How to use Diffusers: what you can do with it

3 use cases

• Generate images from text in a few lines of Python

  Load a pretrained pipeline from the Hub with from_pretrained and generate images from prompts.

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  Using huggingface/diffusers, write a script that loads <model id> with DiffusionPipeline in float16 on CUDA, generates 4 images for <prompt> with fixed seeds, and saves them as PNG files in <folder>.

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• Edit, inpaint or upscale images with ready-made pipelines

  Use image-to-image, inpainting, InstructPix2Pix, ControlNet or upscale pipelines on your own pictures.

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  With huggingface/diffusers, build a small tool that takes <input image> and either upscales it with the Stable Diffusion x4 upscaler or inpaints a masked area with the inpainting checkpoint, depending on a command line flag. Save outputs next to the input.

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• Trade speed for quality by swapping schedulers

  Combine pretrained models with different noise schedulers to control how many steps generation takes.

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  Using huggingface/diffusers, write a denoising loop with DDPMScheduler and UNet2DModel from google/ddpm-cat-256 as in the README, then try two other schedulers at fewer steps and save a side-by-side image comparing speed and quality.

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Quick start with Diffusers

1. Check you have what it needs: Python in a virtual environment; PyTorch; install with `pip install --upgrade diffusers[torch]` or conda; An NVIDIA GPU for the README examples, which call to cuda; Apple Silicon has its own guide.
2. Open huggingface/diffusers on GitHub and read the README and license.
3. Install with pip install --upgrade diffusers[torch]
4. Load a pipeline: DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", dtype=torch.float16)
5. Move it to the GPU with pipeline.to("cuda")
6. Call the pipeline with a prompt such as An image of a squirrel in Picasso style and take images[0]

These steps come from our top use case, Generate images from text in a few lines of Python, which also has a copy-paste agent prompt.

Using the Deduped MCP, get the setup steps for huggingface/diffusers

claude mcp add --transport http deduped https://dedupedai.com/mcp

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About this repo

• Who it is forPython developers and ML practitioners who want to generate, edit or upscale images in code, or train their own diffusion models.
• Why it is hot35k stars, talked about in 2 AI sources we track
• LanguagePython
• Sourcegithub.com/huggingface/diffusers · Back to Hot repos

Requirements

• Python in a virtual environment
• PyTorch; install with `pip install --upgrade diffusers[torch]` or conda
• An NVIDIA GPU for the README examples, which call to cuda; Apple Silicon has its own guide

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