Stable diffusion huggingface

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, stable diffusion huggingface. This model card gives an overview of all available model checkpoints. For more in-detail model cards, please have a look at the model repositories listed under Model Access. For the first version 4 model checkpoints are released.

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.

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.

During training. We aim at generating a beautiful photograph of an old stable diffusion huggingface chief and will later try to find the best prompt to generate such a photograph. More specifically: stable-diffusion-v : The checkpoint is randomly initialized and has been trained onsteps at resolution x on laion2B-en.

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.

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. 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.

Stable diffusion huggingface

The Stable Diffusion 2. The text-to-image models in this release can generate images with default resolutions of both x pixels and x pixels. For more details about how Stable Diffusion 2 works and how it differs from the original Stable Diffusion, please refer to the official announcement post. Stable Diffusion 2 is available for tasks like text-to-image, inpainting, super-resolution, and depth-to-image:. Here are some examples for how to use Stable Diffusion 2 for each task:.

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Impersonating individuals without their consent. View all files. Mis- and disinformation Representations of egregious violence and gore Sharing of copyrighted or licensed material in violation of its terms of use. Sign Up to get started. You signed out in another tab or window. For inpainting, the UNet has 5 additional input channels 4 for the encoded masked-image and 1 for the mask itself whose weights were zero-initialized after restoring the non-inpainting checkpoint. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. Taking Diffusers Beyond Images. Specific pipeline examples. People mentioned that 2. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. 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.

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.

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. We strongly suggest always running your pipelines in float16 as so far we have very rarely seen degradations in quality because of it. 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. During training,. We also want to thank heejkoo for the very helpful overview of papers, code and resources on diffusion models, available here as well as crowsonkb and rromb for useful discussions and insights. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. Memory and Speed Torch2. Guides for how to train a diffusion model for different tasks with different training techniques. Stable Diffusion v2 Model Card This model card focuses on the model associated with the Stable Diffusion v2 model, available here. Reload to refresh your session. 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 :. Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. Feb 13,