diffusers/main/en/api/pipelines/stable_diffusion/sdxl_turbo.html

<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;SDXL Turbo&quot;,&quot;local&quot;:&quot;sdxl-turbo&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Tips&quot;,&quot;local&quot;:&quot;tips&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}">
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		<link rel="modulepreload" href="/docs/diffusers/main/en/_app/immutable/chunks/EditOnGithub.1e64e623.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;SDXL Turbo&quot;,&quot;local&quot;:&quot;sdxl-turbo&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Tips&quot;,&quot;local&quot;:&quot;tips&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>   <h1 class="relative group"><a id="sdxl-turbo" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#sdxl-turbo"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>SDXL Turbo</span></h1> <p data-svelte-h="svelte-1lov3q2">Stable Diffusion XL (SDXL) Turbo was proposed in <a href="https://stability.ai/research/adversarial-diffusion-distillation" rel="nofollow">Adversarial Diffusion Distillation</a> by Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach.</p> <p data-svelte-h="svelte-1cwsb16">The abstract from the paper is:</p> <p data-svelte-h="svelte-g4uhaw"><em>We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that efficiently samples large-scale foundational image diffusion models in just 1–4 steps while maintaining high image quality. We use score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal in combination with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. Our analyses show that our model clearly outperforms existing few-step methods (GANs,Latent Consistency Models) in a single step and reaches the performance of state-of-the-art diffusion models (SDXL) in only four steps. ADD is the first method to unlock single-step, real-time image synthesis with foundation models.</em></p>  <h2 class="relative group"><a id="tips" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#tips"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Tips</span></h2> <ul data-svelte-h="svelte-1s9reyj"><li>SDXL Turbo uses the exact same architecture as <a href="./stable_diffusion_xl">SDXL</a>, which means it also has the same API. Please refer to the <a href="./stable_diffusion_xl">SDXL</a> API reference for more details.</li> <li>SDXL Turbo should disable guidance scale by setting <code>guidance_scale=0.0</code>.</li> <li>SDXL Turbo should use <code>timestep_spacing=&#39;trailing&#39;</code> for the scheduler and use between 1 and 4 steps.</li> <li>SDXL Turbo has been trained to generate images of size 512x512.</li> <li>SDXL Turbo is open-access, but not open-source meaning that one might have to buy a model license in order to use it for commercial applications. Make sure to read the <a href="https://huggingface.co/stabilityai/sdxl-turbo" rel="nofollow">official model card</a> to learn more.</li></ul>  <div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p data-svelte-h="svelte-neqfre">To learn how to use SDXL Turbo for various tasks, how to optimize performance, and other usage examples, take a look at the <a href="../../../using-diffusers/sdxl_turbo">SDXL Turbo</a> guide.</p> <p data-svelte-h="svelte-1n0z285">Check out the <a href="https://huggingface.co/stabilityai" rel="nofollow">Stability AI</a> Hub organization for the official base and refiner model checkpoints!</p></div> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/stable_diffusion/sdxl_turbo.md" target="_blank"><span data-svelte-h="svelte-1kd6by1">&lt;</span> <span data-svelte-h="svelte-x0xyl0">&gt;</span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a>  <p></p> 
			
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