{{ message }}
Repository navigation
Expand file tree
/
Copy pathconvert_sana_video_to_diffusers.py
More file actions
327 lines (282 loc) · 13.2 KB
/
Copy pathconvert_sana_video_to_diffusers.py
File metadata and controls
327 lines (282 loc) · 13.2 KB
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
#!/usr/bin/env python
from __future__ import annotations
import argparse
import os
from contextlib import nullcontext
import torch
from accelerate import init_empty_weights
from huggingface_hub import hf_hub_download, snapshot_download
from termcolor import colored
from transformers import AutoModelForCausalLM, AutoTokenizer
from diffusers import (
AutoencoderKLWan,
DPMSolverMultistepScheduler,
FlowMatchEulerDiscreteScheduler,
SanaVideoPipeline,
SanaVideoTransformer3DModel,
UniPCMultistepScheduler,
)
from diffusers.utils.import_utils import is_accelerate_available
CTX = init_empty_weights if is_accelerate_available else nullcontext
ckpt_ids = ["Efficient-Large-Model/SANA-Video_2B_480p/checkpoints/SANA_Video_2B_480p.pth"]
# https://github.com/NVlabs/Sana/blob/main/inference_video_scripts/inference_sana_video.py
def main(args):
cache_dir_path = os.path.expanduser("~/.cache/huggingface/hub")
if args.orig_ckpt_path is None or args.orig_ckpt_path in ckpt_ids:
ckpt_id = args.orig_ckpt_path or ckpt_ids[0]
snapshot_download(
repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}",
cache_dir=cache_dir_path,
repo_type="model",
)
file_path = hf_hub_download(
repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}",
filename=f"{'/'.join(ckpt_id.split('/')[2:])}",
cache_dir=cache_dir_path,
repo_type="model",
)
else:
file_path = args.orig_ckpt_path
print(colored(f"Loading checkpoint from {file_path}", "green", attrs=["bold"]))
all_state_dict = torch.load(file_path, weights_only=True)
state_dict = all_state_dict.pop("state_dict")
converted_state_dict = {}
# Patch embeddings.
converted_state_dict["patch_embedding.weight"] = state_dict.pop("x_embedder.proj.weight")
converted_state_dict["patch_embedding.bias"] = state_dict.pop("x_embedder.proj.bias")
# Caption projection.
converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight")
converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias")
converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight")
converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias")
converted_state_dict["time_embed.emb.timestep_embedder.linear_1.weight"] = state_dict.pop(
"t_embedder.mlp.0.weight"
)
converted_state_dict["time_embed.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias")
converted_state_dict["time_embed.emb.timestep_embedder.linear_2.weight"] = state_dict.pop(
"t_embedder.mlp.2.weight"
)
converted_state_dict["time_embed.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias")
# Shared norm.
converted_state_dict["time_embed.linear.weight"] = state_dict.pop("t_block.1.weight")
converted_state_dict["time_embed.linear.bias"] = state_dict.pop("t_block.1.bias")
# y norm
converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight")
# scheduler
flow_shift = 8.0
if args.task == "i2v":
assert args.scheduler_type == "flow-euler", "Scheduler type must be flow-euler for i2v task."
# model config
layer_num = 20
# Positional embedding interpolation scale.
qk_norm = True
# sample size
if args.video_size == 480:
sample_size = 30 # Wan-VAE: 8xp2 downsample factor
patch_size = (1, 2, 2)
elif args.video_size == 720:
sample_size = 22 # Wan-VAE: 32xp1 downsample factor
patch_size = (1, 1, 1)
else:
raise ValueError(f"Video size {args.video_size} is not supported.")
for depth in range(layer_num):
# Transformer blocks.
converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop(
f"blocks.{depth}.scale_shift_table"
)
# Linear Attention is all you need 🤘
# Self attention.
q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0)
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v
if qk_norm is not None:
# Add Q/K normalization for self-attention (attn1) - needed for Sana-Sprint and Sana-1.5
converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_q.weight"] = state_dict.pop(
f"blocks.{depth}.attn.q_norm.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_k.weight"] = state_dict.pop(
f"blocks.{depth}.attn.k_norm.weight"
)
# Projection.
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop(
f"blocks.{depth}.attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop(
f"blocks.{depth}.attn.proj.bias"
)
# Feed-forward.
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.inverted_conv.conv.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.bias"] = state_dict.pop(
f"blocks.{depth}.mlp.inverted_conv.conv.bias"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.depth_conv.conv.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.bias"] = state_dict.pop(
f"blocks.{depth}.mlp.depth_conv.conv.bias"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_point.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.point_conv.conv.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_temp.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.t_conv.weight"
)
# Cross-attention.
q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight")
q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias")
k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0)
k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0)
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias
if qk_norm is not None:
# Add Q/K normalization for cross-attention (attn2) - needed for Sana-Sprint and Sana-1.5
converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_q.weight"] = state_dict.pop(
f"blocks.{depth}.cross_attn.q_norm.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_k.weight"] = state_dict.pop(
f"blocks.{depth}.cross_attn.k_norm.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop(
f"blocks.{depth}.cross_attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop(
f"blocks.{depth}.cross_attn.proj.bias"
)
# Final block.
converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight")
converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias")
converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table")
# Transformer
with CTX():
transformer_kwargs = {
"in_channels": 16,
"out_channels": 16,
"num_attention_heads": 20,
"attention_head_dim": 112,
"num_layers": 20,
"num_cross_attention_heads": 20,
"cross_attention_head_dim": 112,
"cross_attention_dim": 2240,
"caption_channels": 2304,
"mlp_ratio": 3.0,
"attention_bias": False,
"sample_size": sample_size,
"patch_size": patch_size,
"norm_elementwise_affine": False,
"norm_eps": 1e-6,
"qk_norm": "rms_norm_across_heads",
"rope_max_seq_len": 1024,
}
transformer = SanaVideoTransformer3DModel(**transformer_kwargs)
transformer.load_state_dict(converted_state_dict, strict=True, assign=True)
try:
state_dict.pop("y_embedder.y_embedding")
state_dict.pop("pos_embed")
state_dict.pop("logvar_linear.weight")
state_dict.pop("logvar_linear.bias")
except KeyError:
print("y_embedder.y_embedding or pos_embed not found in the state_dict")
assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}"
num_model_params = sum(p.numel() for p in transformer.parameters())
print(f"Total number of transformer parameters: {num_model_params}")
transformer = transformer.to(weight_dtype)
if not args.save_full_pipeline:
print(
colored(
f"Only saving transformer model of {args.model_type}. "
f"Set --save_full_pipeline to save the whole Pipeline",
"green",
attrs=["bold"],
)
)
transformer.save_pretrained(
os.path.join(args.dump_path, "transformer"), safe_serialization=True, max_shard_size="5GB"
)
else:
print(colored(f"Saving the whole Pipeline containing {args.model_type}", "green", attrs=["bold"]))
# VAE
vae = AutoencoderKLWan.from_pretrained(
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32
)
# Text Encoder
text_encoder_model_path = "Efficient-Large-Model/gemma-2-2b-it"
tokenizer = AutoTokenizer.from_pretrained(text_encoder_model_path)
tokenizer.padding_side = "right"
text_encoder = AutoModelForCausalLM.from_pretrained(
text_encoder_model_path, torch_dtype=torch.bfloat16
).get_decoder()
# Choose the appropriate pipeline and scheduler based on model type
# Original Sana scheduler
if args.scheduler_type == "flow-dpm_solver":
scheduler = DPMSolverMultistepScheduler(
flow_shift=flow_shift,
use_flow_sigmas=True,
prediction_type="flow_prediction",
)
elif args.scheduler_type == "flow-euler":
scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift)
elif args.scheduler_type == "uni-pc":
scheduler = UniPCMultistepScheduler(
prediction_type="flow_prediction",
use_flow_sigmas=True,
num_train_timesteps=1000,
flow_shift=flow_shift,
)
else:
raise ValueError(f"Scheduler type {args.scheduler_type} is not supported")
pipe = SanaVideoPipeline(
tokenizer=tokenizer,
text_encoder=text_encoder,
transformer=transformer,
vae=vae,
scheduler=scheduler,
)
pipe.save_pretrained(args.dump_path, safe_serialization=True, max_shard_size="5GB")
DTYPE_MAPPING = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--video_size",
default=480,
type=int,
choices=[480, 720],
required=False,
help="Video size of pretrained model, 480 or 720.",
)
parser.add_argument(
"--model_type",
default="SanaVideo",
type=str,
choices=[
"SanaVideo",
],
)
parser.add_argument(
"--scheduler_type",
default="flow-dpm_solver",
type=str,
choices=["flow-dpm_solver", "flow-euler", "uni-pc"],
help="Scheduler type to use.",
)
parser.add_argument("--task", default="t2v", type=str, required=True, help="Task to convert, t2v or i2v.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.")
parser.add_argument("--save_full_pipeline", action="store_true", help="save all the pipeline elements in one.")
parser.add_argument("--dtype", default="fp32", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.")
args = parser.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
weight_dtype = DTYPE_MAPPING[args.dtype]
main(args)
You can’t perform that action at this time.