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 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.

Stable diffusion huggingface

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.

You are viewing v0. Stable Diffusion v Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.

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.

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:. Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! Diffusers documentation Stable Diffusion 2. Get started.

Stable diffusion huggingface

Initially, a base model produces preliminary latents, which are then refined by a specialized model found here that focuses on the final denoising. The base model is also functional independently. Alternatively, a dual-stage process can be employed: The base model first creates latents of the required output size.

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Overview Create a dataset for training Adapt a model to a new task Models. Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Note that you have to "click-request" them on each respective model repository. For more detailed instructions, use-cases and examples in JAX follow the instructions here. The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. Mis- and disinformation Representations of egregious violence and gore Sharing of copyrighted or licensed material in violation of its terms of use. This checker works by checking model outputs against known hard-coded NSFW concepts. During training,. Join the Hugging Face community. Resources for more information: GitHub Repository , Paper. Main Classes.

Getting the DiffusionPipeline to generate images in a certain style or include what you want can be tricky.

For more information, you can check out the official blog post. You are viewing v0. This notebook walks you through the improvements one-by-one so you can best leverage StableDiffusionPipeline for inference. For more details on how the whole Stable Diffusion pipeline works, please have a look at this blog post. Reinforcement Learning Audio Other Modalities. No additional measures were used to deduplicate the dataset. To save memory and use the same components across multiple pipelines, use the. Applications in educational or creative tools. The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. Code of conduct. Philosophy Controlled generation How to contribute? During training,. This includes, but is not limited to:. 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.