nodes/kijai-ComfyUI-WanVideoWrapper
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Cleanup

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kijai
2025-12-26 13:51:05 +02:00
parent b2c520ca44
commit 027bed8c3d
2 changed files with 13 additions and 17 deletions
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nodes_sampler.py
+12 -16
View File
@@ -599,12 +599,10 @@ class WanVideoSampler:
portrait_cfg = [portrait_cfg] * (steps + 1)
# MiniMax Remover
minimax_latents = minimax_mask_latents = None
minimax_latents = image_embeds.get("minimax_latents", None)
minimax_mask_latents = image_embeds.get("minimax_mask_latents", None)
if minimax_latents is not None:
log.info(f"minimax_latents: {minimax_latents.shape}")
log.info(f"minimax_mask_latents: {minimax_mask_latents.shape}")
log.info(f"minimax_latents: {minimax_latents.shape}, minimax_mask_latents: {minimax_mask_latents.shape}")
minimax_latents = minimax_latents.to(device, dtype)
minimax_mask_latents = minimax_mask_latents.to(device, dtype)
@@ -691,7 +689,7 @@ class WanVideoSampler:
framepack = False
s2v_audio_embeds = image_embeds.get("audio_embeds", None)
if s2v_audio_embeds is not None:
log.info(f"Using S2V audio embeddings")
log.info("Using S2V audio embeddings")
framepack = s2v_audio_embeds.get("enable_framepack", False)
if framepack and context_options is not None:
raise ValueError("S2V framepack and context windows cannot be used at the same time")
@@ -1685,7 +1683,6 @@ class WanVideoSampler:
log.info(f"Input sequence length: {seq_len}")
log.info(f"Sampling {(latent_video_length-1) * 4 + 1} frames at {latent.shape[3]*vae_upscale_factor}x{latent.shape[2]*vae_upscale_factor} with {steps-ttm_start_step} steps")
intermediate_device = device
# Differential diffusion prep
masks = None
@@ -1826,8 +1823,8 @@ class WanVideoSampler:
enhance_enabled = True
#region context windowing
elif context_options is not None:
counter = torch.zeros_like(latent_model_input, device=intermediate_device)
noise_pred = torch.zeros_like(latent_model_input, device=intermediate_device)
counter = torch.zeros_like(latent_model_input, device=device)
noise_pred = torch.zeros_like(latent_model_input, device=device)
context_queue = list(context(idx, steps, latent_video_length, context_frames, context_stride, context_overlap))
fraction_per_context = 1.0 / len(context_queue)
context_pbar = ProgressBar(steps)
@@ -1857,13 +1854,12 @@ class WanVideoSampler:
else:
positive = text_embeds["prompt_embeds"]
partial_img_emb = None
partial_control_latents = None
partial_img_emb = partial_control_latents = None
if image_cond is not None:
partial_img_emb = image_cond[:, c]
partial_img_emb = image_cond[:, c].to(device)
if c[0] != 0 and context_reference_latent is not None:
if context_reference_latent.shape[0] == 1: #only single extra init latent
new_init_image = context_reference_latent[0, :, 0].to(intermediate_device)
new_init_image = context_reference_latent[0, :, 0].to(device)
# Concatenate the first 4 channels of partial_img_emb with new_init_image to match the required shape
partial_img_emb[:, 0] = torch.cat([image_cond[:4, 0], new_init_image], dim=0)
elif context_reference_latent.shape[0] > 1:
@@ -1872,10 +1868,10 @@ class WanVideoSampler:
extra_init_index = min(int(max(c) / section_size), num_extra_inits - 1)
if context_options["verbose"]:
log.info(f"extra init image index: {extra_init_index}")
new_init_image = context_reference_latent[extra_init_index, :, 0].to(intermediate_device)
new_init_image = context_reference_latent[extra_init_index, :, 0].to(device)
partial_img_emb[:, 0] = torch.cat([image_cond[:4, 0], new_init_image], dim=0)
else:
new_init_image = image_cond[:, 0].to(intermediate_device)
new_init_image = image_cond[:, 0].to(device)
partial_img_emb[:, 0] = new_init_image
if control_latents is not None:
@@ -1893,14 +1889,14 @@ class WanVideoSampler:
if has_ref:
if c[0] != 0 and context_reference_latent is not None:
if context_reference_latent.shape[0] == 1: #only single extra init latent
partial_context[16:32, :1] = context_reference_latent[0, :, :1].to(intermediate_device)
partial_context[16:32, :1] = context_reference_latent[0, :, :1].to(device)
elif context_reference_latent.shape[0] > 1:
num_extra_inits = context_reference_latent.shape[0]
section_size = (latent_video_length / num_extra_inits)
extra_init_index = min(int(max(c) / section_size), num_extra_inits - 1)
if context_options["verbose"]:
log.info(f"extra init image index: {extra_init_index}")
partial_context[16:32, :1] = context_reference_latent[extra_init_index, :, :1].to(intermediate_device)
partial_context[16:32, :1] = context_reference_latent[extra_init_index, :, :1].to(device)
else:
partial_context[:, 0] = vace_entry["context"][0][:, 0]
@@ -2479,7 +2475,7 @@ class WanVideoSampler:
noise_pred = torch.cat([noise_pred[:, latent_video_length - shift_idx:]] + [noise_pred[:, :latent_video_length - shift_idx]], dim=1)
shift_idx = (shift_idx + latent_skip) % latent_video_length
latent = latent.to(intermediate_device)
latent = latent.to(device)
if self.noise_front_pad_num > 0:
noise_pred = noise_pred[:, self.noise_front_pad_num:]
wanvideo/modules/model.py
+1 -1
View File
@@ -2648,7 +2648,7 @@ class WanModel(torch.nn.Module):
device=x.device,
dtype=x.dtype
)
log.info("Generated new RoPE frequencies")
tqdm.write("Generated new RoPE frequencies")
if s2v_ref_latent is not None:
freqs_ref = self.rope_encode_comfy(