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You can also dig into the models and schedulers toolbox to build your own diffusion system: from diffusers import DDPMScheduler, UNet2DModel from PIL import Image import torch import numpy as np scheduler = DDPMScheduler. to ( 'cuda' ) pipeline ( 'An image of a squirrel in Picasso style' ). from_pretrained ( 'runwayml/stable-diffusion-v1-5', torch_dtype = torch. To generate an image from text, use the from_pretrained method to load any pretrained diffusion model (browse the Hub for 4000+ checkpoints): from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline. Generating outputs is super easy with 🤗 Diffusers. Please refer to the How to use Stable Diffusion in Apple Silicon guide. With pip (official package): pip install -upgrade diffusers Apple Silicon (M1/M2) support With conda (maintained by the community): conda install -c conda-forge diffusers With pip (official package): pip install -upgrade diffusers For more details about installing PyTorch and Flax, please refer to their official documentation. We recommend installing 🤗 Diffusers in a virtual environment from PyPi or Conda.

Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.Interchangeable noise schedulers for different diffusion speeds and output quality.

State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code.🤗 Diffusers offers three core components: Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both.

🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules.

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