nodes/kijai-ComfyUI-WanVideoWrapper
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Clean prints

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kijai
2025-12-23 02:02:31 +02:00
parent 1776695e26
commit fd32b14fdc
3 changed files with 9 additions and 8 deletions
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nodes_model_loading.py
+1 -1
View File
@@ -1153,7 +1153,7 @@ class WanVideoModelLoader:
# currently this can be VACE, MTV-Crafter, Lynx or Ovi-audio weights
if extra_model is not None:
for _model in extra_model:
print("Loading extra model: ", _model["path"])
log.info(f"Loading extra model: {_model['path']}")
if gguf:
if not _model["path"].endswith(".gguf"):
raise ValueError("With GGUF main model the extra model must also be GGUF quantized, if the main model already has VACE included, you can disconnect the extra module loader")
nodes_sampler.py
+7 -3
View File
@@ -875,7 +875,9 @@ class WanVideoSampler:
# LongCat-Avatar
longcat_ref_latent = None
longcat_num_ref_latents = longcat_num_cond_latents = 0
longcat_avatar_options = image_embeds.get("longcat_avatar_options", None)
if longcat_avatar_options is not None:
longcat_ref_latent = longcat_avatar_options.get("longcat_ref_latent", None)
if longcat_ref_latent is not None:
@@ -884,8 +886,10 @@ class WanVideoSampler:
seq_len = math.ceil((latent.shape[2] * latent.shape[3]) / 4 * latent.shape[1])
insert_len = longcat_ref_latent.shape[1]
clean_latent_indices = list(range(0, insert_len)) + [i + insert_len for i in clean_latent_indices]
longcat_num_ref_latents = longcat_ref_latent.shape[1]
latent_video_length += insert_len
log.info(f"LongCat clean_latent_indices: {clean_latent_indices}")
longcat_num_cond_latents = len(clean_latent_indices)
log.info(f"LongCat num_cond_latents: {longcat_num_cond_latents} num_ref_latents: {longcat_num_ref_latents}")
audio_stride = 2 if transformer.is_longcat else 1
#controlnet
@@ -1565,8 +1569,8 @@ class WanVideoSampler:
"ovi_negative_text_embeds": ovi_negative_text_embeds, # Audio latent model negative text embeds for Ovi
"flashvsr_LQ_latent": flashvsr_LQ_latent, # FlashVSR LQ latent for upsampling
"flashvsr_strength": flashvsr_strength, # FlashVSR strength
"longcat_num_cond_latents": len(clean_latent_indices) if transformer.is_longcat else 0,
"longcat_num_ref_latents": longcat_ref_latent.shape[1] if longcat_ref_latent is not None else 0,
"longcat_num_cond_latents": longcat_num_cond_latents,
"longcat_num_ref_latents": longcat_num_ref_latents,
"longcat_avatar_options": longcat_avatar_options, # LongCat avatar attention options
"sdancer_input": sdancer_input, # SteadyDancer input
"one_to_all_input": one_to_all_data, # One-to-All input
wanvideo/modules/model.py
+1 -4
View File
@@ -2210,7 +2210,6 @@ class WanModel(torch.nn.Module):
torch.tensor([ref_frame_index], dtype=dtype, device=device),
torch.arange(0, steps_t - longcat_num_ref_latents, dtype=dtype, device=device)
], dim=0)
print("grid_t:", grid_t)
img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + grid_t.reshape(-1, 1, 1)
else:
# Standard temporal encoding
@@ -2639,7 +2638,6 @@ class WanModel(torch.nn.Module):
self.cached_key == cache_key):
freqs = self.cached_freqs
else:
log.info("Generating new RoPE frequencies")
freqs = self.rope_encode_comfy(
F, H, W,
freq_offset=freq_offset,
@@ -2650,6 +2648,7 @@ class WanModel(torch.nn.Module):
device=x.device,
dtype=x.dtype
)
log.info("Generated new RoPE frequencies")
if s2v_ref_latent is not None:
freqs_ref = self.rope_encode_comfy(
@@ -2855,8 +2854,6 @@ class WanModel(torch.nn.Module):
human_num = len(multitalk_audio_embedding)
# LongCat-Avatar specific
tqdm.write(f"longcat_num_cond_latents: {longcat_num_cond_latents}, longcat_num_ref_latents: {longcat_num_ref_latents}")
if longcat_num_ref_latents > 0:
audio_start_ref = multitalk_audio_embedding[:, [0], :, :] # padding
multitalk_audio_embedding = torch.cat([audio_start_ref, multitalk_audio_embedding], dim=1).contiguous()