nodes/kijai-ComfyUI-WanVideoWrapper
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Update nodes.py

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kijai
2025-06-05 19:28:56 +03:00
parent 6139017535
commit efb87445d5
1 changed files with 8 additions and 3 deletions
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nodes.py
+8 -3
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@@ -2929,7 +2929,7 @@ class WanVideoSampler:
#region model pred
def predict_with_cfg(z, cfg_scale, positive_embeds, negative_embeds, timestep, idx, image_cond=None, clip_fea=None,
control_latents=None, vace_data=None, unianim_data=None, audio_proj=None, control_camera_latents=None, teacache_state=None):
control_latents=None, vace_data=None, unianim_data=None, audio_proj=None, control_camera_latents=None, add_cond=None, teacache_state=None):
z = z.to(dtype)
with torch.autocast(device_type=mm.get_autocast_device(device), dtype=dtype, enabled=("fp8" in model["quantization"])):
@@ -2984,6 +2984,7 @@ class WanVideoSampler:
if recammaster is not None:
z = torch.cat([z, recam_latents.to(z)], dim=1)
use_phantom = False
if phantom_latents is not None:
if (phantom_start_percent <= current_step_percentage <= phantom_end_percent) or \
@@ -3400,12 +3401,16 @@ class WanVideoSampler:
"start_percent": unianimate_poses["start_percent"],
"end_percent": unianimate_poses["end_percent"]
}
if add_cond is not None:
partial_add_cond = add_cond[:, :, c].to(device, dtype)
noise_pred_context, new_teacache = predict_with_cfg(
partial_latent_model_input,
cfg[idx], positive,
text_embeds["negative_prompt_embeds"],
timestep, idx, partial_img_emb, clip_fea, partial_control_latents, partial_vace_context, partial_unianim_data,partial_audio_proj,partial_control_camera_latents,
timestep, idx, partial_img_emb, clip_fea, partial_control_latents, partial_vace_context, partial_unianim_data,partial_audio_proj,
partial_control_camera_latents, partial_add_cond,
current_teacache)
if teacache_args is not None:
@@ -3422,7 +3427,7 @@ class WanVideoSampler:
cfg[idx],
text_embeds["prompt_embeds"],
text_embeds["negative_prompt_embeds"],
timestep, idx, image_cond, clip_fea, control_latents, vace_data, unianim_data, audio_proj, control_camera_latents,
timestep, idx, image_cond, clip_fea, control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond,
teacache_state=self.teacache_state)
if latent_shift_loop: