433 lines
17 KiB
Python
433 lines
17 KiB
Python
"""LoraManager integration API routes for PromptManager."""
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import json
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import os
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from pathlib import Path
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from aiohttp import web
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class LoraIntegrationMixin:
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"""Mixin providing LoraManager detection, scanning, and trigger word endpoints."""
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def _register_lora_routes(self, routes):
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@routes.get("/prompt_manager/lora/detect")
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async def lora_detect_route(request):
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return await self.lora_detect(request)
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@routes.get("/prompt_manager/lora/status")
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async def lora_status_route(request):
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return await self.lora_status(request)
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@routes.post("/prompt_manager/lora/enable")
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async def lora_enable_route(request):
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return await self.lora_enable(request)
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@routes.post("/prompt_manager/lora/scan")
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async def lora_scan_route(request):
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return await self.lora_scan(request)
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@routes.get("/prompt_manager/lora/trigger-words")
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async def lora_trigger_words_route(request):
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return await self.lora_trigger_words(request)
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@routes.post("/prompt_manager/lora/refresh-cache")
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async def lora_refresh_cache_route(request):
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return await self.lora_refresh_cache(request)
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# ── Detection ────────────────────────────────────────────────────
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async def lora_detect(self, request):
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"""Auto-detect LoraManager installation."""
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try:
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from ..lora_utils import detect_lora_manager
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path = await self._run_in_executor(detect_lora_manager)
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return web.json_response(
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{
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"success": True,
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"detected": path is not None,
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"path": path or "",
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}
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)
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except Exception as e:
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self.logger.error(f"LoraManager detection failed: {e}")
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return web.json_response({"success": False, "error": str(e)}, status=500)
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# ── Status ───────────────────────────────────────────────────────
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async def lora_status(self, request):
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"""Get current LoraManager integration status."""
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try:
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from ..config import IntegrationConfig
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from ..lora_utils import detect_lora_manager, get_trigger_cache
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config = IntegrationConfig.get_config()["lora_manager"]
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cache = get_trigger_cache()
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# Check if the configured path is still valid
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detected_path = await self._run_in_executor(
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detect_lora_manager, config.get("path", "")
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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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"enabled": config["enabled"],
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"path": config["path"],
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"trigger_words_enabled": config["trigger_words_enabled"],
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"has_civitai_api_key": bool(config.get("civitai_api_key")),
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"detected": detected_path is not None,
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"detected_path": detected_path or "",
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"trigger_cache_loaded": cache.is_loaded,
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}
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)
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except Exception as e:
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self.logger.error(f"LoraManager status check failed: {e}")
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return web.json_response({"success": False, "error": str(e)}, status=500)
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# ── Enable / Disable ─────────────────────────────────────────────
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async def lora_enable(self, request):
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"""Enable or disable LoraManager integration and save to config.json."""
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try:
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data = await request.json()
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enabled = data.get("enabled", False)
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path = data.get("path", "")
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trigger_words = data.get("trigger_words_enabled", False)
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from ..config import IntegrationConfig, PromptManagerConfig
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from ..lora_utils import detect_lora_manager, get_trigger_cache
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# The key is never echoed back to the UI, so a blank or absent
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# field means "unchanged"; clearing is an explicit request.
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civitai_key = IntegrationConfig.CIVITAI_API_KEY
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submitted_key = data.get("civitai_api_key")
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if submitted_key is not None and not isinstance(submitted_key, str):
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return web.json_response(
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{"success": False, "error": "civitai_api_key must be a string"},
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status=400,
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)
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if data.get("clear_civitai_api_key"):
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civitai_key = ""
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elif submitted_key and submitted_key.strip():
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civitai_key = submitted_key.strip()
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# If enabling, validate the path
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if enabled:
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resolved = await self._run_in_executor(detect_lora_manager, path)
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if not resolved:
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return web.json_response(
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{
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"success": False,
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"error": "LoraManager not found at the specified path",
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},
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status=400,
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)
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path = resolved
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# Update in-memory config
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IntegrationConfig.LORA_MANAGER_ENABLED = enabled
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IntegrationConfig.LORA_MANAGER_PATH = path
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IntegrationConfig.LORA_TRIGGER_WORDS_ENABLED = trigger_words
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IntegrationConfig.CIVITAI_API_KEY = civitai_key
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# Persist to config.json
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config_dir = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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config_file = os.path.join(config_dir, "config.json")
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PromptManagerConfig.save_to_file(config_file)
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# Load trigger word cache if enabling
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cache = get_trigger_cache()
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if enabled and trigger_words and path:
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count = await self._run_in_executor(cache.load, path)
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self.logger.info(f"Trigger word cache loaded: {count} LoRAs")
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elif not enabled:
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cache.clear()
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return web.json_response(
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{
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"success": True,
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"enabled": enabled,
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"path": path,
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"trigger_words_enabled": trigger_words,
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}
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)
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except Exception as e:
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self.logger.error(f"LoraManager enable/disable failed: {e}")
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return web.json_response({"success": False, "error": str(e)}, status=500)
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# ── Scan LoRA example images ─────────────────────────────────────
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async def lora_scan(self, request):
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"""Scan LoraManager metadata and import LoRA info + preview images.
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Streams progress as SSE, matching the existing scan pattern.
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"""
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try:
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from ..config import IntegrationConfig
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from ..lora_utils import (
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download_civitai_images,
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find_lora_directories,
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get_example_prompt_from_metadata,
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get_lora_image_cache_dir,
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get_preview_images_from_metadata,
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get_trigger_words_from_metadata,
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get_model_name_from_metadata,
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read_lora_metadata,
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)
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if not IntegrationConfig.LORA_MANAGER_ENABLED:
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return web.json_response(
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{
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"success": False,
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"error": "LoraManager integration is not enabled",
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},
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status=400,
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)
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lm_path = IntegrationConfig.LORA_MANAGER_PATH
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if not lm_path:
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return web.json_response(
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{"success": False, "error": "LoraManager path not configured"},
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status=400,
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)
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response = web.StreamResponse(
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status=200,
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reason="OK",
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headers={"Content-Type": "text/event-stream"},
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)
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await response.prepare(request)
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async def send_progress(data):
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line = f"data: {json.dumps(data)}\n\n"
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await response.write(line.encode("utf-8"))
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await send_progress(
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{
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"type": "progress",
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"status": "Clearing previous lora-manager imports...",
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"progress": 0,
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}
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)
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# Clear previous imports so reimport is always clean
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await self._run_in_executor(
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self.db.delete_prompts_by_category, "lora-manager"
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)
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await send_progress(
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{
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"type": "progress",
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"status": "Finding LoRA directories...",
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"progress": 2,
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}
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)
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lora_dirs = await self._run_in_executor(find_lora_directories, lm_path)
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# Collect all metadata files
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meta_files = []
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for d in lora_dirs:
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dir_path = Path(d)
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meta_files.extend(dir_path.rglob("*.metadata.json"))
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total = len(meta_files)
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imported = 0
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skipped = 0
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await send_progress(
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{
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"type": "progress",
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"status": f"Found {total} LoRA metadata files",
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"progress": 5,
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"total": total,
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}
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)
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cache_dir = get_lora_image_cache_dir()
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for i, meta_file in enumerate(meta_files):
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metadata = await self._run_in_executor(read_lora_metadata, meta_file)
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if not metadata:
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skipped += 1
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continue
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model_name = get_model_name_from_metadata(metadata)
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trigger_words = get_trigger_words_from_metadata(metadata)
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# Collect all images: local previews + downloaded civitai examples
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preview_paths = await self._run_in_executor(
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get_preview_images_from_metadata, metadata, meta_file
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)
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civitai_paths = await self._run_in_executor(
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download_civitai_images,
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metadata,
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meta_file,
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cache_dir,
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IntegrationConfig.CIVITAI_API_KEY,
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)
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# Merge, local first, dedup
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seen = set(preview_paths)
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all_images = list(preview_paths)
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for cp in civitai_paths:
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if cp not in seen:
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all_images.append(cp)
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seen.add(cp)
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# Build prompt text: prefer example prompt, then model name
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example_prompt = get_example_prompt_from_metadata(metadata)
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prompt_text = example_prompt or model_name
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# Build tags
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tags = ["lora-manager", f"lora:{model_name}"]
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tags.extend(trigger_words)
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# Save to database via existing mechanism
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try:
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import hashlib
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prompt_hash = hashlib.sha256(
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prompt_text.strip().lower().encode("utf-8")
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).hexdigest()
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existing = await self._run_in_executor(
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self.db.get_prompt_by_hash, prompt_hash
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)
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if existing:
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# Link all images
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for pp in all_images:
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await self._run_in_executor(
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self.db.link_image_to_prompt,
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existing["id"],
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pp,
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)
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skipped += 1
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else:
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prompt_id = await self._run_in_executor(
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self.db.save_prompt,
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prompt_text,
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"lora-manager", # category
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tags,
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None, # rating
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None, # notes
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prompt_hash,
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)
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if prompt_id:
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for pp in all_images:
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await self._run_in_executor(
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self.db.link_image_to_prompt,
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prompt_id,
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pp,
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)
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imported += 1
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else:
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skipped += 1
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except Exception as e:
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self.logger.warning(f"Failed to import LoRA {model_name}: {e}")
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skipped += 1
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# Progress update for every LoRA
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progress = int(5 + (90 * (i + 1) / max(total, 1)))
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img_count = len(all_images)
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status = f"{model_name}"
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if img_count:
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status += f" ({img_count} images)"
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await send_progress(
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{
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"type": "progress",
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"status": status,
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"progress": progress,
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"processed": i + 1,
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"imported": imported,
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"skipped": skipped,
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}
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)
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await send_progress(
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{
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"type": "complete",
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"progress": 100,
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"total": total,
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"imported": imported,
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"skipped": skipped,
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}
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)
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await response.write_eof()
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return response
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except Exception as e:
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self.logger.error(f"LoRA scan failed: {e}")
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return web.json_response({"success": False, "error": str(e)}, status=500)
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# ── Trigger word endpoints ───────────────────────────────────────
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async def lora_trigger_words(self, request):
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"""Look up trigger words for a specific LoRA name."""
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try:
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from ..config import IntegrationConfig
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from ..lora_utils import get_trigger_cache
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if not IntegrationConfig.LORA_MANAGER_ENABLED:
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return web.json_response(
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{"success": False, "error": "LoraManager integration not enabled"},
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status=400,
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)
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lora_name = request.query.get("name", "")
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if not lora_name:
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return web.json_response(
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{"success": False, "error": "Missing 'name' query parameter"},
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status=400,
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)
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cache = get_trigger_cache()
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if not cache.is_loaded:
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lm_path = IntegrationConfig.LORA_MANAGER_PATH
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if lm_path:
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await self._run_in_executor(cache.load, lm_path)
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words = cache.get_trigger_words(lora_name)
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return web.json_response(
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{"success": True, "lora": lora_name, "trigger_words": words}
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)
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except Exception as e:
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self.logger.error(f"Trigger word lookup failed: {e}")
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return web.json_response({"success": False, "error": str(e)}, status=500)
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async def lora_refresh_cache(self, request):
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"""Force-refresh the trigger word cache from disk."""
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try:
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from ..config import IntegrationConfig
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from ..lora_utils import get_trigger_cache
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if not IntegrationConfig.LORA_MANAGER_ENABLED:
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return web.json_response(
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{"success": False, "error": "LoraManager integration not enabled"},
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status=400,
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)
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lm_path = IntegrationConfig.LORA_MANAGER_PATH
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if not lm_path:
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return web.json_response(
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{"success": False, "error": "LoraManager path not configured"},
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status=400,
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)
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cache = get_trigger_cache()
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count = await self._run_in_executor(cache.load, lm_path)
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return web.json_response(
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{"success": True, "loras_with_trigger_words": count}
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)
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except Exception as e:
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self.logger.error(f"Trigger cache refresh failed: {e}")
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return web.json_response({"success": False, "error": str(e)}, status=500)
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