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diffusers/scripts/convert_wan_to_diffusers.py
dg845 d8e4805816 [WIP]Add Wan2.2 Animate Pipeline (Continuation of #12442 by tolgacangoz) (#12526) 
---------

Co-authored-by: Tolga Cangöz <mtcangoz@gmail.com>
Co-authored-by: Tolga Cangöz <46008593+tolgacangoz@users.noreply.github.com>
2025-11-12 16:52:31 -10:00

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import argparse
import pathlib
from typing import Any, Dict, Tuple
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,
CLIPImageProcessor,
CLIPVisionModel,
CLIPVisionModelWithProjection,
UMT5EncoderModel,
)
from diffusers import (
AutoencoderKLWan,
UniPCMultistepScheduler,
WanAnimatePipeline,
WanAnimateTransformer3DModel,
WanImageToVideoPipeline,
WanPipeline,
WanTransformer3DModel,
WanVACEPipeline,
WanVACETransformer3DModel,
)
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",
}
VACE_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",
"before_proj": "proj_in",
"after_proj": "proj_out",
}
ANIMATE_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",
"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",
# 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",
"cross_attn.k_img": "attn2.to_k_img",
"cross_attn.v_img": "attn2.to_v_img",
"cross_attn.norm_k_img": "attn2.norm_k_img",
# After cross_attn -> attn2 rename, we need to rename the img keys
"attn2.to_k_img": "attn2.add_k_proj",
"attn2.to_v_img": "attn2.add_v_proj",
"attn2.norm_k_img": "attn2.norm_added_k",
# Wan Animate-specific mappings (motion encoder, face encoder, face adapter)
# Motion encoder mappings
# The name mapping is complicated for the convolutional part so we handle that in its own function
"motion_encoder.enc.fc": "motion_encoder.motion_network",
"motion_encoder.dec.direction.weight": "motion_encoder.motion_synthesis_weight",
# Face encoder mappings - CausalConv1d has a .conv submodule that we need to flatten
"face_encoder.conv1_local.conv": "face_encoder.conv1_local",
"face_encoder.conv2.conv": "face_encoder.conv2",
"face_encoder.conv3.conv": "face_encoder.conv3",
# Face adapter mappings are handled in a separate function
}
# TODO: Verify this and simplify if possible.
def convert_animate_motion_encoder_weights(key: str, state_dict: Dict[str, Any], final_conv_idx: int = 8) -> None:
"""
Convert all motion encoder weights for Animate model.
In the original model:
- All Linear layers in fc use EqualLinear
- All Conv2d layers in convs use EqualConv2d (except blur_conv which is initialized separately)
- Blur kernels are stored as buffers in Sequential modules
- ConvLayer is nn.Sequential with indices: [Blur (optional), EqualConv2d, FusedLeakyReLU (optional)]
Conversion strategy:
1. Drop .kernel buffers (blur kernels)
2. Rename sequential indices to named components (e.g., 0 -> conv2d, 1 -> bias_leaky_relu)
"""
# Skip if not a weight, bias, or kernel
if ".weight" not in key and ".bias" not in key and ".kernel" not in key:
return
# Handle Blur kernel buffers from original implementation.
# After renaming, these appear under: motion_encoder.res_blocks.*.conv{2,skip}.blur_kernel
# Diffusers constructs blur kernels as a non-persistent buffer so we must drop these keys
if ".kernel" in key and "motion_encoder" in key:
# Remove unexpected blur kernel buffers to avoid strict load errors
state_dict.pop(key, None)
return
# Rename Sequential indices to named components in ConvLayer and ResBlock
if ".enc.net_app.convs." in key and (".weight" in key or ".bias" in key):
parts = key.split(".")
# Find the sequential index (digit) after convs or after conv1/conv2/skip
# Examples:
# - enc.net_app.convs.0.0.weight -> conv_in.weight (initial conv layer weight)
# - enc.net_app.convs.0.1.bias -> conv_in.act_fn.bias (initial conv layer bias)
# - enc.net_app.convs.{n:1-7}.conv1.0.weight -> res_blocks.{(n-1):0-6}.conv1.weight (conv1 weight)
# - e.g. enc.net_app.convs.1.conv1.0.weight -> res_blocks.0.conv1.weight
# - enc.net_app.convs.{n:1-7}.conv1.1.bias -> res_blocks.{(n-1):0-6}.conv1.act_fn.bias (conv1 bias)
# - e.g. enc.net_app.convs.1.conv1.1.bias -> res_blocks.0.conv1.act_fn.bias
# - enc.net_app.convs.{n:1-7}.conv2.1.weight -> res_blocks.{(n-1):0-6}.conv2.weight (conv2 weight)
# - enc.net_app.convs.1.conv2.2.bias -> res_blocks.0.conv2.act_fn.bias (conv2 bias)
# - enc.net_app.convs.{n:1-7}.skip.1.weight -> res_blocks.{(n-1):0-6}.conv_skip.weight (skip conv weight)
# - enc.net_app.convs.8 -> conv_out (final conv layer)
convs_idx = parts.index("convs") if "convs" in parts else -1
if convs_idx >= 0 and len(parts) - convs_idx >= 2:
bias = False
# The nn.Sequential index will always follow convs
sequential_idx = int(parts[convs_idx + 1])
if sequential_idx == 0:
if key.endswith(".weight"):
new_key = "motion_encoder.conv_in.weight"
elif key.endswith(".bias"):
new_key = "motion_encoder.conv_in.act_fn.bias"
bias = True
elif sequential_idx == final_conv_idx:
if key.endswith(".weight"):
new_key = "motion_encoder.conv_out.weight"
else:
# Intermediate .convs. layers, which get mapped to .res_blocks.
prefix = "motion_encoder.res_blocks."
layer_name = parts[convs_idx + 2]
if layer_name == "skip":
layer_name = "conv_skip"
if key.endswith(".weight"):
param_name = "weight"
elif key.endswith(".bias"):
param_name = "act_fn.bias"
bias = True
suffix_parts = [str(sequential_idx - 1), layer_name, param_name]
suffix = ".".join(suffix_parts)
new_key = prefix + suffix
param = state_dict.pop(key)
if bias:
param = param.squeeze()
state_dict[new_key] = param
return
return
return
def convert_animate_face_adapter_weights(key: str, state_dict: Dict[str, Any]) -> None:
"""
Convert face adapter weights for the Animate model.
The original model uses a fused KV projection but the diffusers models uses separate K and V projections.
"""
# Skip if not a weight or bias
if ".weight" not in key and ".bias" not in key:
return
prefix = "face_adapter."
if ".fuser_blocks." in key:
parts = key.split(".")
module_list_idx = parts.index("fuser_blocks") if "fuser_blocks" in parts else -1
if module_list_idx >= 0 and (len(parts) - 1) - module_list_idx == 3:
block_idx = parts[module_list_idx + 1]
layer_name = parts[module_list_idx + 2]
param_name = parts[module_list_idx + 3]
if layer_name == "linear1_kv":
layer_name_k = "to_k"
layer_name_v = "to_v"
suffix_k = ".".join([block_idx, layer_name_k, param_name])
suffix_v = ".".join([block_idx, layer_name_v, param_name])
new_key_k = prefix + suffix_k
new_key_v = prefix + suffix_v
kv_proj = state_dict.pop(key)
k_proj, v_proj = torch.chunk(kv_proj, 2, dim=0)
state_dict[new_key_k] = k_proj
state_dict[new_key_v] = v_proj
return
else:
if layer_name == "q_norm":
new_layer_name = "norm_q"
elif layer_name == "k_norm":
new_layer_name = "norm_k"
elif layer_name == "linear1_q":
new_layer_name = "to_q"
elif layer_name == "linear2":
new_layer_name = "to_out"
suffix_parts = [block_idx, new_layer_name, param_name]
suffix = ".".join(suffix_parts)
new_key = prefix + suffix
state_dict[new_key] = state_dict.pop(key)
return
return
TRANSFORMER_SPECIAL_KEYS_REMAP = {}
VACE_TRANSFORMER_SPECIAL_KEYS_REMAP = {}
ANIMATE_TRANSFORMER_SPECIAL_KEYS_REMAP = {
"motion_encoder": convert_animate_motion_encoder_weights,
"face_adapter": convert_animate_face_adapter_weights,
}
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) -> Tuple[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,
},
}
RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
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,
},
}
RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
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,
},
}
RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
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,
},
}
RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
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,
},
}
RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
elif model_type == "Wan-VACE-1.3B":
config = {
"model_id": "Wan-AI/Wan2.1-VACE-1.3B",
"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,
"vace_layers": [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28],
"vace_in_channels": 96,
},
}
RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP
elif model_type == "Wan-VACE-14B":
config = {
"model_id": "Wan-AI/Wan2.1-VACE-14B",
"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,
"vace_layers": [0, 5, 10, 15, 20, 25, 30, 35],
"vace_in_channels": 96,
},
}
RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP
elif model_type == "Wan2.2-VACE-Fun-14B":
config = {
"model_id": "alibaba-pai/Wan2.2-VACE-Fun-A14B",
"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,
"vace_layers": [0, 5, 10, 15, 20, 25, 30, 35],
"vace_in_channels": 96,
},
}
RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP
elif model_type == "Wan2.2-I2V-14B-720p":
config = {
"model_id": "Wan-AI/Wan2.2-I2V-A14B",
"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": 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,
},
}
RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
elif model_type == "Wan2.2-T2V-A14B":
config = {
"model_id": "Wan-AI/Wan2.2-T2V-A14B",
"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,
},
}
RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
elif model_type == "Wan2.2-TI2V-5B":
config = {
"model_id": "Wan-AI/Wan2.2-TI2V-5B",
"diffusers_config": {
"added_kv_proj_dim": None,
"attention_head_dim": 128,
"cross_attn_norm": True,
"eps": 1e-06,
"ffn_dim": 14336,
"freq_dim": 256,
"in_channels": 48,
"num_attention_heads": 24,
"num_layers": 30,
"out_channels": 48,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
},
}
RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
elif model_type == "Wan2.2-Animate-14B":
config = {
"model_id": "Wan-AI/Wan2.2-Animate-14B",
"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": None,
"motion_encoder_size": 512, # Start of Wan Animate-specific configs
"motion_style_dim": 512,
"motion_dim": 20,
"motion_encoder_dim": 512,
"face_encoder_hidden_dim": 1024,
"face_encoder_num_heads": 4,
"inject_face_latents_blocks": 5,
},
}
RENAME_DICT = ANIMATE_TRANSFORMER_KEYS_RENAME_DICT
SPECIAL_KEYS_REMAP = ANIMATE_TRANSFORMER_SPECIAL_KEYS_REMAP
return config, RENAME_DICT, SPECIAL_KEYS_REMAP
def convert_transformer(model_type: str, stage: str = None):
config, RENAME_DICT, SPECIAL_KEYS_REMAP = 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"))
if stage is not None:
model_dir = model_dir / stage
original_state_dict = load_sharded_safetensors(model_dir)
with init_empty_weights():
if "Animate" in model_type:
transformer = WanAnimateTransformer3DModel.from_config(diffusers_config)
elif "VACE" in model_type:
transformer = WanVACETransformer3DModel.from_config(diffusers_config)
else:
transformer = WanTransformer3DModel.from_config(diffusers_config)
for key in list(original_state_dict.keys()):
new_key = key[:]
for replace_key, rename_key in 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 SPECIAL_KEYS_REMAP.items():
if special_key not in key:
continue
handler_fn_inplace(key, original_state_dict)
# Load state dict into the meta model, which will materialize the tensors
transformer.load_state_dict(original_state_dict, strict=True, assign=True)
# Move to CPU to ensure all tensors are materialized
transformer = transformer.to("cpu")
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
vae22_diffusers_config = {
"base_dim": 160,
"z_dim": 48,
"is_residual": True,
"in_channels": 12,
"out_channels": 12,
"decoder_base_dim": 256,
"scale_factor_temporal": 4,
"scale_factor_spatial": 16,
"patch_size": 2,
"latents_mean": [
-0.2289,
-0.0052,
-0.1323,
-0.2339,
-0.2799,
0.0174,
0.1838,
0.1557,
-0.1382,
0.0542,
0.2813,
0.0891,
0.1570,
-0.0098,
0.0375,
-0.1825,
-0.2246,
-0.1207,
-0.0698,
0.5109,
0.2665,
-0.2108,
-0.2158,
0.2502,
-0.2055,
-0.0322,
0.1109,
0.1567,
-0.0729,
0.0899,
-0.2799,
-0.1230,
-0.0313,
-0.1649,
0.0117,
0.0723,
-0.2839,
-0.2083,
-0.0520,
0.3748,
0.0152,
0.1957,
0.1433,
-0.2944,
0.3573,
-0.0548,
-0.1681,
-0.0667,
],
"latents_std": [
0.4765,
1.0364,
0.4514,
1.1677,
0.5313,
0.4990,
0.4818,
0.5013,
0.8158,
1.0344,
0.5894,
1.0901,
0.6885,
0.6165,
0.8454,
0.4978,
0.5759,
0.3523,
0.7135,
0.6804,
0.5833,
1.4146,
0.8986,
0.5659,
0.7069,
0.5338,
0.4889,
0.4917,
0.4069,
0.4999,
0.6866,
0.4093,
0.5709,
0.6065,
0.6415,
0.4944,
0.5726,
1.2042,
0.5458,
1.6887,
0.3971,
1.0600,
0.3943,
0.5537,
0.5444,
0.4089,
0.7468,
0.7744,
],
"clip_output": False,
}
def convert_vae_22():
vae_ckpt_path = hf_hub_download("Wan-AI/Wan2.2-TI2V-5B", "Wan2.2_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."):
# Change encoder.downsamples to encoder.down_blocks
new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.")
# Handle residual blocks - change downsamples to resnets and rename components
if "residual" in new_key or "shortcut" in new_key:
# Change the second downsamples to resnets
new_key = new_key.replace(".downsamples.", ".resnets.")
# Rename residual components
if ".residual.0.gamma" in new_key:
new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma")
elif ".residual.2.weight" in new_key:
new_key = new_key.replace(".residual.2.weight", ".conv1.weight")
elif ".residual.2.bias" in new_key:
new_key = new_key.replace(".residual.2.bias", ".conv1.bias")
elif ".residual.3.gamma" in new_key:
new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma")
elif ".residual.6.weight" in new_key:
new_key = new_key.replace(".residual.6.weight", ".conv2.weight")
elif ".residual.6.bias" in new_key:
new_key = new_key.replace(".residual.6.bias", ".conv2.bias")
elif ".shortcut.weight" in new_key:
new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight")
elif ".shortcut.bias" in new_key:
new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias")
# Handle resample blocks - change downsamples to downsampler and remove index
elif "resample" in new_key or "time_conv" in new_key:
# Change the second downsamples to downsampler and remove the index
parts = new_key.split(".")
# Find the pattern: encoder.down_blocks.X.downsamples.Y.resample...
# We want to change it to: encoder.down_blocks.X.downsampler.resample...
if len(parts) >= 4 and parts[3] == "downsamples":
# Remove the index (parts[4]) and change downsamples to downsampler
new_parts = parts[:3] + ["downsampler"] + parts[5:]
new_key = ".".join(new_parts)
new_state_dict[new_key] = value
# Handle decoder upsamples
elif key.startswith("decoder.upsamples."):
# Change decoder.upsamples to decoder.up_blocks
new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
# Handle residual blocks - change upsamples to resnets and rename components
if "residual" in new_key or "shortcut" in new_key:
# Change the second upsamples to resnets
new_key = new_key.replace(".upsamples.", ".resnets.")
# Rename residual components
if ".residual.0.gamma" in new_key:
new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma")
elif ".residual.2.weight" in new_key:
new_key = new_key.replace(".residual.2.weight", ".conv1.weight")
elif ".residual.2.bias" in new_key:
new_key = new_key.replace(".residual.2.bias", ".conv1.bias")
elif ".residual.3.gamma" in new_key:
new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma")
elif ".residual.6.weight" in new_key:
new_key = new_key.replace(".residual.6.weight", ".conv2.weight")
elif ".residual.6.bias" in new_key:
new_key = new_key.replace(".residual.6.bias", ".conv2.bias")
elif ".shortcut.weight" in new_key:
new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight")
elif ".shortcut.bias" in new_key:
new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias")
# Handle resample blocks - change upsamples to upsampler and remove index
elif "resample" in new_key or "time_conv" in new_key:
# Change the second upsamples to upsampler and remove the index
parts = new_key.split(".")
# Find the pattern: encoder.down_blocks.X.downsamples.Y.resample...
# We want to change it to: encoder.down_blocks.X.downsampler.resample...
if len(parts) >= 4 and parts[3] == "upsamples":
# Remove the index (parts[4]) and change upsamples to upsampler
new_parts = parts[:3] + ["upsampler"] + parts[5:]
new_key = ".".join(new_parts)
new_state_dict[new_key] = value
else:
# Keep other keys unchanged
new_state_dict[key] = value
with init_empty_weights():
vae = AutoencoderKLWan(**vae22_diffusers_config)
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", choices=["fp32", "fp16", "bf16", "none"])
return parser.parse_args()
DTYPE_MAPPING = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}
if __name__ == "__main__":
args = get_args()
if "Wan2.2" in args.model_type and "TI2V" not in args.model_type and "Animate" not in args.model_type:
transformer = convert_transformer(args.model_type, stage="high_noise_model")
transformer_2 = convert_transformer(args.model_type, stage="low_noise_model")
else:
transformer = convert_transformer(args.model_type)
transformer_2 = None
if "Wan2.2" in args.model_type and "TI2V" in args.model_type:
vae = convert_vae_22()
else:
vae = convert_vae()
text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl")
if "FLF2V" in args.model_type:
flow_shift = 16.0
elif "TI2V" in args.model_type or "Animate" in args.model_type:
flow_shift = 5.0
else:
flow_shift = 3.0
scheduler = UniPCMultistepScheduler(
prediction_type="flow_prediction", use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift
)
# If user has specified "none", we keep the original dtypes of the state dict without any conversion
if args.dtype != "none":
dtype = DTYPE_MAPPING[args.dtype]
transformer.to(dtype)
if transformer_2 is not None:
transformer_2.to(dtype)
if "Wan2.2" and "I2V" in args.model_type and "TI2V" not in args.model_type:
pipe = WanImageToVideoPipeline(
transformer=transformer,
transformer_2=transformer_2,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
scheduler=scheduler,
boundary_ratio=0.9,
)
elif "Wan2.2" and "T2V" in args.model_type:
pipe = WanPipeline(
transformer=transformer,
transformer_2=transformer_2,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
scheduler=scheduler,
boundary_ratio=0.875,
)
elif "Wan2.2" and "TI2V" in args.model_type:
pipe = WanPipeline(
transformer=transformer,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
scheduler=scheduler,
expand_timesteps=True,
)
elif "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,
)
elif "Wan2.2-VACE" in args.model_type:
pipe = WanVACEPipeline(
transformer=transformer,
transformer_2=transformer_2,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
scheduler=scheduler,
boundary_ratio=0.875,
)
elif "Wan-VACE" in args.model_type:
pipe = WanVACEPipeline(
transformer=transformer,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
scheduler=scheduler,
)
elif "Animate" in args.model_type:
image_encoder = CLIPVisionModel.from_pretrained(
"laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=torch.bfloat16
)
image_processor = CLIPImageProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
pipe = WanAnimatePipeline(
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")
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