diffusers/v0.7.0/en/_app/pages/using-diffusers/unconditional_image_generation.mdx-hf-doc-builder.js

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generator = DiffusionPipeline.from_pretrained("google/ddpm-celebahq-256")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;google/ddpm-celebahq-256&quot;</span>)`}}),E=new le({props:{code:'generator.to("cuda")',highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>generator.to(<span class="hljs-string">&quot;cuda&quot;</span>)'}}),j=new le({props:{code:"image = generator().images[0]",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>image = generator().images[<span class="hljs-number">0</span>]'}}),I=new le({props:{code:'image.save("generated_image.png")',highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;generated_image.png&quot;</span>)'}}),{c(){h=s("meta"),J=m(),g=s("h1"),y=s("a"),Y=s("span"),M(P.$$.fragment),fe=m(),C=s("span"),ue=a("Unconditional Image Generation"),R=m(),v=s("p"),pe=a("The "),q=s("a"),ce=a("DiffusionPipeline"),me=a(" is the easiest way to use a pre-trained diffusion system for inference"),F=m(),p=s("p"),de=a("Start by creating an instance of "),x=s("a"),he=a("DiffusionPipeline"),ge=a(` and specify which pipeline checkpoint you would like to download.
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