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Remove CLIP input and vision re-encoding from chain sampler

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Re-encoding the next scene's conditioning with the previous scene's
last frame is useless for fade-to-black workflows — the last frame
is black, so it's conditioning on nothing. Removing the whole path.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
2026-05-29 01:01:36 +03:00
co-authored by Claude Sonnet 4.6
parent 5b64a06615
commit a58f395231
1 changed files with 1 additions and 38 deletions
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samplers.py
+1 -38
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@@ -808,9 +808,6 @@ class FunPackLTXAVSceneChainSampler:
}),
},
"optional": {
"clip": ("CLIP", {
"tooltip": "Connect the same CLIP as your Studio node. Enables per-scene vision re-encoding from the previous frame.",
}),
"decode_tile_size": ("INT", {
"default": 0, "min": 0, "max": 4096, "step": 64,
"tooltip": "Tile size for VAE decode (0 = no tiling). Set to e.g. 512 if decode OOMs.",
@@ -1358,41 +1355,13 @@ class FunPackLTXAVSceneChainSampler:
return handles, old_wrapper
def _vision_reencoded_conditioning(self, clip, vae, output, next_cond, next_index):
try:
t = clip.cond_stage_model.gemma3_12b.transformer
if not (hasattr(t, "vision_model") and hasattr(t, "multi_modal_projector")):
return None
pixel_frame = self._decode_last_frame(output, vae)
if pixel_frame is None:
return None
existing_meta = next_cond[1] if isinstance(next_cond, (list, tuple)) and len(next_cond) >= 2 and isinstance(next_cond[1], dict) else {}
text = existing_meta.get("funpack_encode_text") or self._scene_text(next_cond, next_index)
if not text:
return None
tokens = clip.tokenize(text, image=pixel_frame, skip_template=False)
encoded = clip.encode_from_tokens_scheduled(tokens)
if not isinstance(encoded, list) or not encoded:
return None
item = encoded[0]
if not isinstance(item, (list, tuple)) or len(item) < 2:
return None
new_cond = item[0]
new_meta = item[1] if isinstance(item[1], dict) else {"pooled_output": None}
if not isinstance(new_cond, torch.Tensor):
return None
merged = {**new_meta, **{k: v for k, v in existing_meta.items() if k.startswith("funpack_")}}
print(f"[FunPackSceneChain] Vision re-encoded scene {next_index + 1} from previous frame")
return (new_cond, merged)
except Exception:
return None
def sample(self, model, vae, positive, negative, sampler, sigmas, seed, latent_template,
num_frames_per_scene, frame_overlap, cfg, max_scenes, use_same_seed=False,
carry_i2v_guides=False,
self_consistency=False, self_consistency_strength=0.05,
transition_duration=16, decode_tile_size=0,
refinement_key_input="", clip=None, unique_id=None, prompt=None):
refinement_key_input="", unique_id=None, prompt=None):
if not isinstance(positive, list) or not positive:
raise ValueError("positive conditioning must contain at least one scene entry.")
if negative is None:
@@ -1471,12 +1440,6 @@ class FunPackLTXAVSceneChainSampler:
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:
updated = self._vision_reencoded_conditioning(
clip, vae, output, scene_conditionings[scene_index + 1], scene_index + 1,
)
if updated is not None:
scene_conditionings[scene_index + 1] = updated
del scene_cond, scene_positive, scene_negative, scene_conditionings, chunk, sampled