# TME 对diffusers的适配 - rebase 20231124 的diffusers, commit id `3003ff4947ea43fb56aa0df3da61c85652f24c69` # 原版 🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](https://huggingface.co/docs/diffusers/conceptual/philosophy#usability-over-performance), [simple over easy](https://huggingface.co/docs/diffusers/conceptual/philosophy#simple-over-easy), and [customizability over abstractions](https://huggingface.co/docs/diffusers/conceptual/philosophy#tweakable-contributorfriendly-over-abstraction). 🤗 Diffusers offers three core components: - State-of-the-art [diffusion pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) that can be run in inference with just a few lines of code. - Interchangeable noise [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview) for different diffusion speeds and output quality. - Pretrained [models](https://huggingface.co/docs/diffusers/api/models/overview) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems. ## Installation We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation. ### PyTorch With `pip` (official package): ```bash pip install --upgrade diffusers[torch] ``` With `conda` (maintained by the community): ```sh conda install -c conda-forge diffusers ``` ### Flax With `pip` (official package): ```bash pip install --upgrade diffusers[flax] ``` ### Apple Silicon (M1/M2) support Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide. ## Quickstart Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 15000+ checkpoints): ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) pipeline.to("cuda") pipeline("An image of a squirrel in Picasso style").images[0] ``` You can also dig into the models and schedulers toolbox to build your own diffusion system: ```python from diffusers import DDPMScheduler, UNet2DModel from PIL import Image import torch scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256") model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda") scheduler.set_timesteps(50) sample_size = model.config.sample_size noise = torch.randn((1, 3, sample_size, sample_size), device="cuda") input = noise for t in scheduler.timesteps: with torch.no_grad(): noisy_residual = model(input, t).sample prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample input = prev_noisy_sample image = (input / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy()[0] image = Image.fromarray((image * 255).round().astype("uint8")) image ``` Check out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to launch your diffusion journey today! ## How to navigate the documentation | **Documentation** | **What can I learn?** | |---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview) | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model. | | [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview) | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. | | [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview) | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library. | | [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview) | Guides for how to optimize your diffusion model to run faster and consume less memory. | | [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques. | ## Contribution We ❤️ contributions from the open-source community! If you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md). You can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library. - See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute - See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines - See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) Also, say 👋 in our public Discord channel . We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or just hang out ☕. ## Popular Tasks & Pipelines

Task	Pipeline	🤗 Hub
Unconditional Image Generation	DDPM	google/ddpm-ema-church-256
Text-to-Image	Stable Diffusion Text-to-Image	runwayml/stable-diffusion-v1-5
Text-to-Image	unCLIP	kakaobrain/karlo-v1-alpha
Text-to-Image	DeepFloyd IF	DeepFloyd/IF-I-XL-v1.0
Text-to-Image	Kandinsky	kandinsky-community/kandinsky-2-2-decoder
Text-guided Image-to-Image	ControlNet	lllyasviel/sd-controlnet-canny
Text-guided Image-to-Image	InstructPix2Pix	timbrooks/instruct-pix2pix
Text-guided Image-to-Image	Stable Diffusion Image-to-Image	runwayml/stable-diffusion-v1-5
Text-guided Image Inpainting	Stable Diffusion Inpainting	runwayml/stable-diffusion-inpainting
Image Variation	Stable Diffusion Image Variation	lambdalabs/sd-image-variations-diffusers
Super Resolution	Stable Diffusion Upscale	stabilityai/stable-diffusion-x4-upscaler
Super Resolution	Stable Diffusion Latent Upscale	stabilityai/sd-x2-latent-upscaler

## Popular libraries using 🧨 Diffusers - https://github.com/microsoft/TaskMatrix - https://github.com/invoke-ai/InvokeAI - https://github.com/apple/ml-stable-diffusion - https://github.com/Sanster/lama-cleaner - https://github.com/IDEA-Research/Grounded-Segment-Anything - https://github.com/ashawkey/stable-dreamfusion - https://github.com/deep-floyd/IF - https://github.com/bentoml/BentoML - https://github.com/bmaltais/kohya_ss - +6000 other amazing GitHub repositories 💪 Thank you for using us ❤️. ## Credits This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today: - @CompVis' latent diffusion models library, available [here](https://github.com/CompVis/latent-diffusion) - @hojonathanho original DDPM implementation, available [here](https://github.com/hojonathanho/diffusion) as well as the extremely useful translation into PyTorch by @pesser, available [here](https://github.com/pesser/pytorch_diffusion) - @ermongroup's DDIM implementation, available [here](https://github.com/ermongroup/ddim) - @yang-song's Score-VE and Score-VP implementations, available [here](https://github.com/yang-song/score_sde_pytorch) We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available [here](https://github.com/heejkoo/Awesome-Diffusion-Models) as well as @crowsonkb and @rromb for useful discussions and insights. ## Citation ```bibtex @misc{von-platen-etal-2022-diffusers, author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf}, title = {Diffusers: State-of-the-art diffusion models}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/huggingface/diffusers}} } ```