import os import einops from omegaconf import OmegaConf import torch import torch as th import torch.nn as nn from modules import devices, lowvram, shared from ldm.modules.diffusionmodules.util import ( conv_nd, linear, zero_module, timestep_embedding, ) from ldm.modules.attention import SpatialTransformer from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock from ldm.util import exists def load_state_dict(ckpt_path, location='cpu'): _, extension = os.path.splitext(ckpt_path) if extension.lower() == ".safetensors": import safetensors.torch state_dict = safetensors.torch.load_file(ckpt_path, device=location) else: state_dict = get_state_dict(torch.load( ckpt_path, map_location=torch.device(location))) state_dict = get_state_dict(state_dict) print(f'Loaded state_dict from [{ckpt_path}]') return state_dict def get_state_dict(d): return d.get('state_dict', d) def align(hint, size): b, c, h1, w1 = hint.shape h, w = size if h != h1 or w != w1: hint = torch.nn.functional.interpolate(hint, size=size, mode="nearest") return hint def get_node_name(name, parent_name): if len(name) <= len(parent_name): return False, '' p = name[:len(parent_name)] if p != parent_name: return False, '' return True, name[len(parent_name):] class PlugableControlModel(nn.Module): def __init__(self, model_path, config_path, weight=1.0, lowvram=False, base_model=None) -> None: super().__init__() # temp code config_path = "/mnt/workspace/stable-diffusion-webui/extensions/sd-webui-controlnet/models/cldm_v15.yaml" print(config_path) config = OmegaConf.load(config_path) self.control_model = ControlNet(**config.model.params.control_stage_config.params) state_dict = load_state_dict(model_path) if any([k.startswith("control_model.") for k, v in state_dict.items()]): is_diff_model = 'difference' in state_dict transfer_ctrl_opt = shared.opts.data.get("control_net_control_transfer", False) and \ any([k.startswith("model.diffusion_model.") for k, v in state_dict.items()]) if (is_diff_model or transfer_ctrl_opt) and base_model is not None: # apply transfer control - https://github.com/lllyasviel/ControlNet/blob/main/tool_transfer_control.py unet_state_dict = base_model.state_dict() unet_state_dict_keys = unet_state_dict.keys() final_state_dict = {} counter = 0 for key in state_dict.keys(): if not key.startswith("control_model."): continue p = state_dict[key] is_control, node_name = get_node_name(key, 'control_') key_name = node_name.replace("model.", "") if is_control else key if key_name in unet_state_dict_keys: if is_diff_model: # transfer control by make difference in advance p_new = p + unet_state_dict[key_name].clone().cpu() else: # transfer control by calculate offsets from (delta = p + current_unet_encoder - frozen_unet_encoder) p_new = p + unet_state_dict[key_name].clone().cpu() - state_dict["model.diffusion_model."+key_name] counter += 1 else: p_new = p final_state_dict[key] = p_new print(f'Offset cloned: {counter} values') state_dict = final_state_dict state_dict = {k.replace("control_model.", ""): v for k, v in state_dict.items() if k.startswith("control_model.")} else: # assume that model is done by user pass self.control_model.load_state_dict(state_dict) self.lowvram = lowvram self.weight = weight self.only_mid_control = False self.control = None self.hint_cond = None if not self.lowvram: self.control_model.to(devices.get_device_for("controlnet")) def hook(self, model, parent_model): outer = self def forward(self, x, timesteps=None, context=None, **kwargs): only_mid_control = outer.only_mid_control # hires stuffs # note that this method may not works if hr_scale < 1.1 if abs(x.shape[-1] - outer.hint_cond.shape[-1] // 8) > 8: only_mid_control = shared.opts.data.get("control_net_only_midctrl_hires", True) # If you want to completely disable control net, uncomment this. # return self._original_forward(x, timesteps=timesteps, context=context, **kwargs) control = outer.control_model(x=x, hint=outer.hint_cond, timesteps=timesteps, context=context) assert timesteps is not None, ValueError(f"insufficient timestep: {timesteps}") hs = [] with torch.no_grad(): t_emb = timestep_embedding( timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) h = x.type(self.dtype) for module in self.input_blocks: h = module(h, emb, context) hs.append(h) h = self.middle_block(h, emb, context) h += control.pop() for i, module in enumerate(self.output_blocks): if only_mid_control: h = torch.cat([h, hs.pop()], dim=1) else: hs_input, control_input = hs.pop(), control.pop() h = align(h, hs_input.shape[-2:]) h = torch.cat([h, hs_input + control_input * outer.weight], dim=1) h = module(h, emb, context) h = h.type(x.dtype) return self.out(h) def forward2(*args, **kwargs): # webui will handle other compoments try: if shared.cmd_opts.lowvram: lowvram.send_everything_to_cpu() if self.lowvram: self.control_model.to(devices.get_device_for("controlnet")) return forward(*args, **kwargs) finally: if self.lowvram: self.control_model.cpu() model._original_forward = model.forward model.forward = forward2.__get__(model, UNetModel) def notify(self, cond_like, weight): self.hint_cond = cond_like self.weight = weight # print(self.hint_cond.shape) def restore(self, model): if not hasattr(model, "_original_forward"): # no such handle, ignore return model.forward = model._original_forward del model._original_forward 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, use_checkpoint=False, use_fp16=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 # custom support for prediction of discrete ids into codebook of first stage vq model n_embed=None, legacy=True, disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, use_linear_in_transformer=False, ): super().__init__() 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(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.use_checkpoint = use_checkpoint self.dtype = th.float16 if use_fp16 else th.float32 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( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)]) self.input_hint_block = TimestepEmbedSequential( conv_nd(dims, hint_channels, 16, 3, padding=1), nn.SiLU(), conv_nd(dims, 16, 16, 3, padding=1), nn.SiLU(), conv_nd(dims, 16, 32, 3, padding=1, stride=2), nn.SiLU(), conv_nd(dims, 32, 32, 3, padding=1), nn.SiLU(), conv_nd(dims, 32, 96, 3, padding=1, stride=2), nn.SiLU(), conv_nd(dims, 96, 96, 3, padding=1), nn.SiLU(), conv_nd(dims, 96, 256, 3, padding=1, stride=2), nn.SiLU(), zero_module(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, ) ] 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( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self.zero_convs.append(self.make_zero_conv(ch)) 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, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch ) ) ) ch = out_ch input_block_chans.append(ch) self.zero_convs.append(self.make_zero_conv(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, ), AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, # always uses a self-attn ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, use_checkpoint=use_checkpoint ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), ) self.middle_block_out = self.make_zero_conv(ch) self._feature_size += ch def make_zero_conv(self, channels): return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0))) def align(self, hint, h, w): c, h1, w1 = hint.shape if h != h1 or w != w1: hint = align(hint.unsqueeze(0), (h, w)) return hint.squeeze(0) return hint def forward(self, x, hint, timesteps, context, **kwargs): t_emb = timestep_embedding( timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) guided_hint = self.input_hint_block(hint, emb, context) outs = [] h1, w1 = x.shape[-2:] guided_hint = self.align(guided_hint, h1, w1) 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