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8b306b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | # Copyright (c) 2025 ByteDance Ltd. and/or its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# coding: utf-8
__all__ = ['WanVideoVAE']
from typing import List
import torch
from torch import Tensor
from einops import rearrange
from common.utils.logging import get_logger
from common.utils.distributed import get_device
from common.utils.misc import AutoEncoderParams
from .vae2_2 import Wan2_2_VAE
def reparameterize(mu, log_var):
std = torch.exp(0.5 * log_var)
eps = torch.randn_like(std)
return eps * std + mu
class WanVideoVAE(object):
__version__ = "v2.2"
__name__ = "WanVideoVAE"
__logger__ = None
def __init__(self, config_path: str = "", **kwargs) -> None:
if self.__class__.__logger__ is None:
self.__class__.__logger__ = get_logger(self.__class__.__name__)
self.logger = self.__class__.__logger__
self.dtype = kwargs.get("dtype", torch.bfloat16)
self.configure_vae_model()
self.use_sample = kwargs.get("use_sample", True)
# wan vae2.2 config is equal to seedance vae
self.vae_config = AutoEncoderParams(
downsample_spatial=16,
downsample_temporal=4,
z_channels=48,
# scale_factor=1.0,
# shift_factor=0.012,
)
def configure_vae_model(self):
device = get_device()
# 从 path_default.yaml 读取 VAE 路径
try:
from config.config_factory import get_model_path
vae_path = get_model_path("vae.wan")
except Exception as e:
# 降级到默认路径
vae_path = "downloads/Wan2.2_VAE.pth"
self.vae: Wan2_2_VAE = Wan2_2_VAE(vae_pth=vae_path, device=device, dtype=self.dtype)
# self.vae.requires_grad_(False).eval()
# self.vae.to(device=get_device())
@torch.no_grad()
def vae_encode(self, samples: List[Tensor], **kwargs) -> List[Tensor]:
device = get_device()
latents = []
with torch.autocast(device_type="cuda", dtype=self.dtype):
for x in samples:
x = x.to(device=device).unsqueeze(0) # 1CTHW
u, log_var = self.vae.encode(x) # [1,48,t,h,w], [1,48,t,h,w]
if self.use_sample:
u = reparameterize(u, log_var) # [1,48,t,h,w]
u = rearrange(u, "b c ... -> b ... c") # -> [1,t,h,w,48] for 兼容
latents.append(u.squeeze(0)) # -> [t,h,w,48]
return latents
@torch.no_grad()
def vae_decode(self, latents: List[Tensor], **kwargs) -> List[Tensor]:
device = get_device()
samples = []
with torch.autocast(device_type="cuda", dtype=self.dtype):
for u in latents:
u = u.unsqueeze(0).to(device=device) # -> [1,t,h,w,48]
u = rearrange(u, "b ... c -> b c ...") # -> [1,48,t,h,w]
x_hat = self.vae.decode(u) # -> [1,3,T,H,W]
samples.append(x_hat.squeeze(0)) # -> List[[3,T,H,W]]
return samples
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