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>
The velocity_refinement_key (namespace for trajectory capture + rescue
clusters) was a separate field defaulting to "default", easy to drift out
of sync with the refinement key Studio actually uses. Now when it is blank
or left at "default", Studio resolves it to the effective refinement key
(same resolution as seed memory / refine_v2), so capture and rescue share
one bucket automatically. An explicit non-default value is still respected.
JS hint added on the velocity key field.
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>
Studio builds its sampler via FunPackHybridEuler2SSampler.get_sampler but
did not pass the new rescue params. Thread rescue_mode/threshold/strength
through _build_one_sampler, add them to the Studio JS hybrid defaults
(high+low) and the sampler config panel, with a note that rescue needs the
velocity-bias memory captured first.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Detects when a denoising trajectory diverges from the learned
good-trajectory (velocity-bias) memory and rotates the prediction
back toward it on that step only. Reactive, read-only on the memory,
audio-safe (direction-only, magnitude-preserved on the denoised latent).
New sampler widgets: rescue_mode / rescue_threshold / rescue_strength,
threaded through get_sampler extra_options. Shares velocity_refinement_key
and aspect bucket with velocity bias; no-op until that memory is captured.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New order: positive signals (broad→specific) → negative signals
(broad→specific) → delta cap → VF gate.
Concept-in-context delta moved before bad conditioning repulsion so
positive phrase signal isn't added after negatives are removed.
Pair repulsion stays last among negatives as most targeted.
VF final gate: after all patches + delta cap, if net effect lowered
VF predicted reward by >0.05, linearly roll back toward original
proportionally to how much worse it got (max 75% rollback). Visible
as vf-rollback:X.XX in axis adjustments.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
search() generates 16 random unit-direction perturbations (2.5% of
conditioning norm each), scores each with the VF, returns the best.
Runs after ascend() in _v2_ascend_conditioning — ascent exploits the
known gradient direction, search explores the neighbourhood around it
for any better nearby region. Total cost: 16 VF forward passes ≈ free.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Learns which phrase combinations in the final encoded prompt correlate with
anatomy/quality failures (reward < -0.10, wrong_appearance, wrong_action+quality).
For each pair of phrases (up to top 8) present in a bad generation, accumulates
the conditioning direction as a per-pair bad slot using the existing
_v2_store_direction mechanism.
At generation time: for each phrase pair in the current final prompt with ≥3
observations, applies a repulsion from the stored bad direction (max 3 pairs,
strength capped at 3.0% of conditioning norm). Visible in axis_actions as
pair-repulsion:N. Prunes to 200 pairs when exceeding 300.
Trigger includes wrong_appearance explicitly (reward=0.0 but signals anatomy
artifacts), missing quality, and all outcomes below -0.10 reward.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The freeze locked prompt text to the previous Perfect output, overriding
intentional prompt changes when intent similarity stayed above 0.50. With
VF + conditioning memory maintaining quality direction, the freeze is no
longer needed and only causes unwanted video repetition.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Learns which phrases in the final encoded prompt (not intent, not raw input —
the actual text that went into CLIP) correlate with reward changes when added
or removed between consecutive generations.
Storage: concept_delta_memory per phrase text — add_sum/count, remove_sum/count.
Learning: on rating, diffs previous_run.final_phrase_texts vs prev_final_phrase_texts;
credits reward to added phrases, records it for removed.
Application: before conditioning memory is applied, computes a weighted direction
from phrase embeddings (via phrase_memory) for phrases new to this final prompt
that have ≥3 add observations and avg_reward > 0.10. Nudge capped at 3.5% of
conditioning norm alongside all other deltas.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Used refinement_key_input/refinement_key directly instead of eff_key
which is assigned ~340 lines later in refine_v2.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two new VF-driven behaviours in _v2_apply_conditioning_memory:
1. Global confidence scalar — VF predicted reward scales all conditioning memory
modifications: confidence_scale = (vf_score+1)/2 clamped to [0.4, 1.0].
High score → full strength (direction is good). Low score → dampened
(be cautious, current conditioning direction looks bad). Applies regardless
of which prompt is active — purely VF-signal driven.
2. Gradient-aligned phrase boosting — VF gradient direction (∂reward/∂conditioning)
is computed and compared to each axis direction slot via cosine similarity.
Boost factor [0.5, 1.5] applied to direction strength: phrases/axes pointing
with the reward gradient are amplified, those pointing against are dampened.
Applies to liked_dir, missing axis repair, and satisfied axis preserve.
VF score and boost factors visible in adaptation status output.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Attention weight accumulation:
- Capture per-token output norm at identity blocks [14,20,21,30,33] during every
generation via the optimized_attention patch (same path as temperature)
- EMA-merge into blessed file on liked ratings (0.8 old + 0.2 new)
- Inject as V-scaling bias on future runs: important tokens get amplified V,
pushing attention toward historically attended positions
- Temp/blessed files cleared on session reset
VF-driven temperature:
- build_enhancements accepts conditioning=None, loads value function, scores it
- _derive_temperature_map(vf_score=) continuously modulates block temperatures:
high VF score → sharpen (0.80×), low → warm/explore (1.20×), regardless of rating
- Rating-driven temperature still applies when reward < 0.8; VF blends with it
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LTX packs all spatial/temporal dims into a single token [1, 1, C*T*H*W]
during sampling. Per-step latent steering requires hooking into the model
internals, not the k-diffusion callback. Removed: compress_latent(),
_load_latent_value_function(), latent VF training, motion floor callback,
latent_value_guidance toggle in Studio.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previously latent_vf_strength was 0 when no latent VF existed, making
motion floor a no-op. Now embed_guidance_strength is always passed so
motion floor applies at the correct strength regardless of latent VF state.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LTX samples in [B, seq, C] token format during sampling, not [B, C, T, H, W].
Use sequence variance as temporal variance proxy — works for both 3D and 5D.
Replace fixed 0.5 sigma threshold with 1-sigma scale so motion floor and
latent VF activate across LTX's distilled high-sigma schedule naturally.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
OUTPUT_NODE = True forces ComfyUI to run the node even with no downstream
connection on the LATENT output. Motion floor now activates as soon as
embed_guidance is on and a refinement key is set, independent of liked_dir.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When temporal variance of the latent drops below 5% of overall variance,
applies a normalised deviation gradient that pushes frames apart from each
other. Fires only at sigma < 0.5 (same window as other latent guidance),
uses embed_guidance_strength as scale. Active whenever embed_guidance is on
and liked_dir is available — no user input needed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Unconstrained ascent was stripping action content from conditioning to reach
a "safe" static-character attractor. Added 8% displacement cap relative to
original conditioning norm — quality direction is still steered but the
prompt content can't be erased. Also raised MIN_SAMPLES from 5 to 10 to
reduce overfitting risk before the value function activates.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
closePicker() didn't remove the mousedown listener, so clicking the button
to re-open the picker would immediately close it. Now stores the cleanup
in module scope and calls it from closePicker().
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Trains a second OnlineValueFunction on rated video latents (compressed via
channel mean-pool). At each denoising step, computes ∂reward/∂latent through
the lightweight MLP and applies it as an in-place drift to the noisy latent —
gradients never touch the 22B transformer. Controlled by embed_guidance_strength
alongside the existing conditioning guidance. Studio toggle: "Latent guidance".
Cleared on session reset alongside the conditioning VF.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Heart modifier now applies full axis pressure (not 50% softened) — meaning
"fix this specific issue but the quality direction was great." The quality
signal comes from boosted reward and conditioning delta, not from reducing
axis pressure. Nailed it + heart removed (that's just Perfect). Positive
grid changed to 1fr 1fr to fill width with 2 items.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The ♥ button on any rating covers the same semantic. Removed from picker
UI and V2_RATING_LABELS; profile kept in Python for backward compat.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Active class was static (set at creation). Now all option elements are
tracked and active state is updated dynamically on each pick.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ComfyUI rejects widget values not in the allowed list. Generate all
valid label|loved combinations at definition time.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Each rating option now has a small ♥ button on its right. Clicking the main
label applies the base rating; clicking ♥ applies label|loved. No toggle
state — simpler and more direct.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Any rating can now be combined with a "Loved it" toggle. The suffix |loved
is appended to the rating string and parsed in normalize_refiner_v2_rating.
When active: reward boosted to max(base, 0.85), axis pressure softened 50%,
conditioning direction delta raised to 0.45, preferred context applied at
0.30 base_delta. Disabled for Perfect (redundant) and -Just forget it-.
Button label shows ♥ when modifier is active.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
addDOMWidget clicks were swallowed by the canvas. Switched to addWidget
button which ComfyUI routes correctly. Picker now positions at mouse coords
from the widget callback event.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replaces the rating dropdown on Studio and Refiner V2 nodes with a button
that opens a categorized floating picker. Each option shows its reward value
and a one-line hint. Rating auto-resets to -Just forget it- after execution
so forgotten ratings don't silently apply to the next run.
Also adds Wrong action + quality (reward -0.40, wrong_axes action, missing
quality) and drops Wrong action reward from +0.10 to -0.10.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Restores the old "Loved it" learning behavior (reward 0.75, intent delta
+0.03, preferred context 0.65, axis conditioning 0.35, LoRA 0.44/0.24)
as a distinct rating. Sits between Perfect and Loved it on the ladder:
prompt adherence is rewarded but not at full strength.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previously a weaker "Perfect". Now means: output looks great, prompt
adherence is irrelevant. Skips missing_intent_phrases learning, skips
preferred context lock-in, zeroes intent phrase reinforcement. Keeps
conditioning direction delta and raises value function reward to 0.85.
Quality/style LoRAs rewarded at Perfect level (0.55x).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two server routes (GET /funpack/value_function/export, POST /funpack/value_function/import)
serve and receive the .pt binary. Studio Session tab gains Export and Import buttons
below the value guidance toggle, keyed to the active refinement key.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
torch.load under inference_mode creates inference tensors; MLP params then
can't participate in backward. Same fix as the earlier value function loader
in samplers.py.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
reset_session now deletes the value function .pt file alongside the
enhancement files. Previously it was orphaned across resets.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Before returning modified_positive, ascend each scene's conditioning tensor
toward higher predicted reward via the value function. Covers both the
standalone Refiner V2 node and FunPack Studio (which delegates to refine_v2).
No-op until value function reaches MIN_SAMPLES; handles transition-split
multi-scene conditioning correctly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
