Huggingface stable diffusion

For more information, huggingface stable diffusion, you can check out the huggingface stable diffusion blog post. Since its public release the community has done an incredible job at working together to make the stable diffusion checkpoints fastermore memory efficientand more performant. This notebook walks you through the improvements one-by-one so you can best leverage StableDiffusionPipeline for inference. So to begin with, it is most important to speed up stable diffusion as much as possible to generate as many pictures as possible in a given amount of time.

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. Follow instructions here. 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 , Paper. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

Huggingface stable diffusion

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, come here. 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 , Paper. You can do so by telling diffusers to expect the weights to be in float16 precision:. Note : If you are limited by TPU memory, please make sure to load the FlaxStableDiffusionPipeline in bfloat16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:. While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.

Use it with the stablediffusion repository: download the v-ema. Overview Load pipelines, huggingface stable diffusion, models, and schedulers Load and compare different schedulers Load community pipelines and components Load safetensors Load different Stable Diffusion formats Load adapters Push files to the Hub. The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.

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.

Why is this important? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a x image being encoded to x Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a x image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable Diffusion 1. Therefore, this kind of model is well suited for usages where efficiency is important. However, with this setup, a much higher compression of images can be achieved.

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.

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Faster examples with accelerated inference. Note that you have to "click-request" them on each respective model repository. For more information, we recommend taking a look at the official documentation here. Get started. We strongly suggest always running your pipelines in float16 as so far we have very rarely seen degradations in quality because of it. A newer version v0. Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. Using Diffusers. For more information, you can check out the official blog post. Most of the memory is taken up by the cross-attention layers. As a result, we observe some degree of memorization for images that are duplicated in the training data. Get started. For more detailed instructions, use-cases and examples in JAX follow the instructions here. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.

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.

Excluded uses are described below. 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. Join the Hugging Face community. Essentially, when doing prompt engineering, one has to think:. The model was trained mainly with English captions and will not work as well in other languages. Model Description: This is a model that can be used to generate and modify images based on text prompts. Overall, we strongly recommend just trying the models out and reading up on advice online e. We aim at generating a beautiful photograph of an old warrior chief and will later try to find the best prompt to generate such a photograph. Get started. Downloads last month 4,, Resources for more information: GitHub Repository. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. Effective and efficient diffusion Speed Memory Quality Better checkpoints Better pipeline components Better prompt engineering Next steps. Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model.