Huggingface stable diffusion

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more detailed instructions, use-cases and examples in JAX follow the instructions here, huggingface stable diffusion.

Welcome to this Hugging Face Inference Endpoints guide on how to deploy Stable Diffusion to generate images for a given input prompt. This guide will not explain how the model works. It supports all the Transformers and Sentence-Transformers tasks as well as diffusers tasks and any arbitrary ML Framework through easy customization by adding a custom inference handler. This custom inference handler can be used to implement simple inference pipelines for ML Frameworks like Keras, Tensorflow, and sci-kit learn or to add custom business logic to your existing transformers pipeline. The first step is to deploy our model as an Inference Endpoint. Therefore we add the Hugging face repository Id of the Stable Diffusion model we want to deploy. Note: If the repository is not showing up in the search it might be gated, e.

Huggingface stable diffusion

Latent diffusion applies the diffusion process over a lower dimensional latent space to reduce memory and compute complexity. For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI announcement and our own blog post for more technical details. You can find the original codebase for Stable Diffusion v1. Explore these organizations to find the best checkpoint for your use-case! The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo:. To help you get the most out of the Stable Diffusion pipelines, here are a few tips for improving performance and usability. These tips are applicable to all Stable Diffusion pipelines. For example, if you want to use the EulerDiscreteScheduler instead of the default:. To save memory and use the same components across multiple pipelines, use the. Diffusers documentation Stable Diffusion pipelines.

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This model card focuses on the model associated with the Stable Diffusion v2 model, available here. This stable-diffusion-2 model is resumed from stable-diffusionbase base-ema. Resumed for another k steps on x images. Model Description: This is a model that can be used to generate and modify images based on text prompts. Resources for more information: GitHub Repository.

We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique. We demonstrate that SDXL shows drastically improved performance compared the previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators. Check out the Stability AI Hub organization for the official base and refiner model checkpoints! This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the library implements for all the pipelines such as downloading or saving, running on a particular device, etc.

Huggingface stable diffusion

Our library is designed with a focus on usability over performance , simple over easy , and customizability over abstractions. For more details about installing PyTorch and Flax , please refer to their official documentation. You can also dig into the models and schedulers toolbox to build your own diffusion system:. Check out the Quickstart to launch your diffusion journey today! If you want to contribute to this library, please check out our Contribution guide. You can look out for issues you'd like to tackle to contribute to the library. This library concretizes previous work by many different authors and would not have been possible without their great research and implementations.

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Overview Understanding pipelines, models and schedulers AutoPipeline Train a diffusion model Load LoRAs for inference Accelerate inference of text-to-image diffusion models. Possible research areas and tasks include Safe deployment of models which have the potential to generate harmful content. During training, Images are encoded through an encoder, which turns images into latent representations. Evaluations with different classifier-free guidance scales 1. Conceptual Guides. During training,. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Probing and understanding the limitations and biases of generative models. Stable Diffusion Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Training Training Data The model developers used the following dataset for training the model: LAION-2B en and subsets thereof see next section Training Procedure Stable Diffusion v is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.

Getting the DiffusionPipeline to generate images in a certain style or include what you want can be tricky. This tutorial walks you through how to generate faster and better with the DiffusionPipeline. One of the simplest ways to speed up inference is to place the pipeline on a GPU the same way you would with any PyTorch module:.

Taking Diffusers Beyond Images. Training Procedure Stable Diffusion v is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. One of the simplest ways to speed up inference is to place the pipeline on a GPU the same way you would with any PyTorch module:. The autoencoding part of the model is lossy The model was trained on a large-scale dataset LAION-5B which contains adult material and is not fit for product use without additional safety mechanisms and considerations. The easiest is to keep the suggested defaults from the application. Evaluations with different classifier-free guidance scales 1. In this tutorial, you learned how to optimize a DiffusionPipeline for computational and memory efficiency as well as improving the quality of generated outputs. Using Diffusers. Faster examples with accelerated inference. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. If you'd like regular pip install, checkout the latest stable version v0.