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by
stevhliu
HF staff
- opened
README.md
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<div class="flex justify-center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/diffusers-org-logo.png"/>
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</div>
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[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.
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The library provides three main classes.
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1. The [Pipeline](https://hf.co/docs/diffusers/api/pipelines/overview) class provides an easy and unified way to perform inference with many models.
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```py
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import torch
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from diffusers import FluxPipeline
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pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
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pipeline.enable_model_cpu_offload()
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prompt = "A cat holding a sign that says hello world"
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image = 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]
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image.save("flux-schnell.png")
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```
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2. 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.
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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.
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Visit 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! 🤗
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