ComfyUI-Prompt-Calibrator/README.md

# 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](../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](docs/METHODOLOGY.md)**. The controller is an **external
> CLI agent** that drives ComfyUI via its HTTP API — see **[docs/AGENT_LOOP.md](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 |
| `SxCP 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](https://github.com/hardik-uppal/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 |
| `generated_image` | IMAGE | — | the candidate to score |
| `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`) |
| `precision` | bf16 / fp16 / fp8 / nf4 | bf16 | `nf4` = 4-bit (run the 30B judge on 32 GB); `fp8` with the `hf_fp8` copy |
| `axes` | STRING | ~20 axes (identity, body, wardrobe, action, affect, camera, render) | scored axes; granular for explicit content. Edit to taste |
| `max_new_tokens` | INT | 512 | |
| `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/` |

**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.

**Outputs**

| name | type | use |
|---|---|---|
| `overall_score` | FLOAT 0..1 | loop stop-condition / objective |
| `axis_scores_json` | STRING (JSON) | per-axis `{score, ref, gen}` — target vs current, for the agent |
| `diff_analysis` | STRING | readable summary, worst axes first (`score  ref:[…] gen:[…]`) |
| `raw` | STRING | raw model output (both passes if `swap_eval`) |

## Install

```bash
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).

**Recommended upgrade (latest Qwen VL + uncensored, fits 32 GB):**
[`huihui-ai/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated`](https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated)
— MoE (3B active, fast), run at `precision=nf4` (~18 GB). The node auto-detects the MoE
class. An easier middle ground is the **8B** abliterated at `bf16` (~17 GB, no quantization).
Qwen3.5-VL abliterated isn't out yet (Qwen3.5 abliterated builds are text-only so far);
Gemma-3-27B-it abliterated (4-bit) is a viable non-Qwen alternative. See
[docs/METHODOLOGY.md](docs/METHODOLOGY.md#model-sizing-on-32-gb-rtx-5090--abliterated-latest-qwen-vl).

## 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. Load `workflow/workflow_api.json` (SDXL `waiIllustriousSDXL_v160` example — swap the checkpoint for Flux/Krea as needed).
3. Drive it from your agent following `docs/CALIBRATION_POLICY.md`:
   ```bash
   python agent_bridge.py --workflow workflow/workflow_api.json \
     --prompt "1 woman, red lingerie, bedroom, full body, warm light" \
     --run-tag iter001 --analysis-dir /media/p5/Comfyui/output/calibrator
   ```
   stdout = the analysis JSON → agent calibrates → next iteration.

## Status

- [x] Methodology + node selection (`docs/METHODOLOGY.md`)
- [x] Qwen3-VL Image Judge node (structured JSON scoring, swap-eval, model caching, file report)
- [x] Agent-driven architecture (`docs/AGENT_LOOP.md`) — Receptor node + `agent_bridge.py`
- [x] Example end-to-end workflow (`workflow/workflow_api.json`)
- [x] Agent calibration policy (`docs/CALIBRATION_POLICY.md`)
- [ ] Optional: structured-config receptor (carry Prompt-Builder knobs instead of a flat string)