olivv-cs-ComfyUI-FunPack/CHANGELOG.md

Changelog

[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.