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* Add wanx pipeline, model and example * wanx_merged_v1 * change WanX into Wan * fix i2v fp32 oom error Link: https://code.alibaba-inc.com/open_wanx2/diffusers/codereview/20607813 * support t2v load fp32 ckpt * add example * final merge v1 * Update autoencoder_kl_wan.py * up * update middle, test up_block * up up * one less nn.sequential * up more * up * more * [refactor] [wip] Wan transformer/pipeline (#10926) * update * update * refactor rope * refactor pipeline * make fix-copies * add transformer test * update * update * make style * update tests * tests * conversion script * conversion script * update * docs * remove unused code * fix _toctree.yml * update dtype * fix test * fix tests: scale * up * more * Apply suggestions from code review * Apply suggestions from code review * style * Update scripts/convert_wan_to_diffusers.py * update docs * fix --------- Co-authored-by: Yitong Huang <huangyitong.hyt@alibaba-inc.com> Co-authored-by: 亚森 <wangjiayu.wjy@alibaba-inc.com> Co-authored-by: Aryan <aryan@huggingface.co>
This commit is contained in:
YiYi Xu
GitHub
423
scripts/convert_wan_to_diffusers.py
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423
scripts/convert_wan_to_diffusers.py
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import argparse
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import pathlib
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from typing import Any, Dict
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import torch
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from accelerate import init_empty_weights
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from huggingface_hub import hf_hub_download, snapshot_download
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from safetensors.torch import load_file
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from transformers import AutoProcessor, AutoTokenizer, CLIPVisionModelWithProjection, UMT5EncoderModel
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from diffusers import (
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AutoencoderKLWan,
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UniPCMultistepScheduler,
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WanImageToVideoPipeline,
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WanPipeline,
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WanTransformer3DModel,
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)
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TRANSFORMER_KEYS_RENAME_DICT = {
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"time_embedding.0": "condition_embedder.time_embedder.linear_1",
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"time_embedding.2": "condition_embedder.time_embedder.linear_2",
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"text_embedding.0": "condition_embedder.text_embedder.linear_1",
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"text_embedding.2": "condition_embedder.text_embedder.linear_2",
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"time_projection.1": "condition_embedder.time_proj",
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"head.modulation": "scale_shift_table",
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"head.head": "proj_out",
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"modulation": "scale_shift_table",
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"ffn.0": "ffn.net.0.proj",
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"ffn.2": "ffn.net.2",
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# Hack to swap the layer names
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# The original model calls the norms in following order: norm1, norm3, norm2
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# We convert it to: norm1, norm2, norm3
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"norm2": "norm__placeholder",
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"norm3": "norm2",
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"norm__placeholder": "norm3",
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# For the I2V model
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"img_emb.proj.0": "condition_embedder.image_embedder.norm1",
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"img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj",
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"img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2",
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"img_emb.proj.4": "condition_embedder.image_embedder.norm2",
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}
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TRANSFORMER_SPECIAL_KEYS_REMAP = {}
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def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]:
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state_dict[new_key] = state_dict.pop(old_key)
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def load_sharded_safetensors(dir: pathlib.Path):
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file_paths = list(dir.glob("diffusion_pytorch_model*.safetensors"))
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state_dict = {}
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for path in file_paths:
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state_dict.update(load_file(path))
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return state_dict
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def get_transformer_config(model_type: str) -> Dict[str, Any]:
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if model_type == "Wan-T2V-1.3B":
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config = {
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"model_id": "StevenZhang/Wan2.1-T2V-1.3B-Diff",
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"diffusers_config": {
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"added_kv_proj_dim": None,
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"attention_head_dim": 128,
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"cross_attn_norm": True,
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"eps": 1e-06,
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"ffn_dim": 8960,
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"freq_dim": 256,
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"in_channels": 16,
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"num_attention_heads": 12,
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"num_layers": 30,
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"out_channels": 16,
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"patch_size": [1, 2, 2],
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"qk_norm": "rms_norm_across_heads",
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"text_dim": 4096,
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},
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}
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elif model_type == "Wan-T2V-14B":
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config = {
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"model_id": "StevenZhang/Wan2.1-T2V-14B-Diff",
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"diffusers_config": {
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"added_kv_proj_dim": None,
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"attention_head_dim": 128,
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"cross_attn_norm": True,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_channels": 16,
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"num_attention_heads": 40,
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"num_layers": 40,
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"out_channels": 16,
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"patch_size": [1, 2, 2],
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"qk_norm": "rms_norm_across_heads",
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"text_dim": 4096,
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},
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}
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elif model_type == "Wan-I2V-14B-480p":
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config = {
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"model_id": "StevenZhang/Wan2.1-I2V-14B-480P-Diff",
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"diffusers_config": {
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"image_dim": 1280,
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"added_kv_proj_dim": 5120,
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"attention_head_dim": 128,
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"cross_attn_norm": True,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_channels": 36,
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"num_attention_heads": 40,
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"num_layers": 40,
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"out_channels": 16,
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"patch_size": [1, 2, 2],
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"qk_norm": "rms_norm_across_heads",
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"text_dim": 4096,
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},
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}
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elif model_type == "Wan-I2V-14B-720p":
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config = {
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"model_id": "StevenZhang/Wan2.1-I2V-14B-720P-Diff",
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"diffusers_config": {
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"image_dim": 1280,
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"added_kv_proj_dim": 5120,
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"attention_head_dim": 128,
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"cross_attn_norm": True,
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"eps": 1e-06,
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"ffn_dim": 13824,
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"freq_dim": 256,
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"in_channels": 36,
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"num_attention_heads": 40,
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"num_layers": 40,
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"out_channels": 16,
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"patch_size": [1, 2, 2],
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"qk_norm": "rms_norm_across_heads",
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"text_dim": 4096,
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},
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}
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return config
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def convert_transformer(model_type: str):
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config = get_transformer_config(model_type)
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diffusers_config = config["diffusers_config"]
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model_id = config["model_id"]
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model_dir = pathlib.Path(snapshot_download(model_id, repo_type="model"))
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original_state_dict = load_sharded_safetensors(model_dir)
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with init_empty_weights():
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transformer = WanTransformer3DModel.from_config(diffusers_config)
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for key in list(original_state_dict.keys()):
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new_key = key[:]
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for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
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new_key = new_key.replace(replace_key, rename_key)
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update_state_dict_(original_state_dict, key, new_key)
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for key in list(original_state_dict.keys()):
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for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items():
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if special_key not in key:
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continue
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handler_fn_inplace(key, original_state_dict)
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transformer.load_state_dict(original_state_dict, strict=True, assign=True)
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return transformer
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def convert_vae():
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vae_ckpt_path = hf_hub_download("Wan-AI/Wan2.1-T2V-14B", "Wan2.1_VAE.pth")
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old_state_dict = torch.load(vae_ckpt_path, weights_only=True)
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new_state_dict = {}
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# Create mappings for specific components
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middle_key_mapping = {
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# Encoder middle block
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"encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma",
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"encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias",
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"encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight",
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"encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma",
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"encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias",
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"encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight",
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"encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma",
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"encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias",
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"encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight",
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"encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma",
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"encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias",
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"encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight",
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# Decoder middle block
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"decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma",
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"decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias",
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"decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight",
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"decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma",
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"decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias",
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"decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight",
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"decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma",
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"decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias",
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"decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight",
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"decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma",
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"decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias",
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"decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight",
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}
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# Create a mapping for attention blocks
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attention_mapping = {
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# Encoder middle attention
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"encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma",
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"encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight",
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"encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias",
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"encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight",
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"encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias",
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# Decoder middle attention
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"decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma",
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"decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight",
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"decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias",
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"decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight",
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"decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias",
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}
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# Create a mapping for the head components
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head_mapping = {
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# Encoder head
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"encoder.head.0.gamma": "encoder.norm_out.gamma",
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"encoder.head.2.bias": "encoder.conv_out.bias",
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"encoder.head.2.weight": "encoder.conv_out.weight",
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# Decoder head
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"decoder.head.0.gamma": "decoder.norm_out.gamma",
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"decoder.head.2.bias": "decoder.conv_out.bias",
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"decoder.head.2.weight": "decoder.conv_out.weight",
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}
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# Create a mapping for the quant components
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quant_mapping = {
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"conv1.weight": "quant_conv.weight",
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"conv1.bias": "quant_conv.bias",
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"conv2.weight": "post_quant_conv.weight",
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"conv2.bias": "post_quant_conv.bias",
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}
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# Process each key in the state dict
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for key, value in old_state_dict.items():
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# Handle middle block keys using the mapping
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if key in middle_key_mapping:
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new_key = middle_key_mapping[key]
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new_state_dict[new_key] = value
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# Handle attention blocks using the mapping
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elif key in attention_mapping:
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new_key = attention_mapping[key]
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new_state_dict[new_key] = value
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# Handle head keys using the mapping
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elif key in head_mapping:
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new_key = head_mapping[key]
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new_state_dict[new_key] = value
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# Handle quant keys using the mapping
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elif key in quant_mapping:
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new_key = quant_mapping[key]
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new_state_dict[new_key] = value
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# Handle encoder conv1
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elif key == "encoder.conv1.weight":
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new_state_dict["encoder.conv_in.weight"] = value
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elif key == "encoder.conv1.bias":
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new_state_dict["encoder.conv_in.bias"] = value
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# Handle decoder conv1
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elif key == "decoder.conv1.weight":
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new_state_dict["decoder.conv_in.weight"] = value
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elif key == "decoder.conv1.bias":
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new_state_dict["decoder.conv_in.bias"] = value
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# Handle encoder downsamples
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elif key.startswith("encoder.downsamples."):
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# Convert to down_blocks
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new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.")
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# Convert residual block naming but keep the original structure
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if ".residual.0.gamma" in new_key:
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new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma")
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elif ".residual.2.bias" in new_key:
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new_key = new_key.replace(".residual.2.bias", ".conv1.bias")
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elif ".residual.2.weight" in new_key:
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new_key = new_key.replace(".residual.2.weight", ".conv1.weight")
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elif ".residual.3.gamma" in new_key:
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new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma")
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elif ".residual.6.bias" in new_key:
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new_key = new_key.replace(".residual.6.bias", ".conv2.bias")
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elif ".residual.6.weight" in new_key:
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new_key = new_key.replace(".residual.6.weight", ".conv2.weight")
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elif ".shortcut.bias" in new_key:
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new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias")
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elif ".shortcut.weight" in new_key:
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new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight")
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new_state_dict[new_key] = value
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# Handle decoder upsamples
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elif key.startswith("decoder.upsamples."):
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# Convert to up_blocks
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parts = key.split(".")
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block_idx = int(parts[2])
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# Group residual blocks
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if "residual" in key:
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if block_idx in [0, 1, 2]:
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new_block_idx = 0
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resnet_idx = block_idx
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elif block_idx in [4, 5, 6]:
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new_block_idx = 1
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resnet_idx = block_idx - 4
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elif block_idx in [8, 9, 10]:
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new_block_idx = 2
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resnet_idx = block_idx - 8
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elif block_idx in [12, 13, 14]:
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new_block_idx = 3
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resnet_idx = block_idx - 12
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else:
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# Keep as is for other blocks
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new_state_dict[key] = value
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continue
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# Convert residual block naming
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if ".residual.0.gamma" in key:
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new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm1.gamma"
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elif ".residual.2.bias" in key:
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new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.bias"
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elif ".residual.2.weight" in key:
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new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.weight"
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elif ".residual.3.gamma" in key:
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new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm2.gamma"
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elif ".residual.6.bias" in key:
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new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.bias"
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elif ".residual.6.weight" in key:
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new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.weight"
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else:
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new_key = key
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new_state_dict[new_key] = value
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# Handle shortcut connections
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elif ".shortcut." in key:
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if block_idx == 4:
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new_key = key.replace(".shortcut.", ".resnets.0.conv_shortcut.")
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new_key = new_key.replace("decoder.upsamples.4", "decoder.up_blocks.1")
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else:
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new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
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new_key = new_key.replace(".shortcut.", ".conv_shortcut.")
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new_state_dict[new_key] = value
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# Handle upsamplers
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elif ".resample." in key or ".time_conv." in key:
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if block_idx == 3:
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new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.0.upsamplers.0")
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elif block_idx == 7:
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new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.1.upsamplers.0")
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elif block_idx == 11:
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new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.2.upsamplers.0")
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else:
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new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
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new_state_dict[new_key] = value
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else:
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new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
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new_state_dict[new_key] = value
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else:
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# Keep other keys unchanged
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new_state_dict[key] = value
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with init_empty_weights():
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||||
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")
|
||||
scheduler = UniPCMultistepScheduler(
|
||||
prediction_type="flow_prediction", use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=3.0
|
||||
)
|
||||
|
||||
if "I2V" 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")
|
||||
Reference in New Issue
Block a user