import torch import contextlib import math from ldm_patched.modules import model_management from ldm_patched.ldm.util import instantiate_from_config from ldm_patched.ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine import yaml import ldm_patched.modules.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 ldm_patched.modules.model_patcher import ldm_patched.modules.lora import ldm_patched.t2ia.adapter import ldm_patched.modules.supported_models_base import ldm_patched.taesd.taesd 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("extra", 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 = ldm_patched.modules.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 = {} if model is not None: key_map = ldm_patched.modules.lora.model_lora_keys_unet(model.model, key_map) if clip is not None: key_map = ldm_patched.modules.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) loaded = ldm_patched.modules.lora.load_lora(lora, key_map) if model is not None: new_modelpatcher = model.clone() k = new_modelpatcher.add_patches(loaded, strength_model) else: k = () new_modelpatcher = None if clip is not None: new_clip = clip.clone() k1 = new_clip.add_patches(loaded, strength_clip) else: k1 = () new_clip = None 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 params['dtype'] = model_management.text_encoder_dtype(load_device) self.cond_stage_model = clip(**(params)) self.tokenizer = tokenizer(embedding_directory=embedding_directory) self.patcher = ldm_patched.modules.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, sd=None, device=None, config=None, dtype=None): if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower) self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) if config is None: if "decoder.mid.block_1.mix_factor" in sd: encoder_config = {'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} decoder_config = encoder_config.copy() decoder_config["video_kernel_size"] = [3, 1, 1] decoder_config["alpha"] = 0.0 self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "ldm_patched.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, decoder_config={'target': "ldm_patched.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) elif "taesd_decoder.1.weight" in sd: self.first_stage_model = ldm_patched.taesd.taesd.TAESD() else: #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=ddconfig, embed_dim=4) else: self.first_stage_model = AutoencoderKL(**(config['params'])) self.first_stage_model = self.first_stage_model.eval() m, u = self.first_stage_model.load_state_dict(sd, strict=False) if len(m) > 0: print("Missing VAE keys", m) if len(u) > 0: print("Leftover VAE keys", u) if device is None: device = model_management.vae_device() self.device = device offload_device = model_management.vae_offload_device() if dtype is None: dtype = model_management.vae_dtype() self.vae_dtype = dtype self.first_stage_model.to(self.vae_dtype) self.output_device = model_management.intermediate_device() self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): steps = samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = ldm_patched.modules.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(( (ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar) + ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, output_device=self.output_device, pbar = pbar) + ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, output_device=self.output_device, 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] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) steps += pixel_samples.shape[0] * ldm_patched.modules.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] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) pbar = ldm_patched.modules.utils.ProgressBar(steps) encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float() samples = ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar) samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar) samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, output_device=self.output_device, pbar=pbar) samples /= 3.0 return samples def decode(self, samples_in): try: memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) model_management.load_models_gpu([self.patcher], memory_required=memory_used) 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=self.output_device) 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).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0) 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) pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) return pixel_samples def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16): model_management.load_model_gpu(self.patcher) output = self.decode_tiled_(samples, tile_x, tile_y, overlap) return output.movedim(1,-1) def encode(self, pixel_samples): pixel_samples = pixel_samples.movedim(-1,1) try: memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) model_management.load_models_gpu([self.patcher], memory_required=memory_used) 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=self.output_device) 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).to(self.output_device).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) return samples def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): model_management.load_model_gpu(self.patcher) pixel_samples = pixel_samples.movedim(-1,1) samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap) 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 = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) keys = model_data.keys() if "style_embedding" in keys: model = ldm_patched.t2ia.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(ldm_patched.modules.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] = ldm_patched.modules.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 = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) model = gligen.load_gligen(data) if model_management.should_use_fp16(): model = model.half() return ldm_patched.modules.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.pop("use_fp16") if fp16: unet_config["dtype"] = torch.float16 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 = ldm_patched.modules.utils.load_torch_file(ckpt_path) class EmptyClass: pass model_config = ldm_patched.modules.supported_models_base.BASE({}) from . import latent_formats model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor) model_config.unet_config = model_detection.convert_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: vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(state_dict, {"first_stage_model.": ""}, filter_keys=True) vae = VAE(sd=vae_sd, config=vae_config) 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 clip = CLIP(clip_target, embedding_directory=embedding_directory) w.cond_stage_model = clip.cond_stage_model.clip_h 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.clip_l load_clip_weights(w, state_dict) return (ldm_patched.modules.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, output_model=True): sd = ldm_patched.modules.utils.load_torch_file(ckpt_path) sd_keys = sd.keys() clip = None clipvision = None vae = None model = None model_patcher = None clip_target = None parameters = ldm_patched.modules.utils.calculate_parameters(sd, "model.diffusion_model.") unet_dtype = model_management.unet_dtype(model_params=parameters) load_device = model_management.get_torch_device() manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) class WeightsLoader(torch.nn.Module): pass model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype) model_config.set_manual_cast(manual_cast_dtype) 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) if output_model: inital_load_device = model_management.unet_inital_load_device(parameters, unet_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_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True) vae_sd = model_config.process_vae_state_dict(vae_sd) vae = VAE(sd=vae_sd) if output_clip: w = WeightsLoader() clip_target = model_config.clip_target() if clip_target is not None: 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) if output_model: model_patcher = ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_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_state_dict(sd): #load unet in diffusers format parameters = ldm_patched.modules.utils.calculate_parameters(sd) unet_dtype = model_management.unet_dtype(model_params=parameters) load_device = model_management.get_torch_device() manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) if "input_blocks.0.0.weight" in sd: #ldm model_config = model_detection.model_config_from_unet(sd, "", unet_dtype) if model_config is None: return None new_sd = sd else: #diffusers model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype) if model_config is None: return None diffusers_keys = ldm_patched.modules.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_config.set_manual_cast(manual_cast_dtype) model = model_config.get_model(new_sd, "") model = model.to(offload_device) model.load_model_weights(new_sd, "") left_over = sd.keys() if len(left_over) > 0: print("left over keys in unet:", left_over) return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) def load_unet(unet_path): sd = ldm_patched.modules.utils.load_torch_file(unet_path) model = load_unet_state_dict(sd) if model is None: print("ERROR UNSUPPORTED UNET", unet_path) raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) return model 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()) ldm_patched.modules.utils.save_torch_file(sd, output_path, metadata=metadata)