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	<link href="/docs/diffusers/pr_14830/en/_app/immutable/assets/0.tn0RQdqM.css" rel="modulepreload"> <!--[--><!--[0--><!--[--><!--[0--><!--[--><p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----></span> <span>Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg><!----></button></div> <!--[-1--><!--]--></div><!----> <!--[0--><h1 class="relative group"><a id="pipeline-callbacks" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipeline-callbacks"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span>Pipeline callbacks</span></h1><!--]--><!----> <p>A callback runs at the end of a denoising step and can change pipeline state or tensors for later steps. Use it to adjust attributes or tensor variables for new behavior without rewriting the pipeline.</p> <p>These callbacks apply to classic <a href="/docs/diffusers/pr_14830/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a> loops. In <a href="../modular_diffusers/overview">Modular Diffusers</a>, you can build and add custom pipeline blocks instead of <code>callback_on_step_end</code>.</p> <p>Diffusers provides several callbacks in the pipeline <a href="../api/pipelines/overview#diffusers.callbacks.PipelineCallback">overview</a>.</p> <p>To enable a callback, configure when the callback is executed after a certain number of denoising steps with one of the following arguments.</p> <ul><li><code>cutoff_step_ratio</code> specifies when a callback is activated as a percentage of the total denoising steps. Use when the cutoff should scale with <code>num_inference_steps</code> (for example, drop CFG after 40% of run).</li> <li><code>cutoff_step_index</code> specifies the exact step number a callback is activated. Use when you care about an absolute step (for example, step <code>10</code> on a fixed 25-step schedule).</li></ul> <p>The example below uses <code>cutoff_step_ratio=0.4</code>, which means the callback is activated once denoising reaches 40% of the total inference steps. <a href="/docs/diffusers/pr_14830/en/api/pipelines/overview#diffusers.callbacks.SDXLCFGCutoffCallback">SDXLCFGCutoffCallback</a> disables classifier-free guidance (CFG) after a certain number of steps, which can help save compute without significantly affecting performance.</p> <p>Define a callback with one of the <code>cutoff</code> arguments and pass it to the <code>callback_on_step_end</code> parameter in the pipeline.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copied</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">import</span> torch
	<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DPMSolverMultistepScheduler, StableDiffusionXLPipeline
	<span class="hljs-keyword">from</span> diffusers.callbacks <span class="hljs-keyword">import</span> SDXLCFGCutoffCallback

	callback = SDXLCFGCutoffCallback(cutoff_step_ratio=<span class="hljs-number">0.4</span>)
	<span class="hljs-comment"># if using cutoff_step_index</span>
	<span class="hljs-comment"># callback = SDXLCFGCutoffCallback(cutoff_step_ratio=None, cutoff_step_index=10)</span>

	pipeline = StableDiffusionXLPipeline.from_pretrained(
	<span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>,
	dtype=torch.float16,
	device_map=<span class="hljs-string">"cuda"</span> <span class="hljs-comment"># or "mps", "xpu", "cpu"</span>
	)
	pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, use_karras_sigmas=<span class="hljs-literal">True</span>)

	prompt = <span class="hljs-string">"a sports car on the road, best quality, high quality, high detail, 8k resolution"</span>
	output = pipeline(
	prompt=prompt,
	negative_prompt=<span class="hljs-string">""</span>,
	guidance_scale=<span class="hljs-number">6.5</span>,
	num_inference_steps=<span class="hljs-number">25</span>,
	callback_on_step_end=callback,
	)<!----></pre></div><!----> <p>Official callbacks set their own tensor inputs. For a custom function, pass <code>callback_on_step_end_tensor_inputs</code> as in <a href="#display-intermediate-images">Display intermediate images</a>.</p> <p>If you want to add a new official callback, feel free to open a <a href="https://github.com/huggingface/diffusers/issues/new/choose" rel="nofollow">feature request</a> or <a href="https://huggingface.co/docs/diffusers/main/en/conceptual/contribution#how-to-open-a-pr" rel="nofollow">submit a PR</a>. Otherwise, you can also create your own callback as shown below.</p> <!--[1--><h2 class="relative group"><a id="early-stopping" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#early-stopping"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span>Early stopping</span></h2><!--]--><!----> <p>Early stopping is useful if you aren’t happy with the intermediate results during generation. This callback sets a hardcoded stop point by setting the <code>_interrupt</code> attribute to <code>True</code>, which makes the denoising loop skip the remaining steps.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copied</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">import</span> torch
	<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

	<span class="hljs-keyword">def</span> <span class="hljs-title function_">interrupt_callback</span>(<span class="hljs-params">pipeline, i, t, callback_kwargs</span>):
	stop_idx = <span class="hljs-number">10</span>
	<span class="hljs-keyword">if</span> i == stop_idx:
	pipeline._interrupt = <span class="hljs-literal">True</span>
	<span class="hljs-keyword">return</span> callback_kwargs

	pipeline = DiffusionPipeline.from_pretrained(
	<span class="hljs-string">"Qwen/Qwen-Image"</span>,
	dtype=torch.bfloat16,
	device_map=<span class="hljs-string">"cuda"</span>, <span class="hljs-comment"># or "mps", "xpu", "cpu"</span>
	)
	pipeline(
	prompt=<span class="hljs-string">"A photo of a cat"</span>,
	num_inference_steps=<span class="hljs-number">50</span>,
	callback_on_step_end=interrupt_callback,
	)<!----></pre></div><!----> <!--[1--><h2 class="relative group"><a id="display-intermediate-images" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#display-intermediate-images"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span>Display intermediate images</span></h2><!--]--><!----> <p>Visualizing intermediate images is useful for progress monitoring. The preview below is SDXL-only. It maps SDXL latents to RGB with a linear transform for a quick look during denoising. Those weights do not transfer to other models. For Qwen-Image and similar checkpoints, decode with the model VAE instead of this helper.</p> <p><a href="https://huggingface.co/blog/TimothyAlexisVass/explaining-the-sdxl-latent-space" rel="nofollow">Convert</a> Stable Diffusion XL latents (4 channels) to RGB tensors (3 channels).</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copied</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">import</span> torch
	<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
	<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image

	<span class="hljs-keyword">def</span> <span class="hljs-title function_">latents_to_rgb</span>(<span class="hljs-params">latents</span>):
	weights = (
	(<span class="hljs-number">60</span>, -<span class="hljs-number">60</span>, <span class="hljs-number">25</span>, -<span class="hljs-number">70</span>),
	(<span class="hljs-number">60</span>, -<span class="hljs-number">5</span>, <span class="hljs-number">15</span>, -<span class="hljs-number">50</span>),
	(<span class="hljs-number">60</span>, <span class="hljs-number">10</span>, -<span class="hljs-number">5</span>, -<span class="hljs-number">35</span>),
	)

	weights_tensor = torch.t(torch.tensor(weights, dtype=latents.dtype).to(latents.device))
	biases_tensor = torch.tensor((<span class="hljs-number">150</span>, <span class="hljs-number">140</span>, <span class="hljs-number">130</span>), dtype=latents.dtype).to(latents.device)
	rgb_tensor = torch.einsum(<span class="hljs-string">"...lxy,lr -> ...rxy"</span>, latents, weights_tensor) + biases_tensor.unsqueeze(-<span class="hljs-number">1</span>).unsqueeze(-<span class="hljs-number">1</span>)
	image_array = rgb_tensor.clamp(<span class="hljs-number">0</span>, <span class="hljs-number">255</span>).byte().cpu().numpy().transpose(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">0</span>)

	<span class="hljs-keyword">return</span> Image.fromarray(image_array)<!----></pre></div><!----> <p>Extract the latents and convert the first image in the batch to RGB. Save the image as a PNG file with the step number.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copied</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">def</span> <span class="hljs-title function_">decode_tensors</span>(<span class="hljs-params">pipe, step, timestep, callback_kwargs</span>):
	latents = callback_kwargs[<span class="hljs-string">"latents"</span>]

	image = latents_to_rgb(latents[<span class="hljs-number">0</span>])
	image.save(<span class="hljs-string">f"<span class="hljs-subst">{step}</span>.png"</span>)

	<span class="hljs-keyword">return</span> callback_kwargs<!----></pre></div><!----> <p>Use <code>callback_on_step_end_tensor_inputs</code> to choose which tensors the callback receives, which in this case, are the latents.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copied</div><!----></button><!----></div> <pre class="language-py "><!---->pipeline = AutoPipelineForText2Image.from_pretrained(
	<span class="hljs-string">"stabilityai/stable-diffusion-xl-base-1.0"</span>,
	dtype=torch.float16,
	device_map=<span class="hljs-string">"cuda"</span> <span class="hljs-comment"># or "mps", "xpu", "cpu"</span>
	)

	image = pipeline(
	prompt=<span class="hljs-string">"A croissant shaped like a cute bear."</span>,
	negative_prompt=<span class="hljs-string">"Deformed, ugly, bad anatomy"</span>,
	callback_on_step_end=decode_tensors,
	callback_on_step_end_tensor_inputs=[<span class="hljs-string">"latents"</span>],
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