FunPackSceneCutWindows: correct scene-cut approach for LTXAV and LTXV.
Instead of context windows (which slice x_in and fail for LTXAV's packed
format), this node patches optimized_attention at the attention level.
All frames denoised in one forward pass; self-attention is blocked across
scene boundaries (tokens in scene i can only attend to tokens in scene i).
Cross-attention to text conditioning is unchanged.
Works by: (1) FunPackSceneCutHandler reads num_scenes from conds in
should_use_context and returns False (no context windowing); (2) a
model_function_wrapper temporarily replaces optimized_attention with a
per-scene version before each model forward, then restores it.
FunPackSceneNoise: generates initial noise with a different seed per
temporal scene region. Pair with KSamplerAdvanced (add_noise=disable)
or alongside FunPackSceneCutWindows for additional bias toward scene
boundaries. Scene count auto-detected from positive conditioning entries.
Seed auto-detectable from Studio/Refiner V2 seed output.
FunPackContextTransitionWindows: kept for long-video windowed denoising
(non-LTXAV, standard-format latents only); description updated to note
LTXAV incompatibility.
Also in this commit:
- Phrase stacking fix: merged pure-transition segments with next segment
(direction was backwards - was appending to prev, now prepends to next)
- New phrases: 'the scene begins with', 'after a brief scene cut',
'as the scene transitions', 'in the next scene', 'the video concludes',
'another scene shows', 'later another scene shows', 'the final scene',
'the last scene'
- Add scene ordinals 'the final scene' / 'the last scene'
- Diagnostic log in should_use_context for context windows
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
New phrases: 'the scene begins with', 'after a brief scene cut',
'as the scene transitions', 'in the next scene', 'later another scene
shows', 'another scene shows', 'the video concludes'.
Stacking fix: when two transition phrases are adjacent ('after a brief
scene cut, in the next scene...'), the pure-transition intermediate
segment is prepended to the following segment rather than left as an
empty conditioning fragment. Fixed direction (was appending to previous,
now prepends to next).
Diagnostic log: context_transition.py now logs cond group sizes,
num_windows, detected dim, and x_in shape at INFO level on every
should_use_context call to confirm routing is working.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
conds = [uncond_list, cond_list] - conds[0] is the negative conditioning
which always has exactly 1 entry. Reading len(conds[0]) always gave
num_windows=1, so the schedule produced a single window covering all
frames, and split_conds_to_windows picked the middle scene's conditioning
for the entire video (center_ratio≈0.5 → region 1 of 3).
Fix: max(len(c) for c in conds) correctly reads the positive conditioning
(3 entries for 3 scenes) and creates 3 isolated windows.
Also added 'the final scene' and 'the last scene' to transition phrases.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
For each context window, apply:
window_i = general + contrast × (scene_i − general)
where general = the full-prompt conditioning (refined, with memory
applied). The character description cancels in the delta because it
appears in both scene_i and general; the push focuses on what is
unique to each scene (action, environment).
- contrast=1.0 → plain scene encoding (original behaviour)
- contrast=1.5 → default, 50% extra push toward scene content
- contrast=2.0 → classic contrastive (2×scene − general)
New parameter: transition_contrast (FLOAT, default 1.5, range 1.0-4.0)
on Refiner V2 and in Studio popup Refiner tab.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Transition phrase list: add 'the scene then cuts to', 'the scene cuts to',
'the scene transitions to', 'the scene fades to', 'the scene dissolves to',
and short forms ('the scene then cuts', 'the scene cuts').
encoded_prompts output: when split_by_transitions is active, append a
'Per-window prompts' section showing the exact text encoded for each
context window. Example:
Window 1: A woman in red, walking in a park
Window 2: A woman in red, then sitting on a bench
Window 3: A woman in red, suddenly she runs away
Also updated _v2_encode_per_window_conditionings to return (conditionings,
window_texts) tuple instead of conditionings only.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Each scene segment is exactly one window covering all its frames.
No sub-windowing, no overlap between scenes, no temporal carryover.
Character consistency is maintained through the shared character
description in each window's conditioning, not through frame bleed.
Also fixed _detect_temporal_dim min_size check: was using
context_overlap+1 which would be 1 with overlap=0, leaving size<=1
dims rejected but nothing else guaranteed to pass. Now simply >1.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Transition words ('then', 'suddenly', 'cut to', etc.) are now kept at
the START of the new scene's conditioning text rather than discarded.
They are valuable tokens that signal a scene change to the model.
Example: 'A woman in red, walking, then sitting on a bench, suddenly runs'
Window 0: 'A woman in red, walking'
Window 1: 'A woman in red, then sitting on a bench'
Window 2: 'A woman in red, suddenly runs'
Also added ordinal scene markers to the detection list:
'the first scene' ... 'the tenth scene'
'scene one' ... 'scene ten'
This lets users explicitly label scenes in the prompt.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Context Transition Windows: auto-detect temporal dim by scanning
x_in's axes for a size that's > overlap and <= 4096. Search order:
dim 2 (standard [B,C,T,H,W]), dim 1, dim 0 (LTXAV packs frames at 0),
then remaining dims. Skip windowing entirely when no suitable dim is
found (packed formats where no axis maps to frame count).
Studio JS: fix 'Split by transitions' toggle showing 'undefined' -
was appending splitToggle.container which doesn't exist; corrected
to splitToggle.wrap (the actual property returned by toggleEl).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
All configuration is now auto-computed at sampling time - no user-facing
settings except the optional total_frames INT. Window count is derived
from the number of conditioning entries Refiner/Studio outputs. Window
size is total_frames (or actual latent size) / num_windows. Boundaries
are placed at equal intervals. split_conds_to_windows always on.
Studio: move split_by_transitions from direct node input to the Refiner
tab in the popup editor. Removed direct input, added toggle + hint text.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Studio now exposes split_by_transitions as an optional BOOLEAN
input on the node face, giving direct control without editing
the raw settings JSON. The direct input takes precedence over
the popup JSON setting when connected.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Refiner V2 and Studio: new split_by_transitions option detects
transition words in the final prompt (then, suddenly, cut to, etc.),
extracts the character description (text before the first comma),
and encodes one conditioning per scene window with the character
description prepended to each segment. Returns a multi-entry
conditioning list that routes per-window via split_conds_to_windows.
Context Transition Windows: new total_frames optional input.
When > 0 and split_conds_to_windows is enabled, context_length
is auto-computed at sampling time as total_frames / num_cond_entries
so windows size themselves to match Refiner's segment count.
Also fixes pre-existing bugs: early error return in refine_v2 was
missing video_latent (7th return value), and test unpacking was stale.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Three bugs causing i2v latent to be lost:
1. eff_key and prev_reward were defined inside 'if patched_model is not
None' but used outside it. NameError was silently caught but could
cause unexpected behavior. Moved both definitions before the block.
2. load_and_apply_creativity_mask returned bare {'samples': ...} dropping
all other keys from the original latent (noise_mask, type, batch_index,
etc.). Now copies the full original dict and only replaces 'samples'.
3. Modified samples were left on CPU (detached earlier) while the original
latent samples may be on CUDA. Now moves modified tensor to match the
original device and dtype before returning.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two bugs:
1. 'LTXV' in 'LTXAV' == False - substring search silently missed LTXAV.
Fixed with explicit set membership: cls_name in {'LTXV', 'LTXAV'}.
2. model_config is on model.model (BaseModel), not on the ModelPatcher
itself - so the unet_config fallback path also never fired.
Fixed by reading inner.model_config instead of model.model_config.
LTX-2.3 (22b) loads as LTXAV. Both LTXV and LTXAV now correctly detected.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
refine_v2: initialise video_latent = latent instead of None so the connected
input passes through even when no key is set or mask computation is skipped.
Mask result only overwrites when it returns something non-None.
load_and_apply_creativity_mask: track explicit_latent separately from the
disk-loaded source. All early-exit and error paths return explicit_latent
(the connected input unchanged) instead of None, preserving the i2v or
previous-generation latent through the node when mask has nothing to add.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Sits between Perfect (1.0) and Missing details (0.35). For when the output
looks great and you'd keep it, but it took creative liberty with the prompt
(man grabbed woman's hand instead of the other way - still a hand grab, still
loved, but not word-for-word correct).
Behaviour vs Perfect:
- axis_feedback: same - no missing/wrong axes, repair cleared
- good_streak: increments (same as Perfect)
- liked_dir: updated (quality direction was good)
- liked_conditioning average: NOT updated (intent drift - only Perfect locks in)
- phrase memory: delta 0.35x vs Perfect's 0.55x; category weights +0.08 vs +0.13
- preferred context: base_delta 0.65 vs 1.0
- phrase forgiven_count: -0.10 vs -0.18
- LoRA weights: quality/style +0.44x, action/character +0.24x (vs Perfect's 0.55x flat)
- Attention anchors: blessed (composition was good regardless of prompt fidelity)
- Advisor: not treated as perfect execution - repair signal still possible
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Removed reward >= 0.8 early returns from build_creativity_mask and
load_and_apply_creativity_mask. The global floor formula already handles
high reward correctly: at 1.0 it produces 0.0 floor, leaving only gentle
spatial variance (0-8% of latent std). A Perfect-rated latent is the best
possible reference - discarding it was wrong.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
clear_refinement_data() removes temp attention maps, blessed attention
maps, and creativity latent for the refinement key. Called from
_v2_load_state() at the start of a reset before fresh state is written.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Action LoRAs can carry micro-movement details tied to the action itself
(subtle motion blur, fluid dynamics, small camera adjustments). Previously
they only responded to the action axis, so 'Missing details' ratings never
adjusted their weight. Now mirrors character type which already had both.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Removed from LORA_BLOCK_TYPE_PROFILES, LORA_TYPE_BLOCK_ZONES,
LORA_REFINER_TYPE_PROFILES, ACTION_LORA_TYPES, the validation
special-case, and the Studio JS LORA_TYPES dropdown.
normalize_lora_type() keeps the concept->action mapping for silent
backwards compatibility with any existing saved stacks.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- _is_ltx_model() checks inner model class name, unet_config image_model
key (ltxv/ltxav), and model_type string. build_enhancements() returns
the model unchanged with a console note when a non-LTX model is detected
(e.g. WAN2.2) - generation continues normally, only the LTX patches skip.
- Update model input tooltip in FunPackVideoRefinerV2 to state LTX-2/LTXAV
family requirement and pass-through behavior for other models.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
apply_model takes (x, t, **c) not (args_dict). Both wrapper call
sites were passing the whole dict as x, leaving t unfilled.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add ltx_enhancements.py with four automatic generation improvements
driven by the refiner rating system:
Technique 2 - Per-block attention temperature: sharper (colder) attention
on semantic anchor blocks 14/19 when quality/concept is missing; softer
on early blocks when details are missing. Capped at 0.72-1.22x range.
Technique 3 - Temporal RoPE style: frame_rate manipulation via
model_function_wrapper. Selector exposed in FunPackVideoRefinerV2 node
(optional input) and in Studio popup Refiner tab.
natural=no change, accelerate=+35% fps, decelerate=-28% fps,
loop/freeze=experimental coordinate tricks.
Technique 4 - Denoise creativity mask: spatial noise weighting derived
from latent variance; high-variance regions get more denoising freedom
scaled by reward. Built but applied by downstream latent consumers.
Technique 5 - Attention anchor transfer: hidden states at anchor blocks
14 and 19 are captured via patches_replace["dit"] on every run and saved
to refinements/attn_maps/temp_{key}.pt. When the user rates a run
Perfect, bless_attention_maps() promotes the temp file to a blessed file.
Subsequent runs inject the blessed maps at 5-28% strength scaled by
reward, transferring semantic structure from good outputs onto new runs.
All enhancements are composable with existing attn2 direction patches
and STG/PAG blocks via patches_replace composition.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add LORA_TYPE_BLOCK_ZONES: type-aware block zones that suppress bad-fit
blocks to 0.0 and mildly boost good-zone blocks per LoRA type
- Add SEMANTIC_ANCHOR_BLOCKS {14, 19}: confirmed semantic focal points
(PAG default=14, STG defaults=14,19) protected from all suppression
- Add _semantic_anchor_quality: uses LoRA energy share in blocks 14+19
as a quality signal, scaling all block weights 0.92-1.08x accordingly
- Stack priority: weaker LoRA keeps base scale on anchor blocks instead
of being damped, letting the dominant signal carry semantic content
- Fix range display: status line now shows effective weights (model_weight
* scale) instead of raw multipliers; adds zeroed= and anchor_q= fields
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Event delegation on the panel root catches every input/change event
from any field inside the popup and schedules a debounced save to the
studio_settings and adjustments widgets. Changes survive page refresh
without needing to click Close.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
renderSampler() now clears the body before rebuilding so changing the
sampler type replaces the settings instead of appending to them.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two new output pairs between seed and loss_graph:
- high_pass_sampler (SAMPLER) + high_pass_sigmas (SIGMAS)
- low_pass_sampler (SAMPLER) + low_pass_sigmas (SIGMAS)
Sampler tab in popup: per-pass selection of Hybrid Euler 2S, Distilled
Flow, or KSampler (standard ComfyUI fallback). Each type shows its own
settings fields. Motion pulse and velocity bias sections collapse when
mode is "off". Sigmas entered as comma-separated floats; empty means
rely on external sigmas input.
Python: _build_one_sampler() and _build_samplers() class methods handle
the instantiation and sigma parsing. Each sampler is built from the
same get_sampler() call as the standalone node.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When Scene Builder mode is not Pass-through, the Refiner tab shows a
blue banner explaining the positive prompt is built in the Scene tab
with a clickable link that switches to it. The intent override field is
grayed out and disabled since intent derives from the scene prompt.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add negative_prompt STRING input. Priority chain for negative output:
1. negative_conditioning (pre-encoded CONDITIONING) - pass through as-is
2. negative_prompt (text) encoded via CLIP - removes need for external CLIPTextEncode
3. Popup default negative_prompt field in Refiner tab
4. Empty list if nothing is provided
Negative prompt field added to Refiner tab in popup with override toggle.
Override system extended with negative_prompt key.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Inputs reordered per spec: model, clip, advisor_clip, positive_conditioning,
negative_conditioning, clip_vision_output, source_image, lora_stack,
positive_prompt, user_intent_prompt, feedback_prompt, refinement_key_input.
Outputs reordered: model, modified_positive, negative, seed, loss_graph,
status, training_info, encoded_prompts.
negative_conditioning: CONDITIONING input passed straight through to the
negative output unchanged. Empty list when not connected.
advisor_clip: external CLIP input now accepted. Takes precedence over the
LLM configured in the Advisor popup tab. Same pattern as other overridable
inputs.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LoRA pipeline is now fully internal to Studio:
1. FunPackApplyLoraWeights reads session weight suggestions for the
current prompt and builds a stack with adjusted model weights
2. FunPackLoraLoader applies the stack to model and clip
3. refine_v2 receives the LoRA-patched model and applies the attn2
direction patch on top
Removes the lora_stack return value - MODEL output already contains
everything (LoRA patches + direction injection). External lora_stack
input still accepted to bypass internal management entirely.
Adds mode (ltx2/wan) and per_block settings to the LoRA tab in popup,
stored in loras_config dict in studio_settings.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Moved from required widget to optional forceInput so it appears as a
connector like all other inputs, not as a text area widget.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add lora_stack (FUNPACK_LORA_STACK) as the 8th return value so the
active stack flows out for downstream use
- Remove positive_prompt from hidden widgets so the text area is visible
on the node face and can be connected as a link input
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Each input that has both a connected link and a popup value now has an
"Override" toggle below it (default off). Off = external input wins,
popup value is a fallback. On = popup value wins regardless.
Applies to: refinement_key (Session tab), feedback_prompt and
user_intent_prompt (Refiner tab). All other settings are popup-only
and unaffected.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Connected value overrides whatever is typed in the Refiner popup.
Useful for wiring dynamic feedback from another node.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
The node was extracting pooled_output from the conditioning meta dict,
which is None for LTX/Gemma3 text encoders. Adjustments were silently
skipped for every phrase.
Use conditioning.mean(dim=(0,1)) on the sequence tensor itself - same
approach V2 uses for all its conditioning memory operations. Phrase
vectors are encoded the same way (mean of their sequence tensor). The
accumulated direction shift is then applied as a uniform broadcast
across all sequence positions so the tensor shape stays intact.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
addCustomWidget failed silently in this ComfyUI version. Switch to
node.addWidget("button",...) which is natively supported. Also add the
same hideWidget retry timing pattern Scene Builder uses (rAF + two
setTimeout calls) so the adjustments text widget reliably disappears
after the node is created or reconfigured.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When refinement_key_input is connected (from FunPack Refinement Key
Loader), the popup fetches phrase_memory from that session via a new
/funpack/phrase_memory endpoint and shows all learned phrases as
clickable chips grouped by category. Click a chip to add it to the
adjustment list with a default strength of 0.1.
Search field filters phrases live. Phrases are sorted by evidence count
so the most-seen ones appear first within each category.
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>
Two issues prevented diagnostics from reliably appearing in Full mode:
1. The analysis prompt asked for "root cause of recurring patterns" which
is too vague for early sessions with no history. Now adapts: simple
per-run analysis when no session history exists, pattern analysis when
history is available.
2. When the LLM fails or produces empty output after thinking-token
stripping, analysis was silently "" and nothing was stored. Added a
rule-based fallback that builds a diagnostic from axis_feedback
(regressed/missing/wrong axes) so the history always accumulates
even when the LLM produces nothing useful.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Both _v2_apply_perfect_repair_phrases (injects phrases from previously
Perfect-rated runs) and _v2_emphasized_prompt (repeats existing phrases
matching missing axes) were running unconditionally regardless of the
prompt_repair toggle. With prompt_repair=False they should be no-ops -
the user has explicitly said they do not want the node modifying the
prompt text from memory.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When Pass 1 generates a diagnostic, append it to the system prompt of
Pass 2 so the LLM has the session root-cause analysis at the highest
priority level rather than only as a user-message field. The analysis
remains in the user message too for reference.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The diagnostic analysis pass now reasons over session history instead of
just the current prompt. It receives the axis feedback (what's missing,
regressed, resolved, wrong), current rating, and the accumulated session
history, and its output asks for root cause identification across runs
rather than per-run "what to fix" instructions.
Diagnostics are now recorded for all advisor modes that generate them
(Full and Only diagnostics), not only "Only diagnostics". Previously the
Full mode ran analysis but discarded the result without storing it, so
the history never built up and subsequent repairs had no session context
to draw on.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Added explicit rules against metaphors, prose, and emotional language.
Requires every output phrase to describe something a camera can directly
capture. Targets the tendency of instruction-tuned LLMs to produce
poetic rewrites that video diffusion models cannot interpret.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
