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Fix guide regression, blur edge artifact, and effect curve shape

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Revert anchor source back to blended cumulative output — prev_sampled
gave scene N+2 a tail dominated by scene N-1 pinned frames, weakening
the scene-N representation and making the i2v guide's reference pull
visually dominant (apparent reset to reference image). Blended anchor
frames are already hidden by the coverage fix so no yellow leaks.

Fix blur edge distortion: avg_pool2d with zero-padding darkened image
edges at high sigma. Now pre-pad with reflect mode and pool with
padding=0, eliminating the dark halo at frame boundaries.

Fix effect curve shape: previous linear V-fade left blended-latent
content visible at ~89% brightness at the blend zone edges, giving a
"generated transition frames" look. New shape holds brightness=0 /
sigma=max flat across the entire blend zone, then ramps back to full
over transition_duration//2 extra frames into clean scene content.
The blend zone is now fully hidden regardless of transition_duration.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
2026-05-26 01:07:24 +03:00
co-authored by Claude Sonnet 4.6
parent 1d5dcd4057
commit e1036b85c0
1 changed files with 23 additions and 16 deletions
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samplers.py
+23 -16
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@@ -1267,17 +1267,27 @@ class FunPackLTXAVSceneChainSampler:
if sigma <= 0:
return frame
k = max(3, int(sigma * 3) | 1)
pad = k // 2
x = frame.permute(2, 0, 1).unsqueeze(0).float()
x = torch.nn.functional.avg_pool2d(x, k, stride=1, padding=k // 2)
x = torch.nn.functional.pad(x, (pad, pad, pad, pad), mode="reflect")
x = torch.nn.functional.avg_pool2d(x, k, stride=1, padding=0)
return x.squeeze(0).permute(1, 2, 0).to(dtype=frame.dtype)
def _apply_effect_on_pixels(self, frames, effect, center, half):
"""Apply one visual transition effect in-place on a [N, H, W, C] float tensor."""
def _apply_effect_on_pixels(self, frames, effect, center, half, blend_half=0):
"""Apply one visual transition effect in-place on a [N, H, W, C] float tensor.
blend_half: number of frames on each side of center that are blended-latent
content and must be fully processed (zero brightness / max blur) regardless
of transition_duration. extra frames beyond blend_half fade/ramp back to
full brightness over the remaining (half - blend_half) range.
"""
n = frames.shape[0]
center = max(half, min(n - half - 1, center))
extra_half = max(1, half - blend_half)
if effect == "fade_to_black":
for i in range(max(0, center - half), min(n, center + half)):
brightness = min(1.0, abs(i - center) / max(1, half))
dist = abs(i - center)
brightness = min(1.0, max(0.0, (dist - blend_half) / extra_half))
frames[i] = frames[i] * brightness
elif effect == "crossfade":
orig = frames.clone()
@@ -1289,7 +1299,9 @@ class FunPackLTXAVSceneChainSampler:
frames[post_i] = (1 - alpha) * orig[post_i] + alpha * orig[pre_i]
elif effect == "blur_out_in":
for i in range(max(0, center - half), min(n, center + half)):
sigma = 8.0 * (1.0 - abs(i - center) / max(1, half))
dist = abs(i - center)
ramp = max(0.0, (dist - blend_half) / extra_half)
sigma = 8.0 * (1.0 - ramp)
if sigma > 0:
frames[i] = self._blur_pixel_frame(frames[i], sigma)
@@ -1315,9 +1327,10 @@ class FunPackLTXAVSceneChainSampler:
return decoded
frames = decoded.clone().float()
for entry in active:
blend_half = entry.get("blend_half_pixels", 1)
half = blend_half + max(0, transition_duration // 2)
self._apply_effect_on_pixels(frames, entry["effect"], int(entry["pixel_frame"]), half)
blend_half = entry.get("blend_half_pixels", 0)
extra = max(1, transition_duration // 2)
half = blend_half + extra
self._apply_effect_on_pixels(frames, entry["effect"], int(entry["pixel_frame"]), half, blend_half=blend_half)
return frames.clamp(0.0, 1.0).to(dtype=decoded.dtype)
def _apply_transitions_latent(self, output, boundary_entries, vae, time_scale, transition_duration):
@@ -1432,7 +1445,6 @@ class FunPackLTXAVSceneChainSampler:
video_overlap = self._overlap_frames(latent_template, frame_overlap, time_scale)
output = None
prev_sampled = None
report_lines = []
carried_guide_frames = 0
boundary_entries = []
@@ -1449,7 +1461,7 @@ class FunPackLTXAVSceneChainSampler:
else:
scene_seed = provided_seed if provided_seed is not None else int(seed) + scene_index
carried = 0
if prev_sampled is None:
if output is None:
chunk = self._clone_latent(latent_template)
else:
# Record boundary (center of the overlap region) before blending
@@ -1466,11 +1478,7 @@ class FunPackLTXAVSceneChainSampler:
"blend_half_latent": blend_half_latent,
"blend_half_pixels": blend_half_pixels,
})
# Use the previous scene's raw sampled output as the anchor, not the
# blended cumulative output. The blended tail contains interpolated
# latent frames (mix of two scenes) that decode with color artifacts
# and would corrupt the anchor context for this scene.
chunk = self._build_continuation_chunk(latent_template, prev_sampled, video_overlap)
chunk = self._build_continuation_chunk(latent_template, output, video_overlap)
if carry_i2v_guides:
chunk, scene_positive, scene_negative, carried = self._append_i2v_guides(
chunk, latent_template, scene_positive, scene_negative, vae,
@@ -1484,7 +1492,6 @@ class FunPackLTXAVSceneChainSampler:
if output is not None:
sampled = self._match_audio_amplitude(sampled, video_overlap, video_frames)
output = sampled if output is None else self._blend_latents(output, sampled, video_overlap)
prev_sampled = sampled
cumulative_latent_frames = self._tensor_frames(self._latent_tensors(output)[0])
report_lines.append(f"Scene {scene_index + 1}: seed={scene_seed}, text={self._scene_text(scene_cond, scene_index)}")