convert_wan_to_diffusers.py

import argparse
import pathlib
from typing import Any, Dict

import torch
from accelerate import init_empty_weights
from huggingface_hub import hf_hub_download, snapshot_download
from safetensors.torch import load_file
from transformers import AutoProcessor, AutoTokenizer, CLIPVisionModelWithProjection, UMT5EncoderModel

from diffusers import (
    AutoencoderKLWan,
    UniPCMultistepScheduler,
    WanImageToVideoPipeline,
    WanPipeline,
    WanTransformer3DModel,
)


TRANSFORMER_KEYS_RENAME_DICT = {
    "time_embedding.0": "condition_embedder.time_embedder.linear_1",
    "time_embedding.2": "condition_embedder.time_embedder.linear_2",
    "text_embedding.0": "condition_embedder.text_embedder.linear_1",
    "text_embedding.2": "condition_embedder.text_embedder.linear_2",
    "time_projection.1": "condition_embedder.time_proj",
    "head.modulation": "scale_shift_table",
    "head.head": "proj_out",
    "modulation": "scale_shift_table",
    "ffn.0": "ffn.net.0.proj",
    "ffn.2": "ffn.net.2",
    # Hack to swap the layer names
    # The original model calls the norms in following order: norm1, norm3, norm2
    # We convert it to: norm1, norm2, norm3
    "norm2": "norm__placeholder",
    "norm3": "norm2",
    "norm__placeholder": "norm3",
    # For the I2V model
    "img_emb.proj.0": "condition_embedder.image_embedder.norm1",
    "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj",
    "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2",
    "img_emb.proj.4": "condition_embedder.image_embedder.norm2",
    # for the FLF2V model
    "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed",
    # Add attention component mappings
    "self_attn.q": "attn1.to_q",
    "self_attn.k": "attn1.to_k",
    "self_attn.v": "attn1.to_v",
    "self_attn.o": "attn1.to_out.0",
    "self_attn.norm_q": "attn1.norm_q",
    "self_attn.norm_k": "attn1.norm_k",
    "cross_attn.q": "attn2.to_q",
    "cross_attn.k": "attn2.to_k",
    "cross_attn.v": "attn2.to_v",
    "cross_attn.o": "attn2.to_out.0",
    "cross_attn.norm_q": "attn2.norm_q",
    "cross_attn.norm_k": "attn2.norm_k",
    "attn2.to_k_img": "attn2.add_k_proj",
    "attn2.to_v_img": "attn2.add_v_proj",
    "attn2.norm_k_img": "attn2.norm_added_k",
}

TRANSFORMER_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 load_sharded_safetensors(dir: pathlib.Path):
    file_paths = list(dir.glob("diffusion_pytorch_model*.safetensors"))
    state_dict = {}
    for path in file_paths:
        state_dict.update(load_file(path))
    return state_dict


def get_transformer_config(model_type: str) -> Dict[str, Any]:
    if model_type == "Wan-T2V-1.3B":
        config = {
            "model_id": "StevenZhang/Wan2.1-T2V-1.3B-Diff",
            "diffusers_config": {
                "added_kv_proj_dim": None,
                "attention_head_dim": 128,
                "cross_attn_norm": True,
                "eps": 1e-06,
                "ffn_dim": 8960,
                "freq_dim": 256,
                "in_channels": 16,
                "num_attention_heads": 12,
                "num_layers": 30,
                "out_channels": 16,
                "patch_size": [1, 2, 2],
                "qk_norm": "rms_norm_across_heads",
                "text_dim": 4096,
            },
        }
    elif model_type == "Wan-T2V-14B":
        config = {
            "model_id": "StevenZhang/Wan2.1-T2V-14B-Diff",
            "diffusers_config": {
                "added_kv_proj_dim": None,
                "attention_head_dim": 128,
                "cross_attn_norm": True,
                "eps": 1e-06,
                "ffn_dim": 13824,
                "freq_dim": 256,
                "in_channels": 16,
                "num_attention_heads": 40,
                "num_layers": 40,
                "out_channels": 16,
                "patch_size": [1, 2, 2],
                "qk_norm": "rms_norm_across_heads",
                "text_dim": 4096,
            },
        }
    elif model_type == "Wan-I2V-14B-480p":
        config = {
            "model_id": "StevenZhang/Wan2.1-I2V-14B-480P-Diff",
            "diffusers_config": {
                "image_dim": 1280,
                "added_kv_proj_dim": 5120,
                "attention_head_dim": 128,
                "cross_attn_norm": True,
                "eps": 1e-06,
                "ffn_dim": 13824,
                "freq_dim": 256,
                "in_channels": 36,
                "num_attention_heads": 40,
                "num_layers": 40,
                "out_channels": 16,
                "patch_size": [1, 2, 2],
                "qk_norm": "rms_norm_across_heads",
                "text_dim": 4096,
            },
        }
    elif model_type == "Wan-I2V-14B-720p":
        config = {
            "model_id": "StevenZhang/Wan2.1-I2V-14B-720P-Diff",
            "diffusers_config": {
                "image_dim": 1280,
                "added_kv_proj_dim": 5120,
                "attention_head_dim": 128,
                "cross_attn_norm": True,
                "eps": 1e-06,
                "ffn_dim": 13824,
                "freq_dim": 256,
                "in_channels": 36,
                "num_attention_heads": 40,
                "num_layers": 40,
                "out_channels": 16,
                "patch_size": [1, 2, 2],
                "qk_norm": "rms_norm_across_heads",
                "text_dim": 4096,
            },
        }
    elif model_type == "Wan-FLF2V-14B-720P":
        config = {
            "model_id": "ypyp/Wan2.1-FLF2V-14B-720P",  # This is just a placeholder
            "diffusers_config": {
                "image_dim": 1280,
                "added_kv_proj_dim": 5120,
                "attention_head_dim": 128,
                "cross_attn_norm": True,
                "eps": 1e-06,
                "ffn_dim": 13824,
                "freq_dim": 256,
                "in_channels": 36,
                "num_attention_heads": 40,
                "num_layers": 40,
                "out_channels": 16,
                "patch_size": [1, 2, 2],
                "qk_norm": "rms_norm_across_heads",
                "text_dim": 4096,
                "rope_max_seq_len": 1024,
                "pos_embed_seq_len": 257 * 2,
            },
        }
    return config


def convert_transformer(model_type: str):
    config = get_transformer_config(model_type)
    diffusers_config = config["diffusers_config"]
    model_id = config["model_id"]
    model_dir = pathlib.Path(snapshot_download(model_id, repo_type="model"))

    original_state_dict = load_sharded_safetensors(model_dir)

    with init_empty_weights():
        transformer = WanTransformer3DModel.from_config(diffusers_config)

    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():
    vae_ckpt_path = hf_hub_download("Wan-AI/Wan2.1-T2V-14B", "Wan2.1_VAE.pth")
    old_state_dict = torch.load(vae_ckpt_path, weights_only=True)
    new_state_dict = {}

    # Create mappings for specific components
    middle_key_mapping = {
        # Encoder middle block
        "encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma",
        "encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias",
        "encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight",
        "encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma",
        "encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias",
        "encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight",
        "encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma",
        "encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias",
        "encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight",
        "encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma",
        "encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias",
        "encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight",
        # Decoder middle block
        "decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma",
        "decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias",
        "decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight",
        "decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma",
        "decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias",
        "decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight",
        "decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma",
        "decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias",
        "decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight",
        "decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma",
        "decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias",
        "decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight",
    }

    # Create a mapping for attention blocks
    attention_mapping = {
        # Encoder middle attention
        "encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma",
        "encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight",
        "encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias",
        "encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight",
        "encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias",
        # Decoder middle attention
        "decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma",
        "decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight",
        "decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias",
        "decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight",
        "decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias",
    }

    # Create a mapping for the head components
    head_mapping = {
        # Encoder head
        "encoder.head.0.gamma": "encoder.norm_out.gamma",
        "encoder.head.2.bias": "encoder.conv_out.bias",
        "encoder.head.2.weight": "encoder.conv_out.weight",
        # Decoder head
        "decoder.head.0.gamma": "decoder.norm_out.gamma",
        "decoder.head.2.bias": "decoder.conv_out.bias",
        "decoder.head.2.weight": "decoder.conv_out.weight",
    }

    # Create a mapping for the quant components
    quant_mapping = {
        "conv1.weight": "quant_conv.weight",
        "conv1.bias": "quant_conv.bias",
        "conv2.weight": "post_quant_conv.weight",
        "conv2.bias": "post_quant_conv.bias",
    }

    # Process each key in the state dict
    for key, value in old_state_dict.items():
        # Handle middle block keys using the mapping
        if key in middle_key_mapping:
            new_key = middle_key_mapping[key]
            new_state_dict[new_key] = value
        # Handle attention blocks using the mapping
        elif key in attention_mapping:
            new_key = attention_mapping[key]
            new_state_dict[new_key] = value
        # Handle head keys using the mapping
        elif key in head_mapping:
            new_key = head_mapping[key]
            new_state_dict[new_key] = value
        # Handle quant keys using the mapping
        elif key in quant_mapping:
            new_key = quant_mapping[key]
            new_state_dict[new_key] = value
        # Handle encoder conv1
        elif key == "encoder.conv1.weight":
            new_state_dict["encoder.conv_in.weight"] = value
        elif key == "encoder.conv1.bias":
            new_state_dict["encoder.conv_in.bias"] = value
        # Handle decoder conv1
        elif key == "decoder.conv1.weight":
            new_state_dict["decoder.conv_in.weight"] = value
        elif key == "decoder.conv1.bias":
            new_state_dict["decoder.conv_in.bias"] = value
        # Handle encoder downsamples
        elif key.startswith("encoder.downsamples."):
            # Convert to down_blocks
            new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.")

            # Convert residual block naming but keep the original structure
            if ".residual.0.gamma" in new_key:
                new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma")
            elif ".residual.2.bias" in new_key:
                new_key = new_key.replace(".residual.2.bias", ".conv1.bias")
            elif ".residual.2.weight" in new_key:
                new_key = new_key.replace(".residual.2.weight", ".conv1.weight")
            elif ".residual.3.gamma" in new_key:
                new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma")
            elif ".residual.6.bias" in new_key:
                new_key = new_key.replace(".residual.6.bias", ".conv2.bias")
            elif ".residual.6.weight" in new_key:
                new_key = new_key.replace(".residual.6.weight", ".conv2.weight")
            elif ".shortcut.bias" in new_key:
                new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias")
            elif ".shortcut.weight" in new_key:
                new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight")

            new_state_dict[new_key] = value

        # Handle decoder upsamples
        elif key.startswith("decoder.upsamples."):
            # Convert to up_blocks
            parts = key.split(".")
            block_idx = int(parts[2])

            # Group residual blocks
            if "residual" in key:
                if block_idx in [0, 1, 2]:
                    new_block_idx = 0
                    resnet_idx = block_idx
                elif block_idx in [4, 5, 6]:
                    new_block_idx = 1
                    resnet_idx = block_idx - 4
                elif block_idx in [8, 9, 10]:
                    new_block_idx = 2
                    resnet_idx = block_idx - 8
                elif block_idx in [12, 13, 14]:
                    new_block_idx = 3
                    resnet_idx = block_idx - 12
                else:
                    # Keep as is for other blocks
                    new_state_dict[key] = value
                    continue

                # Convert residual block naming
                if ".residual.0.gamma" in key:
                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm1.gamma"
                elif ".residual.2.bias" in key:
                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.bias"
                elif ".residual.2.weight" in key:
                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.weight"
                elif ".residual.3.gamma" in key:
                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm2.gamma"
                elif ".residual.6.bias" in key:
                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.bias"
                elif ".residual.6.weight" in key:
                    new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.weight"
                else:
                    new_key = key

                new_state_dict[new_key] = value

            # Handle shortcut connections
            elif ".shortcut." in key:
                if block_idx == 4:
                    new_key = key.replace(".shortcut.", ".resnets.0.conv_shortcut.")
                    new_key = new_key.replace("decoder.upsamples.4", "decoder.up_blocks.1")
                else:
                    new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
                    new_key = new_key.replace(".shortcut.", ".conv_shortcut.")

                new_state_dict[new_key] = value

            # Handle upsamplers
            elif ".resample." in key or ".time_conv." in key:
                if block_idx == 3:
                    new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.0.upsamplers.0")
                elif block_idx == 7:
                    new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.1.upsamplers.0")
                elif block_idx == 11:
                    new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.2.upsamplers.0")
                else:
                    new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")

                new_state_dict[new_key] = value
            else:
                new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
                new_state_dict[new_key] = value
        else:
            # Keep other keys unchanged
            new_state_dict[key] = value

    with init_empty_weights():
        vae = AutoencoderKLWan()
    vae.load_state_dict(new_state_dict, strict=True, assign=True)
    return vae


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_type", type=str, default=None)
    parser.add_argument("--output_path", type=str, required=True)
    parser.add_argument("--dtype", default="fp32")
    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]

    transformer = convert_transformer(args.model_type).to(dtype=dtype)
    vae = convert_vae()
    text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl")
    tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl")
    flow_shift = 16.0 if "FLF2V" in args.model_type else 3.0
    scheduler = UniPCMultistepScheduler(
        prediction_type="flow_prediction", use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift
    )

    if "I2V" in args.model_type or "FLF2V" in args.model_type:
        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
            "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=torch.bfloat16
        )
        image_processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
        pipe = WanImageToVideoPipeline(
            transformer=transformer,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            vae=vae,
            scheduler=scheduler,
            image_encoder=image_encoder,
            image_processor=image_processor,
        )
    else:
        pipe = WanPipeline(
            transformer=transformer,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            vae=vae,
            scheduler=scheduler,
        )

    pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")