Stable Diffusion Xl Base 1.0

Table of Contents

Model Overview

The Stable Diffusion XL model, developed by Stability AI, is a powerful tool for generating and modifying images based on text prompts. It’s a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).

Tasks and rank

Image-to-Image Translation 3rd

Image Generation 5th

Learn More About the Ranking System

Capabilities

The Stable Diffusion XL model is capable of generating high-quality images that are often indistinguishable from real photos. It can be used for a wide range of tasks, from generating artwork to modifying images for educational or creative purposes.

Primary Tasks

• Generate images: The model can create images from scratch based on a text prompt. Want to see a picture of a cat playing the piano? Just ask!
• Modify images: The model can also modify existing images to fit a new text prompt. For example, you can ask it to add a hat to a person in a picture.

Strengths

• High-quality images: The model can generate high-quality images that are often indistinguishable from real photos.
• Flexibility: The model can be used for a wide range of tasks, from generating artwork to modifying images for educational or creative purposes.

How it Works

Here’s a simplified overview of how the model works:

1. Base Model: The base model generates (noisy) latents based on the input text prompt.
2. Refinement Model: The refinement model (available separately) further processes the latents to produce a final, denoised image.

You can use the base model as a standalone module or combine it with the refinement model for even better results.

Alternatives

Grounded SAM 13900 Visual task model

Vit Base Patch16 224 In21k 220 Image classification

Key Features

• Diffusion-based text-to-image generative model
• Latent Diffusion Model with two fixed, pretrained text encoders
• CreativeML Open RAIL++-M License
• Developed by Stability AI

Example Use Cases

• Artistic applications: Generate artworks, design, and other creative projects.
• Education and research: Use in educational tools, research on generative models, and probing limitations and biases.
• Safe deployment: Deploy models that can generate harmful content.

Limitations and Bias

• Not perfect photorealism: The model may not achieve perfect photorealism.
• Legible text: The model cannot render legible text.
• Compositionality: The model struggles with complex tasks involving compositionality.
• Faces and people: The model may not generate faces and people properly.
• Lossy autoencoding: The autoencoding part of the model is lossy.
• Social biases: The model can reinforce or exacerbate social biases.

Performance

The Stable Diffusion XL model is relatively fast compared to other models. It can generate images in a matter of seconds, depending on the complexity of the prompt and the hardware used.

Model	Time to generate image
Stable Diffusion XL	2-5 seconds
==Stable Diffusion 1.5==	5-10 seconds
==Stable Diffusion 2.1==	10-20 seconds

Format

The Stable Diffusion XL model uses a two-stage pipeline to generate images. It supports the following data formats:

• Text prompts for input
• Images (in the form of latents) for output

Examples

Generate an image of a futuristic cityscape with sleek skyscrapers and flying cars. Image generated: A futuristic cityscape with sleek skyscrapers and flying cars in the sky.

Create an image of a fantasy world with a dragon and a medieval castle in the background. Image generated: A fantasy world with a dragon and a medieval castle in the background, surrounded by rolling hills and a misty atmosphere.

Modify an image of a sunny beach to make it look like a stormy day with dark clouds and strong waves. Image modified: The sunny beach now looks like a stormy day with dark clouds and strong waves, with the palm trees swaying in the wind.

Resources

• GitHub Repository: https://github.com/Stability-AI/generative-models
• SDXL Report: https://arxiv.org/abs/…
• Clipdrop: https://clipdrop.co/stable-diffusion