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Rework i2i_scene_cut: truncated sigmas + anchor placed after overlap

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Two fixes:
- Truncate sigmas to start at strength level (_i2i_sigmas). Full schedule
  from σ=1.0 lets the distilled model reconstruct the input unchanged even
  at moderate strength values. Now denoises only from σ≈strength downward.
- Restore _build_continuation_chunk: overlap frames are pinned normally at
  0..video_overlap-1, i2i anchor placed at offset=video_overlap. Scene starts
  as smooth continuation then cuts to the new anchor. Latent blend remains
  no-op because overlap frames match previous output.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
2026-05-28 23:35:17 +03:00
co-authored by Claude Sonnet 4.6
parent b3502d54b8
commit c46f31b7c0
1 changed files with 33 additions and 22 deletions
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samplers.py
+33 -22
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@@ -1023,24 +1023,37 @@ class FunPackLTXAVSceneChainSampler:
return None
def _i2i_sigmas(self, sigmas, strength):
"""Truncate sigmas so i2i starts at the sigma matching strength, not from the top."""
if strength >= 1.0:
return sigmas
positive_sigmas = sigmas[sigmas > 0]
if not positive_sigmas.numel():
return sigmas
max_sigma = float(positive_sigmas.max())
cutoff = float(strength) * max_sigma
above = (sigmas >= cutoff).nonzero(as_tuple=False).squeeze(-1)
start = int(above[-1].item()) if above.numel() > 0 else len(sigmas) - 1
return sigmas[start:]
def _generate_i2i_anchor(self, model, sampler, sigmas, seed, cfg,
positive, negative, reference_latent, strength):
"""Denoise the reference frame (template frame 0) into a new scene starting frame."""
"""Denoise template frame 0 with truncated sigmas to get a new scene starting frame."""
ref_tensors = self._latent_tensors(reference_latent)
if not ref_tensors:
return None
last_frame = self._time_slice(ref_tensors[0], 0, 1)
noise_mask = torch.full(
(last_frame.shape[0], 1, 1, 1, 1),
float(strength),
ref_frame = self._time_slice(ref_tensors[0], 0, 1)
noise_mask = torch.ones(
(ref_frame.shape[0], 1, 1, 1, 1),
dtype=torch.float32,
device=last_frame.device,
device=ref_frame.device,
)
anchor_latent = {"samples": last_frame, "noise_mask": noise_mask}
return self._sample_chunk(model, sampler, sigmas, seed, cfg, positive, negative, anchor_latent)
anchor_latent = {"samples": ref_frame, "noise_mask": noise_mask}
i2i_sigmas = self._i2i_sigmas(sigmas, strength)
return self._sample_chunk(model, sampler, i2i_sigmas, seed, cfg, positive, negative, anchor_latent)
def _apply_i2v_anchor(self, chunk, anchor_latent):
"""Pin the anchor's single frame as frame 0 of the chunk (mask=0, i2v style)."""
def _apply_i2v_anchor(self, chunk, anchor_latent, offset=0):
"""Pin the anchor's single frame at position offset in the chunk (mask=0, i2v style)."""
anchor_tensors = self._latent_tensors(anchor_latent)
if not anchor_tensors:
return chunk
@@ -1048,12 +1061,12 @@ class FunPackLTXAVSceneChainSampler:
tensors = self._latent_tensors(result)
masks = self._latent_masks(result, len(tensors))
anchor_frame = anchor_tensors[0].to(device=tensors[0].device, dtype=tensors[0].dtype)
tensors[0][:, :, :1] = anchor_frame
tensors[0][:, :, offset:offset + 1] = anchor_frame
if masks[0] is None:
masks[0] = torch.ones_like(tensors[0])
else:
masks[0] = masks[0].clone()
masks[0][:, :, :1] = 0.0
masks[0][:, :, offset:offset + 1] = 0.0
if self._is_nested(result.get("samples")):
result["samples"] = comfy.nested_tensor.NestedTensor(tensors)
result["noise_mask"] = comfy.nested_tensor.NestedTensor(masks)
@@ -1386,7 +1399,6 @@ class FunPackLTXAVSceneChainSampler:
scene_seed = provided_seed if provided_seed is not None else int(seed) + scene_index
carried = 0
soft_carried = 0
used_i2i = False
if output is None:
chunk = self._clone_latent(latent_template)
else:
@@ -1400,22 +1412,22 @@ class FunPackLTXAVSceneChainSampler:
"pixel_frame": max(0, boundary_pixel),
"effect": effect,
})
chunk = self._build_continuation_chunk(latent_template, output, video_overlap)
if i2i_scene_cut:
chunk = self._clone_latent(latent_template)
# Place i2i anchor right after the overlap region.
# Overlap frames (0..video_overlap-1) are identical to previous output
# so the latent blend remains a no-op; the anchor starts the new content.
anchor = self._generate_i2i_anchor(
model, sampler, sigmas, scene_seed, cfg,
scene_positive, scene_negative, latent_template, i2i_strength,
)
if anchor is not None:
chunk = self._apply_i2v_anchor(chunk, anchor)
chunk = self._apply_i2v_anchor(chunk, anchor, offset=video_overlap)
anchor_decoded = self._decode_last_frame(anchor, vae)
if anchor_decoded is not None:
reference_frames.append(anchor_decoded)
used_i2i = True
else:
chunk = self._build_continuation_chunk(latent_template, output, video_overlap)
if video_overlap == 0:
chunk, soft_carried = self._prepend_soft_continuation(chunk, output)
elif video_overlap == 0:
chunk, soft_carried = self._prepend_soft_continuation(chunk, output)
if carry_i2v_guides:
chunk, scene_positive, scene_negative, carried = self._append_i2v_guides(
chunk, latent_template, scene_positive, scene_negative,
@@ -1426,8 +1438,7 @@ class FunPackLTXAVSceneChainSampler:
)
if carried + soft_carried > 0:
sampled = self._crop_video_head(sampled, carried + soft_carried)
seam_overlap = 0 if used_i2i else video_overlap
output = sampled if output is None else self._blend_latents(output, sampled, seam_overlap)
output = sampled if output is None else self._blend_latents(output, sampled, video_overlap)
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)}")
if clip is not None and scene_index + 1 < scene_count: