nodes/kijai-ComfyUI-WanVideoWrapper
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Enable vae tiling with end frame

Browse Source
This commit is contained in:
wzxysf
2025-10-24 22:23:31 +08:00
parent d74cfc54e8
commit 6a37c0b2d6
2 changed files with 12 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files
nodes.py
-2
View File
@@ -2023,8 +2023,6 @@ class WanVideoDecode:
images = images.permute(1, 2, 3, 0).cpu().float()
return (images,)
else:
if end_image is not None:
enable_vae_tiling = False
images = vae.decode(latents, device=device, end_=(end_image is not None), tiled=enable_vae_tiling, tile_size=(tile_x//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8))[0]
wanvideo/wan_video_vae.py
+12 -6
View File
@@ -1193,7 +1193,7 @@ class WanVideoVAE(nn.Module):
return mask
def tiled_decode(self, hidden_states, device, tile_size, tile_stride, pbar=True):
def tiled_decode(self, hidden_states, device, tile_size, tile_stride, end_=False, pbar=True):
_, _, T, H, W = hidden_states.shape
size_h, size_w = tile_size
stride_h, stride_w = tile_stride
@@ -1210,14 +1210,20 @@ class WanVideoVAE(nn.Module):
data_device = "cpu"
computation_device = device
out_T = T * 4 - 3
weight = torch.zeros((1, 1, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
values = torch.zeros((1, 3, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
weight, values = None, None
if pbar:
pbar = ProgressBar(len(tasks))
for h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"):
hidden_states_batch = hidden_states[:, :, :, h:h_, w:w_].to(computation_device)
hidden_states_batch = self.model.decode(hidden_states_batch).to(data_device)
if end_:
hidden_states_batch = self.model.decode_2(hidden_states_batch).to(data_device)
else:
hidden_states_batch = self.model.decode(hidden_states_batch).to(data_device)
if weight is None:
weight = torch.zeros((1, 1, hidden_states_batch.shape[2], H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
if values is None:
values = torch.zeros((1, 3, hidden_states_batch.shape[2], H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
mask = self.build_mask(
hidden_states_batch,
@@ -1361,7 +1367,7 @@ class WanVideoVAE(nn.Module):
for hidden_state in hidden_states:
hidden_state = hidden_state.unsqueeze(0)
if tiled:
video = self.tiled_decode(hidden_state, device, tile_size, tile_stride, pbar=pbar)
video = self.tiled_decode(hidden_state, device, tile_size, tile_stride, end_=end_, pbar=pbar)
else:
if end_:
video = self.double_decode(hidden_state, device)