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<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Introduction to 🤗 Diffusers&quot;,&quot;local&quot;:&quot;introduction-to--diffusers&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;What You Will Learn&quot;,&quot;local&quot;:&quot;what-you-will-learn&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Prerequisites&quot;,&quot;local&quot;:&quot;prerequisites&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 1: Setup&quot;,&quot;local&quot;:&quot;step-1-setup&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Dreambooth: A Sneak Peak at What’s to Come&quot;,&quot;local&quot;:&quot;dreambooth-a-sneak-peak-at-whats-to-come&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;MVP (Minimum Viable Pipeline)&quot;,&quot;local&quot;:&quot;mvp-minimum-viable-pipeline&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 2: Download a training dataset&quot;,&quot;local&quot;:&quot;step-2-download-a-training-dataset&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 3: Define the Scheduler&quot;,&quot;local&quot;:&quot;step-3-define-the-scheduler&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 4: Define the Model&quot;,&quot;local&quot;:&quot;step-4-define-the-model&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 5: Create a Training Loop&quot;,&quot;local&quot;:&quot;step-5-create-a-training-loop&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 6: Generate Images&quot;,&quot;local&quot;:&quot;step-6-generate-images&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Option 1: Creating a pipeline:&quot;,&quot;local&quot;:&quot;option-1-creating-a-pipeline&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3},{&quot;title&quot;:&quot;Option 2: Writing a Sampling Loop&quot;,&quot;local&quot;:&quot;option-2-writing-a-sampling-loop&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 7: Push your model to the Hub&quot;,&quot;local&quot;:&quot;step-7-push-your-model-to-the-hub&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}">
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		<link rel="modulepreload" href="/docs/diffusion-course/pr_113/en/_app/immutable/chunks/DocNotebookDropdown.4d86bc1a.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Introduction to 🤗 Diffusers&quot;,&quot;local&quot;:&quot;introduction-to--diffusers&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;What You Will Learn&quot;,&quot;local&quot;:&quot;what-you-will-learn&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Prerequisites&quot;,&quot;local&quot;:&quot;prerequisites&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 1: Setup&quot;,&quot;local&quot;:&quot;step-1-setup&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Dreambooth: A Sneak Peak at What’s to Come&quot;,&quot;local&quot;:&quot;dreambooth-a-sneak-peak-at-whats-to-come&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;MVP (Minimum Viable Pipeline)&quot;,&quot;local&quot;:&quot;mvp-minimum-viable-pipeline&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 2: Download a training dataset&quot;,&quot;local&quot;:&quot;step-2-download-a-training-dataset&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 3: Define the Scheduler&quot;,&quot;local&quot;:&quot;step-3-define-the-scheduler&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 4: Define the Model&quot;,&quot;local&quot;:&quot;step-4-define-the-model&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 5: Create a Training Loop&quot;,&quot;local&quot;:&quot;step-5-create-a-training-loop&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 6: Generate Images&quot;,&quot;local&quot;:&quot;step-6-generate-images&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Option 1: Creating a pipeline:&quot;,&quot;local&quot;:&quot;option-1-creating-a-pipeline&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3},{&quot;title&quot;:&quot;Option 2: Writing a Sampling Loop&quot;,&quot;local&quot;:&quot;option-2-writing-a-sampling-loop&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Step 7: Push your model to the Hub&quot;,&quot;local&quot;:&quot;step-7-push-your-model-to-the-hub&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p> <div class="flex space-x-1 absolute z-10 right-0 top-0" style=""> <a href="https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/chore/update-doc-main-workflow-sha/unit1/01_introduction_to_diffusers.ipynb" target="_blank"><img alt="Open In Colab" class="!m-0" src="https://colab.research.google.com/assets/colab-badge.svg"></a> </div>  <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg></span> <span>Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></button></div> </div>  <h1 class="relative group"><a id="introduction-to--diffusers" 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="#introduction-to--diffusers"><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>Introduction to 🤗 Diffusers</span></h1> <p data-svelte-h="svelte-1ellde6"><img src="https://github.com/huggingface/diffusers/raw/main/docs/source/en/imgs/diffusers_library.jpg" alt="diffusers_library"></p> <p data-svelte-h="svelte-1qijnby">In this notebook, you’ll train your first diffusion model to <strong>generate images of cute butterflies 🦋.</strong> Along the way, you’ll learn about the core components of the 🤗 Diffusers library, which will provide a good foundation for the more advanced applications that we’ll cover later in the course.</p> <p data-svelte-h="svelte-1tyjc61">Let’s dive in!</p>  <h2 class="relative group"><a id="what-you-will-learn" 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="#what-you-will-learn"><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>What You Will Learn</span></h2> <p data-svelte-h="svelte-1st3pyt">In this notebook you will:</p> <ul data-svelte-h="svelte-v1etuj"><li>See a powerful custom diffusion model pipeline in action (with information on how to make your own version)</li> <li>Create your own mini pipeline by:
<ul><li>Recapping the core ideas behind diffusion models</li> <li>Loading in data from the Hub for training</li> <li>Exploring how we add noise to this data with a scheduler</li> <li>Creating and training the UNet model</li> <li>Putting the pieces together into a working pipeline</li></ul></li> <li>Edit and run a script for initializing longer training runs, that will handle
<ul><li>Multi-GPU training via 🤗 Accelerate</li> <li>Experiment logging to track critical stats</li> <li>Uploading the final model to the Hugging Face Hub</li></ul></li></ul> <p data-svelte-h="svelte-1hvsyk8">❓If you have any questions, please post them on the <code>#diffusion-models-class</code> channel on the Hugging Face Discord server. If you haven’t signed up yet, you can do so here: <a href="https://huggingface.co/join/discord" rel="nofollow">https://huggingface.co/join/discord</a></p>  <h2 class="relative group"><a id="prerequisites" 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="#prerequisites"><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>Prerequisites</span></h2> <p data-svelte-h="svelte-1w8x5lm">Before diving into the notebook, you should:</p> <ul data-svelte-h="svelte-4248et"><li>📖 Read the Unit 1 materials</li> <li>🤗 Create an account on the Hugging Face Hub. If you haven’t done so yet, you can do so here: <a href="https://huggingface.co/join" rel="nofollow">https://huggingface.co/join</a></li></ul>  <h2 class="relative group"><a id="step-1-setup" 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="#step-1-setup"><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>Step 1: Setup</span></h2> <p data-svelte-h="svelte-jjbntx">Run the following cell to install the diffusers library as well as a few other requirements:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->%pip install -qq -U diffusers datasets transformers accelerate ftfy pyarrow==<span class="hljs-number">9.0</span><span class="hljs-number">.0</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-gubkv2">Next, head over to <a href="https://huggingface.co/settings/tokens" rel="nofollow">https://huggingface.co/settings/tokens</a> and create an access token with write permission if you don’t already have one:</p> <p data-svelte-h="svelte-w8yie8"><img 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" alt="Screenshot from 2022-11-10 12-23-34.png"></p> <p data-svelte-h="svelte-1l53vqg">You can login with this token using the command line (<code>huggingface-cli login</code>) or by running the following cell:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

<span class="hljs-meta">&gt;&gt;&gt; </span>notebook_login()<!-- HTML_TAG_END --></pre></div> <pre data-svelte-h="svelte-1recgkl">Login successful
Your token has been saved to /root/.huggingface/token
</pre> <p data-svelte-h="svelte-ydl2wl">Then you need to install Git-LFS to upload your model checkpoints:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->%%capture
!sudo apt -qq install git-lfs
!git config --<span class="hljs-keyword">global</span> credential.helper store<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-vbg44i">Finally, let’s import the libraries we’ll be using and define a few convenience functions which we’ll use later in the notebook:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> torch.nn.functional <span class="hljs-keyword">as</span> F
<span class="hljs-keyword">from</span> matplotlib <span class="hljs-keyword">import</span> pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image


<span class="hljs-keyword">def</span> <span class="hljs-title function_">show_images</span>(<span class="hljs-params">x</span>):
    <span class="hljs-string">&quot;&quot;&quot;Given a batch of images x, make a grid and convert to PIL&quot;&quot;&quot;</span>
    x = x * <span class="hljs-number">0.5</span> + <span class="hljs-number">0.5</span>  <span class="hljs-comment"># Map from (-1, 1) back to (0, 1)</span>
    grid = torchvision.utils.make_grid(x)
    grid_im = grid.detach().cpu().permute(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">0</span>).clip(<span class="hljs-number">0</span>, <span class="hljs-number">1</span>) * <span class="hljs-number">255</span>
    grid_im = Image.fromarray(np.array(grid_im).astype(np.uint8))
    <span class="hljs-keyword">return</span> grid_im


<span class="hljs-keyword">def</span> <span class="hljs-title function_">make_grid</span>(<span class="hljs-params">images, size=<span class="hljs-number">64</span></span>):
    <span class="hljs-string">&quot;&quot;&quot;Given a list of PIL images, stack them together into a line for easy viewing&quot;&quot;&quot;</span>
    output_im = Image.new(<span class="hljs-string">&quot;RGB&quot;</span>, (size * <span class="hljs-built_in">len</span>(images), size))
    <span class="hljs-keyword">for</span> i, im <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(images):
        output_im.paste(im.resize((size, size)), (i * size, <span class="hljs-number">0</span>))
    <span class="hljs-keyword">return</span> output_im


<span class="hljs-comment"># Mac users may need device = &#x27;mps&#x27; (untested)</span>
device = torch.device(<span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1if1wli">OK, we’re all set!</p>  <h2 class="relative group"><a id="dreambooth-a-sneak-peak-at-whats-to-come" 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="#dreambooth-a-sneak-peak-at-whats-to-come"><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>Dreambooth: A Sneak Peak at What’s to Come</span></h2> <p data-svelte-h="svelte-635hqz">If you’ve looked at AI-related social media at all in the past few months, you’ve heard about Stable Diffusion. It’s a powerful text-conditioned latent diffusion model (don’t worry, we’ll learn what all that means). But it has a flaw: it doesn’t know what you or I look like unless we’re famous enough to have our images plastered around the internet.</p> <p data-svelte-h="svelte-10xou5">Dreambooth let’s us create our own model variant with some extra knowledge of a specific face, object or style. The Corridor Crew made an excellent video using this to tell stories with consistent characters, which is a great example of what this technique can do:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> IPython.display <span class="hljs-keyword">import</span> YouTubeVideo

<span class="hljs-meta">&gt;&gt;&gt; </span>YouTubeVideo(<span class="hljs-string">&quot;W4Mcuh38wyM&quot;</span>)<!-- HTML_TAG_END --></pre></div> <img 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"> <p data-svelte-h="svelte-xurcx0">Here’s an example using <a href="https://huggingface.co/sd-dreambooth-library/mr-potato-head" rel="nofollow">a model</a> trained on 5 photos of a popular children’s toy called “Mr Potato Head”.</p> <p data-svelte-h="svelte-4fpmek">First, we load the pipeline. This will download model weights etc. from the Hub. Since this will download several gigabytes of data for a one-line demo, you are welcome to skip this cell and simply admire the example output!</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

<span class="hljs-comment"># Check out https://huggingface.co/sd-dreambooth-library for loads of models from the community</span>
model_id = <span class="hljs-string">&quot;sd-dreambooth-library/mr-potato-head&quot;</span>

<span class="hljs-comment"># Load the pipeline</span>
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(
    device
)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-smnof7">Once the pipeline has finished loading, we can generate images with:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;an abstract oil painting of sks mr potato head by picasso&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt, num_inference_steps=<span class="hljs-number">50</span>, guidance_scale=<span class="hljs-number">7.5</span>).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image<!-- HTML_TAG_END --></pre></div> <img 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"> <p data-svelte-h="svelte-1la6c50"><strong>Exercise:</strong> Try it yourself with different prompts. The <code>sks</code> token represents a unique identifier for the novel concept in this case - what happens if you leave that out? You can also experiment with changing the number of sampling steps (how low can you go?) and the <code>guidance_scale</code>, which determines how much the model will try to match the prompt.</p> <p data-svelte-h="svelte-1v26bse">There’s a lot going on in that magical pipeline! By the end of the course you’ll know how it all works. For now, let’s take a look at how we can train a diffusion model from scratch.</p>  <h2 class="relative group"><a id="mvp-minimum-viable-pipeline" 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="#mvp-minimum-viable-pipeline"><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>MVP (Minimum Viable Pipeline)</span></h2> <p data-svelte-h="svelte-1xh0p6f">The core API of 🤗 Diffusers is divided into three main components:</p> <ol data-svelte-h="svelte-thruap"><li><strong>Pipelines</strong>: high-level classes designed to rapidly generate samples from popular trained diffusion models in a user-friendly fashion.</li> <li><strong>Models</strong>: popular architectures for training new diffusion models, <em>e.g.</em> <a href="https://arxiv.org/abs/1505.04597" rel="nofollow">UNet</a>.</li> <li><strong>Schedulers</strong>: various techniques for generating images from noise during <em>inference</em> as well as to generate noisy images for <em>training</em>.</li></ol> <p data-svelte-h="svelte-1gtbzr5">Pipelines are great for end-users, but if you’re here for this course we assume you want to know what is going on under the hood! So, over the rest of this notebook we’re going to build our own pipeline capable of generating small butterfly pictures. Here’s the final result in action:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load the butterfly pipeline</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>butterfly_pipeline = DDPMPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;johnowhitaker/ddpm-butterflies-32px&quot;</span>
<span class="hljs-meta">... </span>).to(device)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Create 8 images</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>images = butterfly_pipeline(batch_size=<span class="hljs-number">8</span>).images

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># View the result</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>make_grid(images)<!-- HTML_TAG_END --></pre></div> <img src="data:image/jpeg;base64,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"> <p data-svelte-h="svelte-1famlj8">Not as impressive as the DreamBooth example perhaps, but then we’re training from scratch with ~0.0001% of the data used to train Stable Diffusion. Speaking of training, recall from the introduction to this unit that training a diffusion model looks something like this:</p> <ol data-svelte-h="svelte-1b8dai1"><li>Load in some images from the training data</li> <li>Add noise, in different amounts.</li> <li>Feed the noisy versions of the inputs into the model</li> <li>Evaluate how well the model does at denoising these inputs</li> <li>Use this information to update the model weights, and repeat</li></ol> <p data-svelte-h="svelte-1x62mi">We’ll explore these steps one by one in the next few sections until we have a complete training loop working, and then we’ll explore how to sample from the trained model and how to package everything up into a pipeline for easy sharing. Let’s begin with the data…</p>  <h2 class="relative group"><a id="step-2-download-a-training-dataset" 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="#step-2-download-a-training-dataset"><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>Step 2: Download a training dataset</span></h2> <p data-svelte-h="svelte-1dcu5pf">For this example, we’ll use a dataset of images from the Hugging Face Hub. Specifically, <a href="https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset" rel="nofollow">this collection of 1000 butterfly pictures</a>. This is a very small dataset, so we’ve also included commented out lines for a few larger options. If you’d prefer to use your own collection of images, you can also use the commented-out code example to load in pictures from a folder instead.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> torchvision
<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">from</span> torchvision <span class="hljs-keyword">import</span> transforms

dataset = load_dataset(<span class="hljs-string">&quot;huggan/smithsonian_butterflies_subset&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)

<span class="hljs-comment"># Or load images from a local folder</span>
<span class="hljs-comment"># dataset = load_dataset(&quot;imagefolder&quot;, data_dir=&quot;path/to/folder&quot;)</span>

<span class="hljs-comment"># We&#x27;ll train on 32-pixel square images, but you can try larger sizes too</span>
image_size = <span class="hljs-number">32</span>
<span class="hljs-comment"># You can lower your batch size if you&#x27;re running out of GPU memory</span>
batch_size = <span class="hljs-number">64</span>

<span class="hljs-comment"># Define data augmentations</span>
preprocess = transforms.Compose(
    [
        transforms.Resize((image_size, image_size)),  <span class="hljs-comment"># Resize</span>
        transforms.RandomHorizontalFlip(),  <span class="hljs-comment"># Randomly flip (data augmentation)</span>
        transforms.ToTensor(),  <span class="hljs-comment"># Convert to tensor (0, 1)</span>
        transforms.Normalize([<span class="hljs-number">0.5</span>], [<span class="hljs-number">0.5</span>]),  <span class="hljs-comment"># Map to (-1, 1)</span>
    ]
)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">transform</span>(<span class="hljs-params">examples</span>):
    images = [preprocess(image.convert(<span class="hljs-string">&quot;RGB&quot;</span>)) <span class="hljs-keyword">for</span> image <span class="hljs-keyword">in</span> examples[<span class="hljs-string">&quot;image&quot;</span>]]
    <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;images&quot;</span>: images}


dataset.set_transform(transform)

<span class="hljs-comment"># Create a dataloader from the dataset to serve up the transformed images in batches</span>
train_dataloader = torch.utils.data.DataLoader(
    dataset, batch_size=batch_size, shuffle=<span class="hljs-literal">True</span>
)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-udpvfc">We can grab a batch of images and view some of them like so:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>xb = <span class="hljs-built_in">next</span>(<span class="hljs-built_in">iter</span>(train_dataloader))[<span class="hljs-string">&quot;images&quot;</span>].to(device)[:<span class="hljs-number">8</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;X shape:&quot;</span>, xb.shape)
<span class="hljs-meta">&gt;&gt;&gt; </span>show_images(xb).resize((<span class="hljs-number">8</span> * <span class="hljs-number">64</span>, <span class="hljs-number">64</span>), resample=Image.NEAREST)<!-- HTML_TAG_END --></pre></div> <pre data-svelte-h="svelte-1yh53qq">X shape: torch.Size([8, 3, 32, 32])
</pre> <p data-svelte-h="svelte-1q35gp7">We’re sticking to a small dataset with 32 pixel images to keep training times manageable in this notebook.</p>  <h2 class="relative group"><a id="step-3-define-the-scheduler" 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="#step-3-define-the-scheduler"><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>Step 3: Define the Scheduler</span></h2> <p data-svelte-h="svelte-13n9uf2">Our plan for training is to take these input images and add noise to them, then feed the noisy images to the model. And during inference, we will use the model predictions to iteratively remove noise. In <code>diffusers</code>,  these processes are both handled by the <strong>scheduler</strong>.</p> <p data-svelte-h="svelte-d5b6fx">The noise schedule determines how much noise is added at different timesteps. Here’s how we might create a scheduler using the default settings for ‘DDPM’ training and sampling (based on the paper <a href="https://arxiv.org/abs/2006.11239" rel="nofollow">“Denoising Diffusion Probabilistic Models”</a>):</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMScheduler

noise_scheduler = DDPMScheduler(num_train_timesteps=<span class="hljs-number">1000</span>)<!-- HTML_TAG_END --></pre></div> <p>The DDPM paper describes a corruption process that adds a small amount of noise for every ‘timestep’. Given <!-- HTML_TAG_START --><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow><annotation encoding="application/x-tex">x_{t-1}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6389em;vertical-align:-0.2083em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">t</span><span class="mbin mtight">−</span><span class="mord mtight">1</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2083em;"><span></span></span></span></span></span></span></span></span></span><!-- HTML_TAG_END --> for some timestep, we can get the next (slightly more noisy) version <!-- HTML_TAG_START --><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>t</mi></msub></mrow><annotation encoding="application/x-tex">x_t</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">t</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><!-- HTML_TAG_END --> with:<br><br></p> <p data-svelte-h="svelte-ktr7ft">$q(\mathbf{x}<em>t \vert \mathbf{x}</em>{t-1}) = \mathcal{N}(\mathbf{x}<em>t; \sqrt{1 - \beta_t} \mathbf{x}</em>{t-1}, \beta<em>t\mathbf{I}) \quad
q(\mathbf{x}</em>{1:T} \vert \mathbf{x}<em>0) = \prod^T</em>{t=1} q(\mathbf{x}<em>t \vert \mathbf{x}</em>{t-1})$<br><br></p> <p>That is, we take $x_{t-1}$, scale it by <!-- HTML_TAG_START --><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msqrt><mrow><mn>1</mn><mo>−</mo><msub><mi>β</mi><mi>t</mi></msub></mrow></msqrt></mrow><annotation encoding="application/x-tex">\sqrt{1 - \beta_t}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.04em;vertical-align:-0.205em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.835em;"><span class="svg-align" style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span class="mord" style="padding-left:0.833em;"><span class="mord">1</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.05278em;">β</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0528em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">t</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-2.795em;"><span class="pstrut" style="height:3em;"></span><span class="hide-tail" style="min-width:0.853em;height:1.08em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="1.08em" viewBox="0 0 400000 1080" preserveAspectRatio="xMinYMin slice"><path d="M95,702
c-2.7,0,-7.17,-2.7,-13.5,-8c-5.8,-5.3,-9.5,-10,-9.5,-14
c0,-2,0.3,-3.3,1,-4c1.3,-2.7,23.83,-20.7,67.5,-54
c44.2,-33.3,65.8,-50.3,66.5,-51c1.3,-1.3,3,-2,5,-2c4.7,0,8.7,3.3,12,10
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M834 80h400000v40h-400000z"/></svg></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.205em;"><span></span></span></span></span></span></span></span></span><!-- HTML_TAG_END --> and add noise scaled by $\beta_t$. This <!-- HTML_TAG_START --><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>β</mi></mrow><annotation encoding="application/x-tex">\beta</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8889em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05278em;">β</span></span></span></span><!-- HTML_TAG_END --> is defined for every t according to some schedule, and determines how much noise is added per timestep. Now, we don’t necessarily want to do this operation 500 times to get <!-- HTML_TAG_START --><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>500</mn></msub></mrow><annotation encoding="application/x-tex">x_{500}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">500</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><!-- HTML_TAG_END --> so we have another formula to get <!-- HTML_TAG_START --><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>t</mi></msub></mrow><annotation encoding="application/x-tex">x_t</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">t</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><!-- HTML_TAG_END --> for any t given $x_0$: <br><br></p> <p>$\begin{aligned}
q(\mathbf{x}_t \vert \mathbf{x}_0) &amp;= \mathcal{N}(\mathbf{x}_t; \sqrt{\bar{\alpha}_t} \mathbf{x}_0, {(1 - \bar{\alpha}_t)} \mathbf{I})
\end{aligned}$ where <!-- HTML_TAG_START --><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mover accent="true"><mi>α</mi><mo>ˉ</mo></mover><mi>t</mi></msub><mo>=</mo><msubsup><mo>∏</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>T</mi></msubsup><msub><mi>α</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">\bar{\alpha}_t = \prod_{i=1}^T \alpha_i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7178em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord accent"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.5678em;"><span style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span></span><span style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span class="accent-body" style="left:-0.2222em;"><span class="mord">ˉ</span></span></span></span></span></span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0037em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">t</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.2809em;vertical-align:-0.2997em;"></span><span class="mop"><span class="mop op-symbol small-op" style="position:relative;top:0em;">∏</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.9812em;"><span style="top:-2.4003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">i</span><span class="mrel mtight">=</span><span class="mord mtight">1</span></span></span></span><span style="top:-3.2029em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">T</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2997em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.0037em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><!-- HTML_TAG_END --> and $\alpha_i = 1-\beta_i$<br><br></p> <p>The maths notation always looks scary! Luckily the scheduler handles all that for us. We can plot <!-- HTML_TAG_START --><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msqrt><msub><mover accent="true"><mi>α</mi><mo>ˉ</mo></mover><mi>t</mi></msub></msqrt></mrow><annotation encoding="application/x-tex">\sqrt{\bar{\alpha}_t}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.04em;vertical-align:-0.2461em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.7939em;"><span class="svg-align" style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span class="mord" style="padding-left:0.833em;"><span class="mord"><span class="mord accent"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.5678em;"><span style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span></span><span style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span class="accent-body" style="left:-0.2222em;"><span class="mord">ˉ</span></span></span></span></span></span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0037em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">t</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-2.7539em;"><span class="pstrut" style="height:3em;"></span><span class="hide-tail" style="min-width:0.853em;height:1.08em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="1.08em" viewBox="0 0 400000 1080" preserveAspectRatio="xMinYMin slice"><path d="M95,702
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M1001 80h400000v40h-400000z"/></svg></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.305em;"><span></span></span></span></span></span></span></span></span><!-- HTML_TAG_END --> (labelled as <code data-svelte-h="svelte-z1hmfn">sqrt_one_minus_alpha_prod</code>) to view how the input (x) and the noise are scaled and mixed across different timesteps:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>plt.plot(noise_scheduler.alphas_cumprod.cpu() ** <span class="hljs-number">0.5</span>, label=<span class="hljs-string">r&quot;${\sqrt{\bar{\alpha}_t}}$&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>plt.plot((<span class="hljs-number">1</span> - noise_scheduler.alphas_cumprod.cpu()) ** <span class="hljs-number">0.5</span>, label=<span class="hljs-string">r&quot;$\sqrt{(1 - \bar{\alpha}_t)}$&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>plt.legend(fontsize=<span class="hljs-string">&quot;x-large&quot;</span>);<!-- HTML_TAG_END --></pre></div> <img 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<p data-svelte-h="svelte-136uvfe"><strong>Exercise:</strong> You can explore how this plot changes with different settings for beta_start, beta_end and beta_schedule by swapping in one of the commented-out options here:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-comment"># One with too little noise added:</span>
<span class="hljs-comment"># noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_start=0.001, beta_end=0.004)</span>
<span class="hljs-comment"># The &#x27;cosine&#x27; schedule, which may be better for small image sizes:</span>
<span class="hljs-comment"># noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule=&#x27;squaredcos_cap_v2&#x27;)</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-wst0cx">Whichever scheduler you’ve chosen, we can now use it to add noise in different amounts using the <code>noise_scheduler.add_noise</code> function like so:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>timesteps = torch.linspace(<span class="hljs-number">0</span>, <span class="hljs-number">999</span>, <span class="hljs-number">8</span>).long().to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>noise = torch.randn_like(xb)
<span class="hljs-meta">&gt;&gt;&gt; </span>noisy_xb = noise_scheduler.add_noise(xb, noise, timesteps)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Noisy X shape&quot;</span>, noisy_xb.shape)
<span class="hljs-meta">&gt;&gt;&gt; </span>show_images(noisy_xb).resize((<span class="hljs-number">8</span> * <span class="hljs-number">64</span>, <span class="hljs-number">64</span>), resample=Image.NEAREST)<!-- HTML_TAG_END --></pre></div> <pre data-svelte-h="svelte-ep4542">Noisy X shape torch.Size([8, 3, 32, 32])
</pre> <p data-svelte-h="svelte-lf0u2s">Again, explore the effect of using different noise schedules and parameters here. <a href="https://www.youtube.com/watch?v=fbLgFrlTnGU" rel="nofollow">This video</a> does a great job explaining some of the maths above in more detail, and is a great introduction to some of these concepts.</p>  <h2 class="relative group"><a id="step-4-define-the-model" 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="#step-4-define-the-model"><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>Step 4: Define the Model</span></h2> <p data-svelte-h="svelte-chlwsb">Now we come to the core component: the model itself.</p> <p data-svelte-h="svelte-on68x0">Most diffusion models use architectures that are some variant of a <a href="https://arxiv.org/abs/1505.04597" rel="nofollow">U-net</a> and that’s what we’ll use here.</p> <p data-svelte-h="svelte-1ygz2gl"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/unet-model.png"></p> <p data-svelte-h="svelte-1cva3rl">In a nutshell:</p> <ul data-svelte-h="svelte-ywp6qy"><li>the model has the input image go through several blocks of ResNet layers, each of which halves the image size by 2</li> <li>then through the same number of blocks that upsample it again.</li> <li>there are skip connections linking the features on the downsample path to the corresponding layers in the upsample path.</li></ul> <p data-svelte-h="svelte-1ymw2z9">A key feature of this model is that it predicts images of the same size as the input, which is exactly what we need here.</p> <p data-svelte-h="svelte-5kd4ay">Diffusers provides us a handy <code>UNet2DModel</code> class which creates the desired architecture in PyTorch.</p> <p data-svelte-h="svelte-kyixi9">Let’s create a U-net for our desired image size.
Note that <code>down_block_types</code> correspond to the downsampling blocks (green on the diagram above), and <code>up_block_types</code> are the upsampling blocks (red on the diagram):</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UNet2DModel

<span class="hljs-comment"># Create a model</span>
model = UNet2DModel(
    sample_size=image_size,  <span class="hljs-comment"># the target image resolution</span>
    in_channels=<span class="hljs-number">3</span>,  <span class="hljs-comment"># the number of input channels, 3 for RGB images</span>
    out_channels=<span class="hljs-number">3</span>,  <span class="hljs-comment"># the number of output channels</span>
    layers_per_block=<span class="hljs-number">2</span>,  <span class="hljs-comment"># how many ResNet layers to use per UNet block</span>
    block_out_channels=(<span class="hljs-number">64</span>, <span class="hljs-number">128</span>, <span class="hljs-number">128</span>, <span class="hljs-number">256</span>),  <span class="hljs-comment"># More channels -&gt; more parameters</span>
    down_block_types=(
        <span class="hljs-string">&quot;DownBlock2D&quot;</span>,  <span class="hljs-comment"># a regular ResNet downsampling block</span>
        <span class="hljs-string">&quot;DownBlock2D&quot;</span>,
        <span class="hljs-string">&quot;AttnDownBlock2D&quot;</span>,  <span class="hljs-comment"># a ResNet downsampling block with spatial self-attention</span>
        <span class="hljs-string">&quot;AttnDownBlock2D&quot;</span>,
    ),
    up_block_types=(
        <span class="hljs-string">&quot;AttnUpBlock2D&quot;</span>,
        <span class="hljs-string">&quot;AttnUpBlock2D&quot;</span>,  <span class="hljs-comment"># a ResNet upsampling block with spatial self-attention</span>
        <span class="hljs-string">&quot;UpBlock2D&quot;</span>,
        <span class="hljs-string">&quot;UpBlock2D&quot;</span>,  <span class="hljs-comment"># a regular ResNet upsampling block</span>
    ),
)
model.to(device);<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1unccnk">When dealing with higher-resolution inputs you may want to use more down and up-blocks, and keep the attention layers only at the lowest resolution (bottom) layers to reduce memory usage. We’ll talk later about how you might experiment to find the best settings for your use-case.</p> <p data-svelte-h="svelte-16rqz6l">We can check that passing in a batch of data and some random timesteps produces an output the same shape as the input data:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">with</span> torch.no_grad():
    model_prediction = model(noisy_xb, timesteps).sample
model_prediction.shape<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-h70d6e">In the next section we’ll see how to train this model.</p>  <h2 class="relative group"><a id="step-5-create-a-training-loop" 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="#step-5-create-a-training-loop"><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>Step 5: Create a Training Loop</span></h2> <p data-svelte-h="svelte-d7eubx">Time to train! Below is a typical optimization loop in PyTorch, where we run through the data batch by batch and update the parameters of our model each step using an optimizer - in this case the AdamW optimizer with a learning rate of 0.0004.</p> <p data-svelte-h="svelte-qjf17p">For each batch of data, we</p> <ul data-svelte-h="svelte-fk2bsi"><li>Sample some random timesteps</li> <li>Noise the data accordingly</li> <li>Feed the noisy data through the model</li> <li>Compare the model predictions with the target (i.e. the noise in this case) using mean squared error as our loss function</li> <li>Update the model parameters via <code>loss.backward()</code> and <code>optimizer.step()</code></li></ul> <p data-svelte-h="svelte-65m7pu">During this process we also log the losses over time for later plotting.</p> <p data-svelte-h="svelte-198eozz">NB: This code takes nearly 10 minutes to run - feel free to skip these two cells and use the pretrained model if you are in a hurry. Alternatively, you can explore how reducing the number of channels in each layer via the model definition above can speed things up.</p> <p data-svelte-h="svelte-48j9jd">The <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb" rel="nofollow">official diffusers training example</a> trains a larger model on this dataset at higher resolution, and is a good reference for what a less minimal training loop looks like:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Set the noise scheduler</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>noise_scheduler = DDPMScheduler(
<span class="hljs-meta">... </span>    num_train_timesteps=<span class="hljs-number">1000</span>, beta_schedule=<span class="hljs-string">&quot;squaredcos_cap_v2&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Training loop</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer = torch.optim.AdamW(model.parameters(), lr=<span class="hljs-number">4e-4</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>losses = []

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">30</span>):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> step, batch <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(train_dataloader):
<span class="hljs-meta">... </span>        clean_images = batch[<span class="hljs-string">&quot;images&quot;</span>].to(device)
<span class="hljs-meta">... </span>        <span class="hljs-comment"># Sample noise to add to the images</span>
<span class="hljs-meta">... </span>        noise = torch.randn(clean_images.shape).to(clean_images.device)
<span class="hljs-meta">... </span>        bs = clean_images.shape[<span class="hljs-number">0</span>]

<span class="hljs-meta">... </span>        <span class="hljs-comment"># Sample a random timestep for each image</span>
<span class="hljs-meta">... </span>        timesteps = torch.randint(
<span class="hljs-meta">... </span>            <span class="hljs-number">0</span>, noise_scheduler.num_train_timesteps, (bs,), device=clean_images.device
<span class="hljs-meta">... </span>        ).long()

<span class="hljs-meta">... </span>        <span class="hljs-comment"># Add noise to the clean images according to the noise magnitude at each timestep</span>
<span class="hljs-meta">... </span>        noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)

<span class="hljs-meta">... </span>        <span class="hljs-comment"># Get the model prediction</span>
<span class="hljs-meta">... </span>        noise_pred = model(noisy_images, timesteps, return_dict=<span class="hljs-literal">False</span>)[<span class="hljs-number">0</span>]

<span class="hljs-meta">... </span>        <span class="hljs-comment"># Calculate the loss</span>
<span class="hljs-meta">... </span>        loss = F.mse_loss(noise_pred, noise)
<span class="hljs-meta">... </span>        loss.backward(loss)
<span class="hljs-meta">... </span>        losses.append(loss.item())

<span class="hljs-meta">... </span>        <span class="hljs-comment"># Update the model parameters with the optimizer</span>
<span class="hljs-meta">... </span>        optimizer.step()
<span class="hljs-meta">... </span>        optimizer.zero_grad()

<span class="hljs-meta">... </span>    <span class="hljs-keyword">if</span> (epoch + <span class="hljs-number">1</span>) % <span class="hljs-number">5</span> == <span class="hljs-number">0</span>:
<span class="hljs-meta">... </span>        loss_last_epoch = <span class="hljs-built_in">sum</span>(losses[-<span class="hljs-built_in">len</span>(train_dataloader) :]) / <span class="hljs-built_in">len</span>(train_dataloader)
<span class="hljs-meta">... </span>        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Epoch:<span class="hljs-subst">{epoch+<span class="hljs-number">1</span>}</span>, loss: <span class="hljs-subst">{loss_last_epoch}</span>&quot;</span>)<!-- HTML_TAG_END --></pre></div> <pre data-svelte-h="svelte-t848hu">Epoch:5, loss: 0.16273280512541533
Epoch:10, loss: 0.11161588924005628
Epoch:15, loss: 0.10206522420048714
Epoch:20, loss: 0.08302505919709802
Epoch:25, loss: 0.07805309211835265
Epoch:30, loss: 0.07474562455900013
</pre> <p data-svelte-h="svelte-1wd5k43">Plotting the loss, we see that the model rapidly improves initially and then continues to get better at a slower rate (which is more obvious if we use a log scale as shown on the right):</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>fig, axs = plt.subplots(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, figsize=(<span class="hljs-number">12</span>, <span class="hljs-number">4</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>axs[<span class="hljs-number">0</span>].plot(losses)
<span class="hljs-meta">&gt;&gt;&gt; </span>axs[<span class="hljs-number">1</span>].plot(np.log(losses))
<span class="hljs-meta">&gt;&gt;&gt; </span>plt.show()<!-- HTML_TAG_END --></pre></div> <img 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"> <p data-svelte-h="svelte-aq8iy3">As an alternative to running the training code above, you can use the model from the pipeline like so:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-comment"># Uncomment to instead load the model I trained earlier:</span>
<span class="hljs-comment"># model = butterfly_pipeline.unet</span><!-- HTML_TAG_END --></pre></div>  <h2 class="relative group"><a id="step-6-generate-images" 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="#step-6-generate-images"><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>Step 6: Generate Images</span></h2> <p data-svelte-h="svelte-ejo3wx">How do we get images with this model?</p>  <h3 class="relative group"><a id="option-1-creating-a-pipeline" 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="#option-1-creating-a-pipeline"><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>Option 1: Creating a pipeline:</span></h3> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMPipeline

image_pipe = DDPMPipeline(unet=model, scheduler=noise_scheduler)<!-- HTML_TAG_END --></pre></div> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>pipeline_output = image_pipe()
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline_output.images[<span class="hljs-number">0</span>]<!-- HTML_TAG_END --></pre></div> <img src="data:image/jpeg;base64,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"> <p data-svelte-h="svelte-1elep04">We can save a pipeline to a local folder like so:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->image_pipe.save_pretrained(<span class="hljs-string">&quot;my_pipeline&quot;</span>)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1jdpjbh">Inspecting the folder contents:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>!ls my_pipeline/<!-- HTML_TAG_END --></pre></div> <pre data-svelte-h="svelte-1a2j1bv">model_index.json  scheduler  unet
</pre> <p data-svelte-h="svelte-n1w9x6">The <code>scheduler</code> and <code>unet</code> subfolders contain everything needed to re-create those components. For example, inside the <code>unet</code> folder you’ll find the model weights (<code>diffusion_pytorch_model.bin</code>) alongside a config file which specifies the UNet architecture.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>!ls my_pipeline/unet/<!-- HTML_TAG_END --></pre></div> <pre data-svelte-h="svelte-1ys5ub0">config.json  diffusion_pytorch_model.bin
</pre> <p data-svelte-h="svelte-1mm6k5j">Together, these files contain everything needed to recreate the pipeline. You can manually upload them to the hub to share the pipeline with others, or check out the code to do this via the API in the next section.</p>  <h3 class="relative group"><a id="option-2-writing-a-sampling-loop" 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="#option-2-writing-a-sampling-loop"><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>Option 2: Writing a Sampling Loop</span></h3> <p data-svelte-h="svelte-qfq5hp">If you inspect the forward method of the pipeline you’ll be able to see what is happening when we run <code>image_pipe()</code>:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-comment"># ??image_pipe.forward</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-gdoalu">We begin with random noise, and run through the scheduler timesteps from most to least noisy, removing a small amount of noise each step based on the model prediction:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Random starting point (8 random images):</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>sample = torch.randn(<span class="hljs-number">8</span>, <span class="hljs-number">3</span>, <span class="hljs-number">32</span>, <span class="hljs-number">32</span>).to(device)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> i, t <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(noise_scheduler.timesteps):

<span class="hljs-meta">... </span>    <span class="hljs-comment"># Get model pred</span>
<span class="hljs-meta">... </span>    <span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>        residual = model(sample, t).sample

<span class="hljs-meta">... </span>    <span class="hljs-comment"># Update sample with step</span>
<span class="hljs-meta">... </span>    sample = noise_scheduler.step(residual, t, sample).prev_sample

<span class="hljs-meta">&gt;&gt;&gt; </span>show_images(sample)<!-- HTML_TAG_END --></pre></div> <img src="data:image/jpeg;base64,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"> <p data-svelte-h="svelte-scgizw">The <code>noise_scheduler.step()</code> function does the maths required to update <code>sample</code> appropriately. There are a number of sampling methods - in the next unit we’ll see how we can swap in a different sampler to speed up image generation with existing models, and talk more about the theory behind sampling from diffusion models.</p>  <h2 class="relative group"><a id="step-7-push-your-model-to-the-hub" 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="#step-7-push-your-model-to-the-hub"><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>Step 7: Push your model to the Hub</span></h2> <p data-svelte-h="svelte-1dyodm3">In the example above we saved our pipeline to a local folder. To push our model to the Hub, we will need to model repository to push our files to. We’ll determine the repository name from the model ID we want to give our model (feel free to replace the <code>model_name</code> with your own choice; it just needs to contain your username, which is what the function <code>get_full_repo_name()</code> does):</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> get_full_repo_name

model_name = <span class="hljs-string">&quot;sd-class-butterflies-32&quot;</span>
hub_model_id = get_full_repo_name(model_name)
hub_model_id<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-2wrr8h">Next, create a model repository on the 🤗 Hub and push our model:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> HfApi, create_repo

create_repo(hub_model_id)
api = HfApi()
api.upload_folder(
    folder_path=<span class="hljs-string">&quot;my_pipeline/scheduler&quot;</span>, path_in_repo=<span class="hljs-string">&quot;&quot;</span>, repo_id=hub_model_id
)
api.upload_folder(folder_path=<span class="hljs-string">&quot;my_pipeline/unet&quot;</span>, path_in_repo=<span class="hljs-string">&quot;&quot;</span>, repo_id=hub_model_id)
api.upload_file(
    path_or_fileobj=<span class="hljs-string">&quot;my_pipeline/model_index.json&quot;</span>,
    path_in_repo=<span class="hljs-string">&quot;model_index.json&quot;</span>,
    repo_id=hub_model_id,
)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-2870qv">The last thing to do is create a nice model card so that our butterfly generator can easily be found on the Hub (feel free to expand and edit the description!):</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> ModelCard

content = <span class="hljs-string">f&quot;&quot;&quot;
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---

# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)

This model is a diffusion model for unconditional image generation of cute 🦋.

## Usage

```python
from diffusers import DDPMPipeline

pipeline = DDPMPipeline.from_pretrained(&#x27;<span class="hljs-subst">{hub_model_id}</span>&#x27;)
image = pipeline().images[0]
image</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-m1z6vm">&quot;&quot;&quot;</p> <p data-svelte-h="svelte-mu78ou">card = ModelCard(content)
card.push_to_hub(hub_model_id)</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->
Now that the model <span class="hljs-keyword">is</span> <span class="hljs-keyword">on</span> the Hub, you can download it <span class="hljs-keyword">from</span> anywhere <span class="hljs-keyword">by</span> <span class="hljs-keyword">using</span> the `from_pretrained()` <span class="hljs-keyword">method</span> <span class="hljs-title function_">of</span> <span class="hljs-title function_">the</span> `<span class="hljs-title function_">DDPMPipeline</span>` <span class="hljs-title function_">as</span> <span class="hljs-title function_">follows</span>&quot;

```<span class="hljs-title function_">python</span>
&gt;&gt;&gt; <span class="hljs-title function_">from</span> <span class="hljs-title function_">diffusers</span> <span class="hljs-title function_">import</span> <span class="hljs-title function_">DDPMPipeline</span>

&gt;&gt;&gt; <span class="hljs-title function_">image_pipe</span> = <span class="hljs-title function_">DDPMPipeline</span>.<span class="hljs-title function_">from_pretrained</span><span class="hljs-params">(hub_model_id)</span>
&gt;&gt;&gt; <span class="hljs-title function_">pipeline_output</span> = <span class="hljs-title function_">image_pipe</span><span class="hljs-params">()</span>
&gt;&gt;&gt; <span class="hljs-title function_">pipeline_output</span>.<span class="hljs-title function_">images</span>[0]<!-- HTML_TAG_END --></pre></div> <img src="data:image/jpeg;base64,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"> <p data-svelte-h="svelte-el0clj">Great it works!</p>  <h1 class="relative group"><a id="scaling-up-with--accelerate" 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="#scaling-up-with--accelerate"><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>Scaling up with 🤗 Accelerate</span></h1> <p data-svelte-h="svelte-1ujsfz4">This notebook was made for learning purposes, and as such I tried to keep the code as minimal and clean as possible. Because of this, we omitted some of the things you might want if you were to try training a larger model on much more data, such as multi-GPU support, logging of progress and example images, gradient checkpointing to support larger batch sizes, automatic uploading of models and so on. Thankfully most of these features are available in the example training script <a href="https://github.com/huggingface/diffusers/raw/main/examples/unconditional_image_generation/train_unconditional.py" rel="nofollow">here</a>.</p> <p data-svelte-h="svelte-1mbytej">You can download the file like so:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->!wget https://github.com/huggingface/diffusers/raw/main/examples/unconditional_image_generation/train_unconditional.py<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-mtqwvc">Open up the file and you’ll see where the model is defined and what settings are available. I ran the script with the following command:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-comment"># Let&#x27;s give our new model a name for the Hub</span>
model_name = <span class="hljs-string">&quot;sd-class-butterflies-64&quot;</span>
hub_model_id = get_full_repo_name(model_name)
hub_model_id<!-- HTML_TAG_END --></pre></div> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->!accelerate launch train_unconditional.py \
  --dataset_name=<span class="hljs-string">&quot;huggan/smithsonian_butterflies_subset&quot;</span> \
  --resolution=<span class="hljs-number">64</span> \
  --output_dir={model_name} \
  --train_batch_size=<span class="hljs-number">32</span> \
  --num_epochs=<span class="hljs-number">50</span> \
  --gradient_accumulation_steps=<span class="hljs-number">1</span> \
  --learning_rate=<span class="hljs-number">1e-4</span> \
  --lr_warmup_steps=<span class="hljs-number">500</span> \
  --mixed_precision=<span class="hljs-string">&quot;no&quot;</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-4flvpc">As before, let’s push the model to the Hub and create a nice model card (and feel free to edit it as you wish!):</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->create_repo(hub_model_id)
api = HfApi()
api.upload_folder(
    folder_path=<span class="hljs-string">f&quot;<span class="hljs-subst">{model_name}</span>/scheduler&quot;</span>, path_in_repo=<span class="hljs-string">&quot;&quot;</span>, repo_id=hub_model_id
)
api.upload_folder(
    folder_path=<span class="hljs-string">f&quot;<span class="hljs-subst">{model_name}</span>/unet&quot;</span>, path_in_repo=<span class="hljs-string">&quot;&quot;</span>, repo_id=hub_model_id
)
api.upload_file(
    path_or_fileobj=<span class="hljs-string">f&quot;<span class="hljs-subst">{model_name}</span>/model_index.json&quot;</span>,
    path_in_repo=<span class="hljs-string">&quot;model_index.json&quot;</span>,
    repo_id=hub_model_id,
)

content = <span class="hljs-string">f&quot;&quot;&quot;
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---

# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)

This model is a diffusion model for unconditional image generation of cute 🦋.

## Usage

```python
from diffusers import DDPMPipeline

pipeline = DDPMPipeline.from_pretrained(&#x27;<span class="hljs-subst">{hub_model_id}</span>&#x27;)
image = pipeline().images[0]
image</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-m1z6vm">&quot;&quot;&quot;</p> <p data-svelte-h="svelte-mu78ou">card = ModelCard(content)
card.push_to_hub(hub_model_id)</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->
About 45 minutes later, this is the result:

```python
<span class="hljs-meta prompt_">&gt;&gt;&gt;</span> <span class="language-python">pipeline = DDPMPipeline.from_pretrained(hub_model_id).to(device)</span>
<span class="hljs-meta prompt_">&gt;&gt;&gt;</span> <span class="language-python">images = pipeline(batch_size=<span class="hljs-number">8</span>).images</span>
<span class="hljs-meta prompt_">&gt;&gt;&gt;</span> <span class="language-python">make_grid(images)</span><!-- HTML_TAG_END --></pre></div> <img 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<p data-svelte-h="svelte-steh6u"><strong>Exercise:</strong> See if you can find training/model settings that give good results in as little time as possible, and share your findings with the community. Dig around in the script to see if you can understand the code, and ask for clarification on anything that looks confusing.</p>  <h1 class="relative group"><a id="avenues-for-further-exploration" 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="#avenues-for-further-exploration"><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>Avenues for Further Exploration</span></h1> <p data-svelte-h="svelte-2eg4hd">Hopefully this has given you a taste of what you can do with the 🤗 Diffusers library! Some possible next steps:</p> <ul data-svelte-h="svelte-5jmngg"><li>Try training an unconditional diffusion model on a new dataset - bonus points if you <a href="https://huggingface.co/docs/datasets/image_dataset" rel="nofollow">create one yourself</a>. You can find some great image datasets for this task in the <a href="https://huggingface.co/huggan" rel="nofollow">HugGan organization</a> on the Hub. Just make sure you downsample them if you don’t want to wait a very long time for the model to train!</li> <li>Try out DreamBooth to create your own customized Stable Diffusion pipeline using either <a href="https://huggingface.co/spaces/multimodalart/dreambooth-training" rel="nofollow">this Space</a> or <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb" rel="nofollow">this notebook</a></li> <li>Modify the training script to explore different UNet hyperparameters (number of layers, channels etc), different noise schedules etc.</li> <li>Check out the <a href="https://github.com/huggingface/diffusion-models-class/blob/main/unit1/02_diffusion_models_from_scratch.ipynb" rel="nofollow">Diffusion Models from Scratch</a> notebook for a different take on the core ideas we’ve covered in this unit</li></ul> <p data-svelte-h="svelte-8xkgoe">Good luck, and stay tuned for Unit 2!</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/diffusion-models-class/blob/main/unit1/01_introduction_to_diffusers.md" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a>  <p></p> 
			
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