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| import torch | |
| from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler | |
| from .tiler import TileWorker | |
| class ControlNetConditioningLayer(torch.nn.Module): | |
| def __init__(self, channels = (3, 16, 32, 96, 256, 320)): | |
| super().__init__() | |
| self.blocks = torch.nn.ModuleList([]) | |
| self.blocks.append(torch.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1)) | |
| self.blocks.append(torch.nn.SiLU()) | |
| for i in range(1, len(channels) - 2): | |
| self.blocks.append(torch.nn.Conv2d(channels[i], channels[i], kernel_size=3, padding=1)) | |
| self.blocks.append(torch.nn.SiLU()) | |
| self.blocks.append(torch.nn.Conv2d(channels[i], channels[i+1], kernel_size=3, padding=1, stride=2)) | |
| self.blocks.append(torch.nn.SiLU()) | |
| self.blocks.append(torch.nn.Conv2d(channels[-2], channels[-1], kernel_size=3, padding=1)) | |
| def forward(self, conditioning): | |
| for block in self.blocks: | |
| conditioning = block(conditioning) | |
| return conditioning | |
| class SDControlNet(torch.nn.Module): | |
| def __init__(self, global_pool=False): | |
| super().__init__() | |
| self.time_proj = Timesteps(320) | |
| self.time_embedding = torch.nn.Sequential( | |
| torch.nn.Linear(320, 1280), | |
| torch.nn.SiLU(), | |
| torch.nn.Linear(1280, 1280) | |
| ) | |
| self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) | |
| self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320)) | |
| self.blocks = torch.nn.ModuleList([ | |
| # CrossAttnDownBlock2D | |
| ResnetBlock(320, 320, 1280), | |
| AttentionBlock(8, 40, 320, 1, 768), | |
| PushBlock(), | |
| ResnetBlock(320, 320, 1280), | |
| AttentionBlock(8, 40, 320, 1, 768), | |
| PushBlock(), | |
| DownSampler(320), | |
| PushBlock(), | |
| # CrossAttnDownBlock2D | |
| ResnetBlock(320, 640, 1280), | |
| AttentionBlock(8, 80, 640, 1, 768), | |
| PushBlock(), | |
| ResnetBlock(640, 640, 1280), | |
| AttentionBlock(8, 80, 640, 1, 768), | |
| PushBlock(), | |
| DownSampler(640), | |
| PushBlock(), | |
| # CrossAttnDownBlock2D | |
| ResnetBlock(640, 1280, 1280), | |
| AttentionBlock(8, 160, 1280, 1, 768), | |
| PushBlock(), | |
| ResnetBlock(1280, 1280, 1280), | |
| AttentionBlock(8, 160, 1280, 1, 768), | |
| PushBlock(), | |
| DownSampler(1280), | |
| PushBlock(), | |
| # DownBlock2D | |
| ResnetBlock(1280, 1280, 1280), | |
| PushBlock(), | |
| ResnetBlock(1280, 1280, 1280), | |
| PushBlock(), | |
| # UNetMidBlock2DCrossAttn | |
| ResnetBlock(1280, 1280, 1280), | |
| AttentionBlock(8, 160, 1280, 1, 768), | |
| ResnetBlock(1280, 1280, 1280), | |
| PushBlock() | |
| ]) | |
| self.controlnet_blocks = torch.nn.ModuleList([ | |
| torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), | |
| torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), | |
| torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), | |
| torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), | |
| torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), | |
| torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False), | |
| torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False), | |
| torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), | |
| torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), | |
| torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), | |
| torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), | |
| torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), | |
| torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), | |
| ]) | |
| self.global_pool = global_pool | |
| def forward( | |
| self, | |
| sample, timestep, encoder_hidden_states, conditioning, | |
| tiled=False, tile_size=64, tile_stride=32, | |
| ): | |
| # 1. time | |
| time_emb = self.time_proj(timestep[None]).to(sample.dtype) | |
| time_emb = self.time_embedding(time_emb) | |
| time_emb = time_emb.repeat(sample.shape[0], 1) | |
| # 2. pre-process | |
| height, width = sample.shape[2], sample.shape[3] | |
| hidden_states = self.conv_in(sample) + self.controlnet_conv_in(conditioning) | |
| text_emb = encoder_hidden_states | |
| res_stack = [hidden_states] | |
| # 3. blocks | |
| for i, block in enumerate(self.blocks): | |
| if tiled and not isinstance(block, PushBlock): | |
| _, _, inter_height, _ = hidden_states.shape | |
| resize_scale = inter_height / height | |
| hidden_states = TileWorker().tiled_forward( | |
| lambda x: block(x, time_emb, text_emb, res_stack)[0], | |
| hidden_states, | |
| int(tile_size * resize_scale), | |
| int(tile_stride * resize_scale), | |
| tile_device=hidden_states.device, | |
| tile_dtype=hidden_states.dtype | |
| ) | |
| else: | |
| hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack) | |
| # 4. ControlNet blocks | |
| controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)] | |
| # pool | |
| if self.global_pool: | |
| controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack] | |
| return controlnet_res_stack | |
| def state_dict_converter(self): | |
| return SDControlNetStateDictConverter() | |
| class SDControlNetStateDictConverter: | |
| def __init__(self): | |
| pass | |
| def from_diffusers(self, state_dict): | |
| # architecture | |
| block_types = [ | |
| 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', | |
| 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', | |
| 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', | |
| 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', | |
| 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', | |
| 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler', | |
| 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', | |
| 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', | |
| 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock' | |
| ] | |
| # controlnet_rename_dict | |
| controlnet_rename_dict = { | |
| "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight", | |
| "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias", | |
| "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight", | |
| "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias", | |
| "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight", | |
| "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias", | |
| "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight", | |
| "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias", | |
| "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight", | |
| "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias", | |
| "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight", | |
| "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias", | |
| "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight", | |
| "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias", | |
| "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight", | |
| "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias", | |
| } | |
| # Rename each parameter | |
| name_list = sorted([name for name in state_dict]) | |
| rename_dict = {} | |
| block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} | |
| last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} | |
| for name in name_list: | |
| names = name.split(".") | |
| if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: | |
| pass | |
| elif name in controlnet_rename_dict: | |
| names = controlnet_rename_dict[name].split(".") | |
| elif names[0] == "controlnet_down_blocks": | |
| names[0] = "controlnet_blocks" | |
| elif names[0] == "controlnet_mid_block": | |
| names = ["controlnet_blocks", "12", names[-1]] | |
| elif names[0] in ["time_embedding", "add_embedding"]: | |
| if names[0] == "add_embedding": | |
| names[0] = "add_time_embedding" | |
| names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] | |
| elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: | |
| if names[0] == "mid_block": | |
| names.insert(1, "0") | |
| block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] | |
| block_type_with_id = ".".join(names[:4]) | |
| if block_type_with_id != last_block_type_with_id[block_type]: | |
| block_id[block_type] += 1 | |
| last_block_type_with_id[block_type] = block_type_with_id | |
| while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: | |
| block_id[block_type] += 1 | |
| block_type_with_id = ".".join(names[:4]) | |
| names = ["blocks", str(block_id[block_type])] + names[4:] | |
| if "ff" in names: | |
| ff_index = names.index("ff") | |
| component = ".".join(names[ff_index:ff_index+3]) | |
| component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] | |
| names = names[:ff_index] + [component] + names[ff_index+3:] | |
| if "to_out" in names: | |
| names.pop(names.index("to_out") + 1) | |
| else: | |
| raise ValueError(f"Unknown parameters: {name}") | |
| rename_dict[name] = ".".join(names) | |
| # Convert state_dict | |
| state_dict_ = {} | |
| for name, param in state_dict.items(): | |
| if ".proj_in." in name or ".proj_out." in name: | |
| param = param.squeeze() | |
| if rename_dict[name] in [ | |
| "controlnet_blocks.1.bias", "controlnet_blocks.2.bias", "controlnet_blocks.3.bias", "controlnet_blocks.5.bias", "controlnet_blocks.6.bias", | |
| "controlnet_blocks.8.bias", "controlnet_blocks.9.bias", "controlnet_blocks.10.bias", "controlnet_blocks.11.bias", "controlnet_blocks.12.bias" | |
| ]: | |
| continue | |
| state_dict_[rename_dict[name]] = param | |
| return state_dict_ | |
| def from_civitai(self, state_dict): | |
| if "mid_block.resnets.1.time_emb_proj.weight" in state_dict: | |
| # For controlnets in diffusers format | |
| return self.from_diffusers(state_dict) | |
| rename_dict = { | |
| "control_model.time_embed.0.weight": "time_embedding.0.weight", | |
| "control_model.time_embed.0.bias": "time_embedding.0.bias", | |
| "control_model.time_embed.2.weight": "time_embedding.2.weight", | |
| "control_model.time_embed.2.bias": "time_embedding.2.bias", | |
| "control_model.input_blocks.0.0.weight": "conv_in.weight", | |
| "control_model.input_blocks.0.0.bias": "conv_in.bias", | |
| "control_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", | |
| "control_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", | |
| "control_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", | |
| "control_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", | |
| "control_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", | |
| "control_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", | |
| "control_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", | |
| "control_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", | |
| "control_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", | |
| "control_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", | |
| "control_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight", | |
| "control_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias", | |
| "control_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight", | |
| "control_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.1.transformer_blocks.0.attn2.to_q.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.1.transformer_blocks.0.attn2.to_v.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.1.transformer_blocks.0.norm1.bias", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.1.transformer_blocks.0.norm2.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.1.transformer_blocks.0.norm2.bias", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.1.transformer_blocks.0.norm3.weight", | |
| "control_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.1.transformer_blocks.0.norm3.bias", | |
| "control_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight", | |
| "control_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias", | |
| "control_model.input_blocks.2.0.in_layers.0.weight": "blocks.3.norm1.weight", | |
| "control_model.input_blocks.2.0.in_layers.0.bias": "blocks.3.norm1.bias", | |
| "control_model.input_blocks.2.0.in_layers.2.weight": "blocks.3.conv1.weight", | |
| "control_model.input_blocks.2.0.in_layers.2.bias": "blocks.3.conv1.bias", | |
| "control_model.input_blocks.2.0.emb_layers.1.weight": "blocks.3.time_emb_proj.weight", | |
| "control_model.input_blocks.2.0.emb_layers.1.bias": "blocks.3.time_emb_proj.bias", | |
| "control_model.input_blocks.2.0.out_layers.0.weight": "blocks.3.norm2.weight", | |
| "control_model.input_blocks.2.0.out_layers.0.bias": "blocks.3.norm2.bias", | |
| "control_model.input_blocks.2.0.out_layers.3.weight": "blocks.3.conv2.weight", | |
| "control_model.input_blocks.2.0.out_layers.3.bias": "blocks.3.conv2.bias", | |
| "control_model.input_blocks.2.1.norm.weight": "blocks.4.norm.weight", | |
| "control_model.input_blocks.2.1.norm.bias": "blocks.4.norm.bias", | |
| "control_model.input_blocks.2.1.proj_in.weight": "blocks.4.proj_in.weight", | |
| "control_model.input_blocks.2.1.proj_in.bias": "blocks.4.proj_in.bias", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_q.weight": "blocks.4.transformer_blocks.0.attn1.to_q.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_k.weight": "blocks.4.transformer_blocks.0.attn1.to_k.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_v.weight": "blocks.4.transformer_blocks.0.attn1.to_v.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.4.transformer_blocks.0.attn1.to_out.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.4.transformer_blocks.0.attn1.to_out.bias", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.4.transformer_blocks.0.act_fn.proj.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.4.transformer_blocks.0.act_fn.proj.bias", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.weight": "blocks.4.transformer_blocks.0.ff.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.bias": "blocks.4.transformer_blocks.0.ff.bias", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_q.weight": "blocks.4.transformer_blocks.0.attn2.to_q.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight": "blocks.4.transformer_blocks.0.attn2.to_k.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_v.weight": "blocks.4.transformer_blocks.0.attn2.to_v.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.4.transformer_blocks.0.attn2.to_out.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.4.transformer_blocks.0.attn2.to_out.bias", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.norm1.weight": "blocks.4.transformer_blocks.0.norm1.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.norm1.bias": "blocks.4.transformer_blocks.0.norm1.bias", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.norm2.weight": "blocks.4.transformer_blocks.0.norm2.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.norm2.bias": "blocks.4.transformer_blocks.0.norm2.bias", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.norm3.weight": "blocks.4.transformer_blocks.0.norm3.weight", | |
| "control_model.input_blocks.2.1.transformer_blocks.0.norm3.bias": "blocks.4.transformer_blocks.0.norm3.bias", | |
| "control_model.input_blocks.2.1.proj_out.weight": "blocks.4.proj_out.weight", | |
| "control_model.input_blocks.2.1.proj_out.bias": "blocks.4.proj_out.bias", | |
| "control_model.input_blocks.3.0.op.weight": "blocks.6.conv.weight", | |
| "control_model.input_blocks.3.0.op.bias": "blocks.6.conv.bias", | |
| "control_model.input_blocks.4.0.in_layers.0.weight": "blocks.8.norm1.weight", | |
| "control_model.input_blocks.4.0.in_layers.0.bias": "blocks.8.norm1.bias", | |
| "control_model.input_blocks.4.0.in_layers.2.weight": "blocks.8.conv1.weight", | |
| "control_model.input_blocks.4.0.in_layers.2.bias": "blocks.8.conv1.bias", | |
| "control_model.input_blocks.4.0.emb_layers.1.weight": "blocks.8.time_emb_proj.weight", | |
| "control_model.input_blocks.4.0.emb_layers.1.bias": "blocks.8.time_emb_proj.bias", | |
| "control_model.input_blocks.4.0.out_layers.0.weight": "blocks.8.norm2.weight", | |
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| "control_model.input_blocks.4.0.out_layers.3.weight": "blocks.8.conv2.weight", | |
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| "control_model.input_hint_block.4.weight": "controlnet_conv_in.blocks.4.weight", | |
| "control_model.input_hint_block.4.bias": "controlnet_conv_in.blocks.4.bias", | |
| "control_model.input_hint_block.6.weight": "controlnet_conv_in.blocks.6.weight", | |
| "control_model.input_hint_block.6.bias": "controlnet_conv_in.blocks.6.bias", | |
| "control_model.input_hint_block.8.weight": "controlnet_conv_in.blocks.8.weight", | |
| "control_model.input_hint_block.8.bias": "controlnet_conv_in.blocks.8.bias", | |
| "control_model.input_hint_block.10.weight": "controlnet_conv_in.blocks.10.weight", | |
| "control_model.input_hint_block.10.bias": "controlnet_conv_in.blocks.10.bias", | |
| "control_model.input_hint_block.12.weight": "controlnet_conv_in.blocks.12.weight", | |
| "control_model.input_hint_block.12.bias": "controlnet_conv_in.blocks.12.bias", | |
| "control_model.input_hint_block.14.weight": "controlnet_conv_in.blocks.14.weight", | |
| "control_model.input_hint_block.14.bias": "controlnet_conv_in.blocks.14.bias", | |
| "control_model.middle_block.0.in_layers.0.weight": "blocks.28.norm1.weight", | |
| "control_model.middle_block.0.in_layers.0.bias": "blocks.28.norm1.bias", | |
| "control_model.middle_block.0.in_layers.2.weight": "blocks.28.conv1.weight", | |
| "control_model.middle_block.0.in_layers.2.bias": "blocks.28.conv1.bias", | |
| "control_model.middle_block.0.emb_layers.1.weight": "blocks.28.time_emb_proj.weight", | |
| "control_model.middle_block.0.emb_layers.1.bias": "blocks.28.time_emb_proj.bias", | |
| "control_model.middle_block.0.out_layers.0.weight": "blocks.28.norm2.weight", | |
| "control_model.middle_block.0.out_layers.0.bias": "blocks.28.norm2.bias", | |
| "control_model.middle_block.0.out_layers.3.weight": "blocks.28.conv2.weight", | |
| "control_model.middle_block.0.out_layers.3.bias": "blocks.28.conv2.bias", | |
| "control_model.middle_block.1.norm.weight": "blocks.29.norm.weight", | |
| "control_model.middle_block.1.norm.bias": "blocks.29.norm.bias", | |
| "control_model.middle_block.1.proj_in.weight": "blocks.29.proj_in.weight", | |
| "control_model.middle_block.1.proj_in.bias": "blocks.29.proj_in.bias", | |
| "control_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight": "blocks.29.transformer_blocks.0.attn1.to_q.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.attn1.to_k.weight": "blocks.29.transformer_blocks.0.attn1.to_k.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.attn1.to_v.weight": "blocks.29.transformer_blocks.0.attn1.to_v.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.29.transformer_blocks.0.attn1.to_out.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.29.transformer_blocks.0.attn1.to_out.bias", | |
| "control_model.middle_block.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.29.transformer_blocks.0.act_fn.proj.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.29.transformer_blocks.0.act_fn.proj.bias", | |
| "control_model.middle_block.1.transformer_blocks.0.ff.net.2.weight": "blocks.29.transformer_blocks.0.ff.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.ff.net.2.bias": "blocks.29.transformer_blocks.0.ff.bias", | |
| "control_model.middle_block.1.transformer_blocks.0.attn2.to_q.weight": "blocks.29.transformer_blocks.0.attn2.to_q.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.attn2.to_k.weight": "blocks.29.transformer_blocks.0.attn2.to_k.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.attn2.to_v.weight": "blocks.29.transformer_blocks.0.attn2.to_v.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.29.transformer_blocks.0.attn2.to_out.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.29.transformer_blocks.0.attn2.to_out.bias", | |
| "control_model.middle_block.1.transformer_blocks.0.norm1.weight": "blocks.29.transformer_blocks.0.norm1.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.norm1.bias": "blocks.29.transformer_blocks.0.norm1.bias", | |
| "control_model.middle_block.1.transformer_blocks.0.norm2.weight": "blocks.29.transformer_blocks.0.norm2.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.norm2.bias": "blocks.29.transformer_blocks.0.norm2.bias", | |
| "control_model.middle_block.1.transformer_blocks.0.norm3.weight": "blocks.29.transformer_blocks.0.norm3.weight", | |
| "control_model.middle_block.1.transformer_blocks.0.norm3.bias": "blocks.29.transformer_blocks.0.norm3.bias", | |
| "control_model.middle_block.1.proj_out.weight": "blocks.29.proj_out.weight", | |
| "control_model.middle_block.1.proj_out.bias": "blocks.29.proj_out.bias", | |
| "control_model.middle_block.2.in_layers.0.weight": "blocks.30.norm1.weight", | |
| "control_model.middle_block.2.in_layers.0.bias": "blocks.30.norm1.bias", | |
| "control_model.middle_block.2.in_layers.2.weight": "blocks.30.conv1.weight", | |
| "control_model.middle_block.2.in_layers.2.bias": "blocks.30.conv1.bias", | |
| "control_model.middle_block.2.emb_layers.1.weight": "blocks.30.time_emb_proj.weight", | |
| "control_model.middle_block.2.emb_layers.1.bias": "blocks.30.time_emb_proj.bias", | |
| "control_model.middle_block.2.out_layers.0.weight": "blocks.30.norm2.weight", | |
| "control_model.middle_block.2.out_layers.0.bias": "blocks.30.norm2.bias", | |
| "control_model.middle_block.2.out_layers.3.weight": "blocks.30.conv2.weight", | |
| "control_model.middle_block.2.out_layers.3.bias": "blocks.30.conv2.bias", | |
| "control_model.middle_block_out.0.weight": "controlnet_blocks.12.weight", | |
| "control_model.middle_block_out.0.bias": "controlnet_blocks.7.bias", | |
| } | |
| state_dict_ = {} | |
| for name in state_dict: | |
| if name in rename_dict: | |
| param = state_dict[name] | |
| if ".proj_in." in name or ".proj_out." in name: | |
| param = param.squeeze() | |
| state_dict_[rename_dict[name]] = param | |
| return state_dict_ | |