File size: 12,902 Bytes
3a25a0a |
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 |
import argparse
import torch
from diffusers import HunyuanDiT2DModel
def main(args):
state_dict = torch.load(args.pt_checkpoint_path, map_location="cpu")
if args.load_key != "none":
try:
state_dict = state_dict[args.load_key]
except KeyError:
raise KeyError(
f"{args.load_key} not found in the checkpoint."
f"Please load from the following keys:{state_dict.keys()}"
)
device = "cuda"
model_config = HunyuanDiT2DModel.load_config("Tencent-Hunyuan/HunyuanDiT-Diffusers", subfolder="transformer")
model_config[
"use_style_cond_and_image_meta_size"
] = args.use_style_cond_and_image_meta_size ### version <= v1.1: True; version >= v1.2: False
# input_size -> sample_size, text_dim -> cross_attention_dim
for key in state_dict:
print("local:", key)
model = HunyuanDiT2DModel.from_config(model_config).to(device)
for key in model.state_dict():
print("diffusers:", key)
num_layers = 40
for i in range(num_layers):
# attn1
# Wkqv -> to_q, to_k, to_v
q, k, v = torch.chunk(state_dict[f"blocks.{i}.attn1.Wqkv.weight"], 3, dim=0)
q_bias, k_bias, v_bias = torch.chunk(state_dict[f"blocks.{i}.attn1.Wqkv.bias"], 3, dim=0)
state_dict[f"blocks.{i}.attn1.to_q.weight"] = q
state_dict[f"blocks.{i}.attn1.to_q.bias"] = q_bias
state_dict[f"blocks.{i}.attn1.to_k.weight"] = k
state_dict[f"blocks.{i}.attn1.to_k.bias"] = k_bias
state_dict[f"blocks.{i}.attn1.to_v.weight"] = v
state_dict[f"blocks.{i}.attn1.to_v.bias"] = v_bias
state_dict.pop(f"blocks.{i}.attn1.Wqkv.weight")
state_dict.pop(f"blocks.{i}.attn1.Wqkv.bias")
# q_norm, k_norm -> norm_q, norm_k
state_dict[f"blocks.{i}.attn1.norm_q.weight"] = state_dict[f"blocks.{i}.attn1.q_norm.weight"]
state_dict[f"blocks.{i}.attn1.norm_q.bias"] = state_dict[f"blocks.{i}.attn1.q_norm.bias"]
state_dict[f"blocks.{i}.attn1.norm_k.weight"] = state_dict[f"blocks.{i}.attn1.k_norm.weight"]
state_dict[f"blocks.{i}.attn1.norm_k.bias"] = state_dict[f"blocks.{i}.attn1.k_norm.bias"]
state_dict.pop(f"blocks.{i}.attn1.q_norm.weight")
state_dict.pop(f"blocks.{i}.attn1.q_norm.bias")
state_dict.pop(f"blocks.{i}.attn1.k_norm.weight")
state_dict.pop(f"blocks.{i}.attn1.k_norm.bias")
# out_proj -> to_out
state_dict[f"blocks.{i}.attn1.to_out.0.weight"] = state_dict[f"blocks.{i}.attn1.out_proj.weight"]
state_dict[f"blocks.{i}.attn1.to_out.0.bias"] = state_dict[f"blocks.{i}.attn1.out_proj.bias"]
state_dict.pop(f"blocks.{i}.attn1.out_proj.weight")
state_dict.pop(f"blocks.{i}.attn1.out_proj.bias")
# attn2
# kq_proj -> to_k, to_v
k, v = torch.chunk(state_dict[f"blocks.{i}.attn2.kv_proj.weight"], 2, dim=0)
k_bias, v_bias = torch.chunk(state_dict[f"blocks.{i}.attn2.kv_proj.bias"], 2, dim=0)
state_dict[f"blocks.{i}.attn2.to_k.weight"] = k
state_dict[f"blocks.{i}.attn2.to_k.bias"] = k_bias
state_dict[f"blocks.{i}.attn2.to_v.weight"] = v
state_dict[f"blocks.{i}.attn2.to_v.bias"] = v_bias
state_dict.pop(f"blocks.{i}.attn2.kv_proj.weight")
state_dict.pop(f"blocks.{i}.attn2.kv_proj.bias")
# q_proj -> to_q
state_dict[f"blocks.{i}.attn2.to_q.weight"] = state_dict[f"blocks.{i}.attn2.q_proj.weight"]
state_dict[f"blocks.{i}.attn2.to_q.bias"] = state_dict[f"blocks.{i}.attn2.q_proj.bias"]
state_dict.pop(f"blocks.{i}.attn2.q_proj.weight")
state_dict.pop(f"blocks.{i}.attn2.q_proj.bias")
# q_norm, k_norm -> norm_q, norm_k
state_dict[f"blocks.{i}.attn2.norm_q.weight"] = state_dict[f"blocks.{i}.attn2.q_norm.weight"]
state_dict[f"blocks.{i}.attn2.norm_q.bias"] = state_dict[f"blocks.{i}.attn2.q_norm.bias"]
state_dict[f"blocks.{i}.attn2.norm_k.weight"] = state_dict[f"blocks.{i}.attn2.k_norm.weight"]
state_dict[f"blocks.{i}.attn2.norm_k.bias"] = state_dict[f"blocks.{i}.attn2.k_norm.bias"]
state_dict.pop(f"blocks.{i}.attn2.q_norm.weight")
state_dict.pop(f"blocks.{i}.attn2.q_norm.bias")
state_dict.pop(f"blocks.{i}.attn2.k_norm.weight")
state_dict.pop(f"blocks.{i}.attn2.k_norm.bias")
# out_proj -> to_out
state_dict[f"blocks.{i}.attn2.to_out.0.weight"] = state_dict[f"blocks.{i}.attn2.out_proj.weight"]
state_dict[f"blocks.{i}.attn2.to_out.0.bias"] = state_dict[f"blocks.{i}.attn2.out_proj.bias"]
state_dict.pop(f"blocks.{i}.attn2.out_proj.weight")
state_dict.pop(f"blocks.{i}.attn2.out_proj.bias")
# switch norm 2 and norm 3
norm2_weight = state_dict[f"blocks.{i}.norm2.weight"]
norm2_bias = state_dict[f"blocks.{i}.norm2.bias"]
state_dict[f"blocks.{i}.norm2.weight"] = state_dict[f"blocks.{i}.norm3.weight"]
state_dict[f"blocks.{i}.norm2.bias"] = state_dict[f"blocks.{i}.norm3.bias"]
state_dict[f"blocks.{i}.norm3.weight"] = norm2_weight
state_dict[f"blocks.{i}.norm3.bias"] = norm2_bias
# norm1 -> norm1.norm
# default_modulation.1 -> norm1.linear
state_dict[f"blocks.{i}.norm1.norm.weight"] = state_dict[f"blocks.{i}.norm1.weight"]
state_dict[f"blocks.{i}.norm1.norm.bias"] = state_dict[f"blocks.{i}.norm1.bias"]
state_dict[f"blocks.{i}.norm1.linear.weight"] = state_dict[f"blocks.{i}.default_modulation.1.weight"]
state_dict[f"blocks.{i}.norm1.linear.bias"] = state_dict[f"blocks.{i}.default_modulation.1.bias"]
state_dict.pop(f"blocks.{i}.norm1.weight")
state_dict.pop(f"blocks.{i}.norm1.bias")
state_dict.pop(f"blocks.{i}.default_modulation.1.weight")
state_dict.pop(f"blocks.{i}.default_modulation.1.bias")
# mlp.fc1 -> ff.net.0, mlp.fc2 -> ff.net.2
state_dict[f"blocks.{i}.ff.net.0.proj.weight"] = state_dict[f"blocks.{i}.mlp.fc1.weight"]
state_dict[f"blocks.{i}.ff.net.0.proj.bias"] = state_dict[f"blocks.{i}.mlp.fc1.bias"]
state_dict[f"blocks.{i}.ff.net.2.weight"] = state_dict[f"blocks.{i}.mlp.fc2.weight"]
state_dict[f"blocks.{i}.ff.net.2.bias"] = state_dict[f"blocks.{i}.mlp.fc2.bias"]
state_dict.pop(f"blocks.{i}.mlp.fc1.weight")
state_dict.pop(f"blocks.{i}.mlp.fc1.bias")
state_dict.pop(f"blocks.{i}.mlp.fc2.weight")
state_dict.pop(f"blocks.{i}.mlp.fc2.bias")
# pooler -> time_extra_emb
state_dict["time_extra_emb.pooler.positional_embedding"] = state_dict["pooler.positional_embedding"]
state_dict["time_extra_emb.pooler.k_proj.weight"] = state_dict["pooler.k_proj.weight"]
state_dict["time_extra_emb.pooler.k_proj.bias"] = state_dict["pooler.k_proj.bias"]
state_dict["time_extra_emb.pooler.q_proj.weight"] = state_dict["pooler.q_proj.weight"]
state_dict["time_extra_emb.pooler.q_proj.bias"] = state_dict["pooler.q_proj.bias"]
state_dict["time_extra_emb.pooler.v_proj.weight"] = state_dict["pooler.v_proj.weight"]
state_dict["time_extra_emb.pooler.v_proj.bias"] = state_dict["pooler.v_proj.bias"]
state_dict["time_extra_emb.pooler.c_proj.weight"] = state_dict["pooler.c_proj.weight"]
state_dict["time_extra_emb.pooler.c_proj.bias"] = state_dict["pooler.c_proj.bias"]
state_dict.pop("pooler.k_proj.weight")
state_dict.pop("pooler.k_proj.bias")
state_dict.pop("pooler.q_proj.weight")
state_dict.pop("pooler.q_proj.bias")
state_dict.pop("pooler.v_proj.weight")
state_dict.pop("pooler.v_proj.bias")
state_dict.pop("pooler.c_proj.weight")
state_dict.pop("pooler.c_proj.bias")
state_dict.pop("pooler.positional_embedding")
# t_embedder -> time_embedding (`TimestepEmbedding`)
state_dict["time_extra_emb.timestep_embedder.linear_1.bias"] = state_dict["t_embedder.mlp.0.bias"]
state_dict["time_extra_emb.timestep_embedder.linear_1.weight"] = state_dict["t_embedder.mlp.0.weight"]
state_dict["time_extra_emb.timestep_embedder.linear_2.bias"] = state_dict["t_embedder.mlp.2.bias"]
state_dict["time_extra_emb.timestep_embedder.linear_2.weight"] = state_dict["t_embedder.mlp.2.weight"]
state_dict.pop("t_embedder.mlp.0.bias")
state_dict.pop("t_embedder.mlp.0.weight")
state_dict.pop("t_embedder.mlp.2.bias")
state_dict.pop("t_embedder.mlp.2.weight")
# x_embedder -> pos_embd (`PatchEmbed`)
state_dict["pos_embed.proj.weight"] = state_dict["x_embedder.proj.weight"]
state_dict["pos_embed.proj.bias"] = state_dict["x_embedder.proj.bias"]
state_dict.pop("x_embedder.proj.weight")
state_dict.pop("x_embedder.proj.bias")
# mlp_t5 -> text_embedder
state_dict["text_embedder.linear_1.bias"] = state_dict["mlp_t5.0.bias"]
state_dict["text_embedder.linear_1.weight"] = state_dict["mlp_t5.0.weight"]
state_dict["text_embedder.linear_2.bias"] = state_dict["mlp_t5.2.bias"]
state_dict["text_embedder.linear_2.weight"] = state_dict["mlp_t5.2.weight"]
state_dict.pop("mlp_t5.0.bias")
state_dict.pop("mlp_t5.0.weight")
state_dict.pop("mlp_t5.2.bias")
state_dict.pop("mlp_t5.2.weight")
# extra_embedder -> extra_embedder
state_dict["time_extra_emb.extra_embedder.linear_1.bias"] = state_dict["extra_embedder.0.bias"]
state_dict["time_extra_emb.extra_embedder.linear_1.weight"] = state_dict["extra_embedder.0.weight"]
state_dict["time_extra_emb.extra_embedder.linear_2.bias"] = state_dict["extra_embedder.2.bias"]
state_dict["time_extra_emb.extra_embedder.linear_2.weight"] = state_dict["extra_embedder.2.weight"]
state_dict.pop("extra_embedder.0.bias")
state_dict.pop("extra_embedder.0.weight")
state_dict.pop("extra_embedder.2.bias")
state_dict.pop("extra_embedder.2.weight")
# model.final_adaLN_modulation.1 -> norm_out.linear
def swap_scale_shift(weight):
shift, scale = weight.chunk(2, dim=0)
new_weight = torch.cat([scale, shift], dim=0)
return new_weight
state_dict["norm_out.linear.weight"] = swap_scale_shift(state_dict["final_layer.adaLN_modulation.1.weight"])
state_dict["norm_out.linear.bias"] = swap_scale_shift(state_dict["final_layer.adaLN_modulation.1.bias"])
state_dict.pop("final_layer.adaLN_modulation.1.weight")
state_dict.pop("final_layer.adaLN_modulation.1.bias")
# final_linear -> proj_out
state_dict["proj_out.weight"] = state_dict["final_layer.linear.weight"]
state_dict["proj_out.bias"] = state_dict["final_layer.linear.bias"]
state_dict.pop("final_layer.linear.weight")
state_dict.pop("final_layer.linear.bias")
# style_embedder
if model_config["use_style_cond_and_image_meta_size"]:
print(state_dict["style_embedder.weight"])
print(state_dict["style_embedder.weight"].shape)
state_dict["time_extra_emb.style_embedder.weight"] = state_dict["style_embedder.weight"][0:1]
state_dict.pop("style_embedder.weight")
model.load_state_dict(state_dict)
from diffusers import HunyuanDiTPipeline
if args.use_style_cond_and_image_meta_size:
pipe = HunyuanDiTPipeline.from_pretrained(
"Tencent-Hunyuan/HunyuanDiT-Diffusers", transformer=model, torch_dtype=torch.float32
)
else:
pipe = HunyuanDiTPipeline.from_pretrained(
"Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers", transformer=model, torch_dtype=torch.float32
)
pipe.to("cuda")
pipe.to(dtype=torch.float32)
if args.save:
pipe.save_pretrained(args.output_checkpoint_path)
# ### NOTE: HunyuanDiT supports both Chinese and English inputs
prompt = "一个宇航员在骑马"
# prompt = "An astronaut riding a horse"
generator = torch.Generator(device="cuda").manual_seed(0)
image = pipe(
height=1024, width=1024, prompt=prompt, generator=generator, num_inference_steps=25, guidance_scale=5.0
).images[0]
image.save("img.png")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not."
)
parser.add_argument(
"--pt_checkpoint_path", default=None, type=str, required=True, help="Path to the .pt pretrained model."
)
parser.add_argument(
"--output_checkpoint_path",
default=None,
type=str,
required=False,
help="Path to the output converted diffusers pipeline.",
)
parser.add_argument(
"--load_key", default="none", type=str, required=False, help="The key to load from the pretrained .pt file"
)
parser.add_argument(
"--use_style_cond_and_image_meta_size",
type=bool,
default=False,
help="version <= v1.1: True; version >= v1.2: False",
)
args = parser.parse_args()
main(args)
|