nodes/kijai-ComfyUI-WanVideoWrapper
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Allow WanMove to work with context windows

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kijai
2025-12-15 01:22:21 +02:00
parent 3611341339
commit 3aae54f220
1 changed files with 14 additions and 9 deletions
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nodes_sampler.py
+14 -9
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@@ -16,6 +16,7 @@ from .utils import(log, print_memory, apply_lora, fourier_filter, optimized_scal
from .cache_methods.cache_methods import cache_report from .cache_methods.cache_methods import cache_report
from .nodes_model_loading import load_weights from .nodes_model_loading import load_weights
from .enhance_a_video.globals import set_enhance_weight, set_num_frames from .enhance_a_video.globals import set_enhance_weight, set_num_frames
from .WanMove.trajectory import replace_feature
from contextlib import nullcontext from contextlib import nullcontext
from comfy import model_management as mm from comfy import model_management as mm
@@ -301,15 +302,6 @@ class WanVideoSampler:
#I2V #I2V
image_cond = image_embeds.get("image_embeds", None) image_cond = image_embeds.get("image_embeds", None)
if image_cond is not None: if image_cond is not None:
# WanMove
wanmove_embeds = image_embeds.get("wanmove_embeds", None)
if wanmove_embeds is not None:
from .WanMove.trajectory import replace_feature
track_pos = wanmove_embeds["track_pos"]
if any(not math.isclose(c, 1.0) for c in cfg):
image_cond_neg = torch.cat([image_embeds["mask"], image_cond])
image_cond = replace_feature(image_cond.unsqueeze(0).clone(), track_pos.unsqueeze(0), wanmove_embeds.get("strength", 1.0))[0]
if transformer.in_dim == 16: if transformer.in_dim == 16:
raise ValueError("T2V (text to video) model detected, encoded images only work with I2V (Image to video) models") raise ValueError("T2V (text to video) model detected, encoded images only work with I2V (Image to video) models")
elif transformer.in_dim not in [48, 32]: # fun 2.1 models don't use the mask elif transformer.in_dim not in [48, 32]: # fun 2.1 models don't use the mask
@@ -1227,6 +1219,16 @@ class WanVideoSampler:
latents_to_not_step = prev_latents.shape[1] latents_to_not_step = prev_latents.shape[1]
one_to_all_data["num_latent_frames_to_replace"] = latents_to_not_step one_to_all_data["num_latent_frames_to_replace"] = latents_to_not_step
# WanMove
if image_cond is not None:
wanmove_embeds = image_embeds.get("wanmove_embeds", None)
if wanmove_embeds is not None:
track_pos = wanmove_embeds["track_pos"]
if any(not math.isclose(c, 1.0) for c in cfg):
image_cond_neg = torch.cat([image_embeds["mask"], image_cond])
if context_options is None:
image_cond = replace_feature(image_cond.unsqueeze(0).clone(), track_pos.unsqueeze(0), wanmove_embeds.get("strength", 1.0))[0]
#region model pred #region model pred
def predict_with_cfg(z, cfg_scale, positive_embeds, negative_embeds, timestep, idx, image_cond=None, clip_fea=None, 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, control_latents=None, vace_data=None, unianim_data=None, audio_proj=None, control_camera_latents=None,
@@ -1465,6 +1467,9 @@ class WanVideoSampler:
if background_latents is not None or foreground_latents is not None: if background_latents is not None or foreground_latents is not None:
z = torch.cat([z, foreground_latents.to(z), background_latents.to(z)], dim=0) z = torch.cat([z, foreground_latents.to(z), background_latents.to(z)], dim=0)
if wanmove_embeds is not None and context_window is not None:
image_cond_input = replace_feature(image_cond_input.unsqueeze(0), track_pos[:, context_window].unsqueeze(0), wanmove_embeds.get("strength", 1.0))[0]
base_params = { base_params = {
'x': [z], # latent 'x': [z], # latent
'y': [image_cond_input] if image_cond_input is not None else None, # image cond 'y': [image_cond_input] if image_cond_input is not None else None, # image cond