🧨Diffusers

non-profit

https://github.com/huggingface/diffusers

\n\n[Diffusers](https://github.com/huggingface/diffusers) is a library of state-of-the-art pretrained diffusion models for *all* of your generative AI needs and use cases.\n\nThe library provides three main classes.\n\n1. The [Pipeline](https://hf.co/docs/diffusers/api/pipelines/overview) class provides an easy and unified way to perform inference with many models.\n\n```py\nimport torch\nfrom diffusers import FluxPipeline\n\npipeline = FluxPipeline.from_pretrained(\"black-forest-labs/FLUX.1-schnell\", torch_dtype=torch.bfloat16)\npipeline.enable_model_cpu_offload()\n\nprompt = \"A cat holding a sign that says hello world\"\nimage = pipeline(prompt, guidance_scale=0.0, output_type=\"pil\", num_inference_steps=4, max_sequence_length=256, generator=torch.Generator(\"cpu\").manual_seed(33)).images[0]\nimage.save(\"flux-schnell.png\")\n```\n\n2. Diffusers also provides [models](https://hf.co/docs/diffusers/api/models/overview) and [schedulers](https://hf.co/docs/diffusers/api/schedulers/overview) that you can mix and match to build or train your own diffusion systems.\n\nIn addition to these classes, Diffusers is invested in lowering the barrier for everyone. The library is optimized to run on memory-constrained hardware, accelerate inference on PyTorch, hardware (GPU/CPU/TPUs), and model accelerators.\n\nVisit the [documentation](https://hf.co/docs/diffusers/index) if you're interested in learning more. We're excited to see what you create with Diffusers! 🤗","html":"

\n\n

Diffusers is a library of state-of-the-art pretrained diffusion models for all of your generative AI needs and use cases.

The library provides three main classes.

The Pipeline class provides an easy and unified way to perform inference with many models.

import torch\nfrom diffusers import FluxPipeline\n\npipeline = FluxPipeline.from_pretrained(\"black-forest-labs/FLUX.1-schnell\", torch_dtype=torch.bfloat16)\npipeline.enable_model_cpu_offload()\n\nprompt = \"A cat holding a sign that says hello world\"\nimage = pipeline(prompt, guidance_scale=0.0, output_type=\"pil\", num_inference_steps=4, max_sequence_length=256, generator=torch.Generator(\"cpu\").manual_seed(33)).images[0]\nimage.save(\"flux-schnell.png\")\n

Diffusers also provides models and schedulers that you can mix and match to build or train your own diffusion systems.

In addition to these classes, Diffusers is invested in lowering the barrier for everyone. The library is optimized to run on memory-constrained hardware, accelerate inference on PyTorch, hardware (GPU/CPU/TPUs), and model accelerators.

Visit the documentation if you're interested in learning more. We're excited to see what you create with Diffusers! 🤗

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