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, huggingface stable diffusion.

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.

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.

Use it with the stablediffusion repository: download the v-ema. Applications in educational or creative tools.

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.

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.

Huggingface stable diffusion

Our library is designed with a focus on usability over performance , simple over easy , and customizability over abstractions. Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques. Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.

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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. Taking Diffusers Beyond Images. The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. Follow instructions here. While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Not optimized for FID scores. Applications in educational or creative tools. Sexual content without consent of the people who might see it. Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. Probing and understanding the limitations and biases of generative models.

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.

The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. 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. 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. We currently provide four checkpoints, which were trained as follows. As a result, we observe some degree of memorization for images that are duplicated in the training data. While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 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. Sign Up to get started. Stable Video Diffusion torch. To improve the prompt, it often helps to add cues that could have been used online to save high-quality photos, as well as add more details.