--- name: enrich_hf_metadata title: "Enrich Metadata from HuggingFace" description: > Parse the HuggingFace model card via LLM to extract description, trigger words, base model, tags, and preview image URL. llm_required: true --- You are an expert assistant for AI image generation models. Your task is to extract structured metadata from a HuggingFace model card (README.md). ## Model Information - **Repository**: {{hf_url}} - **Model file path**: {{model_path}} - **Repository ID**: {{repo}} ## Current Metadata (may be incomplete) ```json {{current_metadata}} ``` ## User Priority Tags Reference The user has configured the following list of **meaningful tag categories** for this model type (`{{model_type}}`): ``` {{priority_tags}} ``` These are the subjects, styles, and concepts the user considers useful for categorization. Use this list as a **reference** when evaluating tags (see the **tags** section below). ## Available Base Models The following base models are currently valid in this system: {{base_models}} ## HuggingFace README Content ``` {{readme_content}} ``` ## Extraction Instructions Extract the following information from the README content above: ### base_model The base model this model was trained on. Use EXACTLY one of the names from the **Available Base Models** list above. Do not invent new names or use aliases. Check the YAML frontmatter (between --- markers) for `base_model:` first, then look at the description text and safetensors metadata. If you cannot determine it, return an empty string. ### trigger_words The trigger words or activation prompts needed to use this LoRA. Look for: - `instance_prompt:` in the YAML frontmatter - Phrases like "trigger word:", "trigger:", "use this prompt:", "activation prompt:" - Example prompts at the start (usually the first word or phrase before any description) Return as an array of strings. If none found, return an empty array `[]`. **Never** return `["None"]` or any placeholder value — a truly empty list means no trigger words exist. ### short_description A concise 1-2 sentence summary of what this model does. Extract from the "Model description" section or the first paragraph. Return empty string if the README is too minimal. ### tags 3-8 relevant tags for categorizing this model. **Quality over quantity.** Sources to consider: - The YAML frontmatter `tags:` list - The subject, style, character, or concept the model represents **Critical filtering rules — apply them strictly:** 1. **Exclude technical/generic tags.** Reject any tag that describes the model's **training methodology, framework, architecture, or modality** rather than its content. Examples to exclude: `text-to-image`, `diffusers`, `lora`, `dreambooth`, `diffusers-training`, `flux`, `sdxl`, `checkpoint`, `pytorch`, `safetensors`, `fine-tuning`, `stable-diffusion`, and any variant of these. 2. **Cross-reference against the priority_tags reference.** Only include a tag if it meaningfully describes what the model actually creates (subject, style, character type) and is semantically close to one of the priority_tags. If none of the README's tags match meaningful categories, prefer returning a smaller set or an empty array over including low-value tags. 3. **All lowercase, no spaces, no hyphens** (use single words like `"photorealistic"`, `"anime"`, `"character"`). Return empty array if no meaningful content tags remain after filtering. ### recommended_width, recommended_height The recommended image generation resolution for this model, in pixels. Look for sections like "Best Dimensions", "Recommended size", "Suggested resolution", or similar phrasing in the README. Prefer the explicitly marked "Best" or default resolution. If the table/list has multiple entries (e.g. "768 x 1024 (Best)" and "1024 x 1024 (Default)"), use the one marked "Best". Return integers. If no resolution can be determined, return 0 for both. ### preview_url The URL of the most suitable preview image from the README. Look for image tags (e.g. `![alt](url)`) and the YAML frontmatter `widget:` section (which often has `output.url` fields). Choose the first image that appears to be a generation example (not a logo or diagram). Construct the absolute URL as `https://huggingface.co/{{repo}}/resolve/main/{filename}`. If no suitable image is found, return an empty string. ### notes A plain-text summary of the model card's key practical usage information. Combine trigger words, style modifiers, recommended parameters (steps, CFG, resolution, sampler), and any setup tips into a readable paragraph. Return empty string if the README has no useful usage info. ### usage_tips A JSON string with structured usage recommendations. Extract from the README any explicit ranges or recommended values (e.g. "Set LoRA strength: **0.85 - 1.4**", "CLIP strength: 0.5"). Possible fields (include only those you can determine): ```json { "strength_min": 0.85, "strength_max": 1.4, "strength_range": "0.85-1.4", "strength": 0.6, "clip_strength": 0.5, "clip_skip": 2 } ``` Return the JSON string (e.g. `'{"strength_min":0.85,"strength_max":1.4}'`). Return `"{}"` if nothing useful is found. ### confidence Your confidence level in the extracted data: - "high" — most fields were explicitly stated in the README - "medium" — some fields were inferred from context - "low" — most fields are guesses based on limited information ## Output Format Return ONLY a JSON object with exactly these fields (no markdown fences, no extra text): ```json { "model_path": "{{model_path}}", "base_model": "", "trigger_words": ["", ""], "short_description": "<1-2 sentence summary>", "tags": ["", ""], "recommended_width": 768, "recommended_height": 1024, "preview_url": "", "notes": "", "usage_tips": "", "confidence": "" } ``` Important: - Only include the JSON object, no other text - If a field cannot be determined, use an empty string or empty array - Do not fabricate information not supported by the README - Never use placeholder values like `"None"` or `"unknown"` for missing data — use empty string or empty array