bvandeusen
d5f29f7056
Better region PROPOSERS feeding the existing crop→SigLIP→max-over-bag heads (no change to the learned-tagging approach; no per-tag cost — propose once, embed each region, all heads in one matmul). - detectors.py: lazy ultralytics YOLO wrapper, each proposer independently optional + guarded (a bad weight spec / inference error self-disables that one, logged, never breaks the worker). Weights resolve from an ultralytics name | http(s) URL | "hf_repo::file", cached under HF_HOME. NMS merge so a figure two detectors both find collapses to one crop. - worker: figure boxes = imgutils detect_person ∪ general COCO person (merged) → CCIP + concept (anime + Western/realistic coverage); booru_yolo anatomy components (head/cat-head/anatomy/…) → concept crops; comic panels → kind= 'panel' concept crops. Capped per frame (MAX_COMPONENTS/MAX_PANELS). - config + compose: PERSON_WEIGHTS (default yolo11n.pt, works OOB), ANATOMY_WEIGHTS + PANEL_WEIGHTS (operator sets booru_yolo URL + mosesb panel hf::file; empty = off). ultralytics added to requirements. - backend: image_region 'kind' doc notes 'panel'; no migration (free String, and the bag scorer keys on a non-null siglip_embedding, not the kind, so any SigLIP region joins the bag automatically). Agent is outside CI — py-compiled here; operator tests on the GPU and checks Western-vs-anime crop quality via /api/ccip observability. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
55 lines
3.0 KiB
Python
55 lines
3.0 KiB
Python
"""Agent config, all from env (the control container is configured at run)."""
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import os
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from dataclasses import dataclass
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@dataclass
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class Config:
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fc_url: str # base URL of the FabledCurator web service
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token: str # the bearer token from Settings → Tagging → GPU agent
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agent_id: str # identifies this agent's leases
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batch_size: int # jobs a worker leases per round
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concurrency: int # INITIAL parallel workers (tunable live from the UI)
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ccip_model: str # imgutils CCIP model name ("" → imgutils default)
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detector_level: str # imgutils person-detector level: n|s|m|x
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poll_idle_seconds: float # wait between empty leases
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embed_dtype: str # torch dtype for the crop embedder: float16|float32
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embed_model_override: str # force a SigLIP-family model ("" → use the one
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# the server announces in the lease)
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auto_start: bool # start the worker pool on boot (so a container restart
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# resumes processing without anyone clicking Start)
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# Crop PROPOSERS (extra YOLO detectors that say where to crop). Each weight
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# spec is an ultralytics name | http(s) URL | "hf_repo::file" ("" = off).
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person_weights: str # general COCO person detector (Western/realistic figs)
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person_conf: float
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anatomy_weights: str # booru_yolo anime/furry/NSFW components
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anatomy_conf: float
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panel_weights: str # comic-panel detector
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panel_conf: float
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max_components: int # cap anatomy component crops per frame
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max_panels: int # cap panel crops per frame
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@classmethod
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def from_env(cls) -> "Config":
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return cls(
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fc_url=os.environ.get("FC_URL", "http://localhost:8000").rstrip("/"),
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token=os.environ.get("FC_TOKEN", ""),
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agent_id=os.environ.get("AGENT_ID", "desktop-agent"),
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batch_size=int(os.environ.get("BATCH_SIZE", "4")),
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concurrency=int(os.environ.get("CONCURRENCY", "1")),
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ccip_model=os.environ.get("CCIP_MODEL", ""),
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detector_level=os.environ.get("DETECTOR_LEVEL", "m"),
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poll_idle_seconds=float(os.environ.get("POLL_IDLE_SECONDS", "10")),
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embed_dtype=os.environ.get("SIGLIP_DTYPE", "float16"),
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embed_model_override=os.environ.get("EMBED_MODEL_NAME", ""),
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auto_start=os.environ.get("AUTO_START", "").lower() in ("1", "true", "yes"),
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person_weights=os.environ.get("PERSON_WEIGHTS", "yolo11n.pt"),
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person_conf=float(os.environ.get("PERSON_CONF", "0.35")),
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anatomy_weights=os.environ.get("ANATOMY_WEIGHTS", ""),
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anatomy_conf=float(os.environ.get("ANATOMY_CONF", "0.30")),
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panel_weights=os.environ.get("PANEL_WEIGHTS", ""),
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panel_conf=float(os.environ.get("PANEL_CONF", "0.30")),
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max_components=int(os.environ.get("MAX_COMPONENTS", "8")),
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max_panels=int(os.environ.get("MAX_PANELS", "8")),
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)
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