Skill: 1tsnakers/ollamadiffuser (version 1.0.0)

--- name: ollamadiffuser description: Local AI image generation using OllamaDiffuser. Use this skill when Claude needs to generate, edit (img2img/inpaint), or control (ControlNet) images locally using models like FLUX, Stable Diffusion, or GGUF quantized models. --- # OllamaDiffuser OllamaDiffuser is a local AI image generation tool that provides an Ollama-like experience for Stable Diffusion and FLUX models. It can be interfaced via CLI, REST API, or MCP. ## Setup & Installation If the tool is not yet installed or needs specific hardware support, use these commands: - **Standard Installation**: `pip install ollamadiffuser` - **Full Suite (Recommended)**: `pip install "ollamadiffuser[full]"` - **Low-VRAM/GGUF Support**: `pip install "ollamadiffuser[gguf]"` - **MCP/Agent Integration**: `pip install "ollamadiffuser[mcp]"` - **Apple Silicon (Metal)**: `CMAKE_ARGS="-DSD_METAL=ON" pip install stable-diffusion-cpp-python` **Authentication**: Gated models (e.g., FLUX.1-dev, SD 3.5) require a Hugging Face token. - `export HF_TOKEN=your_token_here` (Add to `.bashrc` or `.zshrc` for persistence). ## Core Workflows ### 1. Text-to-Image Generation Generate an image from a text prompt. - **Tool/Command**: Use the `generate_image` MCP tool or the REST API `/api/generate`. - **Key Parameters**: - `prompt`: Detailed description of the image. - `width` / `height`: Default is usually 1024x1024 for SDXL/FLUX, 512x512 for SD1.5. - `seed`: Optional for reproducibility. - `response_format`: Set to `b64_json` for agent-friendly base64 responses. ### 2. Model Management Manage which models are downloaded and active in VRAM. - **Listing Models**: Use `list_models` to see installed versions. - **Pulling Models**: Use `ollamadiffuser pull <model-name>` via shell. - **Loading Models**: Use `load_model` to switch active models in memory. - **Recommendations**: Use `ollamadiffuser recommend` to find models that fit the available GPU VRAM. ### 3. Image-to-Image & Inpainting Modify existing images. - **Img2Img**: Use `/api/generate/img2img`. Requires `image` (file/base64) and `strength` (0.0-1.0; lower = closer to original). - **Inpainting**: Use `/api/generate/inpaint`. Requires `image` and a `mask` image. ### 4. Advanced Control (ControlNet) Use structural guides (Canny, Depth, OpenPose) for precise control. - **Workflow**: 1. Ensure a ControlNet model is pulled (e.g., `ollamadiffuser pull controlnet-canny-sd15`). 2. Use `/api/generate/controlnet`. 3. Provide a `control_image` and specify the preprocessor (e.g., "canny"). ## Model Selection Guide | Use Case | Recommended Model | VRAM | Note | | :--- | :--- | :--- | :--- | | **Highest Quality** | `flux.1-dev` | 20GB+ | Requires HF Token | | **Fast & High Quality** | `flux.1-schnell` | 16GB+ | No token needed | | **Budget GPU (6GB)** | `flux.1-dev-gguf-q4ks` | 6GB | GGUF Quantized | | **Ultra Low VRAM** | `flux.1-dev-gguf-q2k` | 3GB | Entry-level | | **Classic/Fast** | `stable-diffusion-1.5` | 4GB+ | Great for img2img | | **Photorealistic** | `realvisxl-v4` | 6GB+ | SDXL based | ## Technical Notes - **API Base URL**: `http://localhost:8000` - **Web UI**: `http://localhost:8001` (Start with `ollamadiffuser --mode ui`) - **HF Tokens**: Gated models (FLUX.1-dev, SD 3.5) require `export HF_TOKEN=your_token`. - **GGUF Support**: Install with `pip install "ollamadiffuser[gguf]"` for memory-efficient runs.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)