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| <link rel="modulepreload" href="/docs/diffusers/v0.25.0/ko/_app/immutable/chunks/IconCopyLink.96bbb92b.js"><!-- HEAD_svelte-1phssyn_START --><!-- HEAD_svelte-1phssyn_END --> <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 data-svelte-h="svelte-14686qn">Reinforcement learning training with DDPO</span></h1> <p data-svelte-h="svelte-8qjd2l">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 <code>~trl.DDPOTrainer</code>.</p> <p data-svelte-h="svelte-uztl8g">For more information, check out the <code>~trl.DDPOTrainer</code> 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> | |
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