Diffusion Models视频生成

前言：目前开源的DiT视频生成模型不是很多，Open-Sora是开发者生态最好的一个，涵盖了DiT、时空DiT、3D VAE、Rectified Flow、因果卷积等Diffusion视频生成的经典知识点。本篇博客从Open-Sora的代码出发，深入解读背后的原理。

目录

3D VAE原理

代码剖析

2D VAE

时间VAE

因果3D卷积

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3D VAE原理

之前绝大多数都是2D VAE，特别是SDXL的VAE相当好用，很多人都拿来直接用了。但是在DiT-based的模型中，时间序列上如果再不做压缩的话，就已经很难训得动了。因此非常有必要在时间序列上进行压缩，3D VAE应运而生。

Open-Sora的方案是在2D VAE的基础上，再添加一个时间VAE，相比于EasyAnimate 和 CogVideoX的方案的Full Attention 存在劣势，但是可以充分利用到2D VAE的权重，成本更低。

代码剖析

2D VAE

来自华为pixart sdxl vae：

    vae_2d = dict(type="VideoAutoencoderKL",from_pretrained="PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers",subfolder="vae",micro_batch_size=micro_batch_size,local_files_only=local_files_only,)

时间VAE

    vae_temporal = dict(type="VAE_Temporal_SD",from_pretrained=None,)

@MODELS.register_module()
class VAE_Temporal(nn.Module):def __init__(self,in_out_channels=4,latent_embed_dim=4,embed_dim=4,filters=128,num_res_blocks=4,channel_multipliers=(1, 2, 2, 4),temporal_downsample=(True, True, False),num_groups=32,  # for nn.GroupNormactivation_fn="swish",):super().__init__()self.time_downsample_factor = 2 ** sum(temporal_downsample)# self.time_padding = self.time_downsample_factor - 1self.patch_size = (self.time_downsample_factor, 1, 1)self.out_channels = in_out_channels# NOTE: following MAGVIT, conv in bias=False in encoder first convself.encoder = Encoder(in_out_channels=in_out_channels,latent_embed_dim=latent_embed_dim * 2,filters=filters,num_res_blocks=num_res_blocks,channel_multipliers=channel_multipliers,temporal_downsample=temporal_downsample,num_groups=num_groups,  # for nn.GroupNormactivation_fn=activation_fn,)self.quant_conv = CausalConv3d(2 * latent_embed_dim, 2 * embed_dim, 1)self.post_quant_conv = CausalConv3d(embed_dim, latent_embed_dim, 1)self.decoder = Decoder(in_out_channels=in_out_channels,latent_embed_dim=latent_embed_dim,filters=filters,num_res_blocks=num_res_blocks,channel_multipliers=channel_multipliers,temporal_downsample=temporal_downsample,num_groups=num_groups,  # for nn.GroupNormactivation_fn=activation_fn,)def get_latent_size(self, input_size):latent_size = []for i in range(3):if input_size[i] is None:lsize = Noneelif i == 0:time_padding = (0if (input_size[i] % self.time_downsample_factor == 0)else self.time_downsample_factor - input_size[i] % self.time_downsample_factor)lsize = (input_size[i] + time_padding) // self.patch_size[i]else:lsize = input_size[i] // self.patch_size[i]latent_size.append(lsize)return latent_sizedef encode(self, x):time_padding = (0if (x.shape[2] % self.time_downsample_factor == 0)else self.time_downsample_factor - x.shape[2] % self.time_downsample_factor)x = pad_at_dim(x, (time_padding, 0), dim=2)encoded_feature = self.encoder(x)moments = self.quant_conv(encoded_feature).to(x.dtype)posterior = DiagonalGaussianDistribution(moments)return posteriordef decode(self, z, num_frames=None):time_padding = (0if (num_frames % self.time_downsample_factor == 0)else self.time_downsample_factor - num_frames % self.time_downsample_factor)z = self.post_quant_conv(z)x = self.decoder(z)x = x[:, :, time_padding:]return xdef forward(self, x, sample_posterior=True):posterior = self.encode(x)if sample_posterior:z = posterior.sample()else:z = posterior.mode()recon_video = self.decode(z, num_frames=x.shape[2])return recon_video, posterior, z

因果3D卷积

class CausalConv3d(nn.Module):def __init__(self,chan_in,chan_out,kernel_size: Union[int, Tuple[int, int, int]],pad_mode="constant",strides=None,  # allow custom stride**kwargs,):super().__init__()kernel_size = cast_tuple(kernel_size, 3)time_kernel_size, height_kernel_size, width_kernel_size = kernel_sizeassert is_odd(height_kernel_size) and is_odd(width_kernel_size)dilation = kwargs.pop("dilation", 1)stride = strides[0] if strides is not None else kwargs.pop("stride", 1)self.pad_mode = pad_modetime_pad = dilation * (time_kernel_size - 1) + (1 - stride)height_pad = height_kernel_size // 2width_pad = width_kernel_size // 2self.time_pad = time_padself.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_pad, 0)stride = strides if strides is not None else (stride, 1, 1)dilation = (dilation, 1, 1)self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)def forward(self, x):x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)x = self.conv(x)return x