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wan: vae: recurse and chunk for 2+2 frames on decode

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Avoid having to clone off slices of 4 frame chunks and reduce the size
of the big 6 frame convolutions down to 4. Save the VRAMs.
This commit is contained in:
Rattus 2026-03-16 21:44:04 +10:00
parent d8fa68084f
commit 0ebd807eb9
1 changed files with 34 additions and 15 deletions
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49
comfy/ldm/wan/vae.py
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@ -378,24 +378,43 @@ class Decoder3d(nn.Module):
else:
x = layer(x)
## upsamples
for layer in self.upsamples:
out_chunks = []
def run_head(x, feat_idx):
for layer in self.head:
if isinstance(layer, CausalConv3d) and feat_cache is not None:
cache_x = x[:, :, -CACHE_T:, :, :].clone()
x = layer(x, feat_cache[feat_idx[0]])
feat_cache[feat_idx[0]] = cache_x
feat_idx[0] += 1
else:
x = layer(x)
out_chunks.append(x)
def run_upsamples(layer_idx, x, feat_idx):
if layer_idx >= len(self.upsamples):
run_head(x, feat_idx)
return
layer = self.upsamples[layer_idx]
if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1:
for frame_idx in range(x.shape[2]):
run_upsamples(
layer_idx,
x[:, :, frame_idx:frame_idx + 1, :, :],
feat_idx.copy(),
)
return
if 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()
x = layer(x, feat_cache[idx])
feat_cache[idx] = cache_x
feat_idx[0] += 1
else:
x = layer(x)
return x
run_upsamples(layer_idx + 1, x, feat_idx)
run_upsamples(0, x, feat_idx)
return out_chunks
def count_cache_layers(model):
@ -483,5 +502,5 @@ class WanVAE(nn.Module):
x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :],
feat_cache=feat_map,
feat_idx=conv_idx)
out = torch.cat([out, out_], 2)
return out
out += out_
return torch.cat(out, 2)