Sinapsis Hugging Face Diffusers

Sinapsis Hugging Face Diffusers provides a powerful and flexible no-code implementation of the Hugging Face Diffusers library. It enables users to easily configure and run diffusion pipelines for generative tasks.

🐍 Installation • 📦 Features • ▶️ Example usage • 🌐 Webapps • 📙 Documentation • 🔍 License

🐍 Installation

Install using your package manager of choice. We encourage the use of uv

Example with uv:

  uv pip install sinapsis-huggingface-diffusers --extra-index-url https://pypi.sinapsis.tech

or with raw pip:

  pip install sinapsis-huggingface-diffusers --extra-index-url https://pypi.sinapsis.tech

[!IMPORTANT] Templates may require extra optional dependencies. For development, we recommend installing the package with all the optional dependencies:

with uv:

  uv pip install sinapsis-huggingface-diffusers[all] --extra-index-url https://pypi.sinapsis.tech

or with raw pip:

  pip install sinapsis-huggingface-diffusers[all] --extra-index-url https://pypi.sinapsis.tech

📦 Features

The templates in this package include functionality to:

• TextToImageDiffusers: Generates images from text prompts.
• ImageToImageDiffusers: Modifies images using text-guided transformations.
• InpaintingDiffusers: Supports selective image editing using masks or bounding boxes.
• ImageToVideoGenXLDiffusers: Converts images into videos using the I2VGen-XL model.

▶️ Example Usage

Below is an example YAML configuration for running a Text-to-Image Diffusion pipeline using Sinapsis.

agent:
  name: text_to_image

templates:
  - template_name: InputTemplate
    class_name: InputTemplate
    attributes: {}

  - template_name: TextToImageDiffusers
    class_name: TextToImageDiffusers
    template_input: InputTemplate
    attributes:
      model_path: stable-diffusion-v1-5/stable-diffusion-v1-5
      device: cuda
      torch_dtype: "float16"
      enable_model_cpu_offload: false
      overwrite_images: true
      generation_params:
        prompt: "A majestic castle on top of a mountain, surrounded by clouds during sunset"
        height: 1024
        width: 1024
        num_inference_steps: 50
        guidance_scale: 7.5
        negative_prompt: "low quality, blurry, distorted"
        num_images_per_prompt: 1

  - template_name: ImageSaver
    class_name: ImageSaver
    template_input: TextToImageDiffusers
    attributes:
      save_dir: ./output_dir
      extension: png

[!IMPORTANT] The ImageSaver template correspond to the sinapsis-data-writers package. If you want to use the example, please make sure you install this packages.

To run the config, use the CLI:

sinapsis run name_of_config.yml

🌐 Webapps

Th Sinapsis web applications provide an interactive way to explore and experiment with AI models. They allow users to generate outputs, test different inputs, and visualize results in real time, making it easy to experience the capabilities of each model. Below are the available webapps and instructions to launch them.

[!IMPORTANT] To run any of the apps, you first need to clone this repo:

git clone git@github.com:Sinapsis-ai/sinapsis-huggingface.git
cd sinapsis-huggingface

[!NOTE] If you'd like to enable external app sharing in Gradio, export GRADIO_SHARE_APP=True

[!NOTE] Agent configuration can be changed through the AGENT_CONFIG_PATH env var. You can check the available configurations in each package configs folder.

docker compose -f docker/compose.yaml build

docker compose -f docker/compose_diffusers.yaml up sinapsis-huggingface-diffusers-gradio -d

docker logs -f sinapsis-huggingface-diffusers-gradio

Running on local URL:  http://127.0.0.1:7860

docker compose -f docker/compose_diffusers.yaml down

uv sync --frozen

uv pip install sinapsis-huggingface[all] --extra-index-url https://pypi.sinapsis.tech

uv run webapps/diffusers_demo.py

Running on local URL:  http://127.0.0.1:7860

📙 Documentation

Documentation is available on the sinapsis website

Tutorials for different projects within sinapsis are available at sinapsis tutorials page

🔍 License

This project is licensed under the AGPLv3 license, which encourages open collaboration and sharing. For more details, please refer to the LICENSE file.

For commercial use, please refer to our official Sinapsis website for information on obtaining a commercial license.