Introduces a new temporal_style option that cycles motion→hold ramps
across each scene's denoise trajectory via a segmented frame_rate wrapper.
Wired through Studio, engine settings, chain sampler conditioning tags,
and build_enhancements for single-scene workflows.
Each mixed scene is its own queue request (max_scenes=1), not continuous
chain overlap. Scene 2+ uses its anchor via graph Img2Video and borrows
hidden guides from the previous scene. Sampler applies guide stacks on
the first chunk for solo mixed runs.
Mixed i2v anchors no longer blend the previous scene tail into the
starting chunk or the stitched output. Dragging an image onto a
continuation scene auto-sets mixed so montage stays one queue request.
Preview in-points skip overlap before mixed scenes.
Scene chain sampler scene_boundaries JSON now reports pixel/latent overlap
zones, boundary contamination ranges, per-scene mechanisms, and global steering
notes. Pytest conftest stubs the parent FunPack package so light tests run in
the ComfyUI venv without importing folder_paths; align timeline/builder/nodes
tests with current prompt and report formats.
i2v anchors are starting latents via LTXVImgToVideoInplace; i2v
guides remain separate (carry_i2v_guides / custom guide stack).
Removed the incorrect anchor-as-guide path; mixed scenes now build
their chunk from Img2Video then apply overlap and carried guides.
Mixed scenes stay in the chain run (overlap/carry) while applying
their own image anchor at frame 0 alongside the Studio default
guide from scene 1. Per-scene media is copied for sampler anchor
encoding; custom guide stack toggles remain opt-in.
Guide stack is off by default: multi-scene runs still force
carry_i2v_guides (scene 1 template at frame 0). Enabling
guide_settings.stack_enabled passes per-scene guide JSON to the
chain sampler with toggles for accumulate, frame_idx, and apply_at.
Wire ProgressBar callbacks through scene-chain _sample_chunk so denoise
steps update the global progress hook the frontend polls. Reset progress
when a new generation is queued; label the readout as sampling N/M.
DynamicConditioning is an unpacked-video technique; on LTXAV (packed AV latent, 3D
mask) it can only no-op (comfy's process_timestep patchifies expecting 5D). Rather
than keep dead knobs in the Chain Sampler UI, remove i2v_anchor_power /
i2v_anchor_first_frame_only, the install block, and _install_i2v_anchor entirely.
The packed-safe path for cross-cut appearance carry (strengthen the Chain Sampler's
own carry/guide masks pre-pack) remains available as a future native feature.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The i2v anchor (DynamicConditioning port) crashed on LTXAV with 'not enough values
to unpack (expected 5, got 3)': comfy's process_timestep patchifies the video denoise
mask expecting unpacked 5D [B,C,F,H,W], but LTXAV packs the AV latent so its mask is
3D [B,C,N]. The else-branch (first_frame_only=False) modified + propagated the 3D mask
unguarded -> patchify blew up.
Hard-guard the whole denoise_mask_fn to genuine 5D masks; packed/non-5D -> clean no-op
(one-time log). The technique is fundamentally unpacked-video-only, so on LTXAV it does
nothing -- documented in the tooltip. Native packed-safe carry-strength is the follow-up.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Two optional Chain Sampler inputs (i2v_anchor_power, i2v_anchor_first_frame_only),
default power=1.0 = off. When >1.0, installs a denoise-mask model patch via the stock
comfy API (set_model_denoise_mask_function) that raises the denoise mask to power**step,
progressively holding the i2v conditioning frame harder as denoising proceeds -> anti-drift
from the input image. No modified model, no extra forward pass, ~zero overhead.
Patched before the batch/main split so both paths inherit it. Fully defensive: missing
mask / unexpected (e.g. packed) shape / any error falls through to the unmodified mask.
EXPERIMENTAL on packed AV latents - validate in ComfyUI. pos_embedding_exp_values.py was
also assessed and is a dead end (internal RoPE precision-matching constant, not a technique).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The Laplacian pyramid blend from the LTXV pack is a SPATIAL pixel-space tool and
doesn't transfer to our temporal latent crossfade (uniform full-frame alpha over a
2-4 latent-frame overlap -> multi-band adds nothing). The actual seam problem is that
a linear lerp of two latents superimposes them and lets magnitude collapse at ~0.5
(ghosting + washout). Fix in-domain instead:
- _slerp_block: spherical interp of the overlap block per (batch, frame), direction
via slerp + magnitude interpolated linearly, so the crossfade keeps its energy.
Uses torch.linalg.vector_norm (Tensor.norm treats a 3-tuple dim as a matrix norm).
- _blend_tensors: smoothstep alpha (less dwell at the 50/50 ghost point) + slerp for
video; audio/any further stream stays on the untouched linear path (audio-safety).
- Verified: exact endpoints, magnitude preserved across the seam vs linear washout.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Reverts the separate FunPackVAEDecoderNoise node (boundary-law violation: if the
Chain Sampler can own it, it should). Decode-time noise is now two optional inputs
on the Scene Chain Sampler (decode_noise_scale / decode_timestep), applied to a
shallow VAE copy via _vae_with_decode_noise before any decode so both the main and
batch-training IMAGES paths benefit. Never mutates the shared input VAE.
decode_noise_scale defaults to 0 (off) -> no behavior change unless enabled.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Phase 1 of the LTXVideo-pack technique pull. Standalone VAE->VAE passthrough that
stamps decode_timestep / decode_noise_scale / seed onto the LTX VAE so its internal
decoder restores fine detail/grain. ~zero cost, scale=0 disables. Place before any
VAE decode (Scene Chain Sampler, VAE Decode).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Ports LTXVNormalizingSampler's between-step scalar normalization onto the FunPack
Normalizing sampler. Two new STRING inputs on FunPackNormalizingSampler:
video_normalization_factors / audio_normalization_factors (comma-separated per-step
scalars; short lists repeat last value; both default '1' = off/no-op).
Applied to the running latent at the end of each euler step via _apply_av_norm_factors,
using the existing _packed_video_mask to scale the video and audio streams independently.
This is the ONLY FunPack lever that touches audio, and it does so audio-safely by latent
magnitude only (never attention/hidden states) - the same mechanism the reference node
uses. Conservative + opt-in: ships off, audio untouched unless the user authors a schedule
(reference default was '1,1,0.25,1,1,0.25,1,1'). The adaptive normalize_strength remains
the smarter video-side lever; these are a manual per-step gain schedule.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds temporal_style='auto': Studio classifies each scene's prompt for motion
energy and bakes a continuous frame_rate multiplier into that scene's
conditioning metadata; the Scene Chain Sampler applies it per scene via a
layered model_function_wrapper (composes with embed guidance, restored in
install order). Concrete styles keep the global build_enhancements path
unchanged.
- ltx_enhancements: TEMPORAL_STYLES gains 'auto'; single-source TEMPORAL_STYLE_MULT
table; classify_temporal_intent() keyword director (energy->punchier mult<1,
stillness->hold mult>1, loop flag); factored make_temporal_wrapper() shared by
global + per-scene paths; build_enhancements skips the global wrapper for auto.
- conditioning: _v2_apply_auto_temporal() bakes funpack_temporal_mult/label/loop
into each entry inside the single finalize hook; both refine_v2 returns pass
temporal_style + the run prompt as per-scene fallback text. 'auto' added to V2
node dropdown.
- samplers: _scene_temporal_mult() reader + per-scene temporal wrapper around
_sample_chunk in the scene loop.
- web: 'auto' in the Studio dropdown + hint text.
Heuristic is the baseline director, structured so a reward-driven override can
replace the multiplier per intent-family later. loop's RoPE coordinate trick is
still unimplemented (inert flag only); auto detects loop intent but does not yet
honor it.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
1) Widget order: moved the refinement_key_input forceInput SOCKET to the
end of optional (after decode_tile_size + embed_guidance_source). A
forceInput placed between widgets desynced ComfyUI's widgets_values
mapping on reload -> the combo's "relative" landed on the decode_tile_size
INT (NaN field).
2) RecursionError: _run_batch_training's no-key fallback called self.sample()
with the variant markers still in positive -> sample() re-detected the
batch -> re-entered _run_batch_training -> infinite loop (hit when
Interactive Guessing/batch is armed without refinement_key_input wired).
Now runs keyless as generate-only: loops every variant, concatenates to
the IMAGES output, skips temp-save/manifest/rating. (Per-variant sub-calls
are single-variant -> _split_batch_variants returns None -> no recursion.)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New variant producer for Batch Training that, instead of seed/shortcut
variation, scales the conditioning's spread (sigma) on VIDEO channels by a
linear ramp across N rungs, freezing everything else including the noise
seed so the only variable is conditioning strength. Up amplifies toward
overbake, down dampens. Each rung records funpack_guess_factor (in cond
meta + batch manifest) so the ladder is interpretable and Phase-3 learning
can read each rung. Audio channels untouched (scale_conditioning_spread
reuses the LTXAV channel split). Wired refine_v2 -> _v2_pack_batch
(guess_mode branch -> _v2_pack_guess_ladder) and the Studio refine_v2 call
(guess_mode/direction/range from refiner settings).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Previously the batch path returned only the last variant's frames on the
IMAGES slot. Collect each variant's decoded frames (CPU) and concatenate
them so the whole batch comes out IMAGES back-to-back — enabling the
"just make N slight variations, don't rate them" use case. Falls back to
the last variant if frame shapes are incompatible. (Phase 1 of Interactive
Guessing.)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Reuse _normalize_video_denoised in _sample_const_rf_full (Hybrid CONST/RF
path) and sample_funpack_distilled_flow: new normalize_strength /
normalize_start_sigma (default 0 = off). Applied to the denoised after
rescue/quality-sharpness, video-only, so audio is never touched and the
samplers keep AB2/Heun/velocity/rescue. Wired node INPUT_TYPES (appended)
-> get_sampler -> Studio _build_one_sampler + JS panels for both.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New SAMPLER for distilled LTXAV at CFG=1: deterministic 1st-order euler
base (clean ddim look + clean audio) plus per-step video-only latent
normalization to counter overbaking / oversaturation / colour drift at
~zero overhead. Tracks the video latent's robust spread, anchors a
reference once x0 is meaningful (normalize_start_sigma), and compresses
inflated later steps (normalize_strength). Benefit-only, no-op when
healthy, audio never touched (whole-latent for single-stream LTXV).
Velocity bias + rescue included, video-only.
Node FunPackNormalizingSampler + Studio sampler option ("Normalizing")
wired through _build_one_sampler and the JS panel. Registered in __init__.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Instead of full AB2 on every step, optionally ramp the AB2 contribution
linearly 0->1 across the schedule: denoised_eff = lerp(raw, ab2, i/(N-1)).
Early/high-sigma steps stay near 1st-order euler (less overshoot); late
steps get full AB2. Reuses the computed AB2 estimate -> no extra model
evals. Aimed at low-step distilled quality. No-op at order=1; audio still
rides plain euler. Wired node -> get_sampler -> Studio panel (+ JS toggle).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The FunPack Hybrid Euler 2S and Distilled Flow samplers applied AB2 denoised
extrapolation and the Heun corrector to the whole packed LTXAV latent, audio
slice included -> degraded audio. (Stock euler/ddim are clean because they're
1st-order; ComfyUI "ddim" == euler.) Confine both to the video stream via
_video_only so audio always rides plain 1st-order euler on the raw model
denoised, regardless of order/correction settings. Distilled Flow s_noise
diversity injection is now video-only too.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
LTXAV splits one conditioning tensor into video|audio channel-slices
(av_model _prepare_context: torch.split(context,[v_dim,a_dim],-1)) and
cross-attends each stream to its own slice. Steering the whole tensor
corrupted the audio's text conditioning -> degraded audio (observed live).
Add protect_audio_channels: restore the audio channel-slice from the
unsteered conditioning so every edit moves video only. Applied at all
steering chokepoints:
- _v2_apply_conditioning_memory (per-prompt: liked/axis/concept/vf-grad)
- _v2_apply_absolute (global taste pull)
- _v2_ascend_conditioning (relative VF ascend+search)
- attn2 K/V direction patch + phrase emphasis
- FunPackConditioningAdjust (manual)
- sampler embed_guidance wrapper + per-scene VF ascend
Split auto-detected from width (7680->3840, 6144->4096); logs once.
Single-stream LTXV / unknown width = no-op (no regression).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Inserting the combo mid-list shifted every saved widgets_values slot in
existing Scene Chain Sampler nodes, so a leftover numeric value validated
against the combo ("0 not in ['relative','absolute']"). Moved it to the end
of optional inputs; old workflows keep their widget positions and fall back
to the 'relative' default.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Relative (default, unchanged) = per-prompt steering. Absolute = a keyless,
prompt-agnostic "global taste" prior that pulls conditioning toward generally-
liked directions regardless of the prompt; Both layers them.
- New keyless Absolute store (FUNPACK_ABSOLUTE_KEY): pooled liked/disliked
direction + global value function, learned across every rated generation.
- Absolute rating semantics: a rating means "details I like in general" (not
prompt adherence) — reward-driven liked/bad-dir storage, axis attribution
bypassed. Perfect = "more of this everywhere".
- _v2_finalize_conditioning replaces the per-key ascend hook on both returns;
pure Absolute also bypasses per-prompt conditioning memory.
- Scene Chain sampler: embed_guidance_source (relative/absolute) lets the
per-step nudge draw from the global direction/value function.
- Studio popup + V2 node expose Steer mode + Absolute strength.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds s_churn/s_tmin/s_tmax to sample_funpack_distilled_flow: optionally
re-noise the latent within [s_tmin, s_tmax] before each eval, then denoise
from the bumped sigma_hat. No extra model calls (still one eval per step,
at a higher noise level). Fights stale/frozen distilled output (more motion)
and can sharpen by letting the model re-resolve. sigma_hat is clamped to the
schedule start so the model never sees a higher-than-trained noise level, the
final jump-to-zero detail step is never churned, and the default 0.5-1.0 band
protects the sub-0.4 detail finish per the empirical sigma findings.
Churn supersedes the legacy s_noise ancestral path while active and is
video-stream-only on packed LTXAV latents. With s_churn=0 the sampler is
byte-identical to the previous deterministic ODE. Wired into the node and
Studio's Distilled Flow config panel.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Port the Hybrid/RF velocity-bias capture/apply and rating-gated reactive
rescue into sample_funpack_distilled_flow, reusing the shared trajectory
memory helpers. Adds velocity_bias_mode/strength/source/key and
rescue_mode/threshold/strength to the FunPackDistilledFlowSampler node and
wires them into Studio's Distilled Flow config panel (same blank/'default'
velocity-key -> refinement-key fallback as Hybrid). Steering is
magnitude-preserving and, on packed LTXAV latents, video-stream only
(audio-safe). With all controls off the sampler is byte-identical to the
previous deterministic ODE.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Batch Training confirmed working end to end (controlled batch with transitions, distinct
per-variant seeds, node rating ignored during a batch, per-entry rating + VF/axis learning
on submit). Stripped the temporary trace prints (Batch Training check / Batch check / ACTUAL
scene seeds), keeping clean progress logs. Gitignore the local .testvenv.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The 'seed=' in the batch log was the loop index seed (base+idx), not the seed that drives
noise. The real noise seed is each scene's funpack_scene_seed (set distinct per variant by
_v2_pack_batch). Print those so we can tell whether distinct seeds actually reach the
sampler vs. stale/old-code identical seeds.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Prints (a) the batch_variants value + mode flags the refiner sees and how many packed
entries it produced, and (b) the positive entry count + variant markers + group count the
Chain Sampler sees. Pinpoints whether the break is the arm/settings (batch_variants==1 at
the refiner), the conditioning path (markers stripped before the sampler), or the split
logic. Temporary, to be removed once the cause is found.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Per feedback: Batch Training is a complete UI thing driven by Studio (the hub), not a
prompt, and it must NOT start generation itself (ComfyUI's Generate does).
- Removed batch_iterations from the Chain Sampler entirely (input + param). The sampler
now enters batch mode ONLY when Studio packs variant markers into positive; the batch
count lives in Studio. _run_batch_training simplified to variant-entries-only.
- The node's 'Batch Training' button now opens a dedicated window (openBatchPanel) that
shows EITHER a config view (set batch size, Arm/Disarm) when no batch is pending, OR the
rating list (preview + per-entry rating + Submit/Forget) when a batch has finished. It
never queues — arming just sets refiner.batch_variants; the user presses Generate.
- Dynamic button label reflects live state: '(off)', '(armed ×N)', '(Processing…)',
'(Finished — rate)'. Driven by execution_start/success/error listeners, which also
one-shot disarm after a batch runs so the next Generate is normal.
The older in-Refiner-tab batch panel remains functional (secondary path); the node-button
window is now the primary entry point.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Producer (Studio owns conditioning, per the boundary law): refine_v2 gains batch_variants.
When >1, _v2_build_batch_variants packs N conditioning entries into the single positive wire,
each tagged 'funpack_batch_variant'. Variant 0 is the fully-processed base; variants 1..N-1
re-resolve shortcuts with seed+i (random option per entry) and get the same conditioning-memory
treatment, deduped by expansion text. No shortcut variability -> all entries resolve identical
and reuse the base cond -> the batch becomes seed-only (sampler varies the seed per entry).
Captures the pre-shortcut prompt to re-resolve. Not combined with split_by_transitions.
Trigger: a 'Batch Training' button on the Studio NODE (next to Open Studio) — prompts for N,
sets refiner.batch_variants, queues one prompt, then auto-reverts to 1 (one-shot). Studio is
the batch trigger (hub principle); no sampler input needed.
Sampler: now enters batch mode when positive carries variant markers, even if batch_iterations
is 1 (markers are the trigger; variant count wins). Each entry already saves its own cond, so
Submit trains the VF per variant — teaching which prompt variant rated better. Panel hints now
point at the button.
End-to-end: press Batch Training -> N -> Studio makes N variant conds -> sampler samples N ->
previews to temp -> rate in Studio panel -> Submit trains VF per entry -> batch cleared.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Per the Studio/sampler boundary law (Studio owns conditioning; sampler only samples), the
sampler no longer encodes anything. It now auto-detects when Studio has packed N shortcut
VARIANTS into positive — each scene entry tagged with 'funpack_batch_variant' = variant
index — via _split_batch_variants: if present, it samples one chain per variant (variant
count wins); otherwise it falls back to seed-only repeats of batch_iterations. Decisions:
'sampler loops, Studio varies' + 'pack into positive + markers'.
Each entry now saves its OWN conditioning (<id>.cond.pt) — identical across a seed-only
batch, distinct per variant — and the manifest records variant index + prompt per item.
Submit (batch_training.py) trains the VF per entry from its own cond, so variant batches
teach the model which prompt variant rated better (not just seed-quality variance). Studio
panel shows the variant tag + prompt per row.
Studio-side variant PRODUCTION (resolve shortcuts N times, encode, pack markers) is the
remaining half — a Studio/refiner feature, tracked next. Sampler side is ready for it.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Phase 1 amended: the engine now also saves the frozen conditioning (cond.pt) per batch
so submit can train on (cond, rating).
Phase 2 (panel + API): new batch_training.py registers /funpack/batch/{list,submit,forget}
(follows the existing /funpack/* PromptServer pattern). Studio gains a 'Batch Training'
section under the refiner: lists the pending batch for the active refinement key with each
entry's filename + seed, a Preview button (loads the temp webp via /view?type=temp), and a
per-entry V2-rating dropdown, plus Submit and 'Forget batch'. Previews served straight from
ComfyUI temp — no custom file route needed.
Phase 3 (learning intake): Submit loads cond.pt and trains the value function on
(cond, reward) for every rated entry — same conditioning, N rewards = variance-reduction
RLHF, which directly sharpens conditioning ascent. '-Just forget it-'/unknown ratings are
skipped. Submit consumes (deletes) the batch; Forget discards it without learning. Either
way the panel clears and the sampler is ready for a new batch.
Human stays the rating oracle throughout (no auto-rating) — 'user over everything'.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New optional 'batch_iterations' on FunPackLTXAVSceneChainSampler. When >1, runs the
ENTIRE scene chain N times frozen except the seed (seed + i) — conditioning/settings
provably identical because each run reuses sample() itself as the per-generation
primitive with only the seed changed. This gives a clean controlled-batch RLHF signal
(same conditioning, only noise varies) instead of the confounded one-at-a-time loop
where conditioning drifts between rated gens.
Each iteration is persisted under ComfyUI's TEMP dir (funpack_batches/<key>/<stamp>/) so
artifacts are wiped on restart (ephemeral rating material) and are servable via
/view?type=temp for the upcoming Studio panel: latent (.latent.pt) + a downscaled
animated-webp preview + a batch.json manifest (seed, scene prompts, subfolder, rating
slots). LATENT output passes through the last iteration so the downstream decode/save
path is unchanged.
Batch semantics: an entry is the full set of provided scenes — scenes within an entry
chain with overlap as normal; separate entries are independent sample() calls with no
cross-entry carry. Requires refinement_key_input (degrades to a single run without it).
Phase 2 (Studio rating panel + /funpack/batch routes) and Phase 3 (batch-train intake
in the refiner) to follow.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Three issues found tracing the full Studio path with all features on:
1) BUG (silent data loss): a custom velocity_refinement_key captured trajectories
under that key but the refiner committed the rating under the effective refinement
key — so custom velocity keys never learned (banks stayed empty, rescue/velocity a
permanent no-op). This undercut per-key cut-routing. Fix: Studio resolves each pass's
velocity key (same logic as _build_one_sampler) and passes them to refine_v2, which
now commits the verdict to the union of {effective key} + velocity keys (dedup).
Backward compatible (callers without _velocity_keys behave as before).
2) Effectiveness: VELOCITY_BIAS_TARGETS (0.90, 0.80) matched only sigma~0.909 on the
standard 8-step LTX schedule, so rescue fired once per gen at a near-noise step and
never at the detail-forming sigmas. Broadened to (0.90, 0.72, 0.42) -> hits 0.909,
0.725, 0.422. Velocity APPLY still structure-only (highest target); rescue now acts
where runs actually go good/bad. (New detail targets fill as gens are rated.)
3) Redundant value: velocity_aspect_bucket was a manual text field never tied to actual
resolution, and the bank key already includes the full latent shape (which encodes
resolution) — so it was doubly redundant. Removed from the key, all sampler plumbing,
INPUT_TYPES, Studio build, and the JS UI/defaults. Persistence loads old stores fine
(the 'aspect' field is simply ignored; key is now (refkey, target, shape)).
Verified: both py files + JS parse clean; all refactored call signatures aligned.
Tests require the ComfyUI venv (torch) to run.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The real limiter wasn't the 0.35 slider cap but the internal fixed 4.5%-of-|x|
delta clamp, which saturated almost immediately — the upper slider range was a
no-op. Now the rotation cap scales with strength (0.30*strength, capped 0.95), so
the whole range is meaningful: ~0.15 subtle spice (matches old feel), 0.3-1.0 clear
action crossover, 2-3 approaches full action replacement without wiping the current
gen. Magnitude-preserving renorm still keeps |x|, so high strength rotates rather
than blows up. Raised the param/UI cap to 3.0 and updated the tooltip.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
eta_final was silently ignored whenever the quality pass was off: the decay was
anchored to quality_sigma_start, which collapses to sigma=0 when high_quality_pct=0,
so effective_eta stayed pinned at eta for every step. Combined with the missing
deterministic finisher, full-strength ancestral noise ran through the final detail
steps with nothing to resolve it -> noisy mess.
New _effective_eta() ramps eta -> eta_final across raw schedule progress
(sigma/sigmas[0]), so eta_final works with the quality pass on or off. Used in both
the RF and eps paths. No-op when eta_final == eta (default), so existing runs are
unchanged.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Root cause of consistently bad LTXAV audio: ComfyUI packs the video+audio
latents into one [B,1,N] tensor for sampling (pack_latents, video first), and
the RF sampler's ancestral re-noising (eta/s_noise) + motion pulses + velocity/
rescue steering sprayed the whole packed tensor — including the audio slice.
Ancestral re-noising every step destroys audio coherence (video tolerates it,
and in fact benefits from the scene variability).
Fix: recover comfy's per-stream latent_shapes from the guider conds, build a
[1,1,N] video-region mask, and confine every FunPack perturbation to the video
slice. Audio now rides the clean deterministic euler-RF step:
- ancestral noise branch restructured into x_det (deterministic, audio) vs x_anc
(full ancestral, video), combined via the mask.
- motion pulses, velocity-bias apply, rescue, and quality sharpness masked too.
Single-stream LTXV (mask None) is unaffected — behavior identical to before.
Only the CONST/RF path needs this (LTXAV is always CONST); eps path sees no AV.
Prints a one-line diagnostic when packed AV is detected.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Compensate the slow quality degradation from velocity-bias 'apply' (additive
mean-pull) with four guards on the Hybrid Euler 2S sampler (both eps and
rectified-flow/LTXAV paths):
- B: apply is now magnitude-preserving — rotate x toward (x+delta) and
renormalize to |x| instead of x+delta, so no energy is injected.
- C: apply fires only at the highest (structure) velocity target and decays
strength with the sigma ratio; detail sigmas left untouched.
- E: new quality_sharpness knob — temporal-average unsharp on the x0 prediction
during the quality phase to restore high-frequency detail (free, no extra eval).
Default 0.0 (off).
- D: new velocity_bias_source = mean|nearest — 'nearest' steers toward the single
best-matching prompt cluster (one real good gen's detail) instead of the
washed-out average. Affects both apply and rescue. Default 'mean' (off).
Plumbed through get_sampler/INPUT_TYPES, Studio _build_one_sampler, and the
Studio JS sampler panel (both presets + UI controls).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The good/bad trajectory banks were in-memory only, so they died on every
ComfyUI restart — losing valuable rating-learned data. Persist them per key
under refinements/attn_maps/velocity_<hash>.pt (same store as blessed maps),
saved on each commit, loaded once-per-process on first access (rescue/apply/
commit). Directions stored as float16 to halve disk. Now only the deliberate
Studio Session-reset button (clear_velocity_bias_memory) deletes the store; a
restart preserves it. Serialized as weights_only-safe lists/dicts (no tuple
keys); load failures degrade gracefully to empty banks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Root cause of "rescued toward an Awful gen": velocity capture was
rating-blind — it wrote every sampled trajectory straight into the one
"good" memory, so a fresh key whose only capture was an awful gen made that
awful run the sole steering target. Ratings (which live in the refiner) never
touched the bank.
Now:
- capture STAGES the current gen's trajectory (VELOCITY_BIAS_PENDING) instead
of committing it; staging runs whenever rescue is on (no separate capture
toggle needed).
- the refiner commits the previous gen's staged trajectory by its rating:
like/loved/nailed -> good bank, awful -> bad bank, everything else dropped.
- rescue steers TOWARD the good bank and AWAY from the bad bank (per prompt
cluster), firing when diverged from good OR aligned with bad beyond
threshold; no-op when neither bank has a prompt match.
- session reset / clear_refinement_data now clears good + bad + pending.
velocity-bias "apply" now also reads the rating-gated good bank. Tooltips and
Studio note updated.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
VELOCITY_BIAS_MEMORY is an in-process dict, so reset_session (which only
removed on-disk enhancement files via clear_refinement_data) left stale
good-trajectory memory behind — rescue and velocity bias would keep steering
toward the previous session's content. Add clear_velocity_bias_memory(key)
in samplers.py (drops global averages + prompt clusters for that key) and
call it from clear_refinement_data. Key matches: refine_v2 and the velocity
key default both resolve to the same effective refinement key.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replaces the minimal RF rescue mirror with _sample_const_rf_full: an
RF-correct port of the hybrid sampler so its features actually run on CONST
models (LTXAV) instead of being swallowed by the euler_ancestral_RF fallback.
- early phase: ancestral RF euler (matches sample_euler_ancestral_RF) with
eta decay, AB2 order-2 denoised extrapolation, motion pulses
- quality phase: deterministic Heun corrector (RF-correct 2nd order; DPM++ 2S
is eps-only) with the progressive correction_blend schedule
- velocity-bias capture/apply + reactive rescue around every model eval
Active by default (matches hybrid defaults) per user request; changes the
LTXAV baseline away from plain euler-ancestral. to_d-based euler is identical
to the RF flow update, so early and quality phases stay consistent.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Root cause of "no rescue output": LTXAV uses CONST (rectified-flow)
sampling, so the hybrid sampler short-circuited to comfy's euler_ancestral
(which routes to euler_ancestral_RF) and skipped the entire custom loop —
velocity bias, motion pulses, and rescue never ran for LTXAV.
Replace the CONST early-return with _sample_const_rf_with_rescue: a faithful
mirror of comfy.sample_euler_ancestral_RF (same noise_scale, downstep/renoise
math) with velocity-bias capture/apply and reactive rescue inserted around
the model eval. With velocity and rescue off it reproduces the stock RF
result step-for-step. Hoist param normalization above the CONST branch so
both paths share it; correct the startup diagnostic accordingly.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
When rescue_mode is on, print at sampler entry: resolved strength/threshold,
whether a prompt signature arrived, whether the model uses CONST sampling
(which bypasses the whole custom loop), and which sigma steps are eligible
velocity targets. Makes it unambiguous why rescue does or does not fire —
no message at all now means the option never reached the hybrid sampler.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The single velocity-target step (norm sigma ~0.9) lands inside the quality
phase on short distilled schedules (8 steps, high_quality_pct=0.35 ->
quality boundary at sigma 0.909, the only matching step). Capture and rescue
were coded only in the early/non-quality branch, so neither ever ran on the
default LTX schedule — no capture, no rescue, no log output. Hoist
velocity_target above the phase split and run capture+apply+rescue after the
quality-phase model eval too. Capture records raw denoised before rescue
edits it in both branches, so the good-trajectory memory stays clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Rescue previously steered toward a prompt-blind average of all captured
good trajectories, risking false positives that fight a legitimately
different prompt. Now the velocity memory keeps per-prompt clusters: each
tagged with a pooled+normalized conditioning signature. Capture merges into
the nearest cluster (cosine >= 0.92) or starts a new one (cap 8/key, evict
lowest-count). Rescue builds its reference from clusters similar to the
current prompt (cosine >= 0.5, >= 2 matched captures), similarity-weighted;
with no confident prompt match it skips rather than steering to the global
mean. Studio pools its positive conditioning into a signature and threads it
through _build_samplers -> get_sampler -> sampler extra_options. Standalone
node (no conditioning) falls back to the prior global-average behavior.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
