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controlnet_model_guess.py
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controlnet_model_guess.py
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import copy
import os
import torch
from pathlib import Path
from typing import NamedTuple
from modules import devices
from scripts.adapter import PlugableAdapter, Adapter, StyleAdapter, Adapter_light
from scripts.controlnet_lllite import PlugableControlLLLite
from scripts.cldm import PlugableControlModel
from scripts.controlnet_sparsectrl import PlugableSparseCtrlModel
from scripts.ipadapter.ipadapter_model import IPAdapterModel
from scripts.ipadapter.plugable_ipadapter import PlugableIPAdapter
from scripts.logging import logger
from scripts.controlnet_diffusers import convert_from_diffuser_state_dict
from scripts.controlnet_lora import controlnet_lora_hijack, force_load_state_dict
from scripts.enums import ControlModelType
controlnet_default_config = {'adm_in_channels': None,
'in_channels': 4,
'model_channels': 320,
'num_res_blocks': 2,
'attention_resolutions': [1, 2, 4],
'transformer_depth': [1, 1, 1, 0],
'channel_mult': [1, 2, 4, 4],
'transformer_depth_middle': 1,
'use_linear_in_transformer': False,
'context_dim': 768,
"num_heads": 8,
"global_average_pooling": False}
controlnet_sdxl_config = {'num_classes': 'sequential',
'adm_in_channels': 2816,
'in_channels': 4,
'model_channels': 320,
'num_res_blocks': 2,
'attention_resolutions': [2, 4],
'transformer_depth': [0, 2, 10],
'channel_mult': [1, 2, 4],
'transformer_depth_middle': 10,
'use_linear_in_transformer': True,
'context_dim': 2048,
"num_head_channels": 64,
"global_average_pooling": False}
controlnet_sdxl_mid_config = {'num_classes': 'sequential',
'adm_in_channels': 2816,
'in_channels': 4,
'model_channels': 320,
'num_res_blocks': 2,
'attention_resolutions': [4],
'transformer_depth': [0, 0, 1],
'channel_mult': [1, 2, 4],
'transformer_depth_middle': 1,
'use_linear_in_transformer': True,
'context_dim': 2048,
"num_head_channels": 64,
"global_average_pooling": False}
controlnet_sdxl_small_config = {'num_classes': 'sequential',
'adm_in_channels': 2816,
'in_channels': 4,
'model_channels': 320,
'num_res_blocks': 2,
'attention_resolutions': [],
'transformer_depth': [0, 0, 0],
'channel_mult': [1, 2, 4],
'transformer_depth_middle': 0,
'use_linear_in_transformer': True,
"num_head_channels": 64,
'context_dim': 1,
"global_average_pooling": False}
t2i_adapter_config = {
'channels': [320, 640, 1280, 1280],
'nums_rb': 2,
'ksize': 1,
'sk': True,
'cin': 192,
'use_conv': False
}
t2i_adapter_light_config = {
'channels': [320, 640, 1280, 1280],
'nums_rb': 4,
'cin': 192,
}
t2i_adapter_style_config = {
'width': 1024,
'context_dim': 768,
'num_head': 8,
'n_layes': 3,
'num_token': 8,
}
# Stolen from https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/utils.py
def state_dict_key_replace(state_dict, keys_to_replace):
for x in keys_to_replace:
if x in state_dict:
state_dict[keys_to_replace[x]] = state_dict.pop(x)
return state_dict
# # Stolen from https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/utils.py
def state_dict_prefix_replace(state_dict, replace_prefix):
for rp in replace_prefix:
replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys())))
for x in replace:
state_dict[x[1]] = state_dict.pop(x[0])
return state_dict
class ControlModel(NamedTuple):
model: torch.nn.Module
type: ControlModelType
def build_model_by_guess(state_dict, unet, model_path: str) -> ControlModel:
if "lora_controlnet" in state_dict:
is_sdxl = "input_blocks.11.0.in_layers.0.weight" not in state_dict
logger.info(f"Using ControlNet lora ({'SDXL' if is_sdxl else 'SD15'})")
del state_dict['lora_controlnet']
config = copy.deepcopy(controlnet_sdxl_config if is_sdxl else controlnet_default_config)
config['global_average_pooling'] = False
config['hint_channels'] = int(state_dict['input_hint_block.0.weight'].shape[1])
config['use_fp16'] = devices.dtype_unet == torch.float16
with controlnet_lora_hijack():
network = PlugableControlModel(config, state_dict=None)
force_load_state_dict(network.control_model, state_dict)
network.is_control_lora = True
network.to(devices.dtype_unet)
return ControlModel(network, ControlModelType.ControlLoRA)
if "down_blocks.0.motion_modules.0.temporal_transformer.norm.weight" in state_dict: # sparsectrl
config = copy.deepcopy(controlnet_default_config)
if "input_hint_block.0.weight" in state_dict: # rgb
config['use_simplified_condition_embedding'] = True
config['conditioning_channels'] = 5
else: # scribble
config['use_simplified_condition_embedding'] = False
config['conditioning_channels'] = 4
config['use_fp16'] = devices.dtype_unet == torch.float16
network = PlugableSparseCtrlModel(config, state_dict)
network.to(devices.dtype_unet)
return ControlModel(network, ControlModelType.SparseCtrl)
if "controlnet_cond_embedding.conv_in.weight" in state_dict: # diffusers
state_dict = convert_from_diffuser_state_dict(state_dict)
if 'adapter.body.0.resnets.0.block1.weight' in state_dict: # diffusers
prefix_replace = {}
for i in range(4):
for j in range(2):
prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j)
prefix_replace["adapter.body.{}.".format(i)] = "body.{}.".format(i * 2)
prefix_replace["adapter."] = ""
state_dict = state_dict_prefix_replace(state_dict, prefix_replace)
if any('image_proj.' in x for x in state_dict.keys()) and any('ip_adapter.' in x for x in state_dict.keys()): # safetensor ipadapters
st_model = {"image_proj": {}, "ip_adapter": {}}
for key in state_dict.keys():
if key.startswith("image_proj."):
st_model["image_proj"][key.replace("image_proj.", "")] = state_dict[key]
elif key.startswith("ip_adapter."):
st_model["ip_adapter"][key.replace("ip_adapter.", "")] = state_dict[key]
# sort keys
model = {"image_proj": st_model["image_proj"], "ip_adapter": {}}
sorted_keys = sorted(st_model["ip_adapter"].keys(), key=lambda x: int(x.split(".")[0]))
for key in sorted_keys:
model["ip_adapter"][key] = st_model["ip_adapter"][key]
state_dict = model
del st_model
model_has_shuffle_in_filename = 'shuffle' in Path(os.path.abspath(model_path)).stem.lower()
state_dict = {k.replace("control_model.", ""): v for k, v in state_dict.items()}
state_dict = {k.replace("adapter.", ""): v for k, v in state_dict.items()}
if 'input_hint_block.0.weight' in state_dict:
if 'label_emb.0.0.bias' not in state_dict:
config = copy.deepcopy(controlnet_default_config)
logger.info('controlnet_default_config')
config['global_average_pooling'] = model_has_shuffle_in_filename
config['hint_channels'] = int(state_dict['input_hint_block.0.weight'].shape[1])
config['context_dim'] = int(state_dict['input_blocks.5.1.transformer_blocks.0.attn2.to_k.weight'].shape[1])
for key in state_dict.keys():
p = state_dict[key]
if 'proj_in.weight' in key or 'proj_out.weight' in key:
if len(p.shape) == 2:
p = p[..., None, None]
state_dict[key] = p
else:
has_full_layers = 'input_blocks.8.1.transformer_blocks.9.norm3.weight' in state_dict
has_mid_layers = 'input_blocks.8.1.transformer_blocks.0.norm3.weight' in state_dict
if has_full_layers:
config = copy.deepcopy(controlnet_sdxl_config)
logger.info('controlnet_sdxl_config')
elif has_mid_layers:
config = copy.deepcopy(controlnet_sdxl_mid_config)
logger.info('controlnet_sdxl_mid_config')
else:
config = copy.deepcopy(controlnet_sdxl_small_config)
logger.info('controlnet_sdxl_small_config')
config['global_average_pooling'] = False
config['hint_channels'] = int(state_dict['input_hint_block.0.weight'].shape[1])
if 'difference' in state_dict and unet is not None:
unet_state_dict = unet.state_dict()
unet_state_dict_keys = unet_state_dict.keys()
final_state_dict = {}
for key in state_dict.keys():
p = state_dict[key]
if key in unet_state_dict_keys:
p_new = p + unet_state_dict[key].clone().cpu()
else:
p_new = p
final_state_dict[key] = p_new
state_dict = final_state_dict
if "control_add_embedding.linear_1.bias" in state_dict: # Controlnet Union
config["union_controlnet_num_control_type"] = state_dict["task_embedding"].shape[0]
final_state_dict = {}
for k in list(state_dict.keys()):
new_k = k.replace('.attn.in_proj_', '.attn.in_proj.')
final_state_dict[new_k] = state_dict.pop(k)
state_dict = final_state_dict
control_model_type = ControlModelType.ControlNetUnion
elif "instant_id" in model_path.lower():
control_model_type = ControlModelType.InstantID
else:
control_model_type = ControlModelType.ControlNet
config['use_fp16'] = devices.dtype_unet == torch.float16
network = PlugableControlModel(config, state_dict)
network.to(devices.dtype_unet)
return ControlModel(network, control_model_type)
if 'conv_in.weight' in state_dict:
logger.info('t2i_adapter_config')
cin = int(state_dict['conv_in.weight'].shape[1])
channel = int(state_dict['conv_in.weight'].shape[0])
ksize = int(state_dict['body.0.block2.weight'].shape[2])
down_opts = tuple(filter(lambda item: item.endswith("down_opt.op.weight"), state_dict))
use_conv = len(down_opts) > 0
is_sdxl = cin == 256 or cin == 768
adapter = Adapter(
cin=cin,
channels=[channel, channel*2, channel*4, channel*4],
nums_rb=2,
ksize=ksize,
sk=True,
use_conv=use_conv,
is_sdxl=is_sdxl
).cpu()
adapter.load_state_dict(state_dict, strict=False)
network = PlugableAdapter(adapter)
return ControlModel(network, ControlModelType.T2I_Adapter)
if 'style_embedding' in state_dict:
config = copy.deepcopy(t2i_adapter_style_config)
logger.info('t2i_adapter_style_config')
adapter = StyleAdapter(**config).cpu()
adapter.load_state_dict(state_dict, strict=False)
network = PlugableAdapter(adapter)
return ControlModel(network, ControlModelType.T2I_StyleAdapter)
if 'body.0.in_conv.weight' in state_dict:
config = copy.deepcopy(t2i_adapter_light_config)
logger.info('t2i_adapter_light_config')
config['cin'] = int(state_dict['body.0.in_conv.weight'].shape[1])
adapter = Adapter_light(**config).cpu()
adapter.load_state_dict(state_dict, strict=False)
network = PlugableAdapter(adapter)
return ControlModel(network, ControlModelType.T2I_Adapter)
if 'ip_adapter' in state_dict:
network = PlugableIPAdapter(IPAdapterModel.load(state_dict, model_path))
return ControlModel(network, ControlModelType.IPAdapter)
if any('lllite' in k for k in state_dict.keys()):
network = PlugableControlLLLite(state_dict)
network.to('cpu')
return ControlModel(network, ControlModelType.Controlllite)
raise Exception('[ControlNet Error] Cannot recognize the ControlModel!')