xinyun/ComfyUI
1
0
Fork 0
mirror of https://git.datalinker.icu/comfyanonymous/ComfyUI synced 2025-12-09 05:54:24 +08:00
Code Issues Packages Projects Releases Wiki Activity

WAN: Fix cache VRAM leak on error (#10141)

Browse Source 
If this suffers an exception (such as a VRAM oom) it will leave the
encode() and decode() methods which skips the cleanup of the WAN
feature cache. The comfy node cache then ultimately keeps a reference
this object which is in turn reffing large tensors from the failed
execution.

The feature cache is currently setup at a class variable on the
encoder/decoder however, the encode and decode functions always clear
it on both entry and exit of normal execution.

Its likely the design intent is this is usable as a streaming encoder
where the input comes in batches, however the functions as they are
today don't support that.

So simplify by bringing the cache back to local variable, so that if
it does VRAM OOM the cache itself is properly garbage when the
encode()/decode() functions dissappear from the stack.
This commit is contained in:
rattus128 2025-10-02 08:42:16 +10:00 committed by GitHub
parent 911331c06c
commit 4965c0e2ac
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 14 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

37
comfy/ldm/wan/vae.py
View File

@ -468,55 +468,46 @@ class WanVAE(nn.Module):
attn_scales, self.temperal_upsample, dropout)
def encode(self, x):
self.clear_cache()
conv_idx = [0]
feat_map = [None] * count_conv3d(self.decoder)
## cache
t = x.shape[2]
iter_ = 1 + (t - 1) // 4
## 对encode输入的x，按时间拆分为1、4、4、4....
for i in range(iter_):
self._enc_conv_idx = [0]
conv_idx = [0]
if i == 0:
out = self.encoder(
x[:, :, :1, :, :],
feat_cache=self._enc_feat_map,
feat_idx=self._enc_conv_idx)
feat_cache=feat_map,
feat_idx=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)
feat_cache=feat_map,
feat_idx=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()
conv_idx = [0]
feat_map = [None] * count_conv3d(self.decoder)
# z: [b,c,t,h,w]
iter_ = z.shape[2]
x = self.conv2(z)
for i in range(iter_):
self._conv_idx = [0]
conv_idx = [0]
if i == 0:
out = self.decoder(
x[:, :, i:i + 1, :, :],
feat_cache=self._feat_map,
feat_idx=self._conv_idx)
feat_cache=feat_map,
feat_idx=conv_idx)
else:
out_ = self.decoder(
x[:, :, i:i + 1, :, :],
feat_cache=self._feat_map,
feat_idx=self._conv_idx)
feat_cache=feat_map,
feat_idx=conv_idx)
out = torch.cat([out, out_], 2)
self.clear_cache()
return out
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