Clean sampling loop: remove _match_audio_amplitude and dead code
_match_audio_amplitude mutated the audio tensor of sampled before _blend_latents, putting rescaled audio into the overlap zone that becomes pinned context for the next scene. LTXAV generates video and audio jointly — artificially rescaled audio context degrades video generation quality, causing progressive burnout across scenes. Also removed dead code: _has_i2v_reference_latent (defined, never called), _build_bridge_chunk_UNUSED, and want_latent (unused after transition-path cleanup). Sampling loop now matches main exactly except for boundary_entries metadata tracking (read-only). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
co-authored by
Claude Sonnet 4.6
Claude Sonnet 4.6
parent
a1ee3bbec3
commit
5e1c5d7cca
+1
-111
@@ -13,21 +13,7 @@ import comfy.utils
|
||||
MOTION_PULSE_MODES = ["off", "balanced", "aggressive", "custom"]
|
||||
|
||||
|
||||
def _has_i2v_reference_latent(latent):
|
||||
"""Return True if the latent has a protected first frame (i2v reference image)."""
|
||||
if not isinstance(latent, dict):
|
||||
return False
|
||||
mask = latent.get("noise_mask")
|
||||
if mask is None:
|
||||
return False
|
||||
if getattr(mask, "is_nested", False):
|
||||
tensors = list(mask.unbind())
|
||||
if not tensors:
|
||||
return False
|
||||
mask = tensors[0]
|
||||
if not isinstance(mask, torch.Tensor) or mask.dim() < 3:
|
||||
return False
|
||||
return float(mask[:, :, 0].float().mean()) < 0.95
|
||||
|
||||
VELOCITY_BIAS_MODES = ["off", "capture", "apply", "capture_and_apply"]
|
||||
VELOCITY_BIAS_TARGETS = (0.90, 0.80)
|
||||
VELOCITY_BIAS_MEMORY = {}
|
||||
@@ -1124,50 +1110,6 @@ class FunPackLTXAVSceneChainSampler:
|
||||
blended = alpha * left[:, :, -overlap:] + (1.0 - alpha) * right[:, :, :overlap].to(left.device, left.dtype)
|
||||
return torch.cat([left[:, :, :-overlap], blended, right[:, :, overlap:].to(left.device, left.dtype)], dim=2)
|
||||
|
||||
def _match_audio_amplitude(self, sampled, video_overlap, video_frames):
|
||||
"""Rescale non-overlap audio frames to match the RMS of the overlap context.
|
||||
|
||||
Continuation chunks have their audio overlap frames copied from the previous
|
||||
scene (real latents, typically low std). The model denoises new frames with
|
||||
this context visible, generating audio at the context's lower amplitude.
|
||||
Rescaling the new frames to match the overlap RMS restores consistent volume.
|
||||
"""
|
||||
if not self._is_nested(sampled.get("samples")):
|
||||
return sampled
|
||||
tensors = self._latent_tensors(sampled)
|
||||
if len(tensors) < 2:
|
||||
return sampled
|
||||
|
||||
# Audio tensor is index 1 in LTXAV nested structure (3-dim: B, C, T)
|
||||
audio = tensors[1]
|
||||
if audio.dim() != 3:
|
||||
return sampled
|
||||
|
||||
audio_frames = self._tensor_frames(audio)
|
||||
audio_overlap = self._derived_overlap(video_overlap, video_frames, audio_frames)
|
||||
if audio_overlap <= 0 or audio_overlap >= audio_frames:
|
||||
return sampled
|
||||
|
||||
overlap_rms = float(audio[:, :, :audio_overlap].float().pow(2).mean().sqrt())
|
||||
new_rms = float(audio[:, :, audio_overlap:].float().pow(2).mean().sqrt())
|
||||
if overlap_rms < 1e-6 or new_rms < 1e-6:
|
||||
return sampled
|
||||
|
||||
scale = overlap_rms / new_rms
|
||||
# Only apply if there's a meaningful discrepancy (>10% difference)
|
||||
if abs(scale - 1.0) < 0.1:
|
||||
return sampled
|
||||
|
||||
audio_fixed = audio.clone()
|
||||
audio_fixed[:, :, audio_overlap:] = (
|
||||
audio[:, :, audio_overlap:].float().mul(scale).to(dtype=audio.dtype)
|
||||
)
|
||||
result = self._clone_latent(sampled)
|
||||
tensors_fixed = list(tensors)
|
||||
tensors_fixed[1] = audio_fixed
|
||||
result["samples"] = comfy.nested_tensor.NestedTensor(tensors_fixed)
|
||||
return result
|
||||
|
||||
def _blend_latents(self, previous, current, video_overlap):
|
||||
result = self._clone_latent(previous)
|
||||
previous_tensors = self._latent_tensors(previous)
|
||||
@@ -1189,55 +1131,6 @@ class FunPackLTXAVSceneChainSampler:
|
||||
result.pop("noise_mask", None)
|
||||
return result
|
||||
|
||||
def _build_bridge_chunk_UNUSED(self, scene_a, scene_b, latent_template, bridge_video_frames, time_scale):
|
||||
"""Build a short first-last-frame latent for bridge shot generation.
|
||||
|
||||
First latent frame = last frame of scene_a (where we came from).
|
||||
Last latent frame = first frame of scene_b (where we are going).
|
||||
Middle frames = noise to be denoised by the model.
|
||||
"""
|
||||
bridge_latent_frames = self._expected_latent_frames(bridge_video_frames, time_scale)
|
||||
bridge_latent_frames = max(3, bridge_latent_frames)
|
||||
|
||||
tensors_a = self._latent_tensors(scene_a)
|
||||
tensors_b = self._latent_tensors(scene_b)
|
||||
template_tensors = self._latent_tensors(latent_template)
|
||||
|
||||
out_tensors = []
|
||||
mask_tensors = []
|
||||
for index, tmpl in enumerate(template_tensors):
|
||||
tmpl_frames = self._tensor_frames(tmpl)
|
||||
t_bridge_frames = bridge_latent_frames if index == 0 else self._derived_overlap(
|
||||
bridge_latent_frames, bridge_latent_frames, tmpl_frames
|
||||
)
|
||||
t_bridge_frames = max(3, t_bridge_frames)
|
||||
|
||||
bridge = tmpl[:, :, :t_bridge_frames].clone()
|
||||
|
||||
# Pin first frame = last frame of scene A
|
||||
if index < len(tensors_a):
|
||||
bridge[:, :, :1] = tensors_a[index][:, :, -1:].to(device=bridge.device, dtype=bridge.dtype)
|
||||
|
||||
# Pin last frame = first frame of scene B
|
||||
if index < len(tensors_b):
|
||||
bridge[:, :, -1:] = tensors_b[index][:, :, :1].to(device=bridge.device, dtype=bridge.dtype)
|
||||
|
||||
mask = torch.ones_like(bridge)
|
||||
mask[:, :, :1] = 0.0
|
||||
mask[:, :, -1:] = 0.0
|
||||
|
||||
out_tensors.append(bridge)
|
||||
mask_tensors.append(mask)
|
||||
|
||||
result = self._clone_latent(latent_template)
|
||||
if self._is_nested(latent_template.get("samples")):
|
||||
result["samples"] = comfy.nested_tensor.NestedTensor(out_tensors)
|
||||
result["noise_mask"] = comfy.nested_tensor.NestedTensor(mask_tensors)
|
||||
else:
|
||||
result["samples"] = out_tensors[0]
|
||||
result["noise_mask"] = mask_tensors[0]
|
||||
return result
|
||||
|
||||
def _sample_chunk(self, model, sampler, sigmas, seed, cfg, positive, negative, latent):
|
||||
if sampler is None:
|
||||
raise ValueError("sampler input is required.")
|
||||
@@ -1465,14 +1358,11 @@ class FunPackLTXAVSceneChainSampler:
|
||||
)
|
||||
if carry_i2v_guides and carried > 0:
|
||||
sampled = self._crop_video_tail(sampled, carried)
|
||||
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)
|
||||
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)}")
|
||||
|
||||
# RETURN_TYPES slot indices: 0=latent, 1=images
|
||||
want_latent = self._output_connected(prompt, unique_id, 0)
|
||||
want_image = self._output_connected(prompt, unique_id, 1)
|
||||
|
||||
# Release all generation-side references so ComfyUI can unload the diffusion
|
||||
|
||||
Reference in New Issue
Block a user