Stable Diffusion

HuggingFace Stable Diffusion XL is a multi-expert pipeline for latent diffusion. 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. Subsequently, a high-resolution model is utilized, employing the SDEdit technique (details at https://arxiv.org/abs/2108.01073, also known as “img2img”) on the initially generated latents, guided by the original prompt. This method is somewhat slower due to additional computational steps.

The source code is accessible at https://github.com/Stability-AI/generative-models.

Model Overview

Creator: Stability AI
Type: Diffusion-based text-to-image generative model
License: CreativeML Open RAIL++-M License
Description: This model, capable of generating and altering images from text prompts, is based on Latent Diffusion. It incorporates two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
Additional Information: Visit our GitHub Repository and the SDXL report on arXiv for more details.

Model Resources

For research, our generative-models GitHub repository (https://github.com/Stability-AI/generative-models) is recommended. It implements popular diffusion frameworks for both training and inference, with future updates like distillation planned. Clipdrop offers free SDXL inference.

Repository: https://github.com/Stability-AI/generative-models
Demo: https://clipdrop.co/stable-diffusion

Evaluation

Uses of HuggingFace Stable Diffusion Model

The model is designed exclusively for research applications. Potential areas and tasks for research encompass:

1. Generating artwork and incorporating it into design and various artistic endeavors.
2. Utilization in educational or creative tools.
3. Studies focusing on generative models.
4. Research into the safe implementation of models capable of producing potentially harmful content.
5. Investigating and comprehending the constraints and biases inherent in generative models.

The model was not developed to create factual or accurate representations of people or events. Consequently, utilizing the model to generate content of this nature is beyond its intended capabilities and scope.

Limitations

Limitations of the Hugging Face Stable Diffusion Model:

1. The model does not attain absolute photorealism.
2. It is unable to produce legible text within generated images.
3. The model encounters difficulties with complex tasks involving compositional elements, such as creating an image that depicts “A red cube on top of a blue sphere.”
4. Generation of faces and human figures may not be accurately rendered.
5. The autoencoding component of the model results in some loss of information.

Find more information about SD model in our Blog.