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<h1 id="adapting-stable-diffusion-xl">Adapting Stable Diffusion XL<a class="headerlink" href="#adapting-stable-diffusion-xl" title="Permanent link">&para;</a></h1>
<p>Stable Diffusion XL (SDXL) is a very popular text-to-image open source foundation model. This guide will show you how to boost its capabilities with Refiners, using iconic adapters the framework supports out-of-the-box, i.e. without the need for tedious prompt engineering. We'll follow a step by step approach, progressively increasing the number of adapters involved to showcase how simple adapter composition is using Refiners. Our use case will be the generation of an image with "a futuristic castle surrounded by a forest, mountains in the background".</p>
<h2 id="prerequisites">Prerequisites<a class="headerlink" href="#prerequisites" title="Permanent link">&para;</a></h2>
<p>Make sure Refiners is installed in your local environment - see <a href="/getting-started/recommended/">Getting started</a> - and you have access to a decent GPU. </p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>As the examples in this guide's code snippets use CUDA, a minimum of 24GB VRAM is needed. </p>
</div>
<p>Before diving into the adapters themselves, let's establish a baseline by simply prompting SDXL with Refiners.</p>
<div class="admonition note">
<p class="admonition-title">Reminder</p>
<p>A StableDiffusion model is composed of three modules: </p>
<ul>
<li>An Autoencoder, responsible for embedding images into a latent space;</li>
<li>A UNet, responsible for the diffusion process;</li>
<li>A prompt encoder, such as CLIP, responsible for encoding the user prompt which will guide the diffusion process.</li>
</ul>
</div>
<p>As Refiners comes with a new model representation - see <a href="/concepts/chain/">Chain</a> - , you need to download and convert the weights of each module by calling our conversion scripts directly from your terminal (make sure you're in your local <code>refiners</code> directory, with your local environment active):</p>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>python<span class="w"> </span>scripts/conversion/convert_transformers_clip_text_model.py<span class="w"> </span>--from<span class="w"> </span><span class="s2">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span><span class="w"> </span>--subfolder2<span class="w"> </span>text_encoder_2<span class="w"> </span>--to<span class="w"> </span>DoubleCLIPTextEncoder.safetensors<span class="w"> </span>--half
</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>python<span class="w"> </span>scripts/conversion/convert_diffusers_unet.py<span class="w"> </span>--from<span class="w"> </span><span class="s2">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span><span class="w"> </span>--to<span class="w"> </span>sdxl-unet.safetensors<span class="w"> </span>--half
</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>python<span class="w"> </span>scripts/conversion/convert_diffusers_autoencoder_kl.py<span class="w"> </span>--from<span class="w"> </span><span class="s2">&quot;madebyollin/sdxl-vae-fp16-fix&quot;</span><span class="w"> </span>--subfolder<span class="w"> </span><span class="s2">&quot;&quot;</span><span class="w"> </span>--to<span class="w"> </span>sdxl-lda.safetensors<span class="w"> </span>--half
</span></code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This will download the original weights from https://huggingface.co/ which takes some time. If you already have this repo cloned locally, use the <code>--from /path/to/stabilityai/stable-diffusion-xl-base-1.0</code> option instead.</p>
</div>
<p>Now, we can write the Python script responsible for inference. Just create a simple <code>inference.py</code> file, and open it in your favorite editor.</p>
<p>Start by instantiating a <a class="autorefs autorefs-internal" href="../../reference/foundationals/latent_diffusion/#refiners.foundationals.latent_diffusion.stable_diffusion_xl.StableDiffusion_XL"><code>StableDiffusion_XL</code></a> model and load it with the converted weights:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">import</span> <span class="nn">torch</span>
</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">manual_seed</span><span class="p">,</span> <span class="n">no_grad</span>
</span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.stable_diffusion_xl</span> <span class="kn">import</span> <span class="n">StableDiffusion_XL</span>
</span><span id="__span-1-5"><a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a>
</span><span id="__span-1-6"><a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a><span class="c1"># Load SDXL</span>
</span><span id="__span-1-7"><a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a><span class="n">sdxl</span> <span class="o">=</span> <span class="n">StableDiffusion_XL</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span> <span class="c1"># Using half-precision for memory efficiency</span>
</span><span id="__span-1-8"><a id="__codelineno-1-8" name="__codelineno-1-8" href="#__codelineno-1-8"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">clip_text_encoder</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;DoubleCLIPTextEncoder.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-1-9"><a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-unet.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-1-10"><a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-lda.safetensors&quot;</span><span class="p">)</span>
</span></code></pre></div>
<p>Then, define the inference parameters by setting the appropriate prompt / seed / inference steps:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="c1"># Hyperparameters</span>
</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;a futuristic castle surrounded by a forest, mountains in the background&quot;</span>
</span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a><span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
</span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_inference_steps</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">first_step</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</span><span id="__span-2-5"><a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a>
</span><span id="__span-2-6"><a id="__codelineno-2-6" name="__codelineno-2-6" href="#__codelineno-2-6"></a><span class="c1"># Enable self-attention guidance to enhance the quality of the generated images</span>
</span><span id="__span-2-7"><a id="__codelineno-2-7" name="__codelineno-2-7" href="#__codelineno-2-7"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_self_attention_guidance</span><span class="p">(</span><span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
</span><span id="__span-2-8"><a id="__codelineno-2-8" name="__codelineno-2-8" href="#__codelineno-2-8"></a>
</span><span id="__span-2-9"><a id="__codelineno-2-9" name="__codelineno-2-9" href="#__codelineno-2-9"></a><span class="c1"># ... Inference process</span>
</span></code></pre></div>
<p>You can now define and run the proper inference process:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="k">with</span> <span class="n">no_grad</span><span class="p">():</span> <span class="c1"># Disable gradient calculation for memory-efficient inference</span>
</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a> <span class="n">clip_text_embedding</span><span class="p">,</span> <span class="n">pooled_text_embedding</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">compute_clip_text_embedding</span><span class="p">(</span>
</span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a> <span class="n">text</span><span class="o">=</span><span class="n">prompt</span> <span class="o">+</span> <span class="s2">&quot;, best quality, high quality&quot;</span><span class="p">,</span>
</span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a> <span class="n">negative_text</span><span class="o">=</span><span class="s2">&quot;monochrome, lowres, bad anatomy, worst quality, low quality&quot;</span><span class="p">,</span>
</span><span id="__span-3-5"><a id="__codelineno-3-5" name="__codelineno-3-5" href="#__codelineno-3-5"></a> <span class="p">)</span>
</span><span id="__span-3-6"><a id="__codelineno-3-6" name="__codelineno-3-6" href="#__codelineno-3-6"></a> <span class="n">time_ids</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">default_time_ids</span>
</span><span id="__span-3-7"><a id="__codelineno-3-7" name="__codelineno-3-7" href="#__codelineno-3-7"></a>
</span><span id="__span-3-8"><a id="__codelineno-3-8" name="__codelineno-3-8" href="#__codelineno-3-8"></a> <span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
</span><span id="__span-3-9"><a id="__codelineno-3-9" name="__codelineno-3-9" href="#__codelineno-3-9"></a>
</span><span id="__span-3-10"><a id="__codelineno-3-10" name="__codelineno-3-10" href="#__codelineno-3-10"></a> <span class="c1"># SDXL typically generates 1024x1024, here we use a higher resolution.</span>
</span><span id="__span-3-11"><a id="__codelineno-3-11" name="__codelineno-3-11" href="#__codelineno-3-11"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">init_latents</span><span class="p">((</span><span class="mi">2048</span><span class="p">,</span> <span class="mi">2048</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">sdxl</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
</span><span id="__span-3-12"><a id="__codelineno-3-12" name="__codelineno-3-12" href="#__codelineno-3-12"></a>
</span><span id="__span-3-13"><a id="__codelineno-3-13" name="__codelineno-3-13" href="#__codelineno-3-13"></a> <span class="c1"># Diffusion process</span>
</span><span id="__span-3-14"><a id="__codelineno-3-14" name="__codelineno-3-14" href="#__codelineno-3-14"></a> <span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">steps</span><span class="p">:</span>
</span><span id="__span-3-15"><a id="__codelineno-3-15" name="__codelineno-3-15" href="#__codelineno-3-15"></a> <span class="k">if</span> <span class="n">step</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
</span><span id="__span-3-16"><a id="__codelineno-3-16" name="__codelineno-3-16" href="#__codelineno-3-16"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Step </span><span class="si">{</span><span class="n">step</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</span><span id="__span-3-17"><a id="__codelineno-3-17" name="__codelineno-3-17" href="#__codelineno-3-17"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="p">(</span>
</span><span id="__span-3-18"><a id="__codelineno-3-18" name="__codelineno-3-18" href="#__codelineno-3-18"></a> <span class="n">x</span><span class="p">,</span>
</span><span id="__span-3-19"><a id="__codelineno-3-19" name="__codelineno-3-19" href="#__codelineno-3-19"></a> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">,</span>
</span><span id="__span-3-20"><a id="__codelineno-3-20" name="__codelineno-3-20" href="#__codelineno-3-20"></a> <span class="n">clip_text_embedding</span><span class="o">=</span><span class="n">clip_text_embedding</span><span class="p">,</span>
</span><span id="__span-3-21"><a id="__codelineno-3-21" name="__codelineno-3-21" href="#__codelineno-3-21"></a> <span class="n">pooled_text_embedding</span><span class="o">=</span><span class="n">pooled_text_embedding</span><span class="p">,</span>
</span><span id="__span-3-22"><a id="__codelineno-3-22" name="__codelineno-3-22" href="#__codelineno-3-22"></a> <span class="n">time_ids</span><span class="o">=</span><span class="n">time_ids</span><span class="p">,</span>
</span><span id="__span-3-23"><a id="__codelineno-3-23" name="__codelineno-3-23" href="#__codelineno-3-23"></a> <span class="p">)</span>
</span><span id="__span-3-24"><a id="__codelineno-3-24" name="__codelineno-3-24" href="#__codelineno-3-24"></a> <span class="n">predicted_image</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">decode_latents</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-3-25"><a id="__codelineno-3-25" name="__codelineno-3-25" href="#__codelineno-3-25"></a>
</span><span id="__span-3-26"><a id="__codelineno-3-26" name="__codelineno-3-26" href="#__codelineno-3-26"></a><span class="n">predicted_image</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;vanilla_sdxl.png&quot;</span><span class="p">)</span>
</span></code></pre></div>
<details class="example">
<summary>Expand to see the entire end-to-end code</summary>
<div class="language-py highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="kn">import</span> <span class="nn">torch</span>
</span><span id="__span-4-2"><a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a>
</span><span id="__span-4-3"><a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">manual_seed</span><span class="p">,</span> <span class="n">no_grad</span>
</span><span id="__span-4-4"><a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.stable_diffusion_xl</span> <span class="kn">import</span> <span class="n">StableDiffusion_XL</span>
</span><span id="__span-4-5"><a id="__codelineno-4-5" name="__codelineno-4-5" href="#__codelineno-4-5"></a>
</span><span id="__span-4-6"><a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a><span class="c1"># Load SDXL</span>
</span><span id="__span-4-7"><a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a><span class="n">sdxl</span> <span class="o">=</span> <span class="n">StableDiffusion_XL</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
</span><span id="__span-4-8"><a id="__codelineno-4-8" name="__codelineno-4-8" href="#__codelineno-4-8"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">clip_text_encoder</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;DoubleCLIPTextEncoder.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-4-9"><a id="__codelineno-4-9" name="__codelineno-4-9" href="#__codelineno-4-9"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-unet.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-4-10"><a id="__codelineno-4-10" name="__codelineno-4-10" href="#__codelineno-4-10"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-lda.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-4-11"><a id="__codelineno-4-11" name="__codelineno-4-11" href="#__codelineno-4-11"></a>
</span><span id="__span-4-12"><a id="__codelineno-4-12" name="__codelineno-4-12" href="#__codelineno-4-12"></a><span class="c1"># Hyperparameters</span>
</span><span id="__span-4-13"><a id="__codelineno-4-13" name="__codelineno-4-13" href="#__codelineno-4-13"></a><span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;a futuristic castle surrounded by a forest, mountains in the background&quot;</span>
</span><span id="__span-4-14"><a id="__codelineno-4-14" name="__codelineno-4-14" href="#__codelineno-4-14"></a><span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
</span><span id="__span-4-15"><a id="__codelineno-4-15" name="__codelineno-4-15" href="#__codelineno-4-15"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_inference_steps</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">first_step</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</span><span id="__span-4-16"><a id="__codelineno-4-16" name="__codelineno-4-16" href="#__codelineno-4-16"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_self_attention_guidance</span><span class="p">(</span>
</span><span id="__span-4-17"><a id="__codelineno-4-17" name="__codelineno-4-17" href="#__codelineno-4-17"></a> <span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.75</span>
</span><span id="__span-4-18"><a id="__codelineno-4-18" name="__codelineno-4-18" href="#__codelineno-4-18"></a><span class="p">)</span> <span class="c1"># Enable self-attention guidance to enhance the quality of the generated images</span>
</span><span id="__span-4-19"><a id="__codelineno-4-19" name="__codelineno-4-19" href="#__codelineno-4-19"></a>
</span><span id="__span-4-20"><a id="__codelineno-4-20" name="__codelineno-4-20" href="#__codelineno-4-20"></a>
</span><span id="__span-4-21"><a id="__codelineno-4-21" name="__codelineno-4-21" href="#__codelineno-4-21"></a><span class="k">with</span> <span class="n">no_grad</span><span class="p">():</span> <span class="c1"># Disable gradient calculation for memory-efficient inference</span>
</span><span id="__span-4-22"><a id="__codelineno-4-22" name="__codelineno-4-22" href="#__codelineno-4-22"></a> <span class="n">clip_text_embedding</span><span class="p">,</span> <span class="n">pooled_text_embedding</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">compute_clip_text_embedding</span><span class="p">(</span>
</span><span id="__span-4-23"><a id="__codelineno-4-23" name="__codelineno-4-23" href="#__codelineno-4-23"></a> <span class="n">text</span><span class="o">=</span><span class="n">prompt</span> <span class="o">+</span> <span class="s2">&quot;, best quality, high quality&quot;</span><span class="p">,</span>
</span><span id="__span-4-24"><a id="__codelineno-4-24" name="__codelineno-4-24" href="#__codelineno-4-24"></a> <span class="n">negative_text</span><span class="o">=</span><span class="s2">&quot;monochrome, lowres, bad anatomy, worst quality, low quality&quot;</span><span class="p">,</span>
</span><span id="__span-4-25"><a id="__codelineno-4-25" name="__codelineno-4-25" href="#__codelineno-4-25"></a> <span class="p">)</span>
</span><span id="__span-4-26"><a id="__codelineno-4-26" name="__codelineno-4-26" href="#__codelineno-4-26"></a> <span class="n">time_ids</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">default_time_ids</span>
</span><span id="__span-4-27"><a id="__codelineno-4-27" name="__codelineno-4-27" href="#__codelineno-4-27"></a>
</span><span id="__span-4-28"><a id="__codelineno-4-28" name="__codelineno-4-28" href="#__codelineno-4-28"></a> <span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
</span><span id="__span-4-29"><a id="__codelineno-4-29" name="__codelineno-4-29" href="#__codelineno-4-29"></a>
</span><span id="__span-4-30"><a id="__codelineno-4-30" name="__codelineno-4-30" href="#__codelineno-4-30"></a> <span class="c1"># SDXL typically generates 1024x1024, here we use a higher resolution.</span>
</span><span id="__span-4-31"><a id="__codelineno-4-31" name="__codelineno-4-31" href="#__codelineno-4-31"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">init_latents</span><span class="p">((</span><span class="mi">2048</span><span class="p">,</span> <span class="mi">2048</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">sdxl</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
</span><span id="__span-4-32"><a id="__codelineno-4-32" name="__codelineno-4-32" href="#__codelineno-4-32"></a>
</span><span id="__span-4-33"><a id="__codelineno-4-33" name="__codelineno-4-33" href="#__codelineno-4-33"></a> <span class="c1"># Diffusion process</span>
</span><span id="__span-4-34"><a id="__codelineno-4-34" name="__codelineno-4-34" href="#__codelineno-4-34"></a> <span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">steps</span><span class="p">:</span>
</span><span id="__span-4-35"><a id="__codelineno-4-35" name="__codelineno-4-35" href="#__codelineno-4-35"></a> <span class="k">if</span> <span class="n">step</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
</span><span id="__span-4-36"><a id="__codelineno-4-36" name="__codelineno-4-36" href="#__codelineno-4-36"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Step </span><span class="si">{</span><span class="n">step</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</span><span id="__span-4-37"><a id="__codelineno-4-37" name="__codelineno-4-37" href="#__codelineno-4-37"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="p">(</span>
</span><span id="__span-4-38"><a id="__codelineno-4-38" name="__codelineno-4-38" href="#__codelineno-4-38"></a> <span class="n">x</span><span class="p">,</span>
</span><span id="__span-4-39"><a id="__codelineno-4-39" name="__codelineno-4-39" href="#__codelineno-4-39"></a> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">,</span>
</span><span id="__span-4-40"><a id="__codelineno-4-40" name="__codelineno-4-40" href="#__codelineno-4-40"></a> <span class="n">clip_text_embedding</span><span class="o">=</span><span class="n">clip_text_embedding</span><span class="p">,</span>
</span><span id="__span-4-41"><a id="__codelineno-4-41" name="__codelineno-4-41" href="#__codelineno-4-41"></a> <span class="n">pooled_text_embedding</span><span class="o">=</span><span class="n">pooled_text_embedding</span><span class="p">,</span>
</span><span id="__span-4-42"><a id="__codelineno-4-42" name="__codelineno-4-42" href="#__codelineno-4-42"></a> <span class="n">time_ids</span><span class="o">=</span><span class="n">time_ids</span><span class="p">,</span>
</span><span id="__span-4-43"><a id="__codelineno-4-43" name="__codelineno-4-43" href="#__codelineno-4-43"></a> <span class="p">)</span>
</span><span id="__span-4-44"><a id="__codelineno-4-44" name="__codelineno-4-44" href="#__codelineno-4-44"></a> <span class="n">predicted_image</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">decode_latents</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-4-45"><a id="__codelineno-4-45" name="__codelineno-4-45" href="#__codelineno-4-45"></a>
</span><span id="__span-4-46"><a id="__codelineno-4-46" name="__codelineno-4-46" href="#__codelineno-4-46"></a><span class="n">predicted_image</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;vanilla_sdxl.png&quot;</span><span class="p">)</span>
</span></code></pre></div>
</details>
<p>It's time to execute your code. The resulting image should look like this:</p>
<figure markdown>
<img src="vanilla_sdxl.webp" alt="Generated image of a castle using default SDXL weights" width="400">
<figcaption>Generated image of a castle using default SDXL weights.</figcaption>
</figure>
<p>It is not really what we prompted the model for, unfortunately. To get a more futuristic-looking castle, you can either go for tedious prompt engineering, or use a pretrainered LoRA tailored to our use case, like the <a href="https://civitai.com/models/105945?modelVersionId=140624">Sci-fi Environments</a> LoRA available on Civitai. We'll now show you how the LoRA option works with Refiners. </p>
<h2 id="single-lora">Single LoRA<a class="headerlink" href="#single-lora" title="Permanent link">&para;</a></h2>
<p>To use the <a href="https://civitai.com/models/105945?modelVersionId=140624">Sci-fi Environments</a> LoRA, all you have to do is download its weights to disk as a <code>.safetensors</code>, and inject them into SDXL using <a class="autorefs autorefs-internal" href="../../reference/foundationals/latent_diffusion/#refiners.foundationals.latent_diffusion.lora.SDLoraManager"><code>SDLoraManager</code></a> right after instantiating <code>StableDiffusion_XL</code>:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">load_from_safetensors</span>
</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.lora</span> <span class="kn">import</span> <span class="n">SDLoraManager</span>
</span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a>
</span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a><span class="c1"># Load LoRA weights from disk and inject them into target</span>
</span><span id="__span-5-5"><a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a><span class="n">manager</span> <span class="o">=</span> <span class="n">SDLoraManager</span><span class="p">(</span><span class="n">sdxl</span><span class="p">)</span>
</span><span id="__span-5-6"><a id="__codelineno-5-6" name="__codelineno-5-6" href="#__codelineno-5-6"></a><span class="n">scifi_lora_weights</span> <span class="o">=</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;Sci-fi_Environments_sdxl.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-5-7"><a id="__codelineno-5-7" name="__codelineno-5-7" href="#__codelineno-5-7"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;scifi-lora&quot;</span><span class="p">,</span> <span class="n">tensors</span><span class="o">=</span><span class="n">scifi_lora_weights</span><span class="p">)</span>
</span></code></pre></div>
<details class="example">
<summary>Expand to see the entire end-to-end code</summary>
<div class="language-py highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a><span class="kn">import</span> <span class="nn">torch</span>
</span><span id="__span-6-2"><a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a>
</span><span id="__span-6-3"><a id="__codelineno-6-3" name="__codelineno-6-3" href="#__codelineno-6-3"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">load_from_safetensors</span><span class="p">,</span> <span class="n">manual_seed</span><span class="p">,</span> <span class="n">no_grad</span>
</span><span id="__span-6-4"><a id="__codelineno-6-4" name="__codelineno-6-4" href="#__codelineno-6-4"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.lora</span> <span class="kn">import</span> <span class="n">SDLoraManager</span>
</span><span id="__span-6-5"><a id="__codelineno-6-5" name="__codelineno-6-5" href="#__codelineno-6-5"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.stable_diffusion_xl</span> <span class="kn">import</span> <span class="n">StableDiffusion_XL</span>
</span><span id="__span-6-6"><a id="__codelineno-6-6" name="__codelineno-6-6" href="#__codelineno-6-6"></a>
</span><span id="__span-6-7"><a id="__codelineno-6-7" name="__codelineno-6-7" href="#__codelineno-6-7"></a><span class="c1"># Load SDXL</span>
</span><span id="__span-6-8"><a id="__codelineno-6-8" name="__codelineno-6-8" href="#__codelineno-6-8"></a><span class="n">sdxl</span> <span class="o">=</span> <span class="n">StableDiffusion_XL</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
</span><span id="__span-6-9"><a id="__codelineno-6-9" name="__codelineno-6-9" href="#__codelineno-6-9"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">clip_text_encoder</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;DoubleCLIPTextEncoder.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-6-10"><a id="__codelineno-6-10" name="__codelineno-6-10" href="#__codelineno-6-10"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-unet.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-6-11"><a id="__codelineno-6-11" name="__codelineno-6-11" href="#__codelineno-6-11"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-lda.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-6-12"><a id="__codelineno-6-12" name="__codelineno-6-12" href="#__codelineno-6-12"></a>
</span><span id="__span-6-13"><a id="__codelineno-6-13" name="__codelineno-6-13" href="#__codelineno-6-13"></a><span class="c1"># Load LoRA weights from disk and inject them into target</span>
</span><span id="__span-6-14"><a id="__codelineno-6-14" name="__codelineno-6-14" href="#__codelineno-6-14"></a><span class="n">manager</span> <span class="o">=</span> <span class="n">SDLoraManager</span><span class="p">(</span><span class="n">sdxl</span><span class="p">)</span>
</span><span id="__span-6-15"><a id="__codelineno-6-15" name="__codelineno-6-15" href="#__codelineno-6-15"></a><span class="n">scifi_lora_weights</span> <span class="o">=</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;Sci-fi_Environments_sdxl.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-6-16"><a id="__codelineno-6-16" name="__codelineno-6-16" href="#__codelineno-6-16"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;scifi-lora&quot;</span><span class="p">,</span> <span class="n">tensors</span><span class="o">=</span><span class="n">scifi_lora_weights</span><span class="p">)</span>
</span><span id="__span-6-17"><a id="__codelineno-6-17" name="__codelineno-6-17" href="#__codelineno-6-17"></a>
</span><span id="__span-6-18"><a id="__codelineno-6-18" name="__codelineno-6-18" href="#__codelineno-6-18"></a><span class="c1"># Hyperparameters</span>
</span><span id="__span-6-19"><a id="__codelineno-6-19" name="__codelineno-6-19" href="#__codelineno-6-19"></a><span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;a futuristic castle surrounded by a forest, mountains in the background&quot;</span>
</span><span id="__span-6-20"><a id="__codelineno-6-20" name="__codelineno-6-20" href="#__codelineno-6-20"></a><span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
</span><span id="__span-6-21"><a id="__codelineno-6-21" name="__codelineno-6-21" href="#__codelineno-6-21"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_inference_steps</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">first_step</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</span><span id="__span-6-22"><a id="__codelineno-6-22" name="__codelineno-6-22" href="#__codelineno-6-22"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_self_attention_guidance</span><span class="p">(</span>
</span><span id="__span-6-23"><a id="__codelineno-6-23" name="__codelineno-6-23" href="#__codelineno-6-23"></a> <span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.75</span>
</span><span id="__span-6-24"><a id="__codelineno-6-24" name="__codelineno-6-24" href="#__codelineno-6-24"></a><span class="p">)</span> <span class="c1"># Enable self-attention guidance to enhance the quality of the generated images</span>
</span><span id="__span-6-25"><a id="__codelineno-6-25" name="__codelineno-6-25" href="#__codelineno-6-25"></a>
</span><span id="__span-6-26"><a id="__codelineno-6-26" name="__codelineno-6-26" href="#__codelineno-6-26"></a><span class="k">with</span> <span class="n">no_grad</span><span class="p">():</span>
</span><span id="__span-6-27"><a id="__codelineno-6-27" name="__codelineno-6-27" href="#__codelineno-6-27"></a> <span class="n">clip_text_embedding</span><span class="p">,</span> <span class="n">pooled_text_embedding</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">compute_clip_text_embedding</span><span class="p">(</span>
</span><span id="__span-6-28"><a id="__codelineno-6-28" name="__codelineno-6-28" href="#__codelineno-6-28"></a> <span class="n">text</span><span class="o">=</span><span class="n">prompt</span> <span class="o">+</span> <span class="s2">&quot;, best quality, high quality&quot;</span><span class="p">,</span>
</span><span id="__span-6-29"><a id="__codelineno-6-29" name="__codelineno-6-29" href="#__codelineno-6-29"></a> <span class="n">negative_text</span><span class="o">=</span><span class="s2">&quot;monochrome, lowres, bad anatomy, worst quality, low quality&quot;</span><span class="p">,</span>
</span><span id="__span-6-30"><a id="__codelineno-6-30" name="__codelineno-6-30" href="#__codelineno-6-30"></a> <span class="p">)</span>
</span><span id="__span-6-31"><a id="__codelineno-6-31" name="__codelineno-6-31" href="#__codelineno-6-31"></a> <span class="n">time_ids</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">default_time_ids</span>
</span><span id="__span-6-32"><a id="__codelineno-6-32" name="__codelineno-6-32" href="#__codelineno-6-32"></a>
</span><span id="__span-6-33"><a id="__codelineno-6-33" name="__codelineno-6-33" href="#__codelineno-6-33"></a> <span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
</span><span id="__span-6-34"><a id="__codelineno-6-34" name="__codelineno-6-34" href="#__codelineno-6-34"></a>
</span><span id="__span-6-35"><a id="__codelineno-6-35" name="__codelineno-6-35" href="#__codelineno-6-35"></a> <span class="c1"># SDXL typically generates 1024x1024, here we use a higher resolution.</span>
</span><span id="__span-6-36"><a id="__codelineno-6-36" name="__codelineno-6-36" href="#__codelineno-6-36"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">init_latents</span><span class="p">((</span><span class="mi">2048</span><span class="p">,</span> <span class="mi">2048</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">sdxl</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
</span><span id="__span-6-37"><a id="__codelineno-6-37" name="__codelineno-6-37" href="#__codelineno-6-37"></a>
</span><span id="__span-6-38"><a id="__codelineno-6-38" name="__codelineno-6-38" href="#__codelineno-6-38"></a> <span class="c1"># Diffusion process</span>
</span><span id="__span-6-39"><a id="__codelineno-6-39" name="__codelineno-6-39" href="#__codelineno-6-39"></a> <span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">steps</span><span class="p">:</span>
</span><span id="__span-6-40"><a id="__codelineno-6-40" name="__codelineno-6-40" href="#__codelineno-6-40"></a> <span class="k">if</span> <span class="n">step</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
</span><span id="__span-6-41"><a id="__codelineno-6-41" name="__codelineno-6-41" href="#__codelineno-6-41"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Step </span><span class="si">{</span><span class="n">step</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</span><span id="__span-6-42"><a id="__codelineno-6-42" name="__codelineno-6-42" href="#__codelineno-6-42"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="p">(</span>
</span><span id="__span-6-43"><a id="__codelineno-6-43" name="__codelineno-6-43" href="#__codelineno-6-43"></a> <span class="n">x</span><span class="p">,</span>
</span><span id="__span-6-44"><a id="__codelineno-6-44" name="__codelineno-6-44" href="#__codelineno-6-44"></a> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">,</span>
</span><span id="__span-6-45"><a id="__codelineno-6-45" name="__codelineno-6-45" href="#__codelineno-6-45"></a> <span class="n">clip_text_embedding</span><span class="o">=</span><span class="n">clip_text_embedding</span><span class="p">,</span>
</span><span id="__span-6-46"><a id="__codelineno-6-46" name="__codelineno-6-46" href="#__codelineno-6-46"></a> <span class="n">pooled_text_embedding</span><span class="o">=</span><span class="n">pooled_text_embedding</span><span class="p">,</span>
</span><span id="__span-6-47"><a id="__codelineno-6-47" name="__codelineno-6-47" href="#__codelineno-6-47"></a> <span class="n">time_ids</span><span class="o">=</span><span class="n">time_ids</span><span class="p">,</span>
</span><span id="__span-6-48"><a id="__codelineno-6-48" name="__codelineno-6-48" href="#__codelineno-6-48"></a> <span class="p">)</span>
</span><span id="__span-6-49"><a id="__codelineno-6-49" name="__codelineno-6-49" href="#__codelineno-6-49"></a> <span class="n">predicted_image</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">decode_latents</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-6-50"><a id="__codelineno-6-50" name="__codelineno-6-50" href="#__codelineno-6-50"></a>
</span><span id="__span-6-51"><a id="__codelineno-6-51" name="__codelineno-6-51" href="#__codelineno-6-51"></a><span class="n">predicted_image</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;scifi_sdxl.png&quot;</span><span class="p">)</span>
</span></code></pre></div>
</details>
<p>You should get something like this - pretty neat, isn't it? </p>
<figure markdown>
<img src="scifi_sdxl.webp" alt="Sci-fi castle" width="400">
<figcaption>Generated image of a castle in sci-fi style.</figcaption>
</figure>
<h2 id="multiple-loras">Multiple LoRAs<a class="headerlink" href="#multiple-loras" title="Permanent link">&para;</a></h2>
<p>Continuing with our futuristic castle example, we might want to turn it, for instance, into a pixel art. </p>
<p>Again, we could either try some tedious prompt engineering,
or instead use another LoRA found on the web, such as <a href="https://civitai.com/models/120096/pixel-art-xl?modelVersionId=135931">Pixel Art LoRA</a>, found on Civitai.
This is dead simple as <a class="autorefs autorefs-internal" href="../../reference/foundationals/latent_diffusion/#refiners.foundationals.latent_diffusion.lora.SDLoraManager"><code>SDLoraManager</code></a> allows loading multiple LoRAs:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-7-1"><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a><span class="c1"># Load LoRAs weights from disk and inject them into target</span>
</span><span id="__span-7-2"><a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a><span class="n">manager</span> <span class="o">=</span> <span class="n">SDLoraManager</span><span class="p">(</span><span class="n">sdxl</span><span class="p">)</span>
</span><span id="__span-7-3"><a id="__codelineno-7-3" name="__codelineno-7-3" href="#__codelineno-7-3"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;scifi-lora&quot;</span><span class="p">,</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;Sci-fi_Environments_sdxl.safetensors&quot;</span><span class="p">))</span>
</span><span id="__span-7-4"><a id="__codelineno-7-4" name="__codelineno-7-4" href="#__codelineno-7-4"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;pixel-art-lora&quot;</span><span class="p">,</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;pixel-art-xl-v1.1.safetensors&quot;</span><span class="p">))</span>
</span></code></pre></div>
<p>Adapters such as LoRAs also have a <a class="autorefs autorefs-internal" href="../../reference/fluxion/adapters/#refiners.fluxion.adapters.Lora.scale">scale</a> (roughly) quantifying the effect of this Adapter.
Refiners allows setting different scales for each Adapter, allowing the user to balance the effect of each Adapter:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-8-1"><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a><span class="c1"># Load LoRAs weights from disk and inject them into target</span>
</span><span id="__span-8-2"><a id="__codelineno-8-2" name="__codelineno-8-2" href="#__codelineno-8-2"></a><span class="n">manager</span> <span class="o">=</span> <span class="n">SDLoraManager</span><span class="p">(</span><span class="n">sdxl</span><span class="p">)</span>
</span><span id="__span-8-3"><a id="__codelineno-8-3" name="__codelineno-8-3" href="#__codelineno-8-3"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;scifi-lora&quot;</span><span class="p">,</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;Sci-fi_Environments_sdxl.safetensors&quot;</span><span class="p">),</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
</span><span id="__span-8-4"><a id="__codelineno-8-4" name="__codelineno-8-4" href="#__codelineno-8-4"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;pixel-art-lora&quot;</span><span class="p">,</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;pixel-art-xl-v1.1.safetensors&quot;</span><span class="p">),</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.4</span><span class="p">)</span>
</span></code></pre></div>
<details class="example">
<summary>Expand to see the entire end-to-end code</summary>
<div class="language-py highlight"><pre><span></span><code><span id="__span-9-1"><a id="__codelineno-9-1" name="__codelineno-9-1" href="#__codelineno-9-1"></a><span class="kn">import</span> <span class="nn">torch</span>
</span><span id="__span-9-2"><a id="__codelineno-9-2" name="__codelineno-9-2" href="#__codelineno-9-2"></a>
</span><span id="__span-9-3"><a id="__codelineno-9-3" name="__codelineno-9-3" href="#__codelineno-9-3"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">load_from_safetensors</span><span class="p">,</span> <span class="n">manual_seed</span><span class="p">,</span> <span class="n">no_grad</span>
</span><span id="__span-9-4"><a id="__codelineno-9-4" name="__codelineno-9-4" href="#__codelineno-9-4"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.lora</span> <span class="kn">import</span> <span class="n">SDLoraManager</span>
</span><span id="__span-9-5"><a id="__codelineno-9-5" name="__codelineno-9-5" href="#__codelineno-9-5"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.stable_diffusion_xl</span> <span class="kn">import</span> <span class="n">StableDiffusion_XL</span>
</span><span id="__span-9-6"><a id="__codelineno-9-6" name="__codelineno-9-6" href="#__codelineno-9-6"></a>
</span><span id="__span-9-7"><a id="__codelineno-9-7" name="__codelineno-9-7" href="#__codelineno-9-7"></a><span class="c1"># Load SDXL</span>
</span><span id="__span-9-8"><a id="__codelineno-9-8" name="__codelineno-9-8" href="#__codelineno-9-8"></a><span class="n">sdxl</span> <span class="o">=</span> <span class="n">StableDiffusion_XL</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
</span><span id="__span-9-9"><a id="__codelineno-9-9" name="__codelineno-9-9" href="#__codelineno-9-9"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">clip_text_encoder</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;DoubleCLIPTextEncoder.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-9-10"><a id="__codelineno-9-10" name="__codelineno-9-10" href="#__codelineno-9-10"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-unet.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-9-11"><a id="__codelineno-9-11" name="__codelineno-9-11" href="#__codelineno-9-11"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-lda.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-9-12"><a id="__codelineno-9-12" name="__codelineno-9-12" href="#__codelineno-9-12"></a>
</span><span id="__span-9-13"><a id="__codelineno-9-13" name="__codelineno-9-13" href="#__codelineno-9-13"></a><span class="c1"># Load LoRAs weights from disk and inject them into target</span>
</span><span id="__span-9-14"><a id="__codelineno-9-14" name="__codelineno-9-14" href="#__codelineno-9-14"></a><span class="n">manager</span> <span class="o">=</span> <span class="n">SDLoraManager</span><span class="p">(</span><span class="n">sdxl</span><span class="p">)</span>
</span><span id="__span-9-15"><a id="__codelineno-9-15" name="__codelineno-9-15" href="#__codelineno-9-15"></a><span class="n">scifi_lora_weights</span> <span class="o">=</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;Sci-fi_Environments_sdxl.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-9-16"><a id="__codelineno-9-16" name="__codelineno-9-16" href="#__codelineno-9-16"></a><span class="n">pixel_art_lora_weights</span> <span class="o">=</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;pixel-art-xl-v1.1.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-9-17"><a id="__codelineno-9-17" name="__codelineno-9-17" href="#__codelineno-9-17"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;scifi-lora&quot;</span><span class="p">,</span> <span class="n">scifi_lora_weights</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
</span><span id="__span-9-18"><a id="__codelineno-9-18" name="__codelineno-9-18" href="#__codelineno-9-18"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;pixel-art-lora&quot;</span><span class="p">,</span> <span class="n">pixel_art_lora_weights</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.4</span><span class="p">)</span>
</span><span id="__span-9-19"><a id="__codelineno-9-19" name="__codelineno-9-19" href="#__codelineno-9-19"></a>
</span><span id="__span-9-20"><a id="__codelineno-9-20" name="__codelineno-9-20" href="#__codelineno-9-20"></a><span class="c1"># Hyperparameters</span>
</span><span id="__span-9-21"><a id="__codelineno-9-21" name="__codelineno-9-21" href="#__codelineno-9-21"></a><span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;a futuristic castle surrounded by a forest, mountains in the background&quot;</span>
</span><span id="__span-9-22"><a id="__codelineno-9-22" name="__codelineno-9-22" href="#__codelineno-9-22"></a><span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
</span><span id="__span-9-23"><a id="__codelineno-9-23" name="__codelineno-9-23" href="#__codelineno-9-23"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_inference_steps</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">first_step</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</span><span id="__span-9-24"><a id="__codelineno-9-24" name="__codelineno-9-24" href="#__codelineno-9-24"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_self_attention_guidance</span><span class="p">(</span>
</span><span id="__span-9-25"><a id="__codelineno-9-25" name="__codelineno-9-25" href="#__codelineno-9-25"></a> <span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.75</span>
</span><span id="__span-9-26"><a id="__codelineno-9-26" name="__codelineno-9-26" href="#__codelineno-9-26"></a><span class="p">)</span> <span class="c1"># Enable self-attention guidance to enhance the quality of the generated images</span>
</span><span id="__span-9-27"><a id="__codelineno-9-27" name="__codelineno-9-27" href="#__codelineno-9-27"></a>
</span><span id="__span-9-28"><a id="__codelineno-9-28" name="__codelineno-9-28" href="#__codelineno-9-28"></a><span class="k">with</span> <span class="n">no_grad</span><span class="p">():</span>
</span><span id="__span-9-29"><a id="__codelineno-9-29" name="__codelineno-9-29" href="#__codelineno-9-29"></a> <span class="n">clip_text_embedding</span><span class="p">,</span> <span class="n">pooled_text_embedding</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">compute_clip_text_embedding</span><span class="p">(</span>
</span><span id="__span-9-30"><a id="__codelineno-9-30" name="__codelineno-9-30" href="#__codelineno-9-30"></a> <span class="n">text</span><span class="o">=</span><span class="n">prompt</span> <span class="o">+</span> <span class="s2">&quot;, best quality, high quality&quot;</span><span class="p">,</span>
</span><span id="__span-9-31"><a id="__codelineno-9-31" name="__codelineno-9-31" href="#__codelineno-9-31"></a> <span class="n">negative_text</span><span class="o">=</span><span class="s2">&quot;monochrome, lowres, bad anatomy, worst quality, low quality&quot;</span><span class="p">,</span>
</span><span id="__span-9-32"><a id="__codelineno-9-32" name="__codelineno-9-32" href="#__codelineno-9-32"></a> <span class="p">)</span>
</span><span id="__span-9-33"><a id="__codelineno-9-33" name="__codelineno-9-33" href="#__codelineno-9-33"></a> <span class="n">time_ids</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">default_time_ids</span>
</span><span id="__span-9-34"><a id="__codelineno-9-34" name="__codelineno-9-34" href="#__codelineno-9-34"></a>
</span><span id="__span-9-35"><a id="__codelineno-9-35" name="__codelineno-9-35" href="#__codelineno-9-35"></a> <span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
</span><span id="__span-9-36"><a id="__codelineno-9-36" name="__codelineno-9-36" href="#__codelineno-9-36"></a>
</span><span id="__span-9-37"><a id="__codelineno-9-37" name="__codelineno-9-37" href="#__codelineno-9-37"></a> <span class="c1"># SDXL typically generates 1024x1024, here we use a higher resolution.</span>
</span><span id="__span-9-38"><a id="__codelineno-9-38" name="__codelineno-9-38" href="#__codelineno-9-38"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">init_latents</span><span class="p">((</span><span class="mi">2048</span><span class="p">,</span> <span class="mi">2048</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">sdxl</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
</span><span id="__span-9-39"><a id="__codelineno-9-39" name="__codelineno-9-39" href="#__codelineno-9-39"></a>
</span><span id="__span-9-40"><a id="__codelineno-9-40" name="__codelineno-9-40" href="#__codelineno-9-40"></a> <span class="c1"># Diffusion process</span>
</span><span id="__span-9-41"><a id="__codelineno-9-41" name="__codelineno-9-41" href="#__codelineno-9-41"></a> <span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">steps</span><span class="p">:</span>
</span><span id="__span-9-42"><a id="__codelineno-9-42" name="__codelineno-9-42" href="#__codelineno-9-42"></a> <span class="k">if</span> <span class="n">step</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
</span><span id="__span-9-43"><a id="__codelineno-9-43" name="__codelineno-9-43" href="#__codelineno-9-43"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Step </span><span class="si">{</span><span class="n">step</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</span><span id="__span-9-44"><a id="__codelineno-9-44" name="__codelineno-9-44" href="#__codelineno-9-44"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="p">(</span>
</span><span id="__span-9-45"><a id="__codelineno-9-45" name="__codelineno-9-45" href="#__codelineno-9-45"></a> <span class="n">x</span><span class="p">,</span>
</span><span id="__span-9-46"><a id="__codelineno-9-46" name="__codelineno-9-46" href="#__codelineno-9-46"></a> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">,</span>
</span><span id="__span-9-47"><a id="__codelineno-9-47" name="__codelineno-9-47" href="#__codelineno-9-47"></a> <span class="n">clip_text_embedding</span><span class="o">=</span><span class="n">clip_text_embedding</span><span class="p">,</span>
</span><span id="__span-9-48"><a id="__codelineno-9-48" name="__codelineno-9-48" href="#__codelineno-9-48"></a> <span class="n">pooled_text_embedding</span><span class="o">=</span><span class="n">pooled_text_embedding</span><span class="p">,</span>
</span><span id="__span-9-49"><a id="__codelineno-9-49" name="__codelineno-9-49" href="#__codelineno-9-49"></a> <span class="n">time_ids</span><span class="o">=</span><span class="n">time_ids</span><span class="p">,</span>
</span><span id="__span-9-50"><a id="__codelineno-9-50" name="__codelineno-9-50" href="#__codelineno-9-50"></a> <span class="p">)</span>
</span><span id="__span-9-51"><a id="__codelineno-9-51" name="__codelineno-9-51" href="#__codelineno-9-51"></a> <span class="n">predicted_image</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">decode_latents</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-9-52"><a id="__codelineno-9-52" name="__codelineno-9-52" href="#__codelineno-9-52"></a>
</span><span id="__span-9-53"><a id="__codelineno-9-53" name="__codelineno-9-53" href="#__codelineno-9-53"></a><span class="n">predicted_image</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;scifi_pixel_sdxl.png&quot;</span><span class="p">)</span>
</span></code></pre></div>
</details>
<p>The results are looking great:</p>
<figure markdown>
<img src="scifi_pixel_sdxl.webp" alt="Sci-fi Pixel Art castle" width="400">
<figcaption>Generated image of a castle in sci-fi, pixel art style.</figcaption>
</figure>
<h2 id="multiple-loras-ip-adapter">Multiple LoRAs + IP-Adapter<a class="headerlink" href="#multiple-loras-ip-adapter" title="Permanent link">&para;</a></h2>
<p>Refiners really shines when it comes to composing different Adapters to fully exploit the possibilities of foundation models.</p>
<p>For instance, IP-Adapter (covered in <a href="https://blog.finegrain.ai/posts/supercharge-stable-diffusion-ip-adapter/">a previous blog post</a>) is a common choice for practictioners wanting to guide the diffusion process towards a specific prompt image.</p>
<p>In our example, consider this image of the <a href="https://en.wikipedia.org/wiki/Neuschwanstein_Castle">Neuschwanstein Castle</a>:</p>
<figure markdown>
<img src="german-castle.jpg" alt="Castle Image" width="400">
<figcaption>Credits: Bayerische Schlösserverwaltung, Anton Brandl</figcaption>
</figure>
<p>We would like to guide the diffusion process to align with this image, using IP-Adapter. First, download the image as well as the weights of IP-Adapter by calling the following commands from your terminal (again, make sure in you're in your local <code>refiners</code> directory):</p>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-10-1"><a id="__codelineno-10-1" name="__codelineno-10-1" href="#__codelineno-10-1"></a>curl<span class="w"> </span>-O<span class="w"> </span>https://refine.rs/guides/adapting_sdxl/german-castle.jpg
</span><span id="__span-10-2"><a id="__codelineno-10-2" name="__codelineno-10-2" href="#__codelineno-10-2"></a>python<span class="w"> </span>scripts/conversion/convert_transformers_clip_image_model.py<span class="w"> </span>--from<span class="w"> </span><span class="s2">&quot;stabilityai/stable-diffusion-2-1-unclip&quot;</span><span class="w"> </span>--to<span class="w"> </span>CLIPImageEncoderH.safetensors<span class="w"> </span>--half
</span><span id="__span-10-3"><a id="__codelineno-10-3" name="__codelineno-10-3" href="#__codelineno-10-3"></a>curl<span class="w"> </span>-LO<span class="w"> </span>https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin
</span><span id="__span-10-4"><a id="__codelineno-10-4" name="__codelineno-10-4" href="#__codelineno-10-4"></a>python<span class="w"> </span>scripts/conversion/convert_diffusers_ip_adapter.py<span class="w"> </span>--from<span class="w"> </span>ip-adapter-plus_sdxl_vit-h.bin<span class="w"> </span>--half
</span></code></pre></div>
<p>This will download and convert both IP-Adapter and CLIP Image Encoder pretrained weights.</p>
<p>Then, in your Python code, simply instantiate a <a class="autorefs autorefs-internal" href="../../reference/foundationals/latent_diffusion/#refiners.foundationals.latent_diffusion.stable_diffusion_xl.SDXLIPAdapter"><code>SDXLIPAdapter</code></a> targeting our <code>sdxl.unet</code>, and inject it using a simple <code>.inject()</code> call:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-11-1"><a id="__codelineno-11-1" name="__codelineno-11-1" href="#__codelineno-11-1"></a><span class="c1"># IP-Adapter</span>
</span><span id="__span-11-2"><a id="__codelineno-11-2" name="__codelineno-11-2" href="#__codelineno-11-2"></a><span class="n">ip_adapter</span> <span class="o">=</span> <span class="n">SDXLIPAdapter</span><span class="p">(</span>
</span><span id="__span-11-3"><a id="__codelineno-11-3" name="__codelineno-11-3" href="#__codelineno-11-3"></a> <span class="n">target</span><span class="o">=</span><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="p">,</span>
</span><span id="__span-11-4"><a id="__codelineno-11-4" name="__codelineno-11-4" href="#__codelineno-11-4"></a> <span class="n">weights</span><span class="o">=</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;ip-adapter-plus_sdxl_vit-h.safetensors&quot;</span><span class="p">),</span>
</span><span id="__span-11-5"><a id="__codelineno-11-5" name="__codelineno-11-5" href="#__codelineno-11-5"></a> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
</span><span id="__span-11-6"><a id="__codelineno-11-6" name="__codelineno-11-6" href="#__codelineno-11-6"></a> <span class="n">fine_grained</span><span class="o">=</span><span class="kc">True</span> <span class="c1"># Use fine-grained IP-Adapter (i.e IP-Adapter Plus)</span>
</span><span id="__span-11-7"><a id="__codelineno-11-7" name="__codelineno-11-7" href="#__codelineno-11-7"></a><span class="p">)</span>
</span><span id="__span-11-8"><a id="__codelineno-11-8" name="__codelineno-11-8" href="#__codelineno-11-8"></a><span class="n">ip_adapter</span><span class="o">.</span><span class="n">clip_image_encoder</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;CLIPImageEncoderH.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-11-9"><a id="__codelineno-11-9" name="__codelineno-11-9" href="#__codelineno-11-9"></a><span class="n">ip_adapter</span><span class="o">.</span><span class="n">inject</span><span class="p">()</span>
</span></code></pre></div>
<p>Then, at runtime, we simply compute the embedding of the image prompt through the <code>ip_adapter</code> object, and set its embedding calling <code>.set_clip_image_embedding()</code>:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-12-1"><a id="__codelineno-12-1" name="__codelineno-12-1" href="#__codelineno-12-1"></a><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
</span><span id="__span-12-2"><a id="__codelineno-12-2" name="__codelineno-12-2" href="#__codelineno-12-2"></a><span class="n">image_prompt</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">&quot;german-castle.jpg&quot;</span><span class="p">)</span>
</span><span id="__span-12-3"><a id="__codelineno-12-3" name="__codelineno-12-3" href="#__codelineno-12-3"></a>
</span><span id="__span-12-4"><a id="__codelineno-12-4" name="__codelineno-12-4" href="#__codelineno-12-4"></a><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
</span><span id="__span-12-5"><a id="__codelineno-12-5" name="__codelineno-12-5" href="#__codelineno-12-5"></a> <span class="n">clip_image_embedding</span> <span class="o">=</span> <span class="n">ip_adapter</span><span class="o">.</span><span class="n">compute_clip_image_embedding</span><span class="p">(</span><span class="n">ip_adapter</span><span class="o">.</span><span class="n">preprocess_image</span><span class="p">(</span><span class="n">image_prompt</span><span class="p">))</span>
</span><span id="__span-12-6"><a id="__codelineno-12-6" name="__codelineno-12-6" href="#__codelineno-12-6"></a> <span class="n">ip_adapter</span><span class="o">.</span><span class="n">set_clip_image_embedding</span><span class="p">(</span><span class="n">clip_image_embedding</span><span class="p">)</span>
</span><span id="__span-12-7"><a id="__codelineno-12-7" name="__codelineno-12-7" href="#__codelineno-12-7"></a>
</span><span id="__span-12-8"><a id="__codelineno-12-8" name="__codelineno-12-8" href="#__codelineno-12-8"></a><span class="c1"># And start the diffusion process</span>
</span></code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Be wary that composing Adapters (especially ones of different natures, such as LoRAs and IP-Adapter) can be tricky, as their respective effects can be adversarial. This is visible in our example below. In the code below, we tuned the LoRAs scales respectively to <code>1.5</code> and <code>1.55</code>. We invite you to try and test different seeds and scales to find the perfect combination!</p>
</div>
<details class="example">
<summary>Expand to see the entire end-to-end code</summary>
<div class="language-py highlight"><pre><span></span><code><span id="__span-13-1"><a id="__codelineno-13-1" name="__codelineno-13-1" href="#__codelineno-13-1"></a><span class="kn">import</span> <span class="nn">torch</span>
</span><span id="__span-13-2"><a id="__codelineno-13-2" name="__codelineno-13-2" href="#__codelineno-13-2"></a><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
</span><span id="__span-13-3"><a id="__codelineno-13-3" name="__codelineno-13-3" href="#__codelineno-13-3"></a>
</span><span id="__span-13-4"><a id="__codelineno-13-4" name="__codelineno-13-4" href="#__codelineno-13-4"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">load_from_safetensors</span><span class="p">,</span> <span class="n">manual_seed</span><span class="p">,</span> <span class="n">no_grad</span>
</span><span id="__span-13-5"><a id="__codelineno-13-5" name="__codelineno-13-5" href="#__codelineno-13-5"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.lora</span> <span class="kn">import</span> <span class="n">SDLoraManager</span>
</span><span id="__span-13-6"><a id="__codelineno-13-6" name="__codelineno-13-6" href="#__codelineno-13-6"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.stable_diffusion_xl</span> <span class="kn">import</span> <span class="n">StableDiffusion_XL</span>
</span><span id="__span-13-7"><a id="__codelineno-13-7" name="__codelineno-13-7" href="#__codelineno-13-7"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.stable_diffusion_xl.image_prompt</span> <span class="kn">import</span> <span class="n">SDXLIPAdapter</span>
</span><span id="__span-13-8"><a id="__codelineno-13-8" name="__codelineno-13-8" href="#__codelineno-13-8"></a>
</span><span id="__span-13-9"><a id="__codelineno-13-9" name="__codelineno-13-9" href="#__codelineno-13-9"></a><span class="c1"># Load SDXL</span>
</span><span id="__span-13-10"><a id="__codelineno-13-10" name="__codelineno-13-10" href="#__codelineno-13-10"></a><span class="n">sdxl</span> <span class="o">=</span> <span class="n">StableDiffusion_XL</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
</span><span id="__span-13-11"><a id="__codelineno-13-11" name="__codelineno-13-11" href="#__codelineno-13-11"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">clip_text_encoder</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;DoubleCLIPTextEncoder.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-13-12"><a id="__codelineno-13-12" name="__codelineno-13-12" href="#__codelineno-13-12"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-unet.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-13-13"><a id="__codelineno-13-13" name="__codelineno-13-13" href="#__codelineno-13-13"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-lda.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-13-14"><a id="__codelineno-13-14" name="__codelineno-13-14" href="#__codelineno-13-14"></a>
</span><span id="__span-13-15"><a id="__codelineno-13-15" name="__codelineno-13-15" href="#__codelineno-13-15"></a><span class="c1"># Load LoRAs weights from disk and inject them into target</span>
</span><span id="__span-13-16"><a id="__codelineno-13-16" name="__codelineno-13-16" href="#__codelineno-13-16"></a><span class="n">manager</span> <span class="o">=</span> <span class="n">SDLoraManager</span><span class="p">(</span><span class="n">sdxl</span><span class="p">)</span>
</span><span id="__span-13-17"><a id="__codelineno-13-17" name="__codelineno-13-17" href="#__codelineno-13-17"></a><span class="n">scifi_lora_weights</span> <span class="o">=</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;Sci-fi_Environments_sdxl.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-13-18"><a id="__codelineno-13-18" name="__codelineno-13-18" href="#__codelineno-13-18"></a><span class="n">pixel_art_lora_weights</span> <span class="o">=</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;pixel-art-xl-v1.1.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-13-19"><a id="__codelineno-13-19" name="__codelineno-13-19" href="#__codelineno-13-19"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;scifi-lora&quot;</span><span class="p">,</span> <span class="n">scifi_lora_weights</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
</span><span id="__span-13-20"><a id="__codelineno-13-20" name="__codelineno-13-20" href="#__codelineno-13-20"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;pixel-art-lora&quot;</span><span class="p">,</span> <span class="n">pixel_art_lora_weights</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.55</span><span class="p">)</span>
</span><span id="__span-13-21"><a id="__codelineno-13-21" name="__codelineno-13-21" href="#__codelineno-13-21"></a>
</span><span id="__span-13-22"><a id="__codelineno-13-22" name="__codelineno-13-22" href="#__codelineno-13-22"></a><span class="c1"># Load IP-Adapter</span>
</span><span id="__span-13-23"><a id="__codelineno-13-23" name="__codelineno-13-23" href="#__codelineno-13-23"></a><span class="n">ip_adapter</span> <span class="o">=</span> <span class="n">SDXLIPAdapter</span><span class="p">(</span>
</span><span id="__span-13-24"><a id="__codelineno-13-24" name="__codelineno-13-24" href="#__codelineno-13-24"></a> <span class="n">target</span><span class="o">=</span><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="p">,</span>
</span><span id="__span-13-25"><a id="__codelineno-13-25" name="__codelineno-13-25" href="#__codelineno-13-25"></a> <span class="n">weights</span><span class="o">=</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;ip-adapter-plus_sdxl_vit-h.safetensors&quot;</span><span class="p">),</span>
</span><span id="__span-13-26"><a id="__codelineno-13-26" name="__codelineno-13-26" href="#__codelineno-13-26"></a> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
</span><span id="__span-13-27"><a id="__codelineno-13-27" name="__codelineno-13-27" href="#__codelineno-13-27"></a> <span class="n">fine_grained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># Use fine-grained IP-Adapter (IP-Adapter Plus)</span>
</span><span id="__span-13-28"><a id="__codelineno-13-28" name="__codelineno-13-28" href="#__codelineno-13-28"></a><span class="p">)</span>
</span><span id="__span-13-29"><a id="__codelineno-13-29" name="__codelineno-13-29" href="#__codelineno-13-29"></a><span class="n">ip_adapter</span><span class="o">.</span><span class="n">clip_image_encoder</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;CLIPImageEncoderH.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-13-30"><a id="__codelineno-13-30" name="__codelineno-13-30" href="#__codelineno-13-30"></a><span class="n">ip_adapter</span><span class="o">.</span><span class="n">inject</span><span class="p">()</span>
</span><span id="__span-13-31"><a id="__codelineno-13-31" name="__codelineno-13-31" href="#__codelineno-13-31"></a>
</span><span id="__span-13-32"><a id="__codelineno-13-32" name="__codelineno-13-32" href="#__codelineno-13-32"></a><span class="c1"># Hyperparameters</span>
</span><span id="__span-13-33"><a id="__codelineno-13-33" name="__codelineno-13-33" href="#__codelineno-13-33"></a><span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;a futuristic castle surrounded by a forest, mountains in the background&quot;</span>
</span><span id="__span-13-34"><a id="__codelineno-13-34" name="__codelineno-13-34" href="#__codelineno-13-34"></a><span class="n">image_prompt</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">&quot;german-castle.jpg&quot;</span><span class="p">)</span>
</span><span id="__span-13-35"><a id="__codelineno-13-35" name="__codelineno-13-35" href="#__codelineno-13-35"></a><span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
</span><span id="__span-13-36"><a id="__codelineno-13-36" name="__codelineno-13-36" href="#__codelineno-13-36"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_inference_steps</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">first_step</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</span><span id="__span-13-37"><a id="__codelineno-13-37" name="__codelineno-13-37" href="#__codelineno-13-37"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_self_attention_guidance</span><span class="p">(</span>
</span><span id="__span-13-38"><a id="__codelineno-13-38" name="__codelineno-13-38" href="#__codelineno-13-38"></a> <span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.75</span>
</span><span id="__span-13-39"><a id="__codelineno-13-39" name="__codelineno-13-39" href="#__codelineno-13-39"></a><span class="p">)</span> <span class="c1"># Enable self-attention guidance to enhance the quality of the generated images</span>
</span><span id="__span-13-40"><a id="__codelineno-13-40" name="__codelineno-13-40" href="#__codelineno-13-40"></a>
</span><span id="__span-13-41"><a id="__codelineno-13-41" name="__codelineno-13-41" href="#__codelineno-13-41"></a><span class="k">with</span> <span class="n">no_grad</span><span class="p">():</span>
</span><span id="__span-13-42"><a id="__codelineno-13-42" name="__codelineno-13-42" href="#__codelineno-13-42"></a> <span class="n">clip_text_embedding</span><span class="p">,</span> <span class="n">pooled_text_embedding</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">compute_clip_text_embedding</span><span class="p">(</span>
</span><span id="__span-13-43"><a id="__codelineno-13-43" name="__codelineno-13-43" href="#__codelineno-13-43"></a> <span class="n">text</span><span class="o">=</span><span class="n">prompt</span> <span class="o">+</span> <span class="s2">&quot;, best quality, high quality&quot;</span><span class="p">,</span>
</span><span id="__span-13-44"><a id="__codelineno-13-44" name="__codelineno-13-44" href="#__codelineno-13-44"></a> <span class="n">negative_text</span><span class="o">=</span><span class="s2">&quot;monochrome, lowres, bad anatomy, worst quality, low quality&quot;</span><span class="p">,</span>
</span><span id="__span-13-45"><a id="__codelineno-13-45" name="__codelineno-13-45" href="#__codelineno-13-45"></a> <span class="p">)</span>
</span><span id="__span-13-46"><a id="__codelineno-13-46" name="__codelineno-13-46" href="#__codelineno-13-46"></a> <span class="n">time_ids</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">default_time_ids</span>
</span><span id="__span-13-47"><a id="__codelineno-13-47" name="__codelineno-13-47" href="#__codelineno-13-47"></a>
</span><span id="__span-13-48"><a id="__codelineno-13-48" name="__codelineno-13-48" href="#__codelineno-13-48"></a> <span class="n">clip_image_embedding</span> <span class="o">=</span> <span class="n">ip_adapter</span><span class="o">.</span><span class="n">compute_clip_image_embedding</span><span class="p">(</span><span class="n">ip_adapter</span><span class="o">.</span><span class="n">preprocess_image</span><span class="p">(</span><span class="n">image_prompt</span><span class="p">))</span>
</span><span id="__span-13-49"><a id="__codelineno-13-49" name="__codelineno-13-49" href="#__codelineno-13-49"></a> <span class="n">ip_adapter</span><span class="o">.</span><span class="n">set_clip_image_embedding</span><span class="p">(</span><span class="n">clip_image_embedding</span><span class="p">)</span>
</span><span id="__span-13-50"><a id="__codelineno-13-50" name="__codelineno-13-50" href="#__codelineno-13-50"></a>
</span><span id="__span-13-51"><a id="__codelineno-13-51" name="__codelineno-13-51" href="#__codelineno-13-51"></a> <span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
</span><span id="__span-13-52"><a id="__codelineno-13-52" name="__codelineno-13-52" href="#__codelineno-13-52"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">init_latents</span><span class="p">((</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">sdxl</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
</span><span id="__span-13-53"><a id="__codelineno-13-53" name="__codelineno-13-53" href="#__codelineno-13-53"></a>
</span><span id="__span-13-54"><a id="__codelineno-13-54" name="__codelineno-13-54" href="#__codelineno-13-54"></a> <span class="c1"># Diffusion process</span>
</span><span id="__span-13-55"><a id="__codelineno-13-55" name="__codelineno-13-55" href="#__codelineno-13-55"></a> <span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">steps</span><span class="p">:</span>
</span><span id="__span-13-56"><a id="__codelineno-13-56" name="__codelineno-13-56" href="#__codelineno-13-56"></a> <span class="k">if</span> <span class="n">step</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
</span><span id="__span-13-57"><a id="__codelineno-13-57" name="__codelineno-13-57" href="#__codelineno-13-57"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Step </span><span class="si">{</span><span class="n">step</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</span><span id="__span-13-58"><a id="__codelineno-13-58" name="__codelineno-13-58" href="#__codelineno-13-58"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="p">(</span>
</span><span id="__span-13-59"><a id="__codelineno-13-59" name="__codelineno-13-59" href="#__codelineno-13-59"></a> <span class="n">x</span><span class="p">,</span>
</span><span id="__span-13-60"><a id="__codelineno-13-60" name="__codelineno-13-60" href="#__codelineno-13-60"></a> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">,</span>
</span><span id="__span-13-61"><a id="__codelineno-13-61" name="__codelineno-13-61" href="#__codelineno-13-61"></a> <span class="n">clip_text_embedding</span><span class="o">=</span><span class="n">clip_text_embedding</span><span class="p">,</span>
</span><span id="__span-13-62"><a id="__codelineno-13-62" name="__codelineno-13-62" href="#__codelineno-13-62"></a> <span class="n">pooled_text_embedding</span><span class="o">=</span><span class="n">pooled_text_embedding</span><span class="p">,</span>
</span><span id="__span-13-63"><a id="__codelineno-13-63" name="__codelineno-13-63" href="#__codelineno-13-63"></a> <span class="n">time_ids</span><span class="o">=</span><span class="n">time_ids</span><span class="p">,</span>
</span><span id="__span-13-64"><a id="__codelineno-13-64" name="__codelineno-13-64" href="#__codelineno-13-64"></a> <span class="p">)</span>
</span><span id="__span-13-65"><a id="__codelineno-13-65" name="__codelineno-13-65" href="#__codelineno-13-65"></a> <span class="n">predicted_image</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">decode_latents</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-13-66"><a id="__codelineno-13-66" name="__codelineno-13-66" href="#__codelineno-13-66"></a>
</span><span id="__span-13-67"><a id="__codelineno-13-67" name="__codelineno-13-67" href="#__codelineno-13-67"></a><span class="n">predicted_image</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;scifi_pixel_IP_sdxl.png&quot;</span><span class="p">)</span>
</span></code></pre></div>
</details>
<p>The result looks convincing: we do get a <em>pixel-art, futuristic-looking Neuschwanstein castle</em>!</p>
<figure markdown>
<img src="scifi_pixel_IP_sdxl.webp" alt="Generated image in sci-fi, pixel art style, using IP-Adapter." width="400">
<figcaption>Generated image in sci-fi, pixel art style, using IP-Adapter.</figcaption>
</figure>
<h2 id="everything-else-t2i-adapter">Everything else + T2I-Adapter<a class="headerlink" href="#everything-else-t2i-adapter" title="Permanent link">&para;</a></h2>
<p>T2I-Adapters<sup id="fnref:1"><a class="footnote-ref" href="#fn:1">1</a></sup> are a powerful class of Adapters aiming at controlling the Text-to-Image (T2I) diffusion process with external control signals, such as canny edges or pose estimations inputs.
In this section, we will compose our previous example with the <a href="https://huggingface.co/TencentARC/t2i-adapter-depth-zoe-sdxl-1.0">Depth-Zoe Adapter</a>, providing a depth condition to the diffusion process using the following depth map as input signal:</p>
<figure markdown>
<img src="zoe-depth-map-german-castle.png" alt="Input depth map of the initial castle image" width="400">
<figcaption>Input depth map of the initial castle image.</figcaption>
</figure>
<p>First, download the image as well as the weights of T2I-Depth-Zoe-Adapter by calling the following commands:</p>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-14-1"><a id="__codelineno-14-1" name="__codelineno-14-1" href="#__codelineno-14-1"></a>curl<span class="w"> </span>-O<span class="w"> </span>https://refine.rs/guides/adapting_sdxl/zoe-depth-map-german-castle.png
</span><span id="__span-14-2"><a id="__codelineno-14-2" name="__codelineno-14-2" href="#__codelineno-14-2"></a>python<span class="w"> </span>scripts/conversion/convert_diffusers_t2i_adapter.py<span class="w"> </span>--from<span class="w"> </span><span class="s2">&quot;TencentARC/t2i-adapter-depth-zoe-sdxl-1.0&quot;</span><span class="w"> </span>--to<span class="w"> </span>t2i_depth_zoe_xl.safetensors<span class="w"> </span>--half
</span></code></pre></div>
<p>Then, just inject it as usual:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-15-1"><a id="__codelineno-15-1" name="__codelineno-15-1" href="#__codelineno-15-1"></a><span class="c1"># Load T2I-Adapter</span>
</span><span id="__span-15-2"><a id="__codelineno-15-2" name="__codelineno-15-2" href="#__codelineno-15-2"></a><span class="n">t2i_adapter</span> <span class="o">=</span> <span class="n">SDXLT2IAdapter</span><span class="p">(</span>
</span><span id="__span-15-3"><a id="__codelineno-15-3" name="__codelineno-15-3" href="#__codelineno-15-3"></a> <span class="n">target</span><span class="o">=</span><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="p">,</span>
</span><span id="__span-15-4"><a id="__codelineno-15-4" name="__codelineno-15-4" href="#__codelineno-15-4"></a> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;zoe-depth&quot;</span><span class="p">,</span>
</span><span id="__span-15-5"><a id="__codelineno-15-5" name="__codelineno-15-5" href="#__codelineno-15-5"></a> <span class="n">weights</span><span class="o">=</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;t2i_depth_zoe_xl.safetensors&quot;</span><span class="p">),</span>
</span><span id="__span-15-6"><a id="__codelineno-15-6" name="__codelineno-15-6" href="#__codelineno-15-6"></a> <span class="n">scale</span><span class="o">=</span><span class="mf">0.72</span><span class="p">,</span>
</span><span id="__span-15-7"><a id="__codelineno-15-7" name="__codelineno-15-7" href="#__codelineno-15-7"></a><span class="p">)</span><span class="o">.</span><span class="n">inject</span><span class="p">()</span>
</span></code></pre></div>
<p>Finally, at runtime, compute the embedding of the input condition through the <code>t2i_adapter</code> object, and set its embedding calling <code>.set_condition_features()</code>:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-16-1"><a id="__codelineno-16-1" name="__codelineno-16-1" href="#__codelineno-16-1"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">image_to_tensor</span><span class="p">,</span> <span class="n">interpolate</span>
</span><span id="__span-16-2"><a id="__codelineno-16-2" name="__codelineno-16-2" href="#__codelineno-16-2"></a>
</span><span id="__span-16-3"><a id="__codelineno-16-3" name="__codelineno-16-3" href="#__codelineno-16-3"></a><span class="n">image_depth_condition</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">&quot;zoe-depth-map-german-castle.png&quot;</span><span class="p">)</span>
</span><span id="__span-16-4"><a id="__codelineno-16-4" name="__codelineno-16-4" href="#__codelineno-16-4"></a>
</span><span id="__span-16-5"><a id="__codelineno-16-5" name="__codelineno-16-5" href="#__codelineno-16-5"></a><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
</span><span id="__span-16-6"><a id="__codelineno-16-6" name="__codelineno-16-6" href="#__codelineno-16-6"></a> <span class="n">condition</span> <span class="o">=</span> <span class="n">image_to_tensor</span><span class="p">(</span><span class="n">image_depth_condition</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">&quot;RGB&quot;</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">sdxl</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">sdxl</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
</span><span id="__span-16-7"><a id="__codelineno-16-7" name="__codelineno-16-7" href="#__codelineno-16-7"></a> <span class="c1"># Spatial dimensions should be divisible by default downscale factor (=16 for T2IAdapter ConditionEncoder)</span>
</span><span id="__span-16-8"><a id="__codelineno-16-8" name="__codelineno-16-8" href="#__codelineno-16-8"></a> <span class="n">condition</span> <span class="o">=</span> <span class="n">interpolate</span><span class="p">(</span><span class="n">condition</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">((</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">)))</span>
</span><span id="__span-16-9"><a id="__codelineno-16-9" name="__codelineno-16-9" href="#__codelineno-16-9"></a> <span class="n">t2i_adapter</span><span class="o">.</span><span class="n">set_condition_features</span><span class="p">(</span><span class="n">features</span><span class="o">=</span><span class="n">t2i_adapter</span><span class="o">.</span><span class="n">compute_condition_features</span><span class="p">(</span><span class="n">condition</span><span class="p">))</span>
</span></code></pre></div>
<details class="example">
<summary>Expand to see the entire end-to-end code</summary>
<div class="language-py highlight"><pre><span></span><code><span id="__span-17-1"><a id="__codelineno-17-1" name="__codelineno-17-1" href="#__codelineno-17-1"></a><span class="kn">import</span> <span class="nn">torch</span>
</span><span id="__span-17-2"><a id="__codelineno-17-2" name="__codelineno-17-2" href="#__codelineno-17-2"></a><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
</span><span id="__span-17-3"><a id="__codelineno-17-3" name="__codelineno-17-3" href="#__codelineno-17-3"></a>
</span><span id="__span-17-4"><a id="__codelineno-17-4" name="__codelineno-17-4" href="#__codelineno-17-4"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">load_from_safetensors</span><span class="p">,</span> <span class="n">manual_seed</span><span class="p">,</span> <span class="n">no_grad</span><span class="p">,</span> <span class="n">image_to_tensor</span>
</span><span id="__span-17-5"><a id="__codelineno-17-5" name="__codelineno-17-5" href="#__codelineno-17-5"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.lora</span> <span class="kn">import</span> <span class="n">SDLoraManager</span>
</span><span id="__span-17-6"><a id="__codelineno-17-6" name="__codelineno-17-6" href="#__codelineno-17-6"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.stable_diffusion_xl</span> <span class="kn">import</span> <span class="n">StableDiffusion_XL</span><span class="p">,</span> <span class="n">SDXLT2IAdapter</span>
</span><span id="__span-17-7"><a id="__codelineno-17-7" name="__codelineno-17-7" href="#__codelineno-17-7"></a><span class="kn">from</span> <span class="nn">refiners.foundationals.latent_diffusion.stable_diffusion_xl.image_prompt</span> <span class="kn">import</span> <span class="n">SDXLIPAdapter</span>
</span><span id="__span-17-8"><a id="__codelineno-17-8" name="__codelineno-17-8" href="#__codelineno-17-8"></a>
</span><span id="__span-17-9"><a id="__codelineno-17-9" name="__codelineno-17-9" href="#__codelineno-17-9"></a><span class="c1"># Load SDXL</span>
</span><span id="__span-17-10"><a id="__codelineno-17-10" name="__codelineno-17-10" href="#__codelineno-17-10"></a><span class="n">sdxl</span> <span class="o">=</span> <span class="n">StableDiffusion_XL</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">)</span>
</span><span id="__span-17-11"><a id="__codelineno-17-11" name="__codelineno-17-11" href="#__codelineno-17-11"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">clip_text_encoder</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;DoubleCLIPTextEncoder.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-17-12"><a id="__codelineno-17-12" name="__codelineno-17-12" href="#__codelineno-17-12"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-unet.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-17-13"><a id="__codelineno-17-13" name="__codelineno-17-13" href="#__codelineno-17-13"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;sdxl-lda.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-17-14"><a id="__codelineno-17-14" name="__codelineno-17-14" href="#__codelineno-17-14"></a>
</span><span id="__span-17-15"><a id="__codelineno-17-15" name="__codelineno-17-15" href="#__codelineno-17-15"></a><span class="c1"># Load LoRAs weights from disk and inject them into target</span>
</span><span id="__span-17-16"><a id="__codelineno-17-16" name="__codelineno-17-16" href="#__codelineno-17-16"></a><span class="n">manager</span> <span class="o">=</span> <span class="n">SDLoraManager</span><span class="p">(</span><span class="n">sdxl</span><span class="p">)</span>
</span><span id="__span-17-17"><a id="__codelineno-17-17" name="__codelineno-17-17" href="#__codelineno-17-17"></a><span class="n">scifi_lora_weights</span> <span class="o">=</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;Sci-fi_Environments_sdxl.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-17-18"><a id="__codelineno-17-18" name="__codelineno-17-18" href="#__codelineno-17-18"></a><span class="n">pixel_art_lora_weights</span> <span class="o">=</span> <span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;pixel-art-xl-v1.1.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-17-19"><a id="__codelineno-17-19" name="__codelineno-17-19" href="#__codelineno-17-19"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;scifi-lora&quot;</span><span class="p">,</span> <span class="n">scifi_lora_weights</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
</span><span id="__span-17-20"><a id="__codelineno-17-20" name="__codelineno-17-20" href="#__codelineno-17-20"></a><span class="n">manager</span><span class="o">.</span><span class="n">add_loras</span><span class="p">(</span><span class="s2">&quot;pixel-art-lora&quot;</span><span class="p">,</span> <span class="n">pixel_art_lora_weights</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.55</span><span class="p">)</span>
</span><span id="__span-17-21"><a id="__codelineno-17-21" name="__codelineno-17-21" href="#__codelineno-17-21"></a>
</span><span id="__span-17-22"><a id="__codelineno-17-22" name="__codelineno-17-22" href="#__codelineno-17-22"></a><span class="c1"># Load IP-Adapter</span>
</span><span id="__span-17-23"><a id="__codelineno-17-23" name="__codelineno-17-23" href="#__codelineno-17-23"></a><span class="n">ip_adapter</span> <span class="o">=</span> <span class="n">SDXLIPAdapter</span><span class="p">(</span>
</span><span id="__span-17-24"><a id="__codelineno-17-24" name="__codelineno-17-24" href="#__codelineno-17-24"></a> <span class="n">target</span><span class="o">=</span><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="p">,</span>
</span><span id="__span-17-25"><a id="__codelineno-17-25" name="__codelineno-17-25" href="#__codelineno-17-25"></a> <span class="n">weights</span><span class="o">=</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;ip-adapter-plus_sdxl_vit-h.safetensors&quot;</span><span class="p">),</span>
</span><span id="__span-17-26"><a id="__codelineno-17-26" name="__codelineno-17-26" href="#__codelineno-17-26"></a> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
</span><span id="__span-17-27"><a id="__codelineno-17-27" name="__codelineno-17-27" href="#__codelineno-17-27"></a> <span class="n">fine_grained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># Use fine-grained IP-Adapter (IP-Adapter Plus)</span>
</span><span id="__span-17-28"><a id="__codelineno-17-28" name="__codelineno-17-28" href="#__codelineno-17-28"></a><span class="p">)</span>
</span><span id="__span-17-29"><a id="__codelineno-17-29" name="__codelineno-17-29" href="#__codelineno-17-29"></a><span class="n">ip_adapter</span><span class="o">.</span><span class="n">clip_image_encoder</span><span class="o">.</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;CLIPImageEncoderH.safetensors&quot;</span><span class="p">)</span>
</span><span id="__span-17-30"><a id="__codelineno-17-30" name="__codelineno-17-30" href="#__codelineno-17-30"></a><span class="n">ip_adapter</span><span class="o">.</span><span class="n">inject</span><span class="p">()</span>
</span><span id="__span-17-31"><a id="__codelineno-17-31" name="__codelineno-17-31" href="#__codelineno-17-31"></a>
</span><span id="__span-17-32"><a id="__codelineno-17-32" name="__codelineno-17-32" href="#__codelineno-17-32"></a><span class="c1"># Load T2I-Adapter</span>
</span><span id="__span-17-33"><a id="__codelineno-17-33" name="__codelineno-17-33" href="#__codelineno-17-33"></a><span class="n">t2i_adapter</span> <span class="o">=</span> <span class="n">SDXLT2IAdapter</span><span class="p">(</span>
</span><span id="__span-17-34"><a id="__codelineno-17-34" name="__codelineno-17-34" href="#__codelineno-17-34"></a> <span class="n">target</span><span class="o">=</span><span class="n">sdxl</span><span class="o">.</span><span class="n">unet</span><span class="p">,</span>
</span><span id="__span-17-35"><a id="__codelineno-17-35" name="__codelineno-17-35" href="#__codelineno-17-35"></a> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;zoe-depth&quot;</span><span class="p">,</span>
</span><span id="__span-17-36"><a id="__codelineno-17-36" name="__codelineno-17-36" href="#__codelineno-17-36"></a> <span class="n">weights</span><span class="o">=</span><span class="n">load_from_safetensors</span><span class="p">(</span><span class="s2">&quot;t2i_depth_zoe_xl.safetensors&quot;</span><span class="p">),</span>
</span><span id="__span-17-37"><a id="__codelineno-17-37" name="__codelineno-17-37" href="#__codelineno-17-37"></a> <span class="n">scale</span><span class="o">=</span><span class="mf">0.72</span><span class="p">,</span>
</span><span id="__span-17-38"><a id="__codelineno-17-38" name="__codelineno-17-38" href="#__codelineno-17-38"></a><span class="p">)</span><span class="o">.</span><span class="n">inject</span><span class="p">()</span>
</span><span id="__span-17-39"><a id="__codelineno-17-39" name="__codelineno-17-39" href="#__codelineno-17-39"></a>
</span><span id="__span-17-40"><a id="__codelineno-17-40" name="__codelineno-17-40" href="#__codelineno-17-40"></a><span class="c1"># Hyperparameters</span>
</span><span id="__span-17-41"><a id="__codelineno-17-41" name="__codelineno-17-41" href="#__codelineno-17-41"></a><span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;a futuristic castle surrounded by a forest, mountains in the background&quot;</span>
</span><span id="__span-17-42"><a id="__codelineno-17-42" name="__codelineno-17-42" href="#__codelineno-17-42"></a><span class="n">image_prompt</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">&quot;german-castle.jpg&quot;</span><span class="p">)</span>
</span><span id="__span-17-43"><a id="__codelineno-17-43" name="__codelineno-17-43" href="#__codelineno-17-43"></a><span class="n">image_depth_condition</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">&quot;zoe-depth-map-german-castle.png&quot;</span><span class="p">)</span>
</span><span id="__span-17-44"><a id="__codelineno-17-44" name="__codelineno-17-44" href="#__codelineno-17-44"></a><span class="n">seed</span> <span class="o">=</span> <span class="mi">42</span>
</span><span id="__span-17-45"><a id="__codelineno-17-45" name="__codelineno-17-45" href="#__codelineno-17-45"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_inference_steps</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">first_step</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</span><span id="__span-17-46"><a id="__codelineno-17-46" name="__codelineno-17-46" href="#__codelineno-17-46"></a><span class="n">sdxl</span><span class="o">.</span><span class="n">set_self_attention_guidance</span><span class="p">(</span>
</span><span id="__span-17-47"><a id="__codelineno-17-47" name="__codelineno-17-47" href="#__codelineno-17-47"></a> <span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.75</span>
</span><span id="__span-17-48"><a id="__codelineno-17-48" name="__codelineno-17-48" href="#__codelineno-17-48"></a><span class="p">)</span> <span class="c1"># Enable self-attention guidance to enhance the quality of the generated images</span>
</span><span id="__span-17-49"><a id="__codelineno-17-49" name="__codelineno-17-49" href="#__codelineno-17-49"></a>
</span><span id="__span-17-50"><a id="__codelineno-17-50" name="__codelineno-17-50" href="#__codelineno-17-50"></a><span class="k">with</span> <span class="n">no_grad</span><span class="p">():</span>
</span><span id="__span-17-51"><a id="__codelineno-17-51" name="__codelineno-17-51" href="#__codelineno-17-51"></a> <span class="n">clip_text_embedding</span><span class="p">,</span> <span class="n">pooled_text_embedding</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">compute_clip_text_embedding</span><span class="p">(</span>
</span><span id="__span-17-52"><a id="__codelineno-17-52" name="__codelineno-17-52" href="#__codelineno-17-52"></a> <span class="n">text</span><span class="o">=</span><span class="n">prompt</span> <span class="o">+</span> <span class="s2">&quot;, best quality, high quality&quot;</span><span class="p">,</span>
</span><span id="__span-17-53"><a id="__codelineno-17-53" name="__codelineno-17-53" href="#__codelineno-17-53"></a> <span class="n">negative_text</span><span class="o">=</span><span class="s2">&quot;monochrome, lowres, bad anatomy, worst quality, low quality&quot;</span><span class="p">,</span>
</span><span id="__span-17-54"><a id="__codelineno-17-54" name="__codelineno-17-54" href="#__codelineno-17-54"></a> <span class="p">)</span>
</span><span id="__span-17-55"><a id="__codelineno-17-55" name="__codelineno-17-55" href="#__codelineno-17-55"></a> <span class="n">time_ids</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">default_time_ids</span>
</span><span id="__span-17-56"><a id="__codelineno-17-56" name="__codelineno-17-56" href="#__codelineno-17-56"></a>
</span><span id="__span-17-57"><a id="__codelineno-17-57" name="__codelineno-17-57" href="#__codelineno-17-57"></a> <span class="n">clip_image_embedding</span> <span class="o">=</span> <span class="n">ip_adapter</span><span class="o">.</span><span class="n">compute_clip_image_embedding</span><span class="p">(</span><span class="n">ip_adapter</span><span class="o">.</span><span class="n">preprocess_image</span><span class="p">(</span><span class="n">image_prompt</span><span class="p">))</span>
</span><span id="__span-17-58"><a id="__codelineno-17-58" name="__codelineno-17-58" href="#__codelineno-17-58"></a> <span class="n">ip_adapter</span><span class="o">.</span><span class="n">set_clip_image_embedding</span><span class="p">(</span><span class="n">clip_image_embedding</span><span class="p">)</span>
</span><span id="__span-17-59"><a id="__codelineno-17-59" name="__codelineno-17-59" href="#__codelineno-17-59"></a>
</span><span id="__span-17-60"><a id="__codelineno-17-60" name="__codelineno-17-60" href="#__codelineno-17-60"></a> <span class="c1"># Spatial dimensions should be divisible by default downscale factor (=16 for T2IAdapter ConditionEncoder)</span>
</span><span id="__span-17-61"><a id="__codelineno-17-61" name="__codelineno-17-61" href="#__codelineno-17-61"></a> <span class="n">condition</span> <span class="o">=</span> <span class="n">image_to_tensor</span><span class="p">(</span><span class="n">image_depth_condition</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">&quot;RGB&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">)),</span> <span class="n">device</span><span class="o">=</span><span class="n">sdxl</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">sdxl</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
</span><span id="__span-17-62"><a id="__codelineno-17-62" name="__codelineno-17-62" href="#__codelineno-17-62"></a> <span class="n">t2i_adapter</span><span class="o">.</span><span class="n">set_condition_features</span><span class="p">(</span><span class="n">features</span><span class="o">=</span><span class="n">t2i_adapter</span><span class="o">.</span><span class="n">compute_condition_features</span><span class="p">(</span><span class="n">condition</span><span class="p">))</span>
</span><span id="__span-17-63"><a id="__codelineno-17-63" name="__codelineno-17-63" href="#__codelineno-17-63"></a>
</span><span id="__span-17-64"><a id="__codelineno-17-64" name="__codelineno-17-64" href="#__codelineno-17-64"></a> <span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
</span><span id="__span-17-65"><a id="__codelineno-17-65" name="__codelineno-17-65" href="#__codelineno-17-65"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">init_latents</span><span class="p">((</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">sdxl</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
</span><span id="__span-17-66"><a id="__codelineno-17-66" name="__codelineno-17-66" href="#__codelineno-17-66"></a>
</span><span id="__span-17-67"><a id="__codelineno-17-67" name="__codelineno-17-67" href="#__codelineno-17-67"></a> <span class="c1"># Diffusion process</span>
</span><span id="__span-17-68"><a id="__codelineno-17-68" name="__codelineno-17-68" href="#__codelineno-17-68"></a> <span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">steps</span><span class="p">:</span>
</span><span id="__span-17-69"><a id="__codelineno-17-69" name="__codelineno-17-69" href="#__codelineno-17-69"></a> <span class="k">if</span> <span class="n">step</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
</span><span id="__span-17-70"><a id="__codelineno-17-70" name="__codelineno-17-70" href="#__codelineno-17-70"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Step </span><span class="si">{</span><span class="n">step</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</span><span id="__span-17-71"><a id="__codelineno-17-71" name="__codelineno-17-71" href="#__codelineno-17-71"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">sdxl</span><span class="p">(</span>
</span><span id="__span-17-72"><a id="__codelineno-17-72" name="__codelineno-17-72" href="#__codelineno-17-72"></a> <span class="n">x</span><span class="p">,</span>
</span><span id="__span-17-73"><a id="__codelineno-17-73" name="__codelineno-17-73" href="#__codelineno-17-73"></a> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">,</span>
</span><span id="__span-17-74"><a id="__codelineno-17-74" name="__codelineno-17-74" href="#__codelineno-17-74"></a> <span class="n">clip_text_embedding</span><span class="o">=</span><span class="n">clip_text_embedding</span><span class="p">,</span>
</span><span id="__span-17-75"><a id="__codelineno-17-75" name="__codelineno-17-75" href="#__codelineno-17-75"></a> <span class="n">pooled_text_embedding</span><span class="o">=</span><span class="n">pooled_text_embedding</span><span class="p">,</span>
</span><span id="__span-17-76"><a id="__codelineno-17-76" name="__codelineno-17-76" href="#__codelineno-17-76"></a> <span class="n">time_ids</span><span class="o">=</span><span class="n">time_ids</span><span class="p">,</span>
</span><span id="__span-17-77"><a id="__codelineno-17-77" name="__codelineno-17-77" href="#__codelineno-17-77"></a> <span class="p">)</span>
</span><span id="__span-17-78"><a id="__codelineno-17-78" name="__codelineno-17-78" href="#__codelineno-17-78"></a> <span class="n">predicted_image</span> <span class="o">=</span> <span class="n">sdxl</span><span class="o">.</span><span class="n">lda</span><span class="o">.</span><span class="n">decode_latents</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-17-79"><a id="__codelineno-17-79" name="__codelineno-17-79" href="#__codelineno-17-79"></a>
</span><span id="__span-17-80"><a id="__codelineno-17-80" name="__codelineno-17-80" href="#__codelineno-17-80"></a><span class="n">predicted_image</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;scifi_pixel_IP_T2I_sdxl.png&quot;</span><span class="p">)</span>
</span></code></pre></div>
</details>
<p>The results look convincing: the depth and proportions of the initial castle are more faithful, while preserving our <em>futuristic, pixel-art style</em>!</p>
<figure markdown>
<img src="scifi_pixel_IP_T2I_sdxl.webp" alt="Generated image in sci-fi, pixel art style, using IP and T2I Adapters" width="400">
<figcaption>Generated image in sci-fi, pixel art style, using IP and T2I Adapters.</figcaption>
</figure>
<h2 id="wrap-up">Wrap up<a class="headerlink" href="#wrap-up" title="Permanent link">&para;</a></h2>
<p>As you can see in this guide, composing Adapters on top of foundation models is pretty seamless in Refiners, allowing practitioners to quickly test out different combinations of Adapters for their needs. We encourage you to try out different ones, and even train some yourselves!</p>
<div class="footnote">
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<ol>
<li id="fn:1">
<p>Mou, C., Wang, X., Xie, L., Zhang, J., Qi, Z., Shan, Y., &amp; Qie, X. (2023). T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.&#160;<a class="footnote-backref" href="#fnref:1" title="Jump back to footnote 1 in the text">&#8617;</a></p>
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