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Fix VRAM leak in tiler fallback in video VAEs (#13073)

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* sd: soft_empty_cache on tiler fallback

This doesnt cost a lot and creates the expected VRAM reduction in
resource monitors when you fallback to tiler.

* wan: vae: Don't recursion in local fns (move run_up)

Moved Decoder3d’s recursive run_up out of forward into a class
method to avoid nested closure self-reference cycles. This avoids
cyclic garbage that delays garbage of tensors which in turn delays
VRAM release before tiled fallback.

* ltx: vae: Don't recursion in local fns (move run_up)

Mov the recursive run_up out of forward into a class
method to avoid nested closure self-reference cycles. This avoids
cyclic garbage that delays garbage of tensors which in turn delays
VRAM release before tiled fallback.
This commit is contained in:
rattus 2026-03-19 19:30:27 -07:00 committed by GitHub
parent 8458ae2686
commit 82b868a45a
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GPG Key ID: B5690EEEBB952194
3 changed files with 88 additions and 84 deletions
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96
comfy/ldm/lightricks/vae/causal_video_autoencoder.py
View File

@ -536,6 +536,53 @@ class Decoder(nn.Module):
c, (ts, hs, ws), to = self._output_scale
return (input_shape[0], c, input_shape[2] * ts - to, input_shape[3] * hs, input_shape[4] * ws)
def run_up(self, idx, sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size):
sample = sample_ref[0]
sample_ref[0] = None
if idx >= len(self.up_blocks):
sample = self.conv_norm_out(sample)
if timestep_shift_scale is not None:
shift, scale = timestep_shift_scale
sample = sample * (1 + scale) + shift
sample = self.conv_act(sample)
if ended:
mark_conv3d_ended(self.conv_out)
sample = self.conv_out(sample, causal=self.causal)
if sample is not None and sample.shape[2] > 0:
sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
t = sample.shape[2]
output_buffer[:, :, output_offset[0]:output_offset[0] + t].copy_(sample)
output_offset[0] += t
return
up_block = self.up_blocks[idx]
if ended:
mark_conv3d_ended(up_block)
if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
sample = checkpoint_fn(up_block)(
sample, causal=self.causal, timestep=scaled_timestep
)
else:
sample = checkpoint_fn(up_block)(sample, causal=self.causal)
if sample is None or sample.shape[2] == 0:
return
total_bytes = sample.numel() * sample.element_size()
num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size
if num_chunks == 1:
# when we are not chunking, detach our x so the callee can free it as soon as they are done
next_sample_ref = [sample]
del sample
self.run_up(idx + 1, next_sample_ref, ended, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)
return
else:
samples = torch.chunk(sample, chunks=num_chunks, dim=2)
for chunk_idx, sample1 in enumerate(samples):
self.run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)
def forward_orig(
self,
sample: torch.FloatTensor,
@ -591,54 +638,7 @@ class Decoder(nn.Module):
max_chunk_size = get_max_chunk_size(sample.device)
def run_up(idx, sample_ref, ended):
sample = sample_ref[0]
sample_ref[0] = None
if idx >= len(self.up_blocks):
sample = self.conv_norm_out(sample)
if timestep_shift_scale is not None:
shift, scale = timestep_shift_scale
sample = sample * (1 + scale) + shift
sample = self.conv_act(sample)
if ended:
mark_conv3d_ended(self.conv_out)
sample = self.conv_out(sample, causal=self.causal)
if sample is not None and sample.shape[2] > 0:
sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
t = sample.shape[2]
output_buffer[:, :, output_offset[0]:output_offset[0] + t].copy_(sample)
output_offset[0] += t
return
up_block = self.up_blocks[idx]
if (ended):
mark_conv3d_ended(up_block)
if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
sample = checkpoint_fn(up_block)(
sample, causal=self.causal, timestep=scaled_timestep
)
else:
sample = checkpoint_fn(up_block)(sample, causal=self.causal)
if sample is None or sample.shape[2] == 0:
return
total_bytes = sample.numel() * sample.element_size()
num_chunks = (total_bytes + max_chunk_size - 1) // max_chunk_size
if num_chunks == 1:
# when we are not chunking, detach our x so the callee can free it as soon as they are done
next_sample_ref = [sample]
del sample
run_up(idx + 1, next_sample_ref, ended)
return
else:
samples = torch.chunk(sample, chunks=num_chunks, dim=2)
for chunk_idx, sample1 in enumerate(samples):
run_up(idx + 1, [sample1], ended and chunk_idx == len(samples) - 1)
run_up(0, [sample], True)
self.run_up(0, [sample], True, timestep_shift_scale, scaled_timestep, checkpoint_fn, output_buffer, output_offset, max_chunk_size)
return output_buffer

74
comfy/ldm/wan/vae.py
View File

@ -360,6 +360,43 @@ class Decoder3d(nn.Module):
RMS_norm(out_dim, images=False), nn.SiLU(),
CausalConv3d(out_dim, output_channels, 3, padding=1))
def run_up(self, layer_idx, x_ref, feat_cache, feat_idx, out_chunks):
x = x_ref[0]
x_ref[0] = None
if layer_idx >= len(self.upsamples):
for layer in self.head:
if isinstance(layer, CausalConv3d) and feat_cache is not None:
cache_x = x[:, :, -CACHE_T:, :, :]
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)
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]):
self.run_up(
layer_idx,
[x[:, :, frame_idx:frame_idx + 1, :, :]],
feat_cache,
feat_idx.copy(),
out_chunks,
)
del x
return
if feat_cache is not None:
x = layer(x, feat_cache, feat_idx)
else:
x = layer(x)
next_x_ref = [x]
del x
self.run_up(layer_idx + 1, next_x_ref, feat_cache, feat_idx, out_chunks)
def forward(self, x, feat_cache=None, feat_idx=[0]):
## conv1
if feat_cache is not None:
@ -380,42 +417,7 @@ class Decoder3d(nn.Module):
out_chunks = []
def run_up(layer_idx, x_ref, feat_idx):
x = x_ref[0]
x_ref[0] = None
if layer_idx >= len(self.upsamples):
for layer in self.head:
if isinstance(layer, CausalConv3d) and feat_cache is not None:
cache_x = x[:, :, -CACHE_T:, :, :]
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)
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_up(
layer_idx,
[x[:, :, frame_idx:frame_idx + 1, :, :]],
feat_idx.copy(),
)
del x
return
if feat_cache is not None:
x = layer(x, feat_cache, feat_idx)
else:
x = layer(x)
next_x_ref = [x]
del x
run_up(layer_idx + 1, next_x_ref, feat_idx)
run_up(0, [x], feat_idx)
self.run_up(0, [x], feat_cache, feat_idx, out_chunks)
return out_chunks

2
comfy/sd.py
View File

@ -978,6 +978,7 @@ class VAE:
do_tile = True
if do_tile:
comfy.model_management.soft_empty_cache()
dims = samples_in.ndim - 2
if dims == 1 or self.extra_1d_channel is not None:
pixel_samples = self.decode_tiled_1d(samples_in)
@ -1059,6 +1060,7 @@ class VAE:
do_tile = True
if do_tile:
comfy.model_management.soft_empty_cache()
if self.latent_dim == 3:
tile = 256
overlap = tile // 4