wangbo/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-05-08 16:22:38 +08:00
Code Issues Actions 26 Packages Projects Releases Wiki Activity

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

Browse Source 
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 2026-03-20 11:38:46 +10:00
parent 7a88c578e0
commit 6b138a8986
1 changed files with 48 additions and 48 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

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