Stable diffusion huggingface

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. This model card gives an overview of all available model checkpoints.

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.

Stable diffusion huggingface

For more information, you can check out the official blog post. Since its public release the community has done an incredible job at working together to make the stable diffusion checkpoints faster , more memory efficient , and 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. 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. See the documentation on reproducibility here for more information. The default run we did above used full float32 precision and ran the default number of inference steps The easiest speed-ups come from switching to float16 or half precision and simply running fewer inference steps. We strongly suggest always running your pipelines in float16 as so far we have very rarely seen degradations in quality because of it. The number of inference steps is associated with the denoising scheduler we use. Choosing a more efficient scheduler could help us decrease the number of steps. For more information, we recommend taking a look at the official documentation here. Usually, the more images per inference run, the more images per second too.

Model Description: This is a model that can be used to generate and modify images based on text prompts.

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. Running the pipeline if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to EulerDiscreteScheduler :. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people.

For more information, you can check out the official blog post. Since its public release the community has done an incredible job at working together to make the stable diffusion checkpoints faster , more memory efficient , and 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. 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. See the documentation on reproducibility here for more information.

Stable diffusion huggingface

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.

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Specifically, the checker compares the class probability of harmful concepts in the embedding space of the CLIPTextModel after generation of the images. You are viewing v0. Conceptual Guides. This includes, but is not limited to:. See the documentation on reproducibility here for more information. Packages 0 No packages published. The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. 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. Faster examples with accelerated inference. During training, Images are encoded through an encoder, which turns images into latent representations. During training, Images are encoded through an encoder, which turns images into latent representations. You can do so by telling diffusers to expect the weights to be in float16 precision:. Collaborate on models, datasets and Spaces.

The Stable Diffusion 2.

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. Not optimized for FID scores. More specifically:. The model was trained mainly with English captions and will not work as well in other languages. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. If you want to contribute to this library, please check out our Contribution guide. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. The additional input channels of the U-Net which process this extra information were zero-initialized. Diffusers documentation Stable Diffusion pipelines. 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. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. Overall, we strongly recommend just trying the models out and reading up on advice online e. This affects the overall output of the model, as white and western cultures are often set as the default. 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. Mis- and disinformation Representations of egregious violence and gore Sharing of copyrighted or licensed material in violation of its terms of use.