forked from huggingface/diffusers
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathconvert_sd3_controlnet_to_diffusers.py
More file actions
185 lines (153 loc) 路 7.85 KB
/
Copy pathconvert_sd3_controlnet_to_diffusers.py
File metadata and controls
185 lines (153 loc) 路 7.85 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
"""
A script to convert Stable Diffusion 3.5 ControlNet checkpoints to the Diffusers format.
Example:
Convert a SD3.5 ControlNet checkpoint to Diffusers format using local file:
```bash
python scripts/convert_sd3_controlnet_to_diffusers.py \
--checkpoint_path "path/to/local/sd3.5_large_controlnet_canny.safetensors" \
--output_path "output/sd35-controlnet-canny" \
--dtype "fp16" # optional, defaults to fp32
```
Or download and convert from HuggingFace repository:
```bash
python scripts/convert_sd3_controlnet_to_diffusers.py \
--original_state_dict_repo_id "stabilityai/stable-diffusion-3.5-controlnets" \
--filename "sd3.5_large_controlnet_canny.safetensors" \
--output_path "/raid/yiyi/sd35-controlnet-canny-diffusers" \
--dtype "fp32" # optional, defaults to fp32
```
Note:
The script supports the following ControlNet types from SD3.5:
- Canny edge detection
- Depth estimation
- Blur detection
The checkpoint files can be downloaded from:
https://huggingface.co/stabilityai/stable-diffusion-3.5-controlnets
"""
import argparse
import safetensors.torch
import torch
from huggingface_hub import hf_hub_download
from diffusers import SD3ControlNetModel
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", type=str, default=None, help="Path to local checkpoint file")
parser.add_argument(
"--original_state_dict_repo_id", type=str, default=None, help="HuggingFace repo ID containing the checkpoint"
)
parser.add_argument("--filename", type=str, default=None, help="Filename of the checkpoint in the HF repo")
parser.add_argument("--output_path", type=str, required=True, help="Path to save the converted model")
parser.add_argument(
"--dtype", type=str, default="fp32", help="Data type for the converted model (fp16, bf16, or fp32)"
)
args = parser.parse_args()
def load_original_checkpoint(args):
if args.original_state_dict_repo_id is not None:
if args.filename is None:
raise ValueError("When using `original_state_dict_repo_id`, `filename` must also be specified")
print(f"Downloading checkpoint from {args.original_state_dict_repo_id}/{args.filename}")
ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename)
elif args.checkpoint_path is not None:
print(f"Loading checkpoint from local path: {args.checkpoint_path}")
ckpt_path = args.checkpoint_path
else:
raise ValueError("Please provide either `original_state_dict_repo_id` or a local `checkpoint_path`")
original_state_dict = safetensors.torch.load_file(ckpt_path)
return original_state_dict
def convert_sd3_controlnet_checkpoint_to_diffusers(original_state_dict):
converted_state_dict = {}
# Direct mappings for controlnet blocks
for i in range(19): # 19 controlnet blocks
converted_state_dict[f"controlnet_blocks.{i}.weight"] = original_state_dict[f"controlnet_blocks.{i}.weight"]
converted_state_dict[f"controlnet_blocks.{i}.bias"] = original_state_dict[f"controlnet_blocks.{i}.bias"]
# Positional embeddings
converted_state_dict["pos_embed_input.proj.weight"] = original_state_dict["pos_embed_input.proj.weight"]
converted_state_dict["pos_embed_input.proj.bias"] = original_state_dict["pos_embed_input.proj.bias"]
# Time and text embeddings
time_text_mappings = {
"time_text_embed.timestep_embedder.linear_1.weight": "time_text_embed.timestep_embedder.linear_1.weight",
"time_text_embed.timestep_embedder.linear_1.bias": "time_text_embed.timestep_embedder.linear_1.bias",
"time_text_embed.timestep_embedder.linear_2.weight": "time_text_embed.timestep_embedder.linear_2.weight",
"time_text_embed.timestep_embedder.linear_2.bias": "time_text_embed.timestep_embedder.linear_2.bias",
"time_text_embed.text_embedder.linear_1.weight": "time_text_embed.text_embedder.linear_1.weight",
"time_text_embed.text_embedder.linear_1.bias": "time_text_embed.text_embedder.linear_1.bias",
"time_text_embed.text_embedder.linear_2.weight": "time_text_embed.text_embedder.linear_2.weight",
"time_text_embed.text_embedder.linear_2.bias": "time_text_embed.text_embedder.linear_2.bias",
}
for new_key, old_key in time_text_mappings.items():
if old_key in original_state_dict:
converted_state_dict[new_key] = original_state_dict[old_key]
# Transformer blocks
for i in range(19):
# Split QKV into separate Q, K, V
qkv_weight = original_state_dict[f"transformer_blocks.{i}.attn.qkv.weight"]
qkv_bias = original_state_dict[f"transformer_blocks.{i}.attn.qkv.bias"]
q, k, v = torch.chunk(qkv_weight, 3, dim=0)
q_bias, k_bias, v_bias = torch.chunk(qkv_bias, 3, dim=0)
block_mappings = {
f"transformer_blocks.{i}.attn.to_q.weight": q,
f"transformer_blocks.{i}.attn.to_q.bias": q_bias,
f"transformer_blocks.{i}.attn.to_k.weight": k,
f"transformer_blocks.{i}.attn.to_k.bias": k_bias,
f"transformer_blocks.{i}.attn.to_v.weight": v,
f"transformer_blocks.{i}.attn.to_v.bias": v_bias,
# Output projections
f"transformer_blocks.{i}.attn.to_out.0.weight": original_state_dict[
f"transformer_blocks.{i}.attn.proj.weight"
],
f"transformer_blocks.{i}.attn.to_out.0.bias": original_state_dict[
f"transformer_blocks.{i}.attn.proj.bias"
],
# Feed forward
f"transformer_blocks.{i}.ff.net.0.proj.weight": original_state_dict[
f"transformer_blocks.{i}.mlp.fc1.weight"
],
f"transformer_blocks.{i}.ff.net.0.proj.bias": original_state_dict[f"transformer_blocks.{i}.mlp.fc1.bias"],
f"transformer_blocks.{i}.ff.net.2.weight": original_state_dict[f"transformer_blocks.{i}.mlp.fc2.weight"],
f"transformer_blocks.{i}.ff.net.2.bias": original_state_dict[f"transformer_blocks.{i}.mlp.fc2.bias"],
# Norms
f"transformer_blocks.{i}.norm1.linear.weight": original_state_dict[
f"transformer_blocks.{i}.adaLN_modulation.1.weight"
],
f"transformer_blocks.{i}.norm1.linear.bias": original_state_dict[
f"transformer_blocks.{i}.adaLN_modulation.1.bias"
],
}
converted_state_dict.update(block_mappings)
return converted_state_dict
def main(args):
original_ckpt = load_original_checkpoint(args)
original_dtype = next(iter(original_ckpt.values())).dtype
# Initialize dtype with fp32 as default
if args.dtype == "fp16":
dtype = torch.float16
elif args.dtype == "bf16":
dtype = torch.bfloat16
elif args.dtype == "fp32":
dtype = torch.float32
else:
raise ValueError(f"Unsupported dtype: {args.dtype}. Must be one of: fp16, bf16, fp32")
if dtype != original_dtype:
print(
f"Converting checkpoint from {original_dtype} to {dtype}. This can lead to unexpected results, proceed with caution."
)
converted_controlnet_state_dict = convert_sd3_controlnet_checkpoint_to_diffusers(original_ckpt)
controlnet = SD3ControlNetModel(
patch_size=2,
in_channels=16,
num_layers=19,
attention_head_dim=64,
num_attention_heads=38,
joint_attention_dim=None,
caption_projection_dim=2048,
pooled_projection_dim=2048,
out_channels=16,
pos_embed_max_size=None,
pos_embed_type=None,
use_pos_embed=False,
force_zeros_for_pooled_projection=False,
)
controlnet.load_state_dict(converted_controlnet_state_dict, strict=True)
print(f"Saving SD3 ControlNet in Diffusers format in {args.output_path}.")
controlnet.to(dtype).save_pretrained(args.output_path)
if __name__ == "__main__":
main(args)