Stable Diffusion XL 1.0 is the latest state-of-the-art latent diffusion model from Stability AI for high-resolution image synthesis. SDXL is open-source, designed to improve the visual quality of generated images while maintaining transparency and reproducibility.
You can now try out Stable Diffusion XL 1.0 in Clarifai Platform and access it through the API.
Running Stable Diffusion XL 1.0 with Python
Stable Diffusion XL 1.0 is an image generation model that excels in producing highly detailed and photorealistic 1024x1024 px image compared to its previous versions, Stable Diffusion 2.1 and Stable Diffusion 1.5.
It can generate realistic faces, legible text within images, and better overall image composition. SDXL achieves these results using shorter and simpler prompts while still offering features like image-to-image prompting, inpainting, and outpainting.
Stable Diffusion XL 1.0 is an enhanced version of the Stable Diffusion model, employing a three times larger UNet backbone to capture more detailed features and produce superior images. To enhance the image quality and diversity, SDXL incorporates innovative conditioning schemes, including multi-scale conditioning, cross-modal attention, and multi-aspect ratio training. These schemes enable SDXL to generate images that closely match the input textual descriptions while covering a wide range of visual styles and variations.
Furthermore, SDXL utilizes a separate refinement model that employs a noising-denoising process on the latents produced by the model. This refinement step helps eliminate artifacts and further improves the overall visual fidelity of the generated images.
You can run Stable Diffusion XL 1.0 Model using the Clarifai's Python client.
Check out the Code Below:
Try out the Stable Diffusion XL 1.0 model here: clarifai.com/stability-ai/stable-diffusion-2/models/stable-diffusion-xl
SDXL can be used for various applications, including but not limited to:
SDXL was evaluated on several datasets, including ImageNet, COCO, and LSUN. They show that SDXL achieves competitive performance with state-of-the-art image generation models, including BigGAN and StyleGAN2. They also provide ablation studies to analyze the contribution of different components of the model to its performance.
Performance of the SDXL model was evaluated using several standard image quality metrics, including Fréchet Inception Distance (FID), Inception Score (IS), and Learned Perceptual Image Patch Similarity (LPIPS).