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Add i2i hard-cut anchor generation and reference_frames output to chain sampler

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When frame_overlap=0 and i2i_scene_cut=True, generates a new starting frame
for each continuation scene by denoising the previous scene's last frame with
the new scene prompt (i2i strength-controlled). The result is pinned as frame 0
of the next chunk (mask=0, i2v style), replacing soft continuation.

reference_frames output collects all generated anchor images as an IMAGE batch.

New inputs: i2i_scene_cut (BOOLEAN), i2i_strength (FLOAT 0.0–1.0).
New output: reference_frames (IMAGE, slot 2 — shifts status/scene_count/etc up by 1).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
2026-05-28 23:09:52 +03:00
co-authored by Claude Sonnet 4.6
parent a0419e866d
commit 84b88de35a
1 changed files with 75 additions and 7 deletions
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samplers.py
+75 -7
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@@ -792,7 +792,15 @@ class FunPackLTXAVSceneChainSampler:
}),
"carry_i2v_guides": ("BOOLEAN", {
"default": False,
"tooltip": "Experimental: carry protected frames from latent_template noise_mask into each continuation chunk after the overlap. WARNING: using this with frame_overlap=0 is confirmed to produce bad results — enable only for testing.",
"tooltip": "Carry the i2v anchor frame from latent_template into each continuation chunk as a style guide. Works best with frame_overlap > 0 or combined with i2i_scene_cut.",
}),
"i2i_scene_cut": ("BOOLEAN", {
"default": False,
"tooltip": "Hard cut mode: before each continuation scene, generate a new starting frame by denoising the previous scene's last frame with the new scene's prompt. Only active when frame_overlap=0. Replaces soft continuation.",
}),
"i2i_strength": ("FLOAT", {
"default": 0.75, "min": 0.0, "max": 1.0, "step": 0.05,
"tooltip": "How much to transform the reference frame during i2i anchor generation. 0.0 = no change, 1.0 = ignore reference. 0.6–0.85 is typical for a new scene while preserving character.",
}),
"transition_duration": ("INT", {
"default": 16, "min": 0, "max": 128, "step": 2,
@@ -820,8 +828,8 @@ class FunPackLTXAVSceneChainSampler:
},
}
RETURN_TYPES = ("LATENT", "IMAGE", "STRING", "INT", "STRING", "STRING")
RETURN_NAMES = ("latent", "images", "status", "scene_count", "scene_report", "scene_boundaries")
RETURN_TYPES = ("LATENT", "IMAGE", "IMAGE", "STRING", "INT", "STRING", "STRING")
RETURN_NAMES = ("latent", "images", "reference_frames", "status", "scene_count", "scene_report", "scene_boundaries")
FUNCTION = "sample"
CATEGORY = "FunPack/Sampling"
DESCRIPTION = (
@@ -1015,6 +1023,45 @@ class FunPackLTXAVSceneChainSampler:
return None
def _generate_i2i_anchor(self, model, sampler, sigmas, seed, cfg,
positive, negative, previous_output, strength):
"""Denoise the last frame of the previous scene into a new starting frame."""
prev_tensors = self._latent_tensors(previous_output)
if not prev_tensors:
return None
last_frame = self._tail(prev_tensors[0], 1)
noise_mask = torch.full(
(last_frame.shape[0], 1, 1, 1, 1),
float(strength),
dtype=torch.float32,
device=last_frame.device,
)
anchor_latent = {"samples": last_frame, "noise_mask": noise_mask}
return self._sample_chunk(model, sampler, 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)."""
anchor_tensors = self._latent_tensors(anchor_latent)
if not anchor_tensors:
return chunk
result = self._clone_latent(chunk)
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
if masks[0] is None:
masks[0] = torch.ones_like(tensors[0])
else:
masks[0] = masks[0].clone()
masks[0][:, :, :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)
else:
result["samples"] = tensors[0]
result["noise_mask"] = masks[0]
return result
def _prepend_soft_continuation(self, chunk, previous, mask_value=0.4, n_frames=4):
chunk_tensors = self._latent_tensors(chunk)
previous_tensors = self._latent_tensors(previous)
@@ -1305,7 +1352,8 @@ class FunPackLTXAVSceneChainSampler:
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, transition_duration=16, decode_tile_size=0,
carry_i2v_guides=False, i2i_scene_cut=False, i2i_strength=0.75,
transition_duration=16, decode_tile_size=0,
refinement_key_input="", clip=None, unique_id=None, prompt=None):
if not isinstance(positive, list) or not positive:
raise ValueError("positive conditioning must contain at least one scene entry.")
@@ -1324,6 +1372,7 @@ class FunPackLTXAVSceneChainSampler:
carried_guide_frames = 0
boundary_entries = []
cumulative_latent_frames = 0
reference_frames = []
first_scene_seed = self._scene_seed(scene_conditionings[0])
if first_scene_seed is None:
first_scene_seed = int(seed)
@@ -1355,7 +1404,18 @@ class FunPackLTXAVSceneChainSampler:
})
chunk = self._build_continuation_chunk(latent_template, output, video_overlap)
if video_overlap == 0:
chunk, soft_carried = self._prepend_soft_continuation(chunk, output)
if i2i_scene_cut:
anchor = self._generate_i2i_anchor(
model, sampler, sigmas, scene_seed, cfg,
scene_positive, scene_negative, output, i2i_strength,
)
if anchor is not None:
chunk = self._apply_i2v_anchor(chunk, anchor)
anchor_decoded = self._decode_last_frame(anchor, vae)
if anchor_decoded is not None:
reference_frames.append(anchor_decoded)
else:
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,
@@ -1378,7 +1438,7 @@ class FunPackLTXAVSceneChainSampler:
del scene_cond, scene_positive, scene_negative, scene_conditionings, chunk, sampled
# RETURN_TYPES slot indices: 0=latent, 1=images
# RETURN_TYPES slot indices: 0=latent, 1=images, 2=reference_frames, 3=status...
# Sampling is fully complete. Latent is untouched and returned as-is.
# IMAGES: decode the whole latent in one pass, then apply transition effects.
want_image = self._output_connected(prompt, unique_id, 1)
@@ -1409,6 +1469,8 @@ class FunPackLTXAVSceneChainSampler:
)
if carry_i2v_guides and carried_guide_frames > 0:
status += f", i2v guide tokens={carried_guide_frames} latent frame(s)"
if reference_frames:
status += f", i2i anchors={len(reference_frames)}"
boundaries_out = [{"pixel_frame": e["pixel_frame"], "effect": e["effect"]} for e in boundary_entries]
if refinement_key_input:
try:
@@ -1423,4 +1485,10 @@ class FunPackLTXAVSceneChainSampler:
})
except Exception as e:
print(f"[FunPackLTXAVSceneChainSampler] Failed to write sampler context: {e}")
return (output, images, status, scene_count, "\n".join(report_lines), _json.dumps(boundaries_out))
if reference_frames:
refs_out = torch.cat(reference_frames, dim=0)
else:
refs_out = torch.zeros(1, 8, 8, 3)
return (output, images, refs_out, status, scene_count, "\n".join(report_lines), _json.dumps(boundaries_out))