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)