sdxl-turbo
Maintainer: stabilityai
2.1K
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
sdxl-turbo is a fast generative text-to-image model developed by Stability AI. It is a distilled version of the SDXL 1.0 Base model, trained using a novel technique called Adversarial Diffusion Distillation (ADD) to enable high-quality image synthesis in just 1-4 steps. This approach leverages a large-scale off-the-shelf image diffusion model as a teacher signal and combines it with an adversarial loss to ensure high fidelity even with fewer sampling steps.
Model Inputs and Outputs
sdxl-turbo is a text-to-image generative model. It takes a text prompt as input and generates a corresponding photorealistic image as output. The model is optimized for real-time synthesis, allowing for fast image generation from a text description.
Inputs
- Text prompt describing the desired image
Outputs
- Photorealistic image generated based on the input text prompt
Capabilities
sdxl-turbo is capable of generating high-quality, photorealistic images from text prompts in a single network evaluation. This makes it suitable for real-time, interactive applications where fast image synthesis is required.
What Can I Use It For?
With sdxl-turbo's fast and high-quality image generation capabilities, you can explore a variety of applications, such as interactive art tools, visual storytelling platforms, or even prototyping and visualization for product design. The model's real-time performance also makes it well-suited for use in live demos or AI-powered creative assistants. For commercial use, please refer to Stability AI's membership options.
Things to Try
One interesting aspect of sdxl-turbo is its ability to generate images with a high degree of fidelity using just 1-4 sampling steps. This makes it possible to experiment with rapid image synthesis, where the user can quickly generate and iterate on visual ideas. Try exploring different text prompts and observe how the model's output changes with the number of sampling steps.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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