Ethanfel
959ec70065
The local VLM now only observes and scores; correction is left to the stronger external agent. Each axis reports the target value (ref), the current value (gen) and the closeness (score) — the target/current/distance an agent needs to calibrate. Expanded to ~20 granular axes (identity/body/wardrobe/action/affect/ camera/render) so the action cluster stays discriminative for explicit content. swap_eval now inverts ref/gen of the swapped pass; diff summary sorts worst-first; default max_new_tokens 1024. Docs aligned. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
87 lines
4.4 KiB
Markdown
87 lines
4.4 KiB
Markdown
# Agent-driven calibration loop
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The controller is an **external CLI agent**, not an in-graph node. ComfyUI is the
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execution environment (prompt receptor → T2I → VLM judge); the agent is the brain that
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reads the analysis, calibrates the prompt generator, and queues the next iteration.
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```
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CLI AGENT (controller / brain) COMFYUI (execution, running with --listen)
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─────────────────────────────── ──────────────────────────────────────────
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1. build/calibrate a prompt
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2. agent_bridge.py --prompt ... ───POST /prompt──► CalibratorPromptReceptor (injection point)
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│ prompt / negative / seed
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▼
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T2I (SDXL / Flux / Krea2)
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│ generated image
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▼
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Qwen3-VL Image Judge
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│ writes calib_<tag>.json + latest.json
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3. poll /history/{id} (bridge does this) ◄───────────┘
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4. read report JSON (overall_score,
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per-axis score + ref/gen values)
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5. steer prompt toward ref on worst axes
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└──► go to 1 until overall_score ≥ target
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```
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## Why API-driven, not file-watch
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A passive "watch a file and auto-run" receptor is fragile in ComfyUI (no native file
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watcher / auto-queue, and prompt↔image↔analysis can desync). Driving `POST /prompt`
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instead makes every iteration **synchronous and ordered** — one `prompt_id` ties the
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prompt, the image, and the analysis together. The receptor node is still the clean
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injection point; the agent just overrides its widgets per queue. (The receptor *also*
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supports a `source_file` for file-first workflows if you ever want it.)
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## The three pieces
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| Piece | Role |
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|---|---|
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| `CalibratorPromptReceptor` (`SxCP External Prompt (Receptor)`) | Stable node the agent injects `prompt/negative/seed` into. Feeds the sampler. |
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| `QwenVLImageJudge` (`Qwen3-VL Image Judge (Calibrator)`) | Scores generated vs reference; writes `calib_<run_tag>.json`, `latest.json`, `calib_<run_tag>.md` to `report_dir`. |
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| `agent_bridge.py` | One CLI call = one iteration: inject prompt → queue → wait → print the analysis JSON to stdout. Stdlib only. |
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## One iteration (what the agent runs)
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```bash
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python agent_bridge.py \
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--server 127.0.0.1:8188 \
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--workflow workflow_api.json \
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--prompt "1 woman, red lingerie, bedroom, full body, warm rim light" \
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--negative "blurry, deformed" \
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--seed 12345 \
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--run-tag iter003 \
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--analysis-dir /media/p5/Comfyui/output/calibrator
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```
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Stdout (captured by the agent) is the report:
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```json
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{
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"run_tag": "iter003",
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"overall_score": 0.62,
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"axes": {
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"position": {"score": 0.40, "ref": "doggy style", "gen": "missionary"},
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"clothing_state": {"score": 0.85, "ref": "red lace lingerie", "gen": "plain bra"}
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},
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"prompt_used": "...",
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"_prompt_id": "…", "_report_path": "…/calib_iter003.json"
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}
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```
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## Agent calibration policy (suggested)
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For the lowest-scoring axes, the agent rewrites that axis's prompt wording to match its
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`ref` value (the target), regenerates, and keeps changes that raise `overall_score`
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(greedy per-axis hill-climb). The local model supplies no fixes — the agent owns the
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correction. Keep the **T2I seed fixed** while searching so the score reflects the prompt,
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not sampler noise; vary the seed only once near target. Stop at `overall_score ≥ target`
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(e.g. 0.85) or a max-iteration budget. Full policy: **[CALIBRATION_POLICY.md](CALIBRATION_POLICY.md)**.
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## Setup checklist
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1. Run ComfyUI with `--listen` (so the bridge can POST). Install this node pack.
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2. Build a workflow with: `CalibratorPromptReceptor` → (Prompt-Builder formatting, optional) → T2I → `QwenVLImageJudge` (feed the **reference** image into `reference_image`, the T2I output into `generated_image`).
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3. Set the Judge's `report_dir` to a known path; pass the same path as `--analysis-dir`.
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4. Export the workflow in **API format** (`workflow_api.json`).
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5. Drive it from the agent with `agent_bridge.py`, once per iteration.
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