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Wan 2.2 support. (#9080)
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@ -457,6 +457,82 @@ class Wan21(LatentFormat):
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latents_std = self.latents_std.to(latent.device, latent.dtype)
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return latent * latents_std / self.scale_factor + latents_mean
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class Wan22(Wan21):
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latent_channels = 48
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latent_dimensions = 3
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latent_rgb_factors = [
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[ 0.0119, 0.0103, 0.0046],
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[-0.1062, -0.0504, 0.0165],
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[ 0.0140, 0.0409, 0.0491],
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[-0.0813, -0.0677, 0.0607],
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[ 0.0656, 0.0851, 0.0808],
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[ 0.0264, 0.0463, 0.0912],
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[ 0.0295, 0.0326, 0.0590],
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[-0.0244, -0.0270, 0.0025],
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[ 0.0443, -0.0102, 0.0288],
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[-0.0465, -0.0090, -0.0205],
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[ 0.0359, 0.0236, 0.0082],
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[-0.0776, 0.0854, 0.1048],
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[ 0.0564, 0.0264, 0.0561],
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[ 0.0006, 0.0594, 0.0418],
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[-0.0319, -0.0542, -0.0637],
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[-0.0268, 0.0024, 0.0260],
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[ 0.0539, 0.0265, 0.0358],
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[-0.0359, -0.0312, -0.0287],
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[-0.0285, -0.1032, -0.1237],
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[ 0.1041, 0.0537, 0.0622],
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[-0.0086, -0.0374, -0.0051],
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[ 0.0390, 0.0670, 0.2863],
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[ 0.0069, 0.0144, 0.0082],
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[ 0.0006, -0.0167, 0.0079],
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[ 0.0313, -0.0574, -0.0232],
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[-0.1454, -0.0902, -0.0481],
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[ 0.0714, 0.0827, 0.0447],
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[-0.0304, -0.0574, -0.0196],
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[ 0.0401, 0.0384, 0.0204],
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[-0.0758, -0.0297, -0.0014],
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[ 0.0568, 0.1307, 0.1372],
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[-0.0055, -0.0310, -0.0380],
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[ 0.0239, -0.0305, 0.0325],
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[-0.0663, -0.0673, -0.0140],
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[-0.0416, -0.0047, -0.0023],
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[ 0.0166, 0.0112, -0.0093],
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[-0.0211, 0.0011, 0.0331],
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[ 0.1833, 0.1466, 0.2250],
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[-0.0368, 0.0370, 0.0295],
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[-0.3441, -0.3543, -0.2008],
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[-0.0479, -0.0489, -0.0420],
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[-0.0660, -0.0153, 0.0800],
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[-0.0101, 0.0068, 0.0156],
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[-0.0690, -0.0452, -0.0927],
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[-0.0145, 0.0041, 0.0015],
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[ 0.0421, 0.0451, 0.0373],
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[ 0.0504, -0.0483, -0.0356],
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[-0.0837, 0.0168, 0.0055]
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]
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latent_rgb_factors_bias = [0.0317, -0.0878, -0.1388]
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def __init__(self):
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self.scale_factor = 1.0
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self.latents_mean = torch.tensor([
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-0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557,
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-0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825,
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-0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502,
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-0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230,
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-0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748,
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0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667,
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]).view(1, self.latent_channels, 1, 1, 1)
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self.latents_std = torch.tensor([
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0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013,
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0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978,
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0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659,
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0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093,
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0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887,
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0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744
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]).view(1, self.latent_channels, 1, 1, 1)
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class Hunyuan3Dv2(LatentFormat):
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latent_channels = 64
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latent_dimensions = 1
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@ -201,8 +201,10 @@ class WanAttentionBlock(nn.Module):
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freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
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"""
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# assert e.dtype == torch.float32
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e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
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if e.ndim < 4:
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e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
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else:
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e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2)
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# assert e[0].dtype == torch.float32
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# self-attention
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@ -325,7 +327,10 @@ class Head(nn.Module):
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e(Tensor): Shape [B, C]
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"""
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# assert e.dtype == torch.float32
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e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)
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if e.ndim < 3:
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e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)
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else:
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e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e.unsqueeze(2)).unbind(2)
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x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
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return x
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@ -506,8 +511,9 @@ class WanModel(torch.nn.Module):
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# time embeddings
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e = self.time_embedding(
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sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
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e0 = self.time_projection(e).unflatten(1, (6, self.dim))
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sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
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e = e.reshape(t.shape[0], -1, e.shape[-1])
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e0 = self.time_projection(e).unflatten(2, (6, self.dim))
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# context
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context = self.text_embedding(context)
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726
comfy/ldm/wan/vae2_2.py
Normal file
726
comfy/ldm/wan/vae2_2.py
Normal file
@ -0,0 +1,726 @@
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# original version: https://github.com/Wan-Video/Wan2.2/blob/main/wan/modules/vae2_2.py
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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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from .vae import AttentionBlock, CausalConv3d, RMS_norm
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import comfy.ops
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ops = comfy.ops.disable_weight_init
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CACHE_T = 2
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class Resample(nn.Module):
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def __init__(self, dim, mode):
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assert mode in (
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"none",
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"upsample2d",
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"upsample3d",
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"downsample2d",
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"downsample3d",
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)
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super().__init__()
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self.dim = dim
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self.mode = mode
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# layers
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if mode == "upsample2d":
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self.resample = nn.Sequential(
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nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
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ops.Conv2d(dim, dim, 3, padding=1),
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)
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elif mode == "upsample3d":
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self.resample = nn.Sequential(
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nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
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ops.Conv2d(dim, dim, 3, padding=1),
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# ops.Conv2d(dim, dim//2, 3, padding=1)
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)
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self.time_conv = CausalConv3d(
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dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
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elif mode == "downsample2d":
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self.resample = nn.Sequential(
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nn.ZeroPad2d((0, 1, 0, 1)),
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ops.Conv2d(dim, dim, 3, stride=(2, 2)))
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elif mode == "downsample3d":
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self.resample = nn.Sequential(
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nn.ZeroPad2d((0, 1, 0, 1)),
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ops.Conv2d(dim, dim, 3, stride=(2, 2)))
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self.time_conv = CausalConv3d(
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dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
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else:
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self.resample = nn.Identity()
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def forward(self, x, feat_cache=None, feat_idx=[0]):
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b, c, t, h, w = x.size()
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if self.mode == "upsample3d":
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if feat_cache is not None:
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idx = feat_idx[0]
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if feat_cache[idx] is None:
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feat_cache[idx] = "Rep"
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feat_idx[0] += 1
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else:
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cache_x = x[:, :, -CACHE_T:, :, :].clone()
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if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and
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feat_cache[idx] != "Rep"):
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# cache last frame of last two chunk
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cache_x = torch.cat(
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[
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
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cache_x.device),
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cache_x,
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],
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dim=2,
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)
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if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and
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feat_cache[idx] == "Rep"):
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cache_x = torch.cat(
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[
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torch.zeros_like(cache_x).to(cache_x.device),
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cache_x
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],
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dim=2,
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)
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if feat_cache[idx] == "Rep":
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x = self.time_conv(x)
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else:
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x = self.time_conv(x, feat_cache[idx])
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feat_cache[idx] = cache_x
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feat_idx[0] += 1
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x = x.reshape(b, 2, c, t, h, w)
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x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),
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3)
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x = x.reshape(b, c, t * 2, h, w)
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t = x.shape[2]
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x = rearrange(x, "b c t h w -> (b t) c h w")
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x = self.resample(x)
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x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
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if self.mode == "downsample3d":
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if feat_cache is not None:
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idx = feat_idx[0]
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if feat_cache[idx] is None:
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feat_cache[idx] = x.clone()
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feat_idx[0] += 1
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else:
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cache_x = x[:, :, -1:, :, :].clone()
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x = self.time_conv(
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torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
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feat_cache[idx] = cache_x
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feat_idx[0] += 1
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return x
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class ResidualBlock(nn.Module):
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def __init__(self, in_dim, out_dim, dropout=0.0):
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super().__init__()
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self.in_dim = in_dim
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self.out_dim = out_dim
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# layers
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self.residual = nn.Sequential(
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RMS_norm(in_dim, images=False),
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nn.SiLU(),
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CausalConv3d(in_dim, out_dim, 3, padding=1),
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RMS_norm(out_dim, images=False),
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nn.SiLU(),
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nn.Dropout(dropout),
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CausalConv3d(out_dim, out_dim, 3, padding=1),
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)
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self.shortcut = (
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CausalConv3d(in_dim, out_dim, 1)
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if in_dim != out_dim else nn.Identity())
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def forward(self, x, feat_cache=None, feat_idx=[0]):
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h = self.shortcut(x)
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for layer in self.residual:
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if isinstance(layer, CausalConv3d) and feat_cache is not None:
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idx = feat_idx[0]
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cache_x = x[:, :, -CACHE_T:, :, :].clone()
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if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
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# cache last frame of last two chunk
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cache_x = torch.cat(
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[
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
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cache_x.device),
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cache_x,
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],
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dim=2,
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)
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x = layer(x, feat_cache[idx])
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feat_cache[idx] = cache_x
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feat_idx[0] += 1
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else:
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x = layer(x)
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return x + h
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def patchify(x, patch_size):
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if patch_size == 1:
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return x
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if x.dim() == 4:
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x = rearrange(
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x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size, r=patch_size)
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elif x.dim() == 5:
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x = rearrange(
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x,
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"b c f (h q) (w r) -> b (c r q) f h w",
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q=patch_size,
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r=patch_size,
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)
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else:
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raise ValueError(f"Invalid input shape: {x.shape}")
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return x
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def unpatchify(x, patch_size):
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if patch_size == 1:
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return x
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if x.dim() == 4:
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x = rearrange(
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x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size, r=patch_size)
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elif x.dim() == 5:
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x = rearrange(
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x,
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"b (c r q) f h w -> b c f (h q) (w r)",
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q=patch_size,
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r=patch_size,
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)
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return x
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class AvgDown3D(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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factor_t,
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factor_s=1,
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):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.factor_t = factor_t
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self.factor_s = factor_s
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self.factor = self.factor_t * self.factor_s * self.factor_s
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assert in_channels * self.factor % out_channels == 0
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self.group_size = in_channels * self.factor // out_channels
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t
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pad = (0, 0, 0, 0, pad_t, 0)
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x = F.pad(x, pad)
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B, C, T, H, W = x.shape
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x = x.view(
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B,
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C,
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T // self.factor_t,
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self.factor_t,
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H // self.factor_s,
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self.factor_s,
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W // self.factor_s,
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self.factor_s,
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)
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x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous()
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x = x.view(
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B,
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C * self.factor,
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T // self.factor_t,
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H // self.factor_s,
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W // self.factor_s,
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)
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x = x.view(
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B,
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self.out_channels,
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self.group_size,
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T // self.factor_t,
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H // self.factor_s,
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W // self.factor_s,
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)
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x = x.mean(dim=2)
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return x
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class DupUp3D(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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factor_t,
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factor_s=1,
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):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.factor_t = factor_t
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self.factor_s = factor_s
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self.factor = self.factor_t * self.factor_s * self.factor_s
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assert out_channels * self.factor % in_channels == 0
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self.repeats = out_channels * self.factor // in_channels
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def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor:
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x = x.repeat_interleave(self.repeats, dim=1)
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x = x.view(
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x.size(0),
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self.out_channels,
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self.factor_t,
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self.factor_s,
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self.factor_s,
|
||||
x.size(2),
|
||||
x.size(3),
|
||||
x.size(4),
|
||||
)
|
||||
x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous()
|
||||
x = x.view(
|
||||
x.size(0),
|
||||
self.out_channels,
|
||||
x.size(2) * self.factor_t,
|
||||
x.size(4) * self.factor_s,
|
||||
x.size(6) * self.factor_s,
|
||||
)
|
||||
if first_chunk:
|
||||
x = x[:, :, self.factor_t - 1:, :, :]
|
||||
return x
|
||||
|
||||
|
||||
class Down_ResidualBlock(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
in_dim,
|
||||
out_dim,
|
||||
dropout,
|
||||
mult,
|
||||
temperal_downsample=False,
|
||||
down_flag=False):
|
||||
super().__init__()
|
||||
|
||||
# Shortcut path with downsample
|
||||
self.avg_shortcut = AvgDown3D(
|
||||
in_dim,
|
||||
out_dim,
|
||||
factor_t=2 if temperal_downsample else 1,
|
||||
factor_s=2 if down_flag else 1,
|
||||
)
|
||||
|
||||
# Main path with residual blocks and downsample
|
||||
downsamples = []
|
||||
for _ in range(mult):
|
||||
downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
|
||||
in_dim = out_dim
|
||||
|
||||
# Add the final downsample block
|
||||
if down_flag:
|
||||
mode = "downsample3d" if temperal_downsample else "downsample2d"
|
||||
downsamples.append(Resample(out_dim, mode=mode))
|
||||
|
||||
self.downsamples = nn.Sequential(*downsamples)
|
||||
|
||||
def forward(self, x, feat_cache=None, feat_idx=[0]):
|
||||
x_copy = x.clone()
|
||||
for module in self.downsamples:
|
||||
x = module(x, feat_cache, feat_idx)
|
||||
|
||||
return x + self.avg_shortcut(x_copy)
|
||||
|
||||
|
||||
class Up_ResidualBlock(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
in_dim,
|
||||
out_dim,
|
||||
dropout,
|
||||
mult,
|
||||
temperal_upsample=False,
|
||||
up_flag=False):
|
||||
super().__init__()
|
||||
# Shortcut path with upsample
|
||||
if up_flag:
|
||||
self.avg_shortcut = DupUp3D(
|
||||
in_dim,
|
||||
out_dim,
|
||||
factor_t=2 if temperal_upsample else 1,
|
||||
factor_s=2 if up_flag else 1,
|
||||
)
|
||||
else:
|
||||
self.avg_shortcut = None
|
||||
|
||||
# Main path with residual blocks and upsample
|
||||
upsamples = []
|
||||
for _ in range(mult):
|
||||
upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
|
||||
in_dim = out_dim
|
||||
|
||||
# Add the final upsample block
|
||||
if up_flag:
|
||||
mode = "upsample3d" if temperal_upsample else "upsample2d"
|
||||
upsamples.append(Resample(out_dim, mode=mode))
|
||||
|
||||
self.upsamples = nn.Sequential(*upsamples)
|
||||
|
||||
def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
|
||||
x_main = x.clone()
|
||||
for module in self.upsamples:
|
||||
x_main = module(x_main, feat_cache, feat_idx)
|
||||
if self.avg_shortcut is not None:
|
||||
x_shortcut = self.avg_shortcut(x, first_chunk)
|
||||
return x_main + x_shortcut
|
||||
else:
|
||||
return x_main
|
||||
|
||||
|
||||
class Encoder3d(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim=128,
|
||||
z_dim=4,
|
||||
dim_mult=[1, 2, 4, 4],
|
||||
num_res_blocks=2,
|
||||
attn_scales=[],
|
||||
temperal_downsample=[True, True, False],
|
||||
dropout=0.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.z_dim = z_dim
|
||||
self.dim_mult = dim_mult
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.attn_scales = attn_scales
|
||||
self.temperal_downsample = temperal_downsample
|
||||
|
||||
# dimensions
|
||||
dims = [dim * u for u in [1] + dim_mult]
|
||||
scale = 1.0
|
||||
|
||||
# init block
|
||||
self.conv1 = CausalConv3d(12, dims[0], 3, padding=1)
|
||||
|
||||
# downsample blocks
|
||||
downsamples = []
|
||||
for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
|
||||
t_down_flag = (
|
||||
temperal_downsample[i]
|
||||
if i < len(temperal_downsample) else False)
|
||||
downsamples.append(
|
||||
Down_ResidualBlock(
|
||||
in_dim=in_dim,
|
||||
out_dim=out_dim,
|
||||
dropout=dropout,
|
||||
mult=num_res_blocks,
|
||||
temperal_downsample=t_down_flag,
|
||||
down_flag=i != len(dim_mult) - 1,
|
||||
))
|
||||
scale /= 2.0
|
||||
self.downsamples = nn.Sequential(*downsamples)
|
||||
|
||||
# middle blocks
|
||||
self.middle = nn.Sequential(
|
||||
ResidualBlock(out_dim, out_dim, dropout),
|
||||
AttentionBlock(out_dim),
|
||||
ResidualBlock(out_dim, out_dim, dropout),
|
||||
)
|
||||
|
||||
# # output blocks
|
||||
self.head = nn.Sequential(
|
||||
RMS_norm(out_dim, images=False),
|
||||
nn.SiLU(),
|
||||
CausalConv3d(out_dim, z_dim, 3, padding=1),
|
||||
)
|
||||
|
||||
def forward(self, x, feat_cache=None, feat_idx=[0]):
|
||||
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat(
|
||||
[
|
||||
feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
|
||||
cache_x.device),
|
||||
cache_x,
|
||||
],
|
||||
dim=2,
|
||||
)
|
||||
x = self.conv1(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv1(x)
|
||||
|
||||
## downsamples
|
||||
for layer in self.downsamples:
|
||||
if feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
## middle
|
||||
for layer in self.middle:
|
||||
if isinstance(layer, ResidualBlock) and feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
## head
|
||||
for layer in self.head:
|
||||
if isinstance(layer, CausalConv3d) and feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat(
|
||||
[
|
||||
feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
|
||||
cache_x.device),
|
||||
cache_x,
|
||||
],
|
||||
dim=2,
|
||||
)
|
||||
x = layer(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Decoder3d(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim=128,
|
||||
z_dim=4,
|
||||
dim_mult=[1, 2, 4, 4],
|
||||
num_res_blocks=2,
|
||||
attn_scales=[],
|
||||
temperal_upsample=[False, True, True],
|
||||
dropout=0.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.z_dim = z_dim
|
||||
self.dim_mult = dim_mult
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.attn_scales = attn_scales
|
||||
self.temperal_upsample = temperal_upsample
|
||||
|
||||
# dimensions
|
||||
dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
|
||||
# init block
|
||||
self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
|
||||
|
||||
# middle blocks
|
||||
self.middle = nn.Sequential(
|
||||
ResidualBlock(dims[0], dims[0], dropout),
|
||||
AttentionBlock(dims[0]),
|
||||
ResidualBlock(dims[0], dims[0], dropout),
|
||||
)
|
||||
|
||||
# upsample blocks
|
||||
upsamples = []
|
||||
for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
|
||||
t_up_flag = temperal_upsample[i] if i < len(
|
||||
temperal_upsample) else False
|
||||
upsamples.append(
|
||||
Up_ResidualBlock(
|
||||
in_dim=in_dim,
|
||||
out_dim=out_dim,
|
||||
dropout=dropout,
|
||||
mult=num_res_blocks + 1,
|
||||
temperal_upsample=t_up_flag,
|
||||
up_flag=i != len(dim_mult) - 1,
|
||||
))
|
||||
self.upsamples = nn.Sequential(*upsamples)
|
||||
|
||||
# output blocks
|
||||
self.head = nn.Sequential(
|
||||
RMS_norm(out_dim, images=False),
|
||||
nn.SiLU(),
|
||||
CausalConv3d(out_dim, 12, 3, padding=1),
|
||||
)
|
||||
|
||||
def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat(
|
||||
[
|
||||
feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
|
||||
cache_x.device),
|
||||
cache_x,
|
||||
],
|
||||
dim=2,
|
||||
)
|
||||
x = self.conv1(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv1(x)
|
||||
|
||||
for layer in self.middle:
|
||||
if isinstance(layer, ResidualBlock) and feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
## upsamples
|
||||
for layer in self.upsamples:
|
||||
if feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx, first_chunk)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
## head
|
||||
for layer in self.head:
|
||||
if isinstance(layer, CausalConv3d) and feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat(
|
||||
[
|
||||
feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
|
||||
cache_x.device),
|
||||
cache_x,
|
||||
],
|
||||
dim=2,
|
||||
)
|
||||
x = layer(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
|
||||
def count_conv3d(model):
|
||||
count = 0
|
||||
for m in model.modules():
|
||||
if isinstance(m, CausalConv3d):
|
||||
count += 1
|
||||
return count
|
||||
|
||||
|
||||
class WanVAE(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim=160,
|
||||
dec_dim=256,
|
||||
z_dim=16,
|
||||
dim_mult=[1, 2, 4, 4],
|
||||
num_res_blocks=2,
|
||||
attn_scales=[],
|
||||
temperal_downsample=[True, True, False],
|
||||
dropout=0.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.z_dim = z_dim
|
||||
self.dim_mult = dim_mult
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.attn_scales = attn_scales
|
||||
self.temperal_downsample = temperal_downsample
|
||||
self.temperal_upsample = temperal_downsample[::-1]
|
||||
|
||||
# modules
|
||||
self.encoder = Encoder3d(
|
||||
dim,
|
||||
z_dim * 2,
|
||||
dim_mult,
|
||||
num_res_blocks,
|
||||
attn_scales,
|
||||
self.temperal_downsample,
|
||||
dropout,
|
||||
)
|
||||
self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
|
||||
self.conv2 = CausalConv3d(z_dim, z_dim, 1)
|
||||
self.decoder = Decoder3d(
|
||||
dec_dim,
|
||||
z_dim,
|
||||
dim_mult,
|
||||
num_res_blocks,
|
||||
attn_scales,
|
||||
self.temperal_upsample,
|
||||
dropout,
|
||||
)
|
||||
|
||||
def encode(self, x):
|
||||
self.clear_cache()
|
||||
x = patchify(x, patch_size=2)
|
||||
t = x.shape[2]
|
||||
iter_ = 1 + (t - 1) // 4
|
||||
for i in range(iter_):
|
||||
self._enc_conv_idx = [0]
|
||||
if i == 0:
|
||||
out = self.encoder(
|
||||
x[:, :, :1, :, :],
|
||||
feat_cache=self._enc_feat_map,
|
||||
feat_idx=self._enc_conv_idx,
|
||||
)
|
||||
else:
|
||||
out_ = self.encoder(
|
||||
x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
|
||||
feat_cache=self._enc_feat_map,
|
||||
feat_idx=self._enc_conv_idx,
|
||||
)
|
||||
out = torch.cat([out, out_], 2)
|
||||
mu, log_var = self.conv1(out).chunk(2, dim=1)
|
||||
self.clear_cache()
|
||||
return mu
|
||||
|
||||
def decode(self, z):
|
||||
self.clear_cache()
|
||||
iter_ = z.shape[2]
|
||||
x = self.conv2(z)
|
||||
for i in range(iter_):
|
||||
self._conv_idx = [0]
|
||||
if i == 0:
|
||||
out = self.decoder(
|
||||
x[:, :, i:i + 1, :, :],
|
||||
feat_cache=self._feat_map,
|
||||
feat_idx=self._conv_idx,
|
||||
first_chunk=True,
|
||||
)
|
||||
else:
|
||||
out_ = self.decoder(
|
||||
x[:, :, i:i + 1, :, :],
|
||||
feat_cache=self._feat_map,
|
||||
feat_idx=self._conv_idx,
|
||||
)
|
||||
out = torch.cat([out, out_], 2)
|
||||
out = unpatchify(out, patch_size=2)
|
||||
self.clear_cache()
|
||||
return out
|
||||
|
||||
def reparameterize(self, mu, log_var):
|
||||
std = torch.exp(0.5 * log_var)
|
||||
eps = torch.randn_like(std)
|
||||
return eps * std + mu
|
||||
|
||||
def sample(self, imgs, deterministic=False):
|
||||
mu, log_var = self.encode(imgs)
|
||||
if deterministic:
|
||||
return mu
|
||||
std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
|
||||
return mu + std * torch.randn_like(std)
|
||||
|
||||
def clear_cache(self):
|
||||
self._conv_num = count_conv3d(self.decoder)
|
||||
self._conv_idx = [0]
|
||||
self._feat_map = [None] * self._conv_num
|
||||
# cache encode
|
||||
self._enc_conv_num = count_conv3d(self.encoder)
|
||||
self._enc_conv_idx = [0]
|
||||
self._enc_feat_map = [None] * self._enc_conv_num
|
||||
@ -1097,8 +1097,9 @@ class WAN21(BaseModel):
|
||||
image[:, i: i + 16] = self.process_latent_in(image[:, i: i + 16])
|
||||
image = utils.resize_to_batch_size(image, noise.shape[0])
|
||||
|
||||
if not self.image_to_video or extra_channels == image.shape[1]:
|
||||
return image
|
||||
if extra_channels != image.shape[1] + 4:
|
||||
if not self.image_to_video or extra_channels == image.shape[1]:
|
||||
return image
|
||||
|
||||
if image.shape[1] > (extra_channels - 4):
|
||||
image = image[:, :(extra_channels - 4)]
|
||||
@ -1182,6 +1183,31 @@ class WAN21_Camera(WAN21):
|
||||
out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)
|
||||
return out
|
||||
|
||||
class WAN22(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)
|
||||
self.image_to_video = image_to_video
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = super().extra_conds(**kwargs)
|
||||
cross_attn = kwargs.get("cross_attn", None)
|
||||
if cross_attn is not None:
|
||||
out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
|
||||
|
||||
denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
|
||||
if denoise_mask is not None:
|
||||
out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask)
|
||||
return out
|
||||
|
||||
def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
|
||||
if denoise_mask is None:
|
||||
return timestep
|
||||
temp_ts = (torch.mean(denoise_mask[:, :, :, ::2, ::2], dim=1, keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1)
|
||||
return temp_ts
|
||||
|
||||
def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
|
||||
return latent_image
|
||||
|
||||
class Hunyuan3Dv2(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2)
|
||||
|
||||
@ -346,7 +346,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
dit_config = {}
|
||||
dit_config["image_model"] = "wan2.1"
|
||||
dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1]
|
||||
out_dim = state_dict['{}head.head.weight'.format(key_prefix)].shape[0] // 4
|
||||
dit_config["dim"] = dim
|
||||
dit_config["out_dim"] = out_dim
|
||||
dit_config["num_heads"] = dim // 128
|
||||
dit_config["ffn_dim"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
|
||||
|
||||
36
comfy/sd.py
36
comfy/sd.py
@ -14,6 +14,7 @@ import comfy.ldm.genmo.vae.model
|
||||
import comfy.ldm.lightricks.vae.causal_video_autoencoder
|
||||
import comfy.ldm.cosmos.vae
|
||||
import comfy.ldm.wan.vae
|
||||
import comfy.ldm.wan.vae2_2
|
||||
import comfy.ldm.hunyuan3d.vae
|
||||
import comfy.ldm.ace.vae.music_dcae_pipeline
|
||||
import yaml
|
||||
@ -420,17 +421,30 @@ class VAE:
|
||||
self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
|
||||
self.working_dtypes = [torch.bfloat16, torch.float32]
|
||||
elif "decoder.middle.0.residual.0.gamma" in sd:
|
||||
self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
|
||||
self.upscale_index_formula = (4, 8, 8)
|
||||
self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
|
||||
self.downscale_index_formula = (4, 8, 8)
|
||||
self.latent_dim = 3
|
||||
self.latent_channels = 16
|
||||
ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
|
||||
self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
|
||||
self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
|
||||
self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
|
||||
self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
|
||||
if "decoder.upsamples.0.upsamples.0.residual.2.weight" in sd: # Wan 2.2 VAE
|
||||
self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)
|
||||
self.upscale_index_formula = (4, 16, 16)
|
||||
self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
|
||||
self.downscale_index_formula = (4, 16, 16)
|
||||
self.latent_dim = 3
|
||||
self.latent_channels = 48
|
||||
ddconfig = {"dim": 160, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
|
||||
self.first_stage_model = comfy.ldm.wan.vae2_2.WanVAE(**ddconfig)
|
||||
self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
|
||||
self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype)
|
||||
self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype)
|
||||
else: # Wan 2.1 VAE
|
||||
self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
|
||||
self.upscale_index_formula = (4, 8, 8)
|
||||
self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
|
||||
self.downscale_index_formula = (4, 8, 8)
|
||||
self.latent_dim = 3
|
||||
self.latent_channels = 16
|
||||
ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
|
||||
self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig)
|
||||
self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
|
||||
self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype)
|
||||
self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype)
|
||||
elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd:
|
||||
self.latent_dim = 1
|
||||
ln_post = "geo_decoder.ln_post.weight" in sd
|
||||
|
||||
@ -1059,6 +1059,19 @@ class WAN21_Vace(WAN21_T2V):
|
||||
out = model_base.WAN21_Vace(self, image_to_video=False, device=device)
|
||||
return out
|
||||
|
||||
class WAN22_T2V(WAN21_T2V):
|
||||
unet_config = {
|
||||
"image_model": "wan2.1",
|
||||
"model_type": "t2v",
|
||||
"out_dim": 48,
|
||||
}
|
||||
|
||||
latent_format = latent_formats.Wan22
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
out = model_base.WAN22(self, image_to_video=True, device=device)
|
||||
return out
|
||||
|
||||
class Hunyuan3Dv2(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"image_model": "hunyuan3d2",
|
||||
@ -1217,6 +1230,6 @@ class Omnigen2(supported_models_base.BASE):
|
||||
return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect))
|
||||
|
||||
|
||||
models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep, Omnigen2]
|
||||
models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep, Omnigen2]
|
||||
|
||||
models += [SVD_img2vid]
|
||||
|
||||
@ -685,6 +685,49 @@ class WanTrackToVideo:
|
||||
out_latent["samples"] = latent
|
||||
return (positive, negative, out_latent)
|
||||
|
||||
|
||||
class Wan22ImageToVideoLatent:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {"vae": ("VAE", ),
|
||||
"width": ("INT", {"default": 1280, "min": 32, "max": nodes.MAX_RESOLUTION, "step": 32}),
|
||||
"height": ("INT", {"default": 704, "min": 32, "max": nodes.MAX_RESOLUTION, "step": 32}),
|
||||
"length": ("INT", {"default": 49, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
|
||||
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
|
||||
},
|
||||
"optional": {"start_image": ("IMAGE", ),
|
||||
}}
|
||||
|
||||
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "encode"
|
||||
|
||||
CATEGORY = "conditioning/inpaint"
|
||||
|
||||
def encode(self, vae, width, height, length, batch_size, start_image=None):
|
||||
latent = torch.zeros([1, 48, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
|
||||
|
||||
if start_image is None:
|
||||
out_latent = {}
|
||||
out_latent["samples"] = latent
|
||||
return (out_latent,)
|
||||
|
||||
mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
|
||||
|
||||
if start_image is not None:
|
||||
start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
|
||||
latent_temp = vae.encode(start_image)
|
||||
latent[:, :, :latent_temp.shape[-3]] = latent_temp
|
||||
mask[:, :, :latent_temp.shape[-3]] *= 0.0
|
||||
|
||||
out_latent = {}
|
||||
latent_format = comfy.latent_formats.Wan22()
|
||||
latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask)
|
||||
out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
|
||||
out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
|
||||
return (out_latent,)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"WanTrackToVideo": WanTrackToVideo,
|
||||
"WanImageToVideo": WanImageToVideo,
|
||||
@ -695,4 +738,5 @@ NODE_CLASS_MAPPINGS = {
|
||||
"TrimVideoLatent": TrimVideoLatent,
|
||||
"WanCameraImageToVideo": WanCameraImageToVideo,
|
||||
"WanPhantomSubjectToVideo": WanPhantomSubjectToVideo,
|
||||
"Wan22ImageToVideoLatent": Wan22ImageToVideoLatent,
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user