Huggingface 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. These weights are intended to be used with the original CompVis Stable Diffusion codebase. 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.

Huggingface stable diffusion

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. These weights are intended to be used with the original CompVis Stable Diffusion codebase. 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. 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.

This stable-diffusion-2 model is resumed from stable-diffusionbase base-ema.

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.

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.

Huggingface stable diffusion

Unconditional image generation is a popular application of diffusion models that generates images that look like those in the dataset used for training. Typically, the best results are obtained from finetuning a pretrained model on a specific dataset. For additional details and context about diffusion models like how they work, check out the notebook! You can login from a notebook and enter your token when prompted. Make sure your token has the write role. Since the model checkpoints are quite large, install Git-LFS to version these large files:. For convenience, create a TrainingConfig class containing the training hyperparameters feel free to adjust them :. Set config. Image which we can visualize:. For example, to create a UNet2DModel :.

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Note that you have to "click-request" them on each respective model repository. Resources for more information: GitHub Repository , Paper. The eye movement slightly changed and looks nice. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. You are viewing v0. The first picture looks nice! To quickly try out the model, you can try out the Stable Diffusion Space. Collaborate on models, datasets and Spaces. Get started. Sign Up to get started.

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:.

We got some very high-quality image generations there. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. Probing and understanding the limitations and biases of generative models. Resumed for another k steps on x images. Evaluations with different classifier-free guidance scales 1. Overview Understanding pipelines, models and schedulers AutoPipeline Train a diffusion model Load LoRAs for inference Accelerate inference of text-to-image diffusion models. For the first version 4 model checkpoints are released. Overview Load pipelines, 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. Philosophy Controlled generation How to contribute? Stable Diffusion Video also accepts micro-conditioning, in addition to the conditioning image, which allows more control over the generated video:. No additional measures were used to deduplicate the dataset. This checker works by checking model outputs against known hard-coded NSFW concepts. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.