FunPackStudio combines Refinement Key, Scene Builder, Refiner V2,
Advisor LLM, LoRA loading, and Conditioning Adjust under a single node
with a tabbed popup editor. Rating widget stays on the node face;
everything else is in the popup.
Python:
- FunPackStudio.run() chains all subsystems: scene builder prompt
construction, advisor LLM load (using shared cache), LoRA loading via
FunPackLoraLoader, FunPackVideoRefinerV2.refine_v2(), and
FunPackConditioningAdjust.adjust(). No code duplication.
- Added optional _seed parameter to FunPackVideoRefinerV2.refine_v2()
so Studio can generate a seed and output it for wiring to samplers.
- Added /funpack/available_loras endpoint for the LoRA picker.
JS (funpack_studio.js):
- Tabbed popup: Session, Scene, Refiner, Advisor, LoRA, Adjustments.
- Session: refinement key, reset toggle, scene builder mode selector.
- Scene: scene load/save, phrase bank from memory, positive prompt
editor (active when scene builder is not Pass-through).
- Refiner: all V2 settings, feedback prompt, intent override.
- Advisor: LLM enable toggle, model path, dtype.
- LoRA: dynamic list of LoRAs with name/type/model-weight/clip-weight,
loaded from /funpack/available_loras.
- Adjustments: phrase/strength pairs with session phrase bank.
- Settings serialized as JSON in hidden studio_settings widget.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Inserts between Refiner V2 and the sampler. Takes CONDITIONING + CLIP,
lets the user specify phrase/strength pairs in a floating popup panel.
For each active pair: encode the phrase, compute unit-norm direction from
base conditioning toward phrase conditioning (in pooled space), apply at
user-set strength. Positive = push toward that phrase, negative = away.
Bypasses V2's category classification entirely - adjustments target the
exact phrase the user typed, no mis-categorization possible.
Popup hides the raw JSON widget, shows "Edit Adjustments" button on the
node face with a compact summary of active adjustments. Close saves.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Strip <think>...</think> blocks in decode so Qwen3 chain-of-thought
doesn't pollute the repaired prompt and cause validation to reject it
- Pass enable_thinking kwarg to apply_chat_template (with TypeError
fallback for models that don't support it); propagate thinking flag
through tokenize -> generate so the token budget is expanded by 2048
when thinking mode is active, giving room for reasoning + response
- Return 1.0 from _v2_text_semantic_similarity when the clip has no
encode_from_tokens_scheduled - generation-only clips can't compute
similarity so they shouldn't be penalized with a 0.0 semantic gate
- Add FunPackAdvisorLLM to standalone (non-package) imports in
__init__.py so tests can run without the full ComfyUI runtime
- Update three stale advisor tests: 5-value _v2_prompt_advisor unpack,
"Encoded as:" prefix change, 6-return-value unpacking after negative
conditioning was removed
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
FunPackAdvisorLLM:
- Loads any HuggingFace CausalLM via AutoModelForCausalLM.from_pretrained
- Wraps it in _FunPackAdvisorLLMWrapper which exposes tokenize/generate/decode
matching the interface _v2_generate_advisor_text already uses
- tokenize() accepts system_prompt kwarg, applies the model's own chat
template via apply_chat_template, absorbs unsupported kwargs silently
- generate() uses max_new_tokens (not max_length) so prompt length does
not eat into the generation budget
- Model cached in _FUNPACK_ADVISOR_LLM_CACHE by (path, dtype) - no
reload on repeated runs
- Returns CLIP type so it plugs directly into advisor_clip input
- Default: huihui-ai/Huihui-Qwen3-8B-abliterated-v2
Session reset fix:
- _v2_empty_state was missing intent_expansion_memory, session_source_mean_count,
liked_dir, bad_dir - these survived reset via setdefault on next run.
Added all four to the empty state so reset is complete.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
New sampler targeting LTX2.3 distilled LoRA and similar 4-8 step
flow-matching distilled video models. Three improvements over plain Euler:
Adams-Bashforth 2-step multistep (order=2, default):
Extrapolates the denoised direction from two consecutive steps using
variable-step-size AB2 coefficients: (1 + r/2) * d_curr - (r/2) * d_prev
where r = h_current / h_previous. Gives second-order accuracy across the
large sigma jumps typical of distilled schedules at zero extra model-call cost.
Heun predictor-corrector on final steps (final_correction_steps=1, default):
Replaces the last N steps with a Heun pass: Euler predictor to sigma_next,
second model call at sigma_next for the corrector direction, averaged update.
Costs one extra model call per corrected step and significantly improves
sharpness and detail in the steps that define the final output.
Multistep history is invalidated after each Heun step.
Optional s_noise (default 0.0 = deterministic):
Small ancestral-style noise injection (scaled by sigma_up) for diversity.
Distilled models are trained as pure ODE; any non-zero value is an
intentional deviation. Keep below 0.15 if used at all.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
