DemonGatanjieuandClaude Opus 5.5
fd9e415123
A large window: one search over every kind (/ jumps to it), kinds on the left with picks and matches, a column by source (each custom node pack with a badge of its own colour, ComfyUI's own, paid API nodes), prompt origin or model type, a card grid drawn as it scrolls, the card in focus in full on the right, and the picks below with thumbnails and a prefer / only switch. A node card is the node in miniature, sockets in ComfyUI's colours; combos, workflows and models show their covers; prompts their text, including the prompts inside combos and workflows (combo:<name>, workflow:<name>), which get_toolbox now resolves. Vector icons, badges and the empty picture follow the theme. It opens at once: node types come from the page's own definitions, the rest in one answer (api/ai_toolbox_catalog.py) read ahead when a task header is drawn and kept a minute, refreshed behind an open window. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
87 lines
4.8 KiB
Python
87 lines
4.8 KiB
Python
"""Everything the materials picker (web/modules/ui_ai_toolbox.js) lists besides node types, in
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one answer (GET /anomalous/ai_toolbox?kind=all): combos with their cover and prompt, saved
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workflows with their cover, tags and prompts, prompts (the saved ones, and the ones inside
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combos and workflows, ids "combo:<name>" and "workflow:<name>"), and models with their cover,
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by the names and ids the AI's tools use. Read in one go and kept a few seconds, so the picker
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opens at once; node types come from the page's own node definitions. prompt_entries() also
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gives get_toolbox the text of picked prompts."""
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import threading
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import time
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from . import mcp_tools
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from .model_catalog import _collect_all_scan_models
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from .notebooks import _list_notebooks
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from .quick_index import compact_models
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from .recipe_store import _list_recipes, get_recipes_dir
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CACHE_SECONDS = 15
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MAX_TEXT = 1500
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MAX_THUMBNAIL = 60_000 # an inline thumbnail larger than this is left out; the cover asset or the default stands in
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_cache = {"at": 0.0, "data": None}
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_lock = threading.Lock()
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def _join(texts, limit=MAX_TEXT):
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text = "\n".join(str(item).strip() for item in texts or [] if str(item or "").strip())
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return text if len(text) <= limit else text[:limit] + "…"
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def _combos():
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out = []
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for note in _list_notebooks():
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data = note.get("data") or {}
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combo, loras = mcp_tools.combo_view(note)
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out.append({"name": combo["name"], "model": combo["main_model"], "loras": loras[:12], "base": combo.get("base_model") or "",
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"prompt": combo["prompt"], "structure": data.get("kind") == "nodes",
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"cover": (data.get("mainModel") or {}).get("preview_url") or "" if isinstance(data.get("mainModel"), dict) else "",
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"image": data.get("source_image") if isinstance(data.get("source_image"), dict) else None})
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return out
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def _workflows():
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out = []
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for recipe in _list_recipes(get_recipes_dir()):
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data = recipe.get("data") or {}
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params = data.get("params") if isinstance(data.get("params"), dict) else {}
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thumbnail = data.get("thumbnail") if isinstance(data.get("thumbnail"), str) and len(data["thumbnail"]) <= MAX_THUMBNAIL else None
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out.append({"name": data.get("name") or recipe["filename"], "filename": recipe["filename"], "tags": (data.get("tags") or [])[:8],
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"notes": str(data.get("notes") or "")[:300], "partial": data.get("workflow_scope") == "partial",
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"nodes": len(params.get("nodes") or []), "base": params.get("baseModel") or "",
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"prompt": _join(params.get("promptPositive")), "negative": _join(params.get("promptNegative"), 600),
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"cover_asset": (data.get("presentation") or {}).get("cover_asset_id"), "thumbnail": thumbnail,
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"image": data.get("source_image") if isinstance(data.get("source_image"), dict) else None,
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"saved": data.get("timestamp") or 0})
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return out
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def _models():
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out = []
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for model in compact_models(_collect_all_scan_models(1, 0).get("models") or []):
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place = f"{model['subfolder']}/{model['filename']}" if model["subfolder"] else model["filename"]
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out.append({"id": f"{model['type']}:{model['path_idx']}:{place}", "name": model["name"] or model["civitai_name"] or model["filename"],
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"file": model["filename"], "type": model["type"], "base": model["base"], "words": model["words"],
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"cover": model["preview_url"], "folder": model["subfolder"]})
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return out
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def prompt_entries(combos, workflows):
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"""The prompts the picker lists: saved ones (id = name), then those of combos and workflows."""
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entries = [{"id": prompt["name"], "name": prompt["name"], "source": "saved", "tags": prompt["tags"],
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"positive": prompt["positive"], "negative": prompt["negative"]} for prompt in mcp_tools.saved_prompts()]
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entries += [{"id": f"combo:{combo['name']}", "name": combo["name"], "source": "combo", "tags": [],
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"positive": combo["prompt"], "negative": ""} for combo in combos if combo["prompt"]]
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entries += [{"id": f"workflow:{flow['name']}", "name": flow["name"], "source": "workflow", "tags": flow["tags"],
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"positive": flow["prompt"], "negative": flow["negative"]} for flow in workflows if flow["prompt"] or flow["negative"]]
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return entries
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def everything(fresh=False):
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"""{combos, workflows, prompts, models}, read at most every CACHE_SECONDS."""
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with _lock:
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if fresh or _cache["data"] is None or time.monotonic() - _cache["at"] > CACHE_SECONDS:
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combos, workflows = _combos(), _workflows()
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_cache.update(at=time.monotonic(), data={"combos": combos, "workflows": workflows,
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"prompts": prompt_entries(combos, workflows), "models": _models()})
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return _cache["data"]
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