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637 lines (589 loc) · 25.6 KB
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import argparse
import os
import pathlib
from typing import Any, Dict
import torch
from accelerate import init_empty_weights
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from transformers import AutoProcessor, AutoTokenizer, CLIPVisionModelWithProjection, UMT5EncoderModel
from diffusers import (
AutoencoderKLWan,
SkyReelsV2DiffusionForcingPipeline,
SkyReelsV2ImageToVideoPipeline,
SkyReelsV2Pipeline,
SkyReelsV2Transformer3DModel,
UniPCMultistepScheduler,
)
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",
"fps_projection.0": "fps_projection.net.0.proj",
"fps_projection.2": "fps_projection.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):
if "720P" in str(dir):
file_paths = list(dir.glob("diffusion_pytorch_model*.safetensors"))
else:
file_paths = list(dir.glob("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 == "SkyReels-V2-DF-1.3B-540P":
config = {
"model_id": "Skywork/SkyReels-V2-DF-1.3B-540P",
"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,
"inject_sample_info": True,
"num_layers": 30,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
},
}
elif model_type == "SkyReels-V2-DF-14B-720P":
config = {
"model_id": "Skywork/SkyReels-V2-DF-14B-720P",
"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,
"inject_sample_info": False,
"num_layers": 40,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
},
}
elif model_type == "SkyReels-V2-DF-14B-540P":
config = {
"model_id": "Skywork/SkyReels-V2-DF-14B-540P",
"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,
"inject_sample_info": False,
"num_layers": 40,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
},
}
elif model_type == "SkyReels-V2-T2V-14B-720P":
config = {
"model_id": "Skywork/SkyReels-V2-T2V-14B-720P",
"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,
"inject_sample_info": False,
"num_layers": 40,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
},
}
elif model_type == "SkyReels-V2-T2V-14B-540P":
config = {
"model_id": "Skywork/SkyReels-V2-T2V-14B-540P",
"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,
"inject_sample_info": False,
"num_layers": 40,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
},
}
elif model_type == "SkyReels-V2-I2V-1.3B-540P":
config = {
"model_id": "Skywork/SkyReels-V2-I2V-1.3B-540P",
"diffusers_config": {
"added_kv_proj_dim": 1536,
"attention_head_dim": 128,
"cross_attn_norm": True,
"eps": 1e-06,
"ffn_dim": 8960,
"freq_dim": 256,
"in_channels": 36,
"num_attention_heads": 12,
"inject_sample_info": False,
"num_layers": 30,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
"image_dim": 1280,
},
}
elif model_type == "SkyReels-V2-I2V-14B-540P":
config = {
"model_id": "Skywork/SkyReels-V2-I2V-14B-540P",
"diffusers_config": {
"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,
"inject_sample_info": False,
"num_layers": 40,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
"image_dim": 1280,
},
}
elif model_type == "SkyReels-V2-I2V-14B-720P":
config = {
"model_id": "Skywork/SkyReels-V2-I2V-14B-720P",
"diffusers_config": {
"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,
"inject_sample_info": False,
"num_layers": 40,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
"image_dim": 1280,
},
}
elif model_type == "SkyReels-V2-FLF2V-1.3B-540P":
config = {
"model_id": "Skywork/SkyReels-V2-I2V-1.3B-540P",
"diffusers_config": {
"added_kv_proj_dim": 1536,
"attention_head_dim": 128,
"cross_attn_norm": True,
"eps": 1e-06,
"ffn_dim": 8960,
"freq_dim": 256,
"in_channels": 36,
"num_attention_heads": 12,
"inject_sample_info": False,
"num_layers": 30,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
"image_dim": 1280,
"pos_embed_seq_len": 514,
},
}
elif model_type == "SkyReels-V2-FLF2V-14B-540P":
config = {
"model_id": "Skywork/SkyReels-V2-I2V-14B-540P",
"diffusers_config": {
"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,
"inject_sample_info": False,
"num_layers": 40,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
"image_dim": 1280,
"pos_embed_seq_len": 514,
},
}
elif model_type == "SkyReels-V2-FLF2V-14B-720P":
config = {
"model_id": "Skywork/SkyReels-V2-I2V-14B-720P",
"diffusers_config": {
"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,
"inject_sample_info": False,
"num_layers": 40,
"out_channels": 16,
"patch_size": [1, 2, 2],
"qk_norm": "rms_norm_across_heads",
"text_dim": 4096,
"image_dim": 1280,
"pos_embed_seq_len": 514,
},
}
return config
def convert_transformer(model_type: str):
config = get_transformer_config(model_type)
diffusers_config = config["diffusers_config"]
model_id = config["model_id"]
if "1.3B" in model_type:
original_state_dict = load_file(hf_hub_download(model_id, "model.safetensors"))
else:
os.makedirs(model_type, exist_ok=True)
model_dir = pathlib.Path(model_type)
if "720P" in model_type:
top_shard = 7 if "I2V" in model_type else 6
zeros = "0" * (4 if "I2V" or "T2V" in model_type else 3)
model_name = "diffusion_pytorch_model"
elif "540P" in model_type:
top_shard = 14 if "I2V" in model_type else 12
model_name = "model"
for i in range(1, top_shard + 1):
shard_path = f"{model_name}-{i:05d}-of-{zeros}{top_shard}.safetensors"
hf_hub_download(model_id, shard_path, local_dir=model_dir)
original_state_dict = load_sharded_safetensors(model_dir)
with init_empty_weights():
transformer = SkyReelsV2Transformer3DModel.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)
if "FLF2V" in model_type:
if (
hasattr(transformer.condition_embedder, "image_embedder")
and hasattr(transformer.condition_embedder.image_embedder, "pos_embed")
and transformer.condition_embedder.image_embedder.pos_embed is not None
):
pos_embed_shape = transformer.condition_embedder.image_embedder.pos_embed.shape
original_state_dict["condition_embedder.image_embedder.pos_embed"] = torch.zeros(pos_embed_shape)
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")
scheduler = UniPCMultistepScheduler(
prediction_type="flow_prediction",
num_train_timesteps=1000,
use_flow_sigmas=True,
)
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")
image_processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
pipe = SkyReelsV2ImageToVideoPipeline(
transformer=transformer,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
scheduler=scheduler,
image_encoder=image_encoder,
image_processor=image_processor,
)
elif "T2V" in args.model_type:
pipe = SkyReelsV2Pipeline(
transformer=transformer,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
scheduler=scheduler,
)
elif "DF" in args.model_type:
pipe = SkyReelsV2DiffusionForcingPipeline(
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",
# push_to_hub=True,
# repo_id=f"<place_holder>/{args.model_type}-Diffusers",
)
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