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Fix cosmos VAE failing with videos longer than 121 frames.

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comfyanonymous 2025-01-16 16:30:06 -05:00
parent 619b8cde74
commit cca96a85ae
1 changed files with 12 additions and 5 deletions
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17
comfy/ldm/cosmos/vae.py
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@ -18,6 +18,7 @@ import logging
import torch import torch
from torch import nn from torch import nn
from enum import Enum from enum import Enum
import math
from .cosmos_tokenizer.layers3d import ( from .cosmos_tokenizer.layers3d import (
EncoderFactorized, EncoderFactorized,
@ -105,17 +106,23 @@ class CausalContinuousVideoTokenizer(nn.Module):
z, posteriors = self.distribution(moments) z, posteriors = self.distribution(moments)
latent_ch = z.shape[1] latent_ch = z.shape[1]
latent_t = z.shape[2] latent_t = z.shape[2]
dtype = z.dtype in_dtype = z.dtype
mean = self.latent_mean.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=dtype, device=z.device) mean = self.latent_mean.view(latent_ch, -1)
std = self.latent_std.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=dtype, device=z.device) std = self.latent_std.view(latent_ch, -1)
mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
return ((z - mean) / std) * self.sigma_data return ((z - mean) / std) * self.sigma_data
def decode(self, z): def decode(self, z):
in_dtype = z.dtype in_dtype = z.dtype
latent_ch = z.shape[1] latent_ch = z.shape[1]
latent_t = z.shape[2] latent_t = z.shape[2]
mean = self.latent_mean.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) mean = self.latent_mean.view(latent_ch, -1)
std = self.latent_std.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) std = self.latent_std.view(latent_ch, -1)
mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
z = z / self.sigma_data z = z / self.sigma_data
z = z * std + mean z = z * std + mean