" && window.requestAnimationFrame ) { + function on_frame() { + if (that.execution_timer_id != -1) { + return; + } + window.requestAnimationFrame(on_frame); + if(that.onBeforeStep) + that.onBeforeStep(); + that.runStep(1, !that.catch_errors); + if(that.onAfterStep) + that.onAfterStep(); + } + this.execution_timer_id = -1; + on_frame(); + } else { //execute every 'interval' ms + this.execution_timer_id = setInterval(function() { + //execute + if(that.onBeforeStep) + that.onBeforeStep(); + that.runStep(1, !that.catch_errors); + if(that.onAfterStep) + that.onAfterStep(); + }, interval); + } + }; + + /** + * Stops the execution loop of the graph + * @method stop execution + */ + + LGraph.prototype.stop = function() { + if (this.status == LGraph.STATUS_STOPPED) { + return; + } + + this.status = LGraph.STATUS_STOPPED; + + if (this.onStopEvent) { + this.onStopEvent(); + } + + if (this.execution_timer_id != null) { + if (this.execution_timer_id != -1) { + clearInterval(this.execution_timer_id); + } + this.execution_timer_id = null; + } + + this.sendEventToAllNodes("onStop"); + }; + + /** + * Run N steps (cycles) of the graph + * @method runStep + * @param {number} num number of steps to run, default is 1 + * @param {Boolean} do_not_catch_errors [optional] if you want to try/catch errors + * @param {number} limit max number of nodes to execute (used to execute from start to a node) + */ + + LGraph.prototype.runStep = function(num, do_not_catch_errors, limit ) { + num = num || 1; + + var start = LiteGraph.getTime(); + this.globaltime = 0.001 * (start - this.starttime); + + var nodes = this._nodes_executable + ? this._nodes_executable + : this._nodes; + if (!nodes) { + return; + } + + limit = limit || nodes.length; + + if (do_not_catch_errors) { + //iterations + for (var i = 0; i < num; i++) { + for (var j = 0; j < limit; ++j) { + var node = nodes[j]; + if (node.mode == LiteGraph.ALWAYS && node.onExecute) { + //wrap node.onExecute(); + node.doExecute(); + } + } + + this.fixedtime += this.fixedtime_lapse; + if (this.onExecuteStep) { + this.onExecuteStep(); + } + } + + if (this.onAfterExecute) { + this.onAfterExecute(); + } + } else { + try { + //iterations + for (var i = 0; i < num; i++) { + for (var j = 0; j < limit; ++j) { + var node = nodes[j]; + if (node.mode == LiteGraph.ALWAYS && node.onExecute) { + node.onExecute(); + } + } + + this.fixedtime += this.fixedtime_lapse; + if (this.onExecuteStep) { + this.onExecuteStep(); + } + } + + if (this.onAfterExecute) { + this.onAfterExecute(); + } + this.errors_in_execution = false; + } catch (err) { + this.errors_in_execution = true; + if (LiteGraph.throw_errors) { + throw err; + } + if (LiteGraph.debug) { + console.log("Error during execution: " + err); + } + this.stop(); + } + } + + var now = LiteGraph.getTime(); + var elapsed = now - start; + if (elapsed == 0) { + elapsed = 1; + } + this.execution_time = 0.001 * elapsed; + this.globaltime += 0.001 * elapsed; + this.iteration += 1; + this.elapsed_time = (now - this.last_update_time) * 0.001; + this.last_update_time = now; + this.nodes_executing = []; + this.nodes_actioning = []; + this.nodes_executedAction = []; + }; + + /** + * Updates the graph execution order according to relevance of the nodes (nodes with only outputs have more relevance than + * nodes with only inputs. + * @method updateExecutionOrder + */ + LGraph.prototype.updateExecutionOrder = function() { + this._nodes_in_order = this.computeExecutionOrder(false); + this._nodes_executable = []; + for (var i = 0; i < this._nodes_in_order.length; ++i) { + if (this._nodes_in_order[i].onExecute) { + this._nodes_executable.push(this._nodes_in_order[i]); + } + } + }; + + //This is more internal, it computes the executable nodes in order and returns it + LGraph.prototype.computeExecutionOrder = function( + only_onExecute, + set_level + ) { + var L = []; + var S = []; + var M = {}; + var visited_links = {}; //to avoid repeating links + var remaining_links = {}; //to a + + //search for the nodes without inputs (starting nodes) + for (var i = 0, l = this._nodes.length; i < l; ++i) { + var node = this._nodes[i]; + if (only_onExecute && !node.onExecute) { + continue; + } + + M[node.id] = node; //add to pending nodes + + var num = 0; //num of input connections + if (node.inputs) { + for (var j = 0, l2 = node.inputs.length; j < l2; j++) { + if (node.inputs[j] && node.inputs[j].link != null) { + num += 1; + } + } + } + + if (num == 0) { + //is a starting node + S.push(node); + if (set_level) { + node._level = 1; + } + } //num of input links + else { + if (set_level) { + node._level = 0; + } + remaining_links[node.id] = num; + } + } + + while (true) { + if (S.length == 0) { + break; + } + + //get an starting node + var node = S.shift(); + L.push(node); //add to ordered list + delete M[node.id]; //remove from the pending nodes + + if (!node.outputs) { + continue; + } + + //for every output + for (var i = 0; i < node.outputs.length; i++) { + var output = node.outputs[i]; + //not connected + if ( + output == null || + output.links == null || + output.links.length == 0 + ) { + continue; + } + + //for every connection + for (var j = 0; j < output.links.length; j++) { + var link_id = output.links[j]; + var link = this.links[link_id]; + if (!link) { + continue; + } + + //already visited link (ignore it) + if (visited_links[link.id]) { + continue; + } + + var target_node = this.getNodeById(link.target_id); + if (target_node == null) { + visited_links[link.id] = true; + continue; + } + + if ( + set_level && + (!target_node._level || + target_node._level <= node._level) + ) { + target_node._level = node._level + 1; + } + + visited_links[link.id] = true; //mark as visited + remaining_links[target_node.id] -= 1; //reduce the number of links remaining + if (remaining_links[target_node.id] == 0) { + S.push(target_node); + } //if no more links, then add to starters array + } + } + } + + //the remaining ones (loops) + for (var i in M) { + L.push(M[i]); + } + + if (L.length != this._nodes.length && LiteGraph.debug) { + console.warn("something went wrong, nodes missing"); + } + + var l = L.length; + + //save order number in the node + for (var i = 0; i < l; ++i) { + L[i].order = i; + } + + //sort now by priority + L = L.sort(function(A, B) { + var Ap = A.constructor.priority || A.priority || 0; + var Bp = B.constructor.priority || B.priority || 0; + if (Ap == Bp) { + //if same priority, sort by order + return A.order - B.order; + } + return Ap - Bp; //sort by priority + }); + + //save order number in the node, again... + for (var i = 0; i < l; ++i) { + L[i].order = i; + } + + return L; + }; + + /** + * Returns all the nodes that could affect this one (ancestors) by crawling all the inputs recursively. + * It doesn't include the node itself + * @method getAncestors + * @return {Array} an array with all the LGraphNodes that affect this node, in order of execution + */ + LGraph.prototype.getAncestors = function(node) { + var ancestors = []; + var pending = [node]; + var visited = {}; + + while (pending.length) { + var current = pending.shift(); + if (!current.inputs) { + continue; + } + if (!visited[current.id] && current != node) { + visited[current.id] = true; + ancestors.push(current); + } + + for (var i = 0; i < current.inputs.length; ++i) { + var input = current.getInputNode(i); + if (input && ancestors.indexOf(input) == -1) { + pending.push(input); + } + } + } + + ancestors.sort(function(a, b) { + return a.order - b.order; + }); + return ancestors; + }; + + /** + * Positions every node in a more readable manner + * @method arrange + */ + LGraph.prototype.arrange = function (margin, layout) { + margin = margin || 100; + + const nodes = this.computeExecutionOrder(false, true); + const columns = []; + for (let i = 0; i < nodes.length; ++i) { + const node = nodes[i]; + const col = node._level || 1; + if (!columns[col]) { + columns[col] = []; + } + columns[col].push(node); + } + + let x = margin; + + for (let i = 0; i < columns.length; ++i) { + const column = columns[i]; + if (!column) { + continue; + } + let max_size = 100; + let y = margin + LiteGraph.NODE_TITLE_HEIGHT; + for (let j = 0; j < column.length; ++j) { + const node = column[j]; + node.pos[0] = (layout == LiteGraph.VERTICAL_LAYOUT) ? y : x; + node.pos[1] = (layout == LiteGraph.VERTICAL_LAYOUT) ? x : y; + const max_size_index = (layout == LiteGraph.VERTICAL_LAYOUT) ? 1 : 0; + if (node.size[max_size_index] > max_size) { + max_size = node.size[max_size_index]; + } + const node_size_index = (layout == LiteGraph.VERTICAL_LAYOUT) ? 0 : 1; + y += node.size[node_size_index] + margin + LiteGraph.NODE_TITLE_HEIGHT; + } + x += max_size + margin; + } + + this.setDirtyCanvas(true, true); + }; + + /** + * Returns the amount of time the graph has been running in milliseconds + * @method getTime + * @return {number} number of milliseconds the graph has been running + */ + LGraph.prototype.getTime = function() { + return this.globaltime; + }; + + /** + * Returns the amount of time accumulated using the fixedtime_lapse var. This is used in context where the time increments should be constant + * @method getFixedTime + * @return {number} number of milliseconds the graph has been running + */ + + LGraph.prototype.getFixedTime = function() { + return this.fixedtime; + }; + + /** + * Returns the amount of time it took to compute the latest iteration. Take into account that this number could be not correct + * if the nodes are using graphical actions + * @method getElapsedTime + * @return {number} number of milliseconds it took the last cycle + */ + + LGraph.prototype.getElapsedTime = function() { + return this.elapsed_time; + }; + + /** + * Sends an event to all the nodes, useful to trigger stuff + * @method sendEventToAllNodes + * @param {String} eventname the name of the event (function to be called) + * @param {Array} params parameters in array format + */ + LGraph.prototype.sendEventToAllNodes = function(eventname, params, mode) { + mode = mode || LiteGraph.ALWAYS; + + var nodes = this._nodes_in_order ? this._nodes_in_order : this._nodes; + if (!nodes) { + return; + } + + for (var j = 0, l = nodes.length; j < l; ++j) { + var node = nodes[j]; + + if ( + node.constructor === LiteGraph.Subgraph && + eventname != "onExecute" + ) { + if (node.mode == mode) { + node.sendEventToAllNodes(eventname, params, mode); + } + continue; + } + + if (!node[eventname] || node.mode != mode) { + continue; + } + if (params === undefined) { + node[eventname](); + } else if (params && params.constructor === Array) { + node[eventname].apply(node, params); + } else { + node[eventname](params); + } + } + }; + + LGraph.prototype.sendActionToCanvas = function(action, params) { + if (!this.list_of_graphcanvas) { + return; + } + + for (var i = 0; i < this.list_of_graphcanvas.length; ++i) { + var c = this.list_of_graphcanvas[i]; + if (c[action]) { + c[action].apply(c, params); + } + } + }; + + /** + * Adds a new node instance to this graph + * @method add + * @param {LGraphNode} node the instance of the node + */ + + LGraph.prototype.add = function(node, skip_compute_order) { + if (!node) { + return; + } + + //groups + if (node.constructor === LGraphGroup) { + this._groups.push(node); + this.setDirtyCanvas(true); + this.change(); + node.graph = this; + this._version++; + return; + } + + //nodes + if (node.id != -1 && this._nodes_by_id[node.id] != null) { + console.warn( + "LiteGraph: there is already a node with this ID, changing it" + ); + if (LiteGraph.use_uuids) { + node.id = LiteGraph.uuidv4(); + } + else { + node.id = ++this.last_node_id; + } + } + + if (this._nodes.length >= LiteGraph.MAX_NUMBER_OF_NODES) { + throw "LiteGraph: max number of nodes in a graph reached"; + } + + //give him an id + if (LiteGraph.use_uuids) { + if (node.id == null || node.id == -1) + node.id = LiteGraph.uuidv4(); + } + else { + if (node.id == null || node.id == -1) { + node.id = ++this.last_node_id; + } else if (this.last_node_id < node.id) { + this.last_node_id = node.id; + } + } + + node.graph = this; + this._version++; + + this._nodes.push(node); + this._nodes_by_id[node.id] = node; + + if (node.onAdded) { + node.onAdded(this); + } + + if (this.config.align_to_grid) { + node.alignToGrid(); + } + + if (!skip_compute_order) { + this.updateExecutionOrder(); + } + + if (this.onNodeAdded) { + this.onNodeAdded(node); + } + + this.setDirtyCanvas(true); + this.change(); + + return node; //to chain actions + }; + + /** + * Removes a node from the graph + * @method remove + * @param {LGraphNode} node the instance of the node + */ + + LGraph.prototype.remove = function(node) { + if (node.constructor === LiteGraph.LGraphGroup) { + var index = this._groups.indexOf(node); + if (index != -1) { + this._groups.splice(index, 1); + } + node.graph = null; + this._version++; + this.setDirtyCanvas(true, true); + this.change(); + return; + } + + if (this._nodes_by_id[node.id] == null) { + return; + } //not found + + if (node.ignore_remove) { + return; + } //cannot be removed + + this.beforeChange(); //sure? - almost sure is wrong + + //disconnect inputs + if (node.inputs) { + for (var i = 0; i < node.inputs.length; i++) { + var slot = node.inputs[i]; + if (slot.link != null) { + node.disconnectInput(i); + } + } + } + + //disconnect outputs + if (node.outputs) { + for (var i = 0; i < node.outputs.length; i++) { + var slot = node.outputs[i]; + if (slot.links != null && slot.links.length) { + node.disconnectOutput(i); + } + } + } + + //node.id = -1; //why? + + //callback + if (node.onRemoved) { + node.onRemoved(); + } + + node.graph = null; + this._version++; + + //remove from canvas render + if (this.list_of_graphcanvas) { + for (var i = 0; i < this.list_of_graphcanvas.length; ++i) { + var canvas = this.list_of_graphcanvas[i]; + if (canvas.selected_nodes[node.id]) { + delete canvas.selected_nodes[node.id]; + } + if (canvas.node_dragged == node) { + canvas.node_dragged = null; + } + } + } + + //remove from containers + var pos = this._nodes.indexOf(node); + if (pos != -1) { + this._nodes.splice(pos, 1); + } + delete this._nodes_by_id[node.id]; + + if (this.onNodeRemoved) { + this.onNodeRemoved(node); + } + + //close panels + this.sendActionToCanvas("checkPanels"); + + this.setDirtyCanvas(true, true); + this.afterChange(); //sure? - almost sure is wrong + this.change(); + + this.updateExecutionOrder(); + }; + + /** + * Returns a node by its id. + * @method getNodeById + * @param {Number} id + */ + + LGraph.prototype.getNodeById = function(id) { + if (id == null) { + return null; + } + return this._nodes_by_id[id]; + }; + + /** + * Returns a list of nodes that matches a class + * @method findNodesByClass + * @param {Class} classObject the class itself (not an string) + * @return {Array} a list with all the nodes of this type + */ + LGraph.prototype.findNodesByClass = function(classObject, result) { + result = result || []; + result.length = 0; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].constructor === classObject) { + result.push(this._nodes[i]); + } + } + return result; + }; + + /** + * Returns a list of nodes that matches a type + * @method findNodesByType + * @param {String} type the name of the node type + * @return {Array} a list with all the nodes of this type + */ + LGraph.prototype.findNodesByType = function(type, result) { + var type = type.toLowerCase(); + result = result || []; + result.length = 0; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].type.toLowerCase() == type) { + result.push(this._nodes[i]); + } + } + return result; + }; + + /** + * Returns the first node that matches a name in its title + * @method findNodeByTitle + * @param {String} name the name of the node to search + * @return {Node} the node or null + */ + LGraph.prototype.findNodeByTitle = function(title) { + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].title == title) { + return this._nodes[i]; + } + } + return null; + }; + + /** + * Returns a list of nodes that matches a name + * @method findNodesByTitle + * @param {String} name the name of the node to search + * @return {Array} a list with all the nodes with this name + */ + LGraph.prototype.findNodesByTitle = function(title) { + var result = []; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + if (this._nodes[i].title == title) { + result.push(this._nodes[i]); + } + } + return result; + }; + + /** + * Returns the top-most node in this position of the canvas + * @method getNodeOnPos + * @param {number} x the x coordinate in canvas space + * @param {number} y the y coordinate in canvas space + * @param {Array} nodes_list a list with all the nodes to search from, by default is all the nodes in the graph + * @return {LGraphNode} the node at this position or null + */ + LGraph.prototype.getNodeOnPos = function(x, y, nodes_list, margin) { + nodes_list = nodes_list || this._nodes; + var nRet = null; + for (var i = nodes_list.length - 1; i >= 0; i--) { + var n = nodes_list[i]; + var skip_title = n.constructor.title_mode == LiteGraph.NO_TITLE; + if (n.isPointInside(x, y, margin, skip_title)) { + // check for lesser interest nodes (TODO check for overlapping, use the top) + /*if (typeof n == "LGraphGroup"){ + nRet = n; + }else{*/ + return n; + /*}*/ + } + } + return nRet; + }; + + /** + * Returns the top-most group in that position + * @method getGroupOnPos + * @param {number} x the x coordinate in canvas space + * @param {number} y the y coordinate in canvas space + * @return {LGraphGroup} the group or null + */ + LGraph.prototype.getGroupOnPos = function(x, y) { + for (var i = this._groups.length - 1; i >= 0; i--) { + var g = this._groups[i]; + if (g.isPointInside(x, y, 2, true)) { + return g; + } + } + return null; + }; + + /** + * Checks that the node type matches the node type registered, used when replacing a nodetype by a newer version during execution + * this replaces the ones using the old version with the new version + * @method checkNodeTypes + */ + LGraph.prototype.checkNodeTypes = function() { + var changes = false; + for (var i = 0; i < this._nodes.length; i++) { + var node = this._nodes[i]; + var ctor = LiteGraph.registered_node_types[node.type]; + if (node.constructor == ctor) { + continue; + } + console.log("node being replaced by newer version: " + node.type); + var newnode = LiteGraph.createNode(node.type); + changes = true; + this._nodes[i] = newnode; + newnode.configure(node.serialize()); + newnode.graph = this; + this._nodes_by_id[newnode.id] = newnode; + if (node.inputs) { + newnode.inputs = node.inputs.concat(); + } + if (node.outputs) { + newnode.outputs = node.outputs.concat(); + } + } + this.updateExecutionOrder(); + }; + + // ********** GLOBALS ***************** + + LGraph.prototype.onAction = function(action, param, options) { + this._input_nodes = this.findNodesByClass( + LiteGraph.GraphInput, + this._input_nodes + ); + for (var i = 0; i < this._input_nodes.length; ++i) { + var node = this._input_nodes[i]; + if (node.properties.name != action) { + continue; + } + //wrap node.onAction(action, param); + node.actionDo(action, param, options); + break; + } + }; + + LGraph.prototype.trigger = function(action, param) { + if (this.onTrigger) { + this.onTrigger(action, param); + } + }; + + /** + * Tell this graph it has a global graph input of this type + * @method addGlobalInput + * @param {String} name + * @param {String} type + * @param {*} value [optional] + */ + LGraph.prototype.addInput = function(name, type, value) { + var input = this.inputs[name]; + if (input) { + //already exist + return; + } + + this.beforeChange(); + this.inputs[name] = { name: name, type: type, value: value }; + this._version++; + this.afterChange(); + + if (this.onInputAdded) { + this.onInputAdded(name, type); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Assign a data to the global graph input + * @method setGlobalInputData + * @param {String} name + * @param {*} data + */ + LGraph.prototype.setInputData = function(name, data) { + var input = this.inputs[name]; + if (!input) { + return; + } + input.value = data; + }; + + /** + * Returns the current value of a global graph input + * @method getInputData + * @param {String} name + * @return {*} the data + */ + LGraph.prototype.getInputData = function(name) { + var input = this.inputs[name]; + if (!input) { + return null; + } + return input.value; + }; + + /** + * Changes the name of a global graph input + * @method renameInput + * @param {String} old_name + * @param {String} new_name + */ + LGraph.prototype.renameInput = function(old_name, name) { + if (name == old_name) { + return; + } + + if (!this.inputs[old_name]) { + return false; + } + + if (this.inputs[name]) { + console.error("there is already one input with that name"); + return false; + } + + this.inputs[name] = this.inputs[old_name]; + delete this.inputs[old_name]; + this._version++; + + if (this.onInputRenamed) { + this.onInputRenamed(old_name, name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Changes the type of a global graph input + * @method changeInputType + * @param {String} name + * @param {String} type + */ + LGraph.prototype.changeInputType = function(name, type) { + if (!this.inputs[name]) { + return false; + } + + if ( + this.inputs[name].type && + String(this.inputs[name].type).toLowerCase() == + String(type).toLowerCase() + ) { + return; + } + + this.inputs[name].type = type; + this._version++; + if (this.onInputTypeChanged) { + this.onInputTypeChanged(name, type); + } + }; + + /** + * Removes a global graph input + * @method removeInput + * @param {String} name + * @param {String} type + */ + LGraph.prototype.removeInput = function(name) { + if (!this.inputs[name]) { + return false; + } + + delete this.inputs[name]; + this._version++; + + if (this.onInputRemoved) { + this.onInputRemoved(name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + return true; + }; + + /** + * Creates a global graph output + * @method addOutput + * @param {String} name + * @param {String} type + * @param {*} value + */ + LGraph.prototype.addOutput = function(name, type, value) { + this.outputs[name] = { name: name, type: type, value: value }; + this._version++; + + if (this.onOutputAdded) { + this.onOutputAdded(name, type); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Assign a data to the global output + * @method setOutputData + * @param {String} name + * @param {String} value + */ + LGraph.prototype.setOutputData = function(name, value) { + var output = this.outputs[name]; + if (!output) { + return; + } + output.value = value; + }; + + /** + * Returns the current value of a global graph output + * @method getOutputData + * @param {String} name + * @return {*} the data + */ + LGraph.prototype.getOutputData = function(name) { + var output = this.outputs[name]; + if (!output) { + return null; + } + return output.value; + }; + + /** + * Renames a global graph output + * @method renameOutput + * @param {String} old_name + * @param {String} new_name + */ + LGraph.prototype.renameOutput = function(old_name, name) { + if (!this.outputs[old_name]) { + return false; + } + + if (this.outputs[name]) { + console.error("there is already one output with that name"); + return false; + } + + this.outputs[name] = this.outputs[old_name]; + delete this.outputs[old_name]; + this._version++; + + if (this.onOutputRenamed) { + this.onOutputRenamed(old_name, name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + }; + + /** + * Changes the type of a global graph output + * @method changeOutputType + * @param {String} name + * @param {String} type + */ + LGraph.prototype.changeOutputType = function(name, type) { + if (!this.outputs[name]) { + return false; + } + + if ( + this.outputs[name].type && + String(this.outputs[name].type).toLowerCase() == + String(type).toLowerCase() + ) { + return; + } + + this.outputs[name].type = type; + this._version++; + if (this.onOutputTypeChanged) { + this.onOutputTypeChanged(name, type); + } + }; + + /** + * Removes a global graph output + * @method removeOutput + * @param {String} name + */ + LGraph.prototype.removeOutput = function(name) { + if (!this.outputs[name]) { + return false; + } + delete this.outputs[name]; + this._version++; + + if (this.onOutputRemoved) { + this.onOutputRemoved(name); + } + + if (this.onInputsOutputsChange) { + this.onInputsOutputsChange(); + } + return true; + }; + + LGraph.prototype.triggerInput = function(name, value) { + var nodes = this.findNodesByTitle(name); + for (var i = 0; i < nodes.length; ++i) { + nodes[i].onTrigger(value); + } + }; + + LGraph.prototype.setCallback = function(name, func) { + var nodes = this.findNodesByTitle(name); + for (var i = 0; i < nodes.length; ++i) { + nodes[i].setTrigger(func); + } + }; + + //used for undo, called before any change is made to the graph + LGraph.prototype.beforeChange = function(info) { + if (this.onBeforeChange) { + this.onBeforeChange(this,info); + } + this.sendActionToCanvas("onBeforeChange", this); + }; + + //used to resend actions, called after any change is made to the graph + LGraph.prototype.afterChange = function(info) { + if (this.onAfterChange) { + this.onAfterChange(this,info); + } + this.sendActionToCanvas("onAfterChange", this); + }; + + LGraph.prototype.connectionChange = function(node, link_info) { + this.updateExecutionOrder(); + if (this.onConnectionChange) { + this.onConnectionChange(node); + } + this._version++; + this.sendActionToCanvas("onConnectionChange"); + }; + + /** + * returns if the graph is in live mode + * @method isLive + */ + + LGraph.prototype.isLive = function() { + if (!this.list_of_graphcanvas) { + return false; + } + + for (var i = 0; i < this.list_of_graphcanvas.length; ++i) { + var c = this.list_of_graphcanvas[i]; + if (c.live_mode) { + return true; + } + } + return false; + }; + + /** + * clears the triggered slot animation in all links (stop visual animation) + * @method clearTriggeredSlots + */ + LGraph.prototype.clearTriggeredSlots = function() { + for (var i in this.links) { + var link_info = this.links[i]; + if (!link_info) { + continue; + } + if (link_info._last_time) { + link_info._last_time = 0; + } + } + }; + + /* Called when something visually changed (not the graph!) */ + LGraph.prototype.change = function() { + if (LiteGraph.debug) { + console.log("Graph changed"); + } + this.sendActionToCanvas("setDirty", [true, true]); + if (this.on_change) { + this.on_change(this); + } + }; + + LGraph.prototype.setDirtyCanvas = function(fg, bg) { + this.sendActionToCanvas("setDirty", [fg, bg]); + }; + + /** + * Destroys a link + * @method removeLink + * @param {Number} link_id + */ + LGraph.prototype.removeLink = function(link_id) { + var link = this.links[link_id]; + if (!link) { + return; + } + var node = this.getNodeById(link.target_id); + if (node) { + node.disconnectInput(link.target_slot); + } + }; + + //save and recover app state *************************************** + /** + * Creates a Object containing all the info about this graph, it can be serialized + * @method serialize + * @return {Object} value of the node + */ + LGraph.prototype.serialize = function() { + var nodes_info = []; + for (var i = 0, l = this._nodes.length; i < l; ++i) { + nodes_info.push(this._nodes[i].serialize()); + } + + //pack link info into a non-verbose format + var links = []; + for (var i in this.links) { + //links is an OBJECT + var link = this.links[i]; + if (!link.serialize) { + //weird bug I havent solved yet + console.warn( + "weird LLink bug, link info is not a LLink but a regular object" + ); + var link2 = new LLink(); + for (var j in link) { + link2[j] = link[j]; + } + this.links[i] = link2; + link = link2; + } + + links.push(link.serialize()); + } + + var groups_info = []; + for (var i = 0; i < this._groups.length; ++i) { + groups_info.push(this._groups[i].serialize()); + } + + var data = { + last_node_id: this.last_node_id, + last_link_id: this.last_link_id, + nodes: nodes_info, + links: links, + groups: groups_info, + config: this.config, + extra: this.extra, + version: LiteGraph.VERSION + }; + + if(this.onSerialize) + this.onSerialize(data); + + return data; + }; + + /** + * Configure a graph from a JSON string + * @method configure + * @param {String} str configure a graph from a JSON string + * @param {Boolean} returns if there was any error parsing + */ + LGraph.prototype.configure = function(data, keep_old) { + if (!data) { + return; + } + + if (!keep_old) { + this.clear(); + } + + var nodes = data.nodes; + + //decode links info (they are very verbose) + if (data.links && data.links.constructor === Array) { + var links = []; + for (var i = 0; i < data.links.length; ++i) { + var link_data = data.links[i]; + if(!link_data) //weird bug + { + console.warn("serialized graph link data contains errors, skipping."); + continue; + } + var link = new LLink(); + link.configure(link_data); + links[link.id] = link; + } + data.links = links; + } + + //copy all stored fields + for (var i in data) { + if(i == "nodes" || i == "groups" ) //links must be accepted + continue; + this[i] = data[i]; + } + + var error = false; + + //create nodes + this._nodes = []; + if (nodes) { + for (var i = 0, l = nodes.length; i < l; ++i) { + var n_info = nodes[i]; //stored info + var node = LiteGraph.createNode(n_info.type, n_info.title); + if (!node) { + if (LiteGraph.debug) { + console.log( + "Node not found or has errors: " + n_info.type + ); + } + + //in case of error we create a replacement node to avoid losing info + node = new LGraphNode(); + node.last_serialization = n_info; + node.has_errors = true; + error = true; + //continue; + } + + node.id = n_info.id; //id it or it will create a new id + this.add(node, true); //add before configure, otherwise configure cannot create links + } + + //configure nodes afterwards so they can reach each other + for (var i = 0, l = nodes.length; i < l; ++i) { + var n_info = nodes[i]; + var node = this.getNodeById(n_info.id); + if (node) { + node.configure(n_info); + } + } + } + + //groups + this._groups.length = 0; + if (data.groups) { + for (var i = 0; i < data.groups.length; ++i) { + var group = new LiteGraph.LGraphGroup(); + group.configure(data.groups[i]); + this.add(group); + } + } + + this.updateExecutionOrder(); + + this.extra = data.extra || {}; + + if(this.onConfigure) + this.onConfigure(data); + + this._version++; + this.setDirtyCanvas(true, true); + return error; + }; + + LGraph.prototype.load = function(url, callback) { + var that = this; + + //from file + if(url.constructor === File || url.constructor === Blob) + { + var reader = new FileReader(); + reader.addEventListener('load', function(event) { + var data = JSON.parse(event.target.result); + that.configure(data); + if(callback) + callback(); + }); + + reader.readAsText(url); + return; + } + + //is a string, then an URL + var req = new XMLHttpRequest(); + req.open("GET", url, true); + req.send(null); + req.onload = function(oEvent) { + if (req.status !== 200) { + console.error("Error loading graph:", req.status, req.response); + return; + } + var data = JSON.parse( req.response ); + that.configure(data); + if(callback) + callback(); + }; + req.onerror = function(err) { + console.error("Error loading graph:", err); + }; + }; + + LGraph.prototype.onNodeTrace = function(node, msg, color) { + //TODO + }; + + //this is the class in charge of storing link information + function LLink(id, type, origin_id, origin_slot, target_id, target_slot) { + this.id = id; + this.type = type; + this.origin_id = origin_id; + this.origin_slot = origin_slot; + this.target_id = target_id; + this.target_slot = target_slot; + + this._data = null; + this._pos = new Float32Array(2); //center + } + + LLink.prototype.configure = function(o) { + if (o.constructor === Array) { + this.id = o[0]; + this.origin_id = o[1]; + this.origin_slot = o[2]; + this.target_id = o[3]; + this.target_slot = o[4]; + this.type = o[5]; + } else { + this.id = o.id; + this.type = o.type; + this.origin_id = o.origin_id; + this.origin_slot = o.origin_slot; + this.target_id = o.target_id; + this.target_slot = o.target_slot; + } + }; + + LLink.prototype.serialize = function() { + return [ + this.id, + this.origin_id, + this.origin_slot, + this.target_id, + this.target_slot, + this.type + ]; + }; + + LiteGraph.LLink = LLink; + + // ************************************************************* + // Node CLASS ******* + // ************************************************************* + + /* + title: string + pos: [x,y] + size: [x,y] + + input|output: every connection + + { name:string, type:string, pos: [x,y]=Optional, direction: "input"|"output", links: Array }); + + general properties: + + clip_area: if you render outside the node, it will be clipped + + unsafe_execution: not allowed for safe execution + + skip_repeated_outputs: when adding new outputs, it wont show if there is one already connected + + resizable: if set to false it wont be resizable with the mouse + + horizontal: slots are distributed horizontally + + widgets_start_y: widgets start at y distance from the top of the node + + flags object: + + collapsed: if it is collapsed + + supported callbacks: + + onAdded: when added to graph (warning: this is called BEFORE the node is configured when loading) + + onRemoved: when removed from graph + + onStart: when the graph starts playing + + onStop: when the graph stops playing + + onDrawForeground: render the inside widgets inside the node + + onDrawBackground: render the background area inside the node (only in edit mode) + + onMouseDown + + onMouseMove + + onMouseUp + + onMouseEnter + + onMouseLeave + + onExecute: execute the node + + onPropertyChanged: when a property is changed in the panel (return true to skip default behaviour) + + onGetInputs: returns an array of possible inputs + + onGetOutputs: returns an array of possible outputs + + onBounding: in case this node has a bigger bounding than the node itself (the callback receives the bounding as [x,y,w,h]) + + onDblClick: double clicked in the node + + onInputDblClick: input slot double clicked (can be used to automatically create a node connected) + + onOutputDblClick: output slot double clicked (can be used to automatically create a node connected) + + onConfigure: called after the node has been configured + + onSerialize: to add extra info when serializing (the callback receives the object that should be filled with the data) + + onSelected + + onDeselected + + onDropItem : DOM item dropped over the node + + onDropFile : file dropped over the node + + onConnectInput : if returns false the incoming connection will be canceled + + onConnectionsChange : a connection changed (new one or removed) (LiteGraph.INPUT or LiteGraph.OUTPUT, slot, true if connected, link_info, input_info ) + + onAction: action slot triggered + + getExtraMenuOptions: to add option to context menu +*/ + + /** + * Base Class for all the node type classes + * @class LGraphNode + * @param {String} name a name for the node + */ + + function LGraphNode(title) { + this._ctor(title); + } + + global.LGraphNode = LiteGraph.LGraphNode = LGraphNode; + + LGraphNode.prototype._ctor = function(title) { + this.title = title || "Unnamed"; + this.size = [LiteGraph.NODE_WIDTH, 60]; + this.graph = null; + + this._pos = new Float32Array(10, 10); + + Object.defineProperty(this, "pos", { + set: function(v) { + if (!v || v.length < 2) { + return; + } + this._pos[0] = v[0]; + this._pos[1] = v[1]; + }, + get: function() { + return this._pos; + }, + enumerable: true + }); + + if (LiteGraph.use_uuids) { + this.id = LiteGraph.uuidv4(); + } + else { + this.id = -1; //not know till not added + } + this.type = null; + + //inputs available: array of inputs + this.inputs = []; + this.outputs = []; + this.connections = []; + + //local data + this.properties = {}; //for the values + this.properties_info = []; //for the info + + this.flags = {}; + }; + + /** + * configure a node from an object containing the serialized info + * @method configure + */ + LGraphNode.prototype.configure = function(info) { + if (this.graph) { + this.graph._version++; + } + for (var j in info) { + if (j == "properties") { + //i don't want to clone properties, I want to reuse the old container + for (var k in info.properties) { + this.properties[k] = info.properties[k]; + if (this.onPropertyChanged) { + this.onPropertyChanged( k, info.properties[k] ); + } + } + continue; + } + + if (info[j] == null) { + continue; + } else if (typeof info[j] == "object") { + //object + if (this[j] && this[j].configure) { + this[j].configure(info[j]); + } else { + this[j] = LiteGraph.cloneObject(info[j], this[j]); + } + } //value + else { + this[j] = info[j]; + } + } + + if (!info.title) { + this.title = this.constructor.title; + } + + if (this.inputs) { + for (var i = 0; i < this.inputs.length; ++i) { + var input = this.inputs[i]; + var link_info = this.graph ? this.graph.links[input.link] : null; + if (this.onConnectionsChange) + this.onConnectionsChange( LiteGraph.INPUT, i, true, link_info, input ); //link_info has been created now, so its updated + + if( this.onInputAdded ) + this.onInputAdded(input); + + } + } + + if (this.outputs) { + for (var i = 0; i < this.outputs.length; ++i) { + var output = this.outputs[i]; + if (!output.links) { + continue; + } + for (var j = 0; j < output.links.length; ++j) { + var link_info = this.graph ? this.graph.links[output.links[j]] : null; + if (this.onConnectionsChange) + this.onConnectionsChange( LiteGraph.OUTPUT, i, true, link_info, output ); //link_info has been created now, so its updated + } + + if( this.onOutputAdded ) + this.onOutputAdded(output); + } + } + + if( this.widgets ) + { + for (var i = 0; i < this.widgets.length; ++i) + { + var w = this.widgets[i]; + if(!w) + continue; + if(w.options && w.options.property && this.properties[ w.options.property ]) + w.value = JSON.parse( JSON.stringify( this.properties[ w.options.property ] ) ); + } + if (info.widgets_values) { + for (var i = 0; i < info.widgets_values.length; ++i) { + if (this.widgets[i]) { + this.widgets[i].value = info.widgets_values[i]; + } + } + } + } + + if (this.onConfigure) { + this.onConfigure(info); + } + }; + + /** + * serialize the content + * @method serialize + */ + + LGraphNode.prototype.serialize = function() { + //create serialization object + var o = { + id: this.id, + type: this.type, + pos: this.pos, + size: this.size, + flags: LiteGraph.cloneObject(this.flags), + order: this.order, + mode: this.mode + }; + + //special case for when there were errors + if (this.constructor === LGraphNode && this.last_serialization) { + return this.last_serialization; + } + + if (this.inputs) { + o.inputs = this.inputs; + } + + if (this.outputs) { + //clear outputs last data (because data in connections is never serialized but stored inside the outputs info) + for (var i = 0; i < this.outputs.length; i++) { + delete this.outputs[i]._data; + } + o.outputs = this.outputs; + } + + if (this.title && this.title != this.constructor.title) { + o.title = this.title; + } + + if (this.properties) { + o.properties = LiteGraph.cloneObject(this.properties); + } + + if (this.widgets && this.serialize_widgets) { + o.widgets_values = []; + for (var i = 0; i < this.widgets.length; ++i) { + if(this.widgets[i]) + o.widgets_values[i] = this.widgets[i].value; + else + o.widgets_values[i] = null; + } + } + + if (!o.type) { + o.type = this.constructor.type; + } + + if (this.color) { + o.color = this.color; + } + if (this.bgcolor) { + o.bgcolor = this.bgcolor; + } + if (this.boxcolor) { + o.boxcolor = this.boxcolor; + } + if (this.shape) { + o.shape = this.shape; + } + + if (this.onSerialize) { + if (this.onSerialize(o)) { + console.warn( + "node onSerialize shouldnt return anything, data should be stored in the object pass in the first parameter" + ); + } + } + + return o; + }; + + /* Creates a clone of this node */ + LGraphNode.prototype.clone = function() { + var node = LiteGraph.createNode(this.type); + if (!node) { + return null; + } + + //we clone it because serialize returns shared containers + var data = LiteGraph.cloneObject(this.serialize()); + + //remove links + if (data.inputs) { + for (var i = 0; i < data.inputs.length; ++i) { + data.inputs[i].link = null; + } + } + + if (data.outputs) { + for (var i = 0; i < data.outputs.length; ++i) { + if (data.outputs[i].links) { + data.outputs[i].links.length = 0; + } + } + } + + delete data["id"]; + + if (LiteGraph.use_uuids) { + data["id"] = LiteGraph.uuidv4() + } + + //remove links + node.configure(data); + + return node; + }; + + /** + * serialize and stringify + * @method toString + */ + + LGraphNode.prototype.toString = function() { + return JSON.stringify(this.serialize()); + }; + //LGraphNode.prototype.deserialize = function(info) {} //this cannot be done from within, must be done in LiteGraph + + /** + * get the title string + * @method getTitle + */ + + LGraphNode.prototype.getTitle = function() { + return this.title || this.constructor.title; + }; + + /** + * sets the value of a property + * @method setProperty + * @param {String} name + * @param {*} value + */ + LGraphNode.prototype.setProperty = function(name, value) { + if (!this.properties) { + this.properties = {}; + } + if( value === this.properties[name] ) + return; + var prev_value = this.properties[name]; + this.properties[name] = value; + if (this.onPropertyChanged) { + if( this.onPropertyChanged(name, value, prev_value) === false ) //abort change + this.properties[name] = prev_value; + } + if(this.widgets) //widgets could be linked to properties + for(var i = 0; i < this.widgets.length; ++i) + { + var w = this.widgets[i]; + if(!w) + continue; + if(w.options.property == name) + { + w.value = value; + break; + } + } + }; + + // Execution ************************* + /** + * sets the output data + * @method setOutputData + * @param {number} slot + * @param {*} data + */ + LGraphNode.prototype.setOutputData = function(slot, data) { + if (!this.outputs) { + return; + } + + //this maybe slow and a niche case + //if(slot && slot.constructor === String) + // slot = this.findOutputSlot(slot); + + if (slot == -1 || slot >= this.outputs.length) { + return; + } + + var output_info = this.outputs[slot]; + if (!output_info) { + return; + } + + //store data in the output itself in case we want to debug + output_info._data = data; + + //if there are connections, pass the data to the connections + if (this.outputs[slot].links) { + for (var i = 0; i < this.outputs[slot].links.length; i++) { + var link_id = this.outputs[slot].links[i]; + var link = this.graph.links[link_id]; + if(link) + link.data = data; + } + } + }; + + /** + * sets the output data type, useful when you want to be able to overwrite the data type + * @method setOutputDataType + * @param {number} slot + * @param {String} datatype + */ + LGraphNode.prototype.setOutputDataType = function(slot, type) { + if (!this.outputs) { + return; + } + if (slot == -1 || slot >= this.outputs.length) { + return; + } + var output_info = this.outputs[slot]; + if (!output_info) { + return; + } + //store data in the output itself in case we want to debug + output_info.type = type; + + //if there are connections, pass the data to the connections + if (this.outputs[slot].links) { + for (var i = 0; i < this.outputs[slot].links.length; i++) { + var link_id = this.outputs[slot].links[i]; + this.graph.links[link_id].type = type; + } + } + }; + + /** + * Retrieves the input data (data traveling through the connection) from one slot + * @method getInputData + * @param {number} slot + * @param {boolean} force_update if set to true it will force the connected node of this slot to output data into this link + * @return {*} data or if it is not connected returns undefined + */ + LGraphNode.prototype.getInputData = function(slot, force_update) { + if (!this.inputs) { + return; + } //undefined; + + if (slot >= this.inputs.length || this.inputs[slot].link == null) { + return; + } + + var link_id = this.inputs[slot].link; + var link = this.graph.links[link_id]; + if (!link) { + //bug: weird case but it happens sometimes + return null; + } + + if (!force_update) { + return link.data; + } + + //special case: used to extract data from the incoming connection before the graph has been executed + var node = this.graph.getNodeById(link.origin_id); + if (!node) { + return link.data; + } + + if (node.updateOutputData) { + node.updateOutputData(link.origin_slot); + } else if (node.onExecute) { + node.onExecute(); + } + + return link.data; + }; + + /** + * Retrieves the input data type (in case this supports multiple input types) + * @method getInputDataType + * @param {number} slot + * @return {String} datatype in string format + */ + LGraphNode.prototype.getInputDataType = function(slot) { + if (!this.inputs) { + return null; + } //undefined; + + if (slot >= this.inputs.length || this.inputs[slot].link == null) { + return null; + } + var link_id = this.inputs[slot].link; + var link = this.graph.links[link_id]; + if (!link) { + //bug: weird case but it happens sometimes + return null; + } + var node = this.graph.getNodeById(link.origin_id); + if (!node) { + return link.type; + } + var output_info = node.outputs[link.origin_slot]; + if (output_info) { + return output_info.type; + } + return null; + }; + + /** + * Retrieves the input data from one slot using its name instead of slot number + * @method getInputDataByName + * @param {String} slot_name + * @param {boolean} force_update if set to true it will force the connected node of this slot to output data into this link + * @return {*} data or if it is not connected returns null + */ + LGraphNode.prototype.getInputDataByName = function( + slot_name, + force_update + ) { + var slot = this.findInputSlot(slot_name); + if (slot == -1) { + return null; + } + return this.getInputData(slot, force_update); + }; + + /** + * tells you if there is a connection in one input slot + * @method isInputConnected + * @param {number} slot + * @return {boolean} + */ + LGraphNode.prototype.isInputConnected = function(slot) { + if (!this.inputs) { + return false; + } + return slot < this.inputs.length && this.inputs[slot].link != null; + }; + + /** + * tells you info about an input connection (which node, type, etc) + * @method getInputInfo + * @param {number} slot + * @return {Object} object or null { link: id, name: string, type: string or 0 } + */ + LGraphNode.prototype.getInputInfo = function(slot) { + if (!this.inputs) { + return null; + } + if (slot < this.inputs.length) { + return this.inputs[slot]; + } + return null; + }; + + /** + * Returns the link info in the connection of an input slot + * @method getInputLink + * @param {number} slot + * @return {LLink} object or null + */ + LGraphNode.prototype.getInputLink = function(slot) { + if (!this.inputs) { + return null; + } + if (slot < this.inputs.length) { + var slot_info = this.inputs[slot]; + return this.graph.links[ slot_info.link ]; + } + return null; + }; + + /** + * returns the node connected in the input slot + * @method getInputNode + * @param {number} slot + * @return {LGraphNode} node or null + */ + LGraphNode.prototype.getInputNode = function(slot) { + if (!this.inputs) { + return null; + } + if (slot >= this.inputs.length) { + return null; + } + var input = this.inputs[slot]; + if (!input || input.link === null) { + return null; + } + var link_info = this.graph.links[input.link]; + if (!link_info) { + return null; + } + return this.graph.getNodeById(link_info.origin_id); + }; + + /** + * returns the value of an input with this name, otherwise checks if there is a property with that name + * @method getInputOrProperty + * @param {string} name + * @return {*} value + */ + LGraphNode.prototype.getInputOrProperty = function(name) { + if (!this.inputs || !this.inputs.length) { + return this.properties ? this.properties[name] : null; + } + + for (var i = 0, l = this.inputs.length; i < l; ++i) { + var input_info = this.inputs[i]; + if (name == input_info.name && input_info.link != null) { + var link = this.graph.links[input_info.link]; + if (link) { + return link.data; + } + } + } + return this.properties[name]; + }; + + /** + * tells you the last output data that went in that slot + * @method getOutputData + * @param {number} slot + * @return {Object} object or null + */ + LGraphNode.prototype.getOutputData = function(slot) { + if (!this.outputs) { + return null; + } + if (slot >= this.outputs.length) { + return null; + } + + var info = this.outputs[slot]; + return info._data; + }; + + /** + * tells you info about an output connection (which node, type, etc) + * @method getOutputInfo + * @param {number} slot + * @return {Object} object or null { name: string, type: string, links: [ ids of links in number ] } + */ + LGraphNode.prototype.getOutputInfo = function(slot) { + if (!this.outputs) { + return null; + } + if (slot < this.outputs.length) { + return this.outputs[slot]; + } + return null; + }; + + /** + * tells you if there is a connection in one output slot + * @method isOutputConnected + * @param {number} slot + * @return {boolean} + */ + LGraphNode.prototype.isOutputConnected = function(slot) { + if (!this.outputs) { + return false; + } + return ( + slot < this.outputs.length && + this.outputs[slot].links && + this.outputs[slot].links.length + ); + }; + + /** + * tells you if there is any connection in the output slots + * @method isAnyOutputConnected + * @return {boolean} + */ + LGraphNode.prototype.isAnyOutputConnected = function() { + if (!this.outputs) { + return false; + } + for (var i = 0; i < this.outputs.length; ++i) { + if (this.outputs[i].links && this.outputs[i].links.length) { + return true; + } + } + return false; + }; + + /** + * retrieves all the nodes connected to this output slot + * @method getOutputNodes + * @param {number} slot + * @return {array} + */ + LGraphNode.prototype.getOutputNodes = function(slot) { + if (!this.outputs || this.outputs.length == 0) { + return null; + } + + if (slot >= this.outputs.length) { + return null; + } + + var output = this.outputs[slot]; + if (!output.links || output.links.length == 0) { + return null; + } + + var r = []; + for (var i = 0; i < output.links.length; i++) { + var link_id = output.links[i]; + var link = this.graph.links[link_id]; + if (link) { + var target_node = this.graph.getNodeById(link.target_id); + if (target_node) { + r.push(target_node); + } + } + } + return r; + }; + + LGraphNode.prototype.addOnTriggerInput = function(){ + var trigS = this.findInputSlot("onTrigger"); + if (trigS == -1){ //!trigS || + var input = this.addInput("onTrigger", LiteGraph.EVENT, {optional: true, nameLocked: true}); + return this.findInputSlot("onTrigger"); + } + return trigS; + } + + LGraphNode.prototype.addOnExecutedOutput = function(){ + var trigS = this.findOutputSlot("onExecuted"); + if (trigS == -1){ //!trigS || + var output = this.addOutput("onExecuted", LiteGraph.ACTION, {optional: true, nameLocked: true}); + return this.findOutputSlot("onExecuted"); + } + return trigS; + } + + LGraphNode.prototype.onAfterExecuteNode = function(param, options){ + var trigS = this.findOutputSlot("onExecuted"); + if (trigS != -1){ + + //console.debug(this.id+":"+this.order+" triggering slot onAfterExecute"); + //console.debug(param); + //console.debug(options); + this.triggerSlot(trigS, param, null, options); + + } + } + + LGraphNode.prototype.changeMode = function(modeTo){ + switch(modeTo){ + case LiteGraph.ON_EVENT: + // this.addOnExecutedOutput(); + break; + + case LiteGraph.ON_TRIGGER: + this.addOnTriggerInput(); + this.addOnExecutedOutput(); + break; + + case LiteGraph.NEVER: + break; + + case LiteGraph.ALWAYS: + break; + + case LiteGraph.ON_REQUEST: + break; + + default: + return false; + break; + } + this.mode = modeTo; + return true; + }; + + /** + * Triggers the node code execution, place a boolean/counter to mark the node as being executed + * @method execute + * @param {*} param + * @param {*} options + */ + LGraphNode.prototype.doExecute = function(param, options) { + options = options || {}; + if (this.onExecute){ + + // enable this to give the event an ID + if (!options.action_call) options.action_call = this.id+"_exec_"+Math.floor(Math.random()*9999); + + this.graph.nodes_executing[this.id] = true; //.push(this.id); + + this.onExecute(param, options); + + this.graph.nodes_executing[this.id] = false; //.pop(); + + // save execution/action ref + this.exec_version = this.graph.iteration; + if(options && options.action_call){ + this.action_call = options.action_call; // if (param) + this.graph.nodes_executedAction[this.id] = options.action_call; + } + } + this.execute_triggered = 2; // the nFrames it will be used (-- each step), means "how old" is the event + if(this.onAfterExecuteNode) this.onAfterExecuteNode(param, options); // callback + }; + + /** + * Triggers an action, wrapped by logics to control execution flow + * @method actionDo + * @param {String} action name + * @param {*} param + */ + LGraphNode.prototype.actionDo = function(action, param, options) { + options = options || {}; + if (this.onAction){ + + // enable this to give the event an ID + if (!options.action_call) options.action_call = this.id+"_"+(action?action:"action")+"_"+Math.floor(Math.random()*9999); + + this.graph.nodes_actioning[this.id] = (action?action:"actioning"); //.push(this.id); + + this.onAction(action, param, options); + + this.graph.nodes_actioning[this.id] = false; //.pop(); + + // save execution/action ref + if(options && options.action_call){ + this.action_call = options.action_call; // if (param) + this.graph.nodes_executedAction[this.id] = options.action_call; + } + } + this.action_triggered = 2; // the nFrames it will be used (-- each step), means "how old" is the event + if(this.onAfterExecuteNode) this.onAfterExecuteNode(param, options); + }; + + /** + * Triggers an event in this node, this will trigger any output with the same name + * @method trigger + * @param {String} event name ( "on_play", ... ) if action is equivalent to false then the event is send to all + * @param {*} param + */ + LGraphNode.prototype.trigger = function(action, param, options) { + if (!this.outputs || !this.outputs.length) { + return; + } + + if (this.graph) + this.graph._last_trigger_time = LiteGraph.getTime(); + + for (var i = 0; i < this.outputs.length; ++i) { + var output = this.outputs[i]; + if ( !output || output.type !== LiteGraph.EVENT || (action && output.name != action) ) + continue; + this.triggerSlot(i, param, null, options); + } + }; + + /** + * Triggers a slot event in this node: cycle output slots and launch execute/action on connected nodes + * @method triggerSlot + * @param {Number} slot the index of the output slot + * @param {*} param + * @param {Number} link_id [optional] in case you want to trigger and specific output link in a slot + */ + LGraphNode.prototype.triggerSlot = function(slot, param, link_id, options) { + options = options || {}; + if (!this.outputs) { + return; + } + + if(slot == null) + { + console.error("slot must be a number"); + return; + } + + if(slot.constructor !== Number) + console.warn("slot must be a number, use node.trigger('name') if you want to use a string"); + + var output = this.outputs[slot]; + if (!output) { + return; + } + + var links = output.links; + if (!links || !links.length) { + return; + } + + if (this.graph) { + this.graph._last_trigger_time = LiteGraph.getTime(); + } + + //for every link attached here + for (var k = 0; k < links.length; ++k) { + var id = links[k]; + if (link_id != null && link_id != id) { + //to skip links + continue; + } + var link_info = this.graph.links[links[k]]; + if (!link_info) { + //not connected + continue; + } + link_info._last_time = LiteGraph.getTime(); + var node = this.graph.getNodeById(link_info.target_id); + if (!node) { + //node not found? + continue; + } + + //used to mark events in graph + var target_connection = node.inputs[link_info.target_slot]; + + if (node.mode === LiteGraph.ON_TRIGGER) + { + // generate unique trigger ID if not present + if (!options.action_call) options.action_call = this.id+"_trigg_"+Math.floor(Math.random()*9999); + if (node.onExecute) { + // -- wrapping node.onExecute(param); -- + node.doExecute(param, options); + } + } + else if (node.onAction) { + // generate unique action ID if not present + if (!options.action_call) options.action_call = this.id+"_act_"+Math.floor(Math.random()*9999); + //pass the action name + var target_connection = node.inputs[link_info.target_slot]; + // wrap node.onAction(target_connection.name, param); + node.actionDo(target_connection.name, param, options); + } + } + }; + + /** + * clears the trigger slot animation + * @method clearTriggeredSlot + * @param {Number} slot the index of the output slot + * @param {Number} link_id [optional] in case you want to trigger and specific output link in a slot + */ + LGraphNode.prototype.clearTriggeredSlot = function(slot, link_id) { + if (!this.outputs) { + return; + } + + var output = this.outputs[slot]; + if (!output) { + return; + } + + var links = output.links; + if (!links || !links.length) { + return; + } + + //for every link attached here + for (var k = 0; k < links.length; ++k) { + var id = links[k]; + if (link_id != null && link_id != id) { + //to skip links + continue; + } + var link_info = this.graph.links[links[k]]; + if (!link_info) { + //not connected + continue; + } + link_info._last_time = 0; + } + }; + + /** + * changes node size and triggers callback + * @method setSize + * @param {vec2} size + */ + LGraphNode.prototype.setSize = function(size) + { + this.size = size; + if(this.onResize) + this.onResize(this.size); + } + + /** + * add a new property to this node + * @method addProperty + * @param {string} name + * @param {*} default_value + * @param {string} type string defining the output type ("vec3","number",...) + * @param {Object} extra_info this can be used to have special properties of the property (like values, etc) + */ + LGraphNode.prototype.addProperty = function( + name, + default_value, + type, + extra_info + ) { + var o = { name: name, type: type, default_value: default_value }; + if (extra_info) { + for (var i in extra_info) { + o[i] = extra_info[i]; + } + } + if (!this.properties_info) { + this.properties_info = []; + } + this.properties_info.push(o); + if (!this.properties) { + this.properties = {}; + } + this.properties[name] = default_value; + return o; + }; + + //connections + + /** + * add a new output slot to use in this node + * @method addOutput + * @param {string} name + * @param {string} type string defining the output type ("vec3","number",...) + * @param {Object} extra_info this can be used to have special properties of an output (label, special color, position, etc) + */ + LGraphNode.prototype.addOutput = function(name, type, extra_info) { + var output = { name: name, type: type, links: null }; + if (extra_info) { + for (var i in extra_info) { + output[i] = extra_info[i]; + } + } + + if (!this.outputs) { + this.outputs = []; + } + this.outputs.push(output); + if (this.onOutputAdded) { + this.onOutputAdded(output); + } + + if (LiteGraph.auto_load_slot_types) LiteGraph.registerNodeAndSlotType(this,type,true); + + this.setSize( this.computeSize() ); + this.setDirtyCanvas(true, true); + return output; + }; + + /** + * add a new output slot to use in this node + * @method addOutputs + * @param {Array} array of triplets like [[name,type,extra_info],[...]] + */ + LGraphNode.prototype.addOutputs = function(array) { + for (var i = 0; i < array.length; ++i) { + var info = array[i]; + var o = { name: info[0], type: info[1], link: null }; + if (array[2]) { + for (var j in info[2]) { + o[j] = info[2][j]; + } + } + + if (!this.outputs) { + this.outputs = []; + } + this.outputs.push(o); + if (this.onOutputAdded) { + this.onOutputAdded(o); + } + + if (LiteGraph.auto_load_slot_types) LiteGraph.registerNodeAndSlotType(this,info[1],true); + + } + + this.setSize( this.computeSize() ); + this.setDirtyCanvas(true, true); + }; + + /** + * remove an existing output slot + * @method removeOutput + * @param {number} slot + */ + LGraphNode.prototype.removeOutput = function(slot) { + this.disconnectOutput(slot); + this.outputs.splice(slot, 1); + for (var i = slot; i < this.outputs.length; ++i) { + if (!this.outputs[i] || !this.outputs[i].links) { + continue; + } + var links = this.outputs[i].links; + for (var j = 0; j < links.length; ++j) { + var link = this.graph.links[links[j]]; + if (!link) { + continue; + } + link.origin_slot -= 1; + } + } + + this.setSize( this.computeSize() ); + if (this.onOutputRemoved) { + this.onOutputRemoved(slot); + } + this.setDirtyCanvas(true, true); + }; + + /** + * add a new input slot to use in this node + * @method addInput + * @param {string} name + * @param {string} type string defining the input type ("vec3","number",...), it its a generic one use 0 + * @param {Object} extra_info this can be used to have special properties of an input (label, color, position, etc) + */ + LGraphNode.prototype.addInput = function(name, type, extra_info) { + type = type || 0; + var input = { name: name, type: type, link: null }; + if (extra_info) { + for (var i in extra_info) { + input[i] = extra_info[i]; + } + } + + if (!this.inputs) { + this.inputs = []; + } + + this.inputs.push(input); + this.setSize( this.computeSize() ); + + if (this.onInputAdded) { + this.onInputAdded(input); + } + + LiteGraph.registerNodeAndSlotType(this,type); + + this.setDirtyCanvas(true, true); + return input; + }; + + /** + * add several new input slots in this node + * @method addInputs + * @param {Array} array of triplets like [[name,type,extra_info],[...]] + */ + LGraphNode.prototype.addInputs = function(array) { + for (var i = 0; i < array.length; ++i) { + var info = array[i]; + var o = { name: info[0], type: info[1], link: null }; + if (array[2]) { + for (var j in info[2]) { + o[j] = info[2][j]; + } + } + + if (!this.inputs) { + this.inputs = []; + } + this.inputs.push(o); + if (this.onInputAdded) { + this.onInputAdded(o); + } + + LiteGraph.registerNodeAndSlotType(this,info[1]); + } + + this.setSize( this.computeSize() ); + this.setDirtyCanvas(true, true); + }; + + /** + * remove an existing input slot + * @method removeInput + * @param {number} slot + */ + LGraphNode.prototype.removeInput = function(slot) { + this.disconnectInput(slot); + var slot_info = this.inputs.splice(slot, 1); + for (var i = slot; i < this.inputs.length; ++i) { + if (!this.inputs[i]) { + continue; + } + var link = this.graph.links[this.inputs[i].link]; + if (!link) { + continue; + } + link.target_slot -= 1; + } + this.setSize( this.computeSize() ); + if (this.onInputRemoved) { + this.onInputRemoved(slot, slot_info[0] ); + } + this.setDirtyCanvas(true, true); + }; + + /** + * add an special connection to this node (used for special kinds of graphs) + * @method addConnection + * @param {string} name + * @param {string} type string defining the input type ("vec3","number",...) + * @param {[x,y]} pos position of the connection inside the node + * @param {string} direction if is input or output + */ + LGraphNode.prototype.addConnection = function(name, type, pos, direction) { + var o = { + name: name, + type: type, + pos: pos, + direction: direction, + links: null + }; + this.connections.push(o); + return o; + }; + + /** + * computes the minimum size of a node according to its inputs and output slots + * @method computeSize + * @param {vec2} minHeight + * @return {vec2} the total size + */ + LGraphNode.prototype.computeSize = function(out) { + if (this.constructor.size) { + return this.constructor.size.concat(); + } + + var rows = Math.max( + this.inputs ? this.inputs.length : 1, + this.outputs ? this.outputs.length : 1 + ); + var size = out || new Float32Array([0, 0]); + rows = Math.max(rows, 1); + var font_size = LiteGraph.NODE_TEXT_SIZE; //although it should be graphcanvas.inner_text_font size + + var title_width = compute_text_size(this.title); + var input_width = 0; + var output_width = 0; + + if (this.inputs) { + for (var i = 0, l = this.inputs.length; i < l; ++i) { + var input = this.inputs[i]; + var text = input.label || input.name || ""; + var text_width = compute_text_size(text); + if (input_width < text_width) { + input_width = text_width; + } + } + } + + if (this.outputs) { + for (var i = 0, l = this.outputs.length; i < l; ++i) { + var output = this.outputs[i]; + var text = output.label || output.name || ""; + var text_width = compute_text_size(text); + if (output_width < text_width) { + output_width = text_width; + } + } + } + + size[0] = Math.max(input_width + output_width + 10, title_width); + size[0] = Math.max(size[0], LiteGraph.NODE_WIDTH); + if (this.widgets && this.widgets.length) { + size[0] = Math.max(size[0], LiteGraph.NODE_WIDTH * 1.5); + } + + size[1] = (this.constructor.slot_start_y || 0) + rows * LiteGraph.NODE_SLOT_HEIGHT; + + var widgets_height = 0; + if (this.widgets && this.widgets.length) { + for (var i = 0, l = this.widgets.length; i < l; ++i) { + if (this.widgets[i].computeSize) + widgets_height += this.widgets[i].computeSize(size[0])[1] + 4; + else + widgets_height += LiteGraph.NODE_WIDGET_HEIGHT + 4; + } + widgets_height += 8; + } + + //compute height using widgets height + if( this.widgets_up ) + size[1] = Math.max( size[1], widgets_height ); + else if( this.widgets_start_y != null ) + size[1] = Math.max( size[1], widgets_height + this.widgets_start_y ); + else + size[1] += widgets_height; + + function compute_text_size(text) { + if (!text) { + return 0; + } + return font_size * text.length * 0.6; + } + + if ( + this.constructor.min_height && + size[1] < this.constructor.min_height + ) { + size[1] = this.constructor.min_height; + } + + size[1] += 6; //margin + + return size; + }; + + LGraphNode.prototype.inResizeCorner = function(canvasX, canvasY) { + var rows = this.outputs ? this.outputs.length : 1; + var outputs_offset = (this.constructor.slot_start_y || 0) + rows * LiteGraph.NODE_SLOT_HEIGHT; + return isInsideRectangle(canvasX, + canvasY, + this.pos[0] + this.size[0] - 15, + this.pos[1] + Math.max(this.size[1] - 15, outputs_offset), + 20, + 20 + ); + } + + /** + * returns all the info available about a property of this node. + * + * @method getPropertyInfo + * @param {String} property name of the property + * @return {Object} the object with all the available info + */ + LGraphNode.prototype.getPropertyInfo = function( property ) + { + var info = null; + + //there are several ways to define info about a property + //legacy mode + if (this.properties_info) { + for (var i = 0; i < this.properties_info.length; ++i) { + if (this.properties_info[i].name == property) { + info = this.properties_info[i]; + break; + } + } + } + //litescene mode using the constructor + if(this.constructor["@" + property]) + info = this.constructor["@" + property]; + + if(this.constructor.widgets_info && this.constructor.widgets_info[property]) + info = this.constructor.widgets_info[property]; + + //litescene mode using the constructor + if (!info && this.onGetPropertyInfo) { + info = this.onGetPropertyInfo(property); + } + + if (!info) + info = {}; + if(!info.type) + info.type = typeof this.properties[property]; + if(info.widget == "combo") + info.type = "enum"; + + return info; + } + + /** + * Defines a widget inside the node, it will be rendered on top of the node, you can control lots of properties + * + * @method addWidget + * @param {String} type the widget type (could be "number","string","combo" + * @param {String} name the text to show on the widget + * @param {String} value the default value + * @param {Function|String} callback function to call when it changes (optionally, it can be the name of the property to modify) + * @param {Object} options the object that contains special properties of this widget + * @return {Object} the created widget object + */ + LGraphNode.prototype.addWidget = function( type, name, value, callback, options ) + { + if (!this.widgets) { + this.widgets = []; + } + + if(!options && callback && callback.constructor === Object) + { + options = callback; + callback = null; + } + + if(options && options.constructor === String) //options can be the property name + options = { property: options }; + + if(callback && callback.constructor === String) //callback can be the property name + { + if(!options) + options = {}; + options.property = callback; + callback = null; + } + + if(callback && callback.constructor !== Function) + { + console.warn("addWidget: callback must be a function"); + callback = null; + } + + var w = { + type: type.toLowerCase(), + name: name, + value: value, + callback: callback, + options: options || {} + }; + + if (w.options.y !== undefined) { + w.y = w.options.y; + } + + if (!callback && !w.options.callback && !w.options.property) { + console.warn("LiteGraph addWidget(...) without a callback or property assigned"); + } + if (type == "combo" && !w.options.values) { + throw "LiteGraph addWidget('combo',...) requires to pass values in options: { values:['red','blue'] }"; + } + this.widgets.push(w); + this.setSize( this.computeSize() ); + return w; + }; + + LGraphNode.prototype.addCustomWidget = function(custom_widget) { + if (!this.widgets) { + this.widgets = []; + } + this.widgets.push(custom_widget); + return custom_widget; + }; + + /** + * returns the bounding of the object, used for rendering purposes + * bounding is: [topleft_cornerx, topleft_cornery, width, height] + * @method getBounding + * @return {Float32Array[4]} the total size + */ + LGraphNode.prototype.getBounding = function(out) { + out = out || new Float32Array(4); + out[0] = this.pos[0] - 4; + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT; + out[2] = this.size[0] + 4; + out[3] = this.flags.collapsed ? LiteGraph.NODE_TITLE_HEIGHT : this.size[1] + LiteGraph.NODE_TITLE_HEIGHT; + + if (this.onBounding) { + this.onBounding(out); + } + return out; + }; + + /** + * checks if a point is inside the shape of a node + * @method isPointInside + * @param {number} x + * @param {number} y + * @return {boolean} + */ + LGraphNode.prototype.isPointInside = function(x, y, margin, skip_title) { + margin = margin || 0; + + var margin_top = this.graph && this.graph.isLive() ? 0 : LiteGraph.NODE_TITLE_HEIGHT; + if (skip_title) { + margin_top = 0; + } + if (this.flags && this.flags.collapsed) { + //if ( distance([x,y], [this.pos[0] + this.size[0]*0.5, this.pos[1] + this.size[1]*0.5]) < LiteGraph.NODE_COLLAPSED_RADIUS) + if ( + isInsideRectangle( + x, + y, + this.pos[0] - margin, + this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT - margin, + (this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH) + + 2 * margin, + LiteGraph.NODE_TITLE_HEIGHT + 2 * margin + ) + ) { + return true; + } + } else if ( + this.pos[0] - 4 - margin < x && + this.pos[0] + this.size[0] + 4 + margin > x && + this.pos[1] - margin_top - margin < y && + this.pos[1] + this.size[1] + margin > y + ) { + return true; + } + return false; + }; + + /** + * checks if a point is inside a node slot, and returns info about which slot + * @method getSlotInPosition + * @param {number} x + * @param {number} y + * @return {Object} if found the object contains { input|output: slot object, slot: number, link_pos: [x,y] } + */ + LGraphNode.prototype.getSlotInPosition = function(x, y) { + //search for inputs + var link_pos = new Float32Array(2); + if (this.inputs) { + for (var i = 0, l = this.inputs.length; i < l; ++i) { + var input = this.inputs[i]; + this.getConnectionPos(true, i, link_pos); + if ( + isInsideRectangle( + x, + y, + link_pos[0] - 10, + link_pos[1] - 5, + 20, + 10 + ) + ) { + return { input: input, slot: i, link_pos: link_pos }; + } + } + } + + if (this.outputs) { + for (var i = 0, l = this.outputs.length; i < l; ++i) { + var output = this.outputs[i]; + this.getConnectionPos(false, i, link_pos); + if ( + isInsideRectangle( + x, + y, + link_pos[0] - 10, + link_pos[1] - 5, + 20, + 10 + ) + ) { + return { output: output, slot: i, link_pos: link_pos }; + } + } + } + + return null; + }; + + /** + * returns the input slot with a given name (used for dynamic slots), -1 if not found + * @method findInputSlot + * @param {string} name the name of the slot + * @param {boolean} returnObj if the obj itself wanted + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findInputSlot = function(name, returnObj) { + if (!this.inputs) { + return -1; + } + for (var i = 0, l = this.inputs.length; i < l; ++i) { + if (name == this.inputs[i].name) { + return !returnObj ? i : this.inputs[i]; + } + } + return -1; + }; + + /** + * returns the output slot with a given name (used for dynamic slots), -1 if not found + * @method findOutputSlot + * @param {string} name the name of the slot + * @param {boolean} returnObj if the obj itself wanted + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findOutputSlot = function(name, returnObj) { + returnObj = returnObj || false; + if (!this.outputs) { + return -1; + } + for (var i = 0, l = this.outputs.length; i < l; ++i) { + if (name == this.outputs[i].name) { + return !returnObj ? i : this.outputs[i]; + } + } + return -1; + }; + + // TODO refactor: USE SINGLE findInput/findOutput functions! :: merge options + + /** + * returns the first free input slot + * @method findInputSlotFree + * @param {object} options + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findInputSlotFree = function(optsIn) { + var optsIn = optsIn || {}; + var optsDef = {returnObj: false + ,typesNotAccepted: [] + }; + var opts = Object.assign(optsDef,optsIn); + if (!this.inputs) { + return -1; + } + for (var i = 0, l = this.inputs.length; i < l; ++i) { + if (this.inputs[i].link && this.inputs[i].link != null) { + continue; + } + if (opts.typesNotAccepted && opts.typesNotAccepted.includes && opts.typesNotAccepted.includes(this.inputs[i].type)){ + continue; + } + return !opts.returnObj ? i : this.inputs[i]; + } + return -1; + }; + + /** + * returns the first output slot free + * @method findOutputSlotFree + * @param {object} options + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findOutputSlotFree = function(optsIn) { + var optsIn = optsIn || {}; + var optsDef = { returnObj: false + ,typesNotAccepted: [] + }; + var opts = Object.assign(optsDef,optsIn); + if (!this.outputs) { + return -1; + } + for (var i = 0, l = this.outputs.length; i < l; ++i) { + if (this.outputs[i].links && this.outputs[i].links != null) { + continue; + } + if (opts.typesNotAccepted && opts.typesNotAccepted.includes && opts.typesNotAccepted.includes(this.outputs[i].type)){ + continue; + } + return !opts.returnObj ? i : this.outputs[i]; + } + return -1; + }; + + /** + * findSlotByType for INPUTS + */ + LGraphNode.prototype.findInputSlotByType = function(type, returnObj, preferFreeSlot, doNotUseOccupied) { + return this.findSlotByType(true, type, returnObj, preferFreeSlot, doNotUseOccupied); + }; + + /** + * findSlotByType for OUTPUTS + */ + LGraphNode.prototype.findOutputSlotByType = function(type, returnObj, preferFreeSlot, doNotUseOccupied) { + return this.findSlotByType(false, type, returnObj, preferFreeSlot, doNotUseOccupied); + }; + + /** + * returns the output (or input) slot with a given type, -1 if not found + * @method findSlotByType + * @param {boolean} input uise inputs instead of outputs + * @param {string} type the type of the slot + * @param {boolean} returnObj if the obj itself wanted + * @param {boolean} preferFreeSlot if we want a free slot (if not found, will return the first of the type anyway) + * @return {number_or_object} the slot (-1 if not found) + */ + LGraphNode.prototype.findSlotByType = function(input, type, returnObj, preferFreeSlot, doNotUseOccupied) { + input = input || false; + returnObj = returnObj || false; + preferFreeSlot = preferFreeSlot || false; + doNotUseOccupied = doNotUseOccupied || false; + var aSlots = input ? this.inputs : this.outputs; + if (!aSlots) { + return -1; + } + // !! empty string type is considered 0, * !! + if (type == "" || type == "*") type = 0; + for (var i = 0, l = aSlots.length; i < l; ++i) { + var tFound = false; + var aSource = (type+"").toLowerCase().split(","); + var aDest = aSlots[i].type=="0"||aSlots[i].type=="*"?"0":aSlots[i].type; + aDest = (aDest+"").toLowerCase().split(","); + for(var sI=0;sI= 0 && target_slot !== null){ + //console.debug("CONNbyTYPE type "+target_slotType+" for "+target_slot) + return this.connect(slot, target_node, target_slot); + }else{ + //console.log("type "+target_slotType+" not found or not free?") + if (opts.createEventInCase && target_slotType == LiteGraph.EVENT){ + // WILL CREATE THE onTrigger IN SLOT + //console.debug("connect WILL CREATE THE onTrigger "+target_slotType+" to "+target_node); + return this.connect(slot, target_node, -1); + } + // connect to the first general output slot if not found a specific type and + if (opts.generalTypeInCase){ + var target_slot = target_node.findInputSlotByType(0, false, true, true); + //console.debug("connect TO a general type (*, 0), if not found the specific type ",target_slotType," to ",target_node,"RES_SLOT:",target_slot); + if (target_slot >= 0){ + return this.connect(slot, target_node, target_slot); + } + } + // connect to the first free input slot if not found a specific type and this output is general + if (opts.firstFreeIfOutputGeneralInCase && (target_slotType == 0 || target_slotType == "*" || target_slotType == "")){ + var target_slot = target_node.findInputSlotFree({typesNotAccepted: [LiteGraph.EVENT] }); + //console.debug("connect TO TheFirstFREE ",target_slotType," to ",target_node,"RES_SLOT:",target_slot); + if (target_slot >= 0){ + return this.connect(slot, target_node, target_slot); + } + } + + console.debug("no way to connect type: ",target_slotType," to targetNODE ",target_node); + //TODO filter + + return null; + } + } + + /** + * connect this node input to the output of another node BY TYPE + * @method connectByType + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {LGraphNode} node the target node + * @param {string} target_type the output slot type of the target node + * @return {Object} the link_info is created, otherwise null + */ + LGraphNode.prototype.connectByTypeOutput = function(slot, source_node, source_slotType, optsIn) { + var optsIn = optsIn || {}; + var optsDef = { createEventInCase: true + ,firstFreeIfInputGeneralInCase: true + ,generalTypeInCase: true + }; + var opts = Object.assign(optsDef,optsIn); + if (source_node && source_node.constructor === Number) { + source_node = this.graph.getNodeById(source_node); + } + var source_slot = source_node.findOutputSlotByType(source_slotType, false, true); + if (source_slot >= 0 && source_slot !== null){ + //console.debug("CONNbyTYPE OUT! type "+source_slotType+" for "+source_slot) + return source_node.connect(source_slot, this, slot); + }else{ + + // connect to the first general output slot if not found a specific type and + if (opts.generalTypeInCase){ + var source_slot = source_node.findOutputSlotByType(0, false, true, true); + if (source_slot >= 0){ + return source_node.connect(source_slot, this, slot); + } + } + + if (opts.createEventInCase && source_slotType == LiteGraph.EVENT){ + // WILL CREATE THE onExecuted OUT SLOT + if (LiteGraph.do_add_triggers_slots){ + var source_slot = source_node.addOnExecutedOutput(); + return source_node.connect(source_slot, this, slot); + } + } + // connect to the first free output slot if not found a specific type and this input is general + if (opts.firstFreeIfInputGeneralInCase && (source_slotType == 0 || source_slotType == "*" || source_slotType == "")){ + var source_slot = source_node.findOutputSlotFree({typesNotAccepted: [LiteGraph.EVENT] }); + if (source_slot >= 0){ + return source_node.connect(source_slot, this, slot); + } + } + + console.debug("no way to connect byOUT type: ",source_slotType," to sourceNODE ",source_node); + //TODO filter + + //console.log("type OUT! "+source_slotType+" not found or not free?") + return null; + } + } + + /** + * connect this node output to the input of another node + * @method connect + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {LGraphNode} node the target node + * @param {number_or_string} target_slot the input slot of the target node (could be the number of the slot or the string with the name of the slot, or -1 to connect a trigger) + * @return {Object} the link_info is created, otherwise null + */ + LGraphNode.prototype.connect = function(slot, target_node, target_slot) { + target_slot = target_slot || 0; + + if (!this.graph) { + //could be connected before adding it to a graph + console.log( + "Connect: Error, node doesn't belong to any graph. Nodes must be added first to a graph before connecting them." + ); //due to link ids being associated with graphs + return null; + } + + //seek for the output slot + if (slot.constructor === String) { + slot = this.findOutputSlot(slot); + if (slot == -1) { + if (LiteGraph.debug) { + console.log("Connect: Error, no slot of name " + slot); + } + return null; + } + } else if (!this.outputs || slot >= this.outputs.length) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return null; + } + + if (target_node && target_node.constructor === Number) { + target_node = this.graph.getNodeById(target_node); + } + if (!target_node) { + throw "target node is null"; + } + + //avoid loopback + if (target_node == this) { + return null; + } + + //you can specify the slot by name + if (target_slot.constructor === String) { + target_slot = target_node.findInputSlot(target_slot); + if (target_slot == -1) { + if (LiteGraph.debug) { + console.log( + "Connect: Error, no slot of name " + target_slot + ); + } + return null; + } + } else if (target_slot === LiteGraph.EVENT) { + + if (LiteGraph.do_add_triggers_slots){ + //search for first slot with event? :: NO this is done outside + //console.log("Connect: Creating triggerEvent"); + // force mode + target_node.changeMode(LiteGraph.ON_TRIGGER); + target_slot = target_node.findInputSlot("onTrigger"); + }else{ + return null; // -- break -- + } + } else if ( + !target_node.inputs || + target_slot >= target_node.inputs.length + ) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return null; + } + + var changed = false; + + var input = target_node.inputs[target_slot]; + var link_info = null; + var output = this.outputs[slot]; + + if (!this.outputs[slot]){ + /*console.debug("Invalid slot passed: "+slot); + console.debug(this.outputs);*/ + return null; + } + + // allow target node to change slot + if (target_node.onBeforeConnectInput) { + // This way node can choose another slot (or make a new one?) + target_slot = target_node.onBeforeConnectInput(target_slot); //callback + } + + //check target_slot and check connection types + if (target_slot===false || target_slot===null || !LiteGraph.isValidConnection(output.type, input.type)) + { + this.setDirtyCanvas(false, true); + if(changed) + this.graph.connectionChange(this, link_info); + return null; + }else{ + //console.debug("valid connection",output.type, input.type); + } + + //allows nodes to block connection, callback + if (target_node.onConnectInput) { + if ( target_node.onConnectInput(target_slot, output.type, output, this, slot) === false ) { + return null; + } + } + if (this.onConnectOutput) { // callback + if ( this.onConnectOutput(slot, input.type, input, target_node, target_slot) === false ) { + return null; + } + } + + //if there is something already plugged there, disconnect + if (target_node.inputs[target_slot] && target_node.inputs[target_slot].link != null) { + this.graph.beforeChange(); + target_node.disconnectInput(target_slot, {doProcessChange: false}); + changed = true; + } + if (output.links !== null && output.links.length){ + switch(output.type){ + case LiteGraph.EVENT: + if (!LiteGraph.allow_multi_output_for_events){ + this.graph.beforeChange(); + this.disconnectOutput(slot, false, {doProcessChange: false}); // Input(target_slot, {doProcessChange: false}); + changed = true; + } + break; + default: + break; + } + } + + var nextId + if (LiteGraph.use_uuids) + nextId = LiteGraph.uuidv4(); + else + nextId = ++this.graph.last_link_id; + + //create link class + link_info = new LLink( + nextId, + input.type || output.type, + this.id, + slot, + target_node.id, + target_slot + ); + + //add to graph links list + this.graph.links[link_info.id] = link_info; + + //connect in output + if (output.links == null) { + output.links = []; + } + output.links.push(link_info.id); + //connect in input + target_node.inputs[target_slot].link = link_info.id; + if (this.graph) { + this.graph._version++; + } + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.OUTPUT, + slot, + true, + link_info, + output + ); + } //link_info has been created now, so its updated + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.INPUT, + target_slot, + true, + link_info, + input + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + target_slot, + this, + slot + ); + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + this, + slot, + target_node, + target_slot + ); + } + + this.setDirtyCanvas(false, true); + this.graph.afterChange(); + this.graph.connectionChange(this, link_info); + + return link_info; + }; + + /** + * disconnect one output to an specific node + * @method disconnectOutput + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {LGraphNode} target_node the target node to which this slot is connected [Optional, if not target_node is specified all nodes will be disconnected] + * @return {boolean} if it was disconnected successfully + */ + LGraphNode.prototype.disconnectOutput = function(slot, target_node) { + if (slot.constructor === String) { + slot = this.findOutputSlot(slot); + if (slot == -1) { + if (LiteGraph.debug) { + console.log("Connect: Error, no slot of name " + slot); + } + return false; + } + } else if (!this.outputs || slot >= this.outputs.length) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return false; + } + + //get output slot + var output = this.outputs[slot]; + if (!output || !output.links || output.links.length == 0) { + return false; + } + + //one of the output links in this slot + if (target_node) { + if (target_node.constructor === Number) { + target_node = this.graph.getNodeById(target_node); + } + if (!target_node) { + throw "Target Node not found"; + } + + for (var i = 0, l = output.links.length; i < l; i++) { + var link_id = output.links[i]; + var link_info = this.graph.links[link_id]; + + //is the link we are searching for... + if (link_info.target_id == target_node.id) { + output.links.splice(i, 1); //remove here + var input = target_node.inputs[link_info.target_slot]; + input.link = null; //remove there + delete this.graph.links[link_id]; //remove the link from the links pool + if (this.graph) { + this.graph._version++; + } + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.INPUT, + link_info.target_slot, + false, + link_info, + input + ); + } //link_info hasn't been modified so its ok + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.OUTPUT, + slot, + false, + link_info, + output + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + this, + slot + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + this, + slot + ); + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + link_info.target_slot + ); + } + break; + } + } + } //all the links in this output slot + else { + for (var i = 0, l = output.links.length; i < l; i++) { + var link_id = output.links[i]; + var link_info = this.graph.links[link_id]; + if (!link_info) { + //bug: it happens sometimes + continue; + } + + var target_node = this.graph.getNodeById(link_info.target_id); + var input = null; + if (this.graph) { + this.graph._version++; + } + if (target_node) { + input = target_node.inputs[link_info.target_slot]; + input.link = null; //remove other side link + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.INPUT, + link_info.target_slot, + false, + link_info, + input + ); + } //link_info hasn't been modified so its ok + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + link_info.target_slot + ); + } + } + delete this.graph.links[link_id]; //remove the link from the links pool + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.OUTPUT, + slot, + false, + link_info, + output + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + this, + slot + ); + this.graph.onNodeConnectionChange( + LiteGraph.INPUT, + target_node, + link_info.target_slot + ); + } + } + output.links = null; + } + + this.setDirtyCanvas(false, true); + this.graph.connectionChange(this); + return true; + }; + + /** + * disconnect one input + * @method disconnectInput + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @return {boolean} if it was disconnected successfully + */ + LGraphNode.prototype.disconnectInput = function(slot) { + //seek for the output slot + if (slot.constructor === String) { + slot = this.findInputSlot(slot); + if (slot == -1) { + if (LiteGraph.debug) { + console.log("Connect: Error, no slot of name " + slot); + } + return false; + } + } else if (!this.inputs || slot >= this.inputs.length) { + if (LiteGraph.debug) { + console.log("Connect: Error, slot number not found"); + } + return false; + } + + var input = this.inputs[slot]; + if (!input) { + return false; + } + + var link_id = this.inputs[slot].link; + if(link_id != null) + { + this.inputs[slot].link = null; + + //remove other side + var link_info = this.graph.links[link_id]; + if (link_info) { + var target_node = this.graph.getNodeById(link_info.origin_id); + if (!target_node) { + return false; + } + + var output = target_node.outputs[link_info.origin_slot]; + if (!output || !output.links || output.links.length == 0) { + return false; + } + + //search in the inputs list for this link + for (var i = 0, l = output.links.length; i < l; i++) { + if (output.links[i] == link_id) { + output.links.splice(i, 1); + break; + } + } + + delete this.graph.links[link_id]; //remove from the pool + if (this.graph) { + this.graph._version++; + } + if (this.onConnectionsChange) { + this.onConnectionsChange( + LiteGraph.INPUT, + slot, + false, + link_info, + input + ); + } + if (target_node.onConnectionsChange) { + target_node.onConnectionsChange( + LiteGraph.OUTPUT, + i, + false, + link_info, + output + ); + } + if (this.graph && this.graph.onNodeConnectionChange) { + this.graph.onNodeConnectionChange( + LiteGraph.OUTPUT, + target_node, + i + ); + this.graph.onNodeConnectionChange(LiteGraph.INPUT, this, slot); + } + } + } //link != null + + this.setDirtyCanvas(false, true); + if(this.graph) + this.graph.connectionChange(this); + return true; + }; + + /** + * returns the center of a connection point in canvas coords + * @method getConnectionPos + * @param {boolean} is_input true if if a input slot, false if it is an output + * @param {number_or_string} slot (could be the number of the slot or the string with the name of the slot) + * @param {vec2} out [optional] a place to store the output, to free garbage + * @return {[x,y]} the position + **/ + LGraphNode.prototype.getConnectionPos = function( + is_input, + slot_number, + out + ) { + out = out || new Float32Array(2); + var num_slots = 0; + if (is_input && this.inputs) { + num_slots = this.inputs.length; + } + if (!is_input && this.outputs) { + num_slots = this.outputs.length; + } + + var offset = LiteGraph.NODE_SLOT_HEIGHT * 0.5; + + if (this.flags.collapsed) { + var w = this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH; + if (this.horizontal) { + out[0] = this.pos[0] + w * 0.5; + if (is_input) { + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT; + } else { + out[1] = this.pos[1]; + } + } else { + if (is_input) { + out[0] = this.pos[0]; + } else { + out[0] = this.pos[0] + w; + } + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT * 0.5; + } + return out; + } + + //weird feature that never got finished + if (is_input && slot_number == -1) { + out[0] = this.pos[0] + LiteGraph.NODE_TITLE_HEIGHT * 0.5; + out[1] = this.pos[1] + LiteGraph.NODE_TITLE_HEIGHT * 0.5; + return out; + } + + //hard-coded pos + if ( + is_input && + num_slots > slot_number && + this.inputs[slot_number].pos + ) { + out[0] = this.pos[0] + this.inputs[slot_number].pos[0]; + out[1] = this.pos[1] + this.inputs[slot_number].pos[1]; + return out; + } else if ( + !is_input && + num_slots > slot_number && + this.outputs[slot_number].pos + ) { + out[0] = this.pos[0] + this.outputs[slot_number].pos[0]; + out[1] = this.pos[1] + this.outputs[slot_number].pos[1]; + return out; + } + + //horizontal distributed slots + if (this.horizontal) { + out[0] = + this.pos[0] + (slot_number + 0.5) * (this.size[0] / num_slots); + if (is_input) { + out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT; + } else { + out[1] = this.pos[1] + this.size[1]; + } + return out; + } + + //default vertical slots + if (is_input) { + out[0] = this.pos[0] + offset; + } else { + out[0] = this.pos[0] + this.size[0] + 1 - offset; + } + out[1] = + this.pos[1] + + (slot_number + 0.7) * LiteGraph.NODE_SLOT_HEIGHT + + (this.constructor.slot_start_y || 0); + return out; + }; + + /* Force align to grid */ + LGraphNode.prototype.alignToGrid = function() { + this.pos[0] = + LiteGraph.CANVAS_GRID_SIZE * + Math.round(this.pos[0] / LiteGraph.CANVAS_GRID_SIZE); + this.pos[1] = + LiteGraph.CANVAS_GRID_SIZE * + Math.round(this.pos[1] / LiteGraph.CANVAS_GRID_SIZE); + }; + + /* Console output */ + LGraphNode.prototype.trace = function(msg) { + if (!this.console) { + this.console = []; + } + + this.console.push(msg); + if (this.console.length > LGraphNode.MAX_CONSOLE) { + this.console.shift(); + } + + if(this.graph.onNodeTrace) + this.graph.onNodeTrace(this, msg); + }; + + /* Forces to redraw or the main canvas (LGraphNode) or the bg canvas (links) */ + LGraphNode.prototype.setDirtyCanvas = function( + dirty_foreground, + dirty_background + ) { + if (!this.graph) { + return; + } + this.graph.sendActionToCanvas("setDirty", [ + dirty_foreground, + dirty_background + ]); + }; + + LGraphNode.prototype.loadImage = function(url) { + var img = new Image(); + img.src = LiteGraph.node_images_path + url; + img.ready = false; + + var that = this; + img.onload = function() { + this.ready = true; + that.setDirtyCanvas(true); + }; + return img; + }; + + //safe LGraphNode action execution (not sure if safe) + /* +LGraphNode.prototype.executeAction = function(action) +{ + if(action == "") return false; + + if( action.indexOf(";") != -1 || action.indexOf("}") != -1) + { + this.trace("Error: Action contains unsafe characters"); + return false; + } + + var tokens = action.split("("); + var func_name = tokens[0]; + if( typeof(this[func_name]) != "function") + { + this.trace("Error: Action not found on node: " + func_name); + return false; + } + + var code = action; + + try + { + var _foo = eval; + eval = null; + (new Function("with(this) { " + code + "}")).call(this); + eval = _foo; + } + catch (err) + { + this.trace("Error executing action {" + action + "} :" + err); + return false; + } + + return true; +} +*/ + + /* Allows to get onMouseMove and onMouseUp events even if the mouse is out of focus */ + LGraphNode.prototype.captureInput = function(v) { + if (!this.graph || !this.graph.list_of_graphcanvas) { + return; + } + + var list = this.graph.list_of_graphcanvas; + + for (var i = 0; i < list.length; ++i) { + var c = list[i]; + //releasing somebody elses capture?! + if (!v && c.node_capturing_input != this) { + continue; + } + + //change + c.node_capturing_input = v ? this : null; + } + }; + + /** + * Collapse the node to make it smaller on the canvas + * @method collapse + **/ + LGraphNode.prototype.collapse = function(force) { + this.graph._version++; + if (this.constructor.collapsable === false && !force) { + return; + } + if (!this.flags.collapsed) { + this.flags.collapsed = true; + } else { + this.flags.collapsed = false; + } + this.setDirtyCanvas(true, true); + }; + + /** + * Forces the node to do not move or realign on Z + * @method pin + **/ + + LGraphNode.prototype.pin = function(v) { + this.graph._version++; + if (v === undefined) { + this.flags.pinned = !this.flags.pinned; + } else { + this.flags.pinned = v; + } + }; + + LGraphNode.prototype.localToScreen = function(x, y, graphcanvas) { + return [ + (x + this.pos[0]) * graphcanvas.scale + graphcanvas.offset[0], + (y + this.pos[1]) * graphcanvas.scale + graphcanvas.offset[1] + ]; + }; + + function LGraphGroup(title) { + this._ctor(title); + } + + global.LGraphGroup = LiteGraph.LGraphGroup = LGraphGroup; + + LGraphGroup.prototype._ctor = function(title) { + this.title = title || "Group"; + this.font_size = 24; + this.color = LGraphCanvas.node_colors.pale_blue + ? LGraphCanvas.node_colors.pale_blue.groupcolor + : "#AAA"; + this._bounding = new Float32Array([10, 10, 140, 80]); + this._pos = this._bounding.subarray(0, 2); + this._size = this._bounding.subarray(2, 4); + this._nodes = []; + this.graph = null; + + Object.defineProperty(this, "pos", { + set: function(v) { + if (!v || v.length < 2) { + return; + } + this._pos[0] = v[0]; + this._pos[1] = v[1]; + }, + get: function() { + return this._pos; + }, + enumerable: true + }); + + Object.defineProperty(this, "size", { + set: function(v) { + if (!v || v.length < 2) { + return; + } + this._size[0] = Math.max(140, v[0]); + this._size[1] = Math.max(80, v[1]); + }, + get: function() { + return this._size; + }, + enumerable: true + }); + }; + + LGraphGroup.prototype.configure = function(o) { + this.title = o.title; + this._bounding.set(o.bounding); + this.color = o.color; + if (o.font_size) { + this.font_size = o.font_size; + } + }; + + LGraphGroup.prototype.serialize = function() { + var b = this._bounding; + return { + title: this.title, + bounding: [ + Math.round(b[0]), + Math.round(b[1]), + Math.round(b[2]), + Math.round(b[3]) + ], + color: this.color, + font_size: this.font_size + }; + }; + + LGraphGroup.prototype.move = function(deltax, deltay, ignore_nodes) { + this._pos[0] += deltax; + this._pos[1] += deltay; + if (ignore_nodes) { + return; + } + for (var i = 0; i < this._nodes.length; ++i) { + var node = this._nodes[i]; + node.pos[0] += deltax; + node.pos[1] += deltay; + } + }; + + LGraphGroup.prototype.recomputeInsideNodes = function() { + this._nodes.length = 0; + var nodes = this.graph._nodes; + var node_bounding = new Float32Array(4); + + for (var i = 0; i < nodes.length; ++i) { + var node = nodes[i]; + node.getBounding(node_bounding); + if (!overlapBounding(this._bounding, node_bounding)) { + continue; + } //out of the visible area + this._nodes.push(node); + } + }; + + LGraphGroup.prototype.isPointInside = LGraphNode.prototype.isPointInside; + LGraphGroup.prototype.setDirtyCanvas = LGraphNode.prototype.setDirtyCanvas; + + //**************************************** + + //Scale and Offset + function DragAndScale(element, skip_events) { + this.offset = new Float32Array([0, 0]); + this.scale = 1; + this.max_scale = 10; + this.min_scale = 0.1; + this.onredraw = null; + this.enabled = true; + this.last_mouse = [0, 0]; + this.element = null; + this.visible_area = new Float32Array(4); + + if (element) { + this.element = element; + if (!skip_events) { + this.bindEvents(element); + } + } + } + + LiteGraph.DragAndScale = DragAndScale; + + DragAndScale.prototype.bindEvents = function(element) { + this.last_mouse = new Float32Array(2); + + this._binded_mouse_callback = this.onMouse.bind(this); + + LiteGraph.pointerListenerAdd(element,"down", this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(element,"move", this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(element,"up", this._binded_mouse_callback); + + element.addEventListener( + "mousewheel", + this._binded_mouse_callback, + false + ); + element.addEventListener("wheel", this._binded_mouse_callback, false); + }; + + DragAndScale.prototype.computeVisibleArea = function( viewport ) { + if (!this.element) { + this.visible_area[0] = this.visible_area[1] = this.visible_area[2] = this.visible_area[3] = 0; + return; + } + var width = this.element.width; + var height = this.element.height; + var startx = -this.offset[0]; + var starty = -this.offset[1]; + if( viewport ) + { + startx += viewport[0] / this.scale; + starty += viewport[1] / this.scale; + width = viewport[2]; + height = viewport[3]; + } + var endx = startx + width / this.scale; + var endy = starty + height / this.scale; + this.visible_area[0] = startx; + this.visible_area[1] = starty; + this.visible_area[2] = endx - startx; + this.visible_area[3] = endy - starty; + }; + + DragAndScale.prototype.onMouse = function(e) { + if (!this.enabled) { + return; + } + + var canvas = this.element; + var rect = canvas.getBoundingClientRect(); + var x = e.clientX - rect.left; + var y = e.clientY - rect.top; + e.canvasx = x; + e.canvasy = y; + e.dragging = this.dragging; + + var is_inside = !this.viewport || ( this.viewport && x >= this.viewport[0] && x < (this.viewport[0] + this.viewport[2]) && y >= this.viewport[1] && y < (this.viewport[1] + this.viewport[3]) ); + + //console.log("pointerevents: DragAndScale onMouse "+e.type+" "+is_inside); + + var ignore = false; + if (this.onmouse) { + ignore = this.onmouse(e); + } + + if (e.type == LiteGraph.pointerevents_method+"down" && is_inside) { + this.dragging = true; + LiteGraph.pointerListenerRemove(canvas,"move",this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(document,"move",this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(document,"up",this._binded_mouse_callback); + } else if (e.type == LiteGraph.pointerevents_method+"move") { + if (!ignore) { + var deltax = x - this.last_mouse[0]; + var deltay = y - this.last_mouse[1]; + if (this.dragging) { + this.mouseDrag(deltax, deltay); + } + } + } else if (e.type == LiteGraph.pointerevents_method+"up") { + this.dragging = false; + LiteGraph.pointerListenerRemove(document,"move",this._binded_mouse_callback); + LiteGraph.pointerListenerRemove(document,"up",this._binded_mouse_callback); + LiteGraph.pointerListenerAdd(canvas,"move",this._binded_mouse_callback); + } else if ( is_inside && + (e.type == "mousewheel" || + e.type == "wheel" || + e.type == "DOMMouseScroll") + ) { + e.eventType = "mousewheel"; + if (e.type == "wheel") { + e.wheel = -e.deltaY; + } else { + e.wheel = + e.wheelDeltaY != null ? e.wheelDeltaY : e.detail * -60; + } + + //from stack overflow + e.delta = e.wheelDelta + ? e.wheelDelta / 40 + : e.deltaY + ? -e.deltaY / 3 + : 0; + this.changeDeltaScale(1.0 + e.delta * 0.05); + } + + this.last_mouse[0] = x; + this.last_mouse[1] = y; + + if(is_inside) + { + e.preventDefault(); + e.stopPropagation(); + return false; + } + }; + + DragAndScale.prototype.toCanvasContext = function(ctx) { + ctx.scale(this.scale, this.scale); + ctx.translate(this.offset[0], this.offset[1]); + }; + + DragAndScale.prototype.convertOffsetToCanvas = function(pos) { + //return [pos[0] / this.scale - this.offset[0], pos[1] / this.scale - this.offset[1]]; + return [ + (pos[0] + this.offset[0]) * this.scale, + (pos[1] + this.offset[1]) * this.scale + ]; + }; + + DragAndScale.prototype.convertCanvasToOffset = function(pos, out) { + out = out || [0, 0]; + out[0] = pos[0] / this.scale - this.offset[0]; + out[1] = pos[1] / this.scale - this.offset[1]; + return out; + }; + + DragAndScale.prototype.mouseDrag = function(x, y) { + this.offset[0] += x / this.scale; + this.offset[1] += y / this.scale; + + if (this.onredraw) { + this.onredraw(this); + } + }; + + DragAndScale.prototype.changeScale = function(value, zooming_center) { + if (value < this.min_scale) { + value = this.min_scale; + } else if (value > this.max_scale) { + value = this.max_scale; + } + + if (value == this.scale) { + return; + } + + if (!this.element) { + return; + } + + var rect = this.element.getBoundingClientRect(); + if (!rect) { + return; + } + + zooming_center = zooming_center || [ + rect.width * 0.5, + rect.height * 0.5 + ]; + var center = this.convertCanvasToOffset(zooming_center); + this.scale = value; + if (Math.abs(this.scale - 1) < 0.01) { + this.scale = 1; + } + + var new_center = this.convertCanvasToOffset(zooming_center); + var delta_offset = [ + new_center[0] - center[0], + new_center[1] - center[1] + ]; + + this.offset[0] += delta_offset[0]; + this.offset[1] += delta_offset[1]; + + if (this.onredraw) { + this.onredraw(this); + } + }; + + DragAndScale.prototype.changeDeltaScale = function(value, zooming_center) { + this.changeScale(this.scale * value, zooming_center); + }; + + DragAndScale.prototype.reset = function() { + this.scale = 1; + this.offset[0] = 0; + this.offset[1] = 0; + }; + + //********************************************************************************* + // LGraphCanvas: LGraph renderer CLASS + //********************************************************************************* + + /** + * This class is in charge of rendering one graph inside a canvas. And provides all the interaction required. + * Valid callbacks are: onNodeSelected, onNodeDeselected, onShowNodePanel, onNodeDblClicked + * + * @class LGraphCanvas + * @constructor + * @param {HTMLCanvas} canvas the canvas where you want to render (it accepts a selector in string format or the canvas element itself) + * @param {LGraph} graph [optional] + * @param {Object} options [optional] { skip_rendering, autoresize, viewport } + */ + function LGraphCanvas(canvas, graph, options) { + this.options = options = options || {}; + + //if(graph === undefined) + // throw ("No graph assigned"); + this.background_image = LGraphCanvas.DEFAULT_BACKGROUND_IMAGE; + + if (canvas && canvas.constructor === String) { + canvas = document.querySelector(canvas); + } + + this.ds = new DragAndScale(); + this.zoom_modify_alpha = true; //otherwise it generates ugly patterns when scaling down too much + + this.title_text_font = "" + LiteGraph.NODE_TEXT_SIZE + "px Arial"; + this.inner_text_font = + "normal " + LiteGraph.NODE_SUBTEXT_SIZE + "px Arial"; + this.node_title_color = LiteGraph.NODE_TITLE_COLOR; + this.default_link_color = LiteGraph.LINK_COLOR; + this.default_connection_color = { + input_off: "#778", + input_on: "#7F7", //"#BBD" + output_off: "#778", + output_on: "#7F7" //"#BBD" + }; + this.default_connection_color_byType = { + /*number: "#7F7", + string: "#77F", + boolean: "#F77",*/ + } + this.default_connection_color_byTypeOff = { + /*number: "#474", + string: "#447", + boolean: "#744",*/ + }; + + this.highquality_render = true; + this.use_gradients = false; //set to true to render titlebar with gradients + this.editor_alpha = 1; //used for transition + this.pause_rendering = false; + this.clear_background = true; + this.clear_background_color = "#222"; + + this.read_only = false; //if set to true users cannot modify the graph + this.render_only_selected = true; + this.live_mode = false; + this.show_info = true; + this.allow_dragcanvas = true; + this.allow_dragnodes = true; + this.allow_interaction = true; //allow to control widgets, buttons, collapse, etc + this.multi_select = false; //allow selecting multi nodes without pressing extra keys + this.allow_searchbox = true; + this.allow_reconnect_links = true; //allows to change a connection with having to redo it again + this.align_to_grid = false; //snap to grid + + this.drag_mode = false; + this.dragging_rectangle = null; + + this.filter = null; //allows to filter to only accept some type of nodes in a graph + + this.set_canvas_dirty_on_mouse_event = true; //forces to redraw the canvas if the mouse does anything + this.always_render_background = false; + this.render_shadows = true; + this.render_canvas_border = true; + this.render_connections_shadows = false; //too much cpu + this.render_connections_border = true; + this.render_curved_connections = false; + this.render_connection_arrows = false; + this.render_collapsed_slots = true; + this.render_execution_order = false; + this.render_title_colored = true; + this.render_link_tooltip = true; + + this.links_render_mode = LiteGraph.SPLINE_LINK; + + this.mouse = [0, 0]; //mouse in canvas coordinates, where 0,0 is the top-left corner of the blue rectangle + this.graph_mouse = [0, 0]; //mouse in graph coordinates, where 0,0 is the top-left corner of the blue rectangle + this.canvas_mouse = this.graph_mouse; //LEGACY: REMOVE THIS, USE GRAPH_MOUSE INSTEAD + + //to personalize the search box + this.onSearchBox = null; + this.onSearchBoxSelection = null; + + //callbacks + this.onMouse = null; + this.onDrawBackground = null; //to render background objects (behind nodes and connections) in the canvas affected by transform + this.onDrawForeground = null; //to render foreground objects (above nodes and connections) in the canvas affected by transform + this.onDrawOverlay = null; //to render foreground objects not affected by transform (for GUIs) + this.onDrawLinkTooltip = null; //called when rendering a tooltip + this.onNodeMoved = null; //called after moving a node + this.onSelectionChange = null; //called if the selection changes + this.onConnectingChange = null; //called before any link changes + this.onBeforeChange = null; //called before modifying the graph + this.onAfterChange = null; //called after modifying the graph + + this.connections_width = 3; + this.round_radius = 8; + + this.current_node = null; + this.node_widget = null; //used for widgets + this.over_link_center = null; + this.last_mouse_position = [0, 0]; + this.visible_area = this.ds.visible_area; + this.visible_links = []; + + this.viewport = options.viewport || null; //to constraint render area to a portion of the canvas + + //link canvas and graph + if (graph) { + graph.attachCanvas(this); + } + + this.setCanvas(canvas,options.skip_events); + this.clear(); + + if (!options.skip_render) { + this.startRendering(); + } + + this.autoresize = options.autoresize; + } + + global.LGraphCanvas = LiteGraph.LGraphCanvas = LGraphCanvas; + + LGraphCanvas.DEFAULT_BACKGROUND_IMAGE = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAQBJREFUeNrs1rEKwjAUhlETUkj3vP9rdmr1Ysammk2w5wdxuLgcMHyptfawuZX4pJSWZTnfnu/lnIe/jNNxHHGNn//HNbbv+4dr6V+11uF527arU7+u63qfa/bnmh8sWLBgwYJlqRf8MEptXPBXJXa37BSl3ixYsGDBMliwFLyCV/DeLIMFCxYsWLBMwSt4Be/NggXLYMGCBUvBK3iNruC9WbBgwYJlsGApeAWv4L1ZBgsWLFiwYJmCV/AK3psFC5bBggULloJX8BpdwXuzYMGCBctgwVLwCl7Be7MMFixYsGDBsu8FH1FaSmExVfAxBa/gvVmwYMGCZbBg/W4vAQYA5tRF9QYlv/QAAAAASUVORK5CYII="; + + LGraphCanvas.link_type_colors = { + "-1": LiteGraph.EVENT_LINK_COLOR, + number: "#AAA", + node: "#DCA" + }; + LGraphCanvas.gradients = {}; //cache of gradients + + /** + * clears all the data inside + * + * @method clear + */ + LGraphCanvas.prototype.clear = function() { + this.frame = 0; + this.last_draw_time = 0; + this.render_time = 0; + this.fps = 0; + + //this.scale = 1; + //this.offset = [0,0]; + + this.dragging_rectangle = null; + + this.selected_nodes = {}; + this.selected_group = null; + + this.visible_nodes = []; + this.node_dragged = null; + this.node_over = null; + this.node_capturing_input = null; + this.connecting_node = null; + this.highlighted_links = {}; + + this.dragging_canvas = false; + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + this.dirty_area = null; + + this.node_in_panel = null; + this.node_widget = null; + + this.last_mouse = [0, 0]; + this.last_mouseclick = 0; + this.pointer_is_down = false; + this.pointer_is_double = false; + this.visible_area.set([0, 0, 0, 0]); + + if (this.onClear) { + this.onClear(); + } + }; + + /** + * assigns a graph, you can reassign graphs to the same canvas + * + * @method setGraph + * @param {LGraph} graph + */ + LGraphCanvas.prototype.setGraph = function(graph, skip_clear) { + if (this.graph == graph) { + return; + } + + if (!skip_clear) { + this.clear(); + } + + if (!graph && this.graph) { + this.graph.detachCanvas(this); + return; + } + + graph.attachCanvas(this); + + //remove the graph stack in case a subgraph was open + if (this._graph_stack) + this._graph_stack = null; + + this.setDirty(true, true); + }; + + /** + * returns the top level graph (in case there are subgraphs open on the canvas) + * + * @method getTopGraph + * @return {LGraph} graph + */ + LGraphCanvas.prototype.getTopGraph = function() + { + if(this._graph_stack.length) + return this._graph_stack[0]; + return this.graph; + } + + /** + * opens a graph contained inside a node in the current graph + * + * @method openSubgraph + * @param {LGraph} graph + */ + LGraphCanvas.prototype.openSubgraph = function(graph) { + if (!graph) { + throw "graph cannot be null"; + } + + if (this.graph == graph) { + throw "graph cannot be the same"; + } + + this.clear(); + + if (this.graph) { + if (!this._graph_stack) { + this._graph_stack = []; + } + this._graph_stack.push(this.graph); + } + + graph.attachCanvas(this); + this.checkPanels(); + this.setDirty(true, true); + }; + + /** + * closes a subgraph contained inside a node + * + * @method closeSubgraph + * @param {LGraph} assigns a graph + */ + LGraphCanvas.prototype.closeSubgraph = function() { + if (!this._graph_stack || this._graph_stack.length == 0) { + return; + } + var subgraph_node = this.graph._subgraph_node; + var graph = this._graph_stack.pop(); + this.selected_nodes = {}; + this.highlighted_links = {}; + graph.attachCanvas(this); + this.setDirty(true, true); + if (subgraph_node) { + this.centerOnNode(subgraph_node); + this.selectNodes([subgraph_node]); + } + // when close sub graph back to offset [0, 0] scale 1 + this.ds.offset = [0, 0] + this.ds.scale = 1 + }; + + /** + * returns the visually active graph (in case there are more in the stack) + * @method getCurrentGraph + * @return {LGraph} the active graph + */ + LGraphCanvas.prototype.getCurrentGraph = function() { + return this.graph; + }; + + /** + * assigns a canvas + * + * @method setCanvas + * @param {Canvas} assigns a canvas (also accepts the ID of the element (not a selector) + */ + LGraphCanvas.prototype.setCanvas = function(canvas, skip_events) { + var that = this; + + if (canvas) { + if (canvas.constructor === String) { + canvas = document.getElementById(canvas); + if (!canvas) { + throw "Error creating LiteGraph canvas: Canvas not found"; + } + } + } + + if (canvas === this.canvas) { + return; + } + + if (!canvas && this.canvas) { + //maybe detach events from old_canvas + if (!skip_events) { + this.unbindEvents(); + } + } + + this.canvas = canvas; + this.ds.element = canvas; + + if (!canvas) { + return; + } + + //this.canvas.tabindex = "1000"; + canvas.className += " lgraphcanvas"; + canvas.data = this; + canvas.tabindex = "1"; //to allow key events + + //bg canvas: used for non changing stuff + this.bgcanvas = null; + if (!this.bgcanvas) { + this.bgcanvas = document.createElement("canvas"); + this.bgcanvas.width = this.canvas.width; + this.bgcanvas.height = this.canvas.height; + } + + if (canvas.getContext == null) { + if (canvas.localName != "canvas") { + throw "Element supplied for LGraphCanvas must be a element, you passed a " + + canvas.localName; + } + throw "This browser doesn't support Canvas"; + } + + var ctx = (this.ctx = canvas.getContext("2d")); + if (ctx == null) { + if (!canvas.webgl_enabled) { + console.warn( + "This canvas seems to be WebGL, enabling WebGL renderer" + ); + } + this.enableWebGL(); + } + + //input: (move and up could be unbinded) + // why here? this._mousemove_callback = this.processMouseMove.bind(this); + // why here? this._mouseup_callback = this.processMouseUp.bind(this); + + if (!skip_events) { + this.bindEvents(); + } + }; + + //used in some events to capture them + LGraphCanvas.prototype._doNothing = function doNothing(e) { + //console.log("pointerevents: _doNothing "+e.type); + e.preventDefault(); + return false; + }; + LGraphCanvas.prototype._doReturnTrue = function doNothing(e) { + e.preventDefault(); + return true; + }; + + /** + * binds mouse, keyboard, touch and drag events to the canvas + * @method bindEvents + **/ + LGraphCanvas.prototype.bindEvents = function() { + if (this._events_binded) { + console.warn("LGraphCanvas: events already binded"); + return; + } + + //console.log("pointerevents: bindEvents"); + + var canvas = this.canvas; + + var ref_window = this.getCanvasWindow(); + var document = ref_window.document; //hack used when moving canvas between windows + + this._mousedown_callback = this.processMouseDown.bind(this); + this._mousewheel_callback = this.processMouseWheel.bind(this); + // why mousemove and mouseup were not binded here? + this._mousemove_callback = this.processMouseMove.bind(this); + this._mouseup_callback = this.processMouseUp.bind(this); + + //touch events -- TODO IMPLEMENT + //this._touch_callback = this.touchHandler.bind(this); + + LiteGraph.pointerListenerAdd(canvas,"down", this._mousedown_callback, true); //down do not need to store the binded + canvas.addEventListener("mousewheel", this._mousewheel_callback, false); + + LiteGraph.pointerListenerAdd(canvas,"up", this._mouseup_callback, true); // CHECK: ??? binded or not + LiteGraph.pointerListenerAdd(canvas,"move", this._mousemove_callback); + + canvas.addEventListener("contextmenu", this._doNothing); + canvas.addEventListener( + "DOMMouseScroll", + this._mousewheel_callback, + false + ); + + //touch events -- THIS WAY DOES NOT WORK, finish implementing pointerevents, than clean the touchevents + /*if( 'touchstart' in document.documentElement ) + { + canvas.addEventListener("touchstart", this._touch_callback, true); + canvas.addEventListener("touchmove", this._touch_callback, true); + canvas.addEventListener("touchend", this._touch_callback, true); + canvas.addEventListener("touchcancel", this._touch_callback, true); + }*/ + + //Keyboard ****************** + this._key_callback = this.processKey.bind(this); + + canvas.addEventListener("keydown", this._key_callback, true); + document.addEventListener("keyup", this._key_callback, true); //in document, otherwise it doesn't fire keyup + + //Dropping Stuff over nodes ************************************ + this._ondrop_callback = this.processDrop.bind(this); + + canvas.addEventListener("dragover", this._doNothing, false); + canvas.addEventListener("dragend", this._doNothing, false); + canvas.addEventListener("drop", this._ondrop_callback, false); + canvas.addEventListener("dragenter", this._doReturnTrue, false); + + this._events_binded = true; + }; + + /** + * unbinds mouse events from the canvas + * @method unbindEvents + **/ + LGraphCanvas.prototype.unbindEvents = function() { + if (!this._events_binded) { + console.warn("LGraphCanvas: no events binded"); + return; + } + + //console.log("pointerevents: unbindEvents"); + + var ref_window = this.getCanvasWindow(); + var document = ref_window.document; + + LiteGraph.pointerListenerRemove(this.canvas,"move", this._mousedown_callback); + LiteGraph.pointerListenerRemove(this.canvas,"up", this._mousedown_callback); + LiteGraph.pointerListenerRemove(this.canvas,"down", this._mousedown_callback); + this.canvas.removeEventListener( + "mousewheel", + this._mousewheel_callback + ); + this.canvas.removeEventListener( + "DOMMouseScroll", + this._mousewheel_callback + ); + this.canvas.removeEventListener("keydown", this._key_callback); + document.removeEventListener("keyup", this._key_callback); + this.canvas.removeEventListener("contextmenu", this._doNothing); + this.canvas.removeEventListener("drop", this._ondrop_callback); + this.canvas.removeEventListener("dragenter", this._doReturnTrue); + + //touch events -- THIS WAY DOES NOT WORK, finish implementing pointerevents, than clean the touchevents + /*this.canvas.removeEventListener("touchstart", this._touch_callback ); + this.canvas.removeEventListener("touchmove", this._touch_callback ); + this.canvas.removeEventListener("touchend", this._touch_callback ); + this.canvas.removeEventListener("touchcancel", this._touch_callback );*/ + + this._mousedown_callback = null; + this._mousewheel_callback = null; + this._key_callback = null; + this._ondrop_callback = null; + + this._events_binded = false; + }; + + LGraphCanvas.getFileExtension = function(url) { + var question = url.indexOf("?"); + if (question != -1) { + url = url.substr(0, question); + } + var point = url.lastIndexOf("."); + if (point == -1) { + return ""; + } + return url.substr(point + 1).toLowerCase(); + }; + + /** + * this function allows to render the canvas using WebGL instead of Canvas2D + * this is useful if you plant to render 3D objects inside your nodes, it uses litegl.js for webgl and canvas2DtoWebGL to emulate the Canvas2D calls in webGL + * @method enableWebGL + **/ + LGraphCanvas.prototype.enableWebGL = function() { + if (typeof GL === undefined) { + throw "litegl.js must be included to use a WebGL canvas"; + } + if (typeof enableWebGLCanvas === undefined) { + throw "webglCanvas.js must be included to use this feature"; + } + + this.gl = this.ctx = enableWebGLCanvas(this.canvas); + this.ctx.webgl = true; + this.bgcanvas = this.canvas; + this.bgctx = this.gl; + this.canvas.webgl_enabled = true; + + /* + GL.create({ canvas: this.bgcanvas }); + this.bgctx = enableWebGLCanvas( this.bgcanvas ); + window.gl = this.gl; + */ + }; + + /** + * marks as dirty the canvas, this way it will be rendered again + * + * @class LGraphCanvas + * @method setDirty + * @param {bool} fgcanvas if the foreground canvas is dirty (the one containing the nodes) + * @param {bool} bgcanvas if the background canvas is dirty (the one containing the wires) + */ + LGraphCanvas.prototype.setDirty = function(fgcanvas, bgcanvas) { + if (fgcanvas) { + this.dirty_canvas = true; + } + if (bgcanvas) { + this.dirty_bgcanvas = true; + } + }; + + /** + * Used to attach the canvas in a popup + * + * @method getCanvasWindow + * @return {window} returns the window where the canvas is attached (the DOM root node) + */ + LGraphCanvas.prototype.getCanvasWindow = function() { + if (!this.canvas) { + return window; + } + var doc = this.canvas.ownerDocument; + return doc.defaultView || doc.parentWindow; + }; + + /** + * starts rendering the content of the canvas when needed + * + * @method startRendering + */ + LGraphCanvas.prototype.startRendering = function() { + if (this.is_rendering) { + return; + } //already rendering + + this.is_rendering = true; + renderFrame.call(this); + + function renderFrame() { + if (!this.pause_rendering) { + this.draw(); + } + + var window = this.getCanvasWindow(); + if (this.is_rendering) { + window.requestAnimationFrame(renderFrame.bind(this)); + } + } + }; + + /** + * stops rendering the content of the canvas (to save resources) + * + * @method stopRendering + */ + LGraphCanvas.prototype.stopRendering = function() { + this.is_rendering = false; + /* + if(this.rendering_timer_id) + { + clearInterval(this.rendering_timer_id); + this.rendering_timer_id = null; + } + */ + }; + + /* LiteGraphCanvas input */ + + //used to block future mouse events (because of im gui) + LGraphCanvas.prototype.blockClick = function() + { + this.block_click = true; + this.last_mouseclick = 0; + } + + LGraphCanvas.prototype.processMouseDown = function(e) { + + if( this.set_canvas_dirty_on_mouse_event ) + this.dirty_canvas = true; + + if (!this.graph) { + return; + } + + this.adjustMouseEvent(e); + + var ref_window = this.getCanvasWindow(); + var document = ref_window.document; + LGraphCanvas.active_canvas = this; + var that = this; + + var x = e.clientX; + var y = e.clientY; + //console.log(y,this.viewport); + //console.log("pointerevents: processMouseDown pointerId:"+e.pointerId+" which:"+e.which+" isPrimary:"+e.isPrimary+" :: x y "+x+" "+y); + + this.ds.viewport = this.viewport; + var is_inside = !this.viewport || ( this.viewport && x >= this.viewport[0] && x < (this.viewport[0] + this.viewport[2]) && y >= this.viewport[1] && y < (this.viewport[1] + this.viewport[3]) ); + + //move mouse move event to the window in case it drags outside of the canvas + if(!this.options.skip_events) + { + LiteGraph.pointerListenerRemove(this.canvas,"move", this._mousemove_callback); + LiteGraph.pointerListenerAdd(ref_window.document,"move", this._mousemove_callback,true); //catch for the entire window + LiteGraph.pointerListenerAdd(ref_window.document,"up", this._mouseup_callback,true); + } + + if(!is_inside){ + return; + } + + var node = this.graph.getNodeOnPos( e.canvasX, e.canvasY, this.visible_nodes, 5 ); + var skip_dragging = false; + var skip_action = false; + var now = LiteGraph.getTime(); + var is_primary = (e.isPrimary === undefined || !e.isPrimary); + var is_double_click = (now - this.last_mouseclick < 300); + this.mouse[0] = e.clientX; + this.mouse[1] = e.clientY; + this.graph_mouse[0] = e.canvasX; + this.graph_mouse[1] = e.canvasY; + this.last_click_position = [this.mouse[0],this.mouse[1]]; + + if (this.pointer_is_down && is_primary ){ + this.pointer_is_double = true; + //console.log("pointerevents: pointer_is_double start"); + }else{ + this.pointer_is_double = false; + } + this.pointer_is_down = true; + + + this.canvas.focus(); + + LiteGraph.closeAllContextMenus(ref_window); + + if (this.onMouse) + { + if (this.onMouse(e) == true) + return; + } + + //left button mouse / single finger + if (e.which == 1 && !this.pointer_is_double) + { + if (e.ctrlKey) + { + this.dragging_rectangle = new Float32Array(4); + this.dragging_rectangle[0] = e.canvasX; + this.dragging_rectangle[1] = e.canvasY; + this.dragging_rectangle[2] = 1; + this.dragging_rectangle[3] = 1; + skip_action = true; + } + + // clone node ALT dragging + if (LiteGraph.alt_drag_do_clone_nodes && e.altKey && node && this.allow_interaction && !skip_action && !this.read_only) + { + if (cloned = node.clone()){ + cloned.pos[0] += 5; + cloned.pos[1] += 5; + this.graph.add(cloned,false,{doCalcSize: false}); + node = cloned; + skip_action = true; + if (!block_drag_node) { + if (this.allow_dragnodes) { + this.graph.beforeChange(); + this.node_dragged = node; + } + if (!this.selected_nodes[node.id]) { + this.processNodeSelected(node, e); + } + } + } + } + + var clicking_canvas_bg = false; + + //when clicked on top of a node + //and it is not interactive + if (node && (this.allow_interaction || node.flags.allow_interaction) && !skip_action && !this.read_only) { + if (!this.live_mode && !node.flags.pinned) { + this.bringToFront(node); + } //if it wasn't selected? + + //not dragging mouse to connect two slots + if ( this.allow_interaction && !this.connecting_node && !node.flags.collapsed && !this.live_mode ) { + //Search for corner for resize + if ( !skip_action && + node.resizable !== false && node.inResizeCorner(e.canvasX, e.canvasY) + ) { + this.graph.beforeChange(); + this.resizing_node = node; + this.canvas.style.cursor = "se-resize"; + skip_action = true; + } else { + //search for outputs + if (node.outputs) { + for ( var i = 0, l = node.outputs.length; i < l; ++i ) { + var output = node.outputs[i]; + var link_pos = node.getConnectionPos(false, i); + if ( + isInsideRectangle( + e.canvasX, + e.canvasY, + link_pos[0] - 15, + link_pos[1] - 10, + 30, + 20 + ) + ) { + this.connecting_node = node; + this.connecting_output = output; + this.connecting_output.slot_index = i; + this.connecting_pos = node.getConnectionPos( false, i ); + this.connecting_slot = i; + + if (LiteGraph.shift_click_do_break_link_from){ + if (e.shiftKey) { + node.disconnectOutput(i); + } + } + + if (is_double_click) { + if (node.onOutputDblClick) { + node.onOutputDblClick(i, e); + } + } else { + if (node.onOutputClick) { + node.onOutputClick(i, e); + } + } + + skip_action = true; + break; + } + } + } + + //search for inputs + if (node.inputs) { + for ( var i = 0, l = node.inputs.length; i < l; ++i ) { + var input = node.inputs[i]; + var link_pos = node.getConnectionPos(true, i); + if ( + isInsideRectangle( + e.canvasX, + e.canvasY, + link_pos[0] - 15, + link_pos[1] - 10, + 30, + 20 + ) + ) { + if (is_double_click) { + if (node.onInputDblClick) { + node.onInputDblClick(i, e); + } + } else { + if (node.onInputClick) { + node.onInputClick(i, e); + } + } + + if (input.link !== null) { + var link_info = this.graph.links[ + input.link + ]; //before disconnecting + if (LiteGraph.click_do_break_link_to){ + node.disconnectInput(i); + this.dirty_bgcanvas = true; + skip_action = true; + }else{ + // do same action as has not node ? + } + + if ( + this.allow_reconnect_links || + //this.move_destination_link_without_shift || + e.shiftKey + ) { + if (!LiteGraph.click_do_break_link_to){ + node.disconnectInput(i); + } + this.connecting_node = this.graph._nodes_by_id[ + link_info.origin_id + ]; + this.connecting_slot = + link_info.origin_slot; + this.connecting_output = this.connecting_node.outputs[ + this.connecting_slot + ]; + this.connecting_pos = this.connecting_node.getConnectionPos( false, this.connecting_slot ); + + this.dirty_bgcanvas = true; + skip_action = true; + } + + + }else{ + // has not node + } + + if (!skip_action){ + // connect from in to out, from to to from + this.connecting_node = node; + this.connecting_input = input; + this.connecting_input.slot_index = i; + this.connecting_pos = node.getConnectionPos( true, i ); + this.connecting_slot = i; + + this.dirty_bgcanvas = true; + skip_action = true; + } + } + } + } + } //not resizing + } + + //it wasn't clicked on the links boxes + if (!skip_action) { + var block_drag_node = false; + if(node && node.flags && node.flags.pinned) { + block_drag_node = true; + } + var pos = [e.canvasX - node.pos[0], e.canvasY - node.pos[1]]; + + //widgets + var widget = this.processNodeWidgets( node, this.graph_mouse, e ); + if (widget) { + block_drag_node = true; + this.node_widget = [node, widget]; + } + + //double clicking + if (this.allow_interaction && is_double_click && this.selected_nodes[node.id]) { + //double click node + if (node.onDblClick) { + node.onDblClick( e, pos, this ); + } + this.processNodeDblClicked(node); + block_drag_node = true; + } + + //if do not capture mouse + if ( node.onMouseDown && node.onMouseDown( e, pos, this ) ) { + block_drag_node = true; + } else { + //open subgraph button + if(node.subgraph && !node.skip_subgraph_button) + { + if ( !node.flags.collapsed && pos[0] > node.size[0] - LiteGraph.NODE_TITLE_HEIGHT && pos[1] < 0 ) { + var that = this; + setTimeout(function() { + that.openSubgraph(node.subgraph); + }, 10); + } + } + + if (this.live_mode) { + clicking_canvas_bg = true; + block_drag_node = true; + } + } + + if (!block_drag_node) { + if (this.allow_dragnodes) { + this.graph.beforeChange(); + this.node_dragged = node; + } + this.processNodeSelected(node, e); + } else { // double-click + /** + * Don't call the function if the block is already selected. + * Otherwise, it could cause the block to be unselected while its panel is open. + */ + if (!node.is_selected) this.processNodeSelected(node, e); + } + + this.dirty_canvas = true; + } + } //clicked outside of nodes + else { + if (!skip_action){ + //search for link connector + if(!this.read_only) { + for (var i = 0; i < this.visible_links.length; ++i) { + var link = this.visible_links[i]; + var center = link._pos; + if ( + !center || + e.canvasX < center[0] - 4 || + e.canvasX > center[0] + 4 || + e.canvasY < center[1] - 4 || + e.canvasY > center[1] + 4 + ) { + continue; + } + //link clicked + this.showLinkMenu(link, e); + this.over_link_center = null; //clear tooltip + break; + } + } + + this.selected_group = this.graph.getGroupOnPos( e.canvasX, e.canvasY ); + this.selected_group_resizing = false; + if (this.selected_group && !this.read_only ) { + if (e.ctrlKey) { + this.dragging_rectangle = null; + } + + var dist = distance( [e.canvasX, e.canvasY], [ this.selected_group.pos[0] + this.selected_group.size[0], this.selected_group.pos[1] + this.selected_group.size[1] ] ); + if (dist * this.ds.scale < 10) { + this.selected_group_resizing = true; + } else { + this.selected_group.recomputeInsideNodes(); + } + } + + if (is_double_click && !this.read_only && this.allow_searchbox) { + this.showSearchBox(e); + e.preventDefault(); + e.stopPropagation(); + } + + clicking_canvas_bg = true; + } + } + + if (!skip_action && clicking_canvas_bg && this.allow_dragcanvas) { + //console.log("pointerevents: dragging_canvas start"); + this.dragging_canvas = true; + } + + } else if (e.which == 2) { + //middle button + + if (LiteGraph.middle_click_slot_add_default_node){ + if (node && this.allow_interaction && !skip_action && !this.read_only){ + //not dragging mouse to connect two slots + if ( + !this.connecting_node && + !node.flags.collapsed && + !this.live_mode + ) { + var mClikSlot = false; + var mClikSlot_index = false; + var mClikSlot_isOut = false; + //search for outputs + if (node.outputs) { + for ( var i = 0, l = node.outputs.length; i < l; ++i ) { + var output = node.outputs[i]; + var link_pos = node.getConnectionPos(false, i); + if (isInsideRectangle(e.canvasX,e.canvasY,link_pos[0] - 15,link_pos[1] - 10,30,20)) { + mClikSlot = output; + mClikSlot_index = i; + mClikSlot_isOut = true; + break; + } + } + } + + //search for inputs + if (node.inputs) { + for ( var i = 0, l = node.inputs.length; i < l; ++i ) { + var input = node.inputs[i]; + var link_pos = node.getConnectionPos(true, i); + if (isInsideRectangle(e.canvasX,e.canvasY,link_pos[0] - 15,link_pos[1] - 10,30,20)) { + mClikSlot = input; + mClikSlot_index = i; + mClikSlot_isOut = false; + break; + } + } + } + //console.log("middleClickSlots? "+mClikSlot+" & "+(mClikSlot_index!==false)); + if (mClikSlot && mClikSlot_index!==false){ + + var alphaPosY = 0.5-((mClikSlot_index+1)/((mClikSlot_isOut?node.outputs.length:node.inputs.length))); + var node_bounding = node.getBounding(); + // estimate a position: this is a bad semi-bad-working mess .. REFACTOR with a correct autoplacement that knows about the others slots and nodes + var posRef = [ (!mClikSlot_isOut?node_bounding[0]:node_bounding[0]+node_bounding[2])// + node_bounding[0]/this.canvas.width*150 + ,e.canvasY-80// + node_bounding[0]/this.canvas.width*66 // vertical "derive" + ]; + var nodeCreated = this.createDefaultNodeForSlot({ nodeFrom: !mClikSlot_isOut?null:node + ,slotFrom: !mClikSlot_isOut?null:mClikSlot_index + ,nodeTo: !mClikSlot_isOut?node:null + ,slotTo: !mClikSlot_isOut?mClikSlot_index:null + ,position: posRef //,e: e + ,nodeType: "AUTO" //nodeNewType + ,posAdd:[!mClikSlot_isOut?-30:30, -alphaPosY*130] //-alphaPosY*30] + ,posSizeFix:[!mClikSlot_isOut?-1:0, 0] //-alphaPosY*2*/ + }); + skip_action = true; + } + } + } + } + + if (!skip_action && this.allow_dragcanvas) { + //console.log("pointerevents: dragging_canvas start from middle button"); + this.dragging_canvas = true; + } + + + } else if (e.which == 3 || this.pointer_is_double) { + + //right button + if (this.allow_interaction && !skip_action && !this.read_only){ + + // is it hover a node ? + if (node){ + if(Object.keys(this.selected_nodes).length + && (this.selected_nodes[node.id] || e.shiftKey || e.ctrlKey || e.metaKey) + ){ + // is multiselected or using shift to include the now node + if (!this.selected_nodes[node.id]) this.selectNodes([node],true); // add this if not present + }else{ + // update selection + this.selectNodes([node]); + } + } + + // show menu on this node + this.processContextMenu(node, e); + } + + } + + //TODO + //if(this.node_selected != prev_selected) + // this.onNodeSelectionChange(this.node_selected); + + this.last_mouse[0] = e.clientX; + this.last_mouse[1] = e.clientY; + this.last_mouseclick = LiteGraph.getTime(); + this.last_mouse_dragging = true; + + /* + if( (this.dirty_canvas || this.dirty_bgcanvas) && this.rendering_timer_id == null) + this.draw(); + */ + + this.graph.change(); + + //this is to ensure to defocus(blur) if a text input element is on focus + if ( + !ref_window.document.activeElement || + (ref_window.document.activeElement.nodeName.toLowerCase() != + "input" && + ref_window.document.activeElement.nodeName.toLowerCase() != + "textarea") + ) { + e.preventDefault(); + } + e.stopPropagation(); + + if (this.onMouseDown) { + this.onMouseDown(e); + } + + return false; + }; + + /** + * Called when a mouse move event has to be processed + * @method processMouseMove + **/ + LGraphCanvas.prototype.processMouseMove = function(e) { + if (this.autoresize) { + this.resize(); + } + + if( this.set_canvas_dirty_on_mouse_event ) + this.dirty_canvas = true; + + if (!this.graph) { + return; + } + + LGraphCanvas.active_canvas = this; + this.adjustMouseEvent(e); + var mouse = [e.clientX, e.clientY]; + this.mouse[0] = mouse[0]; + this.mouse[1] = mouse[1]; + var delta = [ + mouse[0] - this.last_mouse[0], + mouse[1] - this.last_mouse[1] + ]; + this.last_mouse = mouse; + this.graph_mouse[0] = e.canvasX; + this.graph_mouse[1] = e.canvasY; + + //console.log("pointerevents: processMouseMove "+e.pointerId+" "+e.isPrimary); + + if(this.block_click) + { + //console.log("pointerevents: processMouseMove block_click"); + e.preventDefault(); + return false; + } + + e.dragging = this.last_mouse_dragging; + + if (this.node_widget) { + this.processNodeWidgets( + this.node_widget[0], + this.graph_mouse, + e, + this.node_widget[1] + ); + this.dirty_canvas = true; + } + + //get node over + var node = this.graph.getNodeOnPos(e.canvasX,e.canvasY,this.visible_nodes); + + if (this.dragging_rectangle) + { + this.dragging_rectangle[2] = e.canvasX - this.dragging_rectangle[0]; + this.dragging_rectangle[3] = e.canvasY - this.dragging_rectangle[1]; + this.dirty_canvas = true; + } + else if (this.selected_group && !this.read_only) + { + //moving/resizing a group + if (this.selected_group_resizing) { + this.selected_group.size = [ + e.canvasX - this.selected_group.pos[0], + e.canvasY - this.selected_group.pos[1] + ]; + } else { + var deltax = delta[0] / this.ds.scale; + var deltay = delta[1] / this.ds.scale; + this.selected_group.move(deltax, deltay, e.ctrlKey); + if (this.selected_group._nodes.length) { + this.dirty_canvas = true; + } + } + this.dirty_bgcanvas = true; + } else if (this.dragging_canvas) { + ////console.log("pointerevents: processMouseMove is dragging_canvas"); + this.ds.offset[0] += delta[0] / this.ds.scale; + this.ds.offset[1] += delta[1] / this.ds.scale; + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + } else if ((this.allow_interaction || (node && node.flags.allow_interaction)) && !this.read_only) { + if (this.connecting_node) { + this.dirty_canvas = true; + } + + //remove mouseover flag + for (var i = 0, l = this.graph._nodes.length; i < l; ++i) { + if (this.graph._nodes[i].mouseOver && node != this.graph._nodes[i] ) { + //mouse leave + this.graph._nodes[i].mouseOver = false; + if (this.node_over && this.node_over.onMouseLeave) { + this.node_over.onMouseLeave(e); + } + this.node_over = null; + this.dirty_canvas = true; + } + } + + //mouse over a node + if (node) { + + if(node.redraw_on_mouse) + this.dirty_canvas = true; + + //this.canvas.style.cursor = "move"; + if (!node.mouseOver) { + //mouse enter + node.mouseOver = true; + this.node_over = node; + this.dirty_canvas = true; + + if (node.onMouseEnter) { + node.onMouseEnter(e); + } + } + + //in case the node wants to do something + if (node.onMouseMove) { + node.onMouseMove( e, [e.canvasX - node.pos[0], e.canvasY - node.pos[1]], this ); + } + + //if dragging a link + if (this.connecting_node) { + + if (this.connecting_output){ + + var pos = this._highlight_input || [0, 0]; //to store the output of isOverNodeInput + + //on top of input + if (this.isOverNodeBox(node, e.canvasX, e.canvasY)) { + //mouse on top of the corner box, don't know what to do + } else { + //check if I have a slot below de mouse + var slot = this.isOverNodeInput( node, e.canvasX, e.canvasY, pos ); + if (slot != -1 && node.inputs[slot]) { + var slot_type = node.inputs[slot].type; + if ( LiteGraph.isValidConnection( this.connecting_output.type, slot_type ) ) { + this._highlight_input = pos; + this._highlight_input_slot = node.inputs[slot]; // XXX CHECK THIS + } + } else { + this._highlight_input = null; + this._highlight_input_slot = null; // XXX CHECK THIS + } + } + + }else if(this.connecting_input){ + + var pos = this._highlight_output || [0, 0]; //to store the output of isOverNodeOutput + + //on top of output + if (this.isOverNodeBox(node, e.canvasX, e.canvasY)) { + //mouse on top of the corner box, don't know what to do + } else { + //check if I have a slot below de mouse + var slot = this.isOverNodeOutput( node, e.canvasX, e.canvasY, pos ); + if (slot != -1 && node.outputs[slot]) { + var slot_type = node.outputs[slot].type; + if ( LiteGraph.isValidConnection( this.connecting_input.type, slot_type ) ) { + this._highlight_output = pos; + } + } else { + this._highlight_output = null; + } + } + } + } + + //Search for corner + if (this.canvas) { + if (node.inResizeCorner(e.canvasX, e.canvasY)) { + this.canvas.style.cursor = "se-resize"; + } else { + this.canvas.style.cursor = "crosshair"; + } + } + } else { //not over a node + + //search for link connector + var over_link = null; + for (var i = 0; i < this.visible_links.length; ++i) { + var link = this.visible_links[i]; + var center = link._pos; + if ( + !center || + e.canvasX < center[0] - 4 || + e.canvasX > center[0] + 4 || + e.canvasY < center[1] - 4 || + e.canvasY > center[1] + 4 + ) { + continue; + } + over_link = link; + break; + } + if( over_link != this.over_link_center ) + { + this.over_link_center = over_link; + this.dirty_canvas = true; + } + + if (this.canvas) { + this.canvas.style.cursor = ""; + } + } //end + + //send event to node if capturing input (used with widgets that allow drag outside of the area of the node) + if ( this.node_capturing_input && this.node_capturing_input != node && this.node_capturing_input.onMouseMove ) { + this.node_capturing_input.onMouseMove(e,[e.canvasX - this.node_capturing_input.pos[0],e.canvasY - this.node_capturing_input.pos[1]], this); + } + + //node being dragged + if (this.node_dragged && !this.live_mode) { + //console.log("draggin!",this.selected_nodes); + for (var i in this.selected_nodes) { + var n = this.selected_nodes[i]; + n.pos[0] += delta[0] / this.ds.scale; + n.pos[1] += delta[1] / this.ds.scale; + if (!n.is_selected) this.processNodeSelected(n, e); /* + * Don't call the function if the block is already selected. + * Otherwise, it could cause the block to be unselected while dragging. + */ + } + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + } + + if (this.resizing_node && !this.live_mode) { + //convert mouse to node space + var desired_size = [ e.canvasX - this.resizing_node.pos[0], e.canvasY - this.resizing_node.pos[1] ]; + var min_size = this.resizing_node.computeSize(); + desired_size[0] = Math.max( min_size[0], desired_size[0] ); + desired_size[1] = Math.max( min_size[1], desired_size[1] ); + this.resizing_node.setSize( desired_size ); + + this.canvas.style.cursor = "se-resize"; + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + } + } + + e.preventDefault(); + return false; + }; + + /** + * Called when a mouse up event has to be processed + * @method processMouseUp + **/ + LGraphCanvas.prototype.processMouseUp = function(e) { + + var is_primary = ( e.isPrimary === undefined || e.isPrimary ); + + //early exit for extra pointer + if(!is_primary){ + /*e.stopPropagation(); + e.preventDefault();*/ + //console.log("pointerevents: processMouseUp pointerN_stop "+e.pointerId+" "+e.isPrimary); + return false; + } + + //console.log("pointerevents: processMouseUp "+e.pointerId+" "+e.isPrimary+" :: "+e.clientX+" "+e.clientY); + + if( this.set_canvas_dirty_on_mouse_event ) + this.dirty_canvas = true; + + if (!this.graph) + return; + + var window = this.getCanvasWindow(); + var document = window.document; + LGraphCanvas.active_canvas = this; + + //restore the mousemove event back to the canvas + if(!this.options.skip_events) + { + //console.log("pointerevents: processMouseUp adjustEventListener"); + LiteGraph.pointerListenerRemove(document,"move", this._mousemove_callback,true); + LiteGraph.pointerListenerAdd(this.canvas,"move", this._mousemove_callback,true); + LiteGraph.pointerListenerRemove(document,"up", this._mouseup_callback,true); + } + + this.adjustMouseEvent(e); + var now = LiteGraph.getTime(); + e.click_time = now - this.last_mouseclick; + this.last_mouse_dragging = false; + this.last_click_position = null; + + if(this.block_click) + { + //console.log("pointerevents: processMouseUp block_clicks"); + this.block_click = false; //used to avoid sending twice a click in a immediate button + } + + //console.log("pointerevents: processMouseUp which: "+e.which); + + if (e.which == 1) { + + if( this.node_widget ) + { + this.processNodeWidgets( this.node_widget[0], this.graph_mouse, e ); + } + + //left button + this.node_widget = null; + + if (this.selected_group) { + var diffx = + this.selected_group.pos[0] - + Math.round(this.selected_group.pos[0]); + var diffy = + this.selected_group.pos[1] - + Math.round(this.selected_group.pos[1]); + this.selected_group.move(diffx, diffy, e.ctrlKey); + this.selected_group.pos[0] = Math.round( + this.selected_group.pos[0] + ); + this.selected_group.pos[1] = Math.round( + this.selected_group.pos[1] + ); + if (this.selected_group._nodes.length) { + this.dirty_canvas = true; + } + this.selected_group = null; + } + this.selected_group_resizing = false; + + var node = this.graph.getNodeOnPos( + e.canvasX, + e.canvasY, + this.visible_nodes + ); + + if (this.dragging_rectangle) { + if (this.graph) { + var nodes = this.graph._nodes; + var node_bounding = new Float32Array(4); + + //compute bounding and flip if left to right + var w = Math.abs(this.dragging_rectangle[2]); + var h = Math.abs(this.dragging_rectangle[3]); + var startx = + this.dragging_rectangle[2] < 0 + ? this.dragging_rectangle[0] - w + : this.dragging_rectangle[0]; + var starty = + this.dragging_rectangle[3] < 0 + ? this.dragging_rectangle[1] - h + : this.dragging_rectangle[1]; + this.dragging_rectangle[0] = startx; + this.dragging_rectangle[1] = starty; + this.dragging_rectangle[2] = w; + this.dragging_rectangle[3] = h; + + // test dragging rect size, if minimun simulate a click + if (!node || (w > 10 && h > 10 )){ + //test against all nodes (not visible because the rectangle maybe start outside + var to_select = []; + for (var i = 0; i < nodes.length; ++i) { + var nodeX = nodes[i]; + nodeX.getBounding(node_bounding); + if ( + !overlapBounding( + this.dragging_rectangle, + node_bounding + ) + ) { + continue; + } //out of the visible area + to_select.push(nodeX); + } + if (to_select.length) { + this.selectNodes(to_select,e.shiftKey); // add to selection with shift + } + }else{ + // will select of update selection + this.selectNodes([node],e.shiftKey||e.ctrlKey); // add to selection add to selection with ctrlKey or shiftKey + } + + } + this.dragging_rectangle = null; + } else if (this.connecting_node) { + //dragging a connection + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + + var connInOrOut = this.connecting_output || this.connecting_input; + var connType = connInOrOut.type; + + //node below mouse + if (node) { + + /* no need to condition on event type.. just another type + if ( + connType == LiteGraph.EVENT && + this.isOverNodeBox(node, e.canvasX, e.canvasY) + ) { + + this.connecting_node.connect( + this.connecting_slot, + node, + LiteGraph.EVENT + ); + + } else {*/ + + //slot below mouse? connect + + if (this.connecting_output){ + + var slot = this.isOverNodeInput( + node, + e.canvasX, + e.canvasY + ); + if (slot != -1) { + this.connecting_node.connect(this.connecting_slot, node, slot); + } else { + //not on top of an input + // look for a good slot + this.connecting_node.connectByType(this.connecting_slot,node,connType); + } + + }else if (this.connecting_input){ + + var slot = this.isOverNodeOutput( + node, + e.canvasX, + e.canvasY + ); + + if (slot != -1) { + node.connect(slot, this.connecting_node, this.connecting_slot); // this is inverted has output-input nature like + } else { + //not on top of an input + // look for a good slot + this.connecting_node.connectByTypeOutput(this.connecting_slot,node,connType); + } + + } + + + //} + + }else{ + + // add menu when releasing link in empty space + if (LiteGraph.release_link_on_empty_shows_menu){ + if (e.shiftKey && this.allow_searchbox){ + if(this.connecting_output){ + this.showSearchBox(e,{node_from: this.connecting_node, slot_from: this.connecting_output, type_filter_in: this.connecting_output.type}); + }else if(this.connecting_input){ + this.showSearchBox(e,{node_to: this.connecting_node, slot_from: this.connecting_input, type_filter_out: this.connecting_input.type}); + } + }else{ + if(this.connecting_output){ + this.showConnectionMenu({nodeFrom: this.connecting_node, slotFrom: this.connecting_output, e: e}); + }else if(this.connecting_input){ + this.showConnectionMenu({nodeTo: this.connecting_node, slotTo: this.connecting_input, e: e}); + } + } + } + } + + this.connecting_output = null; + this.connecting_input = null; + this.connecting_pos = null; + this.connecting_node = null; + this.connecting_slot = -1; + } //not dragging connection + else if (this.resizing_node) { + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + this.graph.afterChange(this.resizing_node); + this.resizing_node = null; + } else if (this.node_dragged) { + //node being dragged? + var node = this.node_dragged; + if ( + node && + e.click_time < 300 && + isInsideRectangle( e.canvasX, e.canvasY, node.pos[0], node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT, LiteGraph.NODE_TITLE_HEIGHT, LiteGraph.NODE_TITLE_HEIGHT ) + ) { + node.collapse(); + } + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + this.node_dragged.pos[0] = Math.round(this.node_dragged.pos[0]); + this.node_dragged.pos[1] = Math.round(this.node_dragged.pos[1]); + if (this.graph.config.align_to_grid || this.align_to_grid ) { + this.node_dragged.alignToGrid(); + } + if( this.onNodeMoved ) + this.onNodeMoved( this.node_dragged ); + this.graph.afterChange(this.node_dragged); + this.node_dragged = null; + } //no node being dragged + else { + //get node over + var node = this.graph.getNodeOnPos( + e.canvasX, + e.canvasY, + this.visible_nodes + ); + + if (!node && e.click_time < 300) { + this.deselectAllNodes(); + } + + this.dirty_canvas = true; + this.dragging_canvas = false; + + if (this.node_over && this.node_over.onMouseUp) { + this.node_over.onMouseUp( e, [ e.canvasX - this.node_over.pos[0], e.canvasY - this.node_over.pos[1] ], this ); + } + if ( + this.node_capturing_input && + this.node_capturing_input.onMouseUp + ) { + this.node_capturing_input.onMouseUp(e, [ + e.canvasX - this.node_capturing_input.pos[0], + e.canvasY - this.node_capturing_input.pos[1] + ]); + } + } + } else if (e.which == 2) { + //middle button + //trace("middle"); + this.dirty_canvas = true; + this.dragging_canvas = false; + } else if (e.which == 3) { + //right button + //trace("right"); + this.dirty_canvas = true; + this.dragging_canvas = false; + } + + /* + if((this.dirty_canvas || this.dirty_bgcanvas) && this.rendering_timer_id == null) + this.draw(); + */ + + if (is_primary) + { + this.pointer_is_down = false; + this.pointer_is_double = false; + } + + this.graph.change(); + + //console.log("pointerevents: processMouseUp stopPropagation"); + e.stopPropagation(); + e.preventDefault(); + return false; + }; + + /** + * Called when a mouse wheel event has to be processed + * @method processMouseWheel + **/ + LGraphCanvas.prototype.processMouseWheel = function(e) { + if (!this.graph || !this.allow_dragcanvas) { + return; + } + + var delta = e.wheelDeltaY != null ? e.wheelDeltaY : e.detail * -60; + + this.adjustMouseEvent(e); + + var x = e.clientX; + var y = e.clientY; + var is_inside = !this.viewport || ( this.viewport && x >= this.viewport[0] && x < (this.viewport[0] + this.viewport[2]) && y >= this.viewport[1] && y < (this.viewport[1] + this.viewport[3]) ); + if(!is_inside) + return; + + var scale = this.ds.scale; + + if (delta > 0) { + scale *= 1.1; + } else if (delta < 0) { + scale *= 1 / 1.1; + } + + //this.setZoom( scale, [ e.clientX, e.clientY ] ); + this.ds.changeScale(scale, [e.clientX, e.clientY]); + + this.graph.change(); + + e.preventDefault(); + return false; // prevent default + }; + + /** + * returns true if a position (in graph space) is on top of a node little corner box + * @method isOverNodeBox + **/ + LGraphCanvas.prototype.isOverNodeBox = function(node, canvasx, canvasy) { + var title_height = LiteGraph.NODE_TITLE_HEIGHT; + if ( + isInsideRectangle( + canvasx, + canvasy, + node.pos[0] + 2, + node.pos[1] + 2 - title_height, + title_height - 4, + title_height - 4 + ) + ) { + return true; + } + return false; + }; + + /** + * returns the INDEX if a position (in graph space) is on top of a node input slot + * @method isOverNodeInput + **/ + LGraphCanvas.prototype.isOverNodeInput = function( + node, + canvasx, + canvasy, + slot_pos + ) { + if (node.inputs) { + for (var i = 0, l = node.inputs.length; i < l; ++i) { + var input = node.inputs[i]; + var link_pos = node.getConnectionPos(true, i); + var is_inside = false; + if (node.horizontal) { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 5, + link_pos[1] - 10, + 10, + 20 + ); + } else { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 10, + link_pos[1] - 5, + 40, + 10 + ); + } + if (is_inside) { + if (slot_pos) { + slot_pos[0] = link_pos[0]; + slot_pos[1] = link_pos[1]; + } + return i; + } + } + } + return -1; + }; + + /** + * returns the INDEX if a position (in graph space) is on top of a node output slot + * @method isOverNodeOuput + **/ + LGraphCanvas.prototype.isOverNodeOutput = function( + node, + canvasx, + canvasy, + slot_pos + ) { + if (node.outputs) { + for (var i = 0, l = node.outputs.length; i < l; ++i) { + var output = node.outputs[i]; + var link_pos = node.getConnectionPos(false, i); + var is_inside = false; + if (node.horizontal) { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 5, + link_pos[1] - 10, + 10, + 20 + ); + } else { + is_inside = isInsideRectangle( + canvasx, + canvasy, + link_pos[0] - 10, + link_pos[1] - 5, + 40, + 10 + ); + } + if (is_inside) { + if (slot_pos) { + slot_pos[0] = link_pos[0]; + slot_pos[1] = link_pos[1]; + } + return i; + } + } + } + return -1; + }; + + /** + * process a key event + * @method processKey + **/ + LGraphCanvas.prototype.processKey = function(e) { + if (!this.graph) { + return; + } + + var block_default = false; + //console.log(e); //debug + + if (e.target.localName == "input") { + return; + } + + if (e.type == "keydown") { + if (e.keyCode == 32) { + //space + this.dragging_canvas = true; + block_default = true; + } + + if (e.keyCode == 27) { + //esc + if(this.node_panel) this.node_panel.close(); + if(this.options_panel) this.options_panel.close(); + block_default = true; + } + + //select all Control A + if (e.keyCode == 65 && e.ctrlKey) { + this.selectNodes(); + block_default = true; + } + + if ((e.keyCode === 67) && (e.metaKey || e.ctrlKey) && !e.shiftKey) { + //copy + if (this.selected_nodes) { + this.copyToClipboard(); + block_default = true; + } + } + + if ((e.keyCode === 86) && (e.metaKey || e.ctrlKey)) { + //paste + this.pasteFromClipboard(e.shiftKey); + } + + //delete or backspace + if (e.keyCode == 46 || e.keyCode == 8) { + if ( + e.target.localName != "input" && + e.target.localName != "textarea" + ) { + this.deleteSelectedNodes(); + block_default = true; + } + } + + //collapse + //... + + //TODO + if (this.selected_nodes) { + for (var i in this.selected_nodes) { + if (this.selected_nodes[i].onKeyDown) { + this.selected_nodes[i].onKeyDown(e); + } + } + } + } else if (e.type == "keyup") { + if (e.keyCode == 32) { + // space + this.dragging_canvas = false; + } + + if (this.selected_nodes) { + for (var i in this.selected_nodes) { + if (this.selected_nodes[i].onKeyUp) { + this.selected_nodes[i].onKeyUp(e); + } + } + } + } + + this.graph.change(); + + if (block_default) { + e.preventDefault(); + e.stopImmediatePropagation(); + return false; + } + }; + + LGraphCanvas.prototype.copyToClipboard = function() { + var clipboard_info = { + nodes: [], + links: [] + }; + var index = 0; + var selected_nodes_array = []; + for (var i in this.selected_nodes) { + var node = this.selected_nodes[i]; + if (node.clonable === false) + continue; + node._relative_id = index; + selected_nodes_array.push(node); + index += 1; + } + + for (var i = 0; i < selected_nodes_array.length; ++i) { + var node = selected_nodes_array[i]; + var cloned = node.clone(); + if(!cloned) + { + console.warn("node type not found: " + node.type ); + continue; + } + clipboard_info.nodes.push(cloned.serialize()); + if (node.inputs && node.inputs.length) { + for (var j = 0; j < node.inputs.length; ++j) { + var input = node.inputs[j]; + if (!input || input.link == null) { + continue; + } + var link_info = this.graph.links[input.link]; + if (!link_info) { + continue; + } + var target_node = this.graph.getNodeById( + link_info.origin_id + ); + if (!target_node) { + continue; + } + clipboard_info.links.push([ + target_node._relative_id, + link_info.origin_slot, //j, + node._relative_id, + link_info.target_slot, + target_node.id + ]); + } + } + } + localStorage.setItem( + "litegrapheditor_clipboard", + JSON.stringify(clipboard_info) + ); + }; + + LGraphCanvas.prototype.pasteFromClipboard = function(isConnectUnselected = false) { + // if ctrl + shift + v is off, return when isConnectUnselected is true (shift is pressed) to maintain old behavior + if (!LiteGraph.ctrl_shift_v_paste_connect_unselected_outputs && isConnectUnselected) { + return; + } + var data = localStorage.getItem("litegrapheditor_clipboard"); + if (!data) { + return; + } + + this.graph.beforeChange(); + + //create nodes + var clipboard_info = JSON.parse(data); + // calculate top-left node, could work without this processing but using diff with last node pos :: clipboard_info.nodes[clipboard_info.nodes.length-1].pos + var posMin = false; + var posMinIndexes = false; + for (var i = 0; i < clipboard_info.nodes.length; ++i) { + if (posMin){ + if(posMin[0]>clipboard_info.nodes[i].pos[0]){ + posMin[0] = clipboard_info.nodes[i].pos[0]; + posMinIndexes[0] = i; + } + if(posMin[1]>clipboard_info.nodes[i].pos[1]){ + posMin[1] = clipboard_info.nodes[i].pos[1]; + posMinIndexes[1] = i; + } + } + else{ + posMin = [clipboard_info.nodes[i].pos[0], clipboard_info.nodes[i].pos[1]]; + posMinIndexes = [i, i]; + } + } + var nodes = []; + for (var i = 0; i < clipboard_info.nodes.length; ++i) { + var node_data = clipboard_info.nodes[i]; + var node = LiteGraph.createNode(node_data.type); + if (node) { + node.configure(node_data); + + //paste in last known mouse position + node.pos[0] += this.graph_mouse[0] - posMin[0]; //+= 5; + node.pos[1] += this.graph_mouse[1] - posMin[1]; //+= 5; + + this.graph.add(node,{doProcessChange:false}); + + nodes.push(node); + } + } + + //create links + for (var i = 0; i < clipboard_info.links.length; ++i) { + var link_info = clipboard_info.links[i]; + var origin_node; + var origin_node_relative_id = link_info[0]; + if (origin_node_relative_id != null) { + origin_node = nodes[origin_node_relative_id]; + } else if (LiteGraph.ctrl_shift_v_paste_connect_unselected_outputs && isConnectUnselected) { + var origin_node_id = link_info[4]; + if (origin_node_id) { + origin_node = this.graph.getNodeById(origin_node_id); + } + } + var target_node = nodes[link_info[2]]; + if( origin_node && target_node ) + origin_node.connect(link_info[1], target_node, link_info[3]); + else + console.warn("Warning, nodes missing on pasting"); + } + + this.selectNodes(nodes); + + this.graph.afterChange(); + }; + + /** + * process a item drop event on top the canvas + * @method processDrop + **/ + LGraphCanvas.prototype.processDrop = function(e) { + e.preventDefault(); + this.adjustMouseEvent(e); + var x = e.clientX; + var y = e.clientY; + var is_inside = !this.viewport || ( this.viewport && x >= this.viewport[0] && x < (this.viewport[0] + this.viewport[2]) && y >= this.viewport[1] && y < (this.viewport[1] + this.viewport[3]) ); + if(!is_inside){ + return; + // --- BREAK --- + } + + var pos = [e.canvasX, e.canvasY]; + + + var node = this.graph ? this.graph.getNodeOnPos(pos[0], pos[1]) : null; + + if (!node) { + var r = null; + if (this.onDropItem) { + r = this.onDropItem(event); + } + if (!r) { + this.checkDropItem(e); + } + return; + } + + if (node.onDropFile || node.onDropData) { + var files = e.dataTransfer.files; + if (files && files.length) { + for (var i = 0; i < files.length; i++) { + var file = e.dataTransfer.files[0]; + var filename = file.name; + var ext = LGraphCanvas.getFileExtension(filename); + //console.log(file); + + if (node.onDropFile) { + node.onDropFile(file); + } + + if (node.onDropData) { + //prepare reader + var reader = new FileReader(); + reader.onload = function(event) { + //console.log(event.target); + var data = event.target.result; + node.onDropData(data, filename, file); + }; + + //read data + var type = file.type.split("/")[0]; + if (type == "text" || type == "") { + reader.readAsText(file); + } else if (type == "image") { + reader.readAsDataURL(file); + } else { + reader.readAsArrayBuffer(file); + } + } + } + } + } + + if (node.onDropItem) { + if (node.onDropItem(event)) { + return true; + } + } + + if (this.onDropItem) { + return this.onDropItem(event); + } + + return false; + }; + + //called if the graph doesn't have a default drop item behaviour + LGraphCanvas.prototype.checkDropItem = function(e) { + if (e.dataTransfer.files.length) { + var file = e.dataTransfer.files[0]; + var ext = LGraphCanvas.getFileExtension(file.name).toLowerCase(); + var nodetype = LiteGraph.node_types_by_file_extension[ext]; + if (nodetype) { + this.graph.beforeChange(); + var node = LiteGraph.createNode(nodetype.type); + node.pos = [e.canvasX, e.canvasY]; + this.graph.add(node); + if (node.onDropFile) { + node.onDropFile(file); + } + this.graph.afterChange(); + } + } + }; + + LGraphCanvas.prototype.processNodeDblClicked = function(n) { + if (this.onShowNodePanel) { + this.onShowNodePanel(n); + } + + if (this.onNodeDblClicked) { + this.onNodeDblClicked(n); + } + + this.setDirty(true); + }; + + LGraphCanvas.prototype.processNodeSelected = function(node, e) { + this.selectNode(node, e && (e.shiftKey || e.ctrlKey || this.multi_select)); + if (this.onNodeSelected) { + this.onNodeSelected(node); + } + }; + + /** + * selects a given node (or adds it to the current selection) + * @method selectNode + **/ + LGraphCanvas.prototype.selectNode = function( + node, + add_to_current_selection + ) { + if (node == null) { + this.deselectAllNodes(); + } else { + this.selectNodes([node], add_to_current_selection); + } + }; + + /** + * selects several nodes (or adds them to the current selection) + * @method selectNodes + **/ + LGraphCanvas.prototype.selectNodes = function( nodes, add_to_current_selection ) + { + if (!add_to_current_selection) { + this.deselectAllNodes(); + } + + nodes = nodes || this.graph._nodes; + if (typeof nodes == "string") nodes = [nodes]; + for (var i in nodes) { + var node = nodes[i]; + if (node.is_selected) { + this.deselectNode(node); + continue; + } + + if (!node.is_selected && node.onSelected) { + node.onSelected(); + } + node.is_selected = true; + this.selected_nodes[node.id] = node; + + if (node.inputs) { + for (var j = 0; j < node.inputs.length; ++j) { + this.highlighted_links[node.inputs[j].link] = true; + } + } + if (node.outputs) { + for (var j = 0; j < node.outputs.length; ++j) { + var out = node.outputs[j]; + if (out.links) { + for (var k = 0; k < out.links.length; ++k) { + this.highlighted_links[out.links[k]] = true; + } + } + } + } + } + + if( this.onSelectionChange ) + this.onSelectionChange( this.selected_nodes ); + + this.setDirty(true); + }; + + /** + * removes a node from the current selection + * @method deselectNode + **/ + LGraphCanvas.prototype.deselectNode = function(node) { + if (!node.is_selected) { + return; + } + if (node.onDeselected) { + node.onDeselected(); + } + node.is_selected = false; + + if (this.onNodeDeselected) { + this.onNodeDeselected(node); + } + + //remove highlighted + if (node.inputs) { + for (var i = 0; i < node.inputs.length; ++i) { + delete this.highlighted_links[node.inputs[i].link]; + } + } + if (node.outputs) { + for (var i = 0; i < node.outputs.length; ++i) { + var out = node.outputs[i]; + if (out.links) { + for (var j = 0; j < out.links.length; ++j) { + delete this.highlighted_links[out.links[j]]; + } + } + } + } + }; + + /** + * removes all nodes from the current selection + * @method deselectAllNodes + **/ + LGraphCanvas.prototype.deselectAllNodes = function() { + if (!this.graph) { + return; + } + var nodes = this.graph._nodes; + for (var i = 0, l = nodes.length; i < l; ++i) { + var node = nodes[i]; + if (!node.is_selected) { + continue; + } + if (node.onDeselected) { + node.onDeselected(); + } + node.is_selected = false; + if (this.onNodeDeselected) { + this.onNodeDeselected(node); + } + } + this.selected_nodes = {}; + this.current_node = null; + this.highlighted_links = {}; + if( this.onSelectionChange ) + this.onSelectionChange( this.selected_nodes ); + this.setDirty(true); + }; + + /** + * deletes all nodes in the current selection from the graph + * @method deleteSelectedNodes + **/ + LGraphCanvas.prototype.deleteSelectedNodes = function() { + + this.graph.beforeChange(); + + for (var i in this.selected_nodes) { + var node = this.selected_nodes[i]; + + if(node.block_delete) + continue; + + //autoconnect when possible (very basic, only takes into account first input-output) + if(node.inputs && node.inputs.length && node.outputs && node.outputs.length && LiteGraph.isValidConnection( node.inputs[0].type, node.outputs[0].type ) && node.inputs[0].link && node.outputs[0].links && node.outputs[0].links.length ) + { + var input_link = node.graph.links[ node.inputs[0].link ]; + var output_link = node.graph.links[ node.outputs[0].links[0] ]; + var input_node = node.getInputNode(0); + var output_node = node.getOutputNodes(0)[0]; + if(input_node && output_node) + input_node.connect( input_link.origin_slot, output_node, output_link.target_slot ); + } + this.graph.remove(node); + if (this.onNodeDeselected) { + this.onNodeDeselected(node); + } + } + this.selected_nodes = {}; + this.current_node = null; + this.highlighted_links = {}; + this.setDirty(true); + this.graph.afterChange(); + }; + + /** + * centers the camera on a given node + * @method centerOnNode + **/ + LGraphCanvas.prototype.centerOnNode = function(node) { + this.ds.offset[0] = + -node.pos[0] - + node.size[0] * 0.5 + + (this.canvas.width * 0.5) / this.ds.scale; + this.ds.offset[1] = + -node.pos[1] - + node.size[1] * 0.5 + + (this.canvas.height * 0.5) / this.ds.scale; + this.setDirty(true, true); + }; + + /** + * adds some useful properties to a mouse event, like the position in graph coordinates + * @method adjustMouseEvent + **/ + LGraphCanvas.prototype.adjustMouseEvent = function(e) { + var clientX_rel = 0; + var clientY_rel = 0; + + if (this.canvas) { + var b = this.canvas.getBoundingClientRect(); + clientX_rel = e.clientX - b.left; + clientY_rel = e.clientY - b.top; + } else { + clientX_rel = e.clientX; + clientY_rel = e.clientY; + } + + e.deltaX = clientX_rel - this.last_mouse_position[0]; + e.deltaY = clientY_rel- this.last_mouse_position[1]; + + this.last_mouse_position[0] = clientX_rel; + this.last_mouse_position[1] = clientY_rel; + + e.canvasX = clientX_rel / this.ds.scale - this.ds.offset[0]; + e.canvasY = clientY_rel / this.ds.scale - this.ds.offset[1]; + + //console.log("pointerevents: adjustMouseEvent "+e.clientX+":"+e.clientY+" "+clientX_rel+":"+clientY_rel+" "+e.canvasX+":"+e.canvasY); + }; + + /** + * changes the zoom level of the graph (default is 1), you can pass also a place used to pivot the zoom + * @method setZoom + **/ + LGraphCanvas.prototype.setZoom = function(value, zooming_center) { + this.ds.changeScale(value, zooming_center); + /* + if(!zooming_center && this.canvas) + zooming_center = [this.canvas.width * 0.5,this.canvas.height * 0.5]; + + var center = this.convertOffsetToCanvas( zooming_center ); + + this.ds.scale = value; + + if(this.scale > this.max_zoom) + this.scale = this.max_zoom; + else if(this.scale < this.min_zoom) + this.scale = this.min_zoom; + + var new_center = this.convertOffsetToCanvas( zooming_center ); + var delta_offset = [new_center[0] - center[0], new_center[1] - center[1]]; + + this.offset[0] += delta_offset[0]; + this.offset[1] += delta_offset[1]; + */ + + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + }; + + /** + * converts a coordinate from graph coordinates to canvas2D coordinates + * @method convertOffsetToCanvas + **/ + LGraphCanvas.prototype.convertOffsetToCanvas = function(pos, out) { + return this.ds.convertOffsetToCanvas(pos, out); + }; + + /** + * converts a coordinate from Canvas2D coordinates to graph space + * @method convertCanvasToOffset + **/ + LGraphCanvas.prototype.convertCanvasToOffset = function(pos, out) { + return this.ds.convertCanvasToOffset(pos, out); + }; + + //converts event coordinates from canvas2D to graph coordinates + LGraphCanvas.prototype.convertEventToCanvasOffset = function(e) { + var rect = this.canvas.getBoundingClientRect(); + return this.convertCanvasToOffset([ + e.clientX - rect.left, + e.clientY - rect.top + ]); + }; + + /** + * brings a node to front (above all other nodes) + * @method bringToFront + **/ + LGraphCanvas.prototype.bringToFront = function(node) { + var i = this.graph._nodes.indexOf(node); + if (i == -1) { + return; + } + + this.graph._nodes.splice(i, 1); + this.graph._nodes.push(node); + }; + + /** + * sends a node to the back (below all other nodes) + * @method sendToBack + **/ + LGraphCanvas.prototype.sendToBack = function(node) { + var i = this.graph._nodes.indexOf(node); + if (i == -1) { + return; + } + + this.graph._nodes.splice(i, 1); + this.graph._nodes.unshift(node); + }; + + /* Interaction */ + + /* LGraphCanvas render */ + var temp = new Float32Array(4); + + /** + * checks which nodes are visible (inside the camera area) + * @method computeVisibleNodes + **/ + LGraphCanvas.prototype.computeVisibleNodes = function(nodes, out) { + var visible_nodes = out || []; + visible_nodes.length = 0; + nodes = nodes || this.graph._nodes; + for (var i = 0, l = nodes.length; i < l; ++i) { + var n = nodes[i]; + + //skip rendering nodes in live mode + if (this.live_mode && !n.onDrawBackground && !n.onDrawForeground) { + continue; + } + + if (!overlapBounding(this.visible_area, n.getBounding(temp))) { + continue; + } //out of the visible area + + visible_nodes.push(n); + } + return visible_nodes; + }; + + /** + * renders the whole canvas content, by rendering in two separated canvas, one containing the background grid and the connections, and one containing the nodes) + * @method draw + **/ + LGraphCanvas.prototype.draw = function(force_canvas, force_bgcanvas) { + if (!this.canvas || this.canvas.width == 0 || this.canvas.height == 0) { + return; + } + + //fps counting + var now = LiteGraph.getTime(); + this.render_time = (now - this.last_draw_time) * 0.001; + this.last_draw_time = now; + + if (this.graph) { + this.ds.computeVisibleArea(this.viewport); + } + + if ( + this.dirty_bgcanvas || + force_bgcanvas || + this.always_render_background || + (this.graph && + this.graph._last_trigger_time && + now - this.graph._last_trigger_time < 1000) + ) { + this.drawBackCanvas(); + } + + if (this.dirty_canvas || force_canvas) { + this.drawFrontCanvas(); + } + + this.fps = this.render_time ? 1.0 / this.render_time : 0; + this.frame += 1; + }; + + /** + * draws the front canvas (the one containing all the nodes) + * @method drawFrontCanvas + **/ + LGraphCanvas.prototype.drawFrontCanvas = function() { + this.dirty_canvas = false; + + if (!this.ctx) { + this.ctx = this.bgcanvas.getContext("2d"); + } + var ctx = this.ctx; + if (!ctx) { + //maybe is using webgl... + return; + } + + var canvas = this.canvas; + if ( ctx.start2D && !this.viewport ) { + ctx.start2D(); + ctx.restore(); + ctx.setTransform(1, 0, 0, 1, 0, 0); + } + + //clip dirty area if there is one, otherwise work in full canvas + var area = this.viewport || this.dirty_area; + if (area) { + ctx.save(); + ctx.beginPath(); + ctx.rect( area[0],area[1],area[2],area[3] ); + ctx.clip(); + } + + //clear + //canvas.width = canvas.width; + if (this.clear_background) { + if(area) + ctx.clearRect( area[0],area[1],area[2],area[3] ); + else + ctx.clearRect(0, 0, canvas.width, canvas.height); + } + + //draw bg canvas + if (this.bgcanvas == this.canvas) { + this.drawBackCanvas(); + } else { + ctx.drawImage( this.bgcanvas, 0, 0 ); + } + + //rendering + if (this.onRender) { + this.onRender(canvas, ctx); + } + + //info widget + if (this.show_info) { + this.renderInfo(ctx, area ? area[0] : 0, area ? area[1] : 0 ); + } + + if (this.graph) { + //apply transformations + ctx.save(); + this.ds.toCanvasContext(ctx); + + //draw nodes + var drawn_nodes = 0; + var visible_nodes = this.computeVisibleNodes( + null, + this.visible_nodes + ); + + for (var i = 0; i < visible_nodes.length; ++i) { + var node = visible_nodes[i]; + + //transform coords system + ctx.save(); + ctx.translate(node.pos[0], node.pos[1]); + + //Draw + this.drawNode(node, ctx); + drawn_nodes += 1; + + //Restore + ctx.restore(); + } + + //on top (debug) + if (this.render_execution_order) { + this.drawExecutionOrder(ctx); + } + + //connections ontop? + if (this.graph.config.links_ontop) { + if (!this.live_mode) { + this.drawConnections(ctx); + } + } + + //current connection (the one being dragged by the mouse) + if (this.connecting_pos != null) { + ctx.lineWidth = this.connections_width; + var link_color = null; + + var connInOrOut = this.connecting_output || this.connecting_input; + + var connType = connInOrOut.type; + var connDir = connInOrOut.dir; + if(connDir == null) + { + if (this.connecting_output) + connDir = this.connecting_node.horizontal ? LiteGraph.DOWN : LiteGraph.RIGHT; + else + connDir = this.connecting_node.horizontal ? LiteGraph.UP : LiteGraph.LEFT; + } + var connShape = connInOrOut.shape; + + switch (connType) { + case LiteGraph.EVENT: + link_color = LiteGraph.EVENT_LINK_COLOR; + break; + default: + link_color = LiteGraph.CONNECTING_LINK_COLOR; + } + + //the connection being dragged by the mouse + this.renderLink( + ctx, + this.connecting_pos, + [this.graph_mouse[0], this.graph_mouse[1]], + null, + false, + null, + link_color, + connDir, + LiteGraph.CENTER + ); + + ctx.beginPath(); + if ( + connType === LiteGraph.EVENT || + connShape === LiteGraph.BOX_SHAPE + ) { + ctx.rect( + this.connecting_pos[0] - 6 + 0.5, + this.connecting_pos[1] - 5 + 0.5, + 14, + 10 + ); + ctx.fill(); + ctx.beginPath(); + ctx.rect( + this.graph_mouse[0] - 6 + 0.5, + this.graph_mouse[1] - 5 + 0.5, + 14, + 10 + ); + } else if (connShape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(this.connecting_pos[0] + 8, this.connecting_pos[1] + 0.5); + ctx.lineTo(this.connecting_pos[0] - 4, this.connecting_pos[1] + 6 + 0.5); + ctx.lineTo(this.connecting_pos[0] - 4, this.connecting_pos[1] - 6 + 0.5); + ctx.closePath(); + } + else { + ctx.arc( + this.connecting_pos[0], + this.connecting_pos[1], + 4, + 0, + Math.PI * 2 + ); + ctx.fill(); + ctx.beginPath(); + ctx.arc( + this.graph_mouse[0], + this.graph_mouse[1], + 4, + 0, + Math.PI * 2 + ); + } + ctx.fill(); + + ctx.fillStyle = "#ffcc00"; + if (this._highlight_input) { + ctx.beginPath(); + var shape = this._highlight_input_slot.shape; + if (shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(this._highlight_input[0] + 8, this._highlight_input[1] + 0.5); + ctx.lineTo(this._highlight_input[0] - 4, this._highlight_input[1] + 6 + 0.5); + ctx.lineTo(this._highlight_input[0] - 4, this._highlight_input[1] - 6 + 0.5); + ctx.closePath(); + } else { + ctx.arc( + this._highlight_input[0], + this._highlight_input[1], + 6, + 0, + Math.PI * 2 + ); + } + ctx.fill(); + } + if (this._highlight_output) { + ctx.beginPath(); + if (shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(this._highlight_output[0] + 8, this._highlight_output[1] + 0.5); + ctx.lineTo(this._highlight_output[0] - 4, this._highlight_output[1] + 6 + 0.5); + ctx.lineTo(this._highlight_output[0] - 4, this._highlight_output[1] - 6 + 0.5); + ctx.closePath(); + } else { + ctx.arc( + this._highlight_output[0], + this._highlight_output[1], + 6, + 0, + Math.PI * 2 + ); + } + ctx.fill(); + } + } + + //the selection rectangle + if (this.dragging_rectangle) { + ctx.strokeStyle = "#FFF"; + ctx.strokeRect( + this.dragging_rectangle[0], + this.dragging_rectangle[1], + this.dragging_rectangle[2], + this.dragging_rectangle[3] + ); + } + + //on top of link center + if(this.over_link_center && this.render_link_tooltip) + this.drawLinkTooltip( ctx, this.over_link_center ); + else + if(this.onDrawLinkTooltip) //to remove + this.onDrawLinkTooltip(ctx,null); + + //custom info + if (this.onDrawForeground) { + this.onDrawForeground(ctx, this.visible_rect); + } + + ctx.restore(); + } + + //draws panel in the corner + if (this._graph_stack && this._graph_stack.length) { + this.drawSubgraphPanel( ctx ); + } + + + if (this.onDrawOverlay) { + this.onDrawOverlay(ctx); + } + + if (area){ + ctx.restore(); + } + + if (ctx.finish2D) { + //this is a function I use in webgl renderer + ctx.finish2D(); + } + }; + + /** + * draws the panel in the corner that shows subgraph properties + * @method drawSubgraphPanel + **/ + LGraphCanvas.prototype.drawSubgraphPanel = function (ctx) { + var subgraph = this.graph; + var subnode = subgraph._subgraph_node; + if (!subnode) { + console.warn("subgraph without subnode"); + return; + } + this.drawSubgraphPanelLeft(subgraph, subnode, ctx) + this.drawSubgraphPanelRight(subgraph, subnode, ctx) + } + + LGraphCanvas.prototype.drawSubgraphPanelLeft = function (subgraph, subnode, ctx) { + var num = subnode.inputs ? subnode.inputs.length : 0; + var w = 200; + var h = Math.floor(LiteGraph.NODE_SLOT_HEIGHT * 1.6); + + ctx.fillStyle = "#111"; + ctx.globalAlpha = 0.8; + ctx.beginPath(); + ctx.roundRect(10, 10, w, (num + 1) * h + 50, [8]); + ctx.fill(); + ctx.globalAlpha = 1; + + ctx.fillStyle = "#888"; + ctx.font = "14px Arial"; + ctx.textAlign = "left"; + ctx.fillText("Graph Inputs", 20, 34); + // var pos = this.mouse; + + if (this.drawButton(w - 20, 20, 20, 20, "X", "#151515")) { + this.closeSubgraph(); + return; + } + + var y = 50; + ctx.font = "14px Arial"; + if (subnode.inputs) + for (var i = 0; i < subnode.inputs.length; ++i) { + var input = subnode.inputs[i]; + if (input.not_subgraph_input) + continue; + + //input button clicked + if (this.drawButton(20, y + 2, w - 20, h - 2)) { + var type = subnode.constructor.input_node_type || "graph/input"; + this.graph.beforeChange(); + var newnode = LiteGraph.createNode(type); + if (newnode) { + subgraph.add(newnode); + this.block_click = false; + this.last_click_position = null; + this.selectNodes([newnode]); + this.node_dragged = newnode; + this.dragging_canvas = false; + newnode.setProperty("name", input.name); + newnode.setProperty("type", input.type); + this.node_dragged.pos[0] = this.graph_mouse[0] - 5; + this.node_dragged.pos[1] = this.graph_mouse[1] - 5; + this.graph.afterChange(); + } + else + console.error("graph input node not found:", type); + } + ctx.fillStyle = "#9C9"; + ctx.beginPath(); + ctx.arc(w - 16, y + h * 0.5, 5, 0, 2 * Math.PI); + ctx.fill(); + ctx.fillStyle = "#AAA"; + ctx.fillText(input.name, 30, y + h * 0.75); + // var tw = ctx.measureText(input.name); + ctx.fillStyle = "#777"; + ctx.fillText(input.type, 130, y + h * 0.75); + y += h; + } + //add + button + if (this.drawButton(20, y + 2, w - 20, h - 2, "+", "#151515", "#222")) { + this.showSubgraphPropertiesDialog(subnode); + } + } + LGraphCanvas.prototype.drawSubgraphPanelRight = function (subgraph, subnode, ctx) { + var num = subnode.outputs ? subnode.outputs.length : 0; + var canvas_w = this.bgcanvas.width + var w = 200; + var h = Math.floor(LiteGraph.NODE_SLOT_HEIGHT * 1.6); + + ctx.fillStyle = "#111"; + ctx.globalAlpha = 0.8; + ctx.beginPath(); + ctx.roundRect(canvas_w - w - 10, 10, w, (num + 1) * h + 50, [8]); + ctx.fill(); + ctx.globalAlpha = 1; + + ctx.fillStyle = "#888"; + ctx.font = "14px Arial"; + ctx.textAlign = "left"; + var title_text = "Graph Outputs" + var tw = ctx.measureText(title_text).width + ctx.fillText(title_text, (canvas_w - tw) - 20, 34); + // var pos = this.mouse; + if (this.drawButton(canvas_w - w, 20, 20, 20, "X", "#151515")) { + this.closeSubgraph(); + return; + } + + var y = 50; + ctx.font = "14px Arial"; + if (subnode.outputs) + for (var i = 0; i < subnode.outputs.length; ++i) { + var output = subnode.outputs[i]; + if (output.not_subgraph_input) + continue; + + //output button clicked + if (this.drawButton(canvas_w - w, y + 2, w - 20, h - 2)) { + var type = subnode.constructor.output_node_type || "graph/output"; + this.graph.beforeChange(); + var newnode = LiteGraph.createNode(type); + if (newnode) { + subgraph.add(newnode); + this.block_click = false; + this.last_click_position = null; + this.selectNodes([newnode]); + this.node_dragged = newnode; + this.dragging_canvas = false; + newnode.setProperty("name", output.name); + newnode.setProperty("type", output.type); + this.node_dragged.pos[0] = this.graph_mouse[0] - 5; + this.node_dragged.pos[1] = this.graph_mouse[1] - 5; + this.graph.afterChange(); + } + else + console.error("graph input node not found:", type); + } + ctx.fillStyle = "#9C9"; + ctx.beginPath(); + ctx.arc(canvas_w - w + 16, y + h * 0.5, 5, 0, 2 * Math.PI); + ctx.fill(); + ctx.fillStyle = "#AAA"; + ctx.fillText(output.name, canvas_w - w + 30, y + h * 0.75); + // var tw = ctx.measureText(input.name); + ctx.fillStyle = "#777"; + ctx.fillText(output.type, canvas_w - w + 130, y + h * 0.75); + y += h; + } + //add + button + if (this.drawButton(canvas_w - w, y + 2, w - 20, h - 2, "+", "#151515", "#222")) { + this.showSubgraphPropertiesDialogRight(subnode); + } + } + //Draws a button into the canvas overlay and computes if it was clicked using the immediate gui paradigm + LGraphCanvas.prototype.drawButton = function( x,y,w,h, text, bgcolor, hovercolor, textcolor ) + { + var ctx = this.ctx; + bgcolor = bgcolor || LiteGraph.NODE_DEFAULT_COLOR; + hovercolor = hovercolor || "#555"; + textcolor = textcolor || LiteGraph.NODE_TEXT_COLOR; + var pos = this.ds.convertOffsetToCanvas(this.graph_mouse); + var hover = LiteGraph.isInsideRectangle( pos[0], pos[1], x,y,w,h ); + pos = this.last_click_position ? [this.last_click_position[0], this.last_click_position[1]] : null; + if(pos) { + var rect = this.canvas.getBoundingClientRect(); + pos[0] -= rect.left; + pos[1] -= rect.top; + } + var clicked = pos && LiteGraph.isInsideRectangle( pos[0], pos[1], x,y,w,h ); + + ctx.fillStyle = hover ? hovercolor : bgcolor; + if(clicked) + ctx.fillStyle = "#AAA"; + ctx.beginPath(); + ctx.roundRect(x,y,w,h,[4] ); + ctx.fill(); + + if(text != null) + { + if(text.constructor == String) + { + ctx.fillStyle = textcolor; + ctx.textAlign = "center"; + ctx.font = ((h * 0.65)|0) + "px Arial"; + ctx.fillText( text, x + w * 0.5,y + h * 0.75 ); + ctx.textAlign = "left"; + } + } + + var was_clicked = clicked && !this.block_click; + if(clicked) + this.blockClick(); + return was_clicked; + } + + LGraphCanvas.prototype.isAreaClicked = function( x,y,w,h, hold_click ) + { + var pos = this.mouse; + var hover = LiteGraph.isInsideRectangle( pos[0], pos[1], x,y,w,h ); + pos = this.last_click_position; + var clicked = pos && LiteGraph.isInsideRectangle( pos[0], pos[1], x,y,w,h ); + var was_clicked = clicked && !this.block_click; + if(clicked && hold_click) + this.blockClick(); + return was_clicked; + } + + /** + * draws some useful stats in the corner of the canvas + * @method renderInfo + **/ + LGraphCanvas.prototype.renderInfo = function(ctx, x, y) { + x = x || 10; + y = y || this.canvas.offsetHeight - 80; + + ctx.save(); + ctx.translate(x, y); + + ctx.font = "10px Arial"; + ctx.fillStyle = "#888"; + ctx.textAlign = "left"; + if (this.graph) { + ctx.fillText( "T: " + this.graph.globaltime.toFixed(2) + "s", 5, 13 * 1 ); + ctx.fillText("I: " + this.graph.iteration, 5, 13 * 2 ); + ctx.fillText("N: " + this.graph._nodes.length + " [" + this.visible_nodes.length + "]", 5, 13 * 3 ); + ctx.fillText("V: " + this.graph._version, 5, 13 * 4); + ctx.fillText("FPS:" + this.fps.toFixed(2), 5, 13 * 5); + } else { + ctx.fillText("No graph selected", 5, 13 * 1); + } + ctx.restore(); + }; + + /** + * draws the back canvas (the one containing the background and the connections) + * @method drawBackCanvas + **/ + LGraphCanvas.prototype.drawBackCanvas = function() { + var canvas = this.bgcanvas; + if ( + canvas.width != this.canvas.width || + canvas.height != this.canvas.height + ) { + canvas.width = this.canvas.width; + canvas.height = this.canvas.height; + } + + if (!this.bgctx) { + this.bgctx = this.bgcanvas.getContext("2d"); + } + var ctx = this.bgctx; + if (ctx.start) { + ctx.start(); + } + + var viewport = this.viewport || [0,0,ctx.canvas.width,ctx.canvas.height]; + + //clear + if (this.clear_background) { + ctx.clearRect( viewport[0], viewport[1], viewport[2], viewport[3] ); + } + + //show subgraph stack header + if (this._graph_stack && this._graph_stack.length) { + ctx.save(); + var parent_graph = this._graph_stack[this._graph_stack.length - 1]; + var subgraph_node = this.graph._subgraph_node; + ctx.strokeStyle = subgraph_node.bgcolor; + ctx.lineWidth = 10; + ctx.strokeRect(1, 1, canvas.width - 2, canvas.height - 2); + ctx.lineWidth = 1; + ctx.font = "40px Arial"; + ctx.textAlign = "center"; + ctx.fillStyle = subgraph_node.bgcolor || "#AAA"; + var title = ""; + for (var i = 1; i < this._graph_stack.length; ++i) { + title += + this._graph_stack[i]._subgraph_node.getTitle() + " >> "; + } + ctx.fillText( + title + subgraph_node.getTitle(), + canvas.width * 0.5, + 40 + ); + ctx.restore(); + } + + var bg_already_painted = false; + if (this.onRenderBackground) { + bg_already_painted = this.onRenderBackground(canvas, ctx); + } + + //reset in case of error + if ( !this.viewport ) + { + ctx.restore(); + ctx.setTransform(1, 0, 0, 1, 0, 0); + } + this.visible_links.length = 0; + + if (this.graph) { + //apply transformations + ctx.save(); + this.ds.toCanvasContext(ctx); + + //render BG + if ( this.ds.scale < 1.5 && !bg_already_painted && this.clear_background_color ) + { + ctx.fillStyle = this.clear_background_color; + ctx.fillRect( + this.visible_area[0], + this.visible_area[1], + this.visible_area[2], + this.visible_area[3] + ); + } + + if ( + this.background_image && + this.ds.scale > 0.5 && + !bg_already_painted + ) { + if (this.zoom_modify_alpha) { + ctx.globalAlpha = + (1.0 - 0.5 / this.ds.scale) * this.editor_alpha; + } else { + ctx.globalAlpha = this.editor_alpha; + } + ctx.imageSmoothingEnabled = ctx.imageSmoothingEnabled = false; // ctx.mozImageSmoothingEnabled = + if ( + !this._bg_img || + this._bg_img.name != this.background_image + ) { + this._bg_img = new Image(); + this._bg_img.name = this.background_image; + this._bg_img.src = this.background_image; + var that = this; + this._bg_img.onload = function() { + that.draw(true, true); + }; + } + + var pattern = null; + if (this._pattern == null && this._bg_img.width > 0) { + pattern = ctx.createPattern(this._bg_img, "repeat"); + this._pattern_img = this._bg_img; + this._pattern = pattern; + } else { + pattern = this._pattern; + } + if (pattern) { + ctx.fillStyle = pattern; + ctx.fillRect( + this.visible_area[0], + this.visible_area[1], + this.visible_area[2], + this.visible_area[3] + ); + ctx.fillStyle = "transparent"; + } + + ctx.globalAlpha = 1.0; + ctx.imageSmoothingEnabled = ctx.imageSmoothingEnabled = true; //= ctx.mozImageSmoothingEnabled + } + + //groups + if (this.graph._groups.length && !this.live_mode) { + this.drawGroups(canvas, ctx); + } + + if (this.onDrawBackground) { + this.onDrawBackground(ctx, this.visible_area); + } + if (this.onBackgroundRender) { + //LEGACY + console.error( + "WARNING! onBackgroundRender deprecated, now is named onDrawBackground " + ); + this.onBackgroundRender = null; + } + + //DEBUG: show clipping area + //ctx.fillStyle = "red"; + //ctx.fillRect( this.visible_area[0] + 10, this.visible_area[1] + 10, this.visible_area[2] - 20, this.visible_area[3] - 20); + + //bg + if (this.render_canvas_border) { + ctx.strokeStyle = "#235"; + ctx.strokeRect(0, 0, canvas.width, canvas.height); + } + + if (this.render_connections_shadows) { + ctx.shadowColor = "#000"; + ctx.shadowOffsetX = 0; + ctx.shadowOffsetY = 0; + ctx.shadowBlur = 6; + } else { + ctx.shadowColor = "rgba(0,0,0,0)"; + } + + //draw connections + if (!this.live_mode) { + this.drawConnections(ctx); + } + + ctx.shadowColor = "rgba(0,0,0,0)"; + + //restore state + ctx.restore(); + } + + if (ctx.finish) { + ctx.finish(); + } + + this.dirty_bgcanvas = false; + this.dirty_canvas = true; //to force to repaint the front canvas with the bgcanvas + }; + + var temp_vec2 = new Float32Array(2); + + /** + * draws the given node inside the canvas + * @method drawNode + **/ + LGraphCanvas.prototype.drawNode = function(node, ctx) { + var glow = false; + this.current_node = node; + + var color = node.color || node.constructor.color || LiteGraph.NODE_DEFAULT_COLOR; + var bgcolor = node.bgcolor || node.constructor.bgcolor || LiteGraph.NODE_DEFAULT_BGCOLOR; + + //shadow and glow + if (node.mouseOver) { + glow = true; + } + + var low_quality = this.ds.scale < 0.6; //zoomed out + + //only render if it forces it to do it + if (this.live_mode) { + if (!node.flags.collapsed) { + ctx.shadowColor = "transparent"; + if (node.onDrawForeground) { + node.onDrawForeground(ctx, this, this.canvas); + } + } + return; + } + + var editor_alpha = this.editor_alpha; + ctx.globalAlpha = editor_alpha; + + if (this.render_shadows && !low_quality) { + ctx.shadowColor = LiteGraph.DEFAULT_SHADOW_COLOR; + ctx.shadowOffsetX = 2 * this.ds.scale; + ctx.shadowOffsetY = 2 * this.ds.scale; + ctx.shadowBlur = 3 * this.ds.scale; + } else { + ctx.shadowColor = "transparent"; + } + + //custom draw collapsed method (draw after shadows because they are affected) + if ( + node.flags.collapsed && + node.onDrawCollapsed && + node.onDrawCollapsed(ctx, this) == true + ) { + return; + } + + //clip if required (mask) + var shape = node._shape || LiteGraph.BOX_SHAPE; + var size = temp_vec2; + temp_vec2.set(node.size); + var horizontal = node.horizontal; // || node.flags.horizontal; + + if (node.flags.collapsed) { + ctx.font = this.inner_text_font; + var title = node.getTitle ? node.getTitle() : node.title; + if (title != null) { + node._collapsed_width = Math.min( + node.size[0], + ctx.measureText(title).width + + LiteGraph.NODE_TITLE_HEIGHT * 2 + ); //LiteGraph.NODE_COLLAPSED_WIDTH; + size[0] = node._collapsed_width; + size[1] = 0; + } + } + + if (node.clip_area) { + //Start clipping + ctx.save(); + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE) { + ctx.rect(0, 0, size[0], size[1]); + } else if (shape == LiteGraph.ROUND_SHAPE) { + ctx.roundRect(0, 0, size[0], size[1], [10]); + } else if (shape == LiteGraph.CIRCLE_SHAPE) { + ctx.arc( + size[0] * 0.5, + size[1] * 0.5, + size[0] * 0.5, + 0, + Math.PI * 2 + ); + } + ctx.clip(); + } + + //draw shape + if (node.has_errors) { + bgcolor = "red"; + } + this.drawNodeShape( + node, + ctx, + size, + color, + bgcolor, + node.is_selected, + node.mouseOver + ); + ctx.shadowColor = "transparent"; + + //draw foreground + if (node.onDrawForeground) { + node.onDrawForeground(ctx, this, this.canvas); + } + + //connection slots + ctx.textAlign = horizontal ? "center" : "left"; + ctx.font = this.inner_text_font; + + var render_text = !low_quality; + + var out_slot = this.connecting_output; + var in_slot = this.connecting_input; + ctx.lineWidth = 1; + + var max_y = 0; + var slot_pos = new Float32Array(2); //to reuse + + //render inputs and outputs + if (!node.flags.collapsed) { + //input connection slots + if (node.inputs) { + for (var i = 0; i < node.inputs.length; i++) { + var slot = node.inputs[i]; + + var slot_type = slot.type; + var slot_shape = slot.shape; + + ctx.globalAlpha = editor_alpha; + //change opacity of incompatible slots when dragging a connection + if ( this.connecting_output && !LiteGraph.isValidConnection( slot.type , out_slot.type) ) { + ctx.globalAlpha = 0.4 * editor_alpha; + } + + ctx.fillStyle = + slot.link != null + ? slot.color_on || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.input_on + : slot.color_off || + this.default_connection_color_byTypeOff[slot_type] || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.input_off; + + var pos = node.getConnectionPos(true, i, slot_pos); + pos[0] -= node.pos[0]; + pos[1] -= node.pos[1]; + if (max_y < pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5) { + max_y = pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5; + } + + ctx.beginPath(); + + if (slot_type == "array"){ + slot_shape = LiteGraph.GRID_SHAPE; // place in addInput? addOutput instead? + } + + var doStroke = true; + + if ( + slot.type === LiteGraph.EVENT || + slot.shape === LiteGraph.BOX_SHAPE + ) { + if (horizontal) { + ctx.rect( + pos[0] - 5 + 0.5, + pos[1] - 8 + 0.5, + 10, + 14 + ); + } else { + ctx.rect( + pos[0] - 6 + 0.5, + pos[1] - 5 + 0.5, + 14, + 10 + ); + } + } else if (slot_shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(pos[0] + 8, pos[1] + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] + 6 + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] - 6 + 0.5); + ctx.closePath(); + } else if (slot_shape === LiteGraph.GRID_SHAPE) { + ctx.rect(pos[0] - 4, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 4, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 4, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 1, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 4, pos[1] + 2, 2, 2); + ctx.rect(pos[0] - 1, pos[1] + 2, 2, 2); + ctx.rect(pos[0] + 2, pos[1] + 2, 2, 2); + doStroke = false; + } else { + if(low_quality) + ctx.rect(pos[0] - 4, pos[1] - 4, 8, 8 ); //faster + else + ctx.arc(pos[0], pos[1], 4, 0, Math.PI * 2); + } + ctx.fill(); + + //render name + if (render_text) { + var text = slot.label != null ? slot.label : slot.name; + if (text) { + ctx.fillStyle = LiteGraph.NODE_TEXT_COLOR; + if (horizontal || slot.dir == LiteGraph.UP) { + ctx.fillText(text, pos[0], pos[1] - 10); + } else { + ctx.fillText(text, pos[0] + 10, pos[1] + 5); + } + } + } + } + } + + //output connection slots + + ctx.textAlign = horizontal ? "center" : "right"; + ctx.strokeStyle = "black"; + if (node.outputs) { + for (var i = 0; i < node.outputs.length; i++) { + var slot = node.outputs[i]; + + var slot_type = slot.type; + var slot_shape = slot.shape; + + //change opacity of incompatible slots when dragging a connection + if (this.connecting_input && !LiteGraph.isValidConnection( slot_type , in_slot.type) ) { + ctx.globalAlpha = 0.4 * editor_alpha; + } + + var pos = node.getConnectionPos(false, i, slot_pos); + pos[0] -= node.pos[0]; + pos[1] -= node.pos[1]; + if (max_y < pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5) { + max_y = pos[1] + LiteGraph.NODE_SLOT_HEIGHT * 0.5; + } + + ctx.fillStyle = + slot.links && slot.links.length + ? slot.color_on || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.output_on + : slot.color_off || + this.default_connection_color_byTypeOff[slot_type] || + this.default_connection_color_byType[slot_type] || + this.default_connection_color.output_off; + ctx.beginPath(); + //ctx.rect( node.size[0] - 14,i*14,10,10); + + if (slot_type == "array"){ + slot_shape = LiteGraph.GRID_SHAPE; + } + + var doStroke = true; + + if ( + slot_type === LiteGraph.EVENT || + slot_shape === LiteGraph.BOX_SHAPE + ) { + if (horizontal) { + ctx.rect( + pos[0] - 5 + 0.5, + pos[1] - 8 + 0.5, + 10, + 14 + ); + } else { + ctx.rect( + pos[0] - 6 + 0.5, + pos[1] - 5 + 0.5, + 14, + 10 + ); + } + } else if (slot_shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(pos[0] + 8, pos[1] + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] + 6 + 0.5); + ctx.lineTo(pos[0] - 4, pos[1] - 6 + 0.5); + ctx.closePath(); + } else if (slot_shape === LiteGraph.GRID_SHAPE) { + ctx.rect(pos[0] - 4, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 4, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 4, 2, 2); + ctx.rect(pos[0] - 4, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 1, pos[1] - 1, 2, 2); + ctx.rect(pos[0] + 2, pos[1] - 1, 2, 2); + ctx.rect(pos[0] - 4, pos[1] + 2, 2, 2); + ctx.rect(pos[0] - 1, pos[1] + 2, 2, 2); + ctx.rect(pos[0] + 2, pos[1] + 2, 2, 2); + doStroke = false; + } else { + if(low_quality) + ctx.rect(pos[0] - 4, pos[1] - 4, 8, 8 ); + else + ctx.arc(pos[0], pos[1], 4, 0, Math.PI * 2); + } + + //trigger + //if(slot.node_id != null && slot.slot == -1) + // ctx.fillStyle = "#F85"; + + //if(slot.links != null && slot.links.length) + ctx.fill(); + if(!low_quality && doStroke) + ctx.stroke(); + + //render output name + if (render_text) { + var text = slot.label != null ? slot.label : slot.name; + if (text) { + ctx.fillStyle = LiteGraph.NODE_TEXT_COLOR; + if (horizontal || slot.dir == LiteGraph.DOWN) { + ctx.fillText(text, pos[0], pos[1] - 8); + } else { + ctx.fillText(text, pos[0] - 10, pos[1] + 5); + } + } + } + } + } + + ctx.textAlign = "left"; + ctx.globalAlpha = 1; + + if (node.widgets) { + var widgets_y = max_y; + if (horizontal || node.widgets_up) { + widgets_y = 2; + } + if( node.widgets_start_y != null ) + widgets_y = node.widgets_start_y; + this.drawNodeWidgets( + node, + widgets_y, + ctx, + this.node_widget && this.node_widget[0] == node + ? this.node_widget[1] + : null + ); + } + } else if (this.render_collapsed_slots) { + //if collapsed + var input_slot = null; + var output_slot = null; + + //get first connected slot to render + if (node.inputs) { + for (var i = 0; i < node.inputs.length; i++) { + var slot = node.inputs[i]; + if (slot.link == null) { + continue; + } + input_slot = slot; + break; + } + } + if (node.outputs) { + for (var i = 0; i < node.outputs.length; i++) { + var slot = node.outputs[i]; + if (!slot.links || !slot.links.length) { + continue; + } + output_slot = slot; + } + } + + if (input_slot) { + var x = 0; + var y = LiteGraph.NODE_TITLE_HEIGHT * -0.5; //center + if (horizontal) { + x = node._collapsed_width * 0.5; + y = -LiteGraph.NODE_TITLE_HEIGHT; + } + ctx.fillStyle = "#686"; + ctx.beginPath(); + if ( + slot.type === LiteGraph.EVENT || + slot.shape === LiteGraph.BOX_SHAPE + ) { + ctx.rect(x - 7 + 0.5, y - 4, 14, 8); + } else if (slot.shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(x + 8, y); + ctx.lineTo(x + -4, y - 4); + ctx.lineTo(x + -4, y + 4); + ctx.closePath(); + } else { + ctx.arc(x, y, 4, 0, Math.PI * 2); + } + ctx.fill(); + } + + if (output_slot) { + var x = node._collapsed_width; + var y = LiteGraph.NODE_TITLE_HEIGHT * -0.5; //center + if (horizontal) { + x = node._collapsed_width * 0.5; + y = 0; + } + ctx.fillStyle = "#686"; + ctx.strokeStyle = "black"; + ctx.beginPath(); + if ( + slot.type === LiteGraph.EVENT || + slot.shape === LiteGraph.BOX_SHAPE + ) { + ctx.rect(x - 7 + 0.5, y - 4, 14, 8); + } else if (slot.shape === LiteGraph.ARROW_SHAPE) { + ctx.moveTo(x + 6, y); + ctx.lineTo(x - 6, y - 4); + ctx.lineTo(x - 6, y + 4); + ctx.closePath(); + } else { + ctx.arc(x, y, 4, 0, Math.PI * 2); + } + ctx.fill(); + //ctx.stroke(); + } + } + + if (node.clip_area) { + ctx.restore(); + } + + ctx.globalAlpha = 1.0; + }; + + //used by this.over_link_center + LGraphCanvas.prototype.drawLinkTooltip = function( ctx, link ) + { + var pos = link._pos; + ctx.fillStyle = "black"; + ctx.beginPath(); + ctx.arc( pos[0], pos[1], 3, 0, Math.PI * 2 ); + ctx.fill(); + + if(link.data == null) + return; + + if(this.onDrawLinkTooltip) + if( this.onDrawLinkTooltip(ctx,link,this) == true ) + return; + + var data = link.data; + var text = null; + + if( data.constructor === Number ) + text = data.toFixed(2); + else if( data.constructor === String ) + text = "\"" + data + "\""; + else if( data.constructor === Boolean ) + text = String(data); + else if (data.toToolTip) + text = data.toToolTip(); + else + text = "[" + data.constructor.name + "]"; + + if(text == null) + return; + text = text.substr(0,30); //avoid weird + + ctx.font = "14px Courier New"; + var info = ctx.measureText(text); + var w = info.width + 20; + var h = 24; + ctx.shadowColor = "black"; + ctx.shadowOffsetX = 2; + ctx.shadowOffsetY = 2; + ctx.shadowBlur = 3; + ctx.fillStyle = "#454"; + ctx.beginPath(); + ctx.roundRect( pos[0] - w*0.5, pos[1] - 15 - h, w, h, [3]); + ctx.moveTo( pos[0] - 10, pos[1] - 15 ); + ctx.lineTo( pos[0] + 10, pos[1] - 15 ); + ctx.lineTo( pos[0], pos[1] - 5 ); + ctx.fill(); + ctx.shadowColor = "transparent"; + ctx.textAlign = "center"; + ctx.fillStyle = "#CEC"; + ctx.fillText(text, pos[0], pos[1] - 15 - h * 0.3); + } + + /** + * draws the shape of the given node in the canvas + * @method drawNodeShape + **/ + var tmp_area = new Float32Array(4); + + LGraphCanvas.prototype.drawNodeShape = function( + node, + ctx, + size, + fgcolor, + bgcolor, + selected, + mouse_over + ) { + //bg rect + ctx.strokeStyle = fgcolor; + ctx.fillStyle = bgcolor; + + var title_height = LiteGraph.NODE_TITLE_HEIGHT; + var low_quality = this.ds.scale < 0.5; + + //render node area depending on shape + var shape = + node._shape || node.constructor.shape || LiteGraph.ROUND_SHAPE; + + var title_mode = node.constructor.title_mode; + + var render_title = true; + if (title_mode == LiteGraph.TRANSPARENT_TITLE || title_mode == LiteGraph.NO_TITLE) { + render_title = false; + } else if (title_mode == LiteGraph.AUTOHIDE_TITLE && mouse_over) { + render_title = true; + } + + var area = tmp_area; + area[0] = 0; //x + area[1] = render_title ? -title_height : 0; //y + area[2] = size[0] + 1; //w + area[3] = render_title ? size[1] + title_height : size[1]; //h + + var old_alpha = ctx.globalAlpha; + + //full node shape + //if(node.flags.collapsed) + { + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE || low_quality) { + ctx.fillRect(area[0], area[1], area[2], area[3]); + } else if ( + shape == LiteGraph.ROUND_SHAPE || + shape == LiteGraph.CARD_SHAPE + ) { + ctx.roundRect( + area[0], + area[1], + area[2], + area[3], + shape == LiteGraph.CARD_SHAPE ? [this.round_radius,this.round_radius,0,0] : [this.round_radius] + ); + } else if (shape == LiteGraph.CIRCLE_SHAPE) { + ctx.arc( + size[0] * 0.5, + size[1] * 0.5, + size[0] * 0.5, + 0, + Math.PI * 2 + ); + } + ctx.fill(); + + //separator + if(!node.flags.collapsed && render_title) + { + ctx.shadowColor = "transparent"; + ctx.fillStyle = "rgba(0,0,0,0.2)"; + ctx.fillRect(0, -1, area[2], 2); + } + } + ctx.shadowColor = "transparent"; + + if (node.onDrawBackground) { + node.onDrawBackground(ctx, this, this.canvas, this.graph_mouse ); + } + + //title bg (remember, it is rendered ABOVE the node) + if (render_title || title_mode == LiteGraph.TRANSPARENT_TITLE) { + //title bar + if (node.onDrawTitleBar) { + node.onDrawTitleBar( ctx, title_height, size, this.ds.scale, fgcolor ); + } else if ( + title_mode != LiteGraph.TRANSPARENT_TITLE && + (node.constructor.title_color || this.render_title_colored) + ) { + var title_color = node.constructor.title_color || fgcolor; + + if (node.flags.collapsed) { + ctx.shadowColor = LiteGraph.DEFAULT_SHADOW_COLOR; + } + + //* gradient test + if (this.use_gradients) { + var grad = LGraphCanvas.gradients[title_color]; + if (!grad) { + grad = LGraphCanvas.gradients[ title_color ] = ctx.createLinearGradient(0, 0, 400, 0); + grad.addColorStop(0, title_color); // TODO refactor: validate color !! prevent DOMException + grad.addColorStop(1, "#000"); + } + ctx.fillStyle = grad; + } else { + ctx.fillStyle = title_color; + } + + //ctx.globalAlpha = 0.5 * old_alpha; + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE || low_quality) { + ctx.rect(0, -title_height, size[0] + 1, title_height); + } else if ( shape == LiteGraph.ROUND_SHAPE || shape == LiteGraph.CARD_SHAPE ) { + ctx.roundRect( + 0, + -title_height, + size[0] + 1, + title_height, + node.flags.collapsed ? [this.round_radius] : [this.round_radius,this.round_radius,0,0] + ); + } + ctx.fill(); + ctx.shadowColor = "transparent"; + } + + var colState = false; + if (LiteGraph.node_box_coloured_by_mode){ + if(LiteGraph.NODE_MODES_COLORS[node.mode]){ + colState = LiteGraph.NODE_MODES_COLORS[node.mode]; + } + } + if (LiteGraph.node_box_coloured_when_on){ + colState = node.action_triggered ? "#FFF" : (node.execute_triggered ? "#AAA" : colState); + } + + //title box + var box_size = 10; + if (node.onDrawTitleBox) { + node.onDrawTitleBox(ctx, title_height, size, this.ds.scale); + } else if ( + shape == LiteGraph.ROUND_SHAPE || + shape == LiteGraph.CIRCLE_SHAPE || + shape == LiteGraph.CARD_SHAPE + ) { + if (low_quality) { + ctx.fillStyle = "black"; + ctx.beginPath(); + ctx.arc( + title_height * 0.5, + title_height * -0.5, + box_size * 0.5 + 1, + 0, + Math.PI * 2 + ); + ctx.fill(); + } + + ctx.fillStyle = node.boxcolor || colState || LiteGraph.NODE_DEFAULT_BOXCOLOR; + if(low_quality) + ctx.fillRect( title_height * 0.5 - box_size *0.5, title_height * -0.5 - box_size *0.5, box_size , box_size ); + else + { + ctx.beginPath(); + ctx.arc( + title_height * 0.5, + title_height * -0.5, + box_size * 0.5, + 0, + Math.PI * 2 + ); + ctx.fill(); + } + } else { + if (low_quality) { + ctx.fillStyle = "black"; + ctx.fillRect( + (title_height - box_size) * 0.5 - 1, + (title_height + box_size) * -0.5 - 1, + box_size + 2, + box_size + 2 + ); + } + ctx.fillStyle = node.boxcolor || colState || LiteGraph.NODE_DEFAULT_BOXCOLOR; + ctx.fillRect( + (title_height - box_size) * 0.5, + (title_height + box_size) * -0.5, + box_size, + box_size + ); + } + ctx.globalAlpha = old_alpha; + + //title text + if (node.onDrawTitleText) { + node.onDrawTitleText( + ctx, + title_height, + size, + this.ds.scale, + this.title_text_font, + selected + ); + } + if (!low_quality) { + ctx.font = this.title_text_font; + var title = String(node.getTitle()); + if (title) { + if (selected) { + ctx.fillStyle = LiteGraph.NODE_SELECTED_TITLE_COLOR; + } else { + ctx.fillStyle = + node.constructor.title_text_color || + this.node_title_color; + } + if (node.flags.collapsed) { + ctx.textAlign = "left"; + var measure = ctx.measureText(title); + ctx.fillText( + title.substr(0,20), //avoid urls too long + title_height,// + measure.width * 0.5, + LiteGraph.NODE_TITLE_TEXT_Y - title_height + ); + ctx.textAlign = "left"; + } else { + ctx.textAlign = "left"; + ctx.fillText( + title, + title_height, + LiteGraph.NODE_TITLE_TEXT_Y - title_height + ); + } + } + } + + //subgraph box + if (!node.flags.collapsed && node.subgraph && !node.skip_subgraph_button) { + var w = LiteGraph.NODE_TITLE_HEIGHT; + var x = node.size[0] - w; + var over = LiteGraph.isInsideRectangle( this.graph_mouse[0] - node.pos[0], this.graph_mouse[1] - node.pos[1], x+2, -w+2, w-4, w-4 ); + ctx.fillStyle = over ? "#888" : "#555"; + if( shape == LiteGraph.BOX_SHAPE || low_quality) + ctx.fillRect(x+2, -w+2, w-4, w-4); + else + { + ctx.beginPath(); + ctx.roundRect(x+2, -w+2, w-4, w-4,[4]); + ctx.fill(); + } + ctx.fillStyle = "#333"; + ctx.beginPath(); + ctx.moveTo(x + w * 0.2, -w * 0.6); + ctx.lineTo(x + w * 0.8, -w * 0.6); + ctx.lineTo(x + w * 0.5, -w * 0.3); + ctx.fill(); + } + + //custom title render + if (node.onDrawTitle) { + node.onDrawTitle(ctx); + } + } + + //render selection marker + if (selected) { + if (node.onBounding) { + node.onBounding(area); + } + + if (title_mode == LiteGraph.TRANSPARENT_TITLE) { + area[1] -= title_height; + area[3] += title_height; + } + ctx.lineWidth = 1; + ctx.globalAlpha = 0.8; + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE) { + ctx.rect( + -6 + area[0], + -6 + area[1], + 12 + area[2], + 12 + area[3] + ); + } else if ( + shape == LiteGraph.ROUND_SHAPE || + (shape == LiteGraph.CARD_SHAPE && node.flags.collapsed) + ) { + ctx.roundRect( + -6 + area[0], + -6 + area[1], + 12 + area[2], + 12 + area[3], + [this.round_radius * 2] + ); + } else if (shape == LiteGraph.CARD_SHAPE) { + ctx.roundRect( + -6 + area[0], + -6 + area[1], + 12 + area[2], + 12 + area[3], + [this.round_radius * 2,2,this.round_radius * 2,2] + ); + } else if (shape == LiteGraph.CIRCLE_SHAPE) { + ctx.arc( + size[0] * 0.5, + size[1] * 0.5, + size[0] * 0.5 + 6, + 0, + Math.PI * 2 + ); + } + ctx.strokeStyle = LiteGraph.NODE_BOX_OUTLINE_COLOR; + ctx.stroke(); + ctx.strokeStyle = fgcolor; + ctx.globalAlpha = 1; + } + + // these counter helps in conditioning drawing based on if the node has been executed or an action occurred + if (node.execute_triggered>0) node.execute_triggered--; + if (node.action_triggered>0) node.action_triggered--; + }; + + var margin_area = new Float32Array(4); + var link_bounding = new Float32Array(4); + var tempA = new Float32Array(2); + var tempB = new Float32Array(2); + + /** + * draws every connection visible in the canvas + * OPTIMIZE THIS: pre-catch connections position instead of recomputing them every time + * @method drawConnections + **/ + LGraphCanvas.prototype.drawConnections = function(ctx) { + var now = LiteGraph.getTime(); + var visible_area = this.visible_area; + margin_area[0] = visible_area[0] - 20; + margin_area[1] = visible_area[1] - 20; + margin_area[2] = visible_area[2] + 40; + margin_area[3] = visible_area[3] + 40; + + //draw connections + ctx.lineWidth = this.connections_width; + + ctx.fillStyle = "#AAA"; + ctx.strokeStyle = "#AAA"; + ctx.globalAlpha = this.editor_alpha; + //for every node + var nodes = this.graph._nodes; + for (var n = 0, l = nodes.length; n < l; ++n) { + var node = nodes[n]; + //for every input (we render just inputs because it is easier as every slot can only have one input) + if (!node.inputs || !node.inputs.length) { + continue; + } + + for (var i = 0; i < node.inputs.length; ++i) { + var input = node.inputs[i]; + if (!input || input.link == null) { + continue; + } + var link_id = input.link; + var link = this.graph.links[link_id]; + if (!link) { + continue; + } + + //find link info + var start_node = this.graph.getNodeById(link.origin_id); + if (start_node == null) { + continue; + } + var start_node_slot = link.origin_slot; + var start_node_slotpos = null; + if (start_node_slot == -1) { + start_node_slotpos = [ + start_node.pos[0] + 10, + start_node.pos[1] + 10 + ]; + } else { + start_node_slotpos = start_node.getConnectionPos( + false, + start_node_slot, + tempA + ); + } + var end_node_slotpos = node.getConnectionPos(true, i, tempB); + + //compute link bounding + link_bounding[0] = start_node_slotpos[0]; + link_bounding[1] = start_node_slotpos[1]; + link_bounding[2] = end_node_slotpos[0] - start_node_slotpos[0]; + link_bounding[3] = end_node_slotpos[1] - start_node_slotpos[1]; + if (link_bounding[2] < 0) { + link_bounding[0] += link_bounding[2]; + link_bounding[2] = Math.abs(link_bounding[2]); + } + if (link_bounding[3] < 0) { + link_bounding[1] += link_bounding[3]; + link_bounding[3] = Math.abs(link_bounding[3]); + } + + //skip links outside of the visible area of the canvas + if (!overlapBounding(link_bounding, margin_area)) { + continue; + } + + var start_slot = start_node.outputs[start_node_slot]; + var end_slot = node.inputs[i]; + if (!start_slot || !end_slot) { + continue; + } + var start_dir = + start_slot.dir || + (start_node.horizontal ? LiteGraph.DOWN : LiteGraph.RIGHT); + var end_dir = + end_slot.dir || + (node.horizontal ? LiteGraph.UP : LiteGraph.LEFT); + + this.renderLink( + ctx, + start_node_slotpos, + end_node_slotpos, + link, + false, + 0, + null, + start_dir, + end_dir + ); + + //event triggered rendered on top + if (link && link._last_time && now - link._last_time < 1000) { + var f = 2.0 - (now - link._last_time) * 0.002; + var tmp = ctx.globalAlpha; + ctx.globalAlpha = tmp * f; + this.renderLink( + ctx, + start_node_slotpos, + end_node_slotpos, + link, + true, + f, + "white", + start_dir, + end_dir + ); + ctx.globalAlpha = tmp; + } + } + } + ctx.globalAlpha = 1; + }; + + /** + * draws a link between two points + * @method renderLink + * @param {vec2} a start pos + * @param {vec2} b end pos + * @param {Object} link the link object with all the link info + * @param {boolean} skip_border ignore the shadow of the link + * @param {boolean} flow show flow animation (for events) + * @param {string} color the color for the link + * @param {number} start_dir the direction enum + * @param {number} end_dir the direction enum + * @param {number} num_sublines number of sublines (useful to represent vec3 or rgb) + **/ + LGraphCanvas.prototype.renderLink = function( + ctx, + a, + b, + link, + skip_border, + flow, + color, + start_dir, + end_dir, + num_sublines + ) { + if (link) { + this.visible_links.push(link); + } + + //choose color + if (!color && link) { + color = link.color || LGraphCanvas.link_type_colors[link.type]; + } + if (!color) { + color = this.default_link_color; + } + if (link != null && this.highlighted_links[link.id]) { + color = "#FFF"; + } + + start_dir = start_dir || LiteGraph.RIGHT; + end_dir = end_dir || LiteGraph.LEFT; + + var dist = distance(a, b); + + if (this.render_connections_border && this.ds.scale > 0.6) { + ctx.lineWidth = this.connections_width + 4; + } + ctx.lineJoin = "round"; + num_sublines = num_sublines || 1; + if (num_sublines > 1) { + ctx.lineWidth = 0.5; + } + + //begin line shape + ctx.beginPath(); + for (var i = 0; i < num_sublines; i += 1) { + var offsety = (i - (num_sublines - 1) * 0.5) * 5; + + if (this.links_render_mode == LiteGraph.SPLINE_LINK) { + ctx.moveTo(a[0], a[1] + offsety); + var start_offset_x = 0; + var start_offset_y = 0; + var end_offset_x = 0; + var end_offset_y = 0; + switch (start_dir) { + case LiteGraph.LEFT: + start_offset_x = dist * -0.25; + break; + case LiteGraph.RIGHT: + start_offset_x = dist * 0.25; + break; + case LiteGraph.UP: + start_offset_y = dist * -0.25; + break; + case LiteGraph.DOWN: + start_offset_y = dist * 0.25; + break; + } + switch (end_dir) { + case LiteGraph.LEFT: + end_offset_x = dist * -0.25; + break; + case LiteGraph.RIGHT: + end_offset_x = dist * 0.25; + break; + case LiteGraph.UP: + end_offset_y = dist * -0.25; + break; + case LiteGraph.DOWN: + end_offset_y = dist * 0.25; + break; + } + ctx.bezierCurveTo( + a[0] + start_offset_x, + a[1] + start_offset_y + offsety, + b[0] + end_offset_x, + b[1] + end_offset_y + offsety, + b[0], + b[1] + offsety + ); + } else if (this.links_render_mode == LiteGraph.LINEAR_LINK) { + ctx.moveTo(a[0], a[1] + offsety); + var start_offset_x = 0; + var start_offset_y = 0; + var end_offset_x = 0; + var end_offset_y = 0; + switch (start_dir) { + case LiteGraph.LEFT: + start_offset_x = -1; + break; + case LiteGraph.RIGHT: + start_offset_x = 1; + break; + case LiteGraph.UP: + start_offset_y = -1; + break; + case LiteGraph.DOWN: + start_offset_y = 1; + break; + } + switch (end_dir) { + case LiteGraph.LEFT: + end_offset_x = -1; + break; + case LiteGraph.RIGHT: + end_offset_x = 1; + break; + case LiteGraph.UP: + end_offset_y = -1; + break; + case LiteGraph.DOWN: + end_offset_y = 1; + break; + } + var l = 15; + ctx.lineTo( + a[0] + start_offset_x * l, + a[1] + start_offset_y * l + offsety + ); + ctx.lineTo( + b[0] + end_offset_x * l, + b[1] + end_offset_y * l + offsety + ); + ctx.lineTo(b[0], b[1] + offsety); + } else if (this.links_render_mode == LiteGraph.STRAIGHT_LINK) { + ctx.moveTo(a[0], a[1]); + var start_x = a[0]; + var start_y = a[1]; + var end_x = b[0]; + var end_y = b[1]; + if (start_dir == LiteGraph.RIGHT) { + start_x += 10; + } else { + start_y += 10; + } + if (end_dir == LiteGraph.LEFT) { + end_x -= 10; + } else { + end_y -= 10; + } + ctx.lineTo(start_x, start_y); + ctx.lineTo((start_x + end_x) * 0.5, start_y); + ctx.lineTo((start_x + end_x) * 0.5, end_y); + ctx.lineTo(end_x, end_y); + ctx.lineTo(b[0], b[1]); + } else { + return; + } //unknown + } + + //rendering the outline of the connection can be a little bit slow + if ( + this.render_connections_border && + this.ds.scale > 0.6 && + !skip_border + ) { + ctx.strokeStyle = "rgba(0,0,0,0.5)"; + ctx.stroke(); + } + + ctx.lineWidth = this.connections_width; + ctx.fillStyle = ctx.strokeStyle = color; + ctx.stroke(); + //end line shape + + var pos = this.computeConnectionPoint(a, b, 0.5, start_dir, end_dir); + if (link && link._pos) { + link._pos[0] = pos[0]; + link._pos[1] = pos[1]; + } + + //render arrow in the middle + if ( + this.ds.scale >= 0.6 && + this.highquality_render && + end_dir != LiteGraph.CENTER + ) { + //render arrow + if (this.render_connection_arrows) { + //compute two points in the connection + var posA = this.computeConnectionPoint( + a, + b, + 0.25, + start_dir, + end_dir + ); + var posB = this.computeConnectionPoint( + a, + b, + 0.26, + start_dir, + end_dir + ); + var posC = this.computeConnectionPoint( + a, + b, + 0.75, + start_dir, + end_dir + ); + var posD = this.computeConnectionPoint( + a, + b, + 0.76, + start_dir, + end_dir + ); + + //compute the angle between them so the arrow points in the right direction + var angleA = 0; + var angleB = 0; + if (this.render_curved_connections) { + angleA = -Math.atan2(posB[0] - posA[0], posB[1] - posA[1]); + angleB = -Math.atan2(posD[0] - posC[0], posD[1] - posC[1]); + } else { + angleB = angleA = b[1] > a[1] ? 0 : Math.PI; + } + + //render arrow + ctx.save(); + ctx.translate(posA[0], posA[1]); + ctx.rotate(angleA); + ctx.beginPath(); + ctx.moveTo(-5, -3); + ctx.lineTo(0, +7); + ctx.lineTo(+5, -3); + ctx.fill(); + ctx.restore(); + ctx.save(); + ctx.translate(posC[0], posC[1]); + ctx.rotate(angleB); + ctx.beginPath(); + ctx.moveTo(-5, -3); + ctx.lineTo(0, +7); + ctx.lineTo(+5, -3); + ctx.fill(); + ctx.restore(); + } + + //circle + ctx.beginPath(); + ctx.arc(pos[0], pos[1], 5, 0, Math.PI * 2); + ctx.fill(); + } + + //render flowing points + if (flow) { + ctx.fillStyle = color; + for (var i = 0; i < 5; ++i) { + var f = (LiteGraph.getTime() * 0.001 + i * 0.2) % 1; + var pos = this.computeConnectionPoint( + a, + b, + f, + start_dir, + end_dir + ); + ctx.beginPath(); + ctx.arc(pos[0], pos[1], 5, 0, 2 * Math.PI); + ctx.fill(); + } + } + }; + + //returns the link center point based on curvature + LGraphCanvas.prototype.computeConnectionPoint = function( + a, + b, + t, + start_dir, + end_dir + ) { + start_dir = start_dir || LiteGraph.RIGHT; + end_dir = end_dir || LiteGraph.LEFT; + + var dist = distance(a, b); + var p0 = a; + var p1 = [a[0], a[1]]; + var p2 = [b[0], b[1]]; + var p3 = b; + + switch (start_dir) { + case LiteGraph.LEFT: + p1[0] += dist * -0.25; + break; + case LiteGraph.RIGHT: + p1[0] += dist * 0.25; + break; + case LiteGraph.UP: + p1[1] += dist * -0.25; + break; + case LiteGraph.DOWN: + p1[1] += dist * 0.25; + break; + } + switch (end_dir) { + case LiteGraph.LEFT: + p2[0] += dist * -0.25; + break; + case LiteGraph.RIGHT: + p2[0] += dist * 0.25; + break; + case LiteGraph.UP: + p2[1] += dist * -0.25; + break; + case LiteGraph.DOWN: + p2[1] += dist * 0.25; + break; + } + + var c1 = (1 - t) * (1 - t) * (1 - t); + var c2 = 3 * ((1 - t) * (1 - t)) * t; + var c3 = 3 * (1 - t) * (t * t); + var c4 = t * t * t; + + var x = c1 * p0[0] + c2 * p1[0] + c3 * p2[0] + c4 * p3[0]; + var y = c1 * p0[1] + c2 * p1[1] + c3 * p2[1] + c4 * p3[1]; + return [x, y]; + }; + + LGraphCanvas.prototype.drawExecutionOrder = function(ctx) { + ctx.shadowColor = "transparent"; + ctx.globalAlpha = 0.25; + + ctx.textAlign = "center"; + ctx.strokeStyle = "white"; + ctx.globalAlpha = 0.75; + + var visible_nodes = this.visible_nodes; + for (var i = 0; i < visible_nodes.length; ++i) { + var node = visible_nodes[i]; + ctx.fillStyle = "black"; + ctx.fillRect( + node.pos[0] - LiteGraph.NODE_TITLE_HEIGHT, + node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT + ); + if (node.order == 0) { + ctx.strokeRect( + node.pos[0] - LiteGraph.NODE_TITLE_HEIGHT + 0.5, + node.pos[1] - LiteGraph.NODE_TITLE_HEIGHT + 0.5, + LiteGraph.NODE_TITLE_HEIGHT, + LiteGraph.NODE_TITLE_HEIGHT + ); + } + ctx.fillStyle = "#FFF"; + ctx.fillText( + node.order, + node.pos[0] + LiteGraph.NODE_TITLE_HEIGHT * -0.5, + node.pos[1] - 6 + ); + } + ctx.globalAlpha = 1; + }; + + /** + * draws the widgets stored inside a node + * @method drawNodeWidgets + **/ + LGraphCanvas.prototype.drawNodeWidgets = function( + node, + posY, + ctx, + active_widget + ) { + if (!node.widgets || !node.widgets.length) { + return 0; + } + var width = node.size[0]; + var widgets = node.widgets; + posY += 2; + var H = LiteGraph.NODE_WIDGET_HEIGHT; + var show_text = this.ds.scale > 0.5; + ctx.save(); + ctx.globalAlpha = this.editor_alpha; + var outline_color = LiteGraph.WIDGET_OUTLINE_COLOR; + var background_color = LiteGraph.WIDGET_BGCOLOR; + var text_color = LiteGraph.WIDGET_TEXT_COLOR; + var secondary_text_color = LiteGraph.WIDGET_SECONDARY_TEXT_COLOR; + var margin = 15; + + for (var i = 0; i < widgets.length; ++i) { + var w = widgets[i]; + var y = posY; + if (w.y) { + y = w.y; + } + w.last_y = y; + ctx.strokeStyle = outline_color; + ctx.fillStyle = "#222"; + ctx.textAlign = "left"; + //ctx.lineWidth = 2; + if(w.disabled) + ctx.globalAlpha *= 0.5; + var widget_width = w.width || width; + + switch (w.type) { + case "button": + ctx.fillStyle = background_color; + if (w.clicked) { + ctx.fillStyle = "#AAA"; + w.clicked = false; + this.dirty_canvas = true; + } + ctx.fillRect(margin, y, widget_width - margin * 2, H); + if(show_text && !w.disabled) + ctx.strokeRect( margin, y, widget_width - margin * 2, H ); + if (show_text) { + ctx.textAlign = "center"; + ctx.fillStyle = text_color; + ctx.fillText(w.label || w.name, widget_width * 0.5, y + H * 0.7); + } + break; + case "toggle": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if (show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5]); + else + ctx.rect(margin, y, widget_width - margin * 2, H ); + ctx.fill(); + if(show_text && !w.disabled) + ctx.stroke(); + ctx.fillStyle = w.value ? "#89A" : "#333"; + ctx.beginPath(); + ctx.arc( widget_width - margin * 2, y + H * 0.5, H * 0.36, 0, Math.PI * 2 ); + ctx.fill(); + if (show_text) { + ctx.fillStyle = secondary_text_color; + const label = w.label || w.name; + if (label != null) { + ctx.fillText(label, margin * 2, y + H * 0.7); + } + ctx.fillStyle = w.value ? text_color : secondary_text_color; + ctx.textAlign = "right"; + ctx.fillText( + w.value + ? w.options.on || "true" + : w.options.off || "false", + widget_width - 40, + y + H * 0.7 + ); + } + break; + case "slider": + ctx.fillStyle = background_color; + ctx.fillRect(margin, y, widget_width - margin * 2, H); + var range = w.options.max - w.options.min; + var nvalue = (w.value - w.options.min) / range; + if(nvalue < 0.0) nvalue = 0.0; + if(nvalue > 1.0) nvalue = 1.0; + ctx.fillStyle = w.options.hasOwnProperty("slider_color") ? w.options.slider_color : (active_widget == w ? "#89A" : "#678"); + ctx.fillRect(margin, y, nvalue * (widget_width - margin * 2), H); + if(show_text && !w.disabled) + ctx.strokeRect(margin, y, widget_width - margin * 2, H); + if (w.marker) { + var marker_nvalue = (w.marker - w.options.min) / range; + if(marker_nvalue < 0.0) marker_nvalue = 0.0; + if(marker_nvalue > 1.0) marker_nvalue = 1.0; + ctx.fillStyle = w.options.hasOwnProperty("marker_color") ? w.options.marker_color : "#AA9"; + ctx.fillRect( margin + marker_nvalue * (widget_width - margin * 2), y, 2, H ); + } + if (show_text) { + ctx.textAlign = "center"; + ctx.fillStyle = text_color; + ctx.fillText( + w.label || w.name + " " + Number(w.value).toFixed( + w.options.precision != null + ? w.options.precision + : 3 + ), + widget_width * 0.5, + y + H * 0.7 + ); + } + break; + case "number": + case "combo": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if(show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5] ); + else + ctx.rect(margin, y, widget_width - margin * 2, H ); + ctx.fill(); + if (show_text) { + if(!w.disabled) + ctx.stroke(); + ctx.fillStyle = text_color; + if(!w.disabled) + { + ctx.beginPath(); + ctx.moveTo(margin + 16, y + 5); + ctx.lineTo(margin + 6, y + H * 0.5); + ctx.lineTo(margin + 16, y + H - 5); + ctx.fill(); + ctx.beginPath(); + ctx.moveTo(widget_width - margin - 16, y + 5); + ctx.lineTo(widget_width - margin - 6, y + H * 0.5); + ctx.lineTo(widget_width - margin - 16, y + H - 5); + ctx.fill(); + } + ctx.fillStyle = secondary_text_color; + ctx.fillText(w.label || w.name, margin * 2 + 5, y + H * 0.7); + ctx.fillStyle = text_color; + ctx.textAlign = "right"; + if (w.type == "number") { + ctx.fillText( + Number(w.value).toFixed( + w.options.precision !== undefined + ? w.options.precision + : 3 + ), + widget_width - margin * 2 - 20, + y + H * 0.7 + ); + } else { + var v = w.value; + if( w.options.values ) + { + var values = w.options.values; + if( values.constructor === Function ) + values = values(); + if(values && values.constructor !== Array) + v = values[ w.value ]; + } + ctx.fillText( + v, + widget_width - margin * 2 - 20, + y + H * 0.7 + ); + } + } + break; + case "string": + case "text": + ctx.textAlign = "left"; + ctx.strokeStyle = outline_color; + ctx.fillStyle = background_color; + ctx.beginPath(); + if (show_text) + ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5]); + else + ctx.rect( margin, y, widget_width - margin * 2, H ); + ctx.fill(); + if (show_text) { + if(!w.disabled) + ctx.stroke(); + ctx.save(); + ctx.beginPath(); + ctx.rect(margin, y, widget_width - margin * 2, H); + ctx.clip(); + + //ctx.stroke(); + ctx.fillStyle = secondary_text_color; + const label = w.label || w.name; + if (label != null) { + ctx.fillText(label, margin * 2, y + H * 0.7); + } + ctx.fillStyle = text_color; + ctx.textAlign = "right"; + ctx.fillText(String(w.value).substr(0,30), widget_width - margin * 2, y + H * 0.7); //30 chars max + ctx.restore(); + } + break; + default: + if (w.draw) { + w.draw(ctx, node, widget_width, y, H); + } + break; + } + posY += (w.computeSize ? w.computeSize(widget_width)[1] : H) + 4; + ctx.globalAlpha = this.editor_alpha; + + } + ctx.restore(); + ctx.textAlign = "left"; + }; + + /** + * process an event on widgets + * @method processNodeWidgets + **/ + LGraphCanvas.prototype.processNodeWidgets = function( + node, + pos, + event, + active_widget + ) { + if (!node.widgets || !node.widgets.length || (!this.allow_interaction && !node.flags.allow_interaction)) { + return null; + } + + var x = pos[0] - node.pos[0]; + var y = pos[1] - node.pos[1]; + var width = node.size[0]; + var that = this; + var ref_window = this.getCanvasWindow(); + + for (var i = 0; i < node.widgets.length; ++i) { + var w = node.widgets[i]; + if(!w || w.disabled) + continue; + var widget_height = w.computeSize ? w.computeSize(width)[1] : LiteGraph.NODE_WIDGET_HEIGHT; + var widget_width = w.width || width; + //outside + if ( w != active_widget && + (x < 6 || x > widget_width - 12 || y < w.last_y || y > w.last_y + widget_height || w.last_y === undefined) ) + continue; + + var old_value = w.value; + + //if ( w == active_widget || (x > 6 && x < widget_width - 12 && y > w.last_y && y < w.last_y + widget_height) ) { + //inside widget + switch (w.type) { + case "button": + if (event.type === LiteGraph.pointerevents_method+"down") { + if (w.callback) { + setTimeout(function() { + w.callback(w, that, node, pos, event); + }, 20); + } + w.clicked = true; + this.dirty_canvas = true; + } + break; + case "slider": + var old_value = w.value; + var nvalue = clamp((x - 15) / (widget_width - 30), 0, 1); + if(w.options.read_only) break; + w.value = w.options.min + (w.options.max - w.options.min) * nvalue; + if (old_value != w.value) { + setTimeout(function() { + inner_value_change(w, w.value); + }, 20); + } + this.dirty_canvas = true; + break; + case "number": + case "combo": + var old_value = w.value; + var delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0; + var allow_scroll = true; + if (delta) { + if (x > -3 && x < widget_width + 3) { + allow_scroll = false; + } + } + if (allow_scroll && event.type == LiteGraph.pointerevents_method+"move" && w.type == "number") { + if(event.deltaX) + w.value += event.deltaX * 0.1 * (w.options.step || 1); + if ( w.options.min != null && w.value < w.options.min ) { + w.value = w.options.min; + } + if ( w.options.max != null && w.value > w.options.max ) { + w.value = w.options.max; + } + } else if (event.type == LiteGraph.pointerevents_method+"down") { + var values = w.options.values; + if (values && values.constructor === Function) { + values = w.options.values(w, node); + } + var values_list = null; + + if( w.type != "number") + values_list = values.constructor === Array ? values : Object.keys(values); + + var delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0; + if (w.type == "number") { + w.value += delta * 0.1 * (w.options.step || 1); + if ( w.options.min != null && w.value < w.options.min ) { + w.value = w.options.min; + } + if ( w.options.max != null && w.value > w.options.max ) { + w.value = w.options.max; + } + } else if (delta) { //clicked in arrow, used for combos + var index = -1; + this.last_mouseclick = 0; //avoids dobl click event + if(values.constructor === Object) + index = values_list.indexOf( String( w.value ) ) + delta; + else + index = values_list.indexOf( w.value ) + delta; + if (index >= values_list.length) { + index = values_list.length - 1; + } + if (index < 0) { + index = 0; + } + if( values.constructor === Array ) + w.value = values[index]; + else + w.value = index; + } else { //combo clicked + var text_values = values != values_list ? Object.values(values) : values; + var menu = new LiteGraph.ContextMenu(text_values, { + scale: Math.max(1, this.ds.scale), + event: event, + className: "dark", + callback: inner_clicked.bind(w) + }, + ref_window); + function inner_clicked(v, option, event) { + if(values != values_list) + v = text_values.indexOf(v); + this.value = v; + inner_value_change(this, v); + that.dirty_canvas = true; + return false; + } + } + } //end mousedown + else if(event.type == LiteGraph.pointerevents_method+"up" && w.type == "number") + { + var delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0; + if (event.click_time < 200 && delta == 0) { + this.prompt("Value",w.value,function(v) { + // check if v is a valid equation or a number + if (/^[0-9+\-*/()\s]+|\d+\.\d+$/.test(v)) { + try {//solve the equation if possible + v = eval(v); + } catch (e) { } + } + this.value = Number(v); + inner_value_change(this, this.value); + }.bind(w), + event); + } + } + + if( old_value != w.value ) + setTimeout( + function() { + inner_value_change(this, this.value); + }.bind(w), + 20 + ); + this.dirty_canvas = true; + break; + case "toggle": + if (event.type == LiteGraph.pointerevents_method+"down") { + w.value = !w.value; + setTimeout(function() { + inner_value_change(w, w.value); + }, 20); + } + break; + case "string": + case "text": + if (event.type == LiteGraph.pointerevents_method+"down") { + this.prompt("Value",w.value,function(v) { + inner_value_change(this, v); + }.bind(w), + event,w.options ? w.options.multiline : false ); + } + break; + default: + if (w.mouse) { + this.dirty_canvas = w.mouse(event, [x, y], node); + } + break; + } //end switch + + //value changed + if( old_value != w.value ) + { + if(node.onWidgetChanged) + node.onWidgetChanged( w.name,w.value,old_value,w ); + node.graph._version++; + } + + return w; + }//end for + + function inner_value_change(widget, value) { + if(widget.type == "number"){ + value = Number(value); + } + widget.value = value; + if ( widget.options && widget.options.property && node.properties[widget.options.property] !== undefined ) { + node.setProperty( widget.options.property, value ); + } + if (widget.callback) { + widget.callback(widget.value, that, node, pos, event); + } + } + + return null; + }; + + /** + * draws every group area in the background + * @method drawGroups + **/ + LGraphCanvas.prototype.drawGroups = function(canvas, ctx) { + if (!this.graph) { + return; + } + + var groups = this.graph._groups; + + ctx.save(); + ctx.globalAlpha = 0.5 * this.editor_alpha; + + for (var i = 0; i < groups.length; ++i) { + var group = groups[i]; + + if (!overlapBounding(this.visible_area, group._bounding)) { + continue; + } //out of the visible area + + ctx.fillStyle = group.color || "#335"; + ctx.strokeStyle = group.color || "#335"; + var pos = group._pos; + var size = group._size; + ctx.globalAlpha = 0.25 * this.editor_alpha; + ctx.beginPath(); + ctx.rect(pos[0] + 0.5, pos[1] + 0.5, size[0], size[1]); + ctx.fill(); + ctx.globalAlpha = this.editor_alpha; + ctx.stroke(); + + ctx.beginPath(); + ctx.moveTo(pos[0] + size[0], pos[1] + size[1]); + ctx.lineTo(pos[0] + size[0] - 10, pos[1] + size[1]); + ctx.lineTo(pos[0] + size[0], pos[1] + size[1] - 10); + ctx.fill(); + + var font_size = + group.font_size || LiteGraph.DEFAULT_GROUP_FONT_SIZE; + ctx.font = font_size + "px Arial"; + ctx.textAlign = "left"; + ctx.fillText(group.title, pos[0] + 4, pos[1] + font_size); + } + + ctx.restore(); + }; + + LGraphCanvas.prototype.adjustNodesSize = function() { + var nodes = this.graph._nodes; + for (var i = 0; i < nodes.length; ++i) { + nodes[i].size = nodes[i].computeSize(); + } + this.setDirty(true, true); + }; + + /** + * resizes the canvas to a given size, if no size is passed, then it tries to fill the parentNode + * @method resize + **/ + LGraphCanvas.prototype.resize = function(width, height) { + if (!width && !height) { + var parent = this.canvas.parentNode; + width = parent.offsetWidth; + height = parent.offsetHeight; + } + + if (this.canvas.width == width && this.canvas.height == height) { + return; + } + + this.canvas.width = width; + this.canvas.height = height; + this.bgcanvas.width = this.canvas.width; + this.bgcanvas.height = this.canvas.height; + this.setDirty(true, true); + }; + + /** + * switches to live mode (node shapes are not rendered, only the content) + * this feature was designed when graphs where meant to create user interfaces + * @method switchLiveMode + **/ + LGraphCanvas.prototype.switchLiveMode = function(transition) { + if (!transition) { + this.live_mode = !this.live_mode; + this.dirty_canvas = true; + this.dirty_bgcanvas = true; + return; + } + + var self = this; + var delta = this.live_mode ? 1.1 : 0.9; + if (this.live_mode) { + this.live_mode = false; + this.editor_alpha = 0.1; + } + + var t = setInterval(function() { + self.editor_alpha *= delta; + self.dirty_canvas = true; + self.dirty_bgcanvas = true; + + if (delta < 1 && self.editor_alpha < 0.01) { + clearInterval(t); + if (delta < 1) { + self.live_mode = true; + } + } + if (delta > 1 && self.editor_alpha > 0.99) { + clearInterval(t); + self.editor_alpha = 1; + } + }, 1); + }; + + LGraphCanvas.prototype.onNodeSelectionChange = function(node) { + return; //disabled + }; + + /* this is an implementation for touch not in production and not ready + */ + /*LGraphCanvas.prototype.touchHandler = function(event) { + //alert("foo"); + var touches = event.changedTouches, + first = touches[0], + type = ""; + + switch (event.type) { + case "touchstart": + type = "mousedown"; + break; + case "touchmove": + type = "mousemove"; + break; + case "touchend": + type = "mouseup"; + break; + default: + return; + } + + //initMouseEvent(type, canBubble, cancelable, view, clickCount, + // screenX, screenY, clientX, clientY, ctrlKey, + // altKey, shiftKey, metaKey, button, relatedTarget); + + // this is eventually a Dom object, get the LGraphCanvas back + if(typeof this.getCanvasWindow == "undefined"){ + var window = this.lgraphcanvas.getCanvasWindow(); + }else{ + var window = this.getCanvasWindow(); + } + + var document = window.document; + + var simulatedEvent = document.createEvent("MouseEvent"); + simulatedEvent.initMouseEvent( + type, + true, + true, + window, + 1, + first.screenX, + first.screenY, + first.clientX, + first.clientY, + false, + false, + false, + false, + 0, //left + null + ); + first.target.dispatchEvent(simulatedEvent); + event.preventDefault(); + };*/ + + /* CONTEXT MENU ********************/ + + LGraphCanvas.onGroupAdd = function(info, entry, mouse_event) { + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var group = new LiteGraph.LGraphGroup(); + group.pos = canvas.convertEventToCanvasOffset(mouse_event); + canvas.graph.add(group); + }; + + /** + * Determines the furthest nodes in each direction + * @param nodes {LGraphNode[]} the nodes to from which boundary nodes will be extracted + * @return {{left: LGraphNode, top: LGraphNode, right: LGraphNode, bottom: LGraphNode}} + */ + LGraphCanvas.getBoundaryNodes = function(nodes) { + let top = null; + let right = null; + let bottom = null; + let left = null; + for (const nID in nodes) { + const node = nodes[nID]; + const [x, y] = node.pos; + const [width, height] = node.size; + + if (top === null || y < top.pos[1]) { + top = node; + } + if (right === null || x + width > right.pos[0] + right.size[0]) { + right = node; + } + if (bottom === null || y + height > bottom.pos[1] + bottom.size[1]) { + bottom = node; + } + if (left === null || x < left.pos[0]) { + left = node; + } + } + + return { + "top": top, + "right": right, + "bottom": bottom, + "left": left + }; + } + /** + * Determines the furthest nodes in each direction for the currently selected nodes + * @return {{left: LGraphNode, top: LGraphNode, right: LGraphNode, bottom: LGraphNode}} + */ + LGraphCanvas.prototype.boundaryNodesForSelection = function() { + return LGraphCanvas.getBoundaryNodes(Object.values(this.selected_nodes)); + } + + /** + * + * @param {LGraphNode[]} nodes a list of nodes + * @param {"top"|"bottom"|"left"|"right"} direction Direction to align the nodes + * @param {LGraphNode?} align_to Node to align to (if null, align to the furthest node in the given direction) + */ + LGraphCanvas.alignNodes = function (nodes, direction, align_to) { + if (!nodes) { + return; + } + + const canvas = LGraphCanvas.active_canvas; + let boundaryNodes = [] + if (align_to === undefined) { + boundaryNodes = LGraphCanvas.getBoundaryNodes(nodes) + } else { + boundaryNodes = { + "top": align_to, + "right": align_to, + "bottom": align_to, + "left": align_to + } + } + + for (const [_, node] of Object.entries(canvas.selected_nodes)) { + switch (direction) { + case "right": + node.pos[0] = boundaryNodes["right"].pos[0] + boundaryNodes["right"].size[0] - node.size[0]; + break; + case "left": + node.pos[0] = boundaryNodes["left"].pos[0]; + break; + case "top": + node.pos[1] = boundaryNodes["top"].pos[1]; + break; + case "bottom": + node.pos[1] = boundaryNodes["bottom"].pos[1] + boundaryNodes["bottom"].size[1] - node.size[1]; + break; + } + } + + canvas.dirty_canvas = true; + canvas.dirty_bgcanvas = true; + }; + + LGraphCanvas.onNodeAlign = function(value, options, event, prev_menu, node) { + new LiteGraph.ContextMenu(["Top", "Bottom", "Left", "Right"], { + event: event, + callback: inner_clicked, + parentMenu: prev_menu, + }); + + function inner_clicked(value) { + LGraphCanvas.alignNodes(LGraphCanvas.active_canvas.selected_nodes, value.toLowerCase(), node); + } + } + + LGraphCanvas.onGroupAlign = function(value, options, event, prev_menu) { + new LiteGraph.ContextMenu(["Top", "Bottom", "Left", "Right"], { + event: event, + callback: inner_clicked, + parentMenu: prev_menu, + }); + + function inner_clicked(value) { + LGraphCanvas.alignNodes(LGraphCanvas.active_canvas.selected_nodes, value.toLowerCase()); + } + } + + LGraphCanvas.onMenuAdd = function (node, options, e, prev_menu, callback) { + + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + var graph = canvas.graph; + if (!graph) + return; + + function inner_onMenuAdded(base_category ,prev_menu){ + + var categories = LiteGraph.getNodeTypesCategories(canvas.filter || graph.filter).filter(function(category){return category.startsWith(base_category)}); + var entries = []; + + categories.map(function(category){ + + if (!category) + return; + + var base_category_regex = new RegExp('^(' + base_category + ')'); + var category_name = category.replace(base_category_regex,"").split('/')[0]; + var category_path = base_category === '' ? category_name + '/' : base_category + category_name + '/'; + + var name = category_name; + if(name.indexOf("::") != -1) //in case it has a namespace like "shader::math/rand" it hides the namespace + name = name.split("::")[1]; + + var index = entries.findIndex(function(entry){return entry.value === category_path}); + if (index === -1) { + entries.push({ value: category_path, content: name, has_submenu: true, callback : function(value, event, mouseEvent, contextMenu){ + inner_onMenuAdded(value.value, contextMenu) + }}); + } + + }); + + var nodes = LiteGraph.getNodeTypesInCategory(base_category.slice(0, -1), canvas.filter || graph.filter ); + nodes.map(function(node){ + + if (node.skip_list) + return; + + var entry = { value: node.type, content: node.title, has_submenu: false , callback : function(value, event, mouseEvent, contextMenu){ + + var first_event = contextMenu.getFirstEvent(); + canvas.graph.beforeChange(); + var node = LiteGraph.createNode(value.value); + if (node) { + node.pos = canvas.convertEventToCanvasOffset(first_event); + canvas.graph.add(node); + } + if(callback) + callback(node); + canvas.graph.afterChange(); + + } + } + + entries.push(entry); + + }); + + new LiteGraph.ContextMenu( entries, { event: e, parentMenu: prev_menu }, ref_window ); + + } + + inner_onMenuAdded('',prev_menu); + return false; + + }; + + LGraphCanvas.onMenuCollapseAll = function() {}; + + LGraphCanvas.onMenuNodeEdit = function() {}; + + LGraphCanvas.showMenuNodeOptionalInputs = function( + v, + options, + e, + prev_menu, + node + ) { + if (!node) { + return; + } + + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var options = node.optional_inputs; + if (node.onGetInputs) { + options = node.onGetInputs(); + } + + var entries = []; + if (options) { + for (var i=0; i < options.length; i++) { + var entry = options[i]; + if (!entry) { + entries.push(null); + continue; + } + var label = entry[0]; + if(!entry[2]) + entry[2] = {}; + + if (entry[2].label) { + label = entry[2].label; + } + + entry[2].removable = true; + var data = { content: label, value: entry }; + if (entry[1] == LiteGraph.ACTION) { + data.className = "event"; + } + entries.push(data); + } + } + + if (node.onMenuNodeInputs) { + var retEntries = node.onMenuNodeInputs(entries); + if(retEntries) entries = retEntries; + } + + if (!entries.length) { + console.log("no input entries"); + return; + } + + var menu = new LiteGraph.ContextMenu( + entries, + { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + node: node + }, + ref_window + ); + + function inner_clicked(v, e, prev) { + if (!node) { + return; + } + + if (v.callback) { + v.callback.call(that, node, v, e, prev); + } + + if (v.value) { + node.graph.beforeChange(); + node.addInput(v.value[0], v.value[1], v.value[2]); + + if (node.onNodeInputAdd) { // callback to the node when adding a slot + node.onNodeInputAdd(v.value); + } + node.setDirtyCanvas(true, true); + node.graph.afterChange(); + } + } + + return false; + }; + + LGraphCanvas.showMenuNodeOptionalOutputs = function( + v, + options, + e, + prev_menu, + node + ) { + if (!node) { + return; + } + + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var options = node.optional_outputs; + if (node.onGetOutputs) { + options = node.onGetOutputs(); + } + + var entries = []; + if (options) { + for (var i=0; i < options.length; i++) { + var entry = options[i]; + if (!entry) { + //separator? + entries.push(null); + continue; + } + + if ( + node.flags && + node.flags.skip_repeated_outputs && + node.findOutputSlot(entry[0]) != -1 + ) { + continue; + } //skip the ones already on + var label = entry[0]; + if(!entry[2]) + entry[2] = {}; + if (entry[2].label) { + label = entry[2].label; + } + entry[2].removable = true; + var data = { content: label, value: entry }; + if (entry[1] == LiteGraph.EVENT) { + data.className = "event"; + } + entries.push(data); + } + } + + if (this.onMenuNodeOutputs) { + entries = this.onMenuNodeOutputs(entries); + } + if (LiteGraph.do_add_triggers_slots){ //canvas.allow_addOutSlot_onExecuted + if (node.findOutputSlot("onExecuted") == -1){ + entries.push({content: "On Executed", value: ["onExecuted", LiteGraph.EVENT, {nameLocked: true}], className: "event"}); //, opts: {} + } + } + // add callback for modifing the menu elements onMenuNodeOutputs + if (node.onMenuNodeOutputs) { + var retEntries = node.onMenuNodeOutputs(entries); + if(retEntries) entries = retEntries; + } + + if (!entries.length) { + return; + } + + var menu = new LiteGraph.ContextMenu( + entries, + { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + node: node + }, + ref_window + ); + + function inner_clicked(v, e, prev) { + if (!node) { + return; + } + + if (v.callback) { + v.callback.call(that, node, v, e, prev); + } + + if (!v.value) { + return; + } + + var value = v.value[1]; + + if ( + value && + (value.constructor === Object || value.constructor === Array) + ) { + //submenu why? + var entries = []; + for (var i in value) { + entries.push({ content: i, value: value[i] }); + } + new LiteGraph.ContextMenu(entries, { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + node: node + }); + return false; + } else { + node.graph.beforeChange(); + node.addOutput(v.value[0], v.value[1], v.value[2]); + + if (node.onNodeOutputAdd) { // a callback to the node when adding a slot + node.onNodeOutputAdd(v.value); + } + node.setDirtyCanvas(true, true); + node.graph.afterChange(); + } + } + + return false; + }; + + LGraphCanvas.onShowMenuNodeProperties = function( + value, + options, + e, + prev_menu, + node + ) { + if (!node || !node.properties) { + return; + } + + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var entries = []; + for (var i in node.properties) { + var value = node.properties[i] !== undefined ? node.properties[i] : " "; + if( typeof value == "object" ) + value = JSON.stringify(value); + var info = node.getPropertyInfo(i); + if(info.type == "enum" || info.type == "combo") + value = LGraphCanvas.getPropertyPrintableValue( value, info.values ); + + //value could contain invalid html characters, clean that + value = LGraphCanvas.decodeHTML(value); + entries.push({ + content: + "" + + (info.label ? info.label : i) + + "" + + "" + + value + + "", + value: i + }); + } + if (!entries.length) { + return; + } + + var menu = new LiteGraph.ContextMenu( + entries, + { + event: e, + callback: inner_clicked, + parentMenu: prev_menu, + allow_html: true, + node: node + }, + ref_window + ); + + function inner_clicked(v, options, e, prev) { + if (!node) { + return; + } + var rect = this.getBoundingClientRect(); + canvas.showEditPropertyValue(node, v.value, { + position: [rect.left, rect.top] + }); + } + + return false; + }; + + LGraphCanvas.decodeHTML = function(str) { + var e = document.createElement("div"); + e.innerText = str; + return e.innerHTML; + }; + + LGraphCanvas.onMenuResizeNode = function(value, options, e, menu, node) { + if (!node) { + return; + } + + var fApplyMultiNode = function(node){ + node.size = node.computeSize(); + if (node.onResize) + node.onResize(node.size); + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.prototype.showLinkMenu = function(link, e) { + var that = this; + // console.log(link); + var node_left = that.graph.getNodeById( link.origin_id ); + var node_right = that.graph.getNodeById( link.target_id ); + var fromType = false; + if (node_left && node_left.outputs && node_left.outputs[link.origin_slot]) fromType = node_left.outputs[link.origin_slot].type; + var destType = false; + if (node_right && node_right.outputs && node_right.outputs[link.target_slot]) destType = node_right.inputs[link.target_slot].type; + + var options = ["Add Node",null,"Delete",null]; + + + var menu = new LiteGraph.ContextMenu(options, { + event: e, + title: link.data != null ? link.data.constructor.name : null, + callback: inner_clicked + }); + + function inner_clicked(v,options,e) { + switch (v) { + case "Add Node": + LGraphCanvas.onMenuAdd(null, null, e, menu, function(node){ + // console.debug("node autoconnect"); + if(!node.inputs || !node.inputs.length || !node.outputs || !node.outputs.length){ + return; + } + // leave the connection type checking inside connectByType + if (node_left.connectByType( link.origin_slot, node, fromType )){ + node.connectByType( link.target_slot, node_right, destType ); + node.pos[0] -= node.size[0] * 0.5; + } + }); + break; + + case "Delete": + that.graph.removeLink(link.id); + break; + default: + /*var nodeCreated = createDefaultNodeForSlot({ nodeFrom: node_left + ,slotFrom: link.origin_slot + ,nodeTo: node + ,slotTo: link.target_slot + ,e: e + ,nodeType: "AUTO" + }); + if(nodeCreated) console.log("new node in beetween "+v+" created");*/ + } + } + + return false; + }; + + LGraphCanvas.prototype.createDefaultNodeForSlot = function(optPass) { // addNodeMenu for connection + var optPass = optPass || {}; + var opts = Object.assign({ nodeFrom: null // input + ,slotFrom: null // input + ,nodeTo: null // output + ,slotTo: null // output + ,position: [] // pass the event coords + ,nodeType: null // choose a nodetype to add, AUTO to set at first good + ,posAdd:[0,0] // adjust x,y + ,posSizeFix:[0,0] // alpha, adjust the position x,y based on the new node size w,h + } + ,optPass + ); + var that = this; + + var isFrom = opts.nodeFrom && opts.slotFrom!==null; + var isTo = !isFrom && opts.nodeTo && opts.slotTo!==null; + + if (!isFrom && !isTo){ + console.warn("No data passed to createDefaultNodeForSlot "+opts.nodeFrom+" "+opts.slotFrom+" "+opts.nodeTo+" "+opts.slotTo); + return false; + } + if (!opts.nodeType){ + console.warn("No type to createDefaultNodeForSlot"); + return false; + } + + var nodeX = isFrom ? opts.nodeFrom : opts.nodeTo; + var slotX = isFrom ? opts.slotFrom : opts.slotTo; + + var iSlotConn = false; + switch (typeof slotX){ + case "string": + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX,false) : nodeX.findInputSlot(slotX,false); + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "object": + // ok slotX + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX.name) : nodeX.findInputSlot(slotX.name); + break; + case "number": + iSlotConn = slotX; + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "undefined": + default: + // bad ? + //iSlotConn = 0; + console.warn("Cant get slot information "+slotX); + return false; + } + + if (slotX===false || iSlotConn===false){ + console.warn("createDefaultNodeForSlot bad slotX "+slotX+" "+iSlotConn); + } + + // check for defaults nodes for this slottype + var fromSlotType = slotX.type==LiteGraph.EVENT?"_event_":slotX.type; + var slotTypesDefault = isFrom ? LiteGraph.slot_types_default_out : LiteGraph.slot_types_default_in; + if(slotTypesDefault && slotTypesDefault[fromSlotType]){ + if (slotX.link !== null) { + // is connected + }else{ + // is not not connected + } + nodeNewType = false; + if(typeof slotTypesDefault[fromSlotType] == "object" || typeof slotTypesDefault[fromSlotType] == "array"){ + for(var typeX in slotTypesDefault[fromSlotType]){ + if (opts.nodeType == slotTypesDefault[fromSlotType][typeX] || opts.nodeType == "AUTO"){ + nodeNewType = slotTypesDefault[fromSlotType][typeX]; + // console.log("opts.nodeType == slotTypesDefault[fromSlotType][typeX] :: "+opts.nodeType); + break; // -------- + } + } + }else{ + if (opts.nodeType == slotTypesDefault[fromSlotType] || opts.nodeType == "AUTO") nodeNewType = slotTypesDefault[fromSlotType]; + } + if (nodeNewType) { + var nodeNewOpts = false; + if (typeof nodeNewType == "object" && nodeNewType.node){ + nodeNewOpts = nodeNewType; + nodeNewType = nodeNewType.node; + } + + //that.graph.beforeChange(); + + var newNode = LiteGraph.createNode(nodeNewType); + if(newNode){ + // if is object pass options + if (nodeNewOpts){ + if (nodeNewOpts.properties) { + for (var i in nodeNewOpts.properties) { + newNode.addProperty( i, nodeNewOpts.properties[i] ); + } + } + if (nodeNewOpts.inputs) { + newNode.inputs = []; + for (var i in nodeNewOpts.inputs) { + newNode.addOutput( + nodeNewOpts.inputs[i][0], + nodeNewOpts.inputs[i][1] + ); + } + } + if (nodeNewOpts.outputs) { + newNode.outputs = []; + for (var i in nodeNewOpts.outputs) { + newNode.addOutput( + nodeNewOpts.outputs[i][0], + nodeNewOpts.outputs[i][1] + ); + } + } + if (nodeNewOpts.title) { + newNode.title = nodeNewOpts.title; + } + if (nodeNewOpts.json) { + newNode.configure(nodeNewOpts.json); + } + + } + + // add the node + that.graph.add(newNode); + newNode.pos = [ opts.position[0]+opts.posAdd[0]+(opts.posSizeFix[0]?opts.posSizeFix[0]*newNode.size[0]:0) + ,opts.position[1]+opts.posAdd[1]+(opts.posSizeFix[1]?opts.posSizeFix[1]*newNode.size[1]:0)]; //that.last_click_position; //[e.canvasX+30, e.canvasX+5];*/ + + //that.graph.afterChange(); + + // connect the two! + if (isFrom){ + opts.nodeFrom.connectByType( iSlotConn, newNode, fromSlotType ); + }else{ + opts.nodeTo.connectByTypeOutput( iSlotConn, newNode, fromSlotType ); + } + + // if connecting in between + if (isFrom && isTo){ + // TODO + } + + return true; + + }else{ + console.log("failed creating "+nodeNewType); + } + } + } + return false; + } + + LGraphCanvas.prototype.showConnectionMenu = function(optPass) { // addNodeMenu for connection + var optPass = optPass || {}; + var opts = Object.assign({ nodeFrom: null // input + ,slotFrom: null // input + ,nodeTo: null // output + ,slotTo: null // output + ,e: null + } + ,optPass + ); + var that = this; + + var isFrom = opts.nodeFrom && opts.slotFrom; + var isTo = !isFrom && opts.nodeTo && opts.slotTo; + + if (!isFrom && !isTo){ + console.warn("No data passed to showConnectionMenu"); + return false; + } + + var nodeX = isFrom ? opts.nodeFrom : opts.nodeTo; + var slotX = isFrom ? opts.slotFrom : opts.slotTo; + + var iSlotConn = false; + switch (typeof slotX){ + case "string": + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX,false) : nodeX.findInputSlot(slotX,false); + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + case "object": + // ok slotX + iSlotConn = isFrom ? nodeX.findOutputSlot(slotX.name) : nodeX.findInputSlot(slotX.name); + break; + case "number": + iSlotConn = slotX; + slotX = isFrom ? nodeX.outputs[slotX] : nodeX.inputs[slotX]; + break; + default: + // bad ? + //iSlotConn = 0; + console.warn("Cant get slot information "+slotX); + return false; + } + + var options = ["Add Node",null]; + + if (that.allow_searchbox){ + options.push("Search"); + options.push(null); + } + + // get defaults nodes for this slottype + var fromSlotType = slotX.type==LiteGraph.EVENT?"_event_":slotX.type; + var slotTypesDefault = isFrom ? LiteGraph.slot_types_default_out : LiteGraph.slot_types_default_in; + if(slotTypesDefault && slotTypesDefault[fromSlotType]){ + if(typeof slotTypesDefault[fromSlotType] == "object" || typeof slotTypesDefault[fromSlotType] == "array"){ + for(var typeX in slotTypesDefault[fromSlotType]){ + options.push(slotTypesDefault[fromSlotType][typeX]); + } + }else{ + options.push(slotTypesDefault[fromSlotType]); + } + } + + // build menu + var menu = new LiteGraph.ContextMenu(options, { + event: opts.e, + title: (slotX && slotX.name!="" ? (slotX.name + (fromSlotType?" | ":"")) : "")+(slotX && fromSlotType ? fromSlotType : ""), + callback: inner_clicked + }); + + // callback + function inner_clicked(v,options,e) { + //console.log("Process showConnectionMenu selection"); + switch (v) { + case "Add Node": + LGraphCanvas.onMenuAdd(null, null, e, menu, function(node){ + if (isFrom){ + opts.nodeFrom.connectByType( iSlotConn, node, fromSlotType ); + }else{ + opts.nodeTo.connectByTypeOutput( iSlotConn, node, fromSlotType ); + } + }); + break; + case "Search": + if(isFrom){ + that.showSearchBox(e,{node_from: opts.nodeFrom, slot_from: slotX, type_filter_in: fromSlotType}); + }else{ + that.showSearchBox(e,{node_to: opts.nodeTo, slot_from: slotX, type_filter_out: fromSlotType}); + } + break; + default: + // check for defaults nodes for this slottype + var nodeCreated = that.createDefaultNodeForSlot(Object.assign(opts,{ position: [opts.e.canvasX, opts.e.canvasY] + ,nodeType: v + })); + if (nodeCreated){ + // new node created + //console.log("node "+v+" created") + }else{ + // failed or v is not in defaults + } + break; + } + } + + return false; + }; + + // TODO refactor :: this is used fot title but not for properties! + LGraphCanvas.onShowPropertyEditor = function(item, options, e, menu, node) { + var input_html = ""; + var property = item.property || "title"; + var value = node[property]; + + // TODO refactor :: use createDialog ? + + var dialog = document.createElement("div"); + dialog.is_modified = false; + dialog.className = "graphdialog"; + dialog.innerHTML = + ""; + dialog.close = function() { + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + var title = dialog.querySelector(".name"); + title.innerText = property; + var input = dialog.querySelector(".value"); + if (input) { + input.value = value; + input.addEventListener("blur", function(e) { + this.focus(); + }); + input.addEventListener("keydown", function(e) { + dialog.is_modified = true; + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + inner(); // save + } else if (e.keyCode != 13 && e.target.localName != "textarea") { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + } + + var graphcanvas = LGraphCanvas.active_canvas; + var canvas = graphcanvas.canvas; + + var rect = canvas.getBoundingClientRect(); + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (event) { + dialog.style.left = event.clientX + offsetx + "px"; + dialog.style.top = event.clientY + offsety + "px"; + } else { + dialog.style.left = canvas.width * 0.5 + offsetx + "px"; + dialog.style.top = canvas.height * 0.5 + offsety + "px"; + } + + var button = dialog.querySelector("button"); + button.addEventListener("click", inner); + canvas.parentNode.appendChild(dialog); + + if(input) input.focus(); + + var dialogCloseTimer = null; + dialog.addEventListener("mouseleave", function(e) { + if(LiteGraph.dialog_close_on_mouse_leave) + if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave) + dialogCloseTimer = setTimeout(dialog.close, LiteGraph.dialog_close_on_mouse_leave_delay); //dialog.close(); + }); + dialog.addEventListener("mouseenter", function(e) { + if(LiteGraph.dialog_close_on_mouse_leave) + if(dialogCloseTimer) clearTimeout(dialogCloseTimer); + }); + + function inner() { + if(input) setValue(input.value); + } + + function setValue(value) { + if (item.type == "Number") { + value = Number(value); + } else if (item.type == "Boolean") { + value = Boolean(value); + } + node[property] = value; + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + node.setDirtyCanvas(true, true); + } + }; + + // refactor: there are different dialogs, some uses createDialog some dont + LGraphCanvas.prototype.prompt = function(title, value, callback, event, multiline) { + var that = this; + var input_html = ""; + title = title || ""; + + var dialog = document.createElement("div"); + dialog.is_modified = false; + dialog.className = "graphdialog rounded"; + if(multiline) + dialog.innerHTML = " "; + else + dialog.innerHTML = " "; + dialog.close = function() { + that.prompt_box = null; + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + + var graphcanvas = LGraphCanvas.active_canvas; + var canvas = graphcanvas.canvas; + canvas.parentNode.appendChild(dialog); + + if (this.ds.scale > 1) { + dialog.style.transform = "scale(" + this.ds.scale + ")"; + } + + var dialogCloseTimer = null; + var prevent_timeout = false; + LiteGraph.pointerListenerAdd(dialog,"leave", function(e) { + if (prevent_timeout) + return; + if(LiteGraph.dialog_close_on_mouse_leave) + if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave) + dialogCloseTimer = setTimeout(dialog.close, LiteGraph.dialog_close_on_mouse_leave_delay); //dialog.close(); + }); + LiteGraph.pointerListenerAdd(dialog,"enter", function(e) { + if(LiteGraph.dialog_close_on_mouse_leave) + if(dialogCloseTimer) clearTimeout(dialogCloseTimer); + }); + var selInDia = dialog.querySelectorAll("select"); + if (selInDia){ + // if filtering, check focus changed to comboboxes and prevent closing + selInDia.forEach(function(selIn) { + selIn.addEventListener("click", function(e) { + prevent_timeout++; + }); + selIn.addEventListener("blur", function(e) { + prevent_timeout = 0; + }); + selIn.addEventListener("change", function(e) { + prevent_timeout = -1; + }); + }); + } + + if (that.prompt_box) { + that.prompt_box.close(); + } + that.prompt_box = dialog; + + var first = null; + var timeout = null; + var selected = null; + + var name_element = dialog.querySelector(".name"); + name_element.innerText = title; + var value_element = dialog.querySelector(".value"); + value_element.value = value; + + var input = value_element; + input.addEventListener("keydown", function(e) { + dialog.is_modified = true; + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13 && e.target.localName != "textarea") { + if (callback) { + callback(this.value); + } + dialog.close(); + } else { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + + var button = dialog.querySelector("button"); + button.addEventListener("click", function(e) { + if (callback) { + callback(input.value); + } + that.setDirty(true); + dialog.close(); + }); + + var rect = canvas.getBoundingClientRect(); + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (event) { + dialog.style.left = event.clientX + offsetx + "px"; + dialog.style.top = event.clientY + offsety + "px"; + } else { + dialog.style.left = canvas.width * 0.5 + offsetx + "px"; + dialog.style.top = canvas.height * 0.5 + offsety + "px"; + } + + setTimeout(function() { + input.focus(); + }, 10); + + return dialog; + }; + + LGraphCanvas.search_limit = -1; + LGraphCanvas.prototype.showSearchBox = function(event, options) { + // proposed defaults + var def_options = { slot_from: null + ,node_from: null + ,node_to: null + ,do_type_filter: LiteGraph.search_filter_enabled // TODO check for registered_slot_[in/out]_types not empty // this will be checked for functionality enabled : filter on slot type, in and out + ,type_filter_in: false // these are default: pass to set initially set values + ,type_filter_out: false + ,show_general_if_none_on_typefilter: true + ,show_general_after_typefiltered: true + ,hide_on_mouse_leave: LiteGraph.search_hide_on_mouse_leave + ,show_all_if_empty: true + ,show_all_on_open: LiteGraph.search_show_all_on_open + }; + options = Object.assign(def_options, options || {}); + + //console.log(options); + + var that = this; + var input_html = ""; + var graphcanvas = LGraphCanvas.active_canvas; + var canvas = graphcanvas.canvas; + var root_document = canvas.ownerDocument || document; + + var dialog = document.createElement("div"); + dialog.className = "litegraph litesearchbox graphdialog rounded"; + dialog.innerHTML = "Search "; + if (options.do_type_filter){ + dialog.innerHTML += ""; + dialog.innerHTML += ""; + } + dialog.innerHTML += "
"; + + if( root_document.fullscreenElement ) + root_document.fullscreenElement.appendChild(dialog); + else + { + root_document.body.appendChild(dialog); + root_document.body.style.overflow = "hidden"; + } + // dialog element has been appended + + if (options.do_type_filter){ + var selIn = dialog.querySelector(".slot_in_type_filter"); + var selOut = dialog.querySelector(".slot_out_type_filter"); + } + + dialog.close = function() { + that.search_box = null; + this.blur(); + canvas.focus(); + root_document.body.style.overflow = ""; + + setTimeout(function() { + that.canvas.focus(); + }, 20); //important, if canvas loses focus keys wont be captured + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + + if (this.ds.scale > 1) { + dialog.style.transform = "scale(" + this.ds.scale + ")"; + } + + // hide on mouse leave + if(options.hide_on_mouse_leave){ + var prevent_timeout = false; + var timeout_close = null; + LiteGraph.pointerListenerAdd(dialog,"enter", function(e) { + if (timeout_close) { + clearTimeout(timeout_close); + timeout_close = null; + } + }); + LiteGraph.pointerListenerAdd(dialog,"leave", function(e) { + if (prevent_timeout){ + return; + } + timeout_close = setTimeout(function() { + dialog.close(); + }, 500); + }); + // if filtering, check focus changed to comboboxes and prevent closing + if (options.do_type_filter){ + selIn.addEventListener("click", function(e) { + prevent_timeout++; + }); + selIn.addEventListener("blur", function(e) { + prevent_timeout = 0; + }); + selIn.addEventListener("change", function(e) { + prevent_timeout = -1; + }); + selOut.addEventListener("click", function(e) { + prevent_timeout++; + }); + selOut.addEventListener("blur", function(e) { + prevent_timeout = 0; + }); + selOut.addEventListener("change", function(e) { + prevent_timeout = -1; + }); + } + } + + if (that.search_box) { + that.search_box.close(); + } + that.search_box = dialog; + + var helper = dialog.querySelector(".helper"); + + var first = null; + var timeout = null; + var selected = null; + + var input = dialog.querySelector("input"); + if (input) { + input.addEventListener("blur", function(e) { + this.focus(); + }); + input.addEventListener("keydown", function(e) { + if (e.keyCode == 38) { + //UP + changeSelection(false); + } else if (e.keyCode == 40) { + //DOWN + changeSelection(true); + } else if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + if (selected) { + select(selected.innerHTML); + } else if (first) { + select(first); + } else { + dialog.close(); + } + } else { + if (timeout) { + clearInterval(timeout); + } + timeout = setTimeout(refreshHelper, 10); + return; + } + e.preventDefault(); + e.stopPropagation(); + e.stopImmediatePropagation(); + return true; + }); + } + + // if should filter on type, load and fill selected and choose elements if passed + if (options.do_type_filter){ + if (selIn){ + var aSlots = LiteGraph.slot_types_in; + var nSlots = aSlots.length; // this for object :: Object.keys(aSlots).length; + + if (options.type_filter_in == LiteGraph.EVENT || options.type_filter_in == LiteGraph.ACTION) + options.type_filter_in = "_event_"; + /* this will filter on * .. but better do it manually in case + else if(options.type_filter_in === "" || options.type_filter_in === 0) + options.type_filter_in = "*";*/ + + for (var iK=0; iK (rect.height - 200)) + helper.style.maxHeight = (rect.height - event.layerY - 20) + "px"; + + /* + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (event) { + dialog.style.left = event.clientX + offsetx + "px"; + dialog.style.top = event.clientY + offsety + "px"; + } else { + dialog.style.left = canvas.width * 0.5 + offsetx + "px"; + dialog.style.top = canvas.height * 0.5 + offsety + "px"; + } + canvas.parentNode.appendChild(dialog); + */ + + input.focus(); + if (options.show_all_on_open) refreshHelper(); + + function select(name) { + if (name) { + if (that.onSearchBoxSelection) { + that.onSearchBoxSelection(name, event, graphcanvas); + } else { + var extra = LiteGraph.searchbox_extras[name.toLowerCase()]; + if (extra) { + name = extra.type; + } + + graphcanvas.graph.beforeChange(); + var node = LiteGraph.createNode(name); + if (node) { + node.pos = graphcanvas.convertEventToCanvasOffset( + event + ); + graphcanvas.graph.add(node, false); + } + + if (extra && extra.data) { + if (extra.data.properties) { + for (var i in extra.data.properties) { + node.addProperty( i, extra.data.properties[i] ); + } + } + if (extra.data.inputs) { + node.inputs = []; + for (var i in extra.data.inputs) { + node.addOutput( + extra.data.inputs[i][0], + extra.data.inputs[i][1] + ); + } + } + if (extra.data.outputs) { + node.outputs = []; + for (var i in extra.data.outputs) { + node.addOutput( + extra.data.outputs[i][0], + extra.data.outputs[i][1] + ); + } + } + if (extra.data.title) { + node.title = extra.data.title; + } + if (extra.data.json) { + node.configure(extra.data.json); + } + + } + + // join node after inserting + if (options.node_from){ + var iS = false; + switch (typeof options.slot_from){ + case "string": + iS = options.node_from.findOutputSlot(options.slot_from); + break; + case "object": + if (options.slot_from.name){ + iS = options.node_from.findOutputSlot(options.slot_from.name); + }else{ + iS = -1; + } + if (iS==-1 && typeof options.slot_from.slot_index !== "undefined") iS = options.slot_from.slot_index; + break; + case "number": + iS = options.slot_from; + break; + default: + iS = 0; // try with first if no name set + } + if (typeof options.node_from.outputs[iS] !== undefined){ + if (iS!==false && iS>-1){ + options.node_from.connectByType( iS, node, options.node_from.outputs[iS].type ); + } + }else{ + // console.warn("cant find slot " + options.slot_from); + } + } + if (options.node_to){ + var iS = false; + switch (typeof options.slot_from){ + case "string": + iS = options.node_to.findInputSlot(options.slot_from); + break; + case "object": + if (options.slot_from.name){ + iS = options.node_to.findInputSlot(options.slot_from.name); + }else{ + iS = -1; + } + if (iS==-1 && typeof options.slot_from.slot_index !== "undefined") iS = options.slot_from.slot_index; + break; + case "number": + iS = options.slot_from; + break; + default: + iS = 0; // try with first if no name set + } + if (typeof options.node_to.inputs[iS] !== undefined){ + if (iS!==false && iS>-1){ + // try connection + options.node_to.connectByTypeOutput(iS,node,options.node_to.inputs[iS].type); + } + }else{ + // console.warn("cant find slot_nodeTO " + options.slot_from); + } + } + + graphcanvas.graph.afterChange(); + } + } + + dialog.close(); + } + + function changeSelection(forward) { + var prev = selected; + if (selected) { + selected.classList.remove("selected"); + } + if (!selected) { + selected = forward + ? helper.childNodes[0] + : helper.childNodes[helper.childNodes.length]; + } else { + selected = forward + ? selected.nextSibling + : selected.previousSibling; + if (!selected) { + selected = prev; + } + } + if (!selected) { + return; + } + selected.classList.add("selected"); + selected.scrollIntoView({block: "end", behavior: "smooth"}); + } + + function refreshHelper() { + timeout = null; + var str = input.value; + first = null; + helper.innerHTML = ""; + if (!str && !options.show_all_if_empty) { + return; + } + + if (that.onSearchBox) { + var list = that.onSearchBox(helper, str, graphcanvas); + if (list) { + for (var i = 0; i < list.length; ++i) { + addResult(list[i]); + } + } + } else { + var c = 0; + str = str.toLowerCase(); + var filter = graphcanvas.filter || graphcanvas.graph.filter; + + // filter by type preprocess + if(options.do_type_filter && that.search_box){ + var sIn = that.search_box.querySelector(".slot_in_type_filter"); + var sOut = that.search_box.querySelector(".slot_out_type_filter"); + }else{ + var sIn = false; + var sOut = false; + } + + //extras + for (var i in LiteGraph.searchbox_extras) { + var extra = LiteGraph.searchbox_extras[i]; + if ((!options.show_all_if_empty || str) && extra.desc.toLowerCase().indexOf(str) === -1) { + continue; + } + var ctor = LiteGraph.registered_node_types[ extra.type ]; + if( ctor && ctor.filter != filter ) + continue; + if( ! inner_test_filter(extra.type) ) + continue; + addResult( extra.desc, "searchbox_extra" ); + if ( LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit ) { + break; + } + } + + var filtered = null; + if (Array.prototype.filter) { //filter supported + var keys = Object.keys( LiteGraph.registered_node_types ); //types + var filtered = keys.filter( inner_test_filter ); + } else { + filtered = []; + for (var i in LiteGraph.registered_node_types) { + if( inner_test_filter(i) ) + filtered.push(i); + } + } + + for (var i = 0; i < filtered.length; i++) { + addResult(filtered[i]); + if ( LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit ) { + break; + } + } + + // add general type if filtering + if (options.show_general_after_typefiltered + && (sIn.value || sOut.value) + ){ + filtered_extra = []; + for (var i in LiteGraph.registered_node_types) { + if( inner_test_filter(i, {inTypeOverride: sIn&&sIn.value?"*":false, outTypeOverride: sOut&&sOut.value?"*":false}) ) + filtered_extra.push(i); + } + for (var i = 0; i < filtered_extra.length; i++) { + addResult(filtered_extra[i], "generic_type"); + if ( LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit ) { + break; + } + } + } + + // check il filtering gave no results + if ((sIn.value || sOut.value) && + ( (helper.childNodes.length == 0 && options.show_general_if_none_on_typefilter) ) + ){ + filtered_extra = []; + for (var i in LiteGraph.registered_node_types) { + if( inner_test_filter(i, {skipFilter: true}) ) + filtered_extra.push(i); + } + for (var i = 0; i < filtered_extra.length; i++) { + addResult(filtered_extra[i], "not_in_filter"); + if ( LGraphCanvas.search_limit !== -1 && c++ > LGraphCanvas.search_limit ) { + break; + } + } + } + + function inner_test_filter( type, optsIn ) + { + var optsIn = optsIn || {}; + var optsDef = { skipFilter: false + ,inTypeOverride: false + ,outTypeOverride: false + }; + var opts = Object.assign(optsDef,optsIn); + var ctor = LiteGraph.registered_node_types[ type ]; + if(filter && ctor.filter != filter ) + return false; + if ((!options.show_all_if_empty || str) && type.toLowerCase().indexOf(str) === -1) + return false; + + // filter by slot IN, OUT types + if(options.do_type_filter && !opts.skipFilter){ + var sType = type; + + var sV = sIn.value; + if (opts.inTypeOverride!==false) sV = opts.inTypeOverride; + //if (sV.toLowerCase() == "_event_") sV = LiteGraph.EVENT; // -1 + + if(sIn && sV){ + //console.log("will check filter against "+sV); + if (LiteGraph.registered_slot_in_types[sV] && LiteGraph.registered_slot_in_types[sV].nodes){ // type is stored + //console.debug("check "+sType+" in "+LiteGraph.registered_slot_in_types[sV].nodes); + var doesInc = LiteGraph.registered_slot_in_types[sV].nodes.includes(sType); + if (doesInc!==false){ + //console.log(sType+" HAS "+sV); + }else{ + /*console.debug(LiteGraph.registered_slot_in_types[sV]); + console.log(+" DONT includes "+type);*/ + return false; + } + } + } + + var sV = sOut.value; + if (opts.outTypeOverride!==false) sV = opts.outTypeOverride; + //if (sV.toLowerCase() == "_event_") sV = LiteGraph.EVENT; // -1 + + if(sOut && sV){ + //console.log("search will check filter against "+sV); + if (LiteGraph.registered_slot_out_types[sV] && LiteGraph.registered_slot_out_types[sV].nodes){ // type is stored + //console.debug("check "+sType+" in "+LiteGraph.registered_slot_out_types[sV].nodes); + var doesInc = LiteGraph.registered_slot_out_types[sV].nodes.includes(sType); + if (doesInc!==false){ + //console.log(sType+" HAS "+sV); + }else{ + /*console.debug(LiteGraph.registered_slot_out_types[sV]); + console.log(+" DONT includes "+type);*/ + return false; + } + } + } + } + return true; + } + } + + function addResult(type, className) { + var help = document.createElement("div"); + if (!first) { + first = type; + } + help.innerText = type; + help.dataset["type"] = escape(type); + help.className = "litegraph lite-search-item"; + if (className) { + help.className += " " + className; + } + help.addEventListener("click", function(e) { + select(unescape(this.dataset["type"])); + }); + helper.appendChild(help); + } + } + + return dialog; + }; + + LGraphCanvas.prototype.showEditPropertyValue = function( node, property, options ) { + if (!node || node.properties[property] === undefined) { + return; + } + + options = options || {}; + var that = this; + + var info = node.getPropertyInfo(property); + var type = info.type; + + var input_html = ""; + + if (type == "string" || type == "number" || type == "array" || type == "object") { + input_html = ""; + } else if ( (type == "enum" || type == "combo") && info.values) { + input_html = ""; + } else if (type == "boolean" || type == "toggle") { + input_html = + ""; + } else { + console.warn("unknown type: " + type); + return; + } + + var dialog = this.createDialog( + "" + + (info.label ? info.label : property) + + "" + + input_html + + "", + options + ); + + var input = false; + if ((type == "enum" || type == "combo") && info.values) { + input = dialog.querySelector("select"); + input.addEventListener("change", function(e) { + dialog.modified(); + setValue(e.target.value); + //var index = e.target.value; + //setValue( e.options[e.selectedIndex].value ); + }); + } else if (type == "boolean" || type == "toggle") { + input = dialog.querySelector("input"); + if (input) { + input.addEventListener("click", function(e) { + dialog.modified(); + setValue(!!input.checked); + }); + } + } else { + input = dialog.querySelector("input"); + if (input) { + input.addEventListener("blur", function(e) { + this.focus(); + }); + + var v = node.properties[property] !== undefined ? node.properties[property] : ""; + if (type !== 'string') { + v = JSON.stringify(v); + } + + input.value = v; + input.addEventListener("keydown", function(e) { + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + // ENTER + inner(); // save + } else if (e.keyCode != 13) { + dialog.modified(); + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + } + } + if (input) input.focus(); + + var button = dialog.querySelector("button"); + button.addEventListener("click", inner); + + function inner() { + setValue(input.value); + } + + function setValue(value) { + + if(info && info.values && info.values.constructor === Object && info.values[value] != undefined ) + value = info.values[value]; + + if (typeof node.properties[property] == "number") { + value = Number(value); + } + if (type == "array" || type == "object") { + value = JSON.parse(value); + } + node.properties[property] = value; + if (node.graph) { + node.graph._version++; + } + if (node.onPropertyChanged) { + node.onPropertyChanged(property, value); + } + if(options.onclose) + options.onclose(); + dialog.close(); + node.setDirtyCanvas(true, true); + } + + return dialog; + }; + + // TODO refactor, theer are different dialog, some uses createDialog, some dont + LGraphCanvas.prototype.createDialog = function(html, options) { + var def_options = { checkForInput: false, closeOnLeave: true, closeOnLeave_checkModified: true }; + options = Object.assign(def_options, options || {}); + + var dialog = document.createElement("div"); + dialog.className = "graphdialog"; + dialog.innerHTML = html; + dialog.is_modified = false; + + var rect = this.canvas.getBoundingClientRect(); + var offsetx = -20; + var offsety = -20; + if (rect) { + offsetx -= rect.left; + offsety -= rect.top; + } + + if (options.position) { + offsetx += options.position[0]; + offsety += options.position[1]; + } else if (options.event) { + offsetx += options.event.clientX; + offsety += options.event.clientY; + } //centered + else { + offsetx += this.canvas.width * 0.5; + offsety += this.canvas.height * 0.5; + } + + dialog.style.left = offsetx + "px"; + dialog.style.top = offsety + "px"; + + this.canvas.parentNode.appendChild(dialog); + + // acheck for input and use default behaviour: save on enter, close on esc + if (options.checkForInput){ + var aI = []; + var focused = false; + if (aI = dialog.querySelectorAll("input")){ + aI.forEach(function(iX) { + iX.addEventListener("keydown",function(e){ + dialog.modified(); + if (e.keyCode == 27) { + dialog.close(); + } else if (e.keyCode != 13) { + return; + } + // set value ? + e.preventDefault(); + e.stopPropagation(); + }); + if (!focused) iX.focus(); + }); + } + } + + dialog.modified = function(){ + dialog.is_modified = true; + } + dialog.close = function() { + if (dialog.parentNode) { + dialog.parentNode.removeChild(dialog); + } + }; + + var dialogCloseTimer = null; + var prevent_timeout = false; + dialog.addEventListener("mouseleave", function(e) { + if (prevent_timeout) + return; + if(options.closeOnLeave || LiteGraph.dialog_close_on_mouse_leave) + if (!dialog.is_modified && LiteGraph.dialog_close_on_mouse_leave) + dialogCloseTimer = setTimeout(dialog.close, LiteGraph.dialog_close_on_mouse_leave_delay); //dialog.close(); + }); + dialog.addEventListener("mouseenter", function(e) { + if(options.closeOnLeave || LiteGraph.dialog_close_on_mouse_leave) + if(dialogCloseTimer) clearTimeout(dialogCloseTimer); + }); + var selInDia = dialog.querySelectorAll("select"); + if (selInDia){ + // if filtering, check focus changed to comboboxes and prevent closing + selInDia.forEach(function(selIn) { + selIn.addEventListener("click", function(e) { + prevent_timeout++; + }); + selIn.addEventListener("blur", function(e) { + prevent_timeout = 0; + }); + selIn.addEventListener("change", function(e) { + prevent_timeout = -1; + }); + }); + } + + return dialog; + }; + + LGraphCanvas.prototype.createPanel = function(title, options) { + options = options || {}; + + var ref_window = options.window || window; + var root = document.createElement("div"); + root.className = "litegraph dialog"; + root.innerHTML = "
"; + root.header = root.querySelector(".dialog-header"); + + if(options.width) + root.style.width = options.width + (options.width.constructor === Number ? "px" : ""); + if(options.height) + root.style.height = options.height + (options.height.constructor === Number ? "px" : ""); + if(options.closable) + { + var close = document.createElement("span"); + close.innerHTML = "✕"; + close.classList.add("close"); + close.addEventListener("click",function(){ + root.close(); + }); + root.header.appendChild(close); + } + root.title_element = root.querySelector(".dialog-title"); + root.title_element.innerText = title; + root.content = root.querySelector(".dialog-content"); + root.alt_content = root.querySelector(".dialog-alt-content"); + root.footer = root.querySelector(".dialog-footer"); + + root.close = function() + { + if (root.onClose && typeof root.onClose == "function"){ + root.onClose(); + } + if(root.parentNode) + root.parentNode.removeChild(root); + /* XXX CHECK THIS */ + if(this.parentNode){ + this.parentNode.removeChild(this); + } + /* XXX this was not working, was fixed with an IF, check this */ + } + + // function to swap panel content + root.toggleAltContent = function(force){ + if (typeof force != "undefined"){ + var vTo = force ? "block" : "none"; + var vAlt = force ? "none" : "block"; + }else{ + var vTo = root.alt_content.style.display != "block" ? "block" : "none"; + var vAlt = root.alt_content.style.display != "block" ? "none" : "block"; + } + root.alt_content.style.display = vTo; + root.content.style.display = vAlt; + } + + root.toggleFooterVisibility = function(force){ + if (typeof force != "undefined"){ + var vTo = force ? "block" : "none"; + }else{ + var vTo = root.footer.style.display != "block" ? "block" : "none"; + } + root.footer.style.display = vTo; + } + + root.clear = function() + { + this.content.innerHTML = ""; + } + + root.addHTML = function(code, classname, on_footer) + { + var elem = document.createElement("div"); + if(classname) + elem.className = classname; + elem.innerHTML = code; + if(on_footer) + root.footer.appendChild(elem); + else + root.content.appendChild(elem); + return elem; + } + + root.addButton = function( name, callback, options ) + { + var elem = document.createElement("button"); + elem.innerText = name; + elem.options = options; + elem.classList.add("btn"); + elem.addEventListener("click",callback); + root.footer.appendChild(elem); + return elem; + } + + root.addSeparator = function() + { + var elem = document.createElement("div"); + elem.className = "separator"; + root.content.appendChild(elem); + } + + root.addWidget = function( type, name, value, options, callback ) + { + options = options || {}; + var str_value = String(value); + type = type.toLowerCase(); + if(type == "number") + str_value = value.toFixed(3); + + var elem = document.createElement("div"); + elem.className = "property"; + elem.innerHTML = ""; + elem.querySelector(".property_name").innerText = options.label || name; + var value_element = elem.querySelector(".property_value"); + value_element.innerText = str_value; + elem.dataset["property"] = name; + elem.dataset["type"] = options.type || type; + elem.options = options; + elem.value = value; + + if( type == "code" ) + elem.addEventListener("click", function(e){ root.inner_showCodePad( this.dataset["property"] ); }); + else if (type == "boolean") + { + elem.classList.add("boolean"); + if(value) + elem.classList.add("bool-on"); + elem.addEventListener("click", function(){ + //var v = node.properties[this.dataset["property"]]; + //node.setProperty(this.dataset["property"],!v); this.innerText = v ? "true" : "false"; + var propname = this.dataset["property"]; + this.value = !this.value; + this.classList.toggle("bool-on"); + this.querySelector(".property_value").innerText = this.value ? "true" : "false"; + innerChange(propname, this.value ); + }); + } + else if (type == "string" || type == "number") + { + value_element.setAttribute("contenteditable",true); + value_element.addEventListener("keydown", function(e){ + if(e.code == "Enter" && (type != "string" || !e.shiftKey)) // allow for multiline + { + e.preventDefault(); + this.blur(); + } + }); + value_element.addEventListener("blur", function(){ + var v = this.innerText; + var propname = this.parentNode.dataset["property"]; + var proptype = this.parentNode.dataset["type"]; + if( proptype == "number") + v = Number(v); + innerChange(propname, v); + }); + } + else if (type == "enum" || type == "combo") { + var str_value = LGraphCanvas.getPropertyPrintableValue( value, options.values ); + value_element.innerText = str_value; + + value_element.addEventListener("click", function(event){ + var values = options.values || []; + var propname = this.parentNode.dataset["property"]; + var elem_that = this; + var menu = new LiteGraph.ContextMenu(values,{ + event: event, + className: "dark", + callback: inner_clicked + }, + ref_window); + function inner_clicked(v, option, event) { + //node.setProperty(propname,v); + //graphcanvas.dirty_canvas = true; + elem_that.innerText = v; + innerChange(propname,v); + return false; + } + }); + } + + root.content.appendChild(elem); + + function innerChange(name, value) + { + //console.log("change",name,value); + //that.dirty_canvas = true; + if(options.callback) + options.callback(name,value,options); + if(callback) + callback(name,value,options); + } + + return elem; + } + + if (root.onOpen && typeof root.onOpen == "function") root.onOpen(); + + return root; + }; + + LGraphCanvas.getPropertyPrintableValue = function(value, values) + { + if(!values) + return String(value); + + if(values.constructor === Array) + { + return String(value); + } + + if(values.constructor === Object) + { + var desc_value = ""; + for(var k in values) + { + if(values[k] != value) + continue; + desc_value = k; + break; + } + return String(value) + " ("+desc_value+")"; + } + } + + LGraphCanvas.prototype.closePanels = function(){ + var panel = document.querySelector("#node-panel"); + if(panel) + panel.close(); + var panel = document.querySelector("#option-panel"); + if(panel) + panel.close(); + } + + LGraphCanvas.prototype.showShowGraphOptionsPanel = function(refOpts, obEv, refMenu, refMenu2){ + if(this.constructor && this.constructor.name == "HTMLDivElement"){ + // assume coming from the menu event click + if (!obEv || !obEv.event || !obEv.event.target || !obEv.event.target.lgraphcanvas){ + console.warn("Canvas not found"); // need a ref to canvas obj + /*console.debug(event); + console.debug(event.target);*/ + return; + } + var graphcanvas = obEv.event.target.lgraphcanvas; + }else{ + // assume called internally + var graphcanvas = this; + } + graphcanvas.closePanels(); + var ref_window = graphcanvas.getCanvasWindow(); + panel = graphcanvas.createPanel("Options",{ + closable: true + ,window: ref_window + ,onOpen: function(){ + graphcanvas.OPTIONPANEL_IS_OPEN = true; + } + ,onClose: function(){ + graphcanvas.OPTIONPANEL_IS_OPEN = false; + graphcanvas.options_panel = null; + } + }); + graphcanvas.options_panel = panel; + panel.id = "option-panel"; + panel.classList.add("settings"); + + function inner_refresh(){ + + panel.content.innerHTML = ""; //clear + + var fUpdate = function(name, value, options){ + switch(name){ + /*case "Render mode": + // Case "".. + if (options.values && options.key){ + var kV = Object.values(options.values).indexOf(value); + if (kV>=0 && options.values[kV]){ + console.debug("update graph options: "+options.key+": "+kV); + graphcanvas[options.key] = kV; + //console.debug(graphcanvas); + break; + } + } + console.warn("unexpected options"); + console.debug(options); + break;*/ + default: + //console.debug("want to update graph options: "+name+": "+value); + if (options && options.key){ + name = options.key; + } + if (options.values){ + value = Object.values(options.values).indexOf(value); + } + //console.debug("update graph option: "+name+": "+value); + graphcanvas[name] = value; + break; + } + }; + + // panel.addWidget( "string", "Graph name", "", {}, fUpdate); // implement + + var aProps = LiteGraph.availableCanvasOptions; + aProps.sort(); + for(var pI in aProps){ + var pX = aProps[pI]; + panel.addWidget( "boolean", pX, graphcanvas[pX], {key: pX, on: "True", off: "False"}, fUpdate); + } + + var aLinks = [ graphcanvas.links_render_mode ]; + panel.addWidget( "combo", "Render mode", LiteGraph.LINK_RENDER_MODES[graphcanvas.links_render_mode], {key: "links_render_mode", values: LiteGraph.LINK_RENDER_MODES}, fUpdate); + + panel.addSeparator(); + + panel.footer.innerHTML = ""; // clear + + } + inner_refresh(); + + graphcanvas.canvas.parentNode.appendChild( panel ); + } + + LGraphCanvas.prototype.showShowNodePanel = function( node ) + { + this.SELECTED_NODE = node; + this.closePanels(); + var ref_window = this.getCanvasWindow(); + var that = this; + var graphcanvas = this; + var panel = this.createPanel(node.title || "",{ + closable: true + ,window: ref_window + ,onOpen: function(){ + graphcanvas.NODEPANEL_IS_OPEN = true; + } + ,onClose: function(){ + graphcanvas.NODEPANEL_IS_OPEN = false; + graphcanvas.node_panel = null; + } + }); + graphcanvas.node_panel = panel; + panel.id = "node-panel"; + panel.node = node; + panel.classList.add("settings"); + + function inner_refresh() + { + panel.content.innerHTML = ""; //clear + panel.addHTML(""+node.type+""+(node.constructor.desc || "")+""); + + panel.addHTML("

Properties

"); + + var fUpdate = function(name,value){ + graphcanvas.graph.beforeChange(node); + switch(name){ + case "Title": + node.title = value; + break; + case "Mode": + var kV = Object.values(LiteGraph.NODE_MODES).indexOf(value); + if (kV>=0 && LiteGraph.NODE_MODES[kV]){ + node.changeMode(kV); + }else{ + console.warn("unexpected mode: "+value); + } + break; + case "Color": + if (LGraphCanvas.node_colors[value]){ + node.color = LGraphCanvas.node_colors[value].color; + node.bgcolor = LGraphCanvas.node_colors[value].bgcolor; + }else{ + console.warn("unexpected color: "+value); + } + break; + default: + node.setProperty(name,value); + break; + } + graphcanvas.graph.afterChange(); + graphcanvas.dirty_canvas = true; + }; + + panel.addWidget( "string", "Title", node.title, {}, fUpdate); + + panel.addWidget( "combo", "Mode", LiteGraph.NODE_MODES[node.mode], {values: LiteGraph.NODE_MODES}, fUpdate); + + var nodeCol = ""; + if (node.color !== undefined){ + nodeCol = Object.keys(LGraphCanvas.node_colors).filter(function(nK){ return LGraphCanvas.node_colors[nK].color == node.color; }); + } + + panel.addWidget( "combo", "Color", nodeCol, {values: Object.keys(LGraphCanvas.node_colors)}, fUpdate); + + for(var pName in node.properties) + { + var value = node.properties[pName]; + var info = node.getPropertyInfo(pName); + var type = info.type || "string"; + + //in case the user wants control over the side panel widget + if( node.onAddPropertyToPanel && node.onAddPropertyToPanel(pName,panel) ) + continue; + + panel.addWidget( info.widget || info.type, pName, value, info, fUpdate); + } + + panel.addSeparator(); + + if(node.onShowCustomPanelInfo) + node.onShowCustomPanelInfo(panel); + + panel.footer.innerHTML = ""; // clear + panel.addButton("Delete",function(){ + if(node.block_delete) + return; + node.graph.remove(node); + panel.close(); + }).classList.add("delete"); + } + + panel.inner_showCodePad = function( propname ) + { + panel.classList.remove("settings"); + panel.classList.add("centered"); + + + /*if(window.CodeFlask) //disabled for now + { + panel.content.innerHTML = "
"; + var flask = new CodeFlask( "div.code", { language: 'js' }); + flask.updateCode(node.properties[propname]); + flask.onUpdate( function(code) { + node.setProperty(propname, code); + }); + } + else + {*/ + panel.alt_content.innerHTML = ""; + var textarea = panel.alt_content.querySelector("textarea"); + var fDoneWith = function(){ + panel.toggleAltContent(false); //if(node_prop_div) node_prop_div.style.display = "block"; // panel.close(); + panel.toggleFooterVisibility(true); + textarea.parentNode.removeChild(textarea); + panel.classList.add("settings"); + panel.classList.remove("centered"); + inner_refresh(); + } + textarea.value = node.properties[propname]; + textarea.addEventListener("keydown", function(e){ + if(e.code == "Enter" && e.ctrlKey ) + { + node.setProperty(propname, textarea.value); + fDoneWith(); + } + }); + panel.toggleAltContent(true); + panel.toggleFooterVisibility(false); + textarea.style.height = "calc(100% - 40px)"; + /*}*/ + var assign = panel.addButton( "Assign", function(){ + node.setProperty(propname, textarea.value); + fDoneWith(); + }); + panel.alt_content.appendChild(assign); //panel.content.appendChild(assign); + var button = panel.addButton( "Close", fDoneWith); + button.style.float = "right"; + panel.alt_content.appendChild(button); // panel.content.appendChild(button); + } + + inner_refresh(); + + this.canvas.parentNode.appendChild( panel ); + } + + LGraphCanvas.prototype.showSubgraphPropertiesDialog = function(node) + { + console.log("showing subgraph properties dialog"); + + var old_panel = this.canvas.parentNode.querySelector(".subgraph_dialog"); + if(old_panel) + old_panel.close(); + + var panel = this.createPanel("Subgraph Inputs",{closable:true, width: 500}); + panel.node = node; + panel.classList.add("subgraph_dialog"); + + function inner_refresh() + { + panel.clear(); + + //show currents + if(node.inputs) + for(var i = 0; i < node.inputs.length; ++i) + { + var input = node.inputs[i]; + if(input.not_subgraph_input) + continue; + var html = " "; + var elem = panel.addHTML(html,"subgraph_property"); + elem.dataset["name"] = input.name; + elem.dataset["slot"] = i; + elem.querySelector(".name").innerText = input.name; + elem.querySelector(".type").innerText = input.type; + elem.querySelector("button").addEventListener("click",function(e){ + node.removeInput( Number( this.parentNode.dataset["slot"] ) ); + inner_refresh(); + }); + } + } + + //add extra + var html = " + NameType"; + var elem = panel.addHTML(html,"subgraph_property extra", true); + elem.querySelector("button").addEventListener("click", function(e){ + var elem = this.parentNode; + var name = elem.querySelector(".name").value; + var type = elem.querySelector(".type").value; + if(!name || node.findInputSlot(name) != -1) + return; + node.addInput(name,type); + elem.querySelector(".name").value = ""; + elem.querySelector(".type").value = ""; + inner_refresh(); + }); + + inner_refresh(); + this.canvas.parentNode.appendChild(panel); + return panel; + } + LGraphCanvas.prototype.showSubgraphPropertiesDialogRight = function (node) { + + // console.log("showing subgraph properties dialog"); + var that = this; + // old_panel if old_panel is exist close it + var old_panel = this.canvas.parentNode.querySelector(".subgraph_dialog"); + if (old_panel) + old_panel.close(); + // new panel + var panel = this.createPanel("Subgraph Outputs", { closable: true, width: 500 }); + panel.node = node; + panel.classList.add("subgraph_dialog"); + + function inner_refresh() { + panel.clear(); + //show currents + if (node.outputs) + for (var i = 0; i < node.outputs.length; ++i) { + var input = node.outputs[i]; + if (input.not_subgraph_output) + continue; + var html = " "; + var elem = panel.addHTML(html, "subgraph_property"); + elem.dataset["name"] = input.name; + elem.dataset["slot"] = i; + elem.querySelector(".name").innerText = input.name; + elem.querySelector(".type").innerText = input.type; + elem.querySelector("button").addEventListener("click", function (e) { + node.removeOutput(Number(this.parentNode.dataset["slot"])); + inner_refresh(); + }); + } + } + + //add extra + var html = " + NameType"; + var elem = panel.addHTML(html, "subgraph_property extra", true); + elem.querySelector(".name").addEventListener("keydown", function (e) { + if (e.keyCode == 13) { + addOutput.apply(this) + } + }) + elem.querySelector("button").addEventListener("click", function (e) { + addOutput.apply(this) + }); + function addOutput() { + var elem = this.parentNode; + var name = elem.querySelector(".name").value; + var type = elem.querySelector(".type").value; + if (!name || node.findOutputSlot(name) != -1) + return; + node.addOutput(name, type); + elem.querySelector(".name").value = ""; + elem.querySelector(".type").value = ""; + inner_refresh(); + } + + inner_refresh(); + this.canvas.parentNode.appendChild(panel); + return panel; + } + LGraphCanvas.prototype.checkPanels = function() + { + if(!this.canvas) + return; + var panels = this.canvas.parentNode.querySelectorAll(".litegraph.dialog"); + for(var i = 0; i < panels.length; ++i) + { + var panel = panels[i]; + if( !panel.node ) + continue; + if( !panel.node.graph || panel.graph != this.graph ) + panel.close(); + } + } + + LGraphCanvas.onMenuNodeCollapse = function(value, options, e, menu, node) { + node.graph.beforeChange(/*?*/); + + var fApplyMultiNode = function(node){ + node.collapse(); + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + node.graph.afterChange(/*?*/); + }; + + LGraphCanvas.onMenuNodePin = function(value, options, e, menu, node) { + node.pin(); + }; + + LGraphCanvas.onMenuNodeMode = function(value, options, e, menu, node) { + new LiteGraph.ContextMenu( + LiteGraph.NODE_MODES, + { event: e, callback: inner_clicked, parentMenu: menu, node: node } + ); + + function inner_clicked(v) { + if (!node) { + return; + } + var kV = Object.values(LiteGraph.NODE_MODES).indexOf(v); + var fApplyMultiNode = function(node){ + if (kV>=0 && LiteGraph.NODE_MODES[kV]) + node.changeMode(kV); + else{ + console.warn("unexpected mode: "+v); + node.changeMode(LiteGraph.ALWAYS); + } + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + } + + return false; + }; + + LGraphCanvas.onMenuNodeColors = function(value, options, e, menu, node) { + if (!node) { + throw "no node for color"; + } + + var values = []; + values.push({ + value: null, + content: + "No color" + }); + + for (var i in LGraphCanvas.node_colors) { + var color = LGraphCanvas.node_colors[i]; + var value = { + value: i, + content: + "" + + i + + "" + }; + values.push(value); + } + new LiteGraph.ContextMenu(values, { + event: e, + callback: inner_clicked, + parentMenu: menu, + node: node + }); + + function inner_clicked(v) { + if (!node) { + return; + } + + var color = v.value ? LGraphCanvas.node_colors[v.value] : null; + + var fApplyColor = function(node){ + if (color) { + if (node.constructor === LiteGraph.LGraphGroup) { + node.color = color.groupcolor; + } else { + node.color = color.color; + node.bgcolor = color.bgcolor; + } + } else { + delete node.color; + delete node.bgcolor; + } + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyColor(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyColor(graphcanvas.selected_nodes[i]); + } + } + node.setDirtyCanvas(true, true); + } + + return false; + }; + + LGraphCanvas.onMenuNodeShapes = function(value, options, e, menu, node) { + if (!node) { + throw "no node passed"; + } + + new LiteGraph.ContextMenu(LiteGraph.VALID_SHAPES, { + event: e, + callback: inner_clicked, + parentMenu: menu, + node: node + }); + + function inner_clicked(v) { + if (!node) { + return; + } + node.graph.beforeChange(/*?*/); //node + + var fApplyMultiNode = function(node){ + node.shape = v; + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + node.graph.afterChange(/*?*/); //node + node.setDirtyCanvas(true); + } + + return false; + }; + + LGraphCanvas.onMenuNodeRemove = function(value, options, e, menu, node) { + if (!node) { + throw "no node passed"; + } + + var graph = node.graph; + graph.beforeChange(); + + + var fApplyMultiNode = function(node){ + if (node.removable === false) { + return; + } + graph.remove(node); + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + graph.afterChange(); + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.onMenuNodeToSubgraph = function(value, options, e, menu, node) { + var graph = node.graph; + var graphcanvas = LGraphCanvas.active_canvas; + if(!graphcanvas) //?? + return; + + var nodes_list = Object.values( graphcanvas.selected_nodes || {} ); + if( !nodes_list.length ) + nodes_list = [ node ]; + + var subgraph_node = LiteGraph.createNode("graph/subgraph"); + subgraph_node.pos = node.pos.concat(); + graph.add(subgraph_node); + + subgraph_node.buildFromNodes( nodes_list ); + + graphcanvas.deselectAllNodes(); + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.onMenuNodeClone = function(value, options, e, menu, node) { + + node.graph.beforeChange(); + + var newSelected = {}; + + var fApplyMultiNode = function(node){ + if (node.clonable === false) { + return; + } + var newnode = node.clone(); + if (!newnode) { + return; + } + newnode.pos = [node.pos[0] + 5, node.pos[1] + 5]; + node.graph.add(newnode); + newSelected[newnode.id] = newnode; + } + + var graphcanvas = LGraphCanvas.active_canvas; + if (!graphcanvas.selected_nodes || Object.keys(graphcanvas.selected_nodes).length <= 1){ + fApplyMultiNode(node); + }else{ + for (var i in graphcanvas.selected_nodes) { + fApplyMultiNode(graphcanvas.selected_nodes[i]); + } + } + + if(Object.keys(newSelected).length){ + graphcanvas.selectNodes(newSelected); + } + + node.graph.afterChange(); + + node.setDirtyCanvas(true, true); + }; + + LGraphCanvas.node_colors = { + red: { color: "#322", bgcolor: "#533", groupcolor: "#A88" }, + brown: { color: "#332922", bgcolor: "#593930", groupcolor: "#b06634" }, + green: { color: "#232", bgcolor: "#353", groupcolor: "#8A8" }, + blue: { color: "#223", bgcolor: "#335", groupcolor: "#88A" }, + pale_blue: { + color: "#2a363b", + bgcolor: "#3f5159", + groupcolor: "#3f789e" + }, + cyan: { color: "#233", bgcolor: "#355", groupcolor: "#8AA" }, + purple: { color: "#323", bgcolor: "#535", groupcolor: "#a1309b" }, + yellow: { color: "#432", bgcolor: "#653", groupcolor: "#b58b2a" }, + black: { color: "#222", bgcolor: "#000", groupcolor: "#444" } + }; + + LGraphCanvas.prototype.getCanvasMenuOptions = function() { + var options = null; + var that = this; + if (this.getMenuOptions) { + options = this.getMenuOptions(); + } else { + options = [ + { + content: "Add Node", + has_submenu: true, + callback: LGraphCanvas.onMenuAdd + }, + { content: "Add Group", callback: LGraphCanvas.onGroupAdd }, + //{ content: "Arrange", callback: that.graph.arrange }, + //{content:"Collapse All", callback: LGraphCanvas.onMenuCollapseAll } + ]; + /*if (LiteGraph.showCanvasOptions){ + options.push({ content: "Options", callback: that.showShowGraphOptionsPanel }); + }*/ + + if (Object.keys(this.selected_nodes).length > 1) { + options.push({ + content: "Align", + has_submenu: true, + callback: LGraphCanvas.onGroupAlign, + }) + } + + if (this._graph_stack && this._graph_stack.length > 0) { + options.push(null, { + content: "Close subgraph", + callback: this.closeSubgraph.bind(this) + }); + } + } + + if (this.getExtraMenuOptions) { + var extra = this.getExtraMenuOptions(this, options); + if (extra) { + options = options.concat(extra); + } + } + + return options; + }; + + //called by processContextMenu to extract the menu list + LGraphCanvas.prototype.getNodeMenuOptions = function(node) { + var options = null; + + if (node.getMenuOptions) { + options = node.getMenuOptions(this); + } else { + options = [ + { + content: "Inputs", + has_submenu: true, + disabled: true, + callback: LGraphCanvas.showMenuNodeOptionalInputs + }, + { + content: "Outputs", + has_submenu: true, + disabled: true, + callback: LGraphCanvas.showMenuNodeOptionalOutputs + }, + null, + { + content: "Properties", + has_submenu: true, + callback: LGraphCanvas.onShowMenuNodeProperties + }, + { + content: "Properties Panel", + callback: function(item, options, e, menu, node) { LGraphCanvas.active_canvas.showShowNodePanel(node) } + }, + null, + { + content: "Title", + callback: LGraphCanvas.onShowPropertyEditor + }, + { + content: "Mode", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeMode + }]; + if(node.resizable !== false){ + options.push({ + content: "Resize", callback: LGraphCanvas.onMenuResizeNode + }); + } + options.push( + { + content: "Collapse", + callback: LGraphCanvas.onMenuNodeCollapse + }, + { content: "Pin", callback: LGraphCanvas.onMenuNodePin }, + { + content: "Colors", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeColors + }, + { + content: "Shapes", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeShapes + }, + null + ); + } + + if (node.onGetInputs) { + var inputs = node.onGetInputs(); + if (inputs && inputs.length) { + options[0].disabled = false; + } + } + + if (node.onGetOutputs) { + var outputs = node.onGetOutputs(); + if (outputs && outputs.length) { + options[1].disabled = false; + } + } + + if (node.getExtraMenuOptions) { + var extra = node.getExtraMenuOptions(this, options); + if (extra) { + extra.push(null); + options = extra.concat(options); + } + } + + if (node.clonable !== false) { + options.push({ + content: "Clone", + callback: LGraphCanvas.onMenuNodeClone + }); + } + + if(0) //TODO + options.push({ + content: "To Subgraph", + callback: LGraphCanvas.onMenuNodeToSubgraph + }); + + if (Object.keys(this.selected_nodes).length > 1) { + options.push({ + content: "Align Selected To", + has_submenu: true, + callback: LGraphCanvas.onNodeAlign, + }) + } + + options.push(null, { + content: "Remove", + disabled: !(node.removable !== false && !node.block_delete ), + callback: LGraphCanvas.onMenuNodeRemove + }); + + if (node.graph && node.graph.onGetNodeMenuOptions) { + node.graph.onGetNodeMenuOptions(options, node); + } + + return options; + }; + + LGraphCanvas.prototype.getGroupMenuOptions = function(node) { + var o = [ + { content: "Title", callback: LGraphCanvas.onShowPropertyEditor }, + { + content: "Color", + has_submenu: true, + callback: LGraphCanvas.onMenuNodeColors + }, + { + content: "Font size", + property: "font_size", + type: "Number", + callback: LGraphCanvas.onShowPropertyEditor + }, + null, + { content: "Remove", callback: LGraphCanvas.onMenuNodeRemove } + ]; + + return o; + }; + + LGraphCanvas.prototype.processContextMenu = function(node, event) { + var that = this; + var canvas = LGraphCanvas.active_canvas; + var ref_window = canvas.getCanvasWindow(); + + var menu_info = null; + var options = { + event: event, + callback: inner_option_clicked, + extra: node + }; + + if(node) + options.title = node.type; + + //check if mouse is in input + var slot = null; + if (node) { + slot = node.getSlotInPosition(event.canvasX, event.canvasY); + LGraphCanvas.active_node = node; + } + + if (slot) { + //on slot + menu_info = []; + if (node.getSlotMenuOptions) { + menu_info = node.getSlotMenuOptions(slot); + } else { + if ( + slot && + slot.output && + slot.output.links && + slot.output.links.length + ) { + menu_info.push({ content: "Disconnect Links", slot: slot }); + } + var _slot = slot.input || slot.output; + if (_slot.removable){ + menu_info.push( + _slot.locked + ? "Cannot remove" + : { content: "Remove Slot", slot: slot } + ); + } + if (!_slot.nameLocked){ + menu_info.push({ content: "Rename Slot", slot: slot }); + } + + } + options.title = + (slot.input ? slot.input.type : slot.output.type) || "*"; + if (slot.input && slot.input.type == LiteGraph.ACTION) { + options.title = "Action"; + } + if (slot.output && slot.output.type == LiteGraph.EVENT) { + options.title = "Event"; + } + } else { + if (node) { + //on node + menu_info = this.getNodeMenuOptions(node); + } else { + menu_info = this.getCanvasMenuOptions(); + var group = this.graph.getGroupOnPos( + event.canvasX, + event.canvasY + ); + if (group) { + //on group + menu_info.push(null, { + content: "Edit Group", + has_submenu: true, + submenu: { + title: "Group", + extra: group, + options: this.getGroupMenuOptions(group) + } + }); + } + } + } + + //show menu + if (!menu_info) { + return; + } + + var menu = new LiteGraph.ContextMenu(menu_info, options, ref_window); + + function inner_option_clicked(v, options, e) { + if (!v) { + return; + } + + if (v.content == "Remove Slot") { + var info = v.slot; + node.graph.beforeChange(); + if (info.input) { + node.removeInput(info.slot); + } else if (info.output) { + node.removeOutput(info.slot); + } + node.graph.afterChange(); + return; + } else if (v.content == "Disconnect Links") { + var info = v.slot; + node.graph.beforeChange(); + if (info.output) { + node.disconnectOutput(info.slot); + } else if (info.input) { + node.disconnectInput(info.slot); + } + node.graph.afterChange(); + return; + } else if (v.content == "Rename Slot") { + var info = v.slot; + var slot_info = info.input + ? node.getInputInfo(info.slot) + : node.getOutputInfo(info.slot); + var dialog = that.createDialog( + "Name", + options + ); + var input = dialog.querySelector("input"); + if (input && slot_info) { + input.value = slot_info.label || ""; + } + var inner = function(){ + node.graph.beforeChange(); + if (input.value) { + if (slot_info) { + slot_info.label = input.value; + } + that.setDirty(true); + } + dialog.close(); + node.graph.afterChange(); + } + dialog.querySelector("button").addEventListener("click", inner); + input.addEventListener("keydown", function(e) { + dialog.is_modified = true; + if (e.keyCode == 27) { + //ESC + dialog.close(); + } else if (e.keyCode == 13) { + inner(); // save + } else if (e.keyCode != 13 && e.target.localName != "textarea") { + return; + } + e.preventDefault(); + e.stopPropagation(); + }); + input.focus(); + } + + //if(v.callback) + // return v.callback.call(that, node, options, e, menu, that, event ); + } + }; + + //API ************************************************* + //like rect but rounded corners + if (typeof(window) != "undefined" && window.CanvasRenderingContext2D && !window.CanvasRenderingContext2D.prototype.roundRect) { + window.CanvasRenderingContext2D.prototype.roundRect = function( + x, + y, + w, + h, + radius, + radius_low + ) { + var top_left_radius = 0; + var top_right_radius = 0; + var bottom_left_radius = 0; + var bottom_right_radius = 0; + + if ( radius === 0 ) + { + this.rect(x,y,w,h); + return; + } + + if(radius_low === undefined) + radius_low = radius; + + //make it compatible with official one + if(radius != null && radius.constructor === Array) + { + if(radius.length == 1) + top_left_radius = top_right_radius = bottom_left_radius = bottom_right_radius = radius[0]; + else if(radius.length == 2) + { + top_left_radius = bottom_right_radius = radius[0]; + top_right_radius = bottom_left_radius = radius[1]; + } + else if(radius.length == 4) + { + top_left_radius = radius[0]; + top_right_radius = radius[1]; + bottom_left_radius = radius[2]; + bottom_right_radius = radius[3]; + } + else + return; + } + else //old using numbers + { + top_left_radius = radius || 0; + top_right_radius = radius || 0; + bottom_left_radius = radius_low || 0; + bottom_right_radius = radius_low || 0; + } + + //top right + this.moveTo(x + top_left_radius, y); + this.lineTo(x + w - top_right_radius, y); + this.quadraticCurveTo(x + w, y, x + w, y + top_right_radius); + + //bottom right + this.lineTo(x + w, y + h - bottom_right_radius); + this.quadraticCurveTo( + x + w, + y + h, + x + w - bottom_right_radius, + y + h + ); + + //bottom left + this.lineTo(x + bottom_right_radius, y + h); + this.quadraticCurveTo(x, y + h, x, y + h - bottom_left_radius); + + //top left + this.lineTo(x, y + bottom_left_radius); + this.quadraticCurveTo(x, y, x + top_left_radius, y); + }; + }//if + + function compareObjects(a, b) { + for (var i in a) { + if (a[i] != b[i]) { + return false; + } + } + return true; + } + LiteGraph.compareObjects = compareObjects; + + function distance(a, b) { + return Math.sqrt( + (b[0] - a[0]) * (b[0] - a[0]) + (b[1] - a[1]) * (b[1] - a[1]) + ); + } + LiteGraph.distance = distance; + + function colorToString(c) { + return ( + "rgba(" + + Math.round(c[0] * 255).toFixed() + + "," + + Math.round(c[1] * 255).toFixed() + + "," + + Math.round(c[2] * 255).toFixed() + + "," + + (c.length == 4 ? c[3].toFixed(2) : "1.0") + + ")" + ); + } + LiteGraph.colorToString = colorToString; + + function isInsideRectangle(x, y, left, top, width, height) { + if (left < x && left + width > x && top < y && top + height > y) { + return true; + } + return false; + } + LiteGraph.isInsideRectangle = isInsideRectangle; + + //[minx,miny,maxx,maxy] + function growBounding(bounding, x, y) { + if (x < bounding[0]) { + bounding[0] = x; + } else if (x > bounding[2]) { + bounding[2] = x; + } + + if (y < bounding[1]) { + bounding[1] = y; + } else if (y > bounding[3]) { + bounding[3] = y; + } + } + LiteGraph.growBounding = growBounding; + + //point inside bounding box + function isInsideBounding(p, bb) { + if ( + p[0] < bb[0][0] || + p[1] < bb[0][1] || + p[0] > bb[1][0] || + p[1] > bb[1][1] + ) { + return false; + } + return true; + } + LiteGraph.isInsideBounding = isInsideBounding; + + //bounding overlap, format: [ startx, starty, width, height ] + function overlapBounding(a, b) { + var A_end_x = a[0] + a[2]; + var A_end_y = a[1] + a[3]; + var B_end_x = b[0] + b[2]; + var B_end_y = b[1] + b[3]; + + if ( + a[0] > B_end_x || + a[1] > B_end_y || + A_end_x < b[0] || + A_end_y < b[1] + ) { + return false; + } + return true; + } + LiteGraph.overlapBounding = overlapBounding; + + //Convert a hex value to its decimal value - the inputted hex must be in the + // format of a hex triplet - the kind we use for HTML colours. The function + // will return an array with three values. + function hex2num(hex) { + if (hex.charAt(0) == "#") { + hex = hex.slice(1); + } //Remove the '#' char - if there is one. + hex = hex.toUpperCase(); + var hex_alphabets = "0123456789ABCDEF"; + var value = new Array(3); + var k = 0; + var int1, int2; + for (var i = 0; i < 6; i += 2) { + int1 = hex_alphabets.indexOf(hex.charAt(i)); + int2 = hex_alphabets.indexOf(hex.charAt(i + 1)); + value[k] = int1 * 16 + int2; + k++; + } + return value; + } + + LiteGraph.hex2num = hex2num; + + //Give a array with three values as the argument and the function will return + // the corresponding hex triplet. + function num2hex(triplet) { + var hex_alphabets = "0123456789ABCDEF"; + var hex = "#"; + var int1, int2; + for (var i = 0; i < 3; i++) { + int1 = triplet[i] / 16; + int2 = triplet[i] % 16; + + hex += hex_alphabets.charAt(int1) + hex_alphabets.charAt(int2); + } + return hex; + } + + LiteGraph.num2hex = num2hex; + + /* LiteGraph GUI elements used for canvas editing *************************************/ + + /** + * ContextMenu from LiteGUI + * + * @class ContextMenu + * @constructor + * @param {Array} values (allows object { title: "Nice text", callback: function ... }) + * @param {Object} options [optional] Some options:\ + * - title: title to show on top of the menu + * - callback: function to call when an option is clicked, it receives the item information + * - ignore_item_callbacks: ignores the callback inside the item, it just calls the options.callback + * - event: you can pass a MouseEvent, this way the ContextMenu appears in that position + */ + function ContextMenu(values, options) { + options = options || {}; + this.options = options; + var that = this; + + //to link a menu with its parent + if (options.parentMenu) { + if (options.parentMenu.constructor !== this.constructor) { + console.error( + "parentMenu must be of class ContextMenu, ignoring it" + ); + options.parentMenu = null; + } else { + this.parentMenu = options.parentMenu; + this.parentMenu.lock = true; + this.parentMenu.current_submenu = this; + } + } + + var eventClass = null; + if(options.event) //use strings because comparing classes between windows doesnt work + eventClass = options.event.constructor.name; + if ( eventClass !== "MouseEvent" && + eventClass !== "CustomEvent" && + eventClass !== "PointerEvent" + ) { + console.error( + "Event passed to ContextMenu is not of type MouseEvent or CustomEvent. Ignoring it. ("+eventClass+")" + ); + options.event = null; + } + + var root = document.createElement("div"); + root.className = "litegraph litecontextmenu litemenubar-panel"; + if (options.className) { + root.className += " " + options.className; + } + root.style.minWidth = 100; + root.style.minHeight = 100; + root.style.pointerEvents = "none"; + setTimeout(function() { + root.style.pointerEvents = "auto"; + }, 100); //delay so the mouse up event is not caught by this element + + //this prevents the default context browser menu to open in case this menu was created when pressing right button + LiteGraph.pointerListenerAdd(root,"up", + function(e) { + //console.log("pointerevents: ContextMenu up root prevent"); + e.preventDefault(); + return true; + }, + true + ); + root.addEventListener( + "contextmenu", + function(e) { + if (e.button != 2) { + //right button + return false; + } + e.preventDefault(); + return false; + }, + true + ); + + LiteGraph.pointerListenerAdd(root,"down", + function(e) { + //console.log("pointerevents: ContextMenu down"); + if (e.button == 2) { + that.close(); + e.preventDefault(); + return true; + } + }, + true + ); + + function on_mouse_wheel(e) { + var pos = parseInt(root.style.top); + root.style.top = + (pos + e.deltaY * options.scroll_speed).toFixed() + "px"; + e.preventDefault(); + return true; + } + + if (!options.scroll_speed) { + options.scroll_speed = 0.1; + } + + root.addEventListener("wheel", on_mouse_wheel, true); + root.addEventListener("mousewheel", on_mouse_wheel, true); + + this.root = root; + + //title + if (options.title) { + var element = document.createElement("div"); + element.className = "litemenu-title"; + element.innerHTML = options.title; + root.appendChild(element); + } + + //entries + var num = 0; + for (var i=0; i < values.length; i++) { + var name = values.constructor == Array ? values[i] : i; + if (name != null && name.constructor !== String) { + name = name.content === undefined ? String(name) : name.content; + } + var value = values[i]; + this.addItem(name, value, options); + num++; + } + + //close on leave? touch enabled devices won't work TODO use a global device detector and condition on that + /*LiteGraph.pointerListenerAdd(root,"leave", function(e) { + console.log("pointerevents: ContextMenu leave"); + if (that.lock) { + return; + } + if (root.closing_timer) { + clearTimeout(root.closing_timer); + } + root.closing_timer = setTimeout(that.close.bind(that, e), 500); + //that.close(e); + });*/ + + LiteGraph.pointerListenerAdd(root,"enter", function(e) { + //console.log("pointerevents: ContextMenu enter"); + if (root.closing_timer) { + clearTimeout(root.closing_timer); + } + }); + + //insert before checking position + var root_document = document; + if (options.event) { + root_document = options.event.target.ownerDocument; + } + + if (!root_document) { + root_document = document; + } + + if( root_document.fullscreenElement ) + root_document.fullscreenElement.appendChild(root); + else + root_document.body.appendChild(root); + + //compute best position + var left = options.left || 0; + var top = options.top || 0; + if (options.event) { + left = options.event.clientX - 10; + top = options.event.clientY - 10; + if (options.title) { + top -= 20; + } + + if (options.parentMenu) { + var rect = options.parentMenu.root.getBoundingClientRect(); + left = rect.left + rect.width; + } + + var body_rect = document.body.getBoundingClientRect(); + var root_rect = root.getBoundingClientRect(); + if(body_rect.height == 0) + console.error("document.body height is 0. That is dangerous, set html,body { height: 100%; }"); + + if (body_rect.width && left > body_rect.width - root_rect.width - 10) { + left = body_rect.width - root_rect.width - 10; + } + if (body_rect.height && top > body_rect.height - root_rect.height - 10) { + top = body_rect.height - root_rect.height - 10; + } + } + + root.style.left = left + "px"; + root.style.top = top + "px"; + + if (options.scale) { + root.style.transform = "scale(" + options.scale + ")"; + } + } + + ContextMenu.prototype.addItem = function(name, value, options) { + var that = this; + options = options || {}; + + var element = document.createElement("div"); + element.className = "litemenu-entry submenu"; + + var disabled = false; + + if (value === null) { + element.classList.add("separator"); + //element.innerHTML = "
" + //continue; + } else { + element.innerHTML = value && value.title ? value.title : name; + element.value = value; + + if (value) { + if (value.disabled) { + disabled = true; + element.classList.add("disabled"); + } + if (value.submenu || value.has_submenu) { + element.classList.add("has_submenu"); + } + } + + if (typeof value == "function") { + element.dataset["value"] = name; + element.onclick_callback = value; + } else { + element.dataset["value"] = value; + } + + if (value.className) { + element.className += " " + value.className; + } + } + + this.root.appendChild(element); + if (!disabled) { + element.addEventListener("click", inner_onclick); + } + if (!disabled && options.autoopen) { + LiteGraph.pointerListenerAdd(element,"enter",inner_over); + } + + function inner_over(e) { + var value = this.value; + if (!value || !value.has_submenu) { + return; + } + //if it is a submenu, autoopen like the item was clicked + inner_onclick.call(this, e); + } + + //menu option clicked + function inner_onclick(e) { + var value = this.value; + var close_parent = true; + + if (that.current_submenu) { + that.current_submenu.close(e); + } + + //global callback + if (options.callback) { + var r = options.callback.call( + this, + value, + options, + e, + that, + options.node + ); + if (r === true) { + close_parent = false; + } + } + + //special cases + if (value) { + if ( + value.callback && + !options.ignore_item_callbacks && + value.disabled !== true + ) { + //item callback + var r = value.callback.call( + this, + value, + options, + e, + that, + options.extra + ); + if (r === true) { + close_parent = false; + } + } + if (value.submenu) { + if (!value.submenu.options) { + throw "ContextMenu submenu needs options"; + } + var submenu = new that.constructor(value.submenu.options, { + callback: value.submenu.callback, + event: e, + parentMenu: that, + ignore_item_callbacks: + value.submenu.ignore_item_callbacks, + title: value.submenu.title, + extra: value.submenu.extra, + autoopen: options.autoopen + }); + close_parent = false; + } + } + + if (close_parent && !that.lock) { + that.close(); + } + } + + return element; + }; + + ContextMenu.prototype.close = function(e, ignore_parent_menu) { + if (this.root.parentNode) { + this.root.parentNode.removeChild(this.root); + } + if (this.parentMenu && !ignore_parent_menu) { + this.parentMenu.lock = false; + this.parentMenu.current_submenu = null; + if (e === undefined) { + this.parentMenu.close(); + } else if ( + e && + !ContextMenu.isCursorOverElement(e, this.parentMenu.root) + ) { + ContextMenu.trigger(this.parentMenu.root, LiteGraph.pointerevents_method+"leave", e); + } + } + if (this.current_submenu) { + this.current_submenu.close(e, true); + } + + if (this.root.closing_timer) { + clearTimeout(this.root.closing_timer); + } + + // TODO implement : LiteGraph.contextMenuClosed(); :: keep track of opened / closed / current ContextMenu + // on key press, allow filtering/selecting the context menu elements + }; + + //this code is used to trigger events easily (used in the context menu mouseleave + ContextMenu.trigger = function(element, event_name, params, origin) { + var evt = document.createEvent("CustomEvent"); + evt.initCustomEvent(event_name, true, true, params); //canBubble, cancelable, detail + evt.srcElement = origin; + if (element.dispatchEvent) { + element.dispatchEvent(evt); + } else if (element.__events) { + element.__events.dispatchEvent(evt); + } + //else nothing seems binded here so nothing to do + return evt; + }; + + //returns the top most menu + ContextMenu.prototype.getTopMenu = function() { + if (this.options.parentMenu) { + return this.options.parentMenu.getTopMenu(); + } + return this; + }; + + ContextMenu.prototype.getFirstEvent = function() { + if (this.options.parentMenu) { + return this.options.parentMenu.getFirstEvent(); + } + return this.options.event; + }; + + ContextMenu.isCursorOverElement = function(event, element) { + var left = event.clientX; + var top = event.clientY; + var rect = element.getBoundingClientRect(); + if (!rect) { + return false; + } + if ( + top > rect.top && + top < rect.top + rect.height && + left > rect.left && + left < rect.left + rect.width + ) { + return true; + } + return false; + }; + + LiteGraph.ContextMenu = ContextMenu; + + LiteGraph.closeAllContextMenus = function(ref_window) { + ref_window = ref_window || window; + + var elements = ref_window.document.querySelectorAll(".litecontextmenu"); + if (!elements.length) { + return; + } + + var result = []; + for (var i = 0; i < elements.length; i++) { + result.push(elements[i]); + } + + for (var i=0; i < result.length; i++) { + if (result[i].close) { + result[i].close(); + } else if (result[i].parentNode) { + result[i].parentNode.removeChild(result[i]); + } + } + }; + + LiteGraph.extendClass = function(target, origin) { + for (var i in origin) { + //copy class properties + if (target.hasOwnProperty(i)) { + continue; + } + target[i] = origin[i]; + } + + if (origin.prototype) { + //copy prototype properties + for (var i in origin.prototype) { + //only enumerable + if (!origin.prototype.hasOwnProperty(i)) { + continue; + } + + if (target.prototype.hasOwnProperty(i)) { + //avoid overwriting existing ones + continue; + } + + //copy getters + if (origin.prototype.__lookupGetter__(i)) { + target.prototype.__defineGetter__( + i, + origin.prototype.__lookupGetter__(i) + ); + } else { + target.prototype[i] = origin.prototype[i]; + } + + //and setters + if (origin.prototype.__lookupSetter__(i)) { + target.prototype.__defineSetter__( + i, + origin.prototype.__lookupSetter__(i) + ); + } + } + } + }; + + //used by some widgets to render a curve editor + function CurveEditor( points ) + { + this.points = points; + this.selected = -1; + this.nearest = -1; + this.size = null; //stores last size used + this.must_update = true; + this.margin = 5; + } + + CurveEditor.sampleCurve = function(f,points) + { + if(!points) + return; + for(var i = 0; i < points.length - 1; ++i) + { + var p = points[i]; + var pn = points[i+1]; + if(pn[0] < f) + continue; + var r = (pn[0] - p[0]); + if( Math.abs(r) < 0.00001 ) + return p[1]; + var local_f = (f - p[0]) / r; + return p[1] * (1.0 - local_f) + pn[1] * local_f; + } + return 0; + } + + CurveEditor.prototype.draw = function( ctx, size, graphcanvas, background_color, line_color, inactive ) + { + var points = this.points; + if(!points) + return; + this.size = size; + var w = size[0] - this.margin * 2; + var h = size[1] - this.margin * 2; + + line_color = line_color || "#666"; + + ctx.save(); + ctx.translate(this.margin,this.margin); + + if(background_color) + { + ctx.fillStyle = "#111"; + ctx.fillRect(0,0,w,h); + ctx.fillStyle = "#222"; + ctx.fillRect(w*0.5,0,1,h); + ctx.strokeStyle = "#333"; + ctx.strokeRect(0,0,w,h); + } + ctx.strokeStyle = line_color; + if(inactive) + ctx.globalAlpha = 0.5; + ctx.beginPath(); + for(var i = 0; i < points.length; ++i) + { + var p = points[i]; + ctx.lineTo( p[0] * w, (1.0 - p[1]) * h ); + } + ctx.stroke(); + ctx.globalAlpha = 1; + if(!inactive) + for(var i = 0; i < points.length; ++i) + { + var p = points[i]; + ctx.fillStyle = this.selected == i ? "#FFF" : (this.nearest == i ? "#DDD" : "#AAA"); + ctx.beginPath(); + ctx.arc( p[0] * w, (1.0 - p[1]) * h, 2, 0, Math.PI * 2 ); + ctx.fill(); + } + ctx.restore(); + } + + //localpos is mouse in curve editor space + CurveEditor.prototype.onMouseDown = function( localpos, graphcanvas ) + { + var points = this.points; + if(!points) + return; + if( localpos[1] < 0 ) + return; + + //this.captureInput(true); + var w = this.size[0] - this.margin * 2; + var h = this.size[1] - this.margin * 2; + var x = localpos[0] - this.margin; + var y = localpos[1] - this.margin; + var pos = [x,y]; + var max_dist = 30 / graphcanvas.ds.scale; + //search closer one + this.selected = this.getCloserPoint(pos, max_dist); + //create one + if(this.selected == -1) + { + var point = [x / w, 1 - y / h]; + points.push(point); + points.sort(function(a,b){ return a[0] - b[0]; }); + this.selected = points.indexOf(point); + this.must_update = true; + } + if(this.selected != -1) + return true; + } + + CurveEditor.prototype.onMouseMove = function( localpos, graphcanvas ) + { + var points = this.points; + if(!points) + return; + var s = this.selected; + if(s < 0) + return; + var x = (localpos[0] - this.margin) / (this.size[0] - this.margin * 2 ); + var y = (localpos[1] - this.margin) / (this.size[1] - this.margin * 2 ); + var curvepos = [(localpos[0] - this.margin),(localpos[1] - this.margin)]; + var max_dist = 30 / graphcanvas.ds.scale; + this._nearest = this.getCloserPoint(curvepos, max_dist); + var point = points[s]; + if(point) + { + var is_edge_point = s == 0 || s == points.length - 1; + if( !is_edge_point && (localpos[0] < -10 || localpos[0] > this.size[0] + 10 || localpos[1] < -10 || localpos[1] > this.size[1] + 10) ) + { + points.splice(s,1); + this.selected = -1; + return; + } + if( !is_edge_point ) //not edges + point[0] = clamp(x, 0, 1); + else + point[0] = s == 0 ? 0 : 1; + point[1] = 1.0 - clamp(y, 0, 1); + points.sort(function(a,b){ return a[0] - b[0]; }); + this.selected = points.indexOf(point); + this.must_update = true; + } + } + + CurveEditor.prototype.onMouseUp = function( localpos, graphcanvas ) + { + this.selected = -1; + return false; + } + + CurveEditor.prototype.getCloserPoint = function(pos, max_dist) + { + var points = this.points; + if(!points) + return -1; + max_dist = max_dist || 30; + var w = (this.size[0] - this.margin * 2); + var h = (this.size[1] - this.margin * 2); + var num = points.length; + var p2 = [0,0]; + var min_dist = 1000000; + var closest = -1; + var last_valid = -1; + for(var i = 0; i < num; ++i) + { + var p = points[i]; + p2[0] = p[0] * w; + p2[1] = (1.0 - p[1]) * h; + if(p2[0] < pos[0]) + last_valid = i; + var dist = vec2.distance(pos,p2); + if(dist > min_dist || dist > max_dist) + continue; + closest = i; + min_dist = dist; + } + return closest; + } + + LiteGraph.CurveEditor = CurveEditor; + + //used to create nodes from wrapping functions + LiteGraph.getParameterNames = function(func) { + return (func + "") + .replace(/[/][/].*$/gm, "") // strip single-line comments + .replace(/\s+/g, "") // strip white space + .replace(/[/][*][^/*]*[*][/]/g, "") // strip multi-line comments /**/ + .split("){", 1)[0] + .replace(/^[^(]*[(]/, "") // extract the parameters + .replace(/=[^,]+/g, "") // strip any ES6 defaults + .split(",") + .filter(Boolean); // split & filter [""] + }; + + /* helper for interaction: pointer, touch, mouse Listeners + used by LGraphCanvas DragAndScale ContextMenu*/ + LiteGraph.pointerListenerAdd = function(oDOM, sEvIn, fCall, capture=false) { + if (!oDOM || !oDOM.addEventListener || !sEvIn || typeof fCall!=="function"){ + //console.log("cant pointerListenerAdd "+oDOM+", "+sEvent+", "+fCall); + return; // -- break -- + } + + var sMethod = LiteGraph.pointerevents_method; + var sEvent = sEvIn; + + // UNDER CONSTRUCTION + // convert pointerevents to touch event when not available + if (sMethod=="pointer" && !window.PointerEvent){ + console.warn("sMethod=='pointer' && !window.PointerEvent"); + console.log("Converting pointer["+sEvent+"] : down move up cancel enter TO touchstart touchmove touchend, etc .."); + switch(sEvent){ + case "down":{ + sMethod = "touch"; + sEvent = "start"; + break; + } + case "move":{ + sMethod = "touch"; + //sEvent = "move"; + break; + } + case "up":{ + sMethod = "touch"; + sEvent = "end"; + break; + } + case "cancel":{ + sMethod = "touch"; + //sEvent = "cancel"; + break; + } + case "enter":{ + console.log("debug: Should I send a move event?"); // ??? + break; + } + // case "over": case "out": not used at now + default:{ + console.warn("PointerEvent not available in this browser ? The event "+sEvent+" would not be called"); + } + } + } + + switch(sEvent){ + //both pointer and move events + case "down": case "up": case "move": case "over": case "out": case "enter": + { + oDOM.addEventListener(sMethod+sEvent, fCall, capture); + } + // only pointerevents + case "leave": case "cancel": case "gotpointercapture": case "lostpointercapture": + { + if (sMethod!="mouse"){ + return oDOM.addEventListener(sMethod+sEvent, fCall, capture); + } + } + // not "pointer" || "mouse" + default: + return oDOM.addEventListener(sEvent, fCall, capture); + } + } + LiteGraph.pointerListenerRemove = function(oDOM, sEvent, fCall, capture=false) { + if (!oDOM || !oDOM.removeEventListener || !sEvent || typeof fCall!=="function"){ + //console.log("cant pointerListenerRemove "+oDOM+", "+sEvent+", "+fCall); + return; // -- break -- + } + switch(sEvent){ + //both pointer and move events + case "down": case "up": case "move": case "over": case "out": case "enter": + { + if (LiteGraph.pointerevents_method=="pointer" || LiteGraph.pointerevents_method=="mouse"){ + oDOM.removeEventListener(LiteGraph.pointerevents_method+sEvent, fCall, capture); + } + } + // only pointerevents + case "leave": case "cancel": case "gotpointercapture": case "lostpointercapture": + { + if (LiteGraph.pointerevents_method=="pointer"){ + return oDOM.removeEventListener(LiteGraph.pointerevents_method+sEvent, fCall, capture); + } + } + // not "pointer" || "mouse" + default: + return oDOM.removeEventListener(sEvent, fCall, capture); + } + } + + function clamp(v, a, b) { + return a > v ? a : b < v ? b : v; + }; + global.clamp = clamp; + + if (typeof window != "undefined" && !window["requestAnimationFrame"]) { + window.requestAnimationFrame = + window.webkitRequestAnimationFrame || + window.mozRequestAnimationFrame || + function(callback) { + window.setTimeout(callback, 1000 / 60); + }; + } +})(this); + +if (typeof exports != "undefined") { + exports.LiteGraph = this.LiteGraph; + exports.LGraph = this.LGraph; + exports.LLink = this.LLink; + exports.LGraphNode = this.LGraphNode; + exports.LGraphGroup = this.LGraphGroup; + exports.DragAndScale = this.DragAndScale; + exports.LGraphCanvas = this.LGraphCanvas; + exports.ContextMenu = this.ContextMenu; +} + + diff --git a/web/lib/litegraph.css b/web/lib/litegraph.css new file mode 100644 index 0000000000000000000000000000000000000000..918858f415d85585c2194727d0aa6789e7ed18ad --- /dev/null +++ b/web/lib/litegraph.css @@ -0,0 +1,680 @@ +/* this CSS contains only the basic CSS needed to run the app and use it */ + +.lgraphcanvas { + /*cursor: crosshair;*/ + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + outline: none; + font-family: Tahoma, sans-serif; +} + +.lgraphcanvas * { + box-sizing: border-box; +} + +.litegraph.litecontextmenu { + font-family: Tahoma, sans-serif; + position: fixed; + top: 100px; + left: 100px; + min-width: 100px; + color: #aaf; + padding: 0; + box-shadow: 0 0 10px black !important; + background-color: #2e2e2e !important; + z-index: 10; +} + +.litegraph.litecontextmenu.dark { + background-color: #000 !important; +} + +.litegraph.litecontextmenu .litemenu-title img { + margin-top: 2px; + margin-left: 2px; + margin-right: 4px; +} + +.litegraph.litecontextmenu .litemenu-entry { + margin: 2px; + padding: 2px; +} + +.litegraph.litecontextmenu .litemenu-entry.submenu { + background-color: #2e2e2e !important; +} + +.litegraph.litecontextmenu.dark .litemenu-entry.submenu { + background-color: #000 !important; +} + +.litegraph .litemenubar ul { + font-family: Tahoma, sans-serif; + margin: 0; + padding: 0; +} + +.litegraph .litemenubar li { + font-size: 14px; + color: #999; + display: inline-block; + min-width: 50px; + padding-left: 10px; + padding-right: 10px; + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + cursor: pointer; +} + +.litegraph .litemenubar li:hover { + background-color: #777; + color: #eee; +} + +.litegraph .litegraph .litemenubar-panel { + position: absolute; + top: 5px; + left: 5px; + min-width: 100px; + background-color: #444; + box-shadow: 0 0 3px black; + padding: 4px; + border-bottom: 2px solid #aaf; + z-index: 10; +} + +.litegraph .litemenu-entry, +.litemenu-title { + font-size: 12px; + color: #aaa; + padding: 0 0 0 4px; + margin: 2px; + padding-left: 2px; + -moz-user-select: none; + -webkit-user-select: none; + user-select: none; + cursor: pointer; +} + +.litegraph .litemenu-entry .icon { + display: inline-block; + width: 12px; + height: 12px; + margin: 2px; + vertical-align: top; +} + +.litegraph .litemenu-entry.checked .icon { + background-color: #aaf; +} + +.litegraph .litemenu-entry .more { + float: right; + padding-right: 5px; +} + +.litegraph .litemenu-entry.disabled { + opacity: 0.5; + cursor: default; +} + +.litegraph .litemenu-entry.separator { + display: block; + border-top: 1px solid #333; + border-bottom: 1px solid #666; + width: 100%; + height: 0px; + margin: 3px 0 2px 0; + background-color: transparent; + padding: 0 !important; + cursor: default !important; +} + +.litegraph .litemenu-entry.has_submenu { + border-right: 2px solid cyan; +} + +.litegraph .litemenu-title { + color: #dde; + background-color: #111; + margin: 0; + padding: 2px; + cursor: default; +} + +.litegraph .litemenu-entry:hover:not(.disabled):not(.separator) { + background-color: #444 !important; + color: #eee; + transition: all 0.2s; +} + +.litegraph .litemenu-entry .property_name { + display: inline-block; + text-align: left; + min-width: 80px; + min-height: 1.2em; +} + +.litegraph .litemenu-entry .property_value { + display: inline-block; + background-color: rgba(0, 0, 0, 0.5); + text-align: right; + min-width: 80px; + min-height: 1.2em; + vertical-align: middle; + padding-right: 10px; +} + +.litegraph.litesearchbox { + font-family: Tahoma, sans-serif; + position: absolute; + background-color: rgba(0, 0, 0, 0.5); + padding-top: 4px; +} + +.litegraph.litesearchbox input, +.litegraph.litesearchbox select { + margin-top: 3px; + min-width: 60px; + min-height: 1.5em; + background-color: black; + border: 0; + color: white; + padding-left: 10px; + margin-right: 5px; +} + +.litegraph.litesearchbox .name { + display: inline-block; + min-width: 60px; + min-height: 1.5em; + padding-left: 10px; +} + +.litegraph.litesearchbox .helper { + overflow: auto; + max-height: 200px; + margin-top: 2px; +} + +.litegraph.lite-search-item { + font-family: Tahoma, sans-serif; + background-color: rgba(0, 0, 0, 0.5); + color: white; + padding-top: 2px; +} + +.litegraph.lite-search-item.not_in_filter{ + /*background-color: rgba(50, 50, 50, 0.5);*/ + /*color: #999;*/ + color: #B99; + font-style: italic; +} + +.litegraph.lite-search-item.generic_type{ + /*background-color: rgba(50, 50, 50, 0.5);*/ + /*color: #DD9;*/ + color: #999; + font-style: italic; +} + +.litegraph.lite-search-item:hover, +.litegraph.lite-search-item.selected { + cursor: pointer; + background-color: white; + color: black; +} + +/* DIALOGs ******/ + +.litegraph .dialog { + position: absolute; + top: 50%; + left: 50%; + margin-top: -150px; + margin-left: -200px; + + background-color: #2A2A2A; + + min-width: 400px; + min-height: 200px; + box-shadow: 0 0 4px #111; + border-radius: 6px; +} + +.litegraph .dialog.settings { + left: 10px; + top: 10px; + height: calc( 100% - 20px ); + margin: auto; + max-width: 50%; +} + +.litegraph .dialog.centered { + top: 50px; + left: 50%; + position: absolute; + transform: translateX(-50%); + min-width: 600px; + min-height: 300px; + height: calc( 100% - 100px ); + margin: auto; +} + +.litegraph .dialog .close { + float: right; + margin: 4px; + margin-right: 10px; + cursor: pointer; + font-size: 1.4em; +} + +.litegraph .dialog .close:hover { + color: white; +} + +.litegraph .dialog .dialog-header { + color: #AAA; + border-bottom: 1px solid #161616; +} + +.litegraph .dialog .dialog-header { height: 40px; } +.litegraph .dialog .dialog-footer { height: 50px; padding: 10px; border-top: 1px solid #1a1a1a;} + +.litegraph .dialog .dialog-header .dialog-title { + font: 20px "Arial"; + margin: 4px; + padding: 4px 10px; + display: inline-block; +} + +.litegraph .dialog .dialog-content, .litegraph .dialog .dialog-alt-content { + height: calc(100% - 90px); + width: 100%; + min-height: 100px; + display: inline-block; + color: #AAA; + /*background-color: black;*/ + overflow: auto; +} + +.litegraph .dialog .dialog-content h3 { + margin: 10px; +} + +.litegraph .dialog .dialog-content .connections { + flex-direction: row; +} + +.litegraph .dialog .dialog-content .connections .connections_side { + width: calc(50% - 5px); + min-height: 100px; + background-color: black; + display: flex; +} + +.litegraph .dialog .node_type { + font-size: 1.2em; + display: block; + margin: 10px; +} + +.litegraph .dialog .node_desc { + opacity: 0.5; + display: block; + margin: 10px; +} + +.litegraph .dialog .separator { + display: block; + width: calc( 100% - 4px ); + height: 1px; + border-top: 1px solid #000; + border-bottom: 1px solid #333; + margin: 10px 2px; + padding: 0; +} + +.litegraph .dialog .property { + margin-bottom: 2px; + padding: 4px; +} + +.litegraph .dialog .property:hover { + background: #545454; +} + +.litegraph .dialog .property_name { + color: #737373; + display: inline-block; + text-align: left; + vertical-align: top; + width: 160px; + padding-left: 4px; + overflow: hidden; + margin-right: 6px; +} + +.litegraph .dialog .property:hover .property_name { + color: white; +} + +.litegraph .dialog .property_value { + display: inline-block; + text-align: right; + color: #AAA; + background-color: #1A1A1A; + /*width: calc( 100% - 122px );*/ + max-width: calc( 100% - 162px ); + min-width: 200px; + max-height: 300px; + min-height: 20px; + padding: 4px; + padding-right: 12px; + overflow: hidden; + cursor: pointer; + border-radius: 3px; +} + +.litegraph .dialog .property_value:hover { + color: white; +} + +.litegraph .dialog .property.boolean .property_value { + padding-right: 30px; + color: #A88; + /*width: auto; + float: right;*/ +} + +.litegraph .dialog .property.boolean.bool-on .property_name{ + color: #8A8; +} +.litegraph .dialog .property.boolean.bool-on .property_value{ + color: #8A8; +} + +.litegraph .dialog .btn { + border: 0; + border-radius: 4px; + padding: 4px 20px; + margin-left: 0px; + background-color: #060606; + color: #8e8e8e; +} + +.litegraph .dialog .btn:hover { + background-color: #111; + color: #FFF; +} + +.litegraph .dialog .btn.delete:hover { + background-color: #F33; + color: black; +} + +.litegraph .subgraph_property { + padding: 4px; +} + +.litegraph .subgraph_property:hover { + background-color: #333; +} + +.litegraph .subgraph_property.extra { + margin-top: 8px; +} + +.litegraph .subgraph_property span.name { + font-size: 1.3em; + padding-left: 4px; +} + +.litegraph .subgraph_property span.type { + opacity: 0.5; + margin-right: 20px; + padding-left: 4px; +} + +.litegraph .subgraph_property span.label { + display: inline-block; + width: 60px; + padding: 0px 10px; +} + +.litegraph .subgraph_property input { + width: 140px; + color: #999; + background-color: #1A1A1A; + border-radius: 4px; + border: 0; + margin-right: 10px; + padding: 4px; + padding-left: 10px; +} + +.litegraph .subgraph_property button { + background-color: #1c1c1c; + color: #aaa; + border: 0; + border-radius: 2px; + padding: 4px 10px; + cursor: pointer; +} + +.litegraph .subgraph_property.extra { + color: #ccc; +} + +.litegraph .subgraph_property.extra input { + background-color: #111; +} + +.litegraph .bullet_icon { + margin-left: 10px; + border-radius: 10px; + width: 12px; + height: 12px; + background-color: #666; + display: inline-block; + margin-top: 2px; + margin-right: 4px; + transition: background-color 0.1s ease 0s; + -moz-transition: background-color 0.1s ease 0s; +} + +.litegraph .bullet_icon:hover { + background-color: #698; + cursor: pointer; +} + +/* OLD */ + +.graphcontextmenu { + padding: 4px; + min-width: 100px; +} + +.graphcontextmenu-title { + color: #dde; + background-color: #222; + margin: 0; + padding: 2px; + cursor: default; +} + +.graphmenu-entry { + box-sizing: border-box; + margin: 2px; + padding-left: 20px; + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + transition: all linear 0.3s; +} + +.graphmenu-entry.event, +.litemenu-entry.event { + border-left: 8px solid orange; + padding-left: 12px; +} + +.graphmenu-entry.disabled { + opacity: 0.3; +} + +.graphmenu-entry.submenu { + border-right: 2px solid #eee; +} + +.graphmenu-entry:hover { + background-color: #555; +} + +.graphmenu-entry.separator { + background-color: #111; + border-bottom: 1px solid #666; + height: 1px; + width: calc(100% - 20px); + -moz-width: calc(100% - 20px); + -webkit-width: calc(100% - 20px); +} + +.graphmenu-entry .property_name { + display: inline-block; + text-align: left; + min-width: 80px; + min-height: 1.2em; +} + +.graphmenu-entry .property_value, +.litemenu-entry .property_value { + display: inline-block; + background-color: rgba(0, 0, 0, 0.5); + text-align: right; + min-width: 80px; + min-height: 1.2em; + vertical-align: middle; + padding-right: 10px; +} + +.graphdialog { + position: absolute; + top: 10px; + left: 10px; + min-height: 2em; + background-color: #333; + font-size: 1.2em; + box-shadow: 0 0 10px black !important; + z-index: 10; +} + +.graphdialog.rounded { + border-radius: 12px; + padding-right: 2px; +} + +.graphdialog .name { + display: inline-block; + min-width: 60px; + min-height: 1.5em; + padding-left: 10px; +} + +.graphdialog input, +.graphdialog textarea, +.graphdialog select { + margin: 3px; + min-width: 60px; + min-height: 1.5em; + background-color: black; + border: 0; + color: white; + padding-left: 10px; + outline: none; +} + +.graphdialog textarea { + min-height: 150px; +} + +.graphdialog button { + margin-top: 3px; + vertical-align: top; + background-color: #999; + border: 0; +} + +.graphdialog button.rounded, +.graphdialog input.rounded { + border-radius: 0 12px 12px 0; +} + +.graphdialog .helper { + overflow: auto; + max-height: 200px; +} + +.graphdialog .help-item { + padding-left: 10px; +} + +.graphdialog .help-item:hover, +.graphdialog .help-item.selected { + cursor: pointer; + background-color: white; + color: black; +} + +.litegraph .dialog { + min-height: 0; +} +.litegraph .dialog .dialog-content { +display: block; +} +.litegraph .dialog .dialog-content .subgraph_property { +padding: 5px; +} +.litegraph .dialog .dialog-footer { +margin: 0; +} +.litegraph .dialog .dialog-footer .subgraph_property { +margin-top: 0; +display: flex; +align-items: center; +padding: 5px; +} +.litegraph .dialog .dialog-footer .subgraph_property .name { +flex: 1; +} +.litegraph .graphdialog { +display: flex; +align-items: center; +border-radius: 20px; +padding: 4px 10px; +position: fixed; +} +.litegraph .graphdialog .name { +padding: 0; +min-height: 0; +font-size: 16px; +vertical-align: middle; +} +.litegraph .graphdialog .value { +font-size: 16px; +min-height: 0; +margin: 0 10px; +padding: 2px 5px; +} +.litegraph .graphdialog input[type="checkbox"] { +width: 16px; +height: 16px; +} +.litegraph .graphdialog button { +padding: 4px 18px; +border-radius: 20px; +cursor: pointer; +} + diff --git a/web/lib/litegraph.extensions.js b/web/lib/litegraph.extensions.js new file mode 100644 index 0000000000000000000000000000000000000000..32853fe498f5b89380b490ab23e4421dac0ea243 --- /dev/null +++ b/web/lib/litegraph.extensions.js @@ -0,0 +1,21 @@ +/** + * Changes the background color of the canvas. + * + * @method updateBackground + * @param {image} String + * @param {clearBackgroundColor} String + * @ + */ +LGraphCanvas.prototype.updateBackground = function (image, clearBackgroundColor) { + this._bg_img = new Image(); + this._bg_img.name = image; + this._bg_img.src = image; + this._bg_img.onload = () => { + this.draw(true, true); + }; + this.background_image = image; + + this.clear_background = true; + this.clear_background_color = clearBackgroundColor; + this._pattern = null +} diff --git a/web/scripts/api.js b/web/scripts/api.js new file mode 100644 index 0000000000000000000000000000000000000000..b1d245d73ff192eaa2d28367247290ff756802c1 --- /dev/null +++ b/web/scripts/api.js @@ -0,0 +1,320 @@ +class ComfyApi extends EventTarget { + #registered = new Set(); + + constructor() { + super(); + this.api_host = location.host; + this.api_base = location.pathname.split('/').slice(0, -1).join('/'); + } + + apiURL(route) { + return this.api_base + route; + } + + fetchApi(route, options) { + return fetch(this.apiURL(route), options); + } + + addEventListener(type, callback, options) { + super.addEventListener(type, callback, options); + this.#registered.add(type); + } + + /** + * Poll status for colab and other things that don't support websockets. + */ + #pollQueue() { + setInterval(async () => { + try { + const resp = await this.fetchApi("/prompt"); + const status = await resp.json(); + this.dispatchEvent(new CustomEvent("status", { detail: status })); + } catch (error) { + this.dispatchEvent(new CustomEvent("status", { detail: null })); + } + }, 1000); + } + + /** + * Creates and connects a WebSocket for realtime updates + * @param {boolean} isReconnect If the socket is connection is a reconnect attempt + */ + #createSocket(isReconnect) { + if (this.socket) { + return; + } + + let opened = false; + let existingSession = window.name; + if (existingSession) { + existingSession = "?clientId=" + existingSession; + } + this.socket = new WebSocket( + `ws${window.location.protocol === "https:" ? "s" : ""}://${this.api_host}${this.api_base}/ws${existingSession}` + ); + this.socket.binaryType = "arraybuffer"; + + this.socket.addEventListener("open", () => { + opened = true; + if (isReconnect) { + this.dispatchEvent(new CustomEvent("reconnected")); + } + }); + + this.socket.addEventListener("error", () => { + if (this.socket) this.socket.close(); + if (!isReconnect && !opened) { + this.#pollQueue(); + } + }); + + this.socket.addEventListener("close", () => { + setTimeout(() => { + this.socket = null; + this.#createSocket(true); + }, 300); + if (opened) { + this.dispatchEvent(new CustomEvent("status", { detail: null })); + this.dispatchEvent(new CustomEvent("reconnecting")); + } + }); + + this.socket.addEventListener("message", (event) => { + try { + if (event.data instanceof ArrayBuffer) { + const view = new DataView(event.data); + const eventType = view.getUint32(0); + const buffer = event.data.slice(4); + switch (eventType) { + case 1: + const view2 = new DataView(event.data); + const imageType = view2.getUint32(0) + let imageMime + switch (imageType) { + case 1: + default: + imageMime = "image/jpeg"; + break; + case 2: + imageMime = "image/png" + } + const imageBlob = new Blob([buffer.slice(4)], { type: imageMime }); + this.dispatchEvent(new CustomEvent("b_preview", { detail: imageBlob })); + break; + default: + throw new Error(`Unknown binary websocket message of type ${eventType}`); + } + } + else { + const msg = JSON.parse(event.data); + switch (msg.type) { + case "status": + if (msg.data.sid) { + this.clientId = msg.data.sid; + window.name = this.clientId; + } + this.dispatchEvent(new CustomEvent("status", { detail: msg.data.status })); + break; + case "progress": + this.dispatchEvent(new CustomEvent("progress", { detail: msg.data })); + break; + case "executing": + this.dispatchEvent(new CustomEvent("executing", { detail: msg.data.node })); + break; + case "executed": + this.dispatchEvent(new CustomEvent("executed", { detail: msg.data })); + break; + case "execution_start": + this.dispatchEvent(new CustomEvent("execution_start", { detail: msg.data })); + break; + case "execution_error": + this.dispatchEvent(new CustomEvent("execution_error", { detail: msg.data })); + break; + case "execution_cached": + this.dispatchEvent(new CustomEvent("execution_cached", { detail: msg.data })); + break; + default: + if (this.#registered.has(msg.type)) { + this.dispatchEvent(new CustomEvent(msg.type, { detail: msg.data })); + } else { + throw new Error(`Unknown message type ${msg.type}`); + } + } + } + } catch (error) { + console.warn("Unhandled message:", event.data, error); + } + }); + } + + /** + * Initialises sockets and realtime updates + */ + init() { + this.#createSocket(); + } + + /** + * Gets a list of extension urls + * @returns An array of script urls to import + */ + async getExtensions() { + const resp = await this.fetchApi("/extensions", { cache: "no-store" }); + return await resp.json(); + } + + /** + * Gets a list of embedding names + * @returns An array of script urls to import + */ + async getEmbeddings() { + const resp = await this.fetchApi("/embeddings", { cache: "no-store" }); + return await resp.json(); + } + + /** + * Loads node object definitions for the graph + * @returns The node definitions + */ + async getNodeDefs() { + const resp = await this.fetchApi("/object_info", { cache: "no-store" }); + return await resp.json(); + } + + /** + * + * @param {number} number The index at which to queue the prompt, passing -1 will insert the prompt at the front of the queue + * @param {object} prompt The prompt data to queue + */ + async queuePrompt(number, { output, workflow }) { + const body = { + client_id: this.clientId, + prompt: output, + extra_data: { extra_pnginfo: { workflow } }, + }; + + if (number === -1) { + body.front = true; + } else if (number != 0) { + body.number = number; + } + + const res = await this.fetchApi("/prompt", { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: JSON.stringify(body), + }); + + if (res.status !== 200) { + throw { + response: await res.json(), + }; + } + + return await res.json(); + } + + /** + * Loads a list of items (queue or history) + * @param {string} type The type of items to load, queue or history + * @returns The items of the specified type grouped by their status + */ + async getItems(type) { + if (type === "queue") { + return this.getQueue(); + } + return this.getHistory(); + } + + /** + * Gets the current state of the queue + * @returns The currently running and queued items + */ + async getQueue() { + try { + const res = await this.fetchApi("/queue"); + const data = await res.json(); + return { + // Running action uses a different endpoint for cancelling + Running: data.queue_running.map((prompt) => ({ + prompt, + remove: { name: "Cancel", cb: () => api.interrupt() }, + })), + Pending: data.queue_pending.map((prompt) => ({ prompt })), + }; + } catch (error) { + console.error(error); + return { Running: [], Pending: [] }; + } + } + + /** + * Gets the prompt execution history + * @returns Prompt history including node outputs + */ + async getHistory() { + try { + const res = await this.fetchApi("/history"); + return { History: Object.values(await res.json()) }; + } catch (error) { + console.error(error); + return { History: [] }; + } + } + + /** + * Gets system & device stats + * @returns System stats such as python version, OS, per device info + */ + async getSystemStats() { + const res = await this.fetchApi("/system_stats"); + return await res.json(); + } + + /** + * Sends a POST request to the API + * @param {*} type The endpoint to post to + * @param {*} body Optional POST data + */ + async #postItem(type, body) { + try { + await this.fetchApi("/" + type, { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: body ? JSON.stringify(body) : undefined, + }); + } catch (error) { + console.error(error); + } + } + + /** + * Deletes an item from the specified list + * @param {string} type The type of item to delete, queue or history + * @param {number} id The id of the item to delete + */ + async deleteItem(type, id) { + await this.#postItem(type, { delete: [id] }); + } + + /** + * Clears the specified list + * @param {string} type The type of list to clear, queue or history + */ + async clearItems(type) { + await this.#postItem(type, { clear: true }); + } + + /** + * Interrupts the execution of the running prompt + */ + async interrupt() { + await this.#postItem("interrupt", null); + } +} + +export const api = new ComfyApi(); diff --git a/web/scripts/app.js b/web/scripts/app.js new file mode 100644 index 0000000000000000000000000000000000000000..5efe08c0055977701bca3ca0f56bf9d42d75f00d --- /dev/null +++ b/web/scripts/app.js @@ -0,0 +1,1708 @@ +import { ComfyLogging } from "./logging.js"; +import { ComfyWidgets } from "./widgets.js"; +import { ComfyUI, $el } from "./ui.js"; +import { api } from "./api.js"; +import { defaultGraph } from "./defaultGraph.js"; +import { getPngMetadata, importA1111, getLatentMetadata } from "./pnginfo.js"; + +/** + * @typedef {import("types/comfy").ComfyExtension} ComfyExtension + */ + +export class ComfyApp { + /** + * List of entries to queue + * @type {{number: number, batchCount: number}[]} + */ + #queueItems = []; + /** + * If the queue is currently being processed + * @type {boolean} + */ + #processingQueue = false; + + /** + * Content Clipboard + * @type {serialized node object} + */ + static clipspace = null; + static clipspace_invalidate_handler = null; + static open_maskeditor = null; + static clipspace_return_node = null; + + constructor() { + this.ui = new ComfyUI(this); + this.logging = new ComfyLogging(this); + + /** + * List of extensions that are registered with the app + * @type {ComfyExtension[]} + */ + this.extensions = []; + + /** + * Stores the execution output data for each node + * @type {Record} + */ + this.nodeOutputs = {}; + + /** + * Stores the preview image data for each node + * @type {Record} + */ + this.nodePreviewImages = {}; + + /** + * If the shift key on the keyboard is pressed + * @type {boolean} + */ + this.shiftDown = false; + } + + getPreviewFormatParam() { + let preview_format = this.ui.settings.getSettingValue("Comfy.PreviewFormat"); + if(preview_format) + return `&preview=${preview_format}`; + else + return ""; + } + + static isImageNode(node) { + return node.imgs || (node && node.widgets && node.widgets.findIndex(obj => obj.name === 'image') >= 0); + } + + static onClipspaceEditorSave() { + if(ComfyApp.clipspace_return_node) { + ComfyApp.pasteFromClipspace(ComfyApp.clipspace_return_node); + } + } + + static onClipspaceEditorClosed() { + ComfyApp.clipspace_return_node = null; + } + + static copyToClipspace(node) { + var widgets = null; + if(node.widgets) { + widgets = node.widgets.map(({ type, name, value }) => ({ type, name, value })); + } + + var imgs = undefined; + var orig_imgs = undefined; + if(node.imgs != undefined) { + imgs = []; + orig_imgs = []; + + for (let i = 0; i < node.imgs.length; i++) { + imgs[i] = new Image(); + imgs[i].src = node.imgs[i].src; + orig_imgs[i] = imgs[i]; + } + } + + var selectedIndex = 0; + if(node.imageIndex) { + selectedIndex = node.imageIndex; + } + + ComfyApp.clipspace = { + 'widgets': widgets, + 'imgs': imgs, + 'original_imgs': orig_imgs, + 'images': node.images, + 'selectedIndex': selectedIndex, + 'img_paste_mode': 'selected' // reset to default im_paste_mode state on copy action + }; + + ComfyApp.clipspace_return_node = null; + + if(ComfyApp.clipspace_invalidate_handler) { + ComfyApp.clipspace_invalidate_handler(); + } + } + + static pasteFromClipspace(node) { + if(ComfyApp.clipspace) { + // image paste + if(ComfyApp.clipspace.imgs && node.imgs) { + if(node.images && ComfyApp.clipspace.images) { + if(ComfyApp.clipspace['img_paste_mode'] == 'selected') { + node.images = [ComfyApp.clipspace.images[ComfyApp.clipspace['selectedIndex']]]; + } + else { + node.images = ComfyApp.clipspace.images; + } + + if(app.nodeOutputs[node.id + ""]) + app.nodeOutputs[node.id + ""].images = node.images; + } + + if(ComfyApp.clipspace.imgs) { + // deep-copy to cut link with clipspace + if(ComfyApp.clipspace['img_paste_mode'] == 'selected') { + const img = new Image(); + img.src = ComfyApp.clipspace.imgs[ComfyApp.clipspace['selectedIndex']].src; + node.imgs = [img]; + node.imageIndex = 0; + } + else { + const imgs = []; + for(let i=0; i obj.name === 'image'); + if(index >= 0) { + if(node.widgets[index].type != 'image' && typeof node.widgets[index].value == "string" && clip_image.filename) { + node.widgets[index].value = (clip_image.subfolder?clip_image.subfolder+'/':'') + clip_image.filename + (clip_image.type?` [${clip_image.type}]`:''); + } + else { + node.widgets[index].value = clip_image; + } + } + } + if(ComfyApp.clipspace.widgets) { + ComfyApp.clipspace.widgets.forEach(({ type, name, value }) => { + const prop = Object.values(node.widgets).find(obj => obj.type === type && obj.name === name); + if (prop && prop.type != 'button') { + if(prop.type != 'image' && typeof prop.value == "string" && value.filename) { + prop.value = (value.subfolder?value.subfolder+'/':'') + value.filename + (value.type?` [${value.type}]`:''); + } + else { + prop.value = value; + prop.callback(value); + } + } + }); + } + } + + app.graph.setDirtyCanvas(true); + } + } + + /** + * Invoke an extension callback + * @param {keyof ComfyExtension} method The extension callback to execute + * @param {any[]} args Any arguments to pass to the callback + * @returns + */ + #invokeExtensions(method, ...args) { + let results = []; + for (const ext of this.extensions) { + if (method in ext) { + try { + results.push(ext[method](...args, this)); + } catch (error) { + console.error( + `Error calling extension '${ext.name}' method '${method}'`, + { error }, + { extension: ext }, + { args } + ); + } + } + } + return results; + } + + /** + * Invoke an async extension callback + * Each callback will be invoked concurrently + * @param {string} method The extension callback to execute + * @param {...any} args Any arguments to pass to the callback + * @returns + */ + async #invokeExtensionsAsync(method, ...args) { + return await Promise.all( + this.extensions.map(async (ext) => { + if (method in ext) { + try { + return await ext[method](...args, this); + } catch (error) { + console.error( + `Error calling extension '${ext.name}' method '${method}'`, + { error }, + { extension: ext }, + { args } + ); + } + } + }) + ); + } + + /** + * Adds special context menu handling for nodes + * e.g. this adds Open Image functionality for nodes that show images + * @param {*} node The node to add the menu handler + */ + #addNodeContextMenuHandler(node) { + node.prototype.getExtraMenuOptions = function (_, options) { + if (this.imgs) { + // If this node has images then we add an open in new tab item + let img; + if (this.imageIndex != null) { + // An image is selected so select that + img = this.imgs[this.imageIndex]; + } else if (this.overIndex != null) { + // No image is selected but one is hovered + img = this.imgs[this.overIndex]; + } + if (img) { + options.unshift( + { + content: "Open Image", + callback: () => { + let url = new URL(img.src); + url.searchParams.delete('preview'); + window.open(url, "_blank") + }, + }, + { + content: "Save Image", + callback: () => { + const a = document.createElement("a"); + let url = new URL(img.src); + url.searchParams.delete('preview'); + a.href = url; + a.setAttribute("download", new URLSearchParams(url.search).get("filename")); + document.body.append(a); + a.click(); + requestAnimationFrame(() => a.remove()); + }, + } + ); + } + } + + options.push({ + content: "Bypass", + callback: (obj) => { if (this.mode === 4) this.mode = 0; else this.mode = 4; this.graph.change(); } + }); + + // prevent conflict of clipspace content + if(!ComfyApp.clipspace_return_node) { + options.push({ + content: "Copy (Clipspace)", + callback: (obj) => { ComfyApp.copyToClipspace(this); } + }); + + if(ComfyApp.clipspace != null) { + options.push({ + content: "Paste (Clipspace)", + callback: () => { ComfyApp.pasteFromClipspace(this); } + }); + } + + if(ComfyApp.isImageNode(this)) { + options.push({ + content: "Open in MaskEditor", + callback: (obj) => { + ComfyApp.copyToClipspace(this); + ComfyApp.clipspace_return_node = this; + ComfyApp.open_maskeditor(); + } + }); + } + } + }; + } + + #addNodeKeyHandler(node) { + const app = this; + const origNodeOnKeyDown = node.prototype.onKeyDown; + + node.prototype.onKeyDown = function(e) { + if (origNodeOnKeyDown && origNodeOnKeyDown.apply(this, e) === false) { + return false; + } + + if (this.flags.collapsed || !this.imgs || this.imageIndex === null) { + return; + } + + let handled = false; + + if (e.key === "ArrowLeft" || e.key === "ArrowRight") { + if (e.key === "ArrowLeft") { + this.imageIndex -= 1; + } else if (e.key === "ArrowRight") { + this.imageIndex += 1; + } + this.imageIndex %= this.imgs.length; + + if (this.imageIndex < 0) { + this.imageIndex = this.imgs.length + this.imageIndex; + } + handled = true; + } else if (e.key === "Escape") { + this.imageIndex = null; + handled = true; + } + + if (handled === true) { + e.preventDefault(); + e.stopImmediatePropagation(); + return false; + } + } + } + + /** + * Adds Custom drawing logic for nodes + * e.g. Draws images and handles thumbnail navigation on nodes that output images + * @param {*} node The node to add the draw handler + */ + #addDrawBackgroundHandler(node) { + const app = this; + + function getImageTop(node) { + let shiftY; + if (node.imageOffset != null) { + shiftY = node.imageOffset; + } else { + if (node.widgets?.length) { + const w = node.widgets[node.widgets.length - 1]; + shiftY = w.last_y; + if (w.computeSize) { + shiftY += w.computeSize()[1] + 4; + } + else if(w.computedHeight) { + shiftY += w.computedHeight; + } + else { + shiftY += LiteGraph.NODE_WIDGET_HEIGHT + 4; + } + } else { + shiftY = node.computeSize()[1]; + } + } + return shiftY; + } + + node.prototype.setSizeForImage = function () { + if (this.inputHeight) { + this.setSize(this.size); + return; + } + const minHeight = getImageTop(this) + 220; + if (this.size[1] < minHeight) { + this.setSize([this.size[0], minHeight]); + } + }; + + node.prototype.onDrawBackground = function (ctx) { + if (!this.flags.collapsed) { + let imgURLs = [] + let imagesChanged = false + + const output = app.nodeOutputs[this.id + ""]; + if (output && output.images) { + if (this.images !== output.images) { + this.images = output.images; + imagesChanged = true; + imgURLs = imgURLs.concat(output.images.map(params => { + return api.apiURL("/view?" + new URLSearchParams(params).toString() + app.getPreviewFormatParam()); + })) + } + } + + const preview = app.nodePreviewImages[this.id + ""] + if (this.preview !== preview) { + this.preview = preview + imagesChanged = true; + if (preview != null) { + imgURLs.push(preview); + } + } + + if (imagesChanged) { + this.imageIndex = null; + if (imgURLs.length > 0) { + Promise.all( + imgURLs.map((src) => { + return new Promise((r) => { + const img = new Image(); + img.onload = () => r(img); + img.onerror = () => r(null); + img.src = src + }); + }) + ).then((imgs) => { + if ((!output || this.images === output.images) && (!preview || this.preview === preview)) { + this.imgs = imgs.filter(Boolean); + this.setSizeForImage?.(); + app.graph.setDirtyCanvas(true); + } + }); + } + else { + this.imgs = null; + } + } + + if (this.imgs && this.imgs.length) { + const canvas = graph.list_of_graphcanvas[0]; + const mouse = canvas.graph_mouse; + if (!canvas.pointer_is_down && this.pointerDown) { + if (mouse[0] === this.pointerDown.pos[0] && mouse[1] === this.pointerDown.pos[1]) { + this.imageIndex = this.pointerDown.index; + } + this.pointerDown = null; + } + + let w = this.imgs[0].naturalWidth; + let h = this.imgs[0].naturalHeight; + let imageIndex = this.imageIndex; + const numImages = this.imgs.length; + if (numImages === 1 && !imageIndex) { + this.imageIndex = imageIndex = 0; + } + + const shiftY = getImageTop(this); + + let dw = this.size[0]; + let dh = this.size[1]; + dh -= shiftY; + + if (imageIndex == null) { + let best = 0; + let cellWidth; + let cellHeight; + let cols = 0; + let shiftX = 0; + for (let c = 1; c <= numImages; c++) { + const rows = Math.ceil(numImages / c); + const cW = dw / c; + const cH = dh / rows; + const scaleX = cW / w; + const scaleY = cH / h; + + const scale = Math.min(scaleX, scaleY, 1); + const imageW = w * scale; + const imageH = h * scale; + const area = imageW * imageH * numImages; + + if (area > best) { + best = area; + cellWidth = imageW; + cellHeight = imageH; + cols = c; + shiftX = c * ((cW - imageW) / 2); + } + } + + let anyHovered = false; + this.imageRects = []; + for (let i = 0; i < numImages; i++) { + const img = this.imgs[i]; + const row = Math.floor(i / cols); + const col = i % cols; + const x = col * cellWidth + shiftX; + const y = row * cellHeight + shiftY; + if (!anyHovered) { + anyHovered = LiteGraph.isInsideRectangle( + mouse[0], + mouse[1], + x + this.pos[0], + y + this.pos[1], + cellWidth, + cellHeight + ); + if (anyHovered) { + this.overIndex = i; + let value = 110; + if (canvas.pointer_is_down) { + if (!this.pointerDown || this.pointerDown.index !== i) { + this.pointerDown = { index: i, pos: [...mouse] }; + } + value = 125; + } + ctx.filter = `contrast(${value}%) brightness(${value}%)`; + canvas.canvas.style.cursor = "pointer"; + } + } + this.imageRects.push([x, y, cellWidth, cellHeight]); + + let wratio = cellWidth/img.width; + let hratio = cellHeight/img.height; + var ratio = Math.min(wratio, hratio); + + let imgHeight = ratio * img.height; + let imgY = row * cellHeight + shiftY + (cellHeight - imgHeight)/2; + let imgWidth = ratio * img.width; + let imgX = col * cellWidth + shiftX + (cellWidth - imgWidth)/2; + + ctx.drawImage(img, imgX, imgY, imgWidth, imgHeight); + ctx.filter = "none"; + } + + if (!anyHovered) { + this.pointerDown = null; + this.overIndex = null; + } + } else { + // Draw individual + const scaleX = dw / w; + const scaleY = dh / h; + const scale = Math.min(scaleX, scaleY, 1); + + w *= scale; + h *= scale; + + let x = (dw - w) / 2; + let y = (dh - h) / 2 + shiftY; + ctx.drawImage(this.imgs[imageIndex], x, y, w, h); + + const drawButton = (x, y, sz, text) => { + const hovered = LiteGraph.isInsideRectangle(mouse[0], mouse[1], x + this.pos[0], y + this.pos[1], sz, sz); + let fill = "#333"; + let textFill = "#fff"; + let isClicking = false; + if (hovered) { + canvas.canvas.style.cursor = "pointer"; + if (canvas.pointer_is_down) { + fill = "#1e90ff"; + isClicking = true; + } else { + fill = "#eee"; + textFill = "#000"; + } + } else { + this.pointerWasDown = null; + } + + ctx.fillStyle = fill; + ctx.beginPath(); + ctx.roundRect(x, y, sz, sz, [4]); + ctx.fill(); + ctx.fillStyle = textFill; + ctx.font = "12px Arial"; + ctx.textAlign = "center"; + ctx.fillText(text, x + 15, y + 20); + + return isClicking; + }; + + if (numImages > 1) { + if (drawButton(x + w - 35, y + h - 35, 30, `${this.imageIndex + 1}/${numImages}`)) { + let i = this.imageIndex + 1 >= numImages ? 0 : this.imageIndex + 1; + if (!this.pointerDown || !this.pointerDown.index === i) { + this.pointerDown = { index: i, pos: [...mouse] }; + } + } + + if (drawButton(x + w - 35, y + 5, 30, `x`)) { + if (!this.pointerDown || !this.pointerDown.index === null) { + this.pointerDown = { index: null, pos: [...mouse] }; + } + } + } + } + } + } + }; + } + + /** + * Adds a handler allowing drag+drop of files onto the window to load workflows + */ + #addDropHandler() { + // Get prompt from dropped PNG or json + document.addEventListener("drop", async (event) => { + event.preventDefault(); + event.stopPropagation(); + + const n = this.dragOverNode; + this.dragOverNode = null; + // Node handles file drop, we dont use the built in onDropFile handler as its buggy + // If you drag multiple files it will call it multiple times with the same file + if (n && n.onDragDrop && (await n.onDragDrop(event))) { + return; + } + // Dragging from Chrome->Firefox there is a file but its a bmp, so ignore that + if (event.dataTransfer.files.length && event.dataTransfer.files[0].type !== "image/bmp") { + await this.handleFile(event.dataTransfer.files[0]); + } else { + // Try loading the first URI in the transfer list + const validTypes = ["text/uri-list", "text/x-moz-url"]; + const match = [...event.dataTransfer.types].find((t) => validTypes.find(v => t === v)); + if (match) { + const uri = event.dataTransfer.getData(match)?.split("\n")?.[0]; + if (uri) { + await this.handleFile(await (await fetch(uri)).blob()); + } + } + } + }); + + // Always clear over node on drag leave + this.canvasEl.addEventListener("dragleave", async () => { + if (this.dragOverNode) { + this.dragOverNode = null; + this.graph.setDirtyCanvas(false, true); + } + }); + + // Add handler for dropping onto a specific node + this.canvasEl.addEventListener( + "dragover", + (e) => { + this.canvas.adjustMouseEvent(e); + const node = this.graph.getNodeOnPos(e.canvasX, e.canvasY); + if (node) { + if (node.onDragOver && node.onDragOver(e)) { + this.dragOverNode = node; + + // dragover event is fired very frequently, run this on an animation frame + requestAnimationFrame(() => { + this.graph.setDirtyCanvas(false, true); + }); + return; + } + } + this.dragOverNode = null; + }, + false + ); + } + + /** + * Adds a handler on paste that extracts and loads images or workflows from pasted JSON data + */ + #addPasteHandler() { + document.addEventListener("paste", (e) => { + // ctrl+shift+v is used to paste nodes with connections + // this is handled by litegraph + if(this.shiftDown) return; + + let data = (e.clipboardData || window.clipboardData); + const items = data.items; + + // Look for image paste data + for (const item of items) { + if (item.type.startsWith('image/')) { + var imageNode = null; + + // If an image node is selected, paste into it + if (this.canvas.current_node && + this.canvas.current_node.is_selected && + ComfyApp.isImageNode(this.canvas.current_node)) { + imageNode = this.canvas.current_node; + } + + // No image node selected: add a new one + if (!imageNode) { + const newNode = LiteGraph.createNode("LoadImage"); + newNode.pos = [...this.canvas.graph_mouse]; + imageNode = this.graph.add(newNode); + this.graph.change(); + } + const blob = item.getAsFile(); + imageNode.pasteFile(blob); + return; + } + } + + // No image found. Look for node data + data = data.getData("text/plain"); + let workflow; + try { + data = data.slice(data.indexOf("{")); + workflow = JSON.parse(data); + } catch (err) { + try { + data = data.slice(data.indexOf("workflow\n")); + data = data.slice(data.indexOf("{")); + workflow = JSON.parse(data); + } catch (error) {} + } + + if (workflow && workflow.version && workflow.nodes && workflow.extra) { + this.loadGraphData(workflow); + } + else { + if (e.target.type === "text" || e.target.type === "textarea") { + return; + } + + // Litegraph default paste + this.canvas.pasteFromClipboard(); + } + + + }); + } + + + /** + * Adds a handler on copy that serializes selected nodes to JSON + */ + #addCopyHandler() { + document.addEventListener("copy", (e) => { + if (e.target.type === "text" || e.target.type === "textarea") { + // Default system copy + return; + } + + // copy nodes and clear clipboard + if (e.target.className === "litegraph" && this.canvas.selected_nodes) { + this.canvas.copyToClipboard(); + e.clipboardData.setData('text', ' '); //clearData doesn't remove images from clipboard + e.preventDefault(); + e.stopImmediatePropagation(); + return false; + } + }); + } + + + /** + * Handle mouse + * + * Move group by header + */ + #addProcessMouseHandler() { + const self = this; + + const origProcessMouseDown = LGraphCanvas.prototype.processMouseDown; + LGraphCanvas.prototype.processMouseDown = function(e) { + const res = origProcessMouseDown.apply(this, arguments); + + this.selected_group_moving = false; + + if (this.selected_group && !this.selected_group_resizing) { + var font_size = + this.selected_group.font_size || LiteGraph.DEFAULT_GROUP_FONT_SIZE; + var height = font_size * 1.4; + + // Move group by header + if (LiteGraph.isInsideRectangle(e.canvasX, e.canvasY, this.selected_group.pos[0], this.selected_group.pos[1], this.selected_group.size[0], height)) { + this.selected_group_moving = true; + } + } + + return res; + } + + const origProcessMouseMove = LGraphCanvas.prototype.processMouseMove; + LGraphCanvas.prototype.processMouseMove = function(e) { + const orig_selected_group = this.selected_group; + + if (this.selected_group && !this.selected_group_resizing && !this.selected_group_moving) { + this.selected_group = null; + } + + const res = origProcessMouseMove.apply(this, arguments); + + if (orig_selected_group && !this.selected_group_resizing && !this.selected_group_moving) { + this.selected_group = orig_selected_group; + } + + return res; + }; + } + + /** + * Handle keypress + * + * Ctrl + M mute/unmute selected nodes + */ + #addProcessKeyHandler() { + const self = this; + const origProcessKey = LGraphCanvas.prototype.processKey; + LGraphCanvas.prototype.processKey = function(e) { + if (!this.graph) { + return; + } + + var block_default = false; + + if (e.target.localName == "input") { + return; + } + + if (e.type == "keydown" && !e.repeat) { + + // Ctrl + M mute/unmute + if (e.key === 'm' && e.ctrlKey) { + if (this.selected_nodes) { + for (var i in this.selected_nodes) { + if (this.selected_nodes[i].mode === 2) { // never + this.selected_nodes[i].mode = 0; // always + } else { + this.selected_nodes[i].mode = 2; // never + } + } + } + block_default = true; + } + + // Ctrl + B bypass + if (e.key === 'b' && e.ctrlKey) { + if (this.selected_nodes) { + for (var i in this.selected_nodes) { + if (this.selected_nodes[i].mode === 4) { // never + this.selected_nodes[i].mode = 0; // always + } else { + this.selected_nodes[i].mode = 4; // never + } + } + } + block_default = true; + } + + // Ctrl+C Copy + if ((e.key === 'c') && (e.metaKey || e.ctrlKey)) { + // Trigger onCopy + return true; + } + + // Ctrl+V Paste + if ((e.key === 'v' || e.key == 'V') && (e.metaKey || e.ctrlKey) && !e.shiftKey) { + // Trigger onPaste + return true; + } + } + + this.graph.change(); + + if (block_default) { + e.preventDefault(); + e.stopImmediatePropagation(); + return false; + } + + // Fall through to Litegraph defaults + return origProcessKey.apply(this, arguments); + }; + } + + /** + * Draws group header bar + */ + #addDrawGroupsHandler() { + const self = this; + + const origDrawGroups = LGraphCanvas.prototype.drawGroups; + LGraphCanvas.prototype.drawGroups = function(canvas, ctx) { + if (!this.graph) { + return; + } + + var groups = this.graph._groups; + + ctx.save(); + ctx.globalAlpha = 0.7 * this.editor_alpha; + + for (var i = 0; i < groups.length; ++i) { + var group = groups[i]; + + if (!LiteGraph.overlapBounding(this.visible_area, group._bounding)) { + continue; + } //out of the visible area + + ctx.fillStyle = group.color || "#335"; + ctx.strokeStyle = group.color || "#335"; + var pos = group._pos; + var size = group._size; + ctx.globalAlpha = 0.25 * this.editor_alpha; + ctx.beginPath(); + var font_size = + group.font_size || LiteGraph.DEFAULT_GROUP_FONT_SIZE; + ctx.rect(pos[0] + 0.5, pos[1] + 0.5, size[0], font_size * 1.4); + ctx.fill(); + ctx.globalAlpha = this.editor_alpha; + } + + ctx.restore(); + + const res = origDrawGroups.apply(this, arguments); + return res; + } + } + + /** + * Draws node highlights (executing, drag drop) and progress bar + */ + #addDrawNodeHandler() { + const origDrawNodeShape = LGraphCanvas.prototype.drawNodeShape; + const self = this; + + LGraphCanvas.prototype.drawNodeShape = function (node, ctx, size, fgcolor, bgcolor, selected, mouse_over) { + const res = origDrawNodeShape.apply(this, arguments); + + const nodeErrors = self.lastNodeErrors?.[node.id]; + + let color = null; + let lineWidth = 1; + if (node.id === +self.runningNodeId) { + color = "#0f0"; + } else if (self.dragOverNode && node.id === self.dragOverNode.id) { + color = "dodgerblue"; + } + else if (nodeErrors?.errors) { + color = "red"; + lineWidth = 2; + } + else if (self.lastExecutionError && +self.lastExecutionError.node_id === node.id) { + color = "#f0f"; + lineWidth = 2; + } + + if (color) { + const shape = node._shape || node.constructor.shape || LiteGraph.ROUND_SHAPE; + ctx.lineWidth = lineWidth; + ctx.globalAlpha = 0.8; + ctx.beginPath(); + if (shape == LiteGraph.BOX_SHAPE) + ctx.rect(-6, -6 - LiteGraph.NODE_TITLE_HEIGHT, 12 + size[0] + 1, 12 + size[1] + LiteGraph.NODE_TITLE_HEIGHT); + else if (shape == LiteGraph.ROUND_SHAPE || (shape == LiteGraph.CARD_SHAPE && node.flags.collapsed)) + ctx.roundRect( + -6, + -6 - LiteGraph.NODE_TITLE_HEIGHT, + 12 + size[0] + 1, + 12 + size[1] + LiteGraph.NODE_TITLE_HEIGHT, + this.round_radius * 2 + ); + else if (shape == LiteGraph.CARD_SHAPE) + ctx.roundRect( + -6, + -6 - LiteGraph.NODE_TITLE_HEIGHT, + 12 + size[0] + 1, + 12 + size[1] + LiteGraph.NODE_TITLE_HEIGHT, + [this.round_radius * 2, this.round_radius * 2, 2, 2] + ); + else if (shape == LiteGraph.CIRCLE_SHAPE) + ctx.arc(size[0] * 0.5, size[1] * 0.5, size[0] * 0.5 + 6, 0, Math.PI * 2); + ctx.strokeStyle = color; + ctx.stroke(); + ctx.strokeStyle = fgcolor; + ctx.globalAlpha = 1; + } + + if (self.progress && node.id === +self.runningNodeId) { + ctx.fillStyle = "green"; + ctx.fillRect(0, 0, size[0] * (self.progress.value / self.progress.max), 6); + ctx.fillStyle = bgcolor; + } + + // Highlight inputs that failed validation + if (nodeErrors) { + ctx.lineWidth = 2; + ctx.strokeStyle = "red"; + for (const error of nodeErrors.errors) { + if (error.extra_info && error.extra_info.input_name) { + const inputIndex = node.findInputSlot(error.extra_info.input_name) + if (inputIndex !== -1) { + let pos = node.getConnectionPos(true, inputIndex); + ctx.beginPath(); + ctx.arc(pos[0] - node.pos[0], pos[1] - node.pos[1], 12, 0, 2 * Math.PI, false) + ctx.stroke(); + } + } + } + } + + return res; + }; + + const origDrawNode = LGraphCanvas.prototype.drawNode; + LGraphCanvas.prototype.drawNode = function (node, ctx) { + var editor_alpha = this.editor_alpha; + var old_color = node.bgcolor; + + if (node.mode === 2) { // never + this.editor_alpha = 0.4; + } + + if (node.mode === 4) { // never + node.bgcolor = "#FF00FF"; + this.editor_alpha = 0.2; + } + + const res = origDrawNode.apply(this, arguments); + + this.editor_alpha = editor_alpha; + node.bgcolor = old_color; + + return res; + }; + } + + /** + * Handles updates from the API socket + */ + #addApiUpdateHandlers() { + api.addEventListener("status", ({ detail }) => { + this.ui.setStatus(detail); + }); + + api.addEventListener("reconnecting", () => { + this.ui.dialog.show("Reconnecting..."); + }); + + api.addEventListener("reconnected", () => { + this.ui.dialog.close(); + }); + + api.addEventListener("progress", ({ detail }) => { + this.progress = detail; + this.graph.setDirtyCanvas(true, false); + }); + + api.addEventListener("executing", ({ detail }) => { + this.progress = null; + this.runningNodeId = detail; + this.graph.setDirtyCanvas(true, false); + delete this.nodePreviewImages[this.runningNodeId] + }); + + api.addEventListener("executed", ({ detail }) => { + this.nodeOutputs[detail.node] = detail.output; + const node = this.graph.getNodeById(detail.node); + if (node) { + if (node.onExecuted) + node.onExecuted(detail.output); + } + }); + + api.addEventListener("execution_start", ({ detail }) => { + this.runningNodeId = null; + this.lastExecutionError = null + this.graph._nodes.forEach((node) => { + if (node.onExecutionStart) + node.onExecutionStart() + }) + }); + + api.addEventListener("execution_error", ({ detail }) => { + this.lastExecutionError = detail; + const formattedError = this.#formatExecutionError(detail); + this.ui.dialog.show(formattedError); + this.canvas.draw(true, true); + }); + + api.addEventListener("b_preview", ({ detail }) => { + const id = this.runningNodeId + if (id == null) + return; + + const blob = detail + const blobUrl = URL.createObjectURL(blob) + this.nodePreviewImages[id] = [blobUrl] + }); + + api.init(); + } + + #addKeyboardHandler() { + window.addEventListener("keydown", (e) => { + this.shiftDown = e.shiftKey; + }); + window.addEventListener("keyup", (e) => { + this.shiftDown = e.shiftKey; + }); + } + + /** + * Loads all extensions from the API into the window in parallel + */ + async #loadExtensions() { + const extensions = await api.getExtensions(); + this.logging.addEntry("Comfy.App", "debug", { Extensions: extensions }); + + const extensionPromises = extensions.map(async ext => { + try { + await import(api.apiURL(ext)); + } catch (error) { + console.error("Error loading extension", ext, error); + } + }); + + await Promise.all(extensionPromises); + } + + /** + * Set up the app on the page + */ + async setup() { + await this.#loadExtensions(); + + // Create and mount the LiteGraph in the DOM + const mainCanvas = document.createElement("canvas") + mainCanvas.style.touchAction = "none" + const canvasEl = (this.canvasEl = Object.assign(mainCanvas, { id: "graph-canvas" })); + canvasEl.tabIndex = "1"; + document.body.prepend(canvasEl); + + this.#addProcessMouseHandler(); + this.#addProcessKeyHandler(); + + this.graph = new LGraph(); + const canvas = (this.canvas = new LGraphCanvas(canvasEl, this.graph)); + this.ctx = canvasEl.getContext("2d"); + + LiteGraph.release_link_on_empty_shows_menu = true; + LiteGraph.alt_drag_do_clone_nodes = true; + + this.graph.start(); + + function resizeCanvas() { + // Limit minimal scale to 1, see https://github.com/comfyanonymous/ComfyUI/pull/845 + const scale = Math.max(window.devicePixelRatio, 1); + const { width, height } = canvasEl.getBoundingClientRect(); + canvasEl.width = Math.round(width * scale); + canvasEl.height = Math.round(height * scale); + canvasEl.getContext("2d").scale(scale, scale); + canvas.draw(true, true); + } + + // Ensure the canvas fills the window + resizeCanvas(); + window.addEventListener("resize", resizeCanvas); + + await this.#invokeExtensionsAsync("init"); + await this.registerNodes(); + + // Load previous workflow + let restored = false; + try { + const json = localStorage.getItem("workflow"); + if (json) { + const workflow = JSON.parse(json); + this.loadGraphData(workflow); + restored = true; + } + } catch (err) { + console.error("Error loading previous workflow", err); + } + + // We failed to restore a workflow so load the default + if (!restored) { + this.loadGraphData(); + } + + // Save current workflow automatically + setInterval(() => localStorage.setItem("workflow", JSON.stringify(this.graph.serialize())), 1000); + + this.#addDrawNodeHandler(); + this.#addDrawGroupsHandler(); + this.#addApiUpdateHandlers(); + this.#addDropHandler(); + this.#addCopyHandler(); + this.#addPasteHandler(); + this.#addKeyboardHandler(); + + await this.#invokeExtensionsAsync("setup"); + } + + /** + * Registers nodes with the graph + */ + async registerNodes() { + const app = this; + // Load node definitions from the backend + const defs = await api.getNodeDefs(); + await this.registerNodesFromDefs(defs); + await this.#invokeExtensionsAsync("registerCustomNodes"); + } + + async registerNodesFromDefs(defs) { + await this.#invokeExtensionsAsync("addCustomNodeDefs", defs); + + // Generate list of known widgets + const widgets = Object.assign( + {}, + ComfyWidgets, + ...(await this.#invokeExtensionsAsync("getCustomWidgets")).filter(Boolean) + ); + + // Register a node for each definition + for (const nodeId in defs) { + const nodeData = defs[nodeId]; + const node = Object.assign( + function ComfyNode() { + var inputs = nodeData["input"]["required"]; + if (nodeData["input"]["optional"] != undefined){ + inputs = Object.assign({}, nodeData["input"]["required"], nodeData["input"]["optional"]) + } + const config = { minWidth: 1, minHeight: 1 }; + for (const inputName in inputs) { + const inputData = inputs[inputName]; + const type = inputData[0]; + + let widgetCreated = true; + if (Array.isArray(type)) { + // Enums + Object.assign(config, widgets.COMBO(this, inputName, inputData, app) || {}); + } else if (`${type}:${inputName}` in widgets) { + // Support custom widgets by Type:Name + Object.assign(config, widgets[`${type}:${inputName}`](this, inputName, inputData, app) || {}); + } else if (type in widgets) { + // Standard type widgets + Object.assign(config, widgets[type](this, inputName, inputData, app) || {}); + } else { + // Node connection inputs + this.addInput(inputName, type); + widgetCreated = false; + } + + if(widgetCreated && inputData[1]?.forceInput && config?.widget) { + if (!config.widget.options) config.widget.options = {}; + config.widget.options.forceInput = inputData[1].forceInput; + } + if(widgetCreated && inputData[1]?.defaultInput && config?.widget) { + if (!config.widget.options) config.widget.options = {}; + config.widget.options.defaultInput = inputData[1].defaultInput; + } + } + + for (const o in nodeData["output"]) { + const output = nodeData["output"][o]; + const outputName = nodeData["output_name"][o] || output; + const outputShape = nodeData["output_is_list"][o] ? LiteGraph.GRID_SHAPE : LiteGraph.CIRCLE_SHAPE ; + this.addOutput(outputName, output, { shape: outputShape }); + } + + const s = this.computeSize(); + s[0] = Math.max(config.minWidth, s[0] * 1.5); + s[1] = Math.max(config.minHeight, s[1]); + this.size = s; + this.serialize_widgets = true; + + app.#invokeExtensionsAsync("nodeCreated", this); + }, + { + title: nodeData.display_name || nodeData.name, + comfyClass: nodeData.name, + } + ); + node.prototype.comfyClass = nodeData.name; + + this.#addNodeContextMenuHandler(node); + this.#addDrawBackgroundHandler(node, app); + this.#addNodeKeyHandler(node); + + await this.#invokeExtensionsAsync("beforeRegisterNodeDef", node, nodeData); + LiteGraph.registerNodeType(nodeId, node); + node.category = nodeData.category; + } + } + + /** + * Populates the graph with the specified workflow data + * @param {*} graphData A serialized graph object + */ + loadGraphData(graphData) { + this.clean(); + + let reset_invalid_values = false; + if (!graphData) { + if (typeof structuredClone === "undefined") + { + graphData = JSON.parse(JSON.stringify(defaultGraph)); + }else + { + graphData = structuredClone(defaultGraph); + } + reset_invalid_values = true; + } + + const missingNodeTypes = []; + for (let n of graphData.nodes) { + // Patch T2IAdapterLoader to ControlNetLoader since they are the same node now + if (n.type == "T2IAdapterLoader") n.type = "ControlNetLoader"; + + // Find missing node types + if (!(n.type in LiteGraph.registered_node_types)) { + missingNodeTypes.push(n.type); + } + } + + try { + this.graph.configure(graphData); + } catch (error) { + let errorHint = []; + // Try extracting filename to see if it was caused by an extension script + const filename = error.fileName || (error.stack || "").match(/(\/extensions\/.*\.js)/)?.[1]; + const pos = (filename || "").indexOf("/extensions/"); + if (pos > -1) { + errorHint.push( + $el("span", { textContent: "This may be due to the following script:" }), + $el("br"), + $el("span", { + style: { + fontWeight: "bold", + }, + textContent: filename.substring(pos), + }) + ); + } + + // Show dialog to let the user know something went wrong loading the data + this.ui.dialog.show( + $el("div", [ + $el("p", { textContent: "Loading aborted due to error reloading workflow data" }), + $el("pre", { + style: { padding: "5px", backgroundColor: "rgba(255,0,0,0.2)" }, + textContent: error.toString(), + }), + $el("pre", { + style: { + padding: "5px", + color: "#ccc", + fontSize: "10px", + maxHeight: "50vh", + overflow: "auto", + backgroundColor: "rgba(0,0,0,0.2)", + }, + textContent: error.stack || "No stacktrace available", + }), + ...errorHint, + ]).outerHTML + ); + + return; + } + + for (const node of this.graph._nodes) { + const size = node.computeSize(); + size[0] = Math.max(node.size[0], size[0]); + size[1] = Math.max(node.size[1], size[1]); + node.size = size; + + if (node.widgets) { + // If you break something in the backend and want to patch workflows in the frontend + // This is the place to do this + for (let widget of node.widgets) { + if (node.type == "KSampler" || node.type == "KSamplerAdvanced") { + if (widget.name == "sampler_name") { + if (widget.value.startsWith("sample_")) { + widget.value = widget.value.slice(7); + } + } + } + if (node.type == "KSampler" || node.type == "KSamplerAdvanced" || node.type == "PrimitiveNode") { + if (widget.name == "control_after_generate") { + if (widget.value === true) { + widget.value = "randomize"; + } else if (widget.value === false) { + widget.value = "fixed"; + } + } + } + if (reset_invalid_values) { + if (widget.type == "combo") { + if (!widget.options.values.includes(widget.value) && widget.options.values.length > 0) { + widget.value = widget.options.values[0]; + } + } + } + } + } + + this.#invokeExtensions("loadedGraphNode", node); + } + + if (missingNodeTypes.length) { + this.ui.dialog.show( + `When loading the graph, the following node types were not found:
    ${Array.from(new Set(missingNodeTypes)).map( + (t) => `
  • ${t}
    • ` + ).join("")}
Nodes that have failed to load will show as red on the graph.` + ); + this.logging.addEntry("Comfy.App", "warn", { + MissingNodes: missingNodeTypes, + }); + } + } + + /** + * Converts the current graph workflow for sending to the API + * @returns The workflow and node links + */ + async graphToPrompt() { + const workflow = this.graph.serialize(); + const output = {}; + // Process nodes in order of execution + for (const node of this.graph.computeExecutionOrder(false)) { + const n = workflow.nodes.find((n) => n.id === node.id); + + if (node.isVirtualNode) { + // Don't serialize frontend only nodes but let them make changes + if (node.applyToGraph) { + node.applyToGraph(workflow); + } + continue; + } + + if (node.mode === 2 || node.mode === 4) { + // Don't serialize muted nodes + continue; + } + + const inputs = {}; + const widgets = node.widgets; + + // Store all widget values + if (widgets) { + for (const i in widgets) { + const widget = widgets[i]; + if (!widget.options || widget.options.serialize !== false) { + inputs[widget.name] = widget.serializeValue ? await widget.serializeValue(n, i) : widget.value; + } + } + } + + // Store all node links + for (let i in node.inputs) { + let parent = node.getInputNode(i); + if (parent) { + let link = node.getInputLink(i); + while (parent.mode === 4 || parent.isVirtualNode) { + let found = false; + if (parent.isVirtualNode) { + link = parent.getInputLink(link.origin_slot); + if (link) { + parent = parent.getInputNode(link.target_slot); + if (parent) { + found = true; + } + } + } else if (link && parent.mode === 4) { + let all_inputs = [link.origin_slot]; + if (parent.inputs) { + all_inputs = all_inputs.concat(Object.keys(parent.inputs)) + for (let parent_input in all_inputs) { + parent_input = all_inputs[parent_input]; + if (parent.inputs[parent_input].type === node.inputs[i].type) { + link = parent.getInputLink(parent_input); + if (link) { + parent = parent.getInputNode(parent_input); + } + found = true; + break; + } + } + } + } + + if (!found) { + break; + } + } + + if (link) { + inputs[node.inputs[i].name] = [String(link.origin_id), parseInt(link.origin_slot)]; + } + } + } + + output[String(node.id)] = { + inputs, + class_type: node.comfyClass, + }; + } + + // Remove inputs connected to removed nodes + + for (const o in output) { + for (const i in output[o].inputs) { + if (Array.isArray(output[o].inputs[i]) + && output[o].inputs[i].length === 2 + && !output[output[o].inputs[i][0]]) { + delete output[o].inputs[i]; + } + } + } + + return { workflow, output }; + } + + #formatPromptError(error) { + if (error == null) { + return "(unknown error)" + } + else if (typeof error === "string") { + return error; + } + else if (error.stack && error.message) { + return error.toString() + } + else if (error.response) { + let message = error.response.error.message; + if (error.response.error.details) + message += ": " + error.response.error.details; + for (const [nodeID, nodeError] of Object.entries(error.response.node_errors)) { + message += "\n" + nodeError.class_type + ":" + for (const errorReason of nodeError.errors) { + message += "\n - " + errorReason.message + ": " + errorReason.details + } + } + return message + } + return "(unknown error)" + } + + #formatExecutionError(error) { + if (error == null) { + return "(unknown error)" + } + + const traceback = error.traceback.join("") + const nodeId = error.node_id + const nodeType = error.node_type + + return `Error occurred when executing ${nodeType}:\n\n${error.exception_message}\n\n${traceback}` + } + + async queuePrompt(number, batchCount = 1) { + this.#queueItems.push({ number, batchCount }); + + // Only have one action process the items so each one gets a unique seed correctly + if (this.#processingQueue) { + return; + } + + this.#processingQueue = true; + this.lastNodeErrors = null; + + try { + while (this.#queueItems.length) { + ({ number, batchCount } = this.#queueItems.pop()); + + for (let i = 0; i < batchCount; i++) { + const p = await this.graphToPrompt(); + + try { + const res = await api.queuePrompt(number, p); + this.lastNodeErrors = res.node_errors; + if (this.lastNodeErrors.length > 0) { + this.canvas.draw(true, true); + } + } catch (error) { + const formattedError = this.#formatPromptError(error) + this.ui.dialog.show(formattedError); + if (error.response) { + this.lastNodeErrors = error.response.node_errors; + this.canvas.draw(true, true); + } + break; + } + + for (const n of p.workflow.nodes) { + const node = graph.getNodeById(n.id); + if (node.widgets) { + for (const widget of node.widgets) { + // Allow widgets to run callbacks after a prompt has been queued + // e.g. random seed after every gen + if (widget.afterQueued) { + widget.afterQueued(); + } + } + } + } + + this.canvas.draw(true, true); + await this.ui.queue.update(); + } + } + } finally { + this.#processingQueue = false; + } + } + + /** + * Loads workflow data from the specified file + * @param {File} file + */ + async handleFile(file) { + if (file.type === "image/png") { + const pngInfo = await getPngMetadata(file); + if (pngInfo) { + if (pngInfo.workflow) { + this.loadGraphData(JSON.parse(pngInfo.workflow)); + } else if (pngInfo.parameters) { + importA1111(this.graph, pngInfo.parameters); + } + } + } else if (file.type === "application/json" || file.name?.endsWith(".json")) { + const reader = new FileReader(); + reader.onload = () => { + this.loadGraphData(JSON.parse(reader.result)); + }; + reader.readAsText(file); + } else if (file.name?.endsWith(".latent") || file.name?.endsWith(".safetensors")) { + const info = await getLatentMetadata(file); + if (info.workflow) { + this.loadGraphData(JSON.parse(info.workflow)); + } + } + } + + /** + * Registers a Comfy web extension with the app + * @param {ComfyExtension} extension + */ + registerExtension(extension) { + if (!extension.name) { + throw new Error("Extensions must have a 'name' property."); + } + if (this.extensions.find((ext) => ext.name === extension.name)) { + throw new Error(`Extension named '${extension.name}' already registered.`); + } + this.extensions.push(extension); + } + + /** + * Refresh combo list on whole nodes + */ + async refreshComboInNodes() { + const defs = await api.getNodeDefs(); + + for(let nodeNum in this.graph._nodes) { + const node = this.graph._nodes[nodeNum]; + + const def = defs[node.type]; + + // HOTFIX: The current patch is designed to prevent the rest of the code from breaking due to primitive nodes, + // and additional work is needed to consider the primitive logic in the refresh logic. + if(!def) + continue; + + for(const widgetNum in node.widgets) { + const widget = node.widgets[widgetNum] + if(widget.type == "combo" && def["input"]["required"][widget.name] !== undefined) { + widget.options.values = def["input"]["required"][widget.name][0]; + + if(widget.name != 'image' && !widget.options.values.includes(widget.value)) { + widget.value = widget.options.values[0]; + widget.callback(widget.value); + } + } + } + } + } + + /** + * Clean current state + */ + clean() { + this.nodeOutputs = {}; + this.nodePreviewImages = {} + this.lastNodeErrors = null; + this.lastExecutionError = null; + this.runningNodeId = null; + } +} + +export const app = new ComfyApp(); diff --git a/web/scripts/defaultGraph.js b/web/scripts/defaultGraph.js new file mode 100644 index 0000000000000000000000000000000000000000..9b3cb4a7e6cfa81430a1cf19d9b3dce94c3606db --- /dev/null +++ b/web/scripts/defaultGraph.js @@ -0,0 +1,119 @@ +export const defaultGraph = { + last_node_id: 9, + last_link_id: 9, + nodes: [ + { + id: 7, + type: "CLIPTextEncode", + pos: [413, 389], + size: { 0: 425.27801513671875, 1: 180.6060791015625 }, + flags: {}, + order: 3, + mode: 0, + inputs: [{ name: "clip", type: "CLIP", link: 5 }], + outputs: [{ name: "CONDITIONING", type: "CONDITIONING", links: [6], slot_index: 0 }], + properties: {}, + widgets_values: ["text, watermark"], + }, + { + id: 6, + type: "CLIPTextEncode", + pos: [415, 186], + size: { 0: 422.84503173828125, 1: 164.31304931640625 }, + flags: {}, + order: 2, + mode: 0, + inputs: [{ name: "clip", type: "CLIP", link: 3 }], + outputs: [{ name: "CONDITIONING", type: "CONDITIONING", links: [4], slot_index: 0 }], + properties: {}, + widgets_values: ["beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"], + }, + { + id: 5, + type: "EmptyLatentImage", + pos: [473, 609], + size: { 0: 315, 1: 106 }, + flags: {}, + order: 1, + mode: 0, + outputs: [{ name: "LATENT", type: "LATENT", links: [2], slot_index: 0 }], + properties: {}, + widgets_values: [512, 512, 1], + }, + { + id: 3, + type: "KSampler", + pos: [863, 186], + size: { 0: 315, 1: 262 }, + flags: {}, + order: 4, + mode: 0, + inputs: [ + { name: "model", type: "MODEL", link: 1 }, + { name: "positive", type: "CONDITIONING", link: 4 }, + { name: "negative", type: "CONDITIONING", link: 6 }, + { name: "latent_image", type: "LATENT", link: 2 }, + ], + outputs: [{ name: "LATENT", type: "LATENT", links: [7], slot_index: 0 }], + properties: {}, + widgets_values: [156680208700286, true, 20, 8, "euler", "normal", 1], + }, + { + id: 8, + type: "VAEDecode", + pos: [1209, 188], + size: { 0: 210, 1: 46 }, + flags: {}, + order: 5, + mode: 0, + inputs: [ + { name: "samples", type: "LATENT", link: 7 }, + { name: "vae", type: "VAE", link: 8 }, + ], + outputs: [{ name: "IMAGE", type: "IMAGE", links: [9], slot_index: 0 }], + properties: {}, + }, + { + id: 9, + type: "SaveImage", + pos: [1451, 189], + size: { 0: 210, 1: 26 }, + flags: {}, + order: 6, + mode: 0, + inputs: [{ name: "images", type: "IMAGE", link: 9 }], + properties: {}, + }, + { + id: 4, + type: "CheckpointLoaderSimple", + pos: [26, 474], + size: { 0: 315, 1: 98 }, + flags: {}, + order: 0, + mode: 0, + outputs: [ + { name: "MODEL", type: "MODEL", links: [1], slot_index: 0 }, + { name: "CLIP", type: "CLIP", links: [3, 5], slot_index: 1 }, + { name: "VAE", type: "VAE", links: [8], slot_index: 2 }, + ], + properties: {}, + widgets_values: ["v1-5-pruned-emaonly.ckpt"], + }, + ], + links: [ + [1, 4, 0, 3, 0, "MODEL"], + [2, 5, 0, 3, 3, "LATENT"], + [3, 4, 1, 6, 0, "CLIP"], + [4, 6, 0, 3, 1, "CONDITIONING"], + [5, 4, 1, 7, 0, "CLIP"], + [6, 7, 0, 3, 2, "CONDITIONING"], + [7, 3, 0, 8, 0, "LATENT"], + [8, 4, 2, 8, 1, "VAE"], + [9, 8, 0, 9, 0, "IMAGE"], + ], + groups: [], + config: {}, + extra: {}, + version: 0.4, +}; diff --git a/web/scripts/logging.js b/web/scripts/logging.js new file mode 100644 index 0000000000000000000000000000000000000000..c73462e1ea3896189f4df0e1fd2fcf4e10554049 --- /dev/null +++ b/web/scripts/logging.js @@ -0,0 +1,367 @@ +import { $el, ComfyDialog } from "./ui.js"; +import { api } from "./api.js"; + +$el("style", { + textContent: ` + .comfy-logging-logs { + display: grid; + color: var(--fg-color); + white-space: pre-wrap; + } + .comfy-logging-log { + display: contents; + } + .comfy-logging-title { + background: var(--tr-even-bg-color); + font-weight: bold; + margin-bottom: 5px; + text-align: center; + } + .comfy-logging-log div { + background: var(--row-bg); + padding: 5px; + } + `, + parent: document.body, +}); + +// Stringify function supporting max depth and removal of circular references +// https://stackoverflow.com/a/57193345 +function stringify(val, depth, replacer, space, onGetObjID) { + depth = isNaN(+depth) ? 1 : depth; + var recursMap = new WeakMap(); + function _build(val, depth, o, a, r) { + // (JSON.stringify() has it's own rules, which we respect here by using it for property iteration) + return !val || typeof val != "object" + ? val + : ((r = recursMap.has(val)), + recursMap.set(val, true), + (a = Array.isArray(val)), + r + ? (o = (onGetObjID && onGetObjID(val)) || null) + : JSON.stringify(val, function (k, v) { + if (a || depth > 0) { + if (replacer) v = replacer(k, v); + if (!k) return (a = Array.isArray(v)), (val = v); + !o && (o = a ? [] : {}); + o[k] = _build(v, a ? depth : depth - 1); + } + }), + o === void 0 ? (a ? [] : {}) : o); + } + return JSON.stringify(_build(val, depth), null, space); +} + +const jsonReplacer = (k, v, ui) => { + if (v instanceof Array && v.length === 1) { + v = v[0]; + } + if (v instanceof Date) { + v = v.toISOString(); + if (ui) { + v = v.split("T")[1]; + } + } + if (v instanceof Error) { + let err = ""; + if (v.name) err += v.name + "\n"; + if (v.message) err += v.message + "\n"; + if (v.stack) err += v.stack + "\n"; + if (!err) { + err = v.toString(); + } + v = err; + } + return v; +}; + +const fileInput = $el("input", { + type: "file", + accept: ".json", + style: { display: "none" }, + parent: document.body, +}); + +class ComfyLoggingDialog extends ComfyDialog { + constructor(logging) { + super(); + this.logging = logging; + } + + clear() { + this.logging.clear(); + this.show(); + } + + export() { + const blob = new Blob([stringify([...this.logging.entries], 20, jsonReplacer, "\t")], { + type: "application/json", + }); + const url = URL.createObjectURL(blob); + const a = $el("a", { + href: url, + download: `comfyui-logs-${Date.now()}.json`, + style: { display: "none" }, + parent: document.body, + }); + a.click(); + setTimeout(function () { + a.remove(); + window.URL.revokeObjectURL(url); + }, 0); + } + + import() { + fileInput.onchange = () => { + const reader = new FileReader(); + reader.onload = () => { + fileInput.remove(); + try { + const obj = JSON.parse(reader.result); + if (obj instanceof Array) { + this.show(obj); + } else { + throw new Error("Invalid file selected."); + } + } catch (error) { + alert("Unable to load logs: " + error.message); + } + }; + reader.readAsText(fileInput.files[0]); + }; + fileInput.click(); + } + + createButtons() { + return [ + $el("button", { + type: "button", + textContent: "Clear", + onclick: () => this.clear(), + }), + $el("button", { + type: "button", + textContent: "Export logs...", + onclick: () => this.export(), + }), + $el("button", { + type: "button", + textContent: "View exported logs...", + onclick: () => this.import(), + }), + ...super.createButtons(), + ]; + } + + getTypeColor(type) { + switch (type) { + case "error": + return "red"; + case "warn": + return "orange"; + case "debug": + return "dodgerblue"; + } + } + + show(entries) { + if (!entries) entries = this.logging.entries; + this.element.style.width = "100%"; + const cols = { + source: "Source", + type: "Type", + timestamp: "Timestamp", + message: "Message", + }; + const keys = Object.keys(cols); + const headers = Object.values(cols).map((title) => + $el("div.comfy-logging-title", { + textContent: title, + }) + ); + const rows = entries.map((entry, i) => { + return $el( + "div.comfy-logging-log", + { + $: (el) => el.style.setProperty("--row-bg", `var(--tr-${i % 2 ? "even" : "odd"}-bg-color)`), + }, + keys.map((key) => { + let v = entry[key]; + let color; + if (key === "type") { + color = this.getTypeColor(v); + } else { + v = jsonReplacer(key, v, true); + + if (typeof v === "object") { + v = stringify(v, 5, jsonReplacer, " "); + } + } + + return $el("div", { + style: { + color, + }, + textContent: v, + }); + }) + ); + }); + + const grid = $el( + "div.comfy-logging-logs", + { + style: { + gridTemplateColumns: `repeat(${headers.length}, 1fr)`, + }, + }, + [...headers, ...rows] + ); + const els = [grid]; + if (!this.logging.enabled) { + els.unshift( + $el("h3", { + style: { textAlign: "center" }, + textContent: "Logging is disabled", + }) + ); + } + super.show($el("div", els)); + } +} + +export class ComfyLogging { + /** + * @type Array<{ source: string, type: string, timestamp: Date, message: any }> + */ + entries = []; + + #enabled; + #console = {}; + + get enabled() { + return this.#enabled; + } + + set enabled(value) { + if (value === this.#enabled) return; + if (value) { + this.patchConsole(); + } else { + this.unpatchConsole(); + } + this.#enabled = value; + } + + constructor(app) { + this.app = app; + + this.dialog = new ComfyLoggingDialog(this); + this.addSetting(); + this.catchUnhandled(); + this.addInitData(); + } + + addSetting() { + const settingId = "Comfy.Logging.Enabled"; + const htmlSettingId = settingId.replaceAll(".", "-"); + const setting = this.app.ui.settings.addSetting({ + id: settingId, + name: settingId, + defaultValue: true, + type: (name, setter, value) => { + return $el("tr", [ + $el("td", [ + $el("label", { + textContent: "Logging", + for: htmlSettingId, + }), + ]), + $el("td", [ + $el("input", { + id: htmlSettingId, + type: "checkbox", + checked: value, + onchange: (event) => { + setter((this.enabled = event.target.checked)); + }, + }), + $el("button", { + textContent: "View Logs", + onclick: () => { + this.app.ui.settings.element.close(); + this.dialog.show(); + }, + style: { + fontSize: "14px", + display: "block", + marginTop: "5px", + }, + }), + ]), + ]); + }, + }); + this.enabled = setting.value; + } + + patchConsole() { + // Capture common console outputs + const self = this; + for (const type of ["log", "warn", "error", "debug"]) { + const orig = console[type]; + this.#console[type] = orig; + console[type] = function () { + orig.apply(console, arguments); + self.addEntry("console", type, ...arguments); + }; + } + } + + unpatchConsole() { + // Restore original console functions + for (const type of Object.keys(this.#console)) { + console[type] = this.#console[type]; + } + this.#console = {}; + } + + catchUnhandled() { + // Capture uncaught errors + window.addEventListener("error", (e) => { + this.addEntry("window", "error", e.error ?? "Unknown error"); + return false; + }); + + window.addEventListener("unhandledrejection", (e) => { + this.addEntry("unhandledrejection", "error", e.reason ?? "Unknown error"); + }); + } + + clear() { + this.entries = []; + } + + addEntry(source, type, ...args) { + if (this.enabled) { + this.entries.push({ + source, + type, + timestamp: new Date(), + message: args, + }); + } + } + + log(source, ...args) { + this.addEntry(source, "log", ...args); + } + + async addInitData() { + if (!this.enabled) return; + const source = "ComfyUI.Logging"; + this.addEntry(source, "debug", { UserAgent: navigator.userAgent }); + const systemStats = await api.getSystemStats(); + this.addEntry(source, "debug", systemStats); + } +} diff --git a/web/scripts/pnginfo.js b/web/scripts/pnginfo.js new file mode 100644 index 0000000000000000000000000000000000000000..c5293dfa3326dbb6b99973a67287212c9de08ea9 --- /dev/null +++ b/web/scripts/pnginfo.js @@ -0,0 +1,326 @@ +import { api } from "./api.js"; + +export function getPngMetadata(file) { + return new Promise((r) => { + const reader = new FileReader(); + reader.onload = (event) => { + // Get the PNG data as a Uint8Array + const pngData = new Uint8Array(event.target.result); + const dataView = new DataView(pngData.buffer); + + // Check that the PNG signature is present + if (dataView.getUint32(0) !== 0x89504e47) { + console.error("Not a valid PNG file"); + r(); + return; + } + + // Start searching for chunks after the PNG signature + let offset = 8; + let txt_chunks = {}; + // Loop through the chunks in the PNG file + while (offset < pngData.length) { + // Get the length of the chunk + const length = dataView.getUint32(offset); + // Get the chunk type + const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8)); + if (type === "tEXt") { + // Get the keyword + let keyword_end = offset + 8; + while (pngData[keyword_end] !== 0) { + keyword_end++; + } + const keyword = String.fromCharCode(...pngData.slice(offset + 8, keyword_end)); + // Get the text + const contentArraySegment = pngData.slice(keyword_end + 1, offset + 8 + length); + const contentJson = Array.from(contentArraySegment).map(s=>String.fromCharCode(s)).join('') + txt_chunks[keyword] = contentJson; + } + + offset += 12 + length; + } + + r(txt_chunks); + }; + + reader.readAsArrayBuffer(file); + }); +} + +export function getLatentMetadata(file) { + return new Promise((r) => { + const reader = new FileReader(); + reader.onload = (event) => { + const safetensorsData = new Uint8Array(event.target.result); + const dataView = new DataView(safetensorsData.buffer); + let header_size = dataView.getUint32(0, true); + let offset = 8; + let header = JSON.parse(new TextDecoder().decode(safetensorsData.slice(offset, offset + header_size))); + r(header.__metadata__); + }; + + var slice = file.slice(0, 1024 * 1024 * 4); + reader.readAsArrayBuffer(slice); + }); +} + +export async function importA1111(graph, parameters) { + const p = parameters.lastIndexOf("\nSteps:"); + if (p > -1) { + const embeddings = await api.getEmbeddings(); + const opts = parameters + .substr(p) + .split("\n")[1] + .split(",") + .reduce((p, n) => { + const s = n.split(":"); + p[s[0].trim().toLowerCase()] = s[1].trim(); + return p; + }, {}); + const p2 = parameters.lastIndexOf("\nNegative prompt:", p); + if (p2 > -1) { + let positive = parameters.substr(0, p2).trim(); + let negative = parameters.substring(p2 + 18, p).trim(); + + const ckptNode = LiteGraph.createNode("CheckpointLoaderSimple"); + const clipSkipNode = LiteGraph.createNode("CLIPSetLastLayer"); + const positiveNode = LiteGraph.createNode("CLIPTextEncode"); + const negativeNode = LiteGraph.createNode("CLIPTextEncode"); + const samplerNode = LiteGraph.createNode("KSampler"); + const imageNode = LiteGraph.createNode("EmptyLatentImage"); + const vaeNode = LiteGraph.createNode("VAEDecode"); + const vaeLoaderNode = LiteGraph.createNode("VAELoader"); + const saveNode = LiteGraph.createNode("SaveImage"); + let hrSamplerNode = null; + + const ceil64 = (v) => Math.ceil(v / 64) * 64; + + function getWidget(node, name) { + return node.widgets.find((w) => w.name === name); + } + + function setWidgetValue(node, name, value, isOptionPrefix) { + const w = getWidget(node, name); + if (isOptionPrefix) { + const o = w.options.values.find((w) => w.startsWith(value)); + if (o) { + w.value = o; + } else { + console.warn(`Unknown value '${value}' for widget '${name}'`, node); + w.value = value; + } + } else { + w.value = value; + } + } + + function createLoraNodes(clipNode, text, prevClip, prevModel) { + const loras = []; + text = text.replace(/]+)>/g, function (m, c) { + const s = c.split(":"); + const weight = parseFloat(s[1]); + if (isNaN(weight)) { + console.warn("Invalid LORA", m); + } else { + loras.push({ name: s[0], weight }); + } + return ""; + }); + + for (const l of loras) { + const loraNode = LiteGraph.createNode("LoraLoader"); + graph.add(loraNode); + setWidgetValue(loraNode, "lora_name", l.name, true); + setWidgetValue(loraNode, "strength_model", l.weight); + setWidgetValue(loraNode, "strength_clip", l.weight); + prevModel.node.connect(prevModel.index, loraNode, 0); + prevClip.node.connect(prevClip.index, loraNode, 1); + prevModel = { node: loraNode, index: 0 }; + prevClip = { node: loraNode, index: 1 }; + } + + prevClip.node.connect(1, clipNode, 0); + prevModel.node.connect(0, samplerNode, 0); + if (hrSamplerNode) { + prevModel.node.connect(0, hrSamplerNode, 0); + } + + return { text, prevModel, prevClip }; + } + + function replaceEmbeddings(text) { + if(!embeddings.length) return text; + return text.replaceAll( + new RegExp( + "\\b(" + embeddings.map((e) => e.replace(/[.*+?^${}()|[\]\\]/g, "\\$&")).join("\\b|\\b") + ")\\b", + "ig" + ), + "embedding:$1" + ); + } + + function popOpt(name) { + const v = opts[name]; + delete opts[name]; + return v; + } + + graph.clear(); + graph.add(ckptNode); + graph.add(clipSkipNode); + graph.add(positiveNode); + graph.add(negativeNode); + graph.add(samplerNode); + graph.add(imageNode); + graph.add(vaeNode); + graph.add(vaeLoaderNode); + graph.add(saveNode); + + ckptNode.connect(1, clipSkipNode, 0); + clipSkipNode.connect(0, positiveNode, 0); + clipSkipNode.connect(0, negativeNode, 0); + ckptNode.connect(0, samplerNode, 0); + positiveNode.connect(0, samplerNode, 1); + negativeNode.connect(0, samplerNode, 2); + imageNode.connect(0, samplerNode, 3); + vaeNode.connect(0, saveNode, 0); + samplerNode.connect(0, vaeNode, 0); + vaeLoaderNode.connect(0, vaeNode, 1); + + const handlers = { + model(v) { + setWidgetValue(ckptNode, "ckpt_name", v, true); + }, + "cfg scale"(v) { + setWidgetValue(samplerNode, "cfg", +v); + }, + "clip skip"(v) { + setWidgetValue(clipSkipNode, "stop_at_clip_layer", -v); + }, + sampler(v) { + let name = v.toLowerCase().replace("++", "pp").replaceAll(" ", "_"); + if (name.includes("karras")) { + name = name.replace("karras", "").replace(/_+$/, ""); + setWidgetValue(samplerNode, "scheduler", "karras"); + } else { + setWidgetValue(samplerNode, "scheduler", "normal"); + } + const w = getWidget(samplerNode, "sampler_name"); + const o = w.options.values.find((w) => w === name || w === "sample_" + name); + if (o) { + setWidgetValue(samplerNode, "sampler_name", o); + } + }, + size(v) { + const wxh = v.split("x"); + const w = ceil64(+wxh[0]); + const h = ceil64(+wxh[1]); + const hrUp = popOpt("hires upscale"); + const hrSz = popOpt("hires resize"); + let hrMethod = popOpt("hires upscaler"); + + setWidgetValue(imageNode, "width", w); + setWidgetValue(imageNode, "height", h); + + if (hrUp || hrSz) { + let uw, uh; + if (hrUp) { + uw = w * hrUp; + uh = h * hrUp; + } else { + const s = hrSz.split("x"); + uw = +s[0]; + uh = +s[1]; + } + + let upscaleNode; + let latentNode; + + if (hrMethod.startsWith("Latent")) { + latentNode = upscaleNode = LiteGraph.createNode("LatentUpscale"); + graph.add(upscaleNode); + samplerNode.connect(0, upscaleNode, 0); + + switch (hrMethod) { + case "Latent (nearest-exact)": + hrMethod = "nearest-exact"; + break; + } + setWidgetValue(upscaleNode, "upscale_method", hrMethod, true); + } else { + const decode = LiteGraph.createNode("VAEDecodeTiled"); + graph.add(decode); + samplerNode.connect(0, decode, 0); + vaeLoaderNode.connect(0, decode, 1); + + const upscaleLoaderNode = LiteGraph.createNode("UpscaleModelLoader"); + graph.add(upscaleLoaderNode); + setWidgetValue(upscaleLoaderNode, "model_name", hrMethod, true); + + const modelUpscaleNode = LiteGraph.createNode("ImageUpscaleWithModel"); + graph.add(modelUpscaleNode); + decode.connect(0, modelUpscaleNode, 1); + upscaleLoaderNode.connect(0, modelUpscaleNode, 0); + + upscaleNode = LiteGraph.createNode("ImageScale"); + graph.add(upscaleNode); + modelUpscaleNode.connect(0, upscaleNode, 0); + + const vaeEncodeNode = (latentNode = LiteGraph.createNode("VAEEncodeTiled")); + graph.add(vaeEncodeNode); + upscaleNode.connect(0, vaeEncodeNode, 0); + vaeLoaderNode.connect(0, vaeEncodeNode, 1); + } + + setWidgetValue(upscaleNode, "width", ceil64(uw)); + setWidgetValue(upscaleNode, "height", ceil64(uh)); + + hrSamplerNode = LiteGraph.createNode("KSampler"); + graph.add(hrSamplerNode); + ckptNode.connect(0, hrSamplerNode, 0); + positiveNode.connect(0, hrSamplerNode, 1); + negativeNode.connect(0, hrSamplerNode, 2); + latentNode.connect(0, hrSamplerNode, 3); + hrSamplerNode.connect(0, vaeNode, 0); + } + }, + steps(v) { + setWidgetValue(samplerNode, "steps", +v); + }, + seed(v) { + setWidgetValue(samplerNode, "seed", +v); + }, + }; + + for (const opt in opts) { + if (opt in handlers) { + handlers[opt](popOpt(opt)); + } + } + + if (hrSamplerNode) { + setWidgetValue(hrSamplerNode, "steps", getWidget(samplerNode, "steps").value); + setWidgetValue(hrSamplerNode, "cfg", getWidget(samplerNode, "cfg").value); + setWidgetValue(hrSamplerNode, "scheduler", getWidget(samplerNode, "scheduler").value); + setWidgetValue(hrSamplerNode, "sampler_name", getWidget(samplerNode, "sampler_name").value); + setWidgetValue(hrSamplerNode, "denoise", +(popOpt("denoising strength") || "1")); + } + + let n = createLoraNodes(positiveNode, positive, { node: clipSkipNode, index: 0 }, { node: ckptNode, index: 0 }); + positive = n.text; + n = createLoraNodes(negativeNode, negative, n.prevClip, n.prevModel); + negative = n.text; + + setWidgetValue(positiveNode, "text", replaceEmbeddings(positive)); + setWidgetValue(negativeNode, "text", replaceEmbeddings(negative)); + + graph.arrange(); + + for (const opt of ["model hash", "ensd"]) { + delete opts[opt]; + } + + console.warn("Unhandled parameters:", opts); + } + } +} diff --git a/web/scripts/ui.js b/web/scripts/ui.js new file mode 100644 index 0000000000000000000000000000000000000000..1e7920167a624be3f8f6c6e3ee986512da5ec7cd --- /dev/null +++ b/web/scripts/ui.js @@ -0,0 +1,819 @@ +import {api} from "./api.js"; + +export function $el(tag, propsOrChildren, children) { + const split = tag.split("."); + const element = document.createElement(split.shift()); + if (split.length > 0) { + element.classList.add(...split); + } + + if (propsOrChildren) { + if (Array.isArray(propsOrChildren)) { + element.append(...propsOrChildren); + } else { + const {parent, $: cb, dataset, style} = propsOrChildren; + delete propsOrChildren.parent; + delete propsOrChildren.$; + delete propsOrChildren.dataset; + delete propsOrChildren.style; + + if (Object.hasOwn(propsOrChildren, "for")) { + element.setAttribute("for", propsOrChildren.for) + } + + if (style) { + Object.assign(element.style, style); + } + + if (dataset) { + Object.assign(element.dataset, dataset); + } + + Object.assign(element, propsOrChildren); + if (children) { + element.append(...children); + } + + if (parent) { + parent.append(element); + } + + if (cb) { + cb(element); + } + } + } + return element; +} + +function dragElement(dragEl, settings) { + var posDiffX = 0, + posDiffY = 0, + posStartX = 0, + posStartY = 0, + newPosX = 0, + newPosY = 0; + if (dragEl.getElementsByClassName("drag-handle")[0]) { + // if present, the handle is where you move the DIV from: + dragEl.getElementsByClassName("drag-handle")[0].onmousedown = dragMouseDown; + } else { + // otherwise, move the DIV from anywhere inside the DIV: + dragEl.onmousedown = dragMouseDown; + } + + // When the element resizes (e.g. view queue) ensure it is still in the windows bounds + const resizeObserver = new ResizeObserver(() => { + ensureInBounds(); + }).observe(dragEl); + + function ensureInBounds() { + if (dragEl.classList.contains("comfy-menu-manual-pos")) { + newPosX = Math.min(document.body.clientWidth - dragEl.clientWidth, Math.max(0, dragEl.offsetLeft)); + newPosY = Math.min(document.body.clientHeight - dragEl.clientHeight, Math.max(0, dragEl.offsetTop)); + + positionElement(); + } + } + + function positionElement() { + const halfWidth = document.body.clientWidth / 2; + const anchorRight = newPosX + dragEl.clientWidth / 2 > halfWidth; + + // set the element's new position: + if (anchorRight) { + dragEl.style.left = "unset"; + dragEl.style.right = document.body.clientWidth - newPosX - dragEl.clientWidth + "px"; + } else { + dragEl.style.left = newPosX + "px"; + dragEl.style.right = "unset"; + } + + dragEl.style.top = newPosY + "px"; + dragEl.style.bottom = "unset"; + + if (savePos) { + localStorage.setItem( + "Comfy.MenuPosition", + JSON.stringify({ + x: dragEl.offsetLeft, + y: dragEl.offsetTop, + }) + ); + } + } + + function restorePos() { + let pos = localStorage.getItem("Comfy.MenuPosition"); + if (pos) { + pos = JSON.parse(pos); + newPosX = pos.x; + newPosY = pos.y; + positionElement(); + ensureInBounds(); + } + } + + let savePos = undefined; + settings.addSetting({ + id: "Comfy.MenuPosition", + name: "Save menu position", + type: "boolean", + defaultValue: savePos, + onChange(value) { + if (savePos === undefined && value) { + restorePos(); + } + savePos = value; + }, + }); + + function dragMouseDown(e) { + e = e || window.event; + e.preventDefault(); + // get the mouse cursor position at startup: + posStartX = e.clientX; + posStartY = e.clientY; + document.onmouseup = closeDragElement; + // call a function whenever the cursor moves: + document.onmousemove = elementDrag; + } + + function elementDrag(e) { + e = e || window.event; + e.preventDefault(); + + dragEl.classList.add("comfy-menu-manual-pos"); + + // calculate the new cursor position: + posDiffX = e.clientX - posStartX; + posDiffY = e.clientY - posStartY; + posStartX = e.clientX; + posStartY = e.clientY; + + newPosX = Math.min(document.body.clientWidth - dragEl.clientWidth, Math.max(0, dragEl.offsetLeft + posDiffX)); + newPosY = Math.min(document.body.clientHeight - dragEl.clientHeight, Math.max(0, dragEl.offsetTop + posDiffY)); + + positionElement(); + } + + window.addEventListener("resize", () => { + ensureInBounds(); + }); + + function closeDragElement() { + // stop moving when mouse button is released: + document.onmouseup = null; + document.onmousemove = null; + } +} + +export class ComfyDialog { + constructor() { + this.element = $el("div.comfy-modal", {parent: document.body}, [ + $el("div.comfy-modal-content", [$el("p", {$: (p) => (this.textElement = p)}), ...this.createButtons()]), + ]); + } + + createButtons() { + return [ + $el("button", { + type: "button", + textContent: "Close", + onclick: () => this.close(), + }), + ]; + } + + close() { + this.element.style.display = "none"; + } + + show(html) { + if (typeof html === "string") { + this.textElement.innerHTML = html; + } else { + this.textElement.replaceChildren(html); + } + this.element.style.display = "flex"; + } +} + +class ComfySettingsDialog extends ComfyDialog { + constructor() { + super(); + this.element = $el("dialog", { + id: "comfy-settings-dialog", + parent: document.body, + }, [ + $el("table.comfy-modal-content.comfy-table", [ + $el("caption", {textContent: "Settings"}), + $el("tbody", {$: (tbody) => (this.textElement = tbody)}), + $el("button", { + type: "button", + textContent: "Close", + style: { + cursor: "pointer", + }, + onclick: () => { + this.element.close(); + }, + }), + ]), + ]); + this.settings = []; + } + + getSettingValue(id, defaultValue) { + const settingId = "Comfy.Settings." + id; + const v = localStorage[settingId]; + return v == null ? defaultValue : JSON.parse(v); + } + + setSettingValue(id, value) { + const settingId = "Comfy.Settings." + id; + localStorage[settingId] = JSON.stringify(value); + } + + addSetting({id, name, type, defaultValue, onChange, attrs = {}, tooltip = "", options = undefined}) { + if (!id) { + throw new Error("Settings must have an ID"); + } + + if (this.settings.find((s) => s.id === id)) { + throw new Error(`Setting ${id} of type ${type} must have a unique ID.`); + } + + const settingId = `Comfy.Settings.${id}`; + const v = localStorage[settingId]; + let value = v == null ? defaultValue : JSON.parse(v); + + // Trigger initial setting of value + if (onChange) { + onChange(value, undefined); + } + + this.settings.push({ + render: () => { + const setter = (v) => { + if (onChange) { + onChange(v, value); + } + localStorage[settingId] = JSON.stringify(v); + value = v; + }; + value = this.getSettingValue(id, defaultValue); + + let element; + const htmlID = id.replaceAll(".", "-"); + + const labelCell = $el("td", [ + $el("label", { + for: htmlID, + classList: [tooltip !== "" ? "comfy-tooltip-indicator" : ""], + textContent: name, + }) + ]); + + if (typeof type === "function") { + element = type(name, setter, value, attrs); + } else { + switch (type) { + case "boolean": + element = $el("tr", [ + labelCell, + $el("td", [ + $el("input", { + id: htmlID, + type: "checkbox", + checked: value, + onchange: (event) => { + const isChecked = event.target.checked; + if (onChange !== undefined) { + onChange(isChecked) + } + this.setSettingValue(id, isChecked); + }, + }), + ]), + ]) + break; + case "number": + element = $el("tr", [ + labelCell, + $el("td", [ + $el("input", { + type, + value, + id: htmlID, + oninput: (e) => { + setter(e.target.value); + }, + ...attrs + }), + ]), + ]); + break; + case "slider": + element = $el("tr", [ + labelCell, + $el("td", [ + $el("div", { + style: { + display: "grid", + gridAutoFlow: "column", + }, + }, [ + $el("input", { + ...attrs, + value, + type: "range", + oninput: (e) => { + setter(e.target.value); + e.target.nextElementSibling.value = e.target.value; + }, + }), + $el("input", { + ...attrs, + value, + id: htmlID, + type: "number", + style: {maxWidth: "4rem"}, + oninput: (e) => { + setter(e.target.value); + e.target.previousElementSibling.value = e.target.value; + }, + }), + ]), + ]), + ]); + break; + case "combo": + element = $el("tr", [ + labelCell, + $el("td", [ + $el( + "select", + { + oninput: (e) => { + setter(e.target.value); + }, + }, + (typeof options === "function" ? options(value) : options || []).map((opt) => { + if (typeof opt === "string") { + opt = { text: opt }; + } + const v = opt.value ?? opt.text; + return $el("option", { + value: v, + textContent: opt.text, + selected: value + "" === v + "", + }); + }) + ), + ]), + ]); + break; + case "text": + default: + if (type !== "text") { + console.warn(`Unsupported setting type '${type}, defaulting to text`); + } + + element = $el("tr", [ + labelCell, + $el("td", [ + $el("input", { + value, + id: htmlID, + oninput: (e) => { + setter(e.target.value); + }, + ...attrs, + }), + ]), + ]); + break; + } + } + if (tooltip) { + element.title = tooltip; + } + + return element; + }, + }); + + const self = this; + return { + get value() { + return self.getSettingValue(id, defaultValue); + }, + set value(v) { + self.setSettingValue(id, v); + }, + }; + } + + show() { + this.textElement.replaceChildren( + $el("tr", { + style: {display: "none"}, + }, [ + $el("th"), + $el("th", {style: {width: "33%"}}) + ]), + ...this.settings.map((s) => s.render()), + ) + this.element.showModal(); + } +} + +class ComfyList { + #type; + #text; + #reverse; + + constructor(text, type, reverse) { + this.#text = text; + this.#type = type || text.toLowerCase(); + this.#reverse = reverse || false; + this.element = $el("div.comfy-list"); + this.element.style.display = "none"; + } + + get visible() { + return this.element.style.display !== "none"; + } + + async load() { + const items = await api.getItems(this.#type); + this.element.replaceChildren( + ...Object.keys(items).flatMap((section) => [ + $el("h4", { + textContent: section, + }), + $el("div.comfy-list-items", [ + ...(this.#reverse ? items[section].reverse() : items[section]).map((item) => { + // Allow items to specify a custom remove action (e.g. for interrupt current prompt) + const removeAction = item.remove || { + name: "Delete", + cb: () => api.deleteItem(this.#type, item.prompt[1]), + }; + return $el("div", {textContent: item.prompt[0] + ": "}, [ + $el("button", { + textContent: "Load", + onclick: () => { + app.loadGraphData(item.prompt[3].extra_pnginfo.workflow); + if (item.outputs) { + app.nodeOutputs = item.outputs; + } + }, + }), + $el("button", { + textContent: removeAction.name, + onclick: async () => { + await removeAction.cb(); + await this.update(); + }, + }), + ]); + }), + ]), + ]), + $el("div.comfy-list-actions", [ + $el("button", { + textContent: "Clear " + this.#text, + onclick: async () => { + await api.clearItems(this.#type); + await this.load(); + }, + }), + $el("button", {textContent: "Refresh", onclick: () => this.load()}), + ]) + ); + } + + async update() { + if (this.visible) { + await this.load(); + } + } + + async show() { + this.element.style.display = "block"; + this.button.textContent = "Close"; + + await this.load(); + } + + hide() { + this.element.style.display = "none"; + this.button.textContent = "View " + this.#text; + } + + toggle() { + if (this.visible) { + this.hide(); + return false; + } else { + this.show(); + return true; + } + } +} + +export class ComfyUI { + constructor(app) { + this.app = app; + this.dialog = new ComfyDialog(); + this.settings = new ComfySettingsDialog(); + + this.batchCount = 1; + this.lastQueueSize = 0; + this.queue = new ComfyList("Queue"); + this.history = new ComfyList("History", "history", true); + + api.addEventListener("status", () => { + this.queue.update(); + this.history.update(); + }); + + const confirmClear = this.settings.addSetting({ + id: "Comfy.ConfirmClear", + name: "Require confirmation when clearing workflow", + type: "boolean", + defaultValue: true, + }); + + const promptFilename = this.settings.addSetting({ + id: "Comfy.PromptFilename", + name: "Prompt for filename when saving workflow", + type: "boolean", + defaultValue: true, + }); + + /** + * file format for preview + * + * format;quality + * + * ex) + * webp;50 -> webp, quality 50 + * jpeg;80 -> rgb, jpeg, quality 80 + * + * @type {string} + */ + const previewImage = this.settings.addSetting({ + id: "Comfy.PreviewFormat", + name: "When displaying a preview in the image widget, convert it to a lightweight image, e.g. webp, jpeg, webp;50, etc.", + type: "text", + defaultValue: "", + }); + + this.settings.addSetting({ + id: "Comfy.DisableSliders", + name: "Disable sliders.", + type: "boolean", + defaultValue: false, + }); + + this.settings.addSetting({ + id: "Comfy.DisableFloatRounding", + name: "Disable rounding floats (requires page reload).", + type: "boolean", + defaultValue: false, + }); + + this.settings.addSetting({ + id: "Comfy.FloatRoundingPrecision", + name: "Decimal places [0 = auto] (requires page reload).", + type: "slider", + attrs: { + min: 0, + max: 6, + step: 1, + }, + defaultValue: 0, + }); + + const fileInput = $el("input", { + id: "comfy-file-input", + type: "file", + accept: ".json,image/png,.latent,.safetensors", + style: {display: "none"}, + parent: document.body, + onchange: () => { + app.handleFile(fileInput.files[0]); + }, + }); + + this.menuContainer = $el("div.comfy-menu", {parent: document.body}, [ + $el("div.drag-handle", { + style: { + overflow: "hidden", + position: "relative", + width: "100%", + cursor: "default" + } + }, [ + $el("span.drag-handle"), + $el("span", {$: (q) => (this.queueSize = q)}), + $el("button.comfy-settings-btn", {textContent: "⚙️", onclick: () => this.settings.show()}), + ]), + $el("button.comfy-queue-btn", { + id: "queue-button", + textContent: "Queue Prompt", + onclick: () => app.queuePrompt(0, this.batchCount), + }), + $el("div", {}, [ + $el("label", {innerHTML: "Extra options"}, [ + $el("input", { + type: "checkbox", + onchange: (i) => { + document.getElementById("extraOptions").style.display = i.srcElement.checked ? "block" : "none"; + this.batchCount = i.srcElement.checked ? document.getElementById("batchCountInputRange").value : 1; + document.getElementById("autoQueueCheckbox").checked = false; + }, + }), + ]), + ]), + $el("div", {id: "extraOptions", style: {width: "100%", display: "none"}}, [ + $el("div",[ + + $el("label", {innerHTML: "Batch count"}), + $el("input", { + id: "batchCountInputNumber", + type: "number", + value: this.batchCount, + min: "1", + style: {width: "35%", "margin-left": "0.4em"}, + oninput: (i) => { + this.batchCount = i.target.value; + document.getElementById("batchCountInputRange").value = this.batchCount; + }, + }), + $el("input", { + id: "batchCountInputRange", + type: "range", + min: "1", + max: "100", + value: this.batchCount, + oninput: (i) => { + this.batchCount = i.srcElement.value; + document.getElementById("batchCountInputNumber").value = i.srcElement.value; + }, + }), + ]), + + $el("div",[ + $el("label",{ + for:"autoQueueCheckbox", + innerHTML: "Auto Queue" + // textContent: "Auto Queue" + }), + $el("input", { + id: "autoQueueCheckbox", + type: "checkbox", + checked: false, + title: "Automatically queue prompt when the queue size hits 0", + + }), + ]) + ]), + $el("div.comfy-menu-btns", [ + $el("button", { + id: "queue-front-button", + textContent: "Queue Front", + onclick: () => app.queuePrompt(-1, this.batchCount) + }), + $el("button", { + $: (b) => (this.queue.button = b), + id: "comfy-view-queue-button", + textContent: "View Queue", + onclick: () => { + this.history.hide(); + this.queue.toggle(); + }, + }), + $el("button", { + $: (b) => (this.history.button = b), + id: "comfy-view-history-button", + textContent: "View History", + onclick: () => { + this.queue.hide(); + this.history.toggle(); + }, + }), + ]), + this.queue.element, + this.history.element, + $el("button", { + id: "comfy-save-button", + textContent: "Save", + onclick: () => { + let filename = "workflow.json"; + if (promptFilename.value) { + filename = prompt("Save workflow as:", filename); + if (!filename) return; + if (!filename.toLowerCase().endsWith(".json")) { + filename += ".json"; + } + } + const json = JSON.stringify(app.graph.serialize(), null, 2); // convert the data to a JSON string + const blob = new Blob([json], {type: "application/json"}); + const url = URL.createObjectURL(blob); + const a = $el("a", { + href: url, + download: filename, + style: {display: "none"}, + parent: document.body, + }); + a.click(); + setTimeout(function () { + a.remove(); + window.URL.revokeObjectURL(url); + }, 0); + }, + }), + $el("button", { + id: "comfy-dev-save-api-button", + textContent: "Save (API Format)", + style: {width: "100%", display: "none"}, + onclick: () => { + let filename = "workflow_api.json"; + if (promptFilename.value) { + filename = prompt("Save workflow (API) as:", filename); + if (!filename) return; + if (!filename.toLowerCase().endsWith(".json")) { + filename += ".json"; + } + } + app.graphToPrompt().then(p=>{ + const json = JSON.stringify(p.output, null, 2); // convert the data to a JSON string + const blob = new Blob([json], {type: "application/json"}); + const url = URL.createObjectURL(blob); + const a = $el("a", { + href: url, + download: filename, + style: {display: "none"}, + parent: document.body, + }); + a.click(); + setTimeout(function () { + a.remove(); + window.URL.revokeObjectURL(url); + }, 0); + }); + }, + }), + $el("button", {id: "comfy-load-button", textContent: "Load", onclick: () => fileInput.click()}), + $el("button", { + id: "comfy-refresh-button", + textContent: "Refresh", + onclick: () => app.refreshComboInNodes() + }), + $el("button", {id: "comfy-clipspace-button", textContent: "Clipspace", onclick: () => app.openClipspace()}), + $el("button", { + id: "comfy-clear-button", textContent: "Clear", onclick: () => { + if (!confirmClear.value || confirm("Clear workflow?")) { + app.clean(); + app.graph.clear(); + } + } + }), + $el("button", { + id: "comfy-load-default-button", textContent: "Load Default", onclick: () => { + if (!confirmClear.value || confirm("Load default workflow?")) { + app.loadGraphData() + } + } + }), + ]); + + const devMode = this.settings.addSetting({ + id: "Comfy.DevMode", + name: "Enable Dev mode Options", + type: "boolean", + defaultValue: false, + onChange: function(value) { document.getElementById("comfy-dev-save-api-button").style.display = value ? "block" : "none"}, + }); + + dragElement(this.menuContainer, this.settings); + + this.setStatus({exec_info: {queue_remaining: "X"}}); + } + + setStatus(status) { + this.queueSize.textContent = "Queue size: " + (status ? status.exec_info.queue_remaining : "ERR"); + if (status) { + if ( + this.lastQueueSize != 0 && + status.exec_info.queue_remaining == 0 && + document.getElementById("autoQueueCheckbox").checked + ) { + app.queuePrompt(0, this.batchCount); + } + this.lastQueueSize = status.exec_info.queue_remaining; + } + } +} diff --git a/web/scripts/widgets.js b/web/scripts/widgets.js new file mode 100644 index 0000000000000000000000000000000000000000..2b023937415aa1ed73594efb97a1aec32b56e0e8 --- /dev/null +++ b/web/scripts/widgets.js @@ -0,0 +1,498 @@ +import { api } from "./api.js" + +function getNumberDefaults(inputData, defaultStep, precision, enable_rounding) { + let defaultVal = inputData[1]["default"]; + let { min, max, step, round} = inputData[1]; + + if (defaultVal == undefined) defaultVal = 0; + if (min == undefined) min = 0; + if (max == undefined) max = 2048; + if (step == undefined) step = defaultStep; + // precision is the number of decimal places to show. + // by default, display the the smallest number of decimal places such that changes of size step are visible. + if (precision == undefined) { + precision = Math.max(-Math.floor(Math.log10(step)),0); + } + + if (enable_rounding && (round == undefined || round === true)) { + // by default, round the value to those decimal places shown. + round = Math.round(1000000*Math.pow(0.1,precision))/1000000; + } + + return { val: defaultVal, config: { min, max, step: 10.0 * step, round, precision } }; +} + +export function addValueControlWidget(node, targetWidget, defaultValue = "randomize", values) { + const valueControl = node.addWidget("combo", "control_after_generate", defaultValue, function (v) { }, { + values: ["fixed", "increment", "decrement", "randomize"], + serialize: false, // Don't include this in prompt. + }); + valueControl.afterQueued = () => { + + var v = valueControl.value; + + if (targetWidget.type == "combo" && v !== "fixed") { + let current_index = targetWidget.options.values.indexOf(targetWidget.value); + let current_length = targetWidget.options.values.length; + + switch (v) { + case "increment": + current_index += 1; + break; + case "decrement": + current_index -= 1; + break; + case "randomize": + current_index = Math.floor(Math.random() * current_length); + default: + break; + } + current_index = Math.max(0, current_index); + current_index = Math.min(current_length - 1, current_index); + if (current_index >= 0) { + let value = targetWidget.options.values[current_index]; + targetWidget.value = value; + targetWidget.callback(value); + } + } else { //number + let min = targetWidget.options.min; + let max = targetWidget.options.max; + // limit to something that javascript can handle + max = Math.min(1125899906842624, max); + min = Math.max(-1125899906842624, min); + let range = (max - min) / (targetWidget.options.step / 10); + + //adjust values based on valueControl Behaviour + switch (v) { + case "fixed": + break; + case "increment": + targetWidget.value += targetWidget.options.step / 10; + break; + case "decrement": + targetWidget.value -= targetWidget.options.step / 10; + break; + case "randomize": + targetWidget.value = Math.floor(Math.random() * range) * (targetWidget.options.step / 10) + min; + default: + break; + } + /*check if values are over or under their respective + * ranges and set them to min or max.*/ + if (targetWidget.value < min) + targetWidget.value = min; + + if (targetWidget.value > max) + targetWidget.value = max; + } + } + return valueControl; +}; + +function seedWidget(node, inputName, inputData, app) { + const seed = ComfyWidgets.INT(node, inputName, inputData, app); + const seedControl = addValueControlWidget(node, seed.widget, "randomize"); + + seed.widget.linkedWidgets = [seedControl]; + return seed; +} + +const MultilineSymbol = Symbol(); +const MultilineResizeSymbol = Symbol(); + +function addMultilineWidget(node, name, opts, app) { + const MIN_SIZE = 50; + + function computeSize(size) { + if (node.widgets[0].last_y == null) return; + + let y = node.widgets[0].last_y; + let freeSpace = size[1] - y; + + // Compute the height of all non customtext widgets + let widgetHeight = 0; + const multi = []; + for (let i = 0; i < node.widgets.length; i++) { + const w = node.widgets[i]; + if (w.type === "customtext") { + multi.push(w); + } else { + if (w.computeSize) { + widgetHeight += w.computeSize()[1] + 4; + } else { + widgetHeight += LiteGraph.NODE_WIDGET_HEIGHT + 4; + } + } + } + + // See how large each text input can be + freeSpace -= widgetHeight; + freeSpace /= multi.length + (!!node.imgs?.length); + + if (freeSpace < MIN_SIZE) { + // There isnt enough space for all the widgets, increase the size of the node + freeSpace = MIN_SIZE; + node.size[1] = y + widgetHeight + freeSpace * (multi.length + (!!node.imgs?.length)); + node.graph.setDirtyCanvas(true); + } + + // Position each of the widgets + for (const w of node.widgets) { + w.y = y; + if (w.type === "customtext") { + y += freeSpace; + w.computedHeight = freeSpace - multi.length*4; + } else if (w.computeSize) { + y += w.computeSize()[1] + 4; + } else { + y += LiteGraph.NODE_WIDGET_HEIGHT + 4; + } + } + + node.inputHeight = freeSpace; + } + + const widget = { + type: "customtext", + name, + get value() { + return this.inputEl.value; + }, + set value(x) { + this.inputEl.value = x; + }, + draw: function (ctx, _, widgetWidth, y, widgetHeight) { + if (!this.parent.inputHeight) { + // If we are initially offscreen when created we wont have received a resize event + // Calculate it here instead + computeSize(node.size); + } + const visible = app.canvas.ds.scale > 0.5 && this.type === "customtext"; + const margin = 10; + const elRect = ctx.canvas.getBoundingClientRect(); + const transform = new DOMMatrix() + .scaleSelf(elRect.width / ctx.canvas.width, elRect.height / ctx.canvas.height) + .multiplySelf(ctx.getTransform()) + .translateSelf(margin, margin + y); + + const scale = new DOMMatrix().scaleSelf(transform.a, transform.d) + Object.assign(this.inputEl.style, { + transformOrigin: "0 0", + transform: scale, + left: `${transform.a + transform.e}px`, + top: `${transform.d + transform.f}px`, + width: `${widgetWidth - (margin * 2)}px`, + height: `${this.parent.inputHeight - (margin * 2)}px`, + position: "absolute", + background: (!node.color)?'':node.color, + color: (!node.color)?'':'white', + zIndex: app.graph._nodes.indexOf(node), + }); + this.inputEl.hidden = !visible; + }, + }; + widget.inputEl = document.createElement("textarea"); + widget.inputEl.className = "comfy-multiline-input"; + widget.inputEl.value = opts.defaultVal; + widget.inputEl.placeholder = opts.placeholder || ""; + document.addEventListener("mousedown", function (event) { + if (!widget.inputEl.contains(event.target)) { + widget.inputEl.blur(); + } + }); + widget.parent = node; + document.body.appendChild(widget.inputEl); + + node.addCustomWidget(widget); + + app.canvas.onDrawBackground = function () { + // Draw node isnt fired once the node is off the screen + // if it goes off screen quickly, the input may not be removed + // this shifts it off screen so it can be moved back if the node is visible. + for (let n in app.graph._nodes) { + n = graph._nodes[n]; + for (let w in n.widgets) { + let wid = n.widgets[w]; + if (Object.hasOwn(wid, "inputEl")) { + wid.inputEl.style.left = -8000 + "px"; + wid.inputEl.style.position = "absolute"; + } + } + } + }; + + node.onRemoved = function () { + // When removing this node we need to remove the input from the DOM + for (let y in this.widgets) { + if (this.widgets[y].inputEl) { + this.widgets[y].inputEl.remove(); + } + } + }; + + widget.onRemove = () => { + widget.inputEl?.remove(); + + // Restore original size handler if we are the last + if (!--node[MultilineSymbol]) { + node.onResize = node[MultilineResizeSymbol]; + delete node[MultilineSymbol]; + delete node[MultilineResizeSymbol]; + } + }; + + if (node[MultilineSymbol]) { + node[MultilineSymbol]++; + } else { + node[MultilineSymbol] = 1; + const onResize = (node[MultilineResizeSymbol] = node.onResize); + + node.onResize = function (size) { + computeSize(size); + + // Call original resizer handler + if (onResize) { + onResize.apply(this, arguments); + } + }; + } + + return { minWidth: 400, minHeight: 200, widget }; +} + +function isSlider(display, app) { + if (app.ui.settings.getSettingValue("Comfy.DisableSliders")) { + return "number" + } + + return (display==="slider") ? "slider" : "number" +} + +export const ComfyWidgets = { + "INT:seed": seedWidget, + "INT:noise_seed": seedWidget, + FLOAT(node, inputName, inputData, app) { + let widgetType = isSlider(inputData[1]["display"], app); + let precision = app.ui.settings.getSettingValue("Comfy.FloatRoundingPrecision"); + let disable_rounding = app.ui.settings.getSettingValue("Comfy.DisableFloatRounding") + if (precision == 0) precision = undefined; + const { val, config } = getNumberDefaults(inputData, 0.5, precision, !disable_rounding); + return { widget: node.addWidget(widgetType, inputName, val, + function (v) { + if (config.round) { + this.value = Math.round(v/config.round)*config.round; + } else { + this.value = v; + } + }, config) }; + }, + INT(node, inputName, inputData, app) { + let widgetType = isSlider(inputData[1]["display"], app); + const { val, config } = getNumberDefaults(inputData, 1, 0, true); + Object.assign(config, { precision: 0 }); + return { + widget: node.addWidget( + widgetType, + inputName, + val, + function (v) { + const s = this.options.step / 10; + this.value = Math.round(v / s) * s; + }, + config + ), + }; + }, + BOOLEAN(node, inputName, inputData) { + let defaultVal = inputData[1]["default"]; + return { + widget: node.addWidget( + "toggle", + inputName, + defaultVal, + () => {}, + {"on": inputData[1].label_on, "off": inputData[1].label_off} + ) + }; + }, + STRING(node, inputName, inputData, app) { + const defaultVal = inputData[1].default || ""; + const multiline = !!inputData[1].multiline; + + let res; + if (multiline) { + res = addMultilineWidget(node, inputName, { defaultVal, ...inputData[1] }, app); + } else { + res = { widget: node.addWidget("text", inputName, defaultVal, () => {}, {}) }; + } + + if(inputData[1].dynamicPrompts != undefined) + res.widget.dynamicPrompts = inputData[1].dynamicPrompts; + + return res; + }, + COMBO(node, inputName, inputData) { + const type = inputData[0]; + let defaultValue = type[0]; + if (inputData[1] && inputData[1].default) { + defaultValue = inputData[1].default; + } + return { widget: node.addWidget("combo", inputName, defaultValue, () => {}, { values: type }) }; + }, + IMAGEUPLOAD(node, inputName, inputData, app) { + const imageWidget = node.widgets.find((w) => w.name === "image"); + let uploadWidget; + + function showImage(name) { + const img = new Image(); + img.onload = () => { + node.imgs = [img]; + app.graph.setDirtyCanvas(true); + }; + let folder_separator = name.lastIndexOf("/"); + let subfolder = ""; + if (folder_separator > -1) { + subfolder = name.substring(0, folder_separator); + name = name.substring(folder_separator + 1); + } + img.src = api.apiURL(`/view?filename=${encodeURIComponent(name)}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}`); + node.setSizeForImage?.(); + } + + var default_value = imageWidget.value; + Object.defineProperty(imageWidget, "value", { + set : function(value) { + this._real_value = value; + }, + + get : function() { + let value = ""; + if (this._real_value) { + value = this._real_value; + } else { + return default_value; + } + + if (value.filename) { + let real_value = value; + value = ""; + if (real_value.subfolder) { + value = real_value.subfolder + "/"; + } + + value += real_value.filename; + + if(real_value.type && real_value.type !== "input") + value += ` [${real_value.type}]`; + } + return value; + } + }); + + // Add our own callback to the combo widget to render an image when it changes + const cb = node.callback; + imageWidget.callback = function () { + showImage(imageWidget.value); + if (cb) { + return cb.apply(this, arguments); + } + }; + + // On load if we have a value then render the image + // The value isnt set immediately so we need to wait a moment + // No change callbacks seem to be fired on initial setting of the value + requestAnimationFrame(() => { + if (imageWidget.value) { + showImage(imageWidget.value); + } + }); + + async function uploadFile(file, updateNode, pasted = false) { + try { + // Wrap file in formdata so it includes filename + const body = new FormData(); + body.append("image", file); + if (pasted) body.append("subfolder", "pasted"); + const resp = await api.fetchApi("/upload/image", { + method: "POST", + body, + }); + + if (resp.status === 200) { + const data = await resp.json(); + // Add the file to the dropdown list and update the widget value + let path = data.name; + if (data.subfolder) path = data.subfolder + "/" + path; + + if (!imageWidget.options.values.includes(path)) { + imageWidget.options.values.push(path); + } + + if (updateNode) { + showImage(path); + imageWidget.value = path; + } + } else { + alert(resp.status + " - " + resp.statusText); + } + } catch (error) { + alert(error); + } + } + + const fileInput = document.createElement("input"); + Object.assign(fileInput, { + type: "file", + accept: "image/jpeg,image/png,image/webp", + style: "display: none", + onchange: async () => { + if (fileInput.files.length) { + await uploadFile(fileInput.files[0], true); + } + }, + }); + document.body.append(fileInput); + + // Create the button widget for selecting the files + uploadWidget = node.addWidget("button", "choose file to upload", "image", () => { + fileInput.click(); + }); + uploadWidget.serialize = false; + + // Add handler to check if an image is being dragged over our node + node.onDragOver = function (e) { + if (e.dataTransfer && e.dataTransfer.items) { + const image = [...e.dataTransfer.items].find((f) => f.kind === "file"); + return !!image; + } + + return false; + }; + + // On drop upload files + node.onDragDrop = function (e) { + console.log("onDragDrop called"); + let handled = false; + for (const file of e.dataTransfer.files) { + if (file.type.startsWith("image/")) { + uploadFile(file, !handled); // Dont await these, any order is fine, only update on first one + handled = true; + } + } + + return handled; + }; + + node.pasteFile = function(file) { + if (file.type.startsWith("image/")) { + const is_pasted = (file.name === "image.png") && + (file.lastModified - Date.now() < 2000); + uploadFile(file, true, is_pasted); + return true; + } + return false; + } + + return { widget: uploadWidget }; + }, +}; diff --git a/web/style.css b/web/style.css new file mode 100644 index 0000000000000000000000000000000000000000..692fa31d67253a723979939507039330ba640d54 --- /dev/null +++ b/web/style.css @@ -0,0 +1,437 @@ +:root { + --fg-color: #000; + --bg-color: #fff; + --comfy-menu-bg: #353535; + --comfy-input-bg: #222; + --input-text: #ddd; + --descrip-text: #999; + --drag-text: #ccc; + --error-text: #ff4444; + --border-color: #4e4e4e; + --tr-even-bg-color: #222; + --tr-odd-bg-color: #353535; +} + +@media (prefers-color-scheme: dark) { + :root { + --fg-color: #fff; + --bg-color: #202020; + } +} + +body { + width: 100vw; + height: 100vh; + margin: 0; + overflow: hidden; + background-color: var(--bg-color); + color: var(--fg-color); +} + +#graph-canvas { + width: 100%; + height: 100%; +} + +.comfy-multiline-input { + background-color: var(--comfy-input-bg); + color: var(--input-text); + overflow: hidden; + overflow-y: auto; + padding: 2px; + resize: none; + border: none; + box-sizing: border-box; + font-size: 10px; +} + +.comfy-modal { + display: none; /* Hidden by default */ + position: fixed; /* Stay in place */ + z-index: 100; /* Sit on top */ + padding: 30px 30px 10px 30px; + background-color: var(--comfy-menu-bg); /* Modal background */ + color: var(--error-text); + box-shadow: 0 0 20px #888888; + border-radius: 10px; + top: 50%; + left: 50%; + max-width: 80vw; + max-height: 80vh; + transform: translate(-50%, -50%); + overflow: hidden; + justify-content: center; + font-family: monospace; + font-size: 15px; +} + +.comfy-modal-content { + display: flex; + flex-direction: column; +} + +.comfy-modal p { + overflow: auto; + white-space: pre-line; /* This will respect line breaks */ + margin-bottom: 20px; /* Add some margin between the text and the close button*/ +} + +.comfy-modal select, +.comfy-modal input[type=button], +.comfy-modal input[type=checkbox] { + margin: 3px 3px 3px 4px; +} + +.comfy-menu { + font-size: 15px; + position: absolute; + top: 50%; + right: 0; + text-align: center; + z-index: 999; + width: 170px; + display: flex; + flex-direction: column; + align-items: center; + color: var(--descrip-text); + background-color: var(--comfy-menu-bg); + font-family: sans-serif; + padding: 10px; + border-radius: 0 8px 8px 8px; + box-shadow: 3px 3px 8px rgba(0, 0, 0, 0.4); +} + +.comfy-menu button, +.comfy-modal button { + font-size: 20px; +} + +.comfy-menu-btns { + margin-bottom: 10px; + width: 100%; +} + +.comfy-menu-btns button { + font-size: 10px; + width: 50%; + color: var(--descrip-text) !important; +} + +.comfy-menu > button { + width: 100%; +} + +.comfy-menu > button, +.comfy-menu-btns button, +.comfy-menu .comfy-list button, +.comfy-modal button { + color: var(--input-text); + background-color: var(--comfy-input-bg); + border-radius: 8px; + border-color: var(--border-color); + border-style: solid; + margin-top: 2px; +} + +.comfy-menu > button:hover, +.comfy-menu-btns button:hover, +.comfy-menu .comfy-list button:hover, +.comfy-modal button:hover, +.comfy-settings-btn:hover { + filter: brightness(1.2); + cursor: pointer; +} + +.comfy-menu span.drag-handle { + width: 10px; + height: 20px; + display: inline-block; + overflow: hidden; + line-height: 5px; + padding: 3px 4px; + cursor: move; + vertical-align: middle; + margin-top: -.4em; + margin-left: -.2em; + font-size: 12px; + font-family: sans-serif; + letter-spacing: 2px; + color: var(--drag-text); + text-shadow: 1px 0 1px black; + position: absolute; + top: 0; + left: 0; +} + +.comfy-menu span.drag-handle::after { + content: '.. .. ..'; +} + +.comfy-queue-btn { + width: 100%; +} + +.comfy-list { + color: var(--descrip-text); + background-color: var(--comfy-menu-bg); + margin-bottom: 10px; + border-color: var(--border-color); + border-style: solid; +} + +.comfy-list-items { + overflow-y: scroll; + max-height: 100px; + min-height: 25px; + background-color: var(--comfy-input-bg); + padding: 5px; +} + +.comfy-list h4 { + min-width: 160px; + margin: 0; + padding: 3px; + font-weight: normal; +} + +.comfy-list-items button { + font-size: 10px; +} + +.comfy-list-actions { + margin: 5px; + display: flex; + gap: 5px; + justify-content: center; +} + +.comfy-list-actions button { + font-size: 12px; +} + +button.comfy-settings-btn { + background-color: rgba(0, 0, 0, 0); + font-size: 12px; + padding: 0; + position: absolute; + right: 0; + border: none; +} + +button.comfy-queue-btn { + margin: 6px 0 !important; +} + +.comfy-modal.comfy-settings, +.comfy-modal.comfy-manage-templates { + text-align: center; + font-family: sans-serif; + color: var(--descrip-text); + z-index: 99; +} + +.comfy-modal.comfy-settings input[type="range"] { + vertical-align: middle; +} + +.comfy-modal.comfy-settings input[type="range"] + input[type="number"] { + width: 3.5em; +} + +.comfy-modal input, +.comfy-modal select { + color: var(--input-text); + background-color: var(--comfy-input-bg); + border-radius: 8px; + border-color: var(--border-color); + border-style: solid; + font-size: inherit; +} + +.comfy-tooltip-indicator { + text-decoration: underline; + text-decoration-style: dashed; +} + +@media only screen and (max-height: 850px) { + .comfy-menu { + top: 0 !important; + bottom: 0 !important; + left: auto !important; + right: 0 !important; + border-radius: 0; + } + + .comfy-menu span.drag-handle { + visibility: hidden + } +} + +/* Input popup */ + +.graphdialog { + min-height: 1em; + background-color: var(--comfy-menu-bg); +} + +.graphdialog .name { + font-size: 14px; + font-family: sans-serif; + color: var(--descrip-text); +} + +.graphdialog button { + margin-top: unset; + vertical-align: unset; + height: 1.6em; + padding-right: 8px; +} + +.graphdialog input, .graphdialog textarea, .graphdialog select { + background-color: var(--comfy-input-bg); + border: 2px solid; + border-color: var(--border-color); + color: var(--input-text); + border-radius: 12px 0 0 12px; +} + +/* Dialogs */ + +dialog { + box-shadow: 0 0 20px #888888; +} + +dialog::backdrop { + background: rgba(0, 0, 0, 0.5); +} + +#comfy-settings-dialog { + padding: 0; + width: 41rem; +} + +#comfy-settings-dialog tr > td:first-child { + text-align: right; +} + +#comfy-settings-dialog button { + background-color: var(--bg-color); + border: 1px var(--border-color) solid; + border-radius: 0; + color: var(--input-text); + font-size: 1rem; + padding: 0.5rem; +} + +#comfy-settings-dialog button:hover { + background-color: var(--tr-odd-bg-color); +} + +/* General CSS for tables */ + +.comfy-table { + border-collapse: collapse; + color: var(--input-text); + font-family: Arial, sans-serif; + width: 100%; +} + +.comfy-table caption { + background-color: var(--bg-color); + color: var(--input-text); + font-size: 1rem; + font-weight: bold; + padding: 8px; + text-align: center; +} + +.comfy-table tr:nth-child(even) { + background-color: var(--tr-even-bg-color); +} + +.comfy-table tr:nth-child(odd) { + background-color: var(--tr-odd-bg-color); +} + +.comfy-table td, +.comfy-table th { + border: 1px solid var(--border-color); + padding: 8px; +} + +/* Context menu */ + +.litegraph .dialog { + z-index: 1; + font-family: Arial, sans-serif; +} + +.litegraph .litemenu-entry.has_submenu { + position: relative; + padding-right: 20px; +} + +.litemenu-entry.has_submenu::after { + content: ">"; + position: absolute; + top: 0; + right: 2px; +} + +.litegraph.litecontextmenu, +.litegraph.litecontextmenu.dark { + z-index: 9999 !important; + background-color: var(--comfy-menu-bg) !important; + filter: brightness(95%); +} + +.litegraph.litecontextmenu .litemenu-entry:hover:not(.disabled):not(.separator) { + background-color: var(--comfy-menu-bg) !important; + filter: brightness(155%); + color: var(--input-text); +} + +.litegraph.litecontextmenu .litemenu-entry.submenu, +.litegraph.litecontextmenu.dark .litemenu-entry.submenu { + background-color: var(--comfy-menu-bg) !important; + color: var(--input-text); +} + +.litegraph.litecontextmenu input { + background-color: var(--comfy-input-bg) !important; + color: var(--input-text) !important; +} + +.comfy-context-menu-filter { + box-sizing: border-box; + border: 1px solid #999; + margin: 0 0 5px 5px; + width: calc(100% - 10px); +} + +/* Search box */ + +.litegraph.litesearchbox { + z-index: 9999 !important; + background-color: var(--comfy-menu-bg) !important; + overflow: hidden; + display: block; +} + +.litegraph.litesearchbox input, +.litegraph.litesearchbox select { + background-color: var(--comfy-input-bg) !important; + color: var(--input-text); +} + +.litegraph.lite-search-item { + color: var(--input-text); + background-color: var(--comfy-input-bg); + filter: brightness(80%); + padding-left: 0.2em; +} + +.litegraph.lite-search-item.generic_type { + color: var(--input-text); + filter: brightness(50%); +} diff --git a/web/types/comfy.d.ts b/web/types/comfy.d.ts new file mode 100644 index 0000000000000000000000000000000000000000..f7129b55584e86986cee280e1eaf24542f036b7c --- /dev/null +++ b/web/types/comfy.d.ts @@ -0,0 +1,76 @@ +import { LGraphNode, IWidget } from "./litegraph"; +import { ComfyApp } from "../../scripts/app"; + +export interface ComfyExtension { + /** + * The name of the extension + */ + name: string; + /** + * Allows any initialisation, e.g. loading resources. Called after the canvas is created but before nodes are added + * @param app The ComfyUI app instance + */ + init(app: ComfyApp): Promise; + /** + * Allows any additonal setup, called after the application is fully set up and running + * @param app The ComfyUI app instance + */ + setup(app: ComfyApp): Promise; + /** + * Called before nodes are registered with the graph + * @param defs The collection of node definitions, add custom ones or edit existing ones + * @param app The ComfyUI app instance + */ + addCustomNodeDefs(defs: Record, app: ComfyApp): Promise; + /** + * Allows the extension to add custom widgets + * @param app The ComfyUI app instance + * @returns An array of {[widget name]: widget data} + */ + getCustomWidgets( + app: ComfyApp + ): Promise< + Record { widget?: IWidget; minWidth?: number; minHeight?: number }> + >; + /** + * Allows the extension to add additional handling to the node before it is registered with LGraph + * @param nodeType The node class (not an instance) + * @param nodeData The original node object info config object + * @param app The ComfyUI app instance + */ + beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyObjectInfo, app: ComfyApp): Promise; + /** + * Allows the extension to register additional nodes with LGraph after standard nodes are added + * @param app The ComfyUI app instance + */ + registerCustomNodes(app: ComfyApp): Promise; + /** + * Allows the extension to modify a node that has been reloaded onto the graph. + * If you break something in the backend and want to patch workflows in the frontend + * This is the place to do this + * @param node The node that has been loaded + * @param app The ComfyUI app instance + */ + loadedGraphNode(node: LGraphNode, app: ComfyApp); + /** + * Allows the extension to run code after the constructor of the node + * @param node The node that has been created + * @param app The ComfyUI app instance + */ + nodeCreated(node: LGraphNode, app: ComfyApp); +} + +export type ComfyObjectInfo = { + name: string; + display_name?: string; + description?: string; + category: string; + input?: { + required?: Record; + optional?: Record; + }; + output?: string[]; + output_name: string[]; +}; + +export type ComfyObjectInfoConfig = [string | any[]] | [string | any[], any]; diff --git a/web/types/litegraph.d.ts b/web/types/litegraph.d.ts new file mode 100644 index 0000000000000000000000000000000000000000..6629e779ff073d5bfd1e91b0f9cc9a8defe5e812 --- /dev/null +++ b/web/types/litegraph.d.ts @@ -0,0 +1,1506 @@ +// Type definitions for litegraph.js 0.7.0 +// Project: litegraph.js +// Definitions by: NateScarlet + +export type Vector2 = [number, number]; +export type Vector4 = [number, number, number, number]; +export type widgetTypes = + | "number" + | "slider" + | "combo" + | "text" + | "toggle" + | "button"; +export type SlotShape = + | typeof LiteGraph.BOX_SHAPE + | typeof LiteGraph.CIRCLE_SHAPE + | typeof LiteGraph.ARROW_SHAPE + | typeof LiteGraph.SQUARE_SHAPE + | number; // For custom shapes + +/** https://github.com/jagenjo/litegraph.js/tree/master/guides#node-slots */ +export interface INodeSlot { + name: string; + type: string | -1; + label?: string; + dir?: + | typeof LiteGraph.UP + | typeof LiteGraph.RIGHT + | typeof LiteGraph.DOWN + | typeof LiteGraph.LEFT; + color_on?: string; + color_off?: string; + shape?: SlotShape; + locked?: boolean; + nameLocked?: boolean; +} + +export interface INodeInputSlot extends INodeSlot { + link: LLink["id"] | null; +} +export interface INodeOutputSlot extends INodeSlot { + links: LLink["id"][] | null; +} + +export type WidgetCallback = ( + this: T, + value: T["value"], + graphCanvas: LGraphCanvas, + node: LGraphNode, + pos: Vector2, + event?: MouseEvent +) => void; + +export interface IWidget { + name: string | null; + value: TValue; + options?: TOptions; + type?: widgetTypes; + y?: number; + property?: string; + last_y?: number; + clicked?: boolean; + marker?: boolean; + callback?: WidgetCallback; + /** Called by `LGraphCanvas.drawNodeWidgets` */ + draw?( + ctx: CanvasRenderingContext2D, + node: LGraphNode, + width: number, + posY: number, + height: number + ): void; + /** + * Called by `LGraphCanvas.processNodeWidgets` + * https://github.com/jagenjo/litegraph.js/issues/76 + */ + mouse?( + event: MouseEvent, + pos: Vector2, + node: LGraphNode + ): boolean; + /** Called by `LGraphNode.computeSize` */ + computeSize?(width: number): [number, number]; +} +export interface IButtonWidget extends IWidget { + type: "button"; +} +export interface IToggleWidget + extends IWidget { + type: "toggle"; +} +export interface ISliderWidget + extends IWidget { + type: "slider"; +} +export interface INumberWidget extends IWidget { + type: "number"; +} +export interface IComboWidget + extends IWidget< + string[], + { + values: + | string[] + | ((widget: IComboWidget, node: LGraphNode) => string[]); + } + > { + type: "combo"; +} + +export interface ITextWidget extends IWidget { + type: "text"; +} + +export interface IContextMenuItem { + content: string; + callback?: ContextMenuEventListener; + /** Used as innerHTML for extra child element */ + title?: string; + disabled?: boolean; + has_submenu?: boolean; + submenu?: { + options: ContextMenuItem[]; + } & IContextMenuOptions; + className?: string; +} +export interface IContextMenuOptions { + callback?: ContextMenuEventListener; + ignore_item_callbacks?: Boolean; + event?: MouseEvent | CustomEvent; + parentMenu?: ContextMenu; + autoopen?: boolean; + title?: string; + extra?: any; +} + +export type ContextMenuItem = IContextMenuItem | null; +export type ContextMenuEventListener = ( + value: ContextMenuItem, + options: IContextMenuOptions, + event: MouseEvent, + parentMenu: ContextMenu | undefined, + node: LGraphNode +) => boolean | void; + +export const LiteGraph: { + VERSION: number; + + CANVAS_GRID_SIZE: number; + + NODE_TITLE_HEIGHT: number; + NODE_TITLE_TEXT_Y: number; + NODE_SLOT_HEIGHT: number; + NODE_WIDGET_HEIGHT: number; + NODE_WIDTH: number; + NODE_MIN_WIDTH: number; + NODE_COLLAPSED_RADIUS: number; + NODE_COLLAPSED_WIDTH: number; + NODE_TITLE_COLOR: string; + NODE_TEXT_SIZE: number; + NODE_TEXT_COLOR: string; + NODE_SUBTEXT_SIZE: number; + NODE_DEFAULT_COLOR: string; + NODE_DEFAULT_BGCOLOR: string; + NODE_DEFAULT_BOXCOLOR: string; + NODE_DEFAULT_SHAPE: string; + DEFAULT_SHADOW_COLOR: string; + DEFAULT_GROUP_FONT: number; + + LINK_COLOR: string; + EVENT_LINK_COLOR: string; + CONNECTING_LINK_COLOR: string; + + MAX_NUMBER_OF_NODES: number; //avoid infinite loops + DEFAULT_POSITION: Vector2; //default node position + VALID_SHAPES: ["default", "box", "round", "card"]; //,"circle" + + //shapes are used for nodes but also for slots + BOX_SHAPE: 1; + ROUND_SHAPE: 2; + CIRCLE_SHAPE: 3; + CARD_SHAPE: 4; + ARROW_SHAPE: 5; + SQUARE_SHAPE: 6; + + //enums + INPUT: 1; + OUTPUT: 2; + + EVENT: -1; //for outputs + ACTION: -1; //for inputs + + ALWAYS: 0; + ON_EVENT: 1; + NEVER: 2; + ON_TRIGGER: 3; + + UP: 1; + DOWN: 2; + LEFT: 3; + RIGHT: 4; + CENTER: 5; + + STRAIGHT_LINK: 0; + LINEAR_LINK: 1; + SPLINE_LINK: 2; + + NORMAL_TITLE: 0; + NO_TITLE: 1; + TRANSPARENT_TITLE: 2; + AUTOHIDE_TITLE: 3; + + node_images_path: string; + + debug: boolean; + catch_exceptions: boolean; + throw_errors: boolean; + /** if set to true some nodes like Formula would be allowed to evaluate code that comes from unsafe sources (like node configuration), which could lead to exploits */ + allow_scripts: boolean; + /** node types by string */ + registered_node_types: Record; + /** used for dropping files in the canvas */ + node_types_by_file_extension: Record; + /** node types by class name */ + Nodes: Record; + + /** used to add extra features to the search box */ + searchbox_extras: Record< + string, + { + data: { outputs: string[][]; title: string }; + desc: string; + type: string; + } + >; + + createNode(type: string): T; + /** Register a node class so it can be listed when the user wants to create a new one */ + registerNodeType(type: string, base: { new (): LGraphNode }): void; + /** removes a node type from the system */ + unregisterNodeType(type: string): void; + /** Removes all previously registered node's types. */ + clearRegisteredTypes(): void; + /** + * Create a new node type by passing a function, it wraps it with a proper class and generates inputs according to the parameters of the function. + * Useful to wrap simple methods that do not require properties, and that only process some input to generate an output. + * @param name node name with namespace (p.e.: 'math/sum') + * @param func + * @param param_types an array containing the type of every parameter, otherwise parameters will accept any type + * @param return_type string with the return type, otherwise it will be generic + * @param properties properties to be configurable + */ + wrapFunctionAsNode( + name: string, + func: (...args: any[]) => any, + param_types?: string[], + return_type?: string, + properties?: object + ): void; + + /** + * Adds this method to all node types, existing and to be created + * (You can add it to LGraphNode.prototype but then existing node types wont have it) + */ + addNodeMethod(name: string, func: (...args: any[]) => any): void; + + /** + * Create a node of a given type with a name. The node is not attached to any graph yet. + * @param type full name of the node class. p.e. "math/sin" + * @param name a name to distinguish from other nodes + * @param options to set options + */ + createNode( + type: string, + title: string, + options: object + ): T; + + /** + * Returns a registered node type with a given name + * @param type full name of the node class. p.e. "math/sin" + */ + getNodeType(type: string): LGraphNodeConstructor; + + /** + * Returns a list of node types matching one category + * @method getNodeTypesInCategory + * @param {String} category category name + * @param {String} filter only nodes with ctor.filter equal can be shown + * @return {Array} array with all the node classes + */ + getNodeTypesInCategory( + category: string, + filter: string + ): LGraphNodeConstructor[]; + + /** + * Returns a list with all the node type categories + * @method getNodeTypesCategories + * @param {String} filter only nodes with ctor.filter equal can be shown + * @return {Array} array with all the names of the categories + */ + getNodeTypesCategories(filter: string): string[]; + + /** debug purposes: reloads all the js scripts that matches a wildcard */ + reloadNodes(folder_wildcard: string): void; + + getTime(): number; + LLink: typeof LLink; + LGraph: typeof LGraph; + DragAndScale: typeof DragAndScale; + compareObjects(a: object, b: object): boolean; + distance(a: Vector2, b: Vector2): number; + colorToString(c: string): string; + isInsideRectangle( + x: number, + y: number, + left: number, + top: number, + width: number, + height: number + ): boolean; + growBounding(bounding: Vector4, x: number, y: number): Vector4; + isInsideBounding(p: Vector2, bb: Vector4): boolean; + hex2num(hex: string): [number, number, number]; + num2hex(triplet: [number, number, number]): string; + ContextMenu: typeof ContextMenu; + extendClass(target: A, origin: B): A & B; + getParameterNames(func: string): string[]; +}; + +export type serializedLGraph< + TNode = ReturnType, + // https://github.com/jagenjo/litegraph.js/issues/74 + TLink = [number, number, number, number, number, string], + TGroup = ReturnType +> = { + last_node_id: LGraph["last_node_id"]; + last_link_id: LGraph["last_link_id"]; + nodes: TNode[]; + links: TLink[]; + groups: TGroup[]; + config: LGraph["config"]; + version: typeof LiteGraph.VERSION; +}; + +export declare class LGraph { + static supported_types: string[]; + static STATUS_STOPPED: 1; + static STATUS_RUNNING: 2; + + constructor(o?: object); + + filter: string; + catch_errors: boolean; + /** custom data */ + config: object; + elapsed_time: number; + fixedtime: number; + fixedtime_lapse: number; + globaltime: number; + inputs: any; + iteration: number; + last_link_id: number; + last_node_id: number; + last_update_time: number; + links: Record; + list_of_graphcanvas: LGraphCanvas[]; + outputs: any; + runningtime: number; + starttime: number; + status: typeof LGraph.STATUS_RUNNING | typeof LGraph.STATUS_STOPPED; + + private _nodes: LGraphNode[]; + private _groups: LGraphGroup[]; + private _nodes_by_id: Record; + /** nodes that are executable sorted in execution order */ + private _nodes_executable: + | (LGraphNode & { onExecute: NonNullable }[]) + | null; + /** nodes that contain onExecute */ + private _nodes_in_order: LGraphNode[]; + private _version: number; + + getSupportedTypes(): string[]; + /** Removes all nodes from this graph */ + clear(): void; + /** Attach Canvas to this graph */ + attachCanvas(graphCanvas: LGraphCanvas): void; + /** Detach Canvas to this graph */ + detachCanvas(graphCanvas: LGraphCanvas): void; + /** + * Starts running this graph every interval milliseconds. + * @param interval amount of milliseconds between executions, if 0 then it renders to the monitor refresh rate + */ + start(interval?: number): void; + /** Stops the execution loop of the graph */ + stop(): void; + /** + * Run N steps (cycles) of the graph + * @param num number of steps to run, default is 1 + */ + runStep(num?: number, do_not_catch_errors?: boolean): void; + /** + * Updates the graph execution order according to relevance of the nodes (nodes with only outputs have more relevance than + * nodes with only inputs. + */ + updateExecutionOrder(): void; + /** This is more internal, it computes the executable nodes in order and returns it */ + computeExecutionOrder(only_onExecute: boolean, set_level: any): T; + /** + * Returns all the nodes that could affect this one (ancestors) by crawling all the inputs recursively. + * It doesn't include the node itself + * @return an array with all the LGraphNodes that affect this node, in order of execution + */ + getAncestors(node: LGraphNode): LGraphNode[]; + /** + * Positions every node in a more readable manner + */ + arrange(margin?: number,layout?: string): void; + /** + * Returns the amount of time the graph has been running in milliseconds + * @return number of milliseconds the graph has been running + */ + getTime(): number; + + /** + * Returns the amount of time accumulated using the fixedtime_lapse var. This is used in context where the time increments should be constant + * @return number of milliseconds the graph has been running + */ + getFixedTime(): number; + + /** + * Returns the amount of time it took to compute the latest iteration. Take into account that this number could be not correct + * if the nodes are using graphical actions + * @return number of milliseconds it took the last cycle + */ + getElapsedTime(): number; + /** + * Sends an event to all the nodes, useful to trigger stuff + * @param eventName the name of the event (function to be called) + * @param params parameters in array format + */ + sendEventToAllNodes(eventName: string, params: any[], mode?: any): void; + + sendActionToCanvas(action: any, params: any[]): void; + /** + * Adds a new node instance to this graph + * @param node the instance of the node + */ + add(node: LGraphNode, skip_compute_order?: boolean): void; + /** + * Called when a new node is added + * @param node the instance of the node + */ + onNodeAdded(node: LGraphNode): void; + /** Removes a node from the graph */ + remove(node: LGraphNode): void; + /** Returns a node by its id. */ + getNodeById(id: number): LGraphNode | undefined; + /** + * Returns a list of nodes that matches a class + * @param classObject the class itself (not an string) + * @return a list with all the nodes of this type + */ + findNodesByClass( + classObject: LGraphNodeConstructor + ): T[]; + /** + * Returns a list of nodes that matches a type + * @param type the name of the node type + * @return a list with all the nodes of this type + */ + findNodesByType(type: string): T[]; + /** + * Returns the first node that matches a name in its title + * @param title the name of the node to search + * @return the node or null + */ + findNodeByTitle(title: string): T | null; + /** + * Returns a list of nodes that matches a name + * @param title the name of the node to search + * @return a list with all the nodes with this name + */ + findNodesByTitle(title: string): T[]; + /** + * Returns the top-most node in this position of the canvas + * @param x the x coordinate in canvas space + * @param y the y coordinate in canvas space + * @param nodes_list a list with all the nodes to search from, by default is all the nodes in the graph + * @return the node at this position or null + */ + getNodeOnPos( + x: number, + y: number, + node_list?: LGraphNode[], + margin?: number + ): T | null; + /** + * Returns the top-most group in that position + * @param x the x coordinate in canvas space + * @param y the y coordinate in canvas space + * @return the group or null + */ + getGroupOnPos(x: number, y: number): LGraphGroup | null; + + onAction(action: any, param: any): void; + trigger(action: any, param: any): void; + /** Tell this graph it has a global graph input of this type */ + addInput(name: string, type: string, value?: any): void; + /** Assign a data to the global graph input */ + setInputData(name: string, data: any): void; + /** Returns the current value of a global graph input */ + getInputData(name: string): T; + /** Changes the name of a global graph input */ + renameInput(old_name: string, name: string): false | undefined; + /** Changes the type of a global graph input */ + changeInputType(name: string, type: string): false | undefined; + /** Removes a global graph input */ + removeInput(name: string): boolean; + /** Creates a global graph output */ + addOutput(name: string, type: string, value: any): void; + /** Assign a data to the global output */ + setOutputData(name: string, value: string): void; + /** Returns the current value of a global graph output */ + getOutputData(name: string): T; + + /** Renames a global graph output */ + renameOutput(old_name: string, name: string): false | undefined; + /** Changes the type of a global graph output */ + changeOutputType(name: string, type: string): false | undefined; + /** Removes a global graph output */ + removeOutput(name: string): boolean; + triggerInput(name: string, value: any): void; + setCallback(name: string, func: (...args: any[]) => any): void; + beforeChange(info?: LGraphNode): void; + afterChange(info?: LGraphNode): void; + connectionChange(node: LGraphNode): void; + /** returns if the graph is in live mode */ + isLive(): boolean; + /** clears the triggered slot animation in all links (stop visual animation) */ + clearTriggeredSlots(): void; + /* Called when something visually changed (not the graph!) */ + change(): void; + setDirtyCanvas(fg: boolean, bg: boolean): void; + /** Destroys a link */ + removeLink(link_id: number): void; + /** Creates a Object containing all the info about this graph, it can be serialized */ + serialize(): T; + /** + * Configure a graph from a JSON string + * @param data configure a graph from a JSON string + * @returns if there was any error parsing + */ + configure(data: object, keep_old?: boolean): boolean | undefined; + load(url: string): void; +} + +export type SerializedLLink = [number, string, number, number, number, number]; +export declare class LLink { + id: number; + type: string; + origin_id: number; + origin_slot: number; + target_id: number; + target_slot: number; + constructor( + id: number, + type: string, + origin_id: number, + origin_slot: number, + target_id: number, + target_slot: number + ); + configure(o: LLink | SerializedLLink): void; + serialize(): SerializedLLink; +} + +export type SerializedLGraphNode = { + id: T["id"]; + type: T["type"]; + pos: T["pos"]; + size: T["size"]; + flags: T["flags"]; + mode: T["mode"]; + inputs: T["inputs"]; + outputs: T["outputs"]; + title: T["title"]; + properties: T["properties"]; + widgets_values?: IWidget["value"][]; +}; + +/** https://github.com/jagenjo/litegraph.js/blob/master/guides/README.md#lgraphnode */ +export declare class LGraphNode { + static title_color: string; + static title: string; + static type: null | string; + static widgets_up: boolean; + constructor(title?: string); + + title: string; + type: null | string; + size: Vector2; + graph: null | LGraph; + graph_version: number; + pos: Vector2; + is_selected: boolean; + mouseOver: boolean; + + id: number; + + //inputs available: array of inputs + inputs: INodeInputSlot[]; + outputs: INodeOutputSlot[]; + connections: any[]; + + //local data + properties: Record; + properties_info: any[]; + + flags: Partial<{ + collapsed: boolean + }>; + + color: string; + bgcolor: string; + boxcolor: string; + shape: + | typeof LiteGraph.BOX_SHAPE + | typeof LiteGraph.ROUND_SHAPE + | typeof LiteGraph.CIRCLE_SHAPE + | typeof LiteGraph.CARD_SHAPE + | typeof LiteGraph.ARROW_SHAPE; + + serialize_widgets: boolean; + skip_list: boolean; + + /** Used in `LGraphCanvas.onMenuNodeMode` */ + mode?: + | typeof LiteGraph.ON_EVENT + | typeof LiteGraph.ON_TRIGGER + | typeof LiteGraph.NEVER + | typeof LiteGraph.ALWAYS; + + /** If set to true widgets do not start after the slots */ + widgets_up: boolean; + /** widgets start at y distance from the top of the node */ + widgets_start_y: number; + /** if you render outside the node, it will be clipped */ + clip_area: boolean; + /** if set to false it wont be resizable with the mouse */ + resizable: boolean; + /** slots are distributed horizontally */ + horizontal: boolean; + /** if true, the node will show the bgcolor as 'red' */ + has_errors?: boolean; + + /** configure a node from an object containing the serialized info */ + configure(info: SerializedLGraphNode): void; + /** serialize the content */ + serialize(): SerializedLGraphNode; + /** Creates a clone of this node */ + clone(): this; + /** serialize and stringify */ + toString(): string; + /** get the title string */ + getTitle(): string; + /** sets the value of a property */ + setProperty(name: string, value: any): void; + /** sets the output data */ + setOutputData(slot: number, data: any): void; + /** sets the output data */ + setOutputDataType(slot: number, type: string): void; + /** + * Retrieves the input data (data traveling through the connection) from one slot + * @param slot + * @param force_update if set to true it will force the connected node of this slot to output data into this link + * @return data or if it is not connected returns undefined + */ + getInputData(slot: number, force_update?: boolean): T; + /** + * Retrieves the input data type (in case this supports multiple input types) + * @param slot + * @return datatype in string format + */ + getInputDataType(slot: number): string; + /** + * Retrieves the input data from one slot using its name instead of slot number + * @param slot_name + * @param force_update if set to true it will force the connected node of this slot to output data into this link + * @return data or if it is not connected returns null + */ + getInputDataByName(slot_name: string, force_update?: boolean): T; + /** tells you if there is a connection in one input slot */ + isInputConnected(slot: number): boolean; + /** tells you info about an input connection (which node, type, etc) */ + getInputInfo( + slot: number + ): { link: number; name: string; type: string | 0 } | null; + /** returns the node connected in the input slot */ + getInputNode(slot: number): LGraphNode | null; + /** returns the value of an input with this name, otherwise checks if there is a property with that name */ + getInputOrProperty(name: string): T; + /** tells you the last output data that went in that slot */ + getOutputData(slot: number): T | null; + /** tells you info about an output connection (which node, type, etc) */ + getOutputInfo( + slot: number + ): { name: string; type: string; links: number[] } | null; + /** tells you if there is a connection in one output slot */ + isOutputConnected(slot: number): boolean; + /** tells you if there is any connection in the output slots */ + isAnyOutputConnected(): boolean; + /** retrieves all the nodes connected to this output slot */ + getOutputNodes(slot: number): LGraphNode[]; + /** Triggers an event in this node, this will trigger any output with the same name */ + trigger(action: string, param: any): void; + /** + * Triggers an slot event in this node + * @param slot the index of the output slot + * @param param + * @param link_id in case you want to trigger and specific output link in a slot + */ + triggerSlot(slot: number, param: any, link_id?: number): void; + /** + * clears the trigger slot animation + * @param slot the index of the output slot + * @param link_id in case you want to trigger and specific output link in a slot + */ + clearTriggeredSlot(slot: number, link_id?: number): void; + /** + * add a new property to this node + * @param name + * @param default_value + * @param type string defining the output type ("vec3","number",...) + * @param extra_info this can be used to have special properties of the property (like values, etc) + */ + addProperty( + name: string, + default_value: any, + type: string, + extra_info?: object + ): T; + /** + * add a new output slot to use in this node + * @param name + * @param type string defining the output type ("vec3","number",...) + * @param extra_info this can be used to have special properties of an output (label, special color, position, etc) + */ + addOutput( + name: string, + type: string | -1, + extra_info?: Partial + ): INodeOutputSlot; + /** + * add a new output slot to use in this node + * @param array of triplets like [[name,type,extra_info],[...]] + */ + addOutputs( + array: [string, string | -1, Partial | undefined][] + ): void; + /** remove an existing output slot */ + removeOutput(slot: number): void; + /** + * add a new input slot to use in this node + * @param name + * @param type string defining the input type ("vec3","number",...), it its a generic one use 0 + * @param extra_info this can be used to have special properties of an input (label, color, position, etc) + */ + addInput( + name: string, + type: string | -1, + extra_info?: Partial + ): INodeInputSlot; + /** + * add several new input slots in this node + * @param array of triplets like [[name,type,extra_info],[...]] + */ + addInputs( + array: [string, string | -1, Partial | undefined][] + ): void; + /** remove an existing input slot */ + removeInput(slot: number): void; + /** + * add an special connection to this node (used for special kinds of graphs) + * @param name + * @param type string defining the input type ("vec3","number",...) + * @param pos position of the connection inside the node + * @param direction if is input or output + */ + addConnection( + name: string, + type: string, + pos: Vector2, + direction: string + ): { + name: string; + type: string; + pos: Vector2; + direction: string; + links: null; + }; + setValue(v: any): void; + /** computes the size of a node according to its inputs and output slots */ + computeSize(): [number, number]; + /** + * https://github.com/jagenjo/litegraph.js/blob/master/guides/README.md#node-widgets + * @return created widget + */ + addWidget( + type: T["type"], + name: string, + value: T["value"], + callback?: WidgetCallback | string, + options?: T["options"] + ): T; + + addCustomWidget(customWidget: T): T; + + /** + * returns the bounding of the object, used for rendering purposes + * @return [x, y, width, height] + */ + getBounding(): Vector4; + /** checks if a point is inside the shape of a node */ + isPointInside( + x: number, + y: number, + margin?: number, + skipTitle?: boolean + ): boolean; + /** checks if a point is inside a node slot, and returns info about which slot */ + getSlotInPosition( + x: number, + y: number + ): { + input?: INodeInputSlot; + output?: INodeOutputSlot; + slot: number; + link_pos: Vector2; + }; + /** + * returns the input slot with a given name (used for dynamic slots), -1 if not found + * @param name the name of the slot + * @return the slot (-1 if not found) + */ + findInputSlot(name: string): number; + /** + * returns the output slot with a given name (used for dynamic slots), -1 if not found + * @param name the name of the slot + * @return the slot (-1 if not found) + */ + findOutputSlot(name: string): number; + /** + * connect this node output to the input of another node + * @param slot (could be the number of the slot or the string with the name of the slot) + * @param targetNode the target node + * @param targetSlot the input slot of the target node (could be the number of the slot or the string with the name of the slot, or -1 to connect a trigger) + * @return {Object} the link_info is created, otherwise null + */ + connect( + slot: number | string, + targetNode: LGraphNode, + targetSlot: number | string + ): T | null; + /** + * disconnect one output to an specific node + * @param slot (could be the number of the slot or the string with the name of the slot) + * @param target_node the target node to which this slot is connected [Optional, if not target_node is specified all nodes will be disconnected] + * @return if it was disconnected successfully + */ + disconnectOutput(slot: number | string, targetNode?: LGraphNode): boolean; + /** + * disconnect one input + * @param slot (could be the number of the slot or the string with the name of the slot) + * @return if it was disconnected successfully + */ + disconnectInput(slot: number | string): boolean; + /** + * returns the center of a connection point in canvas coords + * @param is_input true if if a input slot, false if it is an output + * @param slot (could be the number of the slot or the string with the name of the slot) + * @param out a place to store the output, to free garbage + * @return the position + **/ + getConnectionPos( + is_input: boolean, + slot: number | string, + out?: Vector2 + ): Vector2; + /** Force align to grid */ + alignToGrid(): void; + /** Console output */ + trace(msg: string): void; + /** Forces to redraw or the main canvas (LGraphNode) or the bg canvas (links) */ + setDirtyCanvas(fg: boolean, bg: boolean): void; + loadImage(url: string): void; + /** Allows to get onMouseMove and onMouseUp events even if the mouse is out of focus */ + captureInput(v: any): void; + /** Collapse the node to make it smaller on the canvas */ + collapse(force: boolean): void; + /** Forces the node to do not move or realign on Z */ + pin(v?: boolean): void; + localToScreen(x: number, y: number, graphCanvas: LGraphCanvas): Vector2; + + // https://github.com/jagenjo/litegraph.js/blob/master/guides/README.md#custom-node-appearance + onDrawBackground?( + ctx: CanvasRenderingContext2D, + canvas: HTMLCanvasElement + ): void; + onDrawForeground?( + ctx: CanvasRenderingContext2D, + canvas: HTMLCanvasElement + ): void; + + // https://github.com/jagenjo/litegraph.js/blob/master/guides/README.md#custom-node-behaviour + onMouseDown?( + event: MouseEvent, + pos: Vector2, + graphCanvas: LGraphCanvas + ): void; + onMouseMove?( + event: MouseEvent, + pos: Vector2, + graphCanvas: LGraphCanvas + ): void; + onMouseUp?( + event: MouseEvent, + pos: Vector2, + graphCanvas: LGraphCanvas + ): void; + onMouseEnter?( + event: MouseEvent, + pos: Vector2, + graphCanvas: LGraphCanvas + ): void; + onMouseLeave?( + event: MouseEvent, + pos: Vector2, + graphCanvas: LGraphCanvas + ): void; + onKey?(event: KeyboardEvent, pos: Vector2, graphCanvas: LGraphCanvas): void; + + /** Called by `LGraphCanvas.selectNodes` */ + onSelected?(): void; + /** Called by `LGraphCanvas.deselectNode` */ + onDeselected?(): void; + /** Called by `LGraph.runStep` `LGraphNode.getInputData` */ + onExecute?(): void; + /** Called by `LGraph.serialize` */ + onSerialize?(o: SerializedLGraphNode): void; + /** Called by `LGraph.configure` */ + onConfigure?(o: SerializedLGraphNode): void; + /** + * when added to graph (warning: this is called BEFORE the node is configured when loading) + * Called by `LGraph.add` + */ + onAdded?(graph: LGraph): void; + /** + * when removed from graph + * Called by `LGraph.remove` `LGraph.clear` + */ + onRemoved?(): void; + /** + * if returns false the incoming connection will be canceled + * Called by `LGraph.connect` + * @param inputIndex target input slot number + * @param outputType type of output slot + * @param outputSlot output slot object + * @param outputNode node containing the output + * @param outputIndex index of output slot + */ + onConnectInput?( + inputIndex: number, + outputType: INodeOutputSlot["type"], + outputSlot: INodeOutputSlot, + outputNode: LGraphNode, + outputIndex: number + ): boolean; + /** + * if returns false the incoming connection will be canceled + * Called by `LGraph.connect` + * @param outputIndex target output slot number + * @param inputType type of input slot + * @param inputSlot input slot object + * @param inputNode node containing the input + * @param inputIndex index of input slot + */ + onConnectOutput?( + outputIndex: number, + inputType: INodeInputSlot["type"], + inputSlot: INodeInputSlot, + inputNode: LGraphNode, + inputIndex: number + ): boolean; + + /** + * Called just before connection (or disconnect - if input is linked). + * A convenient place to switch to another input, or create new one. + * This allow for ability to automatically add slots if needed + * @param inputIndex + * @return selected input slot index, can differ from parameter value + */ + onBeforeConnectInput?( + inputIndex: number + ): number; + + /** a connection changed (new one or removed) (LiteGraph.INPUT or LiteGraph.OUTPUT, slot, true if connected, link_info, input_info or output_info ) */ + onConnectionsChange( + type: number, + slotIndex: number, + isConnected: boolean, + link: LLink, + ioSlot: (INodeOutputSlot | INodeInputSlot) + ): void; + + /** + * if returns false, will abort the `LGraphNode.setProperty` + * Called when a property is changed + * @param property + * @param value + * @param prevValue + */ + onPropertyChanged?(property: string, value: any, prevValue: any): void | boolean; + + /** Called by `LGraphCanvas.processContextMenu` */ + getMenuOptions?(graphCanvas: LGraphCanvas): ContextMenuItem[]; + getSlotMenuOptions?(slot: INodeSlot): ContextMenuItem[]; +} + +export type LGraphNodeConstructor = { + new (): T; +}; + +export type SerializedLGraphGroup = { + title: LGraphGroup["title"]; + bounding: LGraphGroup["_bounding"]; + color: LGraphGroup["color"]; + font: LGraphGroup["font"]; +}; +export declare class LGraphGroup { + title: string; + private _bounding: Vector4; + color: string; + font: string; + + configure(o: SerializedLGraphGroup): void; + serialize(): SerializedLGraphGroup; + move(deltaX: number, deltaY: number, ignoreNodes?: boolean): void; + recomputeInsideNodes(): void; + isPointInside: LGraphNode["isPointInside"]; + setDirtyCanvas: LGraphNode["setDirtyCanvas"]; +} + +export declare class DragAndScale { + constructor(element?: HTMLElement, skipEvents?: boolean); + offset: [number, number]; + scale: number; + max_scale: number; + min_scale: number; + onredraw: Function | null; + enabled: boolean; + last_mouse: Vector2; + element: HTMLElement | null; + visible_area: Vector4; + bindEvents(element: HTMLElement): void; + computeVisibleArea(): void; + onMouse(e: MouseEvent): void; + toCanvasContext(ctx: CanvasRenderingContext2D): void; + convertOffsetToCanvas(pos: Vector2): Vector2; + convertCanvasToOffset(pos: Vector2): Vector2; + mouseDrag(x: number, y: number): void; + changeScale(value: number, zooming_center?: Vector2): void; + changeDeltaScale(value: number, zooming_center?: Vector2): void; + reset(): void; +} + +/** + * This class is in charge of rendering one graph inside a canvas. And provides all the interaction required. + * Valid callbacks are: onNodeSelected, onNodeDeselected, onShowNodePanel, onNodeDblClicked + * + * @param canvas the canvas where you want to render (it accepts a selector in string format or the canvas element itself) + * @param graph + * @param options { skip_rendering, autoresize } + */ +export declare class LGraphCanvas { + static node_colors: Record< + string, + { + color: string; + bgcolor: string; + groupcolor: string; + } + >; + static link_type_colors: Record; + static gradients: object; + static search_limit: number; + + static getFileExtension(url: string): string; + static decodeHTML(str: string): string; + + static onMenuCollapseAll(): void; + static onMenuNodeEdit(): void; + static onShowPropertyEditor( + item: any, + options: any, + e: any, + menu: any, + node: any + ): void; + /** Create menu for `Add Group` */ + static onGroupAdd: ContextMenuEventListener; + /** Create menu for `Add Node` */ + static onMenuAdd: ContextMenuEventListener; + static showMenuNodeOptionalInputs: ContextMenuEventListener; + static showMenuNodeOptionalOutputs: ContextMenuEventListener; + static onShowMenuNodeProperties: ContextMenuEventListener; + static onResizeNode: ContextMenuEventListener; + static onMenuNodeCollapse: ContextMenuEventListener; + static onMenuNodePin: ContextMenuEventListener; + static onMenuNodeMode: ContextMenuEventListener; + static onMenuNodeColors: ContextMenuEventListener; + static onMenuNodeShapes: ContextMenuEventListener; + static onMenuNodeRemove: ContextMenuEventListener; + static onMenuNodeClone: ContextMenuEventListener; + + constructor( + canvas: HTMLCanvasElement | string, + graph?: LGraph, + options?: { + skip_render?: boolean; + autoresize?: boolean; + } + ); + + static active_canvas: HTMLCanvasElement; + + allow_dragcanvas: boolean; + allow_dragnodes: boolean; + /** allow to control widgets, buttons, collapse, etc */ + allow_interaction: boolean; + /** allows to change a connection with having to redo it again */ + allow_reconnect_links: boolean; + /** allow selecting multi nodes without pressing extra keys */ + multi_select: boolean; + /** No effect */ + allow_searchbox: boolean; + always_render_background: boolean; + autoresize?: boolean; + background_image: string; + bgcanvas: HTMLCanvasElement; + bgctx: CanvasRenderingContext2D; + canvas: HTMLCanvasElement; + canvas_mouse: Vector2; + clear_background: boolean; + connecting_node: LGraphNode | null; + connections_width: number; + ctx: CanvasRenderingContext2D; + current_node: LGraphNode | null; + default_connection_color: { + input_off: string; + input_on: string; + output_off: string; + output_on: string; + }; + default_link_color: string; + dirty_area: Vector4 | null; + dirty_bgcanvas?: boolean; + dirty_canvas?: boolean; + drag_mode: boolean; + dragging_canvas: boolean; + dragging_rectangle: Vector4 | null; + ds: DragAndScale; + /** used for transition */ + editor_alpha: number; + filter: any; + fps: number; + frame: number; + graph: LGraph; + highlighted_links: Record; + highquality_render: boolean; + inner_text_font: string; + is_rendering: boolean; + last_draw_time: number; + last_mouse: Vector2; + /** + * Possible duplicated with `last_mouse` + * https://github.com/jagenjo/litegraph.js/issues/70 + */ + last_mouse_position: Vector2; + /** Timestamp of last mouse click, defaults to 0 */ + last_mouseclick: number; + links_render_mode: + | typeof LiteGraph.STRAIGHT_LINK + | typeof LiteGraph.LINEAR_LINK + | typeof LiteGraph.SPLINE_LINK; + live_mode: boolean; + node_capturing_input: LGraphNode | null; + node_dragged: LGraphNode | null; + node_in_panel: LGraphNode | null; + node_over: LGraphNode | null; + node_title_color: string; + node_widget: [LGraphNode, IWidget] | null; + /** Called by `LGraphCanvas.drawBackCanvas` */ + onDrawBackground: + | ((ctx: CanvasRenderingContext2D, visibleArea: Vector4) => void) + | null; + /** Called by `LGraphCanvas.drawFrontCanvas` */ + onDrawForeground: + | ((ctx: CanvasRenderingContext2D, visibleArea: Vector4) => void) + | null; + onDrawOverlay: ((ctx: CanvasRenderingContext2D) => void) | null; + /** Called by `LGraphCanvas.processMouseDown` */ + onMouse: ((event: MouseEvent) => boolean) | null; + /** Called by `LGraphCanvas.drawFrontCanvas` and `LGraphCanvas.drawLinkTooltip` */ + onDrawLinkTooltip: ((ctx: CanvasRenderingContext2D, link: LLink, _this: this) => void) | null; + /** Called by `LGraphCanvas.selectNodes` */ + onNodeMoved: ((node: LGraphNode) => void) | null; + /** Called by `LGraphCanvas.processNodeSelected` */ + onNodeSelected: ((node: LGraphNode) => void) | null; + /** Called by `LGraphCanvas.deselectNode` */ + onNodeDeselected: ((node: LGraphNode) => void) | null; + /** Called by `LGraphCanvas.processNodeDblClicked` */ + onShowNodePanel: ((node: LGraphNode) => void) | null; + /** Called by `LGraphCanvas.processNodeDblClicked` */ + onNodeDblClicked: ((node: LGraphNode) => void) | null; + /** Called by `LGraphCanvas.selectNodes` */ + onSelectionChange: ((nodes: Record) => void) | null; + /** Called by `LGraphCanvas.showSearchBox` */ + onSearchBox: + | (( + helper: Element, + value: string, + graphCanvas: LGraphCanvas + ) => string[]) + | null; + onSearchBoxSelection: + | ((name: string, event: MouseEvent, graphCanvas: LGraphCanvas) => void) + | null; + pause_rendering: boolean; + render_canvas_border: boolean; + render_collapsed_slots: boolean; + render_connection_arrows: boolean; + render_connections_border: boolean; + render_connections_shadows: boolean; + render_curved_connections: boolean; + render_execution_order: boolean; + render_only_selected: boolean; + render_shadows: boolean; + render_title_colored: boolean; + round_radius: number; + selected_group: null | LGraphGroup; + selected_group_resizing: boolean; + selected_nodes: Record; + show_info: boolean; + title_text_font: string; + /** set to true to render title bar with gradients */ + use_gradients: boolean; + visible_area: DragAndScale["visible_area"]; + visible_links: LLink[]; + visible_nodes: LGraphNode[]; + zoom_modify_alpha: boolean; + + /** clears all the data inside */ + clear(): void; + /** assigns a graph, you can reassign graphs to the same canvas */ + setGraph(graph: LGraph, skipClear?: boolean): void; + /** opens a graph contained inside a node in the current graph */ + openSubgraph(graph: LGraph): void; + /** closes a subgraph contained inside a node */ + closeSubgraph(): void; + /** assigns a canvas */ + setCanvas(canvas: HTMLCanvasElement, skipEvents?: boolean): void; + /** binds mouse, keyboard, touch and drag events to the canvas */ + bindEvents(): void; + /** unbinds mouse events from the canvas */ + unbindEvents(): void; + + /** + * this function allows to render the canvas using WebGL instead of Canvas2D + * this is useful if you plant to render 3D objects inside your nodes, it uses litegl.js for webgl and canvas2DtoWebGL to emulate the Canvas2D calls in webGL + **/ + enableWebGL(): void; + + /** + * marks as dirty the canvas, this way it will be rendered again + * @param fg if the foreground canvas is dirty (the one containing the nodes) + * @param bg if the background canvas is dirty (the one containing the wires) + */ + setDirty(fg: boolean, bg: boolean): void; + + /** + * Used to attach the canvas in a popup + * @return the window where the canvas is attached (the DOM root node) + */ + getCanvasWindow(): Window; + /** starts rendering the content of the canvas when needed */ + startRendering(): void; + /** stops rendering the content of the canvas (to save resources) */ + stopRendering(): void; + + processMouseDown(e: MouseEvent): boolean | undefined; + processMouseMove(e: MouseEvent): boolean | undefined; + processMouseUp(e: MouseEvent): boolean | undefined; + processMouseWheel(e: MouseEvent): boolean | undefined; + + /** returns true if a position (in graph space) is on top of a node little corner box */ + isOverNodeBox(node: LGraphNode, canvasX: number, canvasY: number): boolean; + /** returns true if a position (in graph space) is on top of a node input slot */ + isOverNodeInput( + node: LGraphNode, + canvasX: number, + canvasY: number, + slotPos: Vector2 + ): boolean; + + /** process a key event */ + processKey(e: KeyboardEvent): boolean | undefined; + + copyToClipboard(): void; + pasteFromClipboard(): void; + processDrop(e: DragEvent): void; + checkDropItem(e: DragEvent): void; + processNodeDblClicked(n: LGraphNode): void; + processNodeSelected(n: LGraphNode, e: MouseEvent): void; + processNodeDeselected(node: LGraphNode): void; + + /** selects a given node (or adds it to the current selection) */ + selectNode(node: LGraphNode, add?: boolean): void; + /** selects several nodes (or adds them to the current selection) */ + selectNodes(nodes?: LGraphNode[], add?: boolean): void; + /** removes a node from the current selection */ + deselectNode(node: LGraphNode): void; + /** removes all nodes from the current selection */ + deselectAllNodes(): void; + /** deletes all nodes in the current selection from the graph */ + deleteSelectedNodes(): void; + + /** centers the camera on a given node */ + centerOnNode(node: LGraphNode): void; + /** changes the zoom level of the graph (default is 1), you can pass also a place used to pivot the zoom */ + setZoom(value: number, center: Vector2): void; + /** brings a node to front (above all other nodes) */ + bringToFront(node: LGraphNode): void; + /** sends a node to the back (below all other nodes) */ + sendToBack(node: LGraphNode): void; + /** checks which nodes are visible (inside the camera area) */ + computeVisibleNodes(nodes: LGraphNode[]): LGraphNode[]; + /** renders the whole canvas content, by rendering in two separated canvas, one containing the background grid and the connections, and one containing the nodes) */ + draw(forceFG?: boolean, forceBG?: boolean): void; + /** draws the front canvas (the one containing all the nodes) */ + drawFrontCanvas(): void; + /** draws some useful stats in the corner of the canvas */ + renderInfo(ctx: CanvasRenderingContext2D, x: number, y: number): void; + /** draws the back canvas (the one containing the background and the connections) */ + drawBackCanvas(): void; + /** draws the given node inside the canvas */ + drawNode(node: LGraphNode, ctx: CanvasRenderingContext2D): void; + /** draws graphic for node's slot */ + drawSlotGraphic(ctx: CanvasRenderingContext2D, pos: number[], shape: SlotShape, horizontal: boolean): void; + /** draws the shape of the given node in the canvas */ + drawNodeShape( + node: LGraphNode, + ctx: CanvasRenderingContext2D, + size: [number, number], + fgColor: string, + bgColor: string, + selected: boolean, + mouseOver: boolean + ): void; + /** draws every connection visible in the canvas */ + drawConnections(ctx: CanvasRenderingContext2D): void; + /** + * draws a link between two points + * @param a start pos + * @param b end pos + * @param link the link object with all the link info + * @param skipBorder ignore the shadow of the link + * @param flow show flow animation (for events) + * @param color the color for the link + * @param startDir the direction enum + * @param endDir the direction enum + * @param numSublines number of sublines (useful to represent vec3 or rgb) + **/ + renderLink( + a: Vector2, + b: Vector2, + link: object, + skipBorder: boolean, + flow: boolean, + color?: string, + startDir?: number, + endDir?: number, + numSublines?: number + ): void; + + computeConnectionPoint( + a: Vector2, + b: Vector2, + t: number, + startDir?: number, + endDir?: number + ): void; + + drawExecutionOrder(ctx: CanvasRenderingContext2D): void; + /** draws the widgets stored inside a node */ + drawNodeWidgets( + node: LGraphNode, + posY: number, + ctx: CanvasRenderingContext2D, + activeWidget: object + ): void; + /** process an event on widgets */ + processNodeWidgets( + node: LGraphNode, + pos: Vector2, + event: Event, + activeWidget: object + ): void; + /** draws every group area in the background */ + drawGroups(canvas: any, ctx: CanvasRenderingContext2D): void; + adjustNodesSize(): void; + /** resizes the canvas to a given size, if no size is passed, then it tries to fill the parentNode */ + resize(width?: number, height?: number): void; + /** + * switches to live mode (node shapes are not rendered, only the content) + * this feature was designed when graphs where meant to create user interfaces + **/ + switchLiveMode(transition?: boolean): void; + onNodeSelectionChange(): void; + touchHandler(event: TouchEvent): void; + + showLinkMenu(link: LLink, e: any): false; + prompt( + title: string, + value: any, + callback: Function, + event: any + ): HTMLDivElement; + showSearchBox(event?: MouseEvent): void; + showEditPropertyValue(node: LGraphNode, property: any, options: any): void; + createDialog( + html: string, + options?: { position?: Vector2; event?: MouseEvent } + ): void; + + convertOffsetToCanvas: DragAndScale["convertOffsetToCanvas"]; + convertCanvasToOffset: DragAndScale["convertCanvasToOffset"]; + /** converts event coordinates from canvas2D to graph coordinates */ + convertEventToCanvasOffset(e: MouseEvent): Vector2; + /** adds some useful properties to a mouse event, like the position in graph coordinates */ + adjustMouseEvent(e: MouseEvent): void; + + getCanvasMenuOptions(): ContextMenuItem[]; + getNodeMenuOptions(node: LGraphNode): ContextMenuItem[]; + getGroupMenuOptions(): ContextMenuItem[]; + /** Called by `getCanvasMenuOptions`, replace default options */ + getMenuOptions?(): ContextMenuItem[]; + /** Called by `getCanvasMenuOptions`, append to default options */ + getExtraMenuOptions?(): ContextMenuItem[]; + /** Called when mouse right click */ + processContextMenu(node: LGraphNode, event: Event): void; +} + +declare class ContextMenu { + static trigger( + element: HTMLElement, + event_name: string, + params: any, + origin: any + ): void; + static isCursorOverElement(event: MouseEvent, element: HTMLElement): void; + static closeAllContextMenus(window: Window): void; + constructor(values: ContextMenuItem[], options?: IContextMenuOptions, window?: Window); + options: IContextMenuOptions; + parentMenu?: ContextMenu; + lock: boolean; + current_submenu?: ContextMenu; + addItem( + name: string, + value: ContextMenuItem, + options?: IContextMenuOptions + ): void; + close(e?: MouseEvent, ignore_parent_menu?: boolean): void; + getTopMenu(): void; + getFirstEvent(): void; +} + +declare global { + interface CanvasRenderingContext2D { + /** like rect but rounded corners */ + roundRect( + x: number, + y: number, + width: number, + height: number, + radius: number, + radiusLow: number + ): void; + } + + interface Math { + clamp(v: number, min: number, max: number): number; + } +} diff --git a/web/user.css b/web/user.css new file mode 100644 index 0000000000000000000000000000000000000000..8b1af38689e5853fb065714d6a6d322c52f17e72 --- /dev/null +++ b/web/user.css @@ -0,0 +1 @@ +/* Put custom styles here */ \ No newline at end of file