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<h1 class="relative group"><a id="quicktour" 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="#quicktour"><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>Quicktour
	</span></h1>

<p>Get up and running with 🧨 Diffusers quickly!
Whether you’re a developer or an everyday user, this quick tour will help you get started and show you how to use <a href="/docs/diffusers/pr_516/en/using-diffusers/loading#diffusers.DiffusionPipeline">DiffusionPipeline</a> for inference.</p>
<p>Before you begin, make sure you have all the necessary libraries installed:</p>

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	<pre><!-- HTML_TAG_START -->pip install --upgrade diffusers<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="diffusionpipeline" 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="#diffusionpipeline"><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>DiffusionPipeline
	</span></h2>

<p>The <a href="/docs/diffusers/pr_516/en/using-diffusers/loading#diffusers.DiffusionPipeline">DiffusionPipeline</a> is the easiest way to use a pre-trained diffusion system for inference. You can use the <a href="/docs/diffusers/pr_516/en/using-diffusers/loading#diffusers.DiffusionPipeline">DiffusionPipeline</a> out-of-the-box for many tasks across different modalities. Take a look at the table below for some supported tasks:</p>
<table><thead><tr><th><strong>Task</strong></th>
<th><strong>Description</strong></th>
<th><strong>Pipeline</strong></th></tr></thead>
<tbody><tr><td>Unconditional Image Generation</td>
<td>generate an image from gaussian noise</td>
<td><a href="./using-diffusers/unconditional_image_generation%60">unconditional_image_generation</a></td></tr>
<tr><td>Text-Guided Image Generation</td>
<td>generate an image given a text prompt</td>
<td><a href="./using-diffusers/conditional_image_generation">conditional_image_generation</a></td></tr>
<tr><td>Text-Guided Image-to-Image Translation</td>
<td>generate an image given an original image and a text prompt</td>
<td><a href="./using-diffusers/img2img">img2img</a></td></tr>
<tr><td>Text-Guided Image-Inpainting</td>
<td>fill the masked part of an image given the image, the mask and a text prompt</td>
<td><a href="./using-diffusers/inpaint">inpaint</a></td></tr></tbody></table>
<p>For more in-detail information on how diffusion pipelines function for the different tasks, please have a look at the <a href="./using-diffusers/overview"><strong>Using Diffusers</strong></a> section.</p>
<p>As an example, start by creating an instance of <a href="/docs/diffusers/pr_516/en/using-diffusers/loading#diffusers.DiffusionPipeline">DiffusionPipeline</a> and specify which pipeline checkpoint you would like to download.
You can use the <a href="/docs/diffusers/pr_516/en/using-diffusers/loading#diffusers.DiffusionPipeline">DiffusionPipeline</a> for any <a href="https://huggingface.co/models?library=diffusers&sort=downloads" rel="nofollow">Diffusers’ checkpoint</a>.
In this guide though, you’ll use <a href="/docs/diffusers/pr_516/en/using-diffusers/loading#diffusers.DiffusionPipeline">DiffusionPipeline</a> for text-to-image generation with <a href="https://huggingface.co/CompVis/ldm-text2im-large-256" rel="nofollow">Latent Diffusion</a>:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/ldm-text2im-large-256&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>The <a href="/docs/diffusers/pr_516/en/using-diffusers/loading#diffusers.DiffusionPipeline">DiffusionPipeline</a> downloads and caches all modeling, tokenization, and scheduling components.
Because the model consists of roughly 1.4 billion parameters, we strongly recommend running it on GPU.
You can move the generator object to GPU, just like you would in PyTorch.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>generator.to(<span class="hljs-string">&quot;cuda&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>Now you can use the <code>generator</code> on your text prompt:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>image = generator(<span class="hljs-string">&quot;An image of a squirrel in Picasso style&quot;</span>).images[<span class="hljs-number">0</span>]<!-- HTML_TAG_END --></pre></div>
<p>The output is by default wrapped into a <a href="https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class" rel="nofollow">PIL Image object</a>.</p>
<p>You can save the image by simply calling:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;image_of_squirrel_painting.png&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>More advanced models, like <a href="https://huggingface.co/CompVis/stable-diffusion" rel="nofollow">Stable Diffusion</a> require you to accept a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" rel="nofollow">license</a> before running the model.
This is due to the improved image generation capabilities of the model and the potentially harmful content that could be produced with it.
Please, head over to your stable diffusion model of choice, <em>e.g.</em>  <a href="https://huggingface.co/runwayml/stable-diffusion-v1-5" rel="nofollow"><code>runwayml/stable-diffusion-v1-5</code></a>, read the license carefully and tick the checkbox if you agree.
You have to be a registered user in 🤗 Hugging Face Hub, and you’ll also need to use an access token for the code to work. For more information on access tokens, please refer to <a href="https://huggingface.co/docs/hub/security-tokens" rel="nofollow">this section of the documentation</a>.
Having “click-accepted” the license, you can save your token:</p>

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	<pre><!-- HTML_TAG_START -->AUTH_TOKEN = <span class="hljs-string">&quot;&lt;please-fill-with-your-token&gt;&quot;</span><!-- HTML_TAG_END --></pre></div>
<p>You can then load <a href="https://huggingface.co/runwayml/stable-diffusion-v1-5" rel="nofollow"><code>runwayml/stable-diffusion-v1-5</code></a>
just like we did before only that now you need to pass your <code>AUTH_TOKEN</code>:</p>

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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, use_auth_token=AUTH_TOKEN)<!-- HTML_TAG_END --></pre></div>
<p>If you do not pass your authentication token you will see that the diffusion system will not be correctly
downloaded. Forcing the user to pass an authentication token ensures that it can be verified that the
user has indeed read and accepted the license, which also means that an internet connection is required.</p>
<p><strong>Note</strong>: If you do not want to be forced to pass an authentication token, you can also simply download
the weights locally via:</p>

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	<pre><!-- HTML_TAG_START -->git lfs install
git clone https:<span class="hljs-regexp">//</span>huggingface.co<span class="hljs-regexp">/runwayml/</span>stable-diffusion-v1-<span class="hljs-number">5</span><!-- HTML_TAG_END --></pre></div>
<p>and then load locally saved weights into the pipeline. This way, you do not need to pass an authentication
token. Assuming that <code>&quot;./stable-diffusion-v1-5&quot;</code> is the local path to the cloned stable-diffusion-v1-5 repo,
you can also load the pipeline as follows:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>generator = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;./stable-diffusion-v1-5&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>Running the pipeline is then identical to the code above as it’s the same model architecture.</p>

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	<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><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>generator.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = generator(<span class="hljs-string">&quot;An image of a squirrel in Picasso style&quot;</span>).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;image_of_squirrel_painting.png&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<p>Diffusion systems can be used with multiple different <a href="./api/schedulers">schedulers</a> each with their
pros and cons. By default, Stable Diffusion runs with <a href="/docs/diffusers/pr_516/en/api/schedulers#diffusers.PNDMScheduler">PNDMScheduler</a>, but it’s very simple to
use a different scheduler. <em>E.g.</em> if you would instead like to use the <a href="/docs/diffusers/pr_516/en/api/schedulers#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a> scheduler,
you could use it as follows:</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><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> LMSDiscreteScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>scheduler = LMSDiscreteScheduler(beta_start=<span class="hljs-number">0.00085</span>, beta_end=<span class="hljs-number">0.012</span>, beta_schedule=<span class="hljs-string">&quot;scaled_linear&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = StableDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, scheduler=scheduler, use_auth_token=AUTH_TOKEN
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p><a href="https://stability.ai/" rel="nofollow">Stability AI’s</a> Stable Diffusion model is an impressive image generation model
and can do much more than just generating images from text. We have dedicated a whole documentation page,
just for Stable Diffusion <a href="./conceptual/stable_diffusion">here</a>.</p>
<p>If you want to know how to optimize Stable Diffusion to run on less memory, higher inference speeds, on specific hardware, such as Mac, or with <a href="https://onnxruntime.ai/" rel="nofollow">ONNX Runtime</a>, please have a look at our
optimization pages:</p>
<ul><li><a href="./optimization/fp16">Optimized PyTorch on GPU</a></li>
<li><a href="./optimization/mps">Mac OS with PyTorch</a></li>
<li><a href="./optimization/onnx">ONNX</a></li>
<li><a href="./optimization/open_vino">OpenVINO</a></li></ul>
<p>If you want to fine-tune or train your diffusion model, please have a look at the <a href="./training/overview"><strong>training section</strong></a></p>
<p>Finally, please be considerate when distributing generated images publicly 🤗.</p>


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