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

Add error indicating incompatible input size

Browse Source 
More relevant now with 5B VAE requiring things to be divisible by 32
This commit is contained in:
kijai
2025-08-05 00:35:52 +03:00
parent 6ec463288f
commit 0e9b1973b0
1 changed files with 7 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
nodes.py
+7 -5
View File
@@ -13,7 +13,7 @@ from .wanvideo.schedulers import get_scheduler, get_sampling_sigmas, retrieve_ti
from .gguf.gguf import set_lora_params
from .multitalk.multitalk import timestep_transform, add_noise
from .utils import(log, print_memory, apply_lora, clip_encode_image_tiled, fourier_filter,
is_image_black, add_noise_to_reference_video, optimized_scale, setup_radial_attention,
add_noise_to_reference_video, optimized_scale, setup_radial_attention,
compile_model, dict_to_device, tangential_projection, set_module_tensor_to_device)
from .cache_methods.cache_methods import cache_report
from .enhance_a_video.globals import set_enhance_weight, set_num_frames
@@ -1183,7 +1183,7 @@ class WanVideoVACEEncode:
if input_masks is None:
input_masks = torch.ones_like(input_frames, device=device)
else:
print("input_masks shape", input_masks.shape)
log.info(f"input_masks shape: {input_masks.shape}")
input_masks = input_masks[:num_frames]
input_masks = common_upscale(input_masks.clone().unsqueeze(1), width, height, "nearest-exact", "disabled").squeeze(1)
input_masks = input_masks.to(vae.dtype).to(device)
@@ -1262,7 +1262,6 @@ class WanVideoVACEEncode:
if masks is None:
ref_latent = vae.encode(refs, device=device, tiled=tiled_vae)
else:
print("refs shape", refs.shape)#torch.Size([3, 1, 512, 512])
ref_latent = vae.encode(refs, device=device, tiled=tiled_vae)
ref_latent = [torch.cat((u, torch.zeros_like(u)), dim=0) for u in ref_latent]
assert all([x.shape[1] == 1 for x in ref_latent])
@@ -1743,6 +1742,9 @@ class WanVideoSampler:
latent_video_length = noise.shape[1]
if noise.shape[2] % (vae_upscale_factor/4) != 0 or noise.shape[3] % (vae_upscale_factor/4) != 0:
raise ValueError(f"Width ({noise.shape[3] * vae_upscale_factor}) and height ({noise.shape[2] * vae_upscale_factor}) must be divisible by {vae_upscale_factor*2}. Got {noise.shape[3] * vae_upscale_factor}x{noise.shape[2] * vae_upscale_factor}.")
# Initialize FreeInit filter if enabled
freq_filter = None
if freeinit_args is not None:
@@ -2233,7 +2235,7 @@ class WanVideoSampler:
for human_idx in range(human_num):
audio_start = context_window[0] * 4
audio_end = context_window[-1] * 4 + 1
print("audio_start: ", audio_start, "audio_end: ", audio_end)
#print("audio_start: ", audio_start, "audio_end: ", audio_end)
center_indices = torch.arange(audio_start, audio_end, 1).unsqueeze(1) + indices.unsqueeze(0)
center_indices = torch.clamp(center_indices, min=0, max=audio_embedding[human_idx].shape[0] - 1)
audio_emb = audio_embedding[human_idx][center_indices].unsqueeze(0).to(device)
@@ -2531,7 +2533,7 @@ class WanVideoSampler:
if extra_latents is not None:
if 'all_indices' in locals() and all_indices:
timestep[:, all_indices] = 0
print("timestep: ", timestep)
#print("timestep: ", timestep)
### latent shift
if latent_shift_loop: