Publish pinned Lance Wan2.2 VAE with strict native component validation

d52c11d verified 2 days ago

3.91 kB

# 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.device = torch.device(kwargs.get("device", get_device()))
        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 = self.device

        # Read the VAE path from path_default.yaml.
        try:
            from config.config_factory import get_model_path
            vae_path = get_model_path("vae.wan")
        except Exception as e:
            # Fall back to the default local path.
            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())

    def to(self, device) -> "WanVideoVAE":
        self.device = torch.device(device)
        self.vae.model.to(device=self.device, dtype=self.dtype)
        self.vae.scale = [item.to(device=self.device) for item in self.vae.scale]
        return self

    @torch.no_grad()
    def vae_encode(self, samples: List[Tensor], **kwargs) -> List[Tensor]:
        device = self.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 compatibility

                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 = self.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

	# 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.device = torch.device(kwargs.get("device", get_device()))
	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 = self.device

	# Read the VAE path from path_default.yaml.
	try:
	from config.config_factory import get_model_path
	vae_path = get_model_path("vae.wan")
	except Exception as e:
	# Fall back to the default local path.
	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())

	def to(self, device) -> "WanVideoVAE":
	self.device = torch.device(device)
	self.vae.model.to(device=self.device, dtype=self.dtype)
	self.vae.scale = [item.to(device=self.device) for item in self.vae.scale]
	return self

	@torch.no_grad()
	def vae_encode(self, samples: List[Tensor], **kwargs) -> List[Tensor]:
	device = self.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 compatibility

	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 = self.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