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566
diffsynth/models/sd_controlnet.py
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566
diffsynth/models/sd_controlnet.py
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import torch
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from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler
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class ControlNetConditioningLayer(torch.nn.Module):
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def __init__(self, channels = (3, 16, 32, 96, 256, 320)):
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super().__init__()
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self.blocks = torch.nn.ModuleList([])
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self.blocks.append(torch.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1))
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self.blocks.append(torch.nn.SiLU())
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for i in range(1, len(channels) - 2):
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self.blocks.append(torch.nn.Conv2d(channels[i], channels[i], kernel_size=3, padding=1))
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self.blocks.append(torch.nn.SiLU())
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self.blocks.append(torch.nn.Conv2d(channels[i], channels[i+1], kernel_size=3, padding=1, stride=2))
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self.blocks.append(torch.nn.SiLU())
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self.blocks.append(torch.nn.Conv2d(channels[-2], channels[-1], kernel_size=3, padding=1))
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def forward(self, conditioning):
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for block in self.blocks:
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conditioning = block(conditioning)
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return conditioning
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class SDControlNet(torch.nn.Module):
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def __init__(self, global_pool=False):
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super().__init__()
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self.time_proj = Timesteps(320)
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self.time_embedding = torch.nn.Sequential(
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torch.nn.Linear(320, 1280),
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torch.nn.SiLU(),
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torch.nn.Linear(1280, 1280)
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)
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self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
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self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320))
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self.blocks = torch.nn.ModuleList([
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# CrossAttnDownBlock2D
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ResnetBlock(320, 320, 1280),
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AttentionBlock(8, 40, 320, 1, 768),
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PushBlock(),
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ResnetBlock(320, 320, 1280),
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AttentionBlock(8, 40, 320, 1, 768),
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PushBlock(),
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DownSampler(320),
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PushBlock(),
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# CrossAttnDownBlock2D
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ResnetBlock(320, 640, 1280),
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AttentionBlock(8, 80, 640, 1, 768),
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PushBlock(),
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ResnetBlock(640, 640, 1280),
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AttentionBlock(8, 80, 640, 1, 768),
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PushBlock(),
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DownSampler(640),
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PushBlock(),
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# CrossAttnDownBlock2D
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ResnetBlock(640, 1280, 1280),
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AttentionBlock(8, 160, 1280, 1, 768),
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PushBlock(),
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ResnetBlock(1280, 1280, 1280),
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AttentionBlock(8, 160, 1280, 1, 768),
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PushBlock(),
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DownSampler(1280),
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PushBlock(),
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# DownBlock2D
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ResnetBlock(1280, 1280, 1280),
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PushBlock(),
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ResnetBlock(1280, 1280, 1280),
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PushBlock(),
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# UNetMidBlock2DCrossAttn
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ResnetBlock(1280, 1280, 1280),
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AttentionBlock(8, 160, 1280, 1, 768),
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ResnetBlock(1280, 1280, 1280),
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PushBlock()
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])
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self.controlnet_blocks = torch.nn.ModuleList([
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torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
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torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
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torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
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torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
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torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
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torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False),
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torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False),
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torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
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torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
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torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
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torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
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torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
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torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
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])
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self.global_pool = global_pool
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def forward(self, sample, timestep, encoder_hidden_states, conditioning):
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# 1. time
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time_emb = self.time_proj(timestep[None]).to(sample.dtype)
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time_emb = self.time_embedding(time_emb)
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time_emb = time_emb.repeat(sample.shape[0], 1)
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# 2. pre-process
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hidden_states = self.conv_in(sample) + self.controlnet_conv_in(conditioning)
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text_emb = encoder_hidden_states
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res_stack = [hidden_states]
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# 3. blocks
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for i, block in enumerate(self.blocks):
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hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
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# 4. ControlNet blocks
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controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)]
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# pool
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if self.global_pool:
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controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack]
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return controlnet_res_stack
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def state_dict_converter(self):
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return SDControlNetStateDictConverter()
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class SDControlNetStateDictConverter:
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def __init__(self):
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pass
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def from_diffusers(self, state_dict):
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# architecture
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block_types = [
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'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
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'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
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'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
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'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock',
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'ResnetBlock', 'AttentionBlock', 'ResnetBlock',
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'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler',
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'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
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'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
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'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock'
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]
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# controlnet_rename_dict
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controlnet_rename_dict = {
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"controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight",
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"controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias",
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"controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight",
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"controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias",
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"controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight",
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"controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias",
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"controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight",
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"controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias",
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"controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight",
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"controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias",
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"controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight",
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"controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias",
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"controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight",
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"controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias",
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"controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight",
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"controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias",
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}
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# Rename each parameter
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name_list = sorted([name for name in state_dict])
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rename_dict = {}
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block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
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last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
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for name in name_list:
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names = name.split(".")
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if names[0] in ["conv_in", "conv_norm_out", "conv_out"]:
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pass
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elif name in controlnet_rename_dict:
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names = controlnet_rename_dict[name].split(".")
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elif names[0] == "controlnet_down_blocks":
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names[0] = "controlnet_blocks"
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elif names[0] == "controlnet_mid_block":
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names = ["controlnet_blocks", "12", names[-1]]
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elif names[0] in ["time_embedding", "add_embedding"]:
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if names[0] == "add_embedding":
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names[0] = "add_time_embedding"
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names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
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elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
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if names[0] == "mid_block":
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names.insert(1, "0")
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block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
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block_type_with_id = ".".join(names[:4])
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if block_type_with_id != last_block_type_with_id[block_type]:
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block_id[block_type] += 1
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last_block_type_with_id[block_type] = block_type_with_id
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while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
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block_id[block_type] += 1
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block_type_with_id = ".".join(names[:4])
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names = ["blocks", str(block_id[block_type])] + names[4:]
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if "ff" in names:
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ff_index = names.index("ff")
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component = ".".join(names[ff_index:ff_index+3])
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component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
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names = names[:ff_index] + [component] + names[ff_index+3:]
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if "to_out" in names:
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names.pop(names.index("to_out") + 1)
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else:
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raise ValueError(f"Unknown parameters: {name}")
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rename_dict[name] = ".".join(names)
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# Convert state_dict
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state_dict_ = {}
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for name, param in state_dict.items():
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if ".proj_in." in name or ".proj_out." in name:
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param = param.squeeze()
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if rename_dict[name] in [
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"controlnet_blocks.1.bias", "controlnet_blocks.2.bias", "controlnet_blocks.3.bias", "controlnet_blocks.5.bias", "controlnet_blocks.6.bias",
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"controlnet_blocks.8.bias", "controlnet_blocks.9.bias", "controlnet_blocks.10.bias", "controlnet_blocks.11.bias", "controlnet_blocks.12.bias"
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]:
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continue
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state_dict_[rename_dict[name]] = param
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return state_dict_
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def from_civitai(self, state_dict):
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rename_dict = {
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"control_model.time_embed.0.weight": "time_embedding.0.weight",
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"control_model.time_embed.0.bias": "time_embedding.0.bias",
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"control_model.time_embed.2.weight": "time_embedding.2.weight",
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"control_model.time_embed.2.bias": "time_embedding.2.bias",
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"control_model.input_blocks.0.0.weight": "conv_in.weight",
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"control_model.input_blocks.0.0.bias": "conv_in.bias",
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"control_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",
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"control_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",
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"control_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight",
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"control_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias",
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"control_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",
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"control_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",
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"control_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight",
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"control_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias",
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"control_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight",
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"control_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias",
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"control_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight",
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"control_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias",
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"control_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight",
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"control_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias",
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"control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias",
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"control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias",
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"control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias",
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"control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.1.transformer_blocks.0.attn2.to_q.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.1.transformer_blocks.0.attn2.to_v.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias",
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"control_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.1.transformer_blocks.0.norm1.bias",
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"control_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.1.transformer_blocks.0.norm2.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.1.transformer_blocks.0.norm2.bias",
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"control_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.1.transformer_blocks.0.norm3.weight",
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"control_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.1.transformer_blocks.0.norm3.bias",
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"control_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight",
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"control_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias",
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"control_model.input_blocks.2.0.in_layers.0.weight": "blocks.3.norm1.weight",
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"control_model.input_blocks.2.0.in_layers.0.bias": "blocks.3.norm1.bias",
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"control_model.input_blocks.2.0.in_layers.2.weight": "blocks.3.conv1.weight",
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"control_model.input_blocks.2.0.in_layers.2.bias": "blocks.3.conv1.bias",
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"control_model.input_blocks.2.0.emb_layers.1.weight": "blocks.3.time_emb_proj.weight",
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"control_model.input_blocks.2.0.emb_layers.1.bias": "blocks.3.time_emb_proj.bias",
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"control_model.input_blocks.2.0.out_layers.0.weight": "blocks.3.norm2.weight",
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"control_model.input_blocks.2.0.out_layers.0.bias": "blocks.3.norm2.bias",
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"control_model.input_blocks.2.0.out_layers.3.weight": "blocks.3.conv2.weight",
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"control_model.input_blocks.2.0.out_layers.3.bias": "blocks.3.conv2.bias",
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"control_model.input_blocks.2.1.norm.weight": "blocks.4.norm.weight",
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"control_model.input_blocks.2.1.norm.bias": "blocks.4.norm.bias",
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"control_model.input_blocks.2.1.proj_in.weight": "blocks.4.proj_in.weight",
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"control_model.input_blocks.2.1.proj_in.bias": "blocks.4.proj_in.bias",
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||||
"control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_q.weight": "blocks.4.transformer_blocks.0.attn1.to_q.weight",
|
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"control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_k.weight": "blocks.4.transformer_blocks.0.attn1.to_k.weight",
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"control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_v.weight": "blocks.4.transformer_blocks.0.attn1.to_v.weight",
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||||
"control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.4.transformer_blocks.0.attn1.to_out.weight",
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"control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.4.transformer_blocks.0.attn1.to_out.bias",
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||||
"control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.4.transformer_blocks.0.act_fn.proj.weight",
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"control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.4.transformer_blocks.0.act_fn.proj.bias",
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||||
"control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.weight": "blocks.4.transformer_blocks.0.ff.weight",
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||||
"control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.bias": "blocks.4.transformer_blocks.0.ff.bias",
|
||||
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"control_model.input_blocks.10.0.in_layers.0.weight": "blocks.24.norm1.weight",
|
||||
"control_model.input_blocks.10.0.in_layers.0.bias": "blocks.24.norm1.bias",
|
||||
"control_model.input_blocks.10.0.in_layers.2.weight": "blocks.24.conv1.weight",
|
||||
"control_model.input_blocks.10.0.in_layers.2.bias": "blocks.24.conv1.bias",
|
||||
"control_model.input_blocks.10.0.emb_layers.1.weight": "blocks.24.time_emb_proj.weight",
|
||||
"control_model.input_blocks.10.0.emb_layers.1.bias": "blocks.24.time_emb_proj.bias",
|
||||
"control_model.input_blocks.10.0.out_layers.0.weight": "blocks.24.norm2.weight",
|
||||
"control_model.input_blocks.10.0.out_layers.0.bias": "blocks.24.norm2.bias",
|
||||
"control_model.input_blocks.10.0.out_layers.3.weight": "blocks.24.conv2.weight",
|
||||
"control_model.input_blocks.10.0.out_layers.3.bias": "blocks.24.conv2.bias",
|
||||
"control_model.input_blocks.11.0.in_layers.0.weight": "blocks.26.norm1.weight",
|
||||
"control_model.input_blocks.11.0.in_layers.0.bias": "blocks.26.norm1.bias",
|
||||
"control_model.input_blocks.11.0.in_layers.2.weight": "blocks.26.conv1.weight",
|
||||
"control_model.input_blocks.11.0.in_layers.2.bias": "blocks.26.conv1.bias",
|
||||
"control_model.input_blocks.11.0.emb_layers.1.weight": "blocks.26.time_emb_proj.weight",
|
||||
"control_model.input_blocks.11.0.emb_layers.1.bias": "blocks.26.time_emb_proj.bias",
|
||||
"control_model.input_blocks.11.0.out_layers.0.weight": "blocks.26.norm2.weight",
|
||||
"control_model.input_blocks.11.0.out_layers.0.bias": "blocks.26.norm2.bias",
|
||||
"control_model.input_blocks.11.0.out_layers.3.weight": "blocks.26.conv2.weight",
|
||||
"control_model.input_blocks.11.0.out_layers.3.bias": "blocks.26.conv2.bias",
|
||||
"control_model.zero_convs.0.0.weight": "controlnet_blocks.0.weight",
|
||||
"control_model.zero_convs.0.0.bias": "controlnet_blocks.0.bias",
|
||||
"control_model.zero_convs.1.0.weight": "controlnet_blocks.1.weight",
|
||||
"control_model.zero_convs.1.0.bias": "controlnet_blocks.0.bias",
|
||||
"control_model.zero_convs.2.0.weight": "controlnet_blocks.2.weight",
|
||||
"control_model.zero_convs.2.0.bias": "controlnet_blocks.0.bias",
|
||||
"control_model.zero_convs.3.0.weight": "controlnet_blocks.3.weight",
|
||||
"control_model.zero_convs.3.0.bias": "controlnet_blocks.0.bias",
|
||||
"control_model.zero_convs.4.0.weight": "controlnet_blocks.4.weight",
|
||||
"control_model.zero_convs.4.0.bias": "controlnet_blocks.4.bias",
|
||||
"control_model.zero_convs.5.0.weight": "controlnet_blocks.5.weight",
|
||||
"control_model.zero_convs.5.0.bias": "controlnet_blocks.4.bias",
|
||||
"control_model.zero_convs.6.0.weight": "controlnet_blocks.6.weight",
|
||||
"control_model.zero_convs.6.0.bias": "controlnet_blocks.4.bias",
|
||||
"control_model.zero_convs.7.0.weight": "controlnet_blocks.7.weight",
|
||||
"control_model.zero_convs.7.0.bias": "controlnet_blocks.7.bias",
|
||||
"control_model.zero_convs.8.0.weight": "controlnet_blocks.8.weight",
|
||||
"control_model.zero_convs.8.0.bias": "controlnet_blocks.7.bias",
|
||||
"control_model.zero_convs.9.0.weight": "controlnet_blocks.9.weight",
|
||||
"control_model.zero_convs.9.0.bias": "controlnet_blocks.7.bias",
|
||||
"control_model.zero_convs.10.0.weight": "controlnet_blocks.10.weight",
|
||||
"control_model.zero_convs.10.0.bias": "controlnet_blocks.7.bias",
|
||||
"control_model.zero_convs.11.0.weight": "controlnet_blocks.11.weight",
|
||||
"control_model.zero_convs.11.0.bias": "controlnet_blocks.7.bias",
|
||||
"control_model.input_hint_block.0.weight": "controlnet_conv_in.blocks.0.weight",
|
||||
"control_model.input_hint_block.0.bias": "controlnet_conv_in.blocks.0.bias",
|
||||
"control_model.input_hint_block.2.weight": "controlnet_conv_in.blocks.2.weight",
|
||||
"control_model.input_hint_block.2.bias": "controlnet_conv_in.blocks.2.bias",
|
||||
"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_
|
||||
Reference in New Issue
Block a user