ComfyUI-Prompt-Calibratror

A fully local prompt calibration loop for ComfyUI. A vision-language model (Qwen3-VL) judges how close a generated image is to a reference image and returns a structured score + per-axis difference analysis, which is used to calibrate the prompt-generation method (ComfyUI-Prompt-Builder) until the generated image matches the reference.

Full design rationale, controller options, and VLM-as-judge variance mitigations are in docs/METHODOLOGY.md. The controller is an external CLI agent that drives ComfyUI via its HTTP API — see docs/AGENT_LOOP.md.

Nodes & tools

Component	What it is
Qwen3-VL Image Judge (Calibrator)	scores generated vs reference, writes analysis to disk for the agent
External Prompt (Receptor)	stable injection point; the agent sets prompt/negative/seed here per queue
agent_bridge.py	one CLI call = one iteration (inject → POST /prompt → wait → print analysis JSON)

The "vllm node": Qwen3-VL Image Judge (Calibrator)

The core node (nodes/qwen_judge.py). It reuses the standard transformers Qwen3-VL inference plumbing (same approach as ComfyUI-QwenVL-MultiImage — the recommended reuse base) but forces strict JSON output so an automated loop can act on it.

Inputs

name	type	default	notes
reference_image	IMAGE	—	the target
mode	compare / describe / chat	compare	compare = score ref vs generated. describe = first pass over the reference → caption + target spec. chat = general VLM: your system_prompt + user_prompt over the image(s) → raw text
profile	general / oral / penetration / handjob / solo	general	analysis profile — act-specialized axis set; the act-critical axes are distance/proximity-aware (e.g. mouth_genital_distance) so magnitude isn't hidden behind a coarse label
generated_image	IMAGE (optional)	—	the candidate to score (required for compare, ignored for describe)
model_select	dropdown (model name)	4B local	which judge (transformers/safetensors, auto-downloaded): Qwen3-VL 4B/8B/30B-A3B, Qwen3.5-9B, Qwen3.6-27B/35B-A3B (newer, natively multimodal). Param size shown in the label
precision	bf16 / fp8 / nf4	bf16	the quant — applies to the selected model (VRAM table below)
model_path	STRING	"" (empty)	manual override of the dropdown — local dir, HF repo id, or alias (8b/30b-a3b/3.5-9b/3.6-27b/3.6-35b). Empty = use model_select
axes	STRING input	—	(socket) optional override of the profile's axis set; wire a text node or leave unconnected to use profile
max_new_tokens	INT	3072	reasoning models (Qwen3.5/3.6) need room; raise it if the verdict gets cut off
enable_thinking	BOOL	true	let the model reason before judging. Keep on for accurate verdicts — off makes reasoning models rubber-stamp match. Off is faster
temperature	FLOAT	0.0	0 = greedy/repeatable
swap_eval	BOOL	true	run twice with images swapped, average → cuts position bias
keep_loaded	BOOL	true	cache weights across loop iterations
auto_download	BOOL	true	if model_path is a repo id/alias and not local, fetch it from HF into models/prompt_generator/
system_prompt	STRING input	—	(socket) chat mode: wire your system prompt from a text node
user_prompt	STRING input	—	(socket) chat mode: wire your instruction from a text node
reference_description	STRING input	—	(socket) compare: wire describe's canonical output here to anchor the reference

Auto-download: set model_path to 30b-a3b (alias) or any org/name repo id and leave auto_download on — the node snapshot-downloads it on first run (into ComfyUI's models/prompt_generator/<name>) and reuses the local copy afterward. Local paths and the default skip download entirely.

General VLM (chat mode): set mode=chat and the node becomes a plain vision-language node — feed an image (and optionally a second), write your own system_prompt/user_prompt, and read the model's text from the analysis output. Reuses the same model dropdown, quant, and auto-download as the judge, so it's a one-node abliterated VLM for captioning, tagging, Q&A, prompt-from-image, etc. (CLI: agent_bridge.py --mode chat --user-prompt "..."). Set json_output=true for JSON-producing system prompts — it extracts the JSON object from the reply (stripping any reasoning, prose, or ```fences) and returns it clean and re-serialized (falls back to raw text if none parses). Works even with enable_thinking on.

Audio-guided prompts (Audio Prompt Guide)

For audio-driven video (e.g. LTX prompt-relay timed to music), the AudioPromptGuide node turns an audio clip + your free-text motion notes into inputs the vision model can use:

• waveform_image (IMAGE) — the energy envelope with beat markers + segment boundaries, so the model can see the audio's shape.
• audio_summary (STRING) — duration, tempo/beats (if librosa is installed), a per-segment energy breakdown with 8n+1-snapped frame counts and stage hints (establish→build→peak→settle), plus your notes.

Wire it into the judge node in chat mode. To give the model both your reference subject image and the waveform, use the two image slots (chat mode sees both):

your subject image ───────────────────────────────────► Judge.reference_image  (IMAGE 1)
LoadAudio ─► Audio Prompt Guide ─┬─ waveform_image ─► Judge.generated_image (IMAGE 2)
   (notes: "seg3: fast on the drop")  └─ audio_summary  ─► Judge.user_prompt
   (prompts/ltx_audio_relay_system_prompt.txt) ─────────► Judge.system_prompt
                                        Judge (mode=chat, json_output=true) ─► LTX beats JSON

The audio-aware system prompt is in prompts/ltx_audio_relay_system_prompt.txt — it tells the model IMAGE 1 = appearance, IMAGE 2 = energy shape, and to build one beat per segment (copying its frames), with segment NOTES overriding the energy default.

The model then gets the audio's timing/energy + your motion notes and writes beat durations, camera moves, and deltas that escalate with the music. librosa is optional (BPM/beats); without it you still get the energy envelope + segments.

Notes can target a segment (the note overrides that segment's energy default, and is drawn on the waveform). Syntax in the notes box, one per line:

seg1: slow dreamy intro          # by segment number (also "1:" or "S1:")
10s-15s: explosive drop          # by time range (also "12.3s:")
cinematic, moody grade           # no prefix = global (applies throughout)

Interactive: Audio Wave + Segments

Same outputs, but with an in-node waveform widget: upload an audio clip, play it, and click the waveform to place segment boundaries — click to add a split, drag to move, double-click a segment for its note, shift/right-click to delete. The boundaries + notes are saved to a hidden segments_json and drive the segmentation (falls back to auto-split / the notes syntax if none placed). Outputs waveform_image, audio_summary, and audio. Needs torchaudio/soundfile/librosa to load the file (torchaudio usually ships with torch).

Performance / speed

This node runs models through transformers .generate() — the simplest path, but the slowest: no PagedAttention / continuous batching / fused kernels like vLLM, SGLang, or llama.cpp. With enable_thinking on, the model also emits thousands of reasoning tokens (each token = one forward pass) — that's the cost of accurate verdicts. Levers, fastest first:

• swap_eval = false — halves the work (one reasoned pass instead of two). Biggest free win.
• flash-attention — the node auto-uses flash_attention_2 if flash-attn is installed, else sdpa. pip install flash-attn for the speedup.
• smaller model / fewer axes — Qwen3.5-9B bf16 over the 27B/35B; trim axes or use a focused profile.
• enable_thinking = false — much faster, but reasoning models then rubber-stamp match; only for quick smoke tests.
• avoid nf4 for speed — bitsandbytes dequantizes every step; bf16/fp8 decode faster (nf4 is for fitting the big models, not speed).

The real fix for production speed is a different inference engine (vLLM/SGLang serve these models many× faster) — a heavier, separate-server setup not built into this node.

Outputs

name	type	use
overall_score	FLOAT 0..1	compare: mean verdict (computed here, not by the model). describe: 1.0 placeholder
axis_scores_json	STRING (JSON)	compare: per-axis {verdict, ref, gen} (verdict = match/partial/mismatch). describe: {axis: value}
analysis	STRING	compare: header (overall, N mismatches) + axes worst-first (VERDICT ref:[…] gen:[…]). describe: the caption. chat: the model's response
raw	STRING	raw model output (both passes if swap_eval)
report_path	STRING	path to the written calib_<tag>.json (carries mismatch_count)

Install

cd /media/p5/Comfyui/custom_nodes
ln -s /media/p5/ComfyUI-Prompt-Calibratror .     # or git clone
/media/p5/Comfyui/venv/bin/pip install -r /media/p5/ComfyUI-Prompt-Calibratror/requirements.txt

The node defaults to the huihui-ai Qwen3-VL-4B-Instruct abliterated weights already converted at /media/p5/qwen3vl_4b_abliterated_comfy_convert/ so it runs out of the box (the abliterated/uncensored variant won't refuse to analyze adult imagery, which would otherwise break the loop).

Pick a model in model_select and a quant in precision. All are abliterated, multimodal safetensors (transformers), auto-downloaded. The newer Qwen3.5/3.6 are natively multimodal (need a recent transformers — they load via AutoModelForMultimodalLM).

VRAM by quant on the RTX 5090 32 GB (✅ fits / ⚠ tight / ❌):

model	bf16	fp8	nf4	note
Qwen3-VL-4B (local)	✅ ~9	✅ ~5	✅ ~3	fast, weak
Qwen3-VL-8B	✅ ~17	✅ ~9	✅ ~6	solid, fast
Qwen3.5-9B	✅ ~20	✅ ~10	✅ ~7	newer, fast — recommended
Qwen3-VL-30B-A3B (MoE)	❌ ~62	⚠ ~31	✅ ~18	nf4 slow
Qwen3.6-27B (dense)	❌ ~56	⚠ ~28	✅ ~16	nf4 slow, strong
Qwen3.6-35B-A3B (MoE)	❌ ~70	❌	✅ ~20	nf4 slow, top quality

nf4 (bitsandbytes) fits the big ones but is slow (dequant overhead) — that's the bottleneck, not the model. fp8 is fast but only when a real fp8 checkpoint exists (the local 4B has one; precision=fp8 on a bf16-only repo won't quantize). For speed + recency, Qwen3.5-9B at bf16 is the sweet spot. See docs/METHODOLOGY.md.

Loop sketch

Prompt-Builder (SxCP) ──prompt──▶ T2I (SDXL/Flux/Krea2) ──image──▶ Qwen3-VL Image Judge
        ▲                                                                │
        └──────── knob overrides ◀── Controller ◀── overall_score + diff ┘

Use the Prompt-Builder For-Loop Start/End + Accumulator nodes to drive iterations and route overall_score into the stop condition. Controller options (greedy hill-climb → black-box optimizer → LLM-in-the-loop) are in the methodology doc.

End-to-end loop

1. Run ComfyUI with --listen, install this node pack, put your reference at ComfyUI/input/reference.png.
2. First pass (describe): the judge looks at the reference alone and emits one canonical scene description (coherent paragraph + per-axis target spec) to seed the prompt and anchor the loop:

   python agent_bridge.py --mode describe --workflow workflow/workflow_describe_api.json \
     --run-tag seed --analysis-dir /media/p5/Comfyui/output/calibrator
   

3. Compare loop: load workflow/workflow_api.json (SDXL waiIllustriousSDXL_v160 example — swap the checkpoint for Flux/Krea as needed) and iterate, following docs/CALIBRATION_POLICY.md. Pass --ref-desc-file so compare anchors on the canonical reference (the ref side stays fixed; only the generated image is re-read each turn):

   python agent_bridge.py --workflow workflow/workflow_api.json \
     --prompt "<description from step 2, then calibrated>" \
     --ref-desc-file /media/p5/Comfyui/output/calibrator/calib_seed.json \
     --run-tag iter001 --analysis-dir /media/p5/Comfyui/output/calibrator
   

   stdout = the analysis JSON ({verdict, ref, gen} per axis) → agent steers toward ref → next iteration.

Status

• Methodology + node selection (docs/METHODOLOGY.md)
• Qwen3-VL Image Judge node — describe (first pass) + compare (scoring), swap-eval, file report
• Agent-driven architecture (docs/AGENT_LOOP.md) — Receptor node + agent_bridge.py (--mode)
• Example workflows: workflow_describe_api.json (first pass) + workflow_api.json (compare loop)
• Agent calibration policy (docs/CALIBRATION_POLICY.md)
• Optional: structured-config receptor (carry Prompt-Builder knobs instead of a flat string)