-
-
Notifications
You must be signed in to change notification settings - Fork 80
/
Copy pathmodel_loading.py
1146 lines (999 loc) · 50.7 KB
/
model_loading.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
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
import os
import json
import folder_paths
import comfy.model_management as mm
from typing import Union
def patched_write_atomic(
path_: str,
content: Union[str, bytes],
make_dirs: bool = False,
encode_utf_8: bool = False,
) -> None:
# Write into temporary file first to avoid conflicts between threads
# Avoid using a named temporary file, as those have restricted permissions
from pathlib import Path
import os
import shutil
import threading
assert isinstance(
content, (str, bytes)
), "Only strings and byte arrays can be saved in the cache"
path = Path(path_)
if make_dirs:
path.parent.mkdir(parents=True, exist_ok=True)
tmp_path = path.parent / f".{os.getpid()}.{threading.get_ident()}.tmp"
write_mode = "w" if isinstance(content, str) else "wb"
with tmp_path.open(write_mode, encoding="utf-8" if encode_utf_8 else None) as f:
f.write(content)
shutil.copy2(src=tmp_path, dst=path) #changed to allow overwriting cache files
os.remove(tmp_path)
try:
import torch._inductor.codecache
torch._inductor.codecache.write_atomic = patched_write_atomic
except:
pass
import torch
import torch.nn as nn
from diffusers.models import AutoencoderKLCogVideoX
from diffusers.schedulers import CogVideoXDDIMScheduler
from .custom_cogvideox_transformer_3d import CogVideoXTransformer3DModel
from .pipeline_cogvideox import CogVideoXPipeline
from contextlib import nullcontext
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
from .utils import remove_specific_blocks, log
from comfy.utils import load_torch_file
script_directory = os.path.dirname(os.path.abspath(__file__))
class CogVideoLoraSelect:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"lora": (folder_paths.get_filename_list("cogvideox_loras"),
{"tooltip": "LORA models are expected to be in ComfyUI/models/CogVideo/loras with .safetensors extension"}),
"strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}),
},
"optional": {
"prev_lora":("COGLORA", {"default": None, "tooltip": "For loading multiple LoRAs"}),
"fuse_lora": ("BOOLEAN", {"default": False, "tooltip": "Fuse the LoRA weights into the transformer"}),
}
}
RETURN_TYPES = ("COGLORA",)
RETURN_NAMES = ("lora", )
FUNCTION = "getlorapath"
CATEGORY = "CogVideoWrapper"
DESCRIPTION = "Select a LoRA model from ComfyUI/models/CogVideo/loras"
def getlorapath(self, lora, strength, prev_lora=None, fuse_lora=False):
cog_loras_list = []
cog_lora = {
"path": folder_paths.get_full_path("cogvideox_loras", lora),
"strength": strength,
"name": lora.split(".")[0],
"fuse_lora": fuse_lora
}
if prev_lora is not None:
cog_loras_list.extend(prev_lora)
cog_loras_list.append(cog_lora)
print(cog_loras_list)
return (cog_loras_list,)
class CogVideoLoraSelectComfy:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"lora": (folder_paths.get_filename_list("loras"),
{"tooltip": "LORA models are expected to be in ComfyUI/models/loras with .safetensors extension"}),
"strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.0001, "tooltip": "LORA strength, set to 0.0 to unmerge the LORA"}),
},
"optional": {
"prev_lora":("COGLORA", {"default": None, "tooltip": "For loading multiple LoRAs"}),
"fuse_lora": ("BOOLEAN", {"default": False, "tooltip": "Fuse the LoRA weights into the transformer"}),
}
}
RETURN_TYPES = ("COGLORA",)
RETURN_NAMES = ("lora", )
FUNCTION = "getlorapath"
CATEGORY = "CogVideoWrapper"
DESCRIPTION = "Select a LoRA model from ComfyUI/models/loras"
def getlorapath(self, lora, strength, prev_lora=None, fuse_lora=False):
cog_loras_list = []
cog_lora = {
"path": folder_paths.get_full_path("loras", lora),
"strength": strength,
"name": lora.split(".")[0],
"fuse_lora": fuse_lora
}
if prev_lora is not None:
cog_loras_list.extend(prev_lora)
cog_loras_list.append(cog_lora)
print(cog_loras_list)
return (cog_loras_list,)
#region DownloadAndLoadCogVideoModel
class DownloadAndLoadCogVideoModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (
[
"THUDM/CogVideoX-2b",
"THUDM/CogVideoX-5b",
"THUDM/CogVideoX-5b-I2V",
"kijai/CogVideoX-5b-1.5-T2V",
"kijai/CogVideoX-5b-1.5-I2V",
"bertjiazheng/KoolCogVideoX-5b",
"kijai/CogVideoX-Fun-2b",
"kijai/CogVideoX-Fun-5b",
"kijai/CogVideoX-5b-Tora",
"alibaba-pai/CogVideoX-Fun-V1.1-2b-InP",
"alibaba-pai/CogVideoX-Fun-V1.1-5b-InP",
"alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose",
"alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose",
"alibaba-pai/CogVideoX-Fun-V1.1-5b-Control",
"alibaba-pai/CogVideoX-Fun-V1.5-5b-InP",
"feizhengcong/CogvideoX-Interpolation",
"NimVideo/cogvideox-2b-img2vid"
],
),
},
"optional": {
"precision": (["fp16", "fp32", "bf16"],
{"default": "bf16", "tooltip": "official recommendation is that 2b model should be fp16, 5b model should be bf16"}
),
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fastmode', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6"], {"default": 'disabled', "tooltip": "enabled casts the transformer to torch.float8_e4m3fn, fastmode is only for latest nvidia GPUs and requires torch 2.4.0 and cu124 minimum"}),
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
"block_edit": ("TRANSFORMERBLOCKS", {"default": None}),
"lora": ("COGLORA", {"default": None}),
"compile_args":("COMPILEARGS", ),
"attention_mode": ([
"sdpa",
"fused_sdpa",
"sageattn",
"fused_sageattn",
"sageattn_qk_int8_pv_fp8_cuda",
"sageattn_qk_int8_pv_fp16_cuda",
"sageattn_qk_int8_pv_fp16_triton",
"fused_sageattn_qk_int8_pv_fp8_cuda",
"fused_sageattn_qk_int8_pv_fp16_cuda",
"fused_sageattn_qk_int8_pv_fp16_triton",
"comfy"
], {"default": "sdpa"}),
"load_device": (["main_device", "offload_device"], {"default": "main_device"}),
}
}
RETURN_TYPES = ("COGVIDEOMODEL", "VAE",)
RETURN_NAMES = ("model", "vae", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
DESCRIPTION = "Downloads and loads the selected CogVideo model from Huggingface to 'ComfyUI/models/CogVideo'"
def loadmodel(self, model, precision, quantization="disabled", compile="disabled",
enable_sequential_cpu_offload=False, block_edit=None, lora=None, compile_args=None,
attention_mode="sdpa", load_device="main_device"):
transformer = None
if "sage" in attention_mode:
try:
from sageattention import sageattn
except Exception as e:
raise ValueError(f"Can't import SageAttention: {str(e)}")
if "qk_int8" in attention_mode:
try:
from sageattention import sageattn_qk_int8_pv_fp16_cuda
except Exception as e:
raise ValueError(f"Can't import SageAttention 2.0.0: {str(e)}")
if precision == "fp16" and "1.5" in model:
raise ValueError("1.5 models do not currently work in fp16")
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
manual_offloading = True
transformer_load_device = device if load_device == "main_device" else offload_device
mm.soft_empty_cache()
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
download_path = folder_paths.get_folder_paths("CogVideo")[0]
if "Fun" in model:
if "1.1" not in model and "1.5" not in model:
repo_id = "kijai/CogVideoX-Fun-pruned"
if "2b" in model:
base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-2b-InP") # location of the official model
if not os.path.exists(base_path):
base_path = os.path.join(download_path, "CogVideoX-Fun-2b-InP")
elif "5b" in model:
base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-5b-InP") # location of the official model
if not os.path.exists(base_path):
base_path = os.path.join(download_path, "CogVideoX-Fun-5b-InP")
else:
repo_id = model
base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", (model.split("/")[-1])) # location of the official model
if not os.path.exists(base_path):
base_path = os.path.join(download_path, (model.split("/")[-1]))
download_path = base_path
subfolder = "transformer"
allow_patterns = ["*transformer*", "*scheduler*", "*vae*"]
elif "2b" in model:
if 'img2vid' in model:
base_path = os.path.join(download_path, "cogvideox-2b-img2vid")
download_path = base_path
repo_id = model
else:
base_path = os.path.join(download_path, "CogVideo2B")
download_path = base_path
repo_id = model
subfolder = "transformer"
allow_patterns = ["*transformer*", "*scheduler*", "*vae*"]
elif "1.5-T2V" in model or "1.5-I2V" in model:
base_path = os.path.join(download_path, "CogVideoX-5b-1.5")
download_path = base_path
subfolder = "transformer_T2V" if "1.5-T2V" in model else "transformer_I2V"
allow_patterns = [f"*{subfolder}*", "*vae*", "*scheduler*"]
repo_id = "kijai/CogVideoX-5b-1.5"
else:
base_path = os.path.join(download_path, (model.split("/")[-1]))
download_path = base_path
repo_id = model
subfolder = "transformer"
allow_patterns = ["*transformer*", "*scheduler*", "*vae*"]
if "2b" in model:
scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
else:
scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
if not os.path.exists(base_path) or not os.path.exists(os.path.join(base_path, subfolder)):
log.info(f"Downloading model to: {base_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id=repo_id,
allow_patterns=allow_patterns,
ignore_patterns=["*text_encoder*", "*tokenizer*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
transformer = CogVideoXTransformer3DModel.from_pretrained(base_path, subfolder=subfolder, attention_mode=attention_mode)
transformer = transformer.to(dtype).to(transformer_load_device)
if "1.5" in model and not "fun" in model:
transformer.config.sample_height = 300
transformer.config.sample_width = 300
if block_edit is not None:
transformer = remove_specific_blocks(transformer, block_edit)
with open(scheduler_path) as f:
scheduler_config = json.load(f)
scheduler = CogVideoXDDIMScheduler.from_config(scheduler_config)
# VAE
vae = AutoencoderKLCogVideoX.from_pretrained(base_path, subfolder="vae").to(dtype).to(offload_device)
#pipeline
pipe = CogVideoXPipeline(
transformer,
scheduler,
dtype=dtype,
is_fun_inpaint="fun" in model.lower() and not ("pose" in model.lower() or "control" in model.lower())
)
if "cogvideox-2b-img2vid" in model:
pipe.input_with_padding = False
#LoRAs
if lora is not None:
dimensionx_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
dimensionx_lora = False
adapter_list = []
adapter_weights = []
for l in lora:
if any(item in l["path"].lower() for item in dimensionx_loras):
dimensionx_lora = True
fuse = True if l["fuse_lora"] else False
lora_sd = load_torch_file(l["path"])
lora_rank = None
for key, val in lora_sd.items():
if "lora_B" in key:
lora_rank = val.shape[1]
break
if lora_rank is not None:
log.info(f"Merging rank {lora_rank} LoRA weights from {l['path']} with strength {l['strength']}")
adapter_name = l['path'].split("/")[-1].split(".")[0]
adapter_weight = l['strength']
pipe.load_lora_weights(l['path'], weight_name=l['path'].split("/")[-1], lora_rank=lora_rank, adapter_name=adapter_name)
adapter_list.append(adapter_name)
adapter_weights.append(adapter_weight)
else:
try: #Fun trainer LoRAs are loaded differently
from .lora_utils import merge_lora
log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
pipe.transformer = merge_lora(pipe.transformer, l["path"], l["strength"], device=transformer_load_device, state_dict=lora_sd)
except:
raise ValueError(f"Can't recognize LoRA {l['path']}")
del lora_sd
mm.soft_empty_cache()
if adapter_list:
pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
if fuse:
lora_scale = 1
if dimensionx_lora:
lora_scale = lora_scale / lora_rank
pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])
pipe.delete_adapters(adapter_list)
if "fused" in attention_mode:
from diffusers.models.attention import Attention
pipe.transformer.fuse_qkv_projections = True
for module in pipe.transformer.modules():
if isinstance(module, Attention):
module.fuse_projections(fuse=True)
if compile_args is not None:
pipe.transformer.to(memory_format=torch.channels_last)
#fp8
if quantization == "fp8_e4m3fn" or quantization == "fp8_e4m3fn_fastmode":
params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding", "norm_k", "norm_q", "to_k.bias", "to_q.bias", "to_v.bias"}
if "1.5" in model:
params_to_keep.update({"norm1.linear.weight", "ofs_embedding", "norm_final", "norm_out", "proj_out"})
for name, param in pipe.transformer.named_parameters():
if not any(keyword in name for keyword in params_to_keep):
param.data = param.data.to(torch.float8_e4m3fn)
if quantization == "fp8_e4m3fn_fastmode":
from .fp8_optimization import convert_fp8_linear
if "1.5" in model:
params_to_keep.update({"ff"}) #otherwise NaNs
convert_fp8_linear(pipe.transformer, dtype, params_to_keep=params_to_keep)
# compilation
if compile_args is not None:
torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
for i, block in enumerate(pipe.transformer.transformer_blocks):
if "CogVideoXBlock" in str(block):
pipe.transformer.transformer_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
if "torchao" in quantization:
try:
from torchao.quantization import (
quantize_,
fpx_weight_only,
float8_dynamic_activation_float8_weight,
int8_dynamic_activation_int8_weight
)
except:
raise ImportError("torchao is not installed, please install torchao to use fp8dq")
def filter_fn(module: nn.Module, fqn: str) -> bool:
target_submodules = {'attn1', 'ff'} # avoid norm layers, 1.5 at least won't work with quantized norm1 #todo: test other models
if any(sub in fqn for sub in target_submodules):
return isinstance(module, nn.Linear)
return False
if "fp6" in quantization: #slower for some reason on 4090
quant_func = fpx_weight_only(3, 2)
elif "fp8dq" in quantization: #very fast on 4090 when compiled
quant_func = float8_dynamic_activation_float8_weight()
elif 'fp8dqrow' in quantization:
from torchao.quantization.quant_api import PerRow
quant_func = float8_dynamic_activation_float8_weight(granularity=PerRow())
elif 'int8dq' in quantization:
quant_func = int8_dynamic_activation_int8_weight()
for i, block in enumerate(pipe.transformer.transformer_blocks):
if "CogVideoXBlock" in str(block):
quantize_(block, quant_func, filter_fn=filter_fn)
manual_offloading = False # to disable manual .to(device) calls
if enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
manual_offloading = False
# CogVideoXBlock(
# (norm1): CogVideoXLayerNormZero(
# (silu): SiLU()
# (linear): Linear(in_features=512, out_features=18432, bias=True)
# (norm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)
# )
# (attn1): Attention(
# (norm_q): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
# (norm_k): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
# (to_q): Linear(in_features=3072, out_features=3072, bias=True)
# (to_k): Linear(in_features=3072, out_features=3072, bias=True)
# (to_v): Linear(in_features=3072, out_features=3072, bias=True)
# (to_out): ModuleList(
# (0): Linear(in_features=3072, out_features=3072, bias=True)
# (1): Dropout(p=0.0, inplace=False)
# )
# )
# (norm2): CogVideoXLayerNormZero(
# (silu): SiLU()
# (linear): Linear(in_features=512, out_features=18432, bias=True)
# (norm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)
# )
# (ff): FeedForward(
# (net): ModuleList(
# (0): GELU(
# (proj): Linear(in_features=3072, out_features=12288, bias=True)
# )
# (1): Dropout(p=0.0, inplace=False)
# (2): Linear(in_features=12288, out_features=3072, bias=True)
# (3): Dropout(p=0.0, inplace=False)
# )
# )
# )
# if compile == "onediff":
# from onediffx import compile_pipe
# os.environ['NEXFORT_FX_FORCE_TRITON_SDPA'] = '1'
# pipe = compile_pipe(
# pipe,
# backend="nexfort",
# options= {"mode": "max-optimize:max-autotune:max-autotune", "memory_format": "channels_last", "options": {"inductor.optimize_linear_epilogue": False, "triton.fuse_attention_allow_fp16_reduction": False}},
# ignores=["vae"],
# fuse_qkv_projections= False,
# )
pipeline = {
"pipe": pipe,
"dtype": dtype,
"quantization": quantization,
"base_path": base_path,
"onediff": True if compile == "onediff" else False,
"cpu_offloading": enable_sequential_cpu_offload,
"manual_offloading": manual_offloading,
"scheduler_config": scheduler_config,
"model_name": model,
}
return (pipeline, vae)
#region GGUF
class DownloadAndLoadCogVideoGGUFModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (
[
"CogVideoX_5b_GGUF_Q4_0.safetensors",
"CogVideoX_5b_I2V_GGUF_Q4_0.safetensors",
"CogVideoX_5b_1_5_I2V_GGUF_Q4_0.safetensors",
"CogVideoX_5b_fun_GGUF_Q4_0.safetensors",
"CogVideoX_5b_fun_1_1_GGUF_Q4_0.safetensors",
"CogVideoX_5b_fun_1_1_Pose_GGUF_Q4_0.safetensors",
"CogVideoX_5b_Interpolation_GGUF_Q4_0.safetensors",
"CogVideoX_5b_Tora_GGUF_Q4_0.safetensors",
],
),
"vae_precision": (["fp16", "fp32", "bf16"], {"default": "bf16", "tooltip": "VAE dtype"}),
"fp8_fastmode": ("BOOLEAN", {"default": False, "tooltip": "only supported on 4090 and later GPUs, also requires torch 2.4.0 with cu124 minimum"}),
"load_device": (["main_device", "offload_device"], {"default": "main_device"}),
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
},
"optional": {
"block_edit": ("TRANSFORMERBLOCKS", {"default": None}),
#"compile_args":("COMPILEARGS", ),
"attention_mode": (["sdpa", "sageattn"], {"default": "sdpa"}),
}
}
RETURN_TYPES = ("COGVIDEOMODEL", "VAE",)
RETURN_NAMES = ("model", "vae",)
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
def loadmodel(self, model, vae_precision, fp8_fastmode, load_device, enable_sequential_cpu_offload,
block_edit=None, compile_args=None, attention_mode="sdpa"):
if "sage" in attention_mode:
try:
from sageattention import sageattn
except Exception as e:
raise ValueError(f"Can't import SageAttention: {str(e)}")
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
mm.soft_empty_cache()
vae_dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[vae_precision]
download_path = os.path.join(folder_paths.models_dir, 'CogVideo', 'GGUF')
gguf_path = os.path.join(folder_paths.models_dir, 'diffusion_models', model) # check MinusZone's model path first
if not os.path.exists(gguf_path):
gguf_path = os.path.join(download_path, model)
if not os.path.exists(gguf_path):
if "I2V" in model or "1_1" in model or "Interpolation" in model or "Tora" in model:
repo_id = "Kijai/CogVideoX_GGUF"
else:
repo_id = "MinusZoneAI/ComfyUI-CogVideoX-MZ"
log.info(f"Downloading model to: {gguf_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id=repo_id,
allow_patterns=[f"*{model}*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
if "5b" in model:
scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
transformer_path = os.path.join(script_directory, 'configs', 'transformer_config_5b.json')
elif "2b" in model:
scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
transformer_path = os.path.join(script_directory, 'configs', 'transformer_config_2b.json')
with open(transformer_path) as f:
transformer_config = json.load(f)
from . import mz_gguf_loader
import importlib
importlib.reload(mz_gguf_loader)
with mz_gguf_loader.quantize_lazy_load():
if "fun" in model:
if "Pose" in model:
transformer_config["in_channels"] = 32
else:
transformer_config["in_channels"] = 33
elif "I2V" in model or "Interpolation" in model:
transformer_config["in_channels"] = 32
if "1_5" in model:
transformer_config["ofs_embed_dim"] = 512
transformer_config["use_learned_positional_embeddings"] = False
transformer_config["patch_size_t"] = 2
transformer_config["patch_bias"] = False
transformer_config["sample_height"] = 300
transformer_config["sample_width"] = 300
else:
transformer_config["in_channels"] = 16
transformer = CogVideoXTransformer3DModel.from_config(transformer_config, attention_mode=attention_mode)
cast_dtype = vae_dtype
params_to_keep = {"patch_embed", "pos_embedding", "time_embedding"}
if "2b" in model:
cast_dtype = torch.float16
elif "1_5" in model:
params_to_keep = {"norm1.linear.weight", "patch_embed", "time_embedding", "ofs_embedding", "norm_final", "norm_out", "proj_out"}
cast_dtype = torch.bfloat16
for name, param in transformer.named_parameters():
if not any(keyword in name for keyword in params_to_keep):
param.data = param.data.to(torch.float8_e4m3fn)
else:
param.data = param.data.to(cast_dtype)
#for name, param in transformer.named_parameters():
# print(name, param.data.dtype)
if block_edit is not None:
transformer = remove_specific_blocks(transformer, block_edit)
transformer.attention_mode = attention_mode
if fp8_fastmode:
params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding"}
if "1.5" in model:
params_to_keep.update({"ff","norm1.linear.weight", "norm_k", "norm_q","ofs_embedding", "norm_final", "norm_out", "proj_out"})
from .fp8_optimization import convert_fp8_linear
convert_fp8_linear(transformer, vae_dtype, params_to_keep=params_to_keep)
with open(scheduler_path) as f:
scheduler_config = json.load(f)
scheduler = CogVideoXDDIMScheduler.from_config(scheduler_config, subfolder="scheduler")
# VAE
vae_dl_path = os.path.join(folder_paths.models_dir, 'CogVideo', 'VAE')
vae_path = os.path.join(vae_dl_path, "cogvideox_vae.safetensors")
if not os.path.exists(vae_path):
log.info(f"Downloading VAE model to: {vae_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Kijai/CogVideoX-Fun-pruned",
allow_patterns=["*cogvideox_vae.safetensors*"],
local_dir=vae_dl_path,
local_dir_use_symlinks=False,
)
with open(os.path.join(script_directory, 'configs', 'vae_config.json')) as f:
vae_config = json.load(f)
#VAE
vae_sd = load_torch_file(vae_path)
vae = AutoencoderKLCogVideoX.from_config(vae_config).to(vae_dtype).to(offload_device)
vae.load_state_dict(vae_sd)
del vae_sd
pipe = CogVideoXPipeline(
transformer,
scheduler,
dtype=vae_dtype,
is_fun_inpaint="fun" in model.lower() and not ("pose" in model.lower() or "control" in model.lower())
)
if enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
sd = load_torch_file(gguf_path)
pipe.transformer = mz_gguf_loader.quantize_load_state_dict(pipe.transformer, sd, device="cpu")
del sd
if load_device == "offload_device":
pipe.transformer.to(offload_device)
else:
pipe.transformer.to(device)
pipeline = {
"pipe": pipe,
"dtype": vae_dtype,
"quantization": "GGUF",
"base_path": model,
"onediff": False,
"cpu_offloading": enable_sequential_cpu_offload,
"scheduler_config": scheduler_config,
"model_name": model,
"manual_offloading": True,
}
return (pipeline, vae)
#region ModelLoader
class CogVideoXModelLoader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
"base_precision": (["fp16", "fp32", "bf16"], {"default": "bf16"}),
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6"], {"default": 'disabled', "tooltip": "optional quantization method"}),
"load_device": (["main_device", "offload_device"], {"default": "main_device"}),
"enable_sequential_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "significantly reducing memory usage and slows down the inference"}),
},
"optional": {
"block_edit": ("TRANSFORMERBLOCKS", {"default": None}),
"lora": ("COGLORA", {"default": None}),
"compile_args":("COMPILEARGS", ),
"attention_mode": ([
"sdpa",
"fused_sdpa",
"sageattn",
"fused_sageattn",
"sageattn_qk_int8_pv_fp8_cuda",
"sageattn_qk_int8_pv_fp16_cuda",
"sageattn_qk_int8_pv_fp16_triton",
"fused_sageattn_qk_int8_pv_fp8_cuda",
"fused_sageattn_qk_int8_pv_fp16_cuda",
"fused_sageattn_qk_int8_pv_fp16_triton",
"comfy"
], {"default": "sdpa"}),
}
}
RETURN_TYPES = ("COGVIDEOMODEL",)
RETURN_NAMES = ("model", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
def loadmodel(self, model, base_precision, load_device, enable_sequential_cpu_offload,
block_edit=None, compile_args=None, lora=None, attention_mode="sdpa", quantization="disabled"):
transformer = None
if "sage" in attention_mode:
try:
from sageattention import sageattn
except Exception as e:
raise ValueError(f"Can't import SageAttention: {str(e)}")
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
manual_offloading = True
transformer_load_device = device if load_device == "main_device" else offload_device
mm.soft_empty_cache()
base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[base_precision]
model_path = folder_paths.get_full_path_or_raise("diffusion_models", model)
sd = load_torch_file(model_path, device=transformer_load_device)
model_type = ""
if sd["patch_embed.proj.weight"].shape == (3072, 33, 2, 2):
model_type = "fun_5b"
elif sd["patch_embed.proj.weight"].shape == (3072, 16, 2, 2):
model_type = "5b"
elif sd["patch_embed.proj.weight"].shape == (3072, 128):
model_type = "5b_1_5"
elif sd["patch_embed.proj.weight"].shape == (3072, 256):
model_type = "5b_I2V_1_5"
elif sd["patch_embed.proj.weight"].shape == (1920, 33, 2, 2):
model_type = "fun_2b"
elif sd["patch_embed.proj.weight"].shape == (1920, 32, 2, 2):
model_type = "cogvideox-2b-img2vid"
elif sd["patch_embed.proj.weight"].shape == (1920, 16, 2, 2):
model_type = "2b"
elif sd["patch_embed.proj.weight"].shape == (3072, 32, 2, 2):
if "pos_embedding" in sd:
model_type = "fun_5b_pose"
else:
model_type = "I2V_5b"
else:
raise Exception("Selected model is not recognized")
log.info(f"Detected CogVideoX model type: {model_type}")
if "5b" in model_type:
scheduler_config_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
transformer_config_path = os.path.join(script_directory, 'configs', 'transformer_config_5b.json')
elif "2b" in model_type:
scheduler_config_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
transformer_config_path = os.path.join(script_directory, 'configs', 'transformer_config_2b.json')
with open(transformer_config_path) as f:
transformer_config = json.load(f)
if model_type in ["I2V", "I2V_5b", "fun_5b_pose", "5b_I2V_1_5", "cogvideox-2b-img2vid"]:
transformer_config["in_channels"] = 32
if "1_5" in model_type:
transformer_config["ofs_embed_dim"] = 512
elif "fun" in model_type:
transformer_config["in_channels"] = 33
else:
transformer_config["in_channels"] = 16
if "1_5" in model_type:
transformer_config["use_learned_positional_embeddings"] = False
transformer_config["patch_size_t"] = 2
transformer_config["patch_bias"] = False
transformer_config["sample_height"] = 300
transformer_config["sample_width"] = 300
with init_empty_weights():
transformer = CogVideoXTransformer3DModel.from_config(transformer_config, attention_mode=attention_mode)
#load weights
#params_to_keep = {}
log.info("Using accelerate to load and assign model weights to device...")
for name, param in transformer.named_parameters():
#dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype
set_module_tensor_to_device(transformer, name, device=transformer_load_device, dtype=base_dtype, value=sd[name])
del sd
# TODO fix for transformer model patch_embed.pos_embedding dtype
# or at add line ComfyUI-CogVideoXWrapper/embeddings.py:129 code
# pos_embedding = pos_embedding.to(embeds.device, dtype=embeds.dtype)
transformer = transformer.to(base_dtype).to(transformer_load_device)
#scheduler
with open(scheduler_config_path) as f:
scheduler_config = json.load(f)
scheduler = CogVideoXDDIMScheduler.from_config(scheduler_config, subfolder="scheduler")
if block_edit is not None:
transformer = remove_specific_blocks(transformer, block_edit)
if "fused" in attention_mode:
from diffusers.models.attention import Attention
transformer.fuse_qkv_projections = True
for module in transformer.modules():
if isinstance(module, Attention):
module.fuse_projections(fuse=True)
transformer.attention_mode = attention_mode
pipe = CogVideoXPipeline(
transformer,
scheduler,
dtype=base_dtype,
is_fun_inpaint="fun" in model.lower() and not ("pose" in model.lower() or "control" in model.lower())
)
if "cogvideox-2b-img2vid" == model_type:
pipe.input_with_padding = False
if enable_sequential_cpu_offload:
pipe.enable_sequential_cpu_offload()
#LoRAs
if lora is not None:
dimensionx_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
dimensionx_lora = False
adapter_list = []
adapter_weights = []
for l in lora:
if any(item in l["path"].lower() for item in dimensionx_loras):
dimensionx_lora = True
fuse = True if l["fuse_lora"] else False
lora_sd = load_torch_file(l["path"])
lora_rank = None
for key, val in lora_sd.items():
if "lora_B" in key:
lora_rank = val.shape[1]
break
if lora_rank is not None:
log.info(f"Merging rank {lora_rank} LoRA weights from {l['path']} with strength {l['strength']}")
adapter_name = l['path'].split("/")[-1].split(".")[0]
adapter_weight = l['strength']
pipe.load_lora_weights(l['path'], weight_name=l['path'].split("/")[-1], lora_rank=lora_rank, adapter_name=adapter_name)
adapter_list.append(adapter_name)
adapter_weights.append(adapter_weight)
else:
try: #Fun trainer LoRAs are loaded differently
from .lora_utils import merge_lora
log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
pipe.transformer = merge_lora(pipe.transformer, l["path"], l["strength"], device=transformer_load_device, state_dict=lora_sd)
except:
raise ValueError(f"Can't recognize LoRA {l['path']}")
if adapter_list:
pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
if fuse:
lora_scale = 1
if dimensionx_lora:
lora_scale = lora_scale / lora_rank
pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])
if compile_args is not None:
pipe.transformer.to(memory_format=torch.channels_last)
#quantization
if quantization == "fp8_e4m3fn" or quantization == "fp8_e4m3fn_fast":
params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding", "norm_k", "norm_q", "to_k.bias", "to_q.bias", "to_v.bias"}
if "1.5" in model:
params_to_keep.update({"norm1.linear.weight", "ofs_embedding", "norm_final", "norm_out", "proj_out"})
for name, param in pipe.transformer.named_parameters():
if not any(keyword in name for keyword in params_to_keep):
param.data = param.data.to(torch.float8_e4m3fn)
if quantization == "fp8_e4m3fn_fast":
from .fp8_optimization import convert_fp8_linear
if "1.5" in model:
params_to_keep.update({"ff"}) #otherwise NaNs
convert_fp8_linear(pipe.transformer, base_dtype, params_to_keep=params_to_keep)
#compile
if compile_args is not None:
torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
for i, block in enumerate(pipe.transformer.transformer_blocks):
if "CogVideoXBlock" in str(block):
pipe.transformer.transformer_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
if "torchao" in quantization:
try:
from torchao.quantization import (
quantize_,
fpx_weight_only,
float8_dynamic_activation_float8_weight,
int8_dynamic_activation_int8_weight
)
except:
raise ImportError("torchao is not installed, please install torchao to use fp8dq")
def filter_fn(module: nn.Module, fqn: str) -> bool:
target_submodules = {'attn1', 'ff'} # avoid norm layers, 1.5 at least won't work with quantized norm1 #todo: test other models
if any(sub in fqn for sub in target_submodules):
return isinstance(module, nn.Linear)
return False
if "fp6" in quantization: #slower for some reason on 4090
quant_func = fpx_weight_only(3, 2)
elif "fp8dq" in quantization: #very fast on 4090 when compiled
quant_func = float8_dynamic_activation_float8_weight()
elif 'fp8dqrow' in quantization:
from torchao.quantization.quant_api import PerRow
quant_func = float8_dynamic_activation_float8_weight(granularity=PerRow())
elif 'int8dq' in quantization:
quant_func = int8_dynamic_activation_int8_weight()
for i, block in enumerate(pipe.transformer.transformer_blocks):
if "CogVideoXBlock" in str(block):
quantize_(block, quant_func, filter_fn=filter_fn)
manual_offloading = False # to disable manual .to(device) calls
log.info(f"Quantized transformer blocks to {quantization}")
pipeline = {
"pipe": pipe,
"dtype": base_dtype,
"quantization": quantization,
"base_path": model,
"onediff": False,
"cpu_offloading": enable_sequential_cpu_offload,
"scheduler_config": scheduler_config,
"model_name": model,
"manual_offloading": manual_offloading,
}
return (pipeline,)
#region VAE
class CogVideoXVAELoader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae'"}),
},
"optional": {
"precision": (["fp16", "fp32", "bf16"],
{"default": "bf16"}
),
"compile_args":("COMPILEARGS", ),
}
}
RETURN_TYPES = ("VAE",)
RETURN_NAMES = ("vae", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
DESCRIPTION = "Loads CogVideoX VAE model from 'ComfyUI/models/vae'"
def loadmodel(self, model_name, precision, compile_args=None):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
with open(os.path.join(script_directory, 'configs', 'vae_config.json')) as f:
vae_config = json.load(f)
model_path = folder_paths.get_full_path("vae", model_name)
vae_sd = load_torch_file(model_path)
vae = AutoencoderKLCogVideoX.from_config(vae_config).to(dtype).to(offload_device)
vae.load_state_dict(vae_sd)
#compile
if compile_args is not None:
torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
vae = torch.compile(vae, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
return (vae,)
#region Tora
class DownloadAndLoadToraModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": (
[
"kijai/CogVideoX-5b-Tora",
"kijai/CogVideoX-5b-Tora-I2V",
],
),
},
}
RETURN_TYPES = ("TORAMODEL",)
RETURN_NAMES = ("tora_model", )
FUNCTION = "loadmodel"
CATEGORY = "CogVideoWrapper"
DESCRIPTION = "Downloads and loads the the Tora model from Huggingface to 'ComfyUI/models/CogVideo/CogVideoX-5b-Tora'"
def loadmodel(self, model):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
mm.soft_empty_cache()
download_path = folder_paths.get_folder_paths("CogVideo")[0]
from .tora.traj_module import MGF
try:
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device