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DiffSynth-Studio/diffsynth/models/sd_controlnet.py
Artiprocher 6f79fd6d77 support sdxl controlnet union
2024-08-01 10:01:39 +08:00

590 lines
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Python
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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,
**kwargs
):
# 1. time
time_emb = self.time_proj(timestep).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
@staticmethod
def state_dict_converter():
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",
"control_model.input_blocks.4.0.out_layers.0.bias": "blocks.8.norm2.bias",
"control_model.input_blocks.4.0.out_layers.3.weight": "blocks.8.conv2.weight",
"control_model.input_blocks.4.0.out_layers.3.bias": "blocks.8.conv2.bias",
"control_model.input_blocks.4.0.skip_connection.weight": "blocks.8.conv_shortcut.weight",
"control_model.input_blocks.4.0.skip_connection.bias": "blocks.8.conv_shortcut.bias",
"control_model.input_blocks.4.1.norm.weight": "blocks.9.norm.weight",
"control_model.input_blocks.4.1.norm.bias": "blocks.9.norm.bias",
"control_model.input_blocks.4.1.proj_in.weight": "blocks.9.proj_in.weight",
"control_model.input_blocks.4.1.proj_in.bias": "blocks.9.proj_in.bias",
"control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q.weight": "blocks.9.transformer_blocks.0.attn1.to_q.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.9.transformer_blocks.0.attn1.to_k.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v.weight": "blocks.9.transformer_blocks.0.attn1.to_v.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.9.transformer_blocks.0.attn1.to_out.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.9.transformer_blocks.0.attn1.to_out.bias",
"control_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.9.transformer_blocks.0.act_fn.proj.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.9.transformer_blocks.0.act_fn.proj.bias",
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"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_
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