olivv-cs-ComfyUI-FunPack/CHANGELOG.md

Changelog

[Unreleased]

Added

Interactive Guessing — a new Batch Training mode. Instead of varying the seed, it freezes everything (including the noise seed) and sweeps the conditioning's spread (its "sigma") on video channels along a linear ramp across the N rungs — up to amplify toward overbake, down to dampen — so you can see exactly where it breaks. Each rung records its factor. When you rate the ladder, it learns your safe steering ceiling for that key and auto-caps future steering: a soft pre-limit on absolute/relative steer strength plus a hard clamp on the output conditioning's spread (the guarantee). Audio is never touched (reuses the LTXAV video/audio channel split). The Batch Training tab gains Mode (Regular / Interactive Guessing), Direction, Range, and a Learning toggle — Learning off (either mode) means pure generation that teaches nothing. Combined with the new IMAGES output (all batch videos come out the Chain Sampler's IMAGES port), you can now generate N slight variations and just watch them without rating.

Changed

The Chain Sampler's IMAGES output now returns every batch video concatenated, not just the last one — for both regular batches and Interactive Guessing.

Latent normalization stacks on the Hybrid and Distilled samplers too. The same video-only anti-overbake normalization from the Normalizing sampler is now an opt-in normalize_strength / normalize_start_sigma on the Hybrid Euler 2S (CONST/RF path) and Distilled Flow samplers, so you can keep their features (AB2, Heun, velocity bias, rescue) and get the latent-health correction. Default off (no behaviour change); audio is never touched. Exposed on both nodes and in their Studio panels.

FunPack Normalizing Sampler — a new SAMPLER (selectable in Studio alongside Hybrid Euler 2S / Distilled Flow / KSampler) built for distilled few-step LTXAV at CFG=1, where the model is already extracted and there's no redundant CFG computation to exploit. It runs a clean deterministic 1st-order euler base (the "ddim look" + clean audio — no AB2/Heun/ancestral) and adds video-only latent normalization: each step it tracks the video latent's robust spread, anchors a reference once x0 is meaningful (normalize_start_sigma), and gently compresses any later step whose spread inflates past it (normalize_strength), countering the overbaking / oversaturation / colour-drift that distilled latents fall into. Benefit-only (never amplifies, no-op when the latent is healthy), ~zero overhead (a few masked reductions), and audio-safe by construction — the audio stream rides plain euler and the normalization never touches it (whole-latent for single-stream LTXV). Velocity bias + reactive rescue are available too, video-only. Node FunPackNormalizingSampler + Studio sampler panel.

Distilled Flow AB2 ramp — graduated 2nd order. New opt-in toggle (off by default) that, instead of applying full Adams-Bashforth 2-step extrapolation on every step, ramps the AB2 contribution linearly from 0→1 across the schedule: early/high-sigma steps stay near plain 1st-order euler (where the denoised estimate is rough and full AB2 tends to overshoot), late/detail steps get full AB2. Reuses the already-computed AB2 estimate, so no extra model evals — aimed at squeezing better quality out of low-step distilled runs. No effect at order=1. Exposed on the Distilled Flow sampler node and in Studio's sampler panel; audio stays on the plain euler path as always.

Fixed

Audio corruption in the FunPack Hybrid Euler 2S and Distilled Flow samplers on LTXAV. Both samplers' second-order machinery — the AB2 (Adams-Bashforth) denoised extrapolation and the Heun predictor-corrector — was applied to the whole packed latent, including the audio stream, which degraded audio. (Stock euler/ddim sound fine because they're plain 1st-order with no AB2/Heun; ddim in ComfyUI is literally euler.) Audio now always rides the plain 1st-order euler update (AB2 and Heun confined to the video stream via _video_only), so audio stays clean at any order/correction_blend/final_correction_steps setting while video keeps the 2nd-order treatment. Distilled Flow's optional s_noise diversity injection is likewise now video-only. For the deterministic "ddim look" plus FunPack steering, run Distilled Flow at order=1, final_correction_steps=0 (pure euler base) — audio is doubly safe.

[2.7.8] - 2026-06-04

Added

Made all conditioning steering audio-safe on LTXAV. LTXAV conditions video and audio from two separate text cross-attentions that the model carves out of one conditioning tensor by splitting its channel dim (comfy/ldm/lightricks/av_model.py _prepare_context: torch.split(context, [v_context_dim, a_context_dim], -1) — leading channels → video attn2, trailing → audio audio_attn2). Previously every steer (relative/absolute pull, value-function ascent + search, embed_guidance, the attn2 direction patch, and manual Conditioning Adjust) shifted the whole tensor, corrupting the audio's own text conditioning and degrading audio. Now a shared protect_audio_channels confines every edit to the video channel-slice and restores the audio slice from the unsteered conditioning — effectively "modified conditioning for video, original for audio", with no model patching or extra forward pass. The split is auto-detected from the channel width (7680→3840, 6144→4096) and logs the detected layout once; single-stream LTXV (unrecognised width) is a clean no-op, so video-only models are unaffected. Note: this protects the audio's direct text conditioning (the dominant lever); the per-block cross-modal video_to_audio_attn can still carry a weaker second-order influence from heavily-steered video.

Added an Absolute / Relative steer mode to Refiner V2 and Studio. Relative (the default, unchanged behaviour) is per-prompt: it learns and applies the best conditioning for one specific prompt. Absolute is prompt-agnostic — it accumulates a single global "taste" prior across every rated generation and pulls conditioning toward it regardless of the prompt. In Absolute mode a rating means "this has (or lacks) details I like in general", not prompt adherence: a Perfect means "I love this, give me more of it everywhere", and a low rating means "this is missing the details I like in general". The pull layers two engines — a pooled liked/disliked direction (the learned, automated analogue of the manual Conditioning Adjust phrase shift) and the keyless value function on top — and is applied at the conditioning output (_v2_finalize_conditioning). Both keeps the per-prompt fit and layers the global prior under it; pure Absolute bypasses per-prompt memory entirely. The global store learns from rated runs even with no refinement key wired (it is keyless by design). The Scene Chain sampler's embed_guidance gains an embed_guidance_source (relative / absolute) so the per-step nudge can draw from the global taste direction too. Studio exposes Steer mode + Absolute strength in the refiner panel.

Ported velocity bias + reactive rescue to the FunPack Distilled Flow sampler (sample_funpack_distilled_flow), reusing the same capture/memory/rescue machinery as the Hybrid Euler-2S and LTXAV/RF samplers. The sampler now exposes velocity_bias_mode, velocity_bias_strength, velocity_bias_source, velocity_refinement_key, rescue_mode, rescue_threshold, and rescue_strength, and these are wired into Studio's Distilled Flow sampler config panel (with the same blank/default velocity-key → wired-refinement-key fallback the Hybrid panel uses). Steering is applied magnitude-preservingly and, on packed LTXAV latents, confined to the video stream (audio-safe). Note: few-step distilled schedules may only land on one or two velocity targets, so apply/rescue fire less often than on an 8-step run; they no-op cleanly when no target matches. With everything off the sampler is byte-identical to the previous deterministic ODE.

[2.7.7] - 2026-06-03

Added

Added Batch Training — a controlled-batch RLHF workflow built around the principle that the cleanest learning signal comes from rating several generations that differ in exactly one thing. When a batch is active, the Scene Chain sampler runs the chain N times with everything frozen except the noise seed, producing N directly-comparable videos. Studio then shows a rating panel where every variant is scored, and on submit the value function trains on the batch's shared (frozen) conditioning with each variant's reward — same conditioning, N rewards, which is the variance-reduced comparison signal. Spans the full pipeline: the engine (controlled N-run mode on the sampler), a Studio variant producer that packs the variants into conditioning, the /funpack/batch server routes and rating window, and the value-function intake on submit. Phase 3b adds deeper learning from batch ratings via axis and direction memory. Batches live in ComfyUI's temp directory and are wiped on restart.

Added a reactive in-flight rescue system to the Hybrid Euler-2S sampler. During sampling the trajectory is compared against conditioning-clustered memory of past good and bad runs; when a step drifts toward a known-bad trajectory it is nudged back. Rescue is rating-gated (separate good/bad trajectory banks, learned only from rated runs), prompt-aware (clustered by conditioning so the right memory is consulted), runs in both sampler phases, and persists its trajectory banks to disk. The full feature set was also ported to the LTXAV rectified-flow (CONST-model) path via a sampler mirror, so velocity capture and rescue now work for LTXV/LTXAV, not just the discrete-step models.

Added Monte Carlo conditioning search after the value-function gradient ascent, plus a VF final gate, so the conditioning chosen for generation is the best of a sampled neighbourhood rather than the raw ascent endpoint.

Added a Studio Variability macro and an active-feature readout for the Hybrid sampler, summarising which steering features (velocity bias, rescue, embed guidance, value guidance) are live for the current configuration.

Changed

Unlocked velocity_bias_strength from a 0.35 cap to 3.0 for deliberate creative action injection. Velocity bias is an artistic / action-injection control (and carries an emergent scene-cut prior — cuts survive the trajectory mean and get reintroduced unprompted); it is not a consistency tool.

Refiner V2 now always trains the value function; value_guidance only gates whether the learned reward is applied during sampling, and defaults on. This means rated runs keep teaching the value function even when guidance is off.

Decoupled eta_final decay from the quality boundary — it now anchors to schedule progress instead, so ancestral-noise decay tracks the sampling timeline rather than a quality threshold.

Fixed

Audio-safe LTXAV sampling: ancestral noise and trajectory steering are now applied to the video latent only, never the audio latent, preventing the joint-attention audio corruption that steering on the combined tensor caused.

Velocity-bias anti-softening: the bias is now applied magnitude-preservingly with sigma-decay and a quality-sharpness term, and sourced from the nearest trajectory cluster, so it injects motion without washing out detail.

Audit fixes: corrected a velocity commit key mismatch, sparse rescue targets, and a redundant aspect-bucket computation. Batch Training fixes: distinct per-variant seeds in both the split-scene and single-scene paths (identical seeds were producing identical videos), correct activation alongside split_by_transitions, the node rating is ignored while a batch is in progress, and the in-node panel was de-duplicated from the Studio Refiner tab.

[2.7.6] - 2026-05-30

Added

Added pre-generation conditioning ascent driven by the value function. Before sampling, the positive conditioning is moved by gradient ascent toward higher predicted reward, in both the single and scene-split Refiner V2 output paths. Displacement is capped to prevent reward hacking (the value function will otherwise push conditioning into degenerate high-score regions). The value function can be exported and imported from Studio and is cleared on session reset.

Added VF-driven conditioning shaping: confidence scaling and gradient-aligned phrase boosting in conditioning memory, attention-weight accumulation, and VF-driven temperature, so the learned reward influences which phrases and attention weights are emphasised.

Added concept-in-context conditioning guidance and concept-pair bad-direction repulsion — conditioning is nudged toward liked concepts in context and away from concept pairs learned to be bad.

Added a motion floor to embed guidance: when temporal variance falls below a threshold the guidance auto-boosts it, fighting static/frozen output. Activation is logged with step, sigma, and variance ratio.

Reworked the rating UI into a rating picker popup. Added a Wrong action and an explicit quality rating, a Nailed it rating (prompt-adherence positive, weaker than Perfect), and replaced the standalone Loved it rating with a per-option heart modifier (an axis-blind quality endorsement layered on top of any rating). The heart is disabled for quality-degraded ratings and for Awful.

Changed

Disabled perfect_freeze — prompt changes made after a Perfect rating are now respected instead of the conditioning being frozen.

Removed

Removed the experimental latent value function (VGG-Flow-style per-step latent steering). It was added in this cycle but pulled: the LTX token format does not expose the latent in a form the per-step steering could act on.

Fixed

Fixed an UnboundLocalError (eff_key not yet assigned when the value function loads), value-function loading needing inference_mode(False), several rating-picker bugs (stale outside-click listener re-opening the picker, active state not updating on option change, clicks landing on a canvas button widget rather than a DOM element), and Save Refinement Latent not executing.

[2.7.5] - 2026-05-29

Added

Added embed guidance — a per-step nudge of the conditioning toward the learned "liked-quality" direction during sampling. Near-free overhead; requires a wired refinement_key_input with enough liked generations to have formed a direction. Exposed as embed_guidance / embed_guidance_strength on the Scene Chain sampler.

Added an online value function for reward-guided sampling — a small MLP trained on rated generations that predicts reward and can steer sampling toward it. Required several fixes to train and run gradients inside ComfyUI's inference_mode execution context.

Added mid-scene guide (mid_scene_guide / mid_scene_guide_strength), replacing the broken self_consistency feature. It uses the LTX guide-attention mechanism rather than post-block hidden-state injection (which corrupted audio through joint attention). At ~0.25–0.3 strength it preserves static-element layout across a scene; capped at 0.5.

Added a vision-conditioning toggle to the Studio popup, and moved reference-image conditioning into Studio, where the source image already lives.

Changed

Removed the predefined transition-phrase list — scene splitting is now fully driven by user-defined and auto-detected transitions.

Removed

Removed the per-scene vision re-encoding and clip input from the Scene Chain sampler, and reverted the per-scene reference-image encoding in Studio (both added in 2.7.4). They were unstable in practice.

Removed self_consistency (corrupted audio via joint attention — superseded by mid_scene_guide), the i2i_scene_cut feature, and dead guide-keyframe / guide-conditioning code paths.

Fixed

Fixed i2i_strength inversion (higher now means stronger reference influence) and replaced the 1-frame i2i anchor with 2-frame i2v anchor generation for hard cuts.

[2.7.4] - 2026-05-28

Added

Added K/V in-context conditioning for LTXAV identity blocks during i2v generation. Reference hidden states are captured at the start of each scene's denoising pass and prepended as extra attention tokens to the identity-formation blocks ([14, 20, 21, 30, 33]). This forces the model to attend to the reference character's appearance during every self-attention step in those blocks. Result: strong character consistency across scene cuts, view changes, and orientation changes with no LoRAs required.

Added Gemma3 vision prompting. When source_image is connected to Studio and the CLIP was loaded with a Gemma3-12B checkpoint via DualCLIPLoader, Studio automatically encodes the reference image through the built-in SigLIP vision encoder and feeds the resulting vision tokens into the text conditioning. This means the model conditions on both the prompt text and the actual pixel content of the reference frame. No extra node is required — DualCLIPLoader already loads the vision weights when present in the checkpoint.

Added per-scene vision re-encoding to FunPack LTXAV Scene Chain Sampler. Connect an optional clip input. After each scene is sampled, the next scene's conditioning is re-encoded using the previous scene's decoded last frame as vision context. This gives identical scene texts genuinely different conditioning based on runtime-generated content, so the model knows what state it came from when building the next scene.

Added duplicate scene text differentiation. When two or more scenes share identical text, the second and subsequent occurrences are encoded with a "Returning to an earlier scene: " prefix. The original text is preserved in metadata for logging. This breaks the shared conditioning cache entry so the model receives distinct input for each occurrence.

Improved

Reworked frame_overlap=0 soft continuation: the previous scene's last 4 frames are now prepended with mask 0.4 (partial denoising) instead of 1 frame at mask 0.0 (fully pinned). The model receives temporal context from the previous scene while retaining enough denoising freedom to commit to orientation and pose changes directed by the text prompt.

Fixed

Fixed anchor text punctuation when joining scene segments. If the character description ends with ., !, ?, or ,, a plain space is used as the separator instead of injecting a redundant , . Previously a description ending with a full stop produced "description., scene text".

Removed

Removed FunPackGemmaVision node. It was redundant: DualCLIPLoader with a Gemma3-12B checkpoint already loads vision_model and multi_modal_projector weights through the normal load_state_dict path, so the manual weight injection the node performed was a second load of the same file. The vision capability check now detects the attributes directly instead of relying on an injected flag.

[2.7.3] - 2026-05-27

Fixed

Fixed carry_i2v_guides soft continuation when frame_overlap=0: the anchor mask is now fully pinned (0.0) to prevent denoising from disturbing guide tokens.

Notes

Using frame_overlap=0 together with carry_i2v_guides=True is confirmed to produce bad results and is not recommended. Both parameters now warn about this in their tooltips and in the sampler documentation. Use this combination only for deliberate testing.

[2.7.2] - 2026-05-22

Added

Added Shortcuts system. Activation phrases in the prompt are replaced with full cinematic descriptions before encoding. Multiple replacement options are randomly picked per seed. Empty replacement removes the matched phrase entirely. Longer phrases always win over shorter overlapping triggers. Managed in the new Studio Shortcuts tab with Add/Save/Delete/Import/Export.

Added Transitions system. User-defined transition phrases extend the built-in split list. Custom entries support a placement override - start (transition opens the new segment), end (transition closes the previous segment), or silent (split happens but the phrase is stripped from output entirely). Managed in the new Studio Transitions tab.

Added global Transition placement setting in Studio Refiner tab (start / end / silent), with per-entry override on each custom transition entry.

Fixed

Removed all single-word temporal markers (next, suddenly, later, finally, etc.) from the built-in transition phrase list. They caused false splits on normal prose.

Fixed _GENERIC_SCENE_LABEL_PATTERN matching "scene proceeds", "scene features", "scene shows" and similar noun-verb constructions as scene labels.

Fixed dangling trailing transition segments (prompt ending with "...cuts to the") being kept as near-empty scenes.

Fixed stray comma artifacts after article words when transition phrase merging occurs.

Fixed custom transition triggers ending with punctuation (e.g. "Scene cut.") not being detected due to a misplaced word boundary.

[2.7.1] - 2026-05-21

Added

Added successful seed memory for FunPack Studio. When the seed output is connected, Perfect and Loved it ratings store the previous run's sampler seed under the active refinement key. Future runs occasionally reuse concept-matched successful seeds while keeping normal fresh seeds as the default path.

Added per-scene seed metadata for prompt-split mode. Studio and Refiner V2 keep the public seed socket as a single integer, while each detected scene conditioning entry can carry its own funpack_scene_seed for the Scene Chain sampler.

Added use_same_seed to FunPack LTXAV Scene Chain Sampler. When enabled, every scene uses the first provided scene seed or the base seed. When disabled, each scene uses scene seed metadata or falls back to seed + scene_index.

Changed

Made Scene Chain i2v guide carry opt-in. The default path no longer appends protected i2v frames as hidden guide tokens, preserving cleaner scene cuts unless the experimental option is deliberately enabled.

Fixed

Fixed compact i2v guide masks failing to concatenate with full spatial Scene Chain masks by broadcasting guide masks to the chunk mask shape.

Updated README release notes and added the Intent section.

[2.7.0] - 2026-05-21

Added

Added FunPack LTXAV Scene Chain Sampler for split-scene LTXV/LTXAV continuation in one ComfyUI run. It consumes multi-entry positive conditioning from FunPack Studio or FunPack Video Refiner V2, samples one scene chunk per conditioning entry, increments the seed per scene, preserves overlap from the previous chunk, and blends/appends chunks in latent space.

Added support for plain LTXV video latents and nested LTXAV video/audio latents in the Scene Chain sampler. For nested AV latents, video and audio tensors are continued together, with audio overlap derived from the video/audio latent length ratio.

Added broad order-only scene splitting for split_by_transitions. Scene labels such as scene ten, scene -999999, and scene minus infinity are transition cues, but their written labels never affect scene numbering or order.

Expanded transition phrase detection with scene progression, camera shift, zoom, final shot, and final transition phrases.

Changed

split_by_transitions=True now returns one conditioning entry per detected scene through the existing modified_positive output. No new Refiner V2 or Studio output sockets were added.

The text before the first transition is treated as a shared character/global anchor and prepended to every detected scene conditioning. This is intended to improve character consistency across generated chunks.

Removed the hard 8-scene cap from Refiner V2 split output and Scene Chain sampler execution. max_scenes still defaults to 8, but users can raise it for longer chains.

Standalone then is no longer a transition trigger. More specific phrases such as and then, camera transitions, scene labels, and explicit cut/transition language still split scenes.

Fixed

Fixed Studio's Scene Builder mode dropdown not refreshing the active tab immediately after selecting a new mode.

Warning

FunPack LTXAV Scene Chain Sampler is resource heavy. Long chains create large final latents and may run out of memory during VAE Decode even when sampling succeeds. Start with short scenes and a modest max_scenes, then increase carefully.

[2.6.0] - 2026-05-16

Added

Added FunPack Studio - a single node that replaces the typical chain of Refinement Key Loader, Scene Builder, Apply LoRA Weights, LoRA Loader, Video Refiner V2, and Conditioning Adjust with a tabbed popup editor. All settings are managed inside the popup; only the rating widget and Open Studio button are visible on the node face.

Studio inputs (in order): model, clip, advisor_clip, positive_conditioning, negative_conditioning, clip_vision_output, source_image, lora_stack, positive_prompt, negative_prompt, user_intent_prompt, feedback_prompt, refinement_key_input.

Studio outputs (in order): model (LoRAs applied + attn2 direction patch), modified_positive, negative (encoded from negative_prompt or passed through from negative_conditioning), seed (for wiring to sampler), high_pass_sampler, high_pass_sigmas, low_pass_sampler, low_pass_sigmas, loss_graph, status, training_info, encoded_prompts.

Studio popup tabs:

• Session: refinement key management and Scene Builder mode selector (Pass-through / Manual / Auto / Learning).
• Scene: scene preset load and save, phrase bank from session memory, positive prompt composer.
• Refiner: all Refiner V2 settings including negative prompt field, feedback, and intent override. Shows a banner and disables the intent field when Scene Builder is active.
• Advisor: enable/configure an internal HuggingFace CausalLM advisor. Uses the same model cache as the standalone Advisor LLM node.
• LoRA: full LoRA pipeline - session weight suggestions are read first, then LoRAs are applied to model and CLIP, then the direction patch is applied on top. Supports model type (ltx2/wan) and per-block settings.
• Sampler: configure Hybrid Euler 2S, Distilled Flow, or any KSampler for the high-pass and low-pass outputs independently. Sigma schedules entered as comma-separated floats.
• Adjustments: phrase-level conditioning adjustments with session phrase bank.

Three text inputs (refinement_key, feedback_prompt, user_intent_prompt) have override toggles: when off, connected inputs win; when on, popup values win.

The popup remembers its last active tab per node via localStorage. All field changes auto-save to the node widget after 600ms, so settings survive page refresh without requiring Close to be clicked.

Added negative_prompt encoding to Studio: when no pre-encoded negative_conditioning is connected, Studio encodes the negative_prompt text via CLIP internally, removing the need for an external CLIPTextEncode node.

Added /funpack/available_loras and /funpack/phrase_memory backend endpoints used by Studio's LoRA picker and phrase banks.

Added FunPackConditioningAdjust standalone node for phrase-level conditioning adjustments. Encodes each phrase via CLIP, computes a unit-norm direction from the base conditioning, and applies it at user-set strength. Positive pushes toward the phrase, negative pushes away. Popup editor with session phrase bank.

Added seed output to FunPack Video Refiner V2 (via optional _seed parameter) so Studio can generate a seed and expose it as an output for wiring to samplers.

Fixed

Fixed FunPackAdvisorLLM tokenize failing with AttributeError when apply_chat_template returns a BatchEncoding instead of a plain tensor in newer transformers versions. Explicit hasattr(result, "input_ids") check now handles both return types.

Fixed advisor generation producing no output for Qwen3 and other chain-of-thought models: thinking tokens (<think>...</think>) were not being stripped because they are special token IDs that disappear with skip_special_tokens=True. Switched to decoding with skip_special_tokens=False then stripping thinking blocks with regex. Truncated thinking blocks (no closing tag, token budget exhausted) are also stripped.

Fixed FunPackAdvisorLLM attention mask warning and erratic output (echoed prompts, missing spaces) caused by pad_token == eos_token without an explicit mask. Now passes torch.ones_like(input_ids) as attention mask to all generate calls.

Fixed FunPackConditioningAdjust adjustments not applying for LTX/Gemma3 conditioning: the node was reading pooled_output which is None for T5-based encoders. Now uses conditioning.mean(dim=(0,1)) on the sequence tensor, matching how V2 handles conditioning internally.

Fixed Refiner V2 advisor diagnostic not being generated in Full mode: the LLM analysis prompt was asking for session-wide pattern recognition when no history existed yet. Now adapts - uses a simple per-run analysis on early sessions and session pattern analysis when history exists. Added a rule-based fallback so diagnostic history always accumulates even when the LLM produces empty output.

Fixed perfect_repair_phrases and _v2_emphasized_prompt injecting phrases regardless of the prompt_repair toggle. Both are now gated behind prompt_repair=False.

Removed the Perfect-rating advisor gate. The advisor previously skipped both analysis and repair when rating was Perfect and no text feedback was provided. Perfect is not a ceiling - the advisor now runs normally for Perfect ratings.

[2.5.3] - 2026-05-16

Fixed

Fixed FunPackAdvisorLLM wrapper not triggering advisor generation. Qwen3 and other chain-of-thought models emit <think>...</think> blocks that were not stripped, causing the parsed repaired prompt to contain reasoning text. This made body-similarity validation reject the result as "too far from intent." Fixed by stripping thinking blocks in decode. Also added enable_thinking kwarg support in tokenize (with TypeError fallback for models that don't support it) and expanded max_new_tokens by 2048 when thinking mode is active so the reasoning budget does not crowd out the actual response. Fixed _v2_text_semantic_similarity returning 0.0 for generation-only clips that have no encode_from_tokens_scheduled - these now return 1.0 (skip the semantic gate) instead of causing spurious rejections. Fixed FunPackAdvisorLLM missing from the standalone import block in __init__.py, which broke the test suite.

Removed Perfect-rating advisor gate. The advisor was silently skipping both the analysis pass and the repair pass whenever the rating was Perfect and no text feedback was provided, even when the user had an active advisor mode. Perfect is not a ceiling - if the user provides feedback_prompt, it must be honored regardless of rating. The only remaining guard is the allow_prompt_change check (Learning mode).

[2.5.1] - 2026-05-16

Added

Added FunPackAdvisorLLM node. Loads any HuggingFace CausalLM (including sharded checkpoints) as an advisor for Refiner V2. Connect the output to advisor_clip. Model is cached after first load so subsequent runs do not reload. Also fully compatible with the built-in TextGenerate and TextGenerateLTX2Prompt nodes - supports skip_template, min_p, presence_penalty, and progressive fallback for unsupported generation parameters.

Added _v2_direction_readout to training_info Adaptation section. Shows each direction memory slot in plain language: run count, magnitude, whether it is in direction mode or lerp fallback, and the role each axis is playing this run.

Changed

Advisor prompt format rewritten to natural language. Both the repair and analysis user messages now read as plain enhancement requests rather than structured field-value pairs. Works with enhancement-type models (Sulphur, Qwen prompt enhancers) as well as instruction-following models. System prompt reduced to one sentence.

Direction-based conditioning now uses max_new_tokens instead of max_length in the FunPackAdvisorLLM wrapper so prompt length does not eat into the generation budget.

Model patch status expanded to show which direction slots are active with run counts and which phrase texts are being emphasized in cross-attention.

Adaptation status block rewritten to multi-line readable format showing strength, reward trend, streak, per-slot mode (direction vs lerp fallback), and axis adjustments applied this run.

Fixed

Fixed _v2_generate_advisor_text returning None when layer 1 tokenization succeeded - generate/decode/return were inside the except TypeError block so they only ran when layer 1 failed.

Fixed session reset not clearing intent_expansion_memory, session_source_mean_count, liked_dir, and bad_dir - these fields were missing from _v2_empty_state and survived reset via setdefault.

Fixed advisor repetition loops: repetition_penalty raised from 1.05 to 1.3, added no_repeat_ngram_size=5, temperature raised from 0.5 to 0.7.

Fixed system prompt bleeding into advisor output by splitting prompts into (system, user) tuples and applying the model's native chat template. Three-layer fallback: native system_prompt kwarg, manual apply_chat_template via BFS, flat string with completion anchor.

Added persistent cross-run encode cache (_V2_PERSISTENT_ENCODE_CACHE, 4096 entry cap) so phrase encodings are not recomputed every run when CLIP and text are unchanged.

[2.5.0] - 2026-05-15

Added

Added a CLIP text-generation advisor to FunPack Video Refiner V2. The advisor runs two sequential passes: an analysis pass that identifies what specifically needs to change in the suggested prompt, followed by a repair pass that applies those findings. Both passes see the current suggested prompt, user intent, previous prompt, memory suggestions, and the full feedback history.

Added advisor_clip input to use a separate generative CLIP/Gemma model for the advisor while the main clip continues handling encoding and similarity checks.

Added advisor_mode dropdown: Off, Only diagnostics, Only prompt, Full. In Full mode both analysis and repair passes run. In Only diagnostics mode only the analysis pass runs and its finding is stored in feedback history for the next run. In Only prompt mode only the repair pass runs silently.

Added feedback_prompt optional input. When connected, the user's natural-language description of what was wrong is placed first in both advisor passes, with the system instructed to follow it exactly and override all other repair logic.

Added persistent advisor_feedback_history stored in V2 session state. Up to ten past feedback entries accumulate across runs, each labelled with the corresponding rating: Missing action: he was supposed to hold her hand not her head. Advisor-generated diagnostics from Only diagnostics runs are stored as Advisor note: entries so they carry forward into subsequent Full runs.

Added Prompt only execution mode to FunPack Video Refiner V2. All prompt shaping runs as normal but conditioning vectors are passed through unchanged. Learning still applies. Useful when conditioning adaptation should be paused while prompt refinement continues.

Added prompt_repair boolean input to FunPack Video Refiner V2 (default on). Turning it off disables the rule-based phrase injection from phrase memory and passes no repair candidates to the advisor. Useful early in a session before enough context has been built.

Added encoded_prompts STRING output to FunPack Video Refiner V2. When the advisor ran and produced a suggestion, the output includes up to four labelled sections: Positive prompt (what was encoded), Advisor suggestion (applied/rejected) (what the advisor generated), Advisor analysis (the diagnostic text from the analysis pass), and Pre-advisor prompt (the prompt before the advisor rewrote it).

Added eta_final parameter to FunPack Hybrid Euler 2S Sampler. When set below eta, ancestral noise strength decays linearly toward this value as sigma approaches the quality phase boundary, smoothing the transition into deterministic refinement. Default 1.0 preserves existing behaviour.

Changed

Replaced the Refiner V2 advisor system prompt with a structured repair format. The advisor now receives four explicit variables — ORIGINAL_USER_INTENT, LAST_PROMPT, RATING, and OPTIONAL_NOTE — and is instructed to rewrite LAST_PROMPT to fix the specific failure described by the rating. Memory suggestions, feedback history, and analysis context are folded into OPTIONAL_NOTE. The repair pass for Only prompt mode outputs a plain prompt string with no labels, matching the instruction to output only the final text.

Removed negative_prompt input and modified_negative conditioning output from FunPack Video Refiner V2. Negative conditioning has no effect at CFG=1.0 with NAG guidance and added a redundant AI generation call in every mode. Both the rule-based negative repair and the negative advisor pass are removed.

Increased advisor token budget: repair pass 800 → 1600 tokens, analysis pass fixed at 1200 tokens (was repair // 2 = 400). The analysis limit is now independent of the repair limit so it does not shrink if the repair budget changes.

FunPack Hybrid Euler 2S Sampler early phase now uses an order-2 ancestral denoised extrapolation (Adams-Bashforth 2-step) in addition to the existing Euler-A update. The previous step's denoised estimate is used to extrapolate a better score direction at zero extra model-call cost. The state resets after any motion pulse.

FunPack Hybrid Euler 2S Sampler quality phase now uses a progressive correction_blend: the first half of quality steps use a single-eval Euler ODE pass; the second half use the configured 2S correction. This reduces model calls in the quality phase while concentrating the expensive correction where sigma is lowest and it has the most impact.

Fixed

Fixed Only prompt advisor mode running two AI generation calls per invocation (one for positive repair, one for the now-removed negative advisor), causing each run to take twice as long.

Fixed encoded_prompts always showing only Positive prompt: regardless of advisor activity. The final return path was calling _v2_encoded_prompts_output without the advisor keyword arguments.

Fixed Refiner V2 advisor generation: do_sample was False, forcing greedy decoding and silently ignoring temperature, top_k, top_p, and all sampling parameters. The model was always producing its highest-probability default output regardless of instructions or feedback. Changed to do_sample=True with temperature 0.5.

Fixed advisor validation silently rejecting valid feedback-driven repairs: the intent-distance check and protected-category checks now bypass when feedback_prompt is connected, allowing the advisor to implement what the user explicitly requested.

Fixed _v2_find_perfect_example_for_intent accessing a field (loved_delta_sources) that was never written. It now correctly reads from perfect_anchors and loved_variants.

Fixed _v2_update_streaks updating conditioning strength signals (avg_reward_ema, good_streak, bad_streak) in Prompt only mode, which contaminated conditioning adaptation for subsequent Refine runs. Rating and axis labels still update for repair continuity.

Fixed advisor rating label on first run or session reset: was forwarding the user's rating widget value even when there was no previous output to apply it to. Now passes "No previous output (first run or session reset)" when has_previous_run is false.

[2.4.2] - 2026-05-15

Added

Added Learning mode to FunPack Video Refiner V2. It still observes prompts, conditioning, ratings, phrase memory, and diagnostics, but passes positive and negative prompt conditioning through without prompt repair, Lucky composition, wildcard cleanup, or conditioning-vector adaptation.

Fixed

Fixed FunPack Scene Builder mode handling so the live Mode widget stays independent from the selected saved scene, including queue-time Learning and Auto behavior.

Fixed FunPack Scene Builder rich prompt editing so the caret can move past a final inline phrase chip with the mouse or right arrow key.

[2.4.1] - 2026-05-14

Fixed

Improved FunPack Scene Builder database rows so long words and phrases show their full text as a hover hint, and double-click editing opens a wider multiline field with explicit OK/Cancel buttons.

[2.4.0] - 2026-05-14

Added

Added FunPack Scene Builder, a scene preset node that replaces FunPack Template Manager. It collects universal prompt phrase memory, lets users manually assign positive/negative scene phrases, passes the current LoRA stack through unchanged, and can auto-apply a saved scene from an intent prompt match.

Simplified FunPack Scene Builder so prompt and intent text are connection-only inputs, removed model-mode and per-block controls, and outputs only scene prompt data plus the pass-through FunPack LoRA stack instead of conditioning.

Added Learning mode to FunPack Scene Builder; it records connected prompt phrases into the selected refinement key's scene memory while passing positive prompt, negative prompt, and LoRA stack through unchanged. Refiner reset clears conditioning-delta learning while preserving the refinement key's Scene Builder memory.

Redesigned FunPack Scene Builder as a compact button-driven node with centered editor menus for scene name, mode, aliases, Positive Prompt, Negative Prompt, and Database controls. First use asks for a scene name before editing, connected prompts now teach useful words as well as phrase chunks, the editor refreshes the selected refinement-key database before opening, prompt editors highlight already-used chips, database words can be double-clicked for inline editing, and wildcard random choice is now a checkbox for adjacent entries instead of a text group.

Added searchable LoRA picking to FunPack Apply LoRA Weights. The compact row UI remains the primary workflow, and saved workflows still serialize through the existing lora_list JSON value.

Added optional clip_vision_output, source_image, and negative_prompt inputs to FunPack Video Refiner V2.

Added a final modified_negative conditioning output to FunPack Video Refiner V2. When negative repair has prompt text to encode and CLIP is connected, the node returns repaired negative conditioning; otherwise it returns an empty conditioning list.

Added advisory V2 vision context storage for source image dimensions, aspect ratio bucket, image fingerprint, CLIP Vision tensor summaries, and changed-image detection. Vision context is diagnostic only and is not blended into positive conditioning.

Added experimental early velocity bias capture/application controls to FunPack Hybrid Euler 2S Sampler, defaulting off.

Changed

Removed public registration for FunPack Template Manager. Use FunPack Scene Builder for new scene/preset workflows.

Updated V2 prompt repair so repaired phrases preserve stopwords and phrase text while still using filtered semantic tokens for matching and categorization.

Reduced repeated Refiner V2 CLIP model calls by caching category and phrase encodes within each run.

Updated negative repair to persist poorly rated or wrong-context tags and append them to future negative prompts before encoding negative conditioning.

[2.3.3] - 2026-05-08

Fixed

Fixed Refiner V2 so CLIP and pre-encoded positive_conditioning can both be optional inputs. When CLIP is connected, V2 keeps owning prompt encoding as before. When CLIP is not connected but positive_conditioning is connected, V2 accepts the finished Gemma3/LTX2 conditioning, uses the prompt for analysis, and loads only the Gemma3 tokenizer.

[2.3.2] - 2026-05-08

Added

Added Refiner V2 original-intent alignment memory. When user_intent_prompt stays the same but an enhancer produces different positive_prompt variants, the refiner now remembers intent-enhance pairs, which variants rated well, which original-intent phrases were omitted, and which enhancer-only phrases were rejected.

Fixed

Fixed Refiner V2 so learned original-intent omissions can be restored on later runs, while repeatedly rejected enhancer-only additions can be removed before encoding. Rejected enhancer-only full words and adjacent word pairs are stored as omit evidence for that original intent.

[2.3.1] - 2026-05-08

Fixed

Fixed Refiner V2 Prompt Repair so missing/wrong ratings only repair from the current prompt or explicit user intent, instead of pulling unrelated learned favorite actions, details, quality cues, camera moves, or styles from memory.

Fixed Prompt Repair memory matching so the same word with different neighboring prompt context is treated as different evidence.

Fixed vague raw user intent handling so prompts like Figure it out let the enhanced positive_prompt drive repair matching when available.

[2.3.0] - 2026-05-08

Added

Added Wrong appearance rating to FunPack Video Refiner V2 for outputs contaminated by remembered clothing, character, subject, or background concepts.

Added FunPack Refinement Key Loader, with a selectable key dropdown, create-on-load behavior, and browser-side JSON import/export buttons.

Added a Discord-friendly Refiner V2 quick guide for new users.

Changed

Updated Refiner V2 Prompt Repair so it only auto-adds safe repair concepts such as action, camera, details, quality, and style. Appearance, subject/character, and environment/background concepts are now blocked from Prompt Repair.

Updated I'm Feeling Lucky in Refiner V2 so appearance, subject/character, and environment/background memory is not auto-injected unless the user explicitly includes that phrase in the current prompt.

Updated legacy Void/Lucky token-bank selection to skip appearance, subject/character, and environment/background tokens.

Updated Refiner V2 and FunPack Apply LoRA Weights so both can accept a linked refinement key from FunPack Refinement Key Loader.

Fixed

Fixed appearance bleed-over where highly liked clothing or character tags could reappear in unrelated image-to-video prompts.

[2.2.1] - 2026-05-07

Fixed

Fixed FunPack Video Refiner V2 prompt phrase categorization so environment and appearance descriptions are not pulled into action learning by generic -ing or -ed words.

Updated Refiner V2 category similarity blending so CLIP category comparisons only help uncertain phrases instead of overriding strong local action, camera, appearance, environment, quality, or detail anchors.

Fixed FunPack Video Refiner V2 so prompt-enhancer refusal text like "I'm sorry, I cannot help..." is passed through without being saved into prompt history, phrase memory, or future learning targets.

Improved FunPack Video Refiner V2 training data output with clearer sections and extra line breaks for run state, learning, prompt analysis, adaptation, guidance, and LoRA diagnostics.

Updated FunPack Video Refiner V2 to remember liked action/detail phrase clusters with their neighbors and use those ordered clusters before weaker ngram or token memory when repairing missing axes.

Added Wrong details, Wrong action, and Wrong details + action ratings for good-looking videos that do not match the requested intent; these preserve satisfied quality/composition signals while marking the mismatched action/detail context for repair.

[2.2.0] - 2026-05-07

Added

Added FunPack Video Refiner V2, a simplified prompt-owned refiner that accepts positive_prompt and a connected CLIP, owns prompt encoding internally, learns from ratings, and returns refined positive conditioning plus diagnostics.

Added FunPack Template Manager, a preset node for storing prompts, activation words, refinement keys, sigma schedules, and FunPack LoRA stacks with import/export support.

Added I'm Feeling Lucky mode to FunPack Video Refiner V2. It works as a preference composer that can inject learned user-preferred actions, camera moves, details, and styles even when the current prompt is vague.

Changed

Updated LTX per-block LoRA loading so supported stacks now compare LoRA block fingerprints across the whole stack and apply type-aware conflict balancing before patches are loaded.

Fixed FunPack Hybrid Euler 2S Sampler restart timing so restart_trigger_pct is respected across the full sigma schedule instead of being clamped to the Euler-to-2S quality transition.

Improved FunPack LoRA Loader rerun performance by caching recently used raw LoRA files, model-mapped LoRA patches, and per-block fingerprint analysis.

Reworked FunPack Video Refiner V2 ratings around explicit missing-axis signals: Perfect, single missing axes, paired missing axes, and Awful.

Removed the Refiner V2 mode input. V2 now accepts whatever connected CLIP the workflow provides and stores state in a CLIP-owned namespace.

Renamed visible Refiner and LoRA intent from concept to action. Old Missing concept ratings and old concept LoRA rows are still accepted as compatibility aliases, but V2 stores and displays action.

Updated I'm Feeling Lucky in Refiner V2 so Lucky only composes prompt text when enabled. When disabled, it may train memory from rated runs but does not compose or alter output.

Fixed

Fixed I'm Feeling Lucky token-bank learning for changing prompt/conditioning workflows by falling back to prompt-order token placement when exact tokenizer position matching cannot find enough words.

Fixed I'm Feeling Lucky rating attribution for changing prompts so ratings update the previous prompt's learned tokens while the current prompt seeds new neutral discovery tokens.

Updated I'm Feeling Lucky filtering to learn poor adjacent token pairs instead of refusing individual tokens outright.

Updated I'm Feeling Lucky with uncapped token, pair, and context memory so it can learn which concepts belong together and call strong missing neighbors when prompt anchors are present.

Fixed I'm Feeling Lucky composition order so the current generation uses already-learned memory first, then seeds current prompt tokens for future runs.

Fixed I'm Feeling Lucky memory-first output so vague or empty incoming conditioning can use the longest compatible learned conditioning canvas instead of being limited to the current prompt's shape/content.

Added an optional clip input to FunPack Video Refiner so I'm Feeling Lucky can compose a learned prompt, re-encode it through the connected CLIP/Gemma text encoder, and refine from that freshly tokenized conditioning.

Improved I'm Feeling Lucky runtime by selecting learned conditioning canvases from saved tensor metadata before decoding, capping CLIP/Gemma Lucky prompts to a practical per-run concept count, and decoding only the token vectors selected for the current generation.

Reduced redundant I'm Feeling Lucky work by keeping Lucky runs in one stable memory history, skipping normal prompt-variant conditioning scans while Lucky is active, validating large Lucky memories once per loaded session, and updating context relationships locally instead of writing all-to-all token graphs every run.

Fixed I'm Feeling Lucky CLIP/Gemma re-encode crashes when the encoded Lucky prompt has a different sequence length than the incoming conditioning by resizing the refinement delta before applying it.

Updated I'm Feeling Lucky CLIP/Gemma prompt composition to preserve learned comma/semicolon-separated concept phrases instead of emitting loose word lists when phrase memory is available.

Added Lucky phrase placement memory so learned prompt phrases remember their rated order positions and CLIP/Gemma Lucky prompts can reassemble phrases into a more coherent prompt order instead of sentence salad.

Fixed I'm Feeling Lucky bootstrap learning so sessions that start with Lucky enabled now create a real discovery history entry, seed prompt tokens/phrases, and can learn from ratings without first running the classic refinement loop.

Updated Lucky memory so normal non-Lucky runs still seed reusable token, phrase, context, and placement memory for later Lucky runs.

Updated all missing-axis ratings so Missing details, Missing concept, Missing quality, and paired missing ratings now mark prompt tokens as wanted-but-underrepresented instead of weak neutral feedback; repeated missing feedback reserves Lucky composition room for those tokens and their compatible neighbours.

Fixed Lucky diagnostics so the collapsed Lucky memory stream reports real Lucky update counts and learned memory size instead of implying the session is still prompt 1 out of 1.

Removed

Removed the old public FunPack Video Refiner node, the FunPackGemmaEmbeddingRefiner compatibility alias, and FunPack Save Refinement Latent from the registered node list.

Removed sigma refinement, latent refinement, manual scheduler controls, and feedback-question inputs from the active Refiner workflow. These systems are not part of Refiner V2.

[2.1.3] - 2026-04-24

Changed

FunPack Apply LoRA Weights now has more user-friendly, compact UI.

FunPack Video Refiner now has updated logic to work more stable when provided different prompts and conditioning with each new generation.

[2.1.1] - 2026-04-24

Added

Added -Just forget it- as a Video Refiner rating. Use it when a generation failed for reasons that should not be learned from, such as a broken reference, bad seed, or workflow mistake.

Added category feedback questions for prompt phrases that the refiner cannot confidently classify. The answer scale is general, concept, style, quality, character, and details.

Added a CLIP Vision output combiner node for workflows that need one combined CLIP Vision output from multiple inputs.

Changed

Updated the Video Refiner rating categories so feedback can separate missing details, missing concept, missing quality, and fully failed output instead of treating all bad results the same way.

Reduced repeated category feedback prompts after the user has already answered enough about the same concept.

Refreshed README and refiner docs for 2.1.1.

Fixed

Fixed LoRA weight row restore order when workflows are loaded.

[2.1.0] - 2026-04-23

Added

Added FunPack Apply LoRA Weights and FunPack LoRA Loader, a prompt-exact LoRA weight workflow designed to work with FunPack Video Refiner.

Added FunPack Save Refinement Latent, which stores latent tensor bundles by refinement key for optional latent refinement in FunPack Video Refiner.

Added hidden LTX per-block LoRA redistribution for supported ltx2 model stacks. The UI still exposes normal LoRA weights, while the loader derives per-block strengths from the LoRA patch magnitudes when the model and LoRA layout support it.

The new workflow uses base LoRA weights on the first run for a prompt, then lets the refiner save prompt-specific suggested LoRA weights into its existing JSON state for later runs.

Changed

Renamed the visible refiner title from FunPack Gemma Embedding Refiner to FunPack Video Refiner. The old node key is still available as a compatibility alias.

Split the old single funpack.py implementation into focused modules:

• conditioning.py
• samplers.py
• image_processing.py
• model_management.py

funpack.py remains as a compatibility re-export for older imports.

Updated FunPack Video Refiner so it can accept a FunPack LoRA stack and save next-run model LoRA weight suggestions based on prompt concepts, LoRA type hints, and user ratings.

Updated FunPack Video Refiner with optional latent input/output refinement. If no matching saved latent exists and both latent input and output are connected, the input latent is saved as the first reference and passed through unchanged.

Updated prompt analysis so quoted speech and backslash-wrapped phrases can be protected as whole prompt units.

Documentation

Documented unintended and edge-case usage for the new refiner workflow, including disconnected latent paths, saved-latent-only runs, wrong LTX audio/AV latent connections, exact-prompt LoRA lookup behavior, base-weight mismatch behavior, zero-weight LoRA skipping, and unsupported per-block fallback behavior.

[1.3.3] - 2026-04-22

Changed

Expanded /docs so every node in funpack.py now has matching documentation, and refreshed the existing node docs to match the current inputs and outputs.

[1.3.2] - 2026-04-19

Changed

Changed the core logic of Self-Refiner. Removed obsolete nodes.

[1.3.0 & 1.3.1] - 2026-04-18

Changed

Added new nodes - User Rating and Gemma Self-Refinement for LTX2.3 video workflows.

Fixed

Device type mismatch in new nodes.

[1.2.3] - 2026-01-30

Fixed

Fixed Transformers library error when running Prompt Enhancer and Story Writer nodes.

[1.2.2] - 2026-01-26

Changed

Changed the logic of processing sequences in Story Writer node. Now doesn't append full instructions and previous context to previous messages with each loop iteration, now fully replaces messages with a system prompt and sequence history without appending.

[1.2.1] - 2026-01-24

Added

Added experimental LoRA recommendation feature and Sanity Check features to Story Writer node.

[1.2.0] - 2026-01-23

Added

Added new Story Writer node, based on existing Prompt Enhancer. It generates up to 5 prompts one after another, based on either user's prompt directly, or on the story generated from the user's prompt.

[1.1.0] - 2026-01-02

Added

Added Creative Template and Lorebook Enhancer nodes. The Creative Template is a wildcard-based node that replaces given keywords in the template with ones provided by user. Lorebook Enhancer is a node that takes SillyTavern format .json lorebooks and enhances your prompt by adding required knowledge.

[1.0.0] - 2026-01-01

Initial release on Comfy Registry.