nodes/kijai-ComfyUI-WanVideoWrapper
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Drastically reduce VRAM usage on I2V encode node.

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kijai
2025-08-24 11:39:56 +03:00
parent 8a151b5402
commit 61aa0e2ffd
1 changed files with 29 additions and 15 deletions
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nodes.py
+29 -15
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@@ -864,40 +864,55 @@ class WanVideoImageToVideoEncode:
# Resize and rearrange the input image dimensions
if start_image is not None:
resized_start_image = common_upscale(start_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1)
if start_image.shape[1] != H or start_image.shape[2] != W:
resized_start_image = common_upscale(start_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1)
else:
resized_start_image = start_image.permute(3, 0, 1, 2) # C, T, H, W
resized_start_image = resized_start_image * 2 - 1
if noise_aug_strength > 0.0:
resized_start_image = add_noise_to_reference_video(resized_start_image, ratio=noise_aug_strength)
if end_image is not None:
resized_end_image = common_upscale(end_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1)
if end_image.shape[1] != H or end_image.shape[2] != W:
resized_end_image = common_upscale(end_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1)
else:
resized_end_image = end_image.permute(3, 0, 1, 2) # C, T, H, W
resized_end_image = resized_end_image * 2 - 1
if noise_aug_strength > 0.0:
resized_end_image = add_noise_to_reference_video(resized_end_image, ratio=noise_aug_strength)
# Concatenate image with zero frames and encode
vae.to(device)
if temporal_mask is None:
if start_image is not None and end_image is None:
zero_frames = torch.zeros(3, num_frames-start_image.shape[0], H, W, device=device)
concatenated = torch.cat([resized_start_image.to(device), zero_frames], dim=1)
zero_frames = torch.zeros(3, num_frames-start_image.shape[0], H, W, device=device, dtype=vae.dtype)
concatenated = torch.cat([resized_start_image.to(device, dtype=vae.dtype), zero_frames], dim=1)
del resized_start_image, zero_frames
elif start_image is None and end_image is not None:
zero_frames = torch.zeros(3, num_frames-end_image.shape[0], H, W, device=device)
concatenated = torch.cat([zero_frames, resized_end_image.to(device)], dim=1)
zero_frames = torch.zeros(3, num_frames-end_image.shape[0], H, W, device=device, dtype=vae.dtype)
concatenated = torch.cat([zero_frames, resized_end_image.to(device, dtype=vae.dtype)], dim=1)
del resized_end_image, zero_frames
elif start_image is None and end_image is None:
concatenated = torch.zeros(3, num_frames, H, W, device=device)
concatenated = torch.zeros(3, num_frames, H, W, device=device, dtype=vae.dtype)
else:
if fun_or_fl2v_model:
zero_frames = torch.zeros(3, num_frames-(start_image.shape[0]+end_image.shape[0]), H, W, device=device)
zero_frames = torch.zeros(3, num_frames-(start_image.shape[0]+end_image.shape[0]), H, W, device=device, dtype=vae.dtype)
else:
zero_frames = torch.zeros(3, num_frames-1, H, W, device=device)
concatenated = torch.cat([resized_start_image.to(device), zero_frames, resized_end_image.to(device)], dim=1)
zero_frames = torch.zeros(3, num_frames-1, H, W, device=device, dtype=vae.dtype)
concatenated = torch.cat([resized_start_image.to(device, dtype=vae.dtype), zero_frames, resized_end_image.to(device, dtype=vae.dtype)], dim=1)
del resized_start_image, resized_end_image, zero_frames
else:
temporal_mask = common_upscale(temporal_mask.unsqueeze(1), W, H, "nearest", "disabled").squeeze(1)
concatenated = resized_start_image[:,:num_frames] * temporal_mask[:num_frames].unsqueeze(0)
del resized_start_image, temporal_mask
mm.soft_empty_cache()
gc.collect()
vae.to(device)
y = vae.encode([concatenated], device, end_=(end_image is not None and not fun_or_fl2v_model),tiled=tiled_vae)[0]
vae.model.clear_cache()
del concatenated
y = vae.encode([concatenated.to(device=device, dtype=vae.dtype)], device, end_=(end_image is not None and not fun_or_fl2v_model),tiled=tiled_vae)[0]
has_ref = False
if extra_latents is not None:
samples = extra_latents["samples"].squeeze(0)
@@ -915,8 +930,7 @@ class WanVideoImageToVideoEncode:
if add_cond_latents is not None:
add_cond_latents["ref_latent_neg"] = vae.encode(torch.zeros(1, 3, 1, H, W, device=device, dtype=vae.dtype), device)
vae.model.clear_cache()
if force_offload:
vae.model.to(offload_device)
mm.soft_empty_cache()