Ethanfel
69c1d6deb4
Describe mode now produces a single coherent, internally-consistent canonical scene description (paragraph + per-axis spec, written to canonical_reference in the report). Compare gains an optional reference_description input: when set, it anchors on that fixed text and shows only the generated image (no swap) — so the reference side never drifts or self-contradicts across iterations; only the generated image is re-described each turn. agent_bridge gains --ref-desc / --ref-desc-file (reads the describe report's canonical_reference). Docs + example workflow updated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
123 lines
7.0 KiB
Markdown
123 lines
7.0 KiB
Markdown
# ComfyUI-Prompt-Calibratror
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A **fully local** prompt calibration loop for ComfyUI. A vision-language model
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(Qwen3-VL) judges how close a *generated* image is to a *reference* image and
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returns a structured score + per-axis difference analysis, which is used to
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**calibrate the prompt-generation method** ([ComfyUI-Prompt-Builder](../ComfyUI-Prompt-Builder))
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until the generated image matches the reference.
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> Full design rationale, controller options, and VLM-as-judge variance mitigations
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> are in **[docs/METHODOLOGY.md](docs/METHODOLOGY.md)**. The controller is an **external
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> CLI agent** that drives ComfyUI via its HTTP API — see **[docs/AGENT_LOOP.md](docs/AGENT_LOOP.md)**.
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## Nodes & tools
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| Component | What it is |
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| `Qwen3-VL Image Judge (Calibrator)` | scores generated vs reference, writes analysis to disk for the agent |
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| `SxCP External Prompt (Receptor)` | stable injection point; the agent sets `prompt/negative/seed` here per queue |
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| `agent_bridge.py` | one CLI call = one iteration (inject → `POST /prompt` → wait → print analysis JSON) |
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## The "vllm node": `Qwen3-VL Image Judge (Calibrator)`
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The core node (`nodes/qwen_judge.py`). It reuses the standard transformers Qwen3-VL
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inference plumbing (same approach as
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[ComfyUI-QwenVL-MultiImage](https://github.com/hardik-uppal/ComfyUI-QwenVL-MultiImage)
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— the recommended reuse base) but **forces strict JSON output** so an automated loop
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can act on it.
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**Inputs**
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| name | type | default | notes |
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| `reference_image` | IMAGE | — | the target |
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| `mode` | compare / describe | compare | `describe` = first pass over the reference only → caption + target spec (seeds the prompt). `compare` = score ref vs generated |
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| `generated_image` | IMAGE (optional) | — | the candidate to score (required for `compare`, ignored for `describe`) |
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| `model_path` | STRING | `/media/p5/qwen3vl_4b_abliterated_comfy_convert/hf_bf16` | local dir, **HF repo id** (`org/name`), or alias (`30b-a3b` / `8b` / `4b`) |
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| `precision` | bf16 / fp16 / fp8 / nf4 | bf16 | `nf4` = 4-bit (run the 30B judge on 32 GB); `fp8` with the `hf_fp8` copy |
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| `axes` | STRING | ~20 axes (identity, body, wardrobe, action, affect, camera, render) | scored/described axes; granular for explicit content. Edit to taste |
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| `max_new_tokens` | INT | 1024 | |
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| `temperature` | FLOAT | 0.0 | 0 = greedy/repeatable |
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| `swap_eval` | BOOL | true | run twice with images swapped, average → cuts position bias |
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| `keep_loaded` | BOOL | true | cache weights across loop iterations |
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| `auto_download` | BOOL | true | if `model_path` is a repo id/alias and not local, fetch it from HF into `models/prompt_generator/` |
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**Auto-download:** set `model_path` to `30b-a3b` (alias) or any `org/name` repo id and leave
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`auto_download` on — the node snapshot-downloads it on first run (into ComfyUI's
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`models/prompt_generator/<name>`) and reuses the local copy afterward. Local paths and the
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default skip download entirely.
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**Outputs**
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| name | type | use |
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| `overall_score` | FLOAT 0..1 | compare: mean verdict (computed here, not by the model). describe: `1.0` placeholder |
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| `axis_scores_json` | STRING (JSON) | compare: per-axis `{verdict, ref, gen}` (verdict = match/partial/mismatch). describe: `{axis: value}` |
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| `analysis` | STRING | compare: header (`overall, N mismatches`) + axes worst-first (`VERDICT ref:[…] gen:[…]`). describe: the `caption` |
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| `raw` | STRING | raw model output (both passes if `swap_eval`) |
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| `report_path` | STRING | path to the written `calib_<tag>.json` (carries `mismatch_count`) |
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## Install
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```bash
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cd /media/p5/Comfyui/custom_nodes
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ln -s /media/p5/ComfyUI-Prompt-Calibratror . # or git clone
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/media/p5/Comfyui/venv/bin/pip install -r /media/p5/ComfyUI-Prompt-Calibratror/requirements.txt
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```
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The node defaults to the **huihui-ai Qwen3-VL-4B-Instruct abliterated** weights already
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converted at `/media/p5/qwen3vl_4b_abliterated_comfy_convert/` so it runs out of the box
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(the abliterated/uncensored variant won't refuse to analyze adult imagery, which would
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otherwise break the loop).
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**Recommended upgrade (latest Qwen VL + uncensored, fits 32 GB):**
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[`huihui-ai/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated`](https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated)
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— MoE (3B active, fast), run at `precision=nf4` (~18 GB). The node auto-detects the MoE
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class. An easier middle ground is the **8B** abliterated at `bf16` (~17 GB, no quantization).
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Qwen3.5-VL abliterated isn't out yet (Qwen3.5 abliterated builds are text-only so far);
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Gemma-3-27B-it abliterated (4-bit) is a viable non-Qwen alternative. See
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[docs/METHODOLOGY.md](docs/METHODOLOGY.md#model-sizing-on-32-gb-rtx-5090--abliterated-latest-qwen-vl).
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## Loop sketch
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```
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Prompt-Builder (SxCP) ──prompt──▶ T2I (SDXL/Flux/Krea2) ──image──▶ Qwen3-VL Image Judge
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▲ │
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└──────── knob overrides ◀── Controller ◀── overall_score + diff ┘
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```
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Use the Prompt-Builder **For-Loop Start/End + Accumulator** nodes to drive iterations and
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route `overall_score` into the stop condition. Controller options (greedy hill-climb →
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black-box optimizer → LLM-in-the-loop) are in the methodology doc.
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## End-to-end loop
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1. Run ComfyUI with `--listen`, install this node pack, put your reference at `ComfyUI/input/reference.png`.
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2. **First pass (describe):** the judge looks at the reference alone and emits **one canonical
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scene description** (coherent paragraph + per-axis target spec) to seed the prompt *and*
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anchor the loop:
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```bash
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python agent_bridge.py --mode describe --workflow workflow/workflow_describe_api.json \
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--run-tag seed --analysis-dir /media/p5/Comfyui/output/calibrator
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```
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3. **Compare loop:** load `workflow/workflow_api.json` (SDXL `waiIllustriousSDXL_v160` example —
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swap the checkpoint for Flux/Krea as needed) and iterate, following `docs/CALIBRATION_POLICY.md`.
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Pass `--ref-desc-file` so compare anchors on the canonical reference (the `ref` side stays
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fixed; only the generated image is re-read each turn):
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```bash
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python agent_bridge.py --workflow workflow/workflow_api.json \
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--prompt "<description from step 2, then calibrated>" \
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--ref-desc-file /media/p5/Comfyui/output/calibrator/calib_seed.json \
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--run-tag iter001 --analysis-dir /media/p5/Comfyui/output/calibrator
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```
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stdout = the analysis JSON (`{verdict, ref, gen}` per axis) → agent steers toward `ref` → next iteration.
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## Status
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- [x] Methodology + node selection (`docs/METHODOLOGY.md`)
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- [x] Qwen3-VL Image Judge node — `describe` (first pass) + `compare` (scoring), swap-eval, file report
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- [x] Agent-driven architecture (`docs/AGENT_LOOP.md`) — Receptor node + `agent_bridge.py` (`--mode`)
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- [x] Example workflows: `workflow_describe_api.json` (first pass) + `workflow_api.json` (compare loop)
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- [x] Agent calibration policy (`docs/CALIBRATION_POLICY.md`)
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- [ ] Optional: structured-config receptor (carry Prompt-Builder knobs instead of a flat string)
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