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import argparse
from typing import Any, Dict
import torch
from accelerate import init_empty_weights
from transformers import AutoModel, AutoTokenizer, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKLHunyuanVideo,
FlowMatchEulerDiscreteScheduler,
HunyuanVideoPipeline,
HunyuanVideoTransformer3DModel,
)
def remap_norm_scale_shift_(key, state_dict):
weight = state_dict.pop(key)
shift, scale = weight.chunk(2, dim=0)
new_weight = torch.cat([scale, shift], dim=0)
state_dict[key.replace("final_layer.adaLN_modulation.1", "norm_out.linear")] = new_weight
def remap_txt_in_(key, state_dict):
def rename_key(key):
new_key = key.replace("individual_token_refiner.blocks", "token_refiner.refiner_blocks")
new_key = new_key.replace("adaLN_modulation.1", "norm_out.linear")
new_key = new_key.replace("txt_in", "context_embedder")
new_key = new_key.replace("t_embedder.mlp.0", "time_text_embed.timestep_embedder.linear_1")
new_key = new_key.replace("t_embedder.mlp.2", "time_text_embed.timestep_embedder.linear_2")
new_key = new_key.replace("c_embedder", "time_text_embed.text_embedder")
new_key = new_key.replace("mlp", "ff")
return new_key
if "self_attn_qkv" in key:
weight = state_dict.pop(key)
to_q, to_k, to_v = weight.chunk(3, dim=0)
state_dict[rename_key(key.replace("self_attn_qkv", "attn.to_q"))] = to_q
state_dict[rename_key(key.replace("self_attn_qkv", "attn.to_k"))] = to_k
state_dict[rename_key(key.replace("self_attn_qkv", "attn.to_v"))] = to_v
else:
state_dict[rename_key(key)] = state_dict.pop(key)
def remap_img_attn_qkv_(key, state_dict):
weight = state_dict.pop(key)
to_q, to_k, to_v = weight.chunk(3, dim=0)
state_dict[key.replace("img_attn_qkv", "attn.to_q")] = to_q
state_dict[key.replace("img_attn_qkv", "attn.to_k")] = to_k
state_dict[key.replace("img_attn_qkv", "attn.to_v")] = to_v
def remap_txt_attn_qkv_(key, state_dict):
weight = state_dict.pop(key)
to_q, to_k, to_v = weight.chunk(3, dim=0)
state_dict[key.replace("txt_attn_qkv", "attn.add_q_proj")] = to_q
state_dict[key.replace("txt_attn_qkv", "attn.add_k_proj")] = to_k
state_dict[key.replace("txt_attn_qkv", "attn.add_v_proj")] = to_v
def remap_single_transformer_blocks_(key, state_dict):
hidden_size = 3072
if "linear1.weight" in key:
linear1_weight = state_dict.pop(key)
split_size = (hidden_size, hidden_size, hidden_size, linear1_weight.size(0) - 3 * hidden_size)
q, k, v, mlp = torch.split(linear1_weight, split_size, dim=0)
new_key = key.replace("single_blocks", "single_transformer_blocks").removesuffix(".linear1.weight")
state_dict[f"{new_key}.attn.to_q.weight"] = q
state_dict[f"{new_key}.attn.to_k.weight"] = k
state_dict[f"{new_key}.attn.to_v.weight"] = v
state_dict[f"{new_key}.proj_mlp.weight"] = mlp
elif "linear1.bias" in key:
linear1_bias = state_dict.pop(key)
split_size = (hidden_size, hidden_size, hidden_size, linear1_bias.size(0) - 3 * hidden_size)
q_bias, k_bias, v_bias, mlp_bias = torch.split(linear1_bias, split_size, dim=0)
new_key = key.replace("single_blocks", "single_transformer_blocks").removesuffix(".linear1.bias")
state_dict[f"{new_key}.attn.to_q.bias"] = q_bias
state_dict[f"{new_key}.attn.to_k.bias"] = k_bias
state_dict[f"{new_key}.attn.to_v.bias"] = v_bias
state_dict[f"{new_key}.proj_mlp.bias"] = mlp_bias
else:
new_key = key.replace("single_blocks", "single_transformer_blocks")
new_key = new_key.replace("linear2", "proj_out")
new_key = new_key.replace("q_norm", "attn.norm_q")
new_key = new_key.replace("k_norm", "attn.norm_k")
state_dict[new_key] = state_dict.pop(key)
TRANSFORMER_KEYS_RENAME_DICT = {
"img_in": "x_embedder",
"time_in.mlp.0": "time_text_embed.timestep_embedder.linear_1",
"time_in.mlp.2": "time_text_embed.timestep_embedder.linear_2",
"guidance_in.mlp.0": "time_text_embed.guidance_embedder.linear_1",
"guidance_in.mlp.2": "time_text_embed.guidance_embedder.linear_2",
"vector_in.in_layer": "time_text_embed.text_embedder.linear_1",
"vector_in.out_layer": "time_text_embed.text_embedder.linear_2",
"double_blocks": "transformer_blocks",
"img_attn_q_norm": "attn.norm_q",
"img_attn_k_norm": "attn.norm_k",
"img_attn_proj": "attn.to_out.0",
"txt_attn_q_norm": "attn.norm_added_q",
"txt_attn_k_norm": "attn.norm_added_k",
"txt_attn_proj": "attn.to_add_out",
"img_mod.linear": "norm1.linear",
"img_norm1": "norm1.norm",
"img_norm2": "norm2",
"img_mlp": "ff",
"txt_mod.linear": "norm1_context.linear",
"txt_norm1": "norm1.norm",
"txt_norm2": "norm2_context",
"txt_mlp": "ff_context",
"self_attn_proj": "attn.to_out.0",
"modulation.linear": "norm.linear",
"pre_norm": "norm.norm",
"final_layer.norm_final": "norm_out.norm",
"final_layer.linear": "proj_out",
"fc1": "net.0.proj",
"fc2": "net.2",
"input_embedder": "proj_in",
}
TRANSFORMER_SPECIAL_KEYS_REMAP = {
"txt_in": remap_txt_in_,
"img_attn_qkv": remap_img_attn_qkv_,
"txt_attn_qkv": remap_txt_attn_qkv_,
"single_blocks": remap_single_transformer_blocks_,
"final_layer.adaLN_modulation.1": remap_norm_scale_shift_,
}
VAE_KEYS_RENAME_DICT = {}
VAE_SPECIAL_KEYS_REMAP = {}
def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]:
state_dict[new_key] = state_dict.pop(old_key)
def get_state_dict(saved_dict: Dict[str, Any]) -> Dict[str, Any]:
state_dict = saved_dict
if "model" in saved_dict.keys():
state_dict = state_dict["model"]
if "module" in saved_dict.keys():
state_dict = state_dict["module"]
if "state_dict" in saved_dict.keys():
state_dict = state_dict["state_dict"]
return state_dict
def convert_transformer(ckpt_path: str):
original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", weights_only=True))
with init_empty_weights():
transformer = HunyuanVideoTransformer3DModel()
for key in list(original_state_dict.keys()):
new_key = key[:]
for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
new_key = new_key.replace(replace_key, rename_key)
update_state_dict_(original_state_dict, key, new_key)
for key in list(original_state_dict.keys()):
for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items():
if special_key not in key:
continue
handler_fn_inplace(key, original_state_dict)
transformer.load_state_dict(original_state_dict, strict=True, assign=True)
return transformer
def convert_vae(ckpt_path: str):
original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", weights_only=True))
with init_empty_weights():
vae = AutoencoderKLHunyuanVideo()
for key in list(original_state_dict.keys()):
new_key = key[:]
for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items():
new_key = new_key.replace(replace_key, rename_key)
update_state_dict_(original_state_dict, key, new_key)
for key in list(original_state_dict.keys()):
for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items():
if special_key not in key:
continue
handler_fn_inplace(key, original_state_dict)
vae.load_state_dict(original_state_dict, strict=True, assign=True)
return vae
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint"
)
parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original VAE checkpoint")
parser.add_argument("--text_encoder_path", type=str, default=None, help="Path to original llama checkpoint")
parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to original llama tokenizer")
parser.add_argument("--text_encoder_2_path", type=str, default=None, help="Path to original clip checkpoint")
parser.add_argument("--save_pipeline", action="store_true")
parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved")
parser.add_argument("--dtype", default="bf16", help="Torch dtype to save the transformer in.")
return parser.parse_args()
DTYPE_MAPPING = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}
if __name__ == "__main__":
args = get_args()
transformer = None
dtype = DTYPE_MAPPING[args.dtype]
if args.save_pipeline:
assert args.transformer_ckpt_path is not None and args.vae_ckpt_path is not None
assert args.text_encoder_path is not None
assert args.tokenizer_path is not None
assert args.text_encoder_2_path is not None
if args.transformer_ckpt_path is not None:
transformer = convert_transformer(args.transformer_ckpt_path)
transformer = transformer.to(dtype=dtype)
if not args.save_pipeline:
transformer.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
if args.vae_ckpt_path is not None:
vae = convert_vae(args.vae_ckpt_path)
if not args.save_pipeline:
vae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
if args.save_pipeline:
text_encoder = AutoModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path, padding_side="right")
text_encoder_2 = CLIPTextModel.from_pretrained(args.text_encoder_2_path, torch_dtype=torch.float16)
tokenizer_2 = CLIPTokenizer.from_pretrained(args.text_encoder_2_path)
scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)
pipe = HunyuanVideoPipeline(
transformer=transformer,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
scheduler=scheduler,
)
pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
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