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Much improved context window blending

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Co-Authored-By: Benjamin Paine <57536852+painebenjamin@users.noreply.github.com>
This commit is contained in:
kijai
2025-03-01 02:52:02 +02:00
co-authored by Benjamin Paine
parent 889efe7371
commit e5a6cc872a
2 changed files with 690 additions and 2 deletions
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example_workflows/wanvideo_long_T2V_example_01.json
+672
View File
@@ -0,0 +1,672 @@
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"high quality nature video featuring a red panda balancing on a bamboo stem while a bird lands on it's head, on the background there is a waterfall",
"色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
true
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"ComfyUI-KJNodes": "dc482957d814a5a78000a3452b6c623a48fbd992",
"ComfyUI-VideoHelperSuite": "2c25b8b53835aaeb63f831b3137c705cf9f85dce"
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nodes.py
+18 -2
View File
@@ -1112,11 +1112,13 @@ class WanVideoSampler:
disable_enhance()
if context_options is not None:
counter = torch.zeros_like(latent_model_input[0], device=offload_device)
noise_pred = torch.zeros_like(latent_model_input[0], device=offload_device)
context_queue = list(context(
i, steps, latent_video_length, context_frames, context_stride, context_overlap,
))
for c in context_queue:
partial_latent_model_input = [latent_model_input[0][:, c, :, :]]
# Model inference - returns [frames, channels, height, width]
@@ -1130,8 +1132,22 @@ class WanVideoSampler:
noise_pred_cond - noise_pred_uncond)
else:
noise_pred_context = noise_pred_cond
noise_pred[:, c, :, :] += noise_pred_context
counter[:, c, :, :] += 1
window_mask = torch.ones_like(noise_pred_context)
# Apply left-side blending for all except first chunk
if min(c) > 0:
ramp_up = torch.linspace(0, 1, context_overlap, device=noise_pred.device)
ramp_up = ramp_up.view(1, -1, 1, 1)
window_mask[:, :context_overlap] = ramp_up
# Apply right-side blending for all except last chunk
if max(c) < latent_video_length - 1:
ramp_down = torch.linspace(1, 0, context_overlap, device=noise_pred.device)
ramp_down = ramp_down.view(1, -1, 1, 1)
window_mask[:, -context_overlap:] = ramp_down
# Apply masked prediction
noise_pred[:, c, :, :] += noise_pred_context * window_mask
counter[:, c, :, :] += window_mask
#model inference end
noise_pred /= counter
else: