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>
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>
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>
Removed three tests that exclusively tested negative conditioning/prompt
functionality which was removed in 2.5.0:
- test_refiner_v2_negative_advisor_repairs_negative_prompt_and_exports_one_string
- test_refiner_v2_returns_empty_negative_conditioning_when_negative_blank
- test_negative_prompt_repair_adds_poorly_rated_tags_persistently
Fixed six tests with stale assertions caused by API changes since they
were written: updated return-value unpack positions after negative output
was removed, dropped CONDITIONING-owned/CLIP-owned status checks that no
longer appear in any output, dropped Gemma3 tokenizer status check that
is only present in the error path now, updated Learning section header
name, and updated RETURN_NAMES assertion to check by value not position.
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>
Full mode now runs two sequential LLM calls per generation:
Pass 1 (analysis): a focused prompt asks the model to identify exactly
what needs to change — what to add, what to remove, what to fix — and
output a single DIAGNOSTIC line. Both user feedback and feedback history
are present. Max tokens is halved since only one line is needed.
Pass 2 (repair): builds on pass 1 by including the analysis result as
"Analysis of what needs to change:" before the task instruction. The
model applies the analysis rather than having to simultaneously diagnose
and rewrite in a single response.
Only diagnostics mode runs only pass 1, stores the analysis in
advisor_feedback_history as an advisor-sourced entry (labelled
"Advisor note:" in future history context), and returns without
modifying the prompt — making it useful as a genuine preview step
before switching to Full mode.
Only prompt mode runs only pass 2 directly (no analysis overhead),
unchanged from before.
Both passes receive feedback_prompt and feedback_history.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Advisor context is now minimal and feedback-first:
- User feedback (current run, placed first when present)
- Current prompt
- User intent
- Previous encoded prompt (anchor for feedback — so 'he held her head'
has a referent)
- Suggestions from memory (repair candidates, max 5)
- Past feedback history (last 10 entries, most recent first)
Removed from context: rating label, axis codes, plain-English axis
translation, encoded-vs-original prompt duplication, advisor-poisoning
note, previous advisor diagnostic, perfect example, V2 recommendations,
source image text note (image is fed directly to vision-capable models
via the existing tokenizer image= path)
New persistent store: global_state["advisor_feedback_history"] — up to
10 entries, each {feedback, rating, iteration}. Only populated when
feedback_prompt is non-empty. Updated at the end of each run so the
current run's feedback is available for future runs, not the current.
History format: "Rating: user's exact feedback text" e.g.
"1. Missing details: I asked for smoke but it didn't appear."
"2. Wrong action: he was supposed to hold her hand, not her head."
Removed dead helpers: _v2_find_perfect_example_for_intent,
_v2_build_v2_recommendations, _v2_axes_to_plain_english,
_AXIS_DESCRIPTIONS — none used by advisor context any more.
Cleaned up _v2_prompt_advisor and _v2_negative_prompt_advisor
signatures: removed rating_label, phrases, perfect_example,
v2_recommendations, has_image params; added feedback_history.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The advisor prompt was flooded with internal refiner state that means
nothing to a language model: phrase category tags, raw axis code labels,
redundant recommendations, and duplicate encoded/original prompt fields.
With 15+ fields of noisy context, explicit user feedback buried at the
bottom was competing with everything else for the model's attention.
Changes:
- Phrase analysis removed entirely from advisor context
- Axis labels translated to plain English descriptions via _v2_axes_to_
plain_english: "missing action axis" → "The previous output was missing:
main action, movement, motion direction"
- V2 recommendations field removed (was a third copy of the same axis/
candidate information)
- Encoded prompt only shown when it actually differs from the original
- Repair candidates capped to 5 and framed as "Consider adding:" rather
than a raw list
- Negative advisor prompt receives the same treatment
- Negative prompt length rejection raised from 900 to 1200 chars
- feedback_prompt placed first in user context in both advisors, before
all other fields
- System prompt OVERRIDE block injected when feedback is present
- Protected-category and intent-distance validation bypassed when user
feedback is provided
- is_perfect analysis-only block and intent-change guard also bypassed
when feedback is present
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Remove hardcoded Gemma chat tokens; advisor prompts now use plain
structured text compatible with any CLIP text generator
- Raise advisor generation limit from 384 to 800 tokens (internal)
- Repair candidates are now passed as suggestions to the advisor before
being applied; rule-based repair applies only if the advisor is off or
produces no valid output
- Add optional feedback_prompt input for user-provided natural-language
feedback, injected at highest priority in the advisor system prompt
- Add Prompt only execution mode: full prompt shaping with conditioning
vectors passed through unchanged
- Rename advisor modes to Off / Only diagnostics / Only prompt / Full;
Only prompt suppresses the diagnostic line for clean output
- Advisor detects when its own previous output caused a bad rating and
notes this explicitly in the next system prompt
- Inject perfect-rated prompt example into advisor system prompt when
one exists for the current intent family
- Inject V2 phrase-memory recommendations into advisor system prompt;
user feedback takes priority over these
- Guard against stale rating attribution when user intent changes between
runs (body similarity < 0.50 skips repair application)
- Advisor no longer skips on Perfect rating; runs in analysis-only mode
to capture what worked
- Previous advisor diagnostic is fed back into the next run's system
prompt, closing the learning loop
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
