mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-09-20 18:51:26 -03:00
Will Miao
e0052cd237
The download modal's location step decided between checkpoint and unet roots using only the CivitAI file-type signal, while the backend also falls back to DIFFUSION_MODEL_BASE_MODELS. Models like Anima (file type "Model") were offered checkpoint roots in the UI even though use_default_paths would route them to the unet root. - Extract the two-tier decision into py/services/download_routing.py and reuse it in DownloadManager._execute_download - Add POST /api/lm/download/routing so the UI asks the backend for the routing decision; fall back to the local file-type check on failure - ModelVersionsTab: search both checkpoint and unet roots when resolving an existing version's download path
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
"""HTTP handler for download target routing decisions."""
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from __future__ import annotations
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import json
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import logging
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from aiohttp import web
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from ...services.download_routing import is_diffusion_model_download
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logger = logging.getLogger(__name__)
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class DownloadRoutingHandler:
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"""Expose the download-time checkpoint/diffusion-model routing decision.
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The web UI calls this when the user reaches the download location step
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so the root dropdown offers the same root set (checkpoint vs unet) that
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the download manager would pick for ``use_default_paths``.
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"""
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async def get_download_routing(self, request: web.Request) -> web.Response:
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try:
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payload = await request.json()
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except json.JSONDecodeError:
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return web.json_response(
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{"success": False, "error": "Invalid JSON payload"}, status=400
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)
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model_type = payload.get("model_type", "")
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base_model = payload.get("base_model") or ""
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file_types = payload.get("file_types") or []
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if not isinstance(model_type, str) or not model_type:
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return web.json_response(
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{"success": False, "error": "model_type is required"}, status=400
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)
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if not isinstance(base_model, str) or not isinstance(file_types, list):
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return web.json_response(
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{
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"success": False,
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"error": "base_model must be a string and file_types a list",
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},
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status=400,
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)
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is_diffusion = is_diffusion_model_download(
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model_type,
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file_types=(str(t) for t in file_types),
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base_model=base_model,
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)
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return web.json_response(
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{
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"success": True,
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"is_diffusion_model": is_diffusion,
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"root_kind": "unet" if is_diffusion else model_type,
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}
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)
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