import argparse import os from collections import defaultdict from pathlib import Path import torch from safetensors.torch import load_file, save_file def convert_diffusers_to_hunyuan_video_lora(diffusers_state_dict): converted_state_dict = {k: diffusers_state_dict.pop(k) for k in list(diffusers_state_dict.keys())} 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", ".single_blocks": ".single_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", "txt_mlp": "ff_context", "img_mlp": "ff", "txt_mod.linear": "norm1_context.linear", "txt_norm1": "norm1.norm", "txt_norm2": "norm2_context", "modulation.linear": "norm.linear", "pre_norm": "norm.norm", "final_layer.norm_final": "norm_out.norm", "final_layer.linear": "proj_out", # "linear2": "proj_out", "fc1": "net.0.proj", "fc2": "net.2", "input_embedder": "proj_in", # txt_in "individual_token_refiner.blocks": "token_refiner.refiner_blocks", "final_layer.adaLN_modulation.1": "norm_out.linear", # "t_embedder.mlp.0": "time_text_embed.timestep_embedder.linear_1", # "t_embedder.mlp.2": "time_text_embed.timestep_embedder.linear_2", "c_embedder": "time_text_embed.text_embedder", "txt_in": "context_embedder", # "mlp": "ff", } TRANSFORMER_KEYS_RENAME_DICT_REVERSE = {v: k for k, v in TRANSFORMER_KEYS_RENAME_DICT.items()} for key in list(converted_state_dict.keys()): if "norm_out.linear" in key: weight = converted_state_dict.pop(key) scale, shift = weight.chunk(2, dim=0) new_weight = torch.cat([shift, scale], dim=0) converted_state_dict[key] = new_weight if "to_q" in key: if "single_transformer_blocks" in key: to_q = converted_state_dict.pop(key) to_k = converted_state_dict.pop(key.replace("to_q", "to_k")) to_v = converted_state_dict.pop(key.replace("to_q", "to_v")) to_out = converted_state_dict.pop(key.replace("attn.to_q", "proj_mlp")) rename_attn_key = "linear1" if "lora_A" in key: converted_state_dict[key.replace("attn.to_q", rename_attn_key)] = to_q else: qkv_mlp = torch.cat([to_q, to_k, to_v, to_out], dim=0) converted_state_dict[key.replace("attn.to_q", rename_attn_key)] = qkv_mlp else: to_q = converted_state_dict.pop(key) to_k = converted_state_dict.pop(key.replace("to_q", "to_k")) to_v = converted_state_dict.pop(key.replace("to_q", "to_v")) if "token_refiner" in key: rename_attn_key = "self_attn_qkv" if "lora_A" in key: converted_state_dict[key.replace("attn.to_q", rename_attn_key)] = to_q else: qkv = torch.cat([to_q, to_k, to_v], dim=0) converted_state_dict[key.replace("attn.to_q", rename_attn_key)] = qkv else: rename_attn_key = "img_attn_qkv" if "lora_A" in key: converted_state_dict[key.replace("attn.to_q", rename_attn_key)] = to_q else: qkv = torch.cat([to_q, to_k, to_v], dim=0) converted_state_dict[key.replace("attn.to_q", rename_attn_key)] = qkv if "add_q_proj" in key: to_q = converted_state_dict.pop(key) to_k = converted_state_dict.pop(key.replace("add_q_proj", "add_k_proj")) to_v = converted_state_dict.pop(key.replace("add_q_proj", "add_v_proj")) rename_attn_key = "txt_attn_qkv" if "lora_A" in key: converted_state_dict[key.replace("attn.add_q_proj", rename_attn_key)] = to_q else: qkv = torch.cat([to_q, to_k, to_v], dim=0) converted_state_dict[key.replace("attn.add_q_proj", rename_attn_key)] = qkv for key in list(converted_state_dict.keys()): new_key = key[:] if "token_refiner" in key and "attn.to_out.0" in new_key: new_key = new_key.replace("attn.to_out.0", "self_attn_proj") if "token_refiner" in key and "ff" in new_key: new_key = new_key.replace("ff", "mlp") if "token_refiner" in key and "norm_out.linear" in new_key: new_key = new_key.replace("norm_out.linear", "adaLN_modulation.1") if "context_embedder" in key and "time_text_embed.text_embedder.linear_1" in new_key: new_key = new_key.replace("time_text_embed.text_embedder.linear_1", "c_embedder.linear_1") if "context_embedder" in key and "time_text_embed.text_embedder.linear_2" in new_key: new_key = new_key.replace("time_text_embed.text_embedder.linear_2", "c_embedder.linear_2") if "context_embedder" in key and "time_text_embed.timestep_embedder.linear_1" in new_key: new_key = new_key.replace("time_text_embed.timestep_embedder.linear_1", "t_embedder.mlp.0") if "context_embedder" in key and "time_text_embed.timestep_embedder.linear_2" in new_key: new_key = new_key.replace("time_text_embed.timestep_embedder.linear_2", "t_embedder.mlp.2") if "single_transformer_blocks" in key and "proj_out" in new_key: new_key = new_key.replace("proj_out", "linear2") for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT_REVERSE.items(): new_key = new_key.replace(replace_key, rename_key) converted_state_dict[new_key] = converted_state_dict.pop(key) # Remove "transformer." prefix for key in list(converted_state_dict.keys()): if key.startswith("transformer."): converted_state_dict[key[len("transformer."):]] = converted_state_dict.pop(key) # Add back "diffusion_model." prefix for key in list(converted_state_dict.keys()): converted_state_dict[f"diffusion_model.{key}"] = converted_state_dict.pop(key) return converted_state_dict def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--ckpt_path", type=str, required=True) parser.add_argument("--output_path_or_name", type=str, required=True) return parser.parse_args() if __name__ == "__main__": args = get_args() if args.ckpt_path.endswith(".pt"): diffusers_state_dict = torch.load(args.ckpt_path, map_location="cpu", weights_only=True) elif args.ckpt_path.endswith(".safetensors"): diffusers_state_dict = load_file(args.ckpt_path) original_format_state_dict = convert_diffusers_to_hunyuan_video_lora(diffusers_state_dict) output_path_or_name = Path(args.output_path_or_name) if output_path_or_name.as_posix().endswith(".safetensors"): os.makedirs(output_path_or_name.parent, exist_ok=True) save_file(original_format_state_dict, output_path_or_name) else: os.makedirs(output_path_or_name, exist_ok=True) output_path_or_name = output_path_or_name / "pytorch_lora_weights.safetensors" save_file(original_format_state_dict, output_path_or_name)