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import argparse
import torch
from diffusers import HunyuanDiT2DControlNetModel
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."
"Please load from the following keys:{state_dict.keys()}"
)
device = "cuda"
model_config = HunyuanDiT2DControlNetModel.load_config(
"Tencent-Hunyuan/HunyuanDiT-v1.2-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
print(model_config)
for key in state_dict:
print("local:", key)
model = HunyuanDiT2DControlNetModel.from_config(model_config).to(device)
for key in model.state_dict():
print("diffusers:", key)
num_layers = 19
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")
# after_proj_list -> controlnet_blocks
state_dict[f"controlnet_blocks.{i}.weight"] = state_dict[f"after_proj_list.{i}.weight"]
state_dict[f"controlnet_blocks.{i}.bias"] = state_dict[f"after_proj_list.{i}.bias"]
state_dict.pop(f"after_proj_list.{i}.weight")
state_dict.pop(f"after_proj_list.{i}.bias")
# before_proj -> input_block
state_dict["input_block.weight"] = state_dict["before_proj.weight"]
state_dict["input_block.bias"] = state_dict["before_proj.bias"]
state_dict.pop("before_proj.weight")
state_dict.pop("before_proj.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")
# 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)
if args.save:
model.save_pretrained(args.output_checkpoint_path)
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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