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Copy pathconvert_hunyuandit_to_diffusers.py
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267 lines (226 loc) · 12.6 KB
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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)
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