wehub-resource-sync
115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# coding: utf-8
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__all__ = ['WanVideoVAE']
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from typing import List
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import torch
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from torch import Tensor
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from einops import rearrange
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from common.utils.logging import get_logger
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from common.utils.distributed import get_device
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from common.utils.misc import AutoEncoderParams
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from .vae2_2 import Wan2_2_VAE
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def reparameterize(mu, log_var):
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std = torch.exp(0.5 * log_var)
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eps = torch.randn_like(std)
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return eps * std + mu
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class WanVideoVAE(object):
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__version__ = "v2.2"
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__name__ = "WanVideoVAE"
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__logger__ = None
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def __init__(self, config_path: str = "", **kwargs) -> None:
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if self.__class__.__logger__ is None:
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self.__class__.__logger__ = get_logger(self.__class__.__name__)
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self.logger = self.__class__.__logger__
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self.dtype = kwargs.get("dtype", torch.bfloat16)
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self.device = torch.device(kwargs.get("device", get_device()))
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self.configure_vae_model()
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self.use_sample = kwargs.get("use_sample", True)
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# wan vae2.2 config is equal to seedance vae
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self.vae_config = AutoEncoderParams(
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downsample_spatial=16,
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downsample_temporal=4,
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z_channels=48,
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# scale_factor=1.0,
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# shift_factor=0.012,
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)
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def configure_vae_model(self):
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device = self.device
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# Read the VAE path from path_default.yaml.
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try:
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from config.config_factory import get_model_path
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vae_path = get_model_path("vae.wan")
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except Exception as e:
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# Fall back to the default local path.
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vae_path = "downloads/Wan2.2_VAE.pth"
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self.vae: Wan2_2_VAE = Wan2_2_VAE(vae_pth=vae_path, device=device, dtype=self.dtype)
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# self.vae.requires_grad_(False).eval()
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# self.vae.to(device=get_device())
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def to(self, device) -> "WanVideoVAE":
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self.device = torch.device(device)
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self.vae.model.to(device=self.device, dtype=self.dtype)
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self.vae.scale = [item.to(device=self.device) for item in self.vae.scale]
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return self
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@torch.no_grad()
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def vae_encode(self, samples: List[Tensor], **kwargs) -> List[Tensor]:
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device = self.device
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latents = []
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with torch.autocast(device_type="cuda", dtype=self.dtype):
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for x in samples:
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x = x.to(device=device).unsqueeze(0) # 1CTHW
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u, log_var = self.vae.encode(x) # [1,48,t,h,w], [1,48,t,h,w]
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if self.use_sample:
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u = reparameterize(u, log_var) # [1,48,t,h,w]
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u = rearrange(u, "b c ... -> b ... c") # -> [1,t,h,w,48] for compatibility
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latents.append(u.squeeze(0)) # -> [t,h,w,48]
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return latents
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@torch.no_grad()
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def vae_decode(self, latents: List[Tensor], **kwargs) -> List[Tensor]:
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device = self.device
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samples = []
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with torch.autocast(device_type="cuda", dtype=self.dtype):
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for u in latents:
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u = u.unsqueeze(0).to(device=device) # -> [1,t,h,w,48]
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u = rearrange(u, "b ... c -> b c ...") # -> [1,48,t,h,w]
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x_hat = self.vae.decode(u) # -> [1,3,T,H,W]
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samples.append(x_hat.squeeze(0)) # -> List[[3,T,H,W]]
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return samples
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