--- pipeline_tag: text-to-image inference: false license: other license_name: sai-nc-community license_link: https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICENSE.md --- # SDXL-Turbo Model Card  SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. A real-time demo is available here: http://clipdrop.co/stable-diffusion-turbo Please note: For commercial use, please refer to https://stability.ai/license. ## Model Details ### Model Description SDXL-Turbo is a distilled version of [SDXL 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), trained for real-time synthesis. SDXL-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the [technical report](https://stability.ai/research/adversarial-diffusion-distillation)), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high image quality. This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. - **Developed by:** Stability AI - **Funded by:** Stability AI - **Model type:** Generative text-to-image model - **Finetuned from model:** [SDXL 1.0 Base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) ### Model Sources For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference). - **Repository:** https://github.com/Stability-AI/generative-models - **Paper:** https://stability.ai/research/adversarial-diffusion-distillation - **Demo:** http://clipdrop.co/stable-diffusion-turbo ## Evaluation   The charts above evaluate user preference for SDXL-Turbo over other single- and multi-step models. SDXL-Turbo evaluated at a single step is preferred by human voters in terms of image quality and prompt following over LCM-XL evaluated at four (or fewer) steps. In addition, we see that using four steps for SDXL-Turbo further improves performance. For details on the user study, we refer to the [research paper](https://stability.ai/research/adversarial-diffusion-distillation). ## Uses ### Direct Use The model is intended for both non-commercial and commercial usage. You can use this model for non-commercial or research purposes under this [license](https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICENSE.md). Possible research areas and tasks include - Research on generative models. - Research on real-time applications of generative models. - Research on the impact of real-time generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. For commercial use, please refer to https://stability.ai/membership. Excluded uses are described below. ### Diffusers ``` pip install diffusers transformers accelerate --upgrade ``` - **Text-to-image**: SDXL-Turbo does not make use of `guidance_scale` or `negative_prompt`, we disable it with `guidance_scale=0.0`. Preferably, the model generates images of size 512x512 but higher image sizes work as well. A **single step** is enough to generate high quality images. ```py from diffusers import AutoPipelineForText2Image import torch pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16") pipe.to("cuda") prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe." image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0] ``` - **Image-to-image**: When using SDXL-Turbo for image-to-image generation, make sure that `num_inference_steps` * `strength` is larger or equal to 1. The image-to-image pipeline will run for `int(num_inference_steps * strength)` steps, *e.g.* 0.5 * 2.0 = 1 step in our example below. ```py from diffusers import AutoPipelineForImage2Image from diffusers.utils import load_image import torch pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16") pipe.to("cuda") init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png").resize((512, 512)) prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k" image = pipe(prompt, image=init_image, num_inference_steps=2, strength=0.5, guidance_scale=0.0).images[0] ``` ### Out-of-Scope Use 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. The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy). ## Limitations and Bias ### Limitations - The generated images are of a fixed resolution (512x512 pix), and the model does not achieve perfect photorealism. - The model cannot render legible text. - Faces and people in general may not be generated properly. - The autoencoding part of the model is lossy. ### Recommendations The model is intended for both non-commercial and commercial usage. ## How to Get Started with the Model Check out https://github.com/Stability-AI/generative-models
