-
Notifications
You must be signed in to change notification settings - Fork 26
/
hook_comfyui_kolors_v2.py
318 lines (254 loc) · 14.7 KB
/
hook_comfyui_kolors_v2.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
import os
from types import MethodType
import warnings
from comfy.model_detection import *
import comfy.model_detection as model_detection
import comfy.supported_models
import comfy.utils
import torch
from comfy import model_base
from comfy.model_base import sdxl_pooled, CLIPEmbeddingNoiseAugmentation, Timestep, ModelType
from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel
from comfy.cldm.cldm import ControlNet
# try:
# import comfy.samplers as samplers
# original_CFGGuider_inner_set_conds = samplers.CFGGuider.set_conds
# def patched_set_conds(self, positive, negative):
# if isinstance(self.model_patcher.model, KolorsSDXL):
# import copy
# if "control" in positive[0][1]:
# if hasattr(positive[0][1]["control"], "control_model"):
# if positive[0][1]["control"].control_model.label_emb.shape[1] == 5632:
# return
# warnings.warn("该方法不再维护")
# positive = copy.deepcopy(positive)
# negative = copy.deepcopy(negative)
# hid_proj = self.model_patcher.model.encoder_hid_proj
# if hid_proj is not None:
# positive[0][0] = hid_proj(positive[0][0])
# negative[0][0] = hid_proj(negative[0][0])
# if "control" in positive[0][1]:
# if hasattr(positive[0][1]["control"], "control_model"):
# positive[0][1]["control"].control_model.label_emb = self.model_patcher.model.diffusion_model.label_emb
# if "control" in negative[0][1]:
# if hasattr(negative[0][1]["control"], "control_model"):
# negative[0][1]["control"].control_model.label_emb = self.model_patcher.model.diffusion_model.label_emb
# return original_CFGGuider_inner_set_conds(self, positive, negative)
# samplers.CFGGuider.set_conds = patched_set_conds
# except ImportError:
# print("CFGGuider not found, skipping patching")
class KolorsUNetModel(UNetModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.encoder_hid_proj = nn.Linear(
4096, 2048, bias=True)
def forward(self, *args, **kwargs):
with torch.cuda.amp.autocast(enabled=True):
if "context" in kwargs:
kwargs["context"] = self.encoder_hid_proj(
kwargs["context"])
# if "y" in kwargs:
# if kwargs["y"].shape[1] == 2816:
# # 扩展至5632
# kwargs["y"] = torch.cat(
# torch.zeros(kwargs["y"].shape[0], 2816).to(kwargs["y"].device), kwargs["y"], dim=1)
result = super().forward(*args, **kwargs)
return result
class KolorsSDXL(model_base.SDXL):
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
model_config.sampling_settings["beta_schedule"] = "linear"
model_config.sampling_settings["linear_start"] = 0.00085
model_config.sampling_settings["linear_end"] = 0.014
model_config.sampling_settings["timesteps"] = 1100
model_type = ModelType.EPS
model_base.BaseModel.__init__(
self, model_config, model_type, device=device, unet_model=KolorsUNetModel)
self.embedder = Timestep(256)
self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(
**{"noise_schedule_config": {"timesteps": 1100, "beta_schedule": "linear", "linear_start": 0.00085, "linear_end": 0.014}, "timestep_dim": 1280})
def encode_adm(self, **kwargs):
clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
width = kwargs.get("width", 768)
height = kwargs.get("height", 768)
crop_w = kwargs.get("crop_w", 0)
crop_h = kwargs.get("crop_h", 0)
target_width = kwargs.get("target_width", width)
target_height = kwargs.get("target_height", height)
out = []
out.append(self.embedder(torch.Tensor([height])))
out.append(self.embedder(torch.Tensor([width])))
out.append(self.embedder(torch.Tensor([crop_h])))
out.append(self.embedder(torch.Tensor([crop_w])))
out.append(self.embedder(torch.Tensor([target_height])))
out.append(self.embedder(torch.Tensor([target_width])))
flat = torch.flatten(torch.cat(out)).unsqueeze(
dim=0).repeat(clip_pooled.shape[0], 1)
return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
class KolorsSupported(comfy.supported_models.SDXL):
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 0, 2, 2, 10, 10],
"context_dim": 2048,
"adm_in_channels": 5632,
"use_temporal_attention": False,
}
def get_model(self, state_dict, prefix="", device=None):
out = KolorsSDXL(self, model_type=self.model_type(
state_dict, prefix), device=device,)
out.__class__ = model_base.SDXL
if self.inpaint_model():
out.set_inpaint()
return out
def kolors_unet_config_from_diffusers_unet(state_dict, dtype=None):
match = {}
transformer_depth = []
attn_res = 1
down_blocks = count_blocks(state_dict, "down_blocks.{}")
for i in range(down_blocks):
attn_blocks = count_blocks(
state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
res_blocks = count_blocks(
state_dict, "down_blocks.{}.resnets.".format(i) + '{}')
for ab in range(attn_blocks):
transformer_count = count_blocks(
state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
transformer_depth.append(transformer_count)
if transformer_count > 0:
match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(
i, ab)].shape[1]
attn_res *= 2
if attn_blocks == 0:
for i in range(res_blocks):
transformer_depth.append(0)
match["transformer_depth"] = transformer_depth
match["model_channels"] = state_dict["conv_in.weight"].shape[0]
match["in_channels"] = state_dict["conv_in.weight"].shape[1]
match["adm_in_channels"] = None
if "class_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
elif "add_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
Kolors = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
Kolors_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
Kolors_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 5632, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0,
'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}
supported_models = [Kolors, Kolors_inpaint,
Kolors_ip2p, SDXL, SDXL_mid_cnet, SDXL_small_cnet]
for unet_config in supported_models:
matches = True
for k in match:
if match[k] != unet_config[k]:
print("key {} does not match".format(
k), match[k], "||", unet_config[k])
matches = False
break
if matches:
return convert_config(unet_config)
return None
import comfy.ldm.modules.diffusionmodules.openaimodel
from torch import nn
def load_clipvision_336_from_sd(sd, prefix="", convert_keys=False):
from comfy.clip_vision import ClipVisionModel, convert_to_transformers
json_config = os.path.join(os.path.dirname(
os.path.realpath(__file__)), "clip_vit_336", "config.json")
clip = ClipVisionModel(json_config)
m, u = clip.load_sd(sd)
if len(m) > 0:
logging.warning("missing clip vision: {}".format(m))
u = set(u)
keys = list(sd.keys())
for k in keys:
if k not in u:
t = sd.pop(k)
del t
# def vis_forward(self, pixel_values, attention_mask=None, intermediate_output=None):
# pixel_values = nn.functional.interpolate(
# pixel_values, size=(336, 336), mode='bilinear', align_corners=False)
# x = self.embeddings(pixel_values)
# x = self.pre_layrnorm(x)
# # TODO: attention_mask?
# x, i = self.encoder(
# x, mask=None, intermediate_output=intermediate_output)
# pooled_output = self.post_layernorm(x[:, 0, :])
# return x, i, pooled_output
# clip.model.vision_model.forward = MethodType(
# vis_forward, clip.model.vision_model
# )
return clip
class KolorsControlNet(ControlNet):
def __init__(self, *args, **kwargs):
adm_in_channels = kwargs["adm_in_channels"]
if adm_in_channels == 2816:
# 异常: 该加载器不支持SDXL类型, 请使用ControlNet加载器+KolorsControlNetPatch节点
raise Exception(
"This loader does not support SDXL type, please use ControlNet loader + KolorsControlNetPatch node")
super().__init__(*args, **kwargs)
self.encoder_hid_proj = nn.Linear(
4096, 2048, bias=True)
def forward(self, *args, **kwargs):
with torch.cuda.amp.autocast(enabled=True):
if "context" in kwargs:
kwargs["context"] = self.encoder_hid_proj(
kwargs["context"])
result = super().forward(*args, **kwargs)
return result
class apply_kolors:
def __enter__(self):
import comfy.ldm.modules.diffusionmodules.openaimodel
import comfy.cldm.cldm
import comfy.utils
import comfy.clip_vision
self.original_load_clipvision_from_sd = comfy.clip_vision.load_clipvision_from_sd
comfy.clip_vision.load_clipvision_from_sd = load_clipvision_336_from_sd
self.original_UNET_MAP_BASIC = comfy.utils.UNET_MAP_BASIC.copy()
comfy.utils.UNET_MAP_BASIC.add(
("encoder_hid_proj.weight", "encoder_hid_proj.weight"),
)
comfy.utils.UNET_MAP_BASIC.add(
("encoder_hid_proj.bias", "encoder_hid_proj.bias"),
)
self.original_unet_config_from_diffusers_unet = model_detection.unet_config_from_diffusers_unet
model_detection.unet_config_from_diffusers_unet = kolors_unet_config_from_diffusers_unet
import comfy.supported_models
self.original_supported_models = comfy.supported_models.models
comfy.supported_models.models = [KolorsSupported]
self.original_controlnet = comfy.cldm.cldm.ControlNet
comfy.cldm.cldm.ControlNet = KolorsControlNet
def __exit__(self, type, value, traceback):
import comfy.ldm.modules.diffusionmodules.openaimodel
import comfy.cldm.cldm
import comfy.utils
comfy.utils.UNET_MAP_BASIC = self.original_UNET_MAP_BASIC
model_detection.unet_config_from_diffusers_unet = self.original_unet_config_from_diffusers_unet
import comfy.supported_models
comfy.supported_models.models = self.original_supported_models
import comfy.clip_vision
comfy.clip_vision.load_clipvision_from_sd = self.original_load_clipvision_from_sd
comfy.cldm.cldm.ControlNet = self.original_controlnet