diffusers/main/en/training/ddpo.html

<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Reinforcement learning training with DDPO&quot;,&quot;local&quot;:&quot;reinforcement-learning-training-with-ddpo&quot;,&quot;sections&quot;:[],&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;Reinforcement learning training with DDPO&quot;,&quot;local&quot;:&quot;reinforcement-learning-training-with-ddpo&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>   <h1 class="relative group"><a id="reinforcement-learning-training-with-ddpo" 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="#reinforcement-learning-training-with-ddpo"><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>Reinforcement learning training with DDPO</span></h1> <p data-svelte-h="svelte-1kjmrxq">You can fine-tune Stable Diffusion on a reward function via reinforcement learning with the 🤗 TRL library and 🤗 Diffusers. This is done with the Denoising Diffusion Policy Optimization (DDPO) algorithm introduced by Black et al. in <a href="https://arxiv.org/abs/2305.13301" rel="nofollow">Training Diffusion Models with Reinforcement Learning</a>, which is implemented in 🤗 TRL with the <a href="https://huggingface.co/docs/trl/main/en/trainer#trl.DDPOTrainer" rel="nofollow">DDPOTrainer</a>.</p> <p data-svelte-h="svelte-jwyq5v">For more information, check out the <a href="https://huggingface.co/docs/trl/main/en/trainer#trl.DDPOTrainer" rel="nofollow">DDPOTrainer</a> API reference and the <a href="https://huggingface.co/blog/trl-ddpo" rel="nofollow">Finetune Stable Diffusion Models with DDPO via TRL</a> blog post.</p> <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/training/ddpo.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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