ae7ba961c1
- app.py: add subprocess import; add _ensure_mcp_repo() to clone/pull danbooru-mcp from https://git.liveaodh.com/aodhan/danbooru-mcp into tools/danbooru-mcp/ at startup; add ensure_mcp_server_running() which calls _ensure_mcp_repo() then starts the Docker container if not running; add GET /api/status/comfyui and GET /api/status/mcp health endpoints; fix call_llm() to retry up to 3 times on unexpected response format (KeyError/IndexError), logging the raw response and prompting the LLM to respond with valid JSON before each retry - templates/layout.html: add ComfyUI and MCP status dot indicators to navbar; add polling JS that checks both endpoints on load and every 30s - static/style.css: add .service-status, .status-dot, .status-ok, .status-error, .status-checking styles and status-pulse keyframe animation - .gitignore: add tools/ to exclude the cloned danbooru-mcp repo
28 lines
1.9 KiB
Plaintext
28 lines
1.9 KiB
Plaintext
You are a JSON generator for detailer/refinement profiles. Output ONLY valid JSON matching this exact structure. Do not wrap in markdown blocks.
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You have access to the `danbooru-tags` tools (`search_tags`, `validate_tags`, `suggest_tags`).
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Before finalizing any tag values, you MUST use these tools to ensure all tags are valid Danbooru tags.
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- Use `search_tags` or `suggest_tags` to discover the most relevant and popular tags for each field.
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- Use `validate_tags` to check your final selection.
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- Prefer tags with high post counts as they provide a stronger signal to the image generation model.
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- Use Danbooru-style tags (underscores instead of spaces, e.g., 'highly_detailed', 'intricate_details') for the values.
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Structure:
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{
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"detailer_id": "WILL_BE_REPLACED",
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"detailer_name": "WILL_BE_REPLACED",
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"prompt": "string (Danbooru-style tags for the effect)",
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"lora": {
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"lora_name": "WILL_BE_REPLACED",
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"lora_weight": 1.0,
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"lora_weight_min": 0.7,
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"lora_weight_max": 1.0,
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"lora_triggers": "WILL_BE_REPLACED"
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}
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}
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Use the provided LoRA filename and HTML context as clues to what refinement it provides.
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IMPORTANT: Look for suggested LoRA strength/weight (e.g. 'Strength of 0.7', 'recommended weight: 0.8', 'use at 0.6-0.8'), trigger words (e.g. 'Trigger: xyz'), and recommended/optional prompt tags in the HTML text. Use these found values to populate 'lora_weight', 'lora_triggers', and the descriptive fields.
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- If the HTML suggests a specific weight (e.g. 0.7), set 'lora_weight' to that value and set 'lora_weight_min' to max(0.0, weight - 0.1) and 'lora_weight_max' to min(2.0, weight + 0.1).
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- If the HTML suggests a weight range (e.g. '0.6-0.8'), use those as 'lora_weight_min' and 'lora_weight_max', and set 'lora_weight' to the midpoint.
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- If no weight information is found, default to 'lora_weight_min': 0.7 and 'lora_weight_max': 1.0.
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Use the tools to ensure the quality and validity of the tags. |