Buckets:

hf-doc-build
/

doc-dev

Files

xet

hf-doc-build/doc-dev / diffusers /pr_13881 /en /optimization /para_attn.html

HuggingFaceDocBuilder

about 17 hours ago

download

raw

35.8 kB

	<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"ParaAttention","local":"paraattention","sections":[{"title":"First Block Cache","local":"first-block-cache","sections":[],"depth":2},{"title":"fp8 quantization","local":"fp8-quantization","sections":[],"depth":2},{"title":"Context Parallelism","local":"context-parallelism","sections":[],"depth":2},{"title":"Benchmarks","local":"benchmarks","sections":[],"depth":2}],"depth":1}"/>
	<link href="/docs/diffusers/main/en/_app/immutable/entry/start.BKtHvaj4.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/chunks/DA5YANCb.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/chunks/DK803DsY.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/entry/app.Cp32ddTn.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/chunks/DTwaC60R.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/chunks/BTASUwav.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/chunks/C1r7BcyV.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/chunks/DsnmJJEf.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/nodes/0.BPNuemSR.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/chunks/BzOvRKAw.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/nodes/300.D-Yxzi5V.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/chunks/CmJXCtRL.js" rel="modulepreload">
	<link href="/docs/diffusers/main/en/_app/immutable/chunks/BtTdhXOX.js" rel="modulepreload">
	<!--fka9nn--><meta name="hf:doc:metadata" content="{"title":"ParaAttention","local":"paraattention","sections":[{"title":"First Block Cache","local":"first-block-cache","sections":[],"depth":2},{"title":"fp8 quantization","local":"fp8-quantization","sections":[],"depth":2},{"title":"Context Parallelism","local":"context-parallelism","sections":[],"depth":2},{"title":"Benchmarks","local":"benchmarks","sections":[],"depth":2}],"depth":1}"/><!---->
	<link href="/docs/diffusers/main/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="paraattention" 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="#paraattention"><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>ParaAttention</span></h1><!--]--><!----> <div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-performance.png"/></div> <div class="flex justify-center"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/hunyuan-video-performance.png"/></div> <p>Large image and video generation models, such as <a href="https://huggingface.co/black-forest-labs/FLUX.1-dev" rel="nofollow">FLUX.1-dev</a> and <a href="https://huggingface.co/tencent/HunyuanVideo" rel="nofollow">HunyuanVideo</a>, can be an inference challenge for real-time applications and deployment because of their size.</p> <p><a href="https://github.com/chengzeyi/ParaAttention" rel="nofollow">ParaAttention</a> is a library that implements <strong>context parallelism</strong> and <strong>first block cache</strong>, and can be combined with other techniques (torch.compile, fp8 dynamic quantization), to accelerate inference.</p> <p>This guide will show you how to apply ParaAttention to FLUX.1-dev and HunyuanVideo on NVIDIA L20 GPUs.
	No optimizations are applied for our baseline benchmark, except for HunyuanVideo to avoid out-of-memory errors.</p> <p>Our baseline benchmark shows that FLUX.1-dev is able to generate a 1024x1024 resolution image in 28 steps in 26.36 seconds, and HunyuanVideo is able to generate 129 frames at 720p resolution in 30 steps in 3675.71 seconds.</p> <blockquote class="tip"><p>For even faster inference with context parallelism, try using NVIDIA A100 or H100 GPUs (if available) with NVLink support, especially when there is a large number of GPUs.</p></blockquote> <!--[1--><h2 class="relative group"><a id="first-block-cache" 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="#first-block-cache"><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>First Block Cache</span></h2><!--]--><!----> <p>Caching the output of the transformers blocks in the model and reusing them in the next inference steps reduces the computation cost and makes inference faster.</p> <p>However, it is hard to decide when to reuse the cache to ensure quality generated images or videos. ParaAttention directly uses the <strong>residual difference of the first transformer block output</strong> to approximate the difference among model outputs. When the difference is small enough, the residual difference of previous inference steps is reused. In other words, the denoising step is skipped.</p> <p>This achieves a 2x speedup on FLUX.1-dev and HunyuanVideo inference with very good quality.</p> <figure><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/ada-cache.png" alt="Cache in Diffusion Transformer"/> <figcaption>How AdaCache works, First Block Cache is a variant of it</figcaption></figure> <div class="flex space-x-2 items-center my-1.5 mr-8 h-7 !pl-0 -mx-3 md:mx-0"><!--[--><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd border-gray-800 bg-black dark:bg-gray-700 text-white">FLUX-1.dev</div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">HunyuanVideo</div><!--]--></div> <div class="language-select"><!--[0--><p>To apply first block cache on FLUX.1-dev, call <code>apply_cache_on_pipe</code> as shown below. 0.08 is the default residual difference value for FLUX models.</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-python "><!----><span class="hljs-keyword">import</span> time
	<span class="hljs-keyword">import</span> torch
	<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FluxPipeline

	pipe = FluxPipeline.from_pretrained(
	<span class="hljs-string">"black-forest-labs/FLUX.1-dev"</span>,
	torch_dtype=torch.bfloat16,
	).to(<span class="hljs-string">"cuda"</span>)

	<span class="hljs-keyword">from</span> para_attn.first_block_cache.diffusers_adapters <span class="hljs-keyword">import</span> apply_cache_on_pipe

	apply_cache_on_pipe(pipe, residual_diff_threshold=<span class="hljs-number">0.08</span>)

	<span class="hljs-comment"># Enable memory savings</span>
	<span class="hljs-comment"># pipe.enable_model_cpu_offload()</span>
	<span class="hljs-comment"># pipe.enable_sequential_cpu_offload()</span>

	begin = time.time()
	image = pipe(
	<span class="hljs-string">"A cat holding a sign that says hello world"</span>,
	num_inference_steps=<span class="hljs-number">28</span>,
	).images[<span class="hljs-number">0</span>]
	end = time.time()
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Time: <span class="hljs-subst">{end - begin:<span class="hljs-number">.2</span>f}</span>s"</span>)

	<span class="hljs-built_in">print</span>(<span class="hljs-string">"Saving image to flux.png"</span>)
	image.save(<span class="hljs-string">"flux.png"</span>)<!----></pre></div><!----> <table><thead><tr><th>Optimizations</th><th>Original</th><th>FBCache rdt=0.06</th><th>FBCache rdt=0.08</th><th>FBCache rdt=0.10</th><th>FBCache rdt=0.12</th></tr></thead><tbody><tr><td>Preview</td><td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-original.png" alt="Original"/></td><td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.06.png" alt="FBCache rdt=0.06"/></td><td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.08.png" alt="FBCache rdt=0.08"/></td><td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.10.png" alt="FBCache rdt=0.10"/></td><td><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/para-attn/flux-fbc-0.12.png" alt="FBCache rdt=0.12"/></td></tr><tr><td>Wall Time (s)</td><td>26.36</td><td>21.83</td><td>17.01</td><td>16.00</td><td>13.78</td></tr></tbody></table> <p>First Block Cache reduced the inference speed to 17.01 seconds compared to the baseline, or 1.55x faster, while maintaining nearly zero quality loss.</p><!----><!--]--><!----> <!--[-1--><!--]--><!----><!----></div><!----> <!--[1--><h2 class="relative group"><a id="fp8-quantization" 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="#fp8-quantization"><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>fp8 quantization</span></h2><!--]--><!----> <p>fp8 with dynamic quantization further speeds up inference and reduces memory usage. Both the activations and weights must be quantized in order to use the 8-bit <a href="https://www.nvidia.com/en-us/data-center/tensor-cores/" rel="nofollow">NVIDIA Tensor Cores</a>.</p> <p>Use <code>float8_weight_only</code> and <code>float8_dynamic_activation_float8_weight</code> to quantize the text encoder and transformer model.</p> <p>The default quantization method is per tensor quantization, but if your GPU supports row-wise quantization, you can also try it for better accuracy.</p> <p>Install <a href="https://github.com/pytorch/ao/tree/main" rel="nofollow">torchao</a> with the command below.</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-bash "><!---->pip3 install -U torch torchao<!----></pre></div><!----> <p><a href="https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html" rel="nofollow">torch.compile</a> with <code>mode="max-autotune-no-cudagraphs"</code> or <code>mode="max-autotune"</code> selects the best kernel for performance. Compilation can take a long time if it’s the first time the model is called, but it is worth it once the model has been compiled.</p> <p>This example only quantizes the transformer model, but you can also quantize the text encoder to reduce memory usage even more.</p> <blockquote class="tip"><p>Dynamic quantization can significantly change the distribution of the model output, so you need to change the <code>residual_diff_threshold</code> to a larger value for it to take effect.</p></blockquote> <div class="flex space-x-2 items-center my-1.5 mr-8 h-7 !pl-0 -mx-3 md:mx-0"><!--[--><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd border-gray-800 bg-black dark:bg-gray-700 text-white">FLUX-1.dev</div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">HunyuanVideo</div><!--]--></div> <div class="language-select"><!--[0--><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-python "><!----><span class="hljs-keyword">import</span> time
	<span class="hljs-keyword">import</span> torch
	<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FluxPipeline

	pipe = FluxPipeline.from_pretrained(
	<span class="hljs-string">"black-forest-labs/FLUX.1-dev"</span>,
	torch_dtype=torch.bfloat16,
	).to(<span class="hljs-string">"cuda"</span>)

	<span class="hljs-keyword">from</span> para_attn.first_block_cache.diffusers_adapters <span class="hljs-keyword">import</span> apply_cache_on_pipe

	apply_cache_on_pipe(
	pipe,
	residual_diff_threshold=<span class="hljs-number">0.12</span>, <span class="hljs-comment"># Use a larger value to make the cache take effect</span>
	)

	<span class="hljs-keyword">from</span> torchao.quantization <span class="hljs-keyword">import</span> quantize_, float8_dynamic_activation_float8_weight, float8_weight_only

	quantize_(pipe.text_encoder, float8_weight_only())
	quantize_(pipe.transformer, float8_dynamic_activation_float8_weight())
	pipe.transformer = torch.<span class="hljs-built_in">compile</span>(
	pipe.transformer, mode=<span class="hljs-string">"max-autotune-no-cudagraphs"</span>,
	)

	<span class="hljs-comment"># Enable memory savings</span>
	<span class="hljs-comment"># pipe.enable_model_cpu_offload()</span>
	<span class="hljs-comment"># pipe.enable_sequential_cpu_offload()</span>

	<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">2</span>):
	begin = time.time()
	image = pipe(
	<span class="hljs-string">"A cat holding a sign that says hello world"</span>,
	num_inference_steps=<span class="hljs-number">28</span>,
	).images[<span class="hljs-number">0</span>]
	end = time.time()
	<span class="hljs-keyword">if</span> i == <span class="hljs-number">0</span>:
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Warm up time: <span class="hljs-subst">{end - begin:<span class="hljs-number">.2</span>f}</span>s"</span>)
	<span class="hljs-keyword">else</span>:
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Time: <span class="hljs-subst">{end - begin:<span class="hljs-number">.2</span>f}</span>s"</span>)

	<span class="hljs-built_in">print</span>(<span class="hljs-string">"Saving image to flux.png"</span>)
	image.save(<span class="hljs-string">"flux.png"</span>)<!----></pre></div><!----> <p>fp8 dynamic quantization and torch.compile reduced the inference speed to 7.56 seconds compared to the baseline, or 3.48x faster.</p><!----><!--]--><!----> <!--[-1--><!--]--><!----><!----></div><!----> <!--[1--><h2 class="relative group"><a id="context-parallelism" 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="#context-parallelism"><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>Context Parallelism</span></h2><!--]--><!----> <p>Context Parallelism parallelizes inference and scales with multiple GPUs. The ParaAttention compositional design allows you to combine Context Parallelism with First Block Cache and dynamic quantization.</p> <blockquote class="tip"><p>Refer to the <a href="https://github.com/chengzeyi/ParaAttention/tree/main" rel="nofollow">ParaAttention</a> repository for detailed instructions and examples of how to scale inference with multiple GPUs.</p></blockquote> <p>If the inference process needs to be persistent and serviceable, it is suggested to use <a href="https://pytorch.org/docs/stable/multiprocessing.html" rel="nofollow">torch.multiprocessing</a> to write your own inference processor. This can eliminate the overhead of launching the process and loading and recompiling the model.</p> <div class="flex space-x-2 items-center my-1.5 mr-8 h-7 !pl-0 -mx-3 md:mx-0"><!--[--><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd border-gray-800 bg-black dark:bg-gray-700 text-white">FLUX-1.dev</div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">HunyuanVideo</div><!--]--></div> <div class="language-select"><!--[0--><p>The code sample below combines First Block Cache, fp8 dynamic quantization, torch.compile, and Context Parallelism for the fastest inference speed.</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-python "><!----><span class="hljs-keyword">import</span> time
	<span class="hljs-keyword">import</span> torch
	<span class="hljs-keyword">import</span> torch.distributed <span class="hljs-keyword">as</span> dist
	<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FluxPipeline

	dist.init_process_group()

	torch.cuda.set_device(dist.get_rank())

	pipe = FluxPipeline.from_pretrained(
	<span class="hljs-string">"black-forest-labs/FLUX.1-dev"</span>,
	torch_dtype=torch.bfloat16,
	).to(<span class="hljs-string">"cuda"</span>)

	<span class="hljs-keyword">from</span> para_attn.context_parallel <span class="hljs-keyword">import</span> init_context_parallel_mesh
	<span class="hljs-keyword">from</span> para_attn.context_parallel.diffusers_adapters <span class="hljs-keyword">import</span> parallelize_pipe
	<span class="hljs-keyword">from</span> para_attn.parallel_vae.diffusers_adapters <span class="hljs-keyword">import</span> parallelize_vae

	mesh = init_context_parallel_mesh(
	pipe.device.<span class="hljs-built_in">type</span>,
	max_ring_dim_size=<span class="hljs-number">2</span>,
	)
	parallelize_pipe(
	pipe,
	mesh=mesh,
	)
	parallelize_vae(pipe.vae, mesh=mesh._flatten())

	<span class="hljs-keyword">from</span> para_attn.first_block_cache.diffusers_adapters <span class="hljs-keyword">import</span> apply_cache_on_pipe

	apply_cache_on_pipe(
	pipe,
	residual_diff_threshold=<span class="hljs-number">0.12</span>, <span class="hljs-comment"># Use a larger value to make the cache take effect</span>
	)

	<span class="hljs-keyword">from</span> torchao.quantization <span class="hljs-keyword">import</span> quantize_, float8_dynamic_activation_float8_weight, float8_weight_only

	quantize_(pipe.text_encoder, float8_weight_only())
	quantize_(pipe.transformer, float8_dynamic_activation_float8_weight())
	torch._inductor.config.reorder_for_compute_comm_overlap = <span class="hljs-literal">True</span>
	pipe.transformer = torch.<span class="hljs-built_in">compile</span>(
	pipe.transformer, mode=<span class="hljs-string">"max-autotune-no-cudagraphs"</span>,
	)

	<span class="hljs-comment"># Enable memory savings</span>
	<span class="hljs-comment"># pipe.enable_model_cpu_offload(gpu_id=dist.get_rank())</span>
	<span class="hljs-comment"># pipe.enable_sequential_cpu_offload(gpu_id=dist.get_rank())</span>

	<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">2</span>):
	begin = time.time()
	image = pipe(
	<span class="hljs-string">"A cat holding a sign that says hello world"</span>,
	num_inference_steps=<span class="hljs-number">28</span>,
	output_type=<span class="hljs-string">"pil"</span> <span class="hljs-keyword">if</span> dist.get_rank() == <span class="hljs-number">0</span> <span class="hljs-keyword">else</span> <span class="hljs-string">"pt"</span>,
	).images[<span class="hljs-number">0</span>]
	end = time.time()
	<span class="hljs-keyword">if</span> dist.get_rank() == <span class="hljs-number">0</span>:
	<span class="hljs-keyword">if</span> i == <span class="hljs-number">0</span>:
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Warm up time: <span class="hljs-subst">{end - begin:<span class="hljs-number">.2</span>f}</span>s"</span>)
	<span class="hljs-keyword">else</span>:
	<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Time: <span class="hljs-subst">{end - begin:<span class="hljs-number">.2</span>f}</span>s"</span>)

	<span class="hljs-keyword">if</span> dist.get_rank() == <span class="hljs-number">0</span>:
	<span class="hljs-built_in">print</span>(<span class="hljs-string">"Saving image to flux.png"</span>)
	image.save(<span class="hljs-string">"flux.png"</span>)

	dist.destroy_process_group()<!----></pre></div><!----> <p>Save to <code>run_flux.py</code> and launch it with <a href="https://pytorch.org/docs/stable/elastic/run.html" rel="nofollow">torchrun</a>.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg 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-bash "><!----><span class="hljs-comment"># Use --nproc_per_node to specify the number of GPUs</span>
	torchrun --nproc_per_node=2 run_flux.py<!----></pre></div><!----> <p>Inference speed is reduced to 8.20 seconds compared to the baseline, or 3.21x faster, with 2 NVIDIA L20 GPUs. On 4 L20s, inference speed is 3.90 seconds, or 6.75x faster.</p><!----><!--]--><!----> <!--[-1--><!--]--><!----><!----></div><!----> <!--[1--><h2 class="relative group"><a id="benchmarks" 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="#benchmarks"><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>Benchmarks</span></h2><!--]--><!----> <div class="flex space-x-2 items-center my-1.5 mr-8 h-7 !pl-0 -mx-3 md:mx-0"><!--[--><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd border-gray-800 bg-black dark:bg-gray-700 text-white">FLUX-1.dev</div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">HunyuanVideo</div><!--]--></div> <div class="language-select"><!--[0--><table><thead><tr><th>GPU Type</th><th>Number of GPUs</th><th>Optimizations</th><th>Wall Time (s)</th><th>Speedup</th></tr></thead><tbody><tr><td>NVIDIA L20</td><td>1</td><td>Baseline</td><td>26.36</td><td>1.00x</td></tr><tr><td>NVIDIA L20</td><td>1</td><td>FBCache (rdt=0.08)</td><td>17.01</td><td>1.55x</td></tr><tr><td>NVIDIA L20</td><td>1</td><td>FP8 DQ</td><td>13.40</td><td>1.96x</td></tr><tr><td>NVIDIA L20</td><td>1</td><td>FBCache (rdt=0.12) + FP8 DQ</td><td>7.56</td><td>3.48x</td></tr><tr><td>NVIDIA L20</td><td>2</td><td>FBCache (rdt=0.12) + FP8 DQ + CP</td><td>4.92</td><td>5.35x</td></tr><tr><td>NVIDIA L20</td><td>4</td><td>FBCache (rdt=0.12) + FP8 DQ + CP</td><td>3.90</td><td>6.75x</td></tr></tbody></table><!----><!--]--><!----> <!--[-1--><!--]--><!----><!----></div><!----> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/diffusers/blob/main/docs/source/en/optimization/para_attn.md" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg><!----> <span><span class="underline">Update</span> on GitHub</span></a><!----> <p></p><!--]--><!----><!--]--><!--]--><!--]--> <!--[-1--><!--]--><!--]-->

	<script>
	{
	__sveltekit_rmdsov = {
	base: "/docs/diffusers/main/en",
	assets: "/docs/diffusers/main/en"
	};

	const element = document.currentScript.parentElement;

	Promise.all([
	import("/docs/diffusers/main/en/_app/immutable/entry/start.BKtHvaj4.js"),
	import("/docs/diffusers/main/en/_app/immutable/entry/app.Cp32ddTn.js")
	]).then(([kit, app]) => {
	kit.start(app, element, {
	node_ids: [0, 300],
	data: [null,null],
	form: null,
	error: null
	});
	});
	}
	</script>

Xet Storage Details

Size:: 35.8 kB
Xet hash:: 362c0c0e6a4870e9931470719e54abb07f62b7fc8ffe62292883f798e43771fa

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.