ollamadiffuser

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.

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Install skill "ollamadiffuser" with this command: npx skills add 1tsnakers/ollamadiffuser

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 CaseRecommended ModelVRAMNote
Highest Qualityflux.1-dev20GB+Requires HF Token
Fast & High Qualityflux.1-schnell16GB+No token needed
Budget GPU (6GB)flux.1-dev-gguf-q4ks6GBGGUF Quantized
Ultra Low VRAMflux.1-dev-gguf-q2k3GBEntry-level
Classic/Faststable-diffusion-1.54GB+Great for img2img
Photorealisticrealvisxl-v46GB+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.

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