optimum-neuron/v0.3.0/en/_app/immutable/nodes/24.6e28f305.js

import{s as Xe,f as Ne,o as Ze,n as Be}from"../chunks/scheduler.85c25b89.js";import{S as Ge,i as Fe,g as o,s,r as p,A as Ve,h as r,f as l,c as a,j as he,u as m,x as U,k as y,y as _e,a as n,v as u,d as c,t as M,w as d}from"../chunks/index.c9bcf812.js";import{T as We}from"../chunks/Tip.d8f753fa.js";import{D as Ie}from"../chunks/Docstring.e86a2d02.js";import{C as z}from"../chunks/CodeBlock.c004bd26.js";import{H as q}from"../chunks/getInferenceSnippets.5ea0a804.js";function Se(R){let i,j="We recommend using a <code>inf2.24xlarge</code> instance with tensor parallel size 8 for the model compilation and inference.";return{c(){i=o("p"),i.innerHTML=j},l(f){i=r(f,"P",{"data-svelte-h":!0}),U(i)!=="svelte-1s0w8k8"&&(i.innerHTML=j)},m(f,Q){n(f,i,Q)},p:Be,d(f){f&&l(i)}}}function Ae(R){let i,j,f,Q,T,Y,J,ge="Flux is a series of text-to-image generation models based on diffusion transformers.",P,g,D,b,K,C,Ue="<li>Option 1: CLI</li>",O,$,ee,x,je="<li>Option 2: Python API</li>",te,v,le,N,ne,I,Te="<li>The guidance-distilled variant takes about 50 sampling steps for good-quality generation.</li>",se,X,ae,h,Je,ie,Z,oe,B,be="<li>max_sequence_length cannot be more than 256.</li> <li>guidance_scale needs to be 0.</li> <li>As this is a timestep-distilled model, it benefits from fewer sampling steps.</li>",re,G,pe,F,me,w,Ce,ue,V,ce,W,$e="The Flux pipeline for text-to-image generation.",Me,_,S,we,L,A,de,E,xe='Are there any other diffusion features that you want us to support in 🤗<code>Optimum-neuron</code>? Please file an issue to <a href="https://github.com/huggingface/optimum-neuron" rel="nofollow"><code>Optimum-neuron</code> Github repo</a> or discuss with us on <a href="https://discuss.huggingface.co/c/optimum/" rel="nofollow">HuggingFace’s community forum</a>, cheers 🤗 !',ye,H,fe;return T=new q({props:{title:"Flux",local:"flux",headingTag:"h1"}}),g=new We({props:{warning:!1,$$slots:{default:[Se]},$$scope:{ctx:R}}}),b=new q({props:{title:"Export to Neuron",local:"export-to-neuron",headingTag:"h3"}}),$=new z({props:{code:"b3B0aW11bS1jbGklMjBleHBvcnQlMjBuZXVyb24lMjAtLW1vZGVsJTIwYmxhY2stZm9yZXN0LWxhYnMlMkZGTFVYLjEtZGV2JTIwLS10ZW5zb3JfcGFyYWxsZWxfc2l6ZSUyMDglMjAtLWJhdGNoX3NpemUlMjAxJTIwLS1oZWlnaHQlMjAxMDI0JTIwLS13aWR0aCUyMDEwMjQlMjAtLW51bV9pbWFnZXNfcGVyX3Byb21wdCUyMDElMjAtLXRvcmNoX2R0eXBlJTIwYmZsb2F0MTYlMjBmbHV4X2Rldl9uZXVyb24lMkY=",highlighted:'optimum-cli <span class="hljs-built_in">export</span> neuron --model black-forest-labs/FLUX.1-dev --tensor_parallel_size 8 --batch_size 1 --height 1024 --width 1024 --num_images_per_prompt 1 --torch_dtype bfloat16 flux_dev_neuron/',wrap:!1}}),v=new z({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronFluxPipeline

<span class="hljs-keyword">if</span> __name__ == <span class="hljs-string">&quot;__main__&quot;</span>:
    compiler_args = {<span class="hljs-string">&quot;auto_cast&quot;</span>: <span class="hljs-string">&quot;none&quot;</span>}
    input_shapes = {<span class="hljs-string">&quot;batch_size&quot;</span>: <span class="hljs-number">1</span>, <span class="hljs-string">&quot;height&quot;</span>: <span class="hljs-number">1024</span>, <span class="hljs-string">&quot;width&quot;</span>: <span class="hljs-number">1024</span>}

    pipe = NeuronFluxPipeline.from_pretrained(
        <span class="hljs-string">&quot;black-forest-labs/FLUX.1-dev&quot;</span>,
        torch_dtype=torch.bfloat16,
        export=<span class="hljs-literal">True</span>,
        tensor_parallel_size=<span class="hljs-number">8</span>,
        **compiler_args,
        **input_shapes
    )

    <span class="hljs-comment"># Save locally</span>
    pipe.save_pretrained(<span class="hljs-string">&quot;flux_dev_neuron_1024_tp8/&quot;</span>)

    <span class="hljs-comment"># Upload to the HuggingFace Hub</span>
    pipe.push_to_hub(
        <span class="hljs-string">&quot;flux_dev_neuron_1024_tp8/&quot;</span>, repository_id=<span class="hljs-string">&quot;Jingya/FLUX.1-dev-neuronx-1024x1024-tp8&quot;</span>  <span class="hljs-comment"># Replace with your HF Hub repo id</span>
    )`,wrap:!1}}),N=new q({props:{title:"Guidance-distilled",local:"guidance-distilled",headingTag:"h2"}}),X=new z({props:{code:"ZnJvbSUyMG9wdGltdW0ubmV1cm9uJTIwaW1wb3J0JTIwTmV1cm9uRmx1eFBpcGVsaW5lJTBBJTBBcGlwZSUyMCUzRCUyME5ldXJvbkZsdXhQaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIyZmx1eF9kZXZfbmV1cm9uXzEwMjRfdHA4JTJGJTIyKSUwQXByb21wdCUyMCUzRCUyMCUyMkElMjBjYXQlMjBob2xkaW5nJTIwYSUyMHNpZ24lMjB0aGF0JTIwc2F5cyUyMGhlbGxvJTIwd29ybGQlMjIlMEFvdXQlMjAlM0QlMjBwaXBlKCUwQSUyMCUyMCUyMCUyMHByb21wdCUyQyUwQSUyMCUyMCUyMCUyMGd1aWRhbmNlX3NjYWxlJTNEMy41JTJDJTBBJTIwJTIwJTIwJTIwbnVtX2luZmVyZW5jZV9zdGVwcyUzRDUwJTJDJTBBJTIwJTIwJTIwJTIwZ2VuZXJhdG9yJTNEdG9yY2guR2VuZXJhdG9yKCUyMmNwdSUyMikubWFudWFsX3NlZWQoMCklMEEpLmltYWdlcyU1QjAlNUQlMEFvdXQuc2F2ZSglMjJmbHV4X29wdGltdW0ucG5nJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronFluxPipeline

pipe = NeuronFluxPipeline.from_pretrained(<span class="hljs-string">&quot;flux_dev_neuron_1024_tp8/&quot;</span>)
prompt = <span class="hljs-string">&quot;A cat holding a sign that says hello world&quot;</span>
out = pipe(
    prompt,
    guidance_scale=<span class="hljs-number">3.5</span>,
    num_inference_steps=<span class="hljs-number">50</span>,
    generator=torch.Generator(<span class="hljs-string">&quot;cpu&quot;</span>).manual_seed(<span class="hljs-number">0</span>)
).images[<span class="hljs-number">0</span>]
out.save(<span class="hljs-string">&quot;flux_optimum.png&quot;</span>)`,wrap:!1}}),Z=new q({props:{title:"Timestep-distilled",local:"timestep-distilled",headingTag:"h2"}}),G=new z({props:{code:"b3B0aW11bS1jbGklMjBleHBvcnQlMjBuZXVyb24lMjAtLW1vZGVsJTIwYmxhY2stZm9yZXN0LWxhYnMlMkZGTFVYLjEtc2NobmVsbCUyMC0tdGVuc29yX3BhcmFsbGVsX3NpemUlMjA4JTIwLS1iYXRjaF9zaXplJTIwMSUyMC0taGVpZ2h0JTIwMTAyNCUyMC0td2lkdGglMjAxMDI0JTIwLS1udW1faW1hZ2VzX3Blcl9wcm9tcHQlMjAxJTIwLS1zZXF1ZW5jZV9sZW5ndGglMjAyNTYlMjAtLXRvcmNoX2R0eXBlJTIwYmZsb2F0MTYlMjBmbHV4X3NjaG5lbGxfbmV1cm9uXzEwMjRfdHA4JTJG",highlighted:'optimum-cli <span class="hljs-built_in">export</span> neuron --model black-forest-labs/FLUX.1-schnell --tensor_parallel_size 8 --batch_size 1 --height 1024 --width 1024 --num_images_per_prompt 1 --sequence_length 256 --torch_dtype bfloat16 flux_schnell_neuron_1024_tp8/',wrap:!1}}),F=new z({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwb3B0aW11bS5uZXVyb24lMjBpbXBvcnQlMjBOZXVyb25GbHV4UGlwZWxpbmUlMEElMEFwaXBlJTIwJTNEJTIwTmV1cm9uRmx1eFBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJmbHV4X3NjaG5lbGxfbmV1cm9uXzEwMjRfdHA4JTIyKSUwQXByb21wdCUyMCUzRCUyMCUyMkElMjBjYXQlMjBob2xkaW5nJTIwYSUyMHNpZ24lMjB0aGF0JTIwc2F5cyUyMGhlbGxvJTIwd29ybGQlMjIlMEFvdXQlMjAlM0QlMjBwaXBlKHByb21wdCUyQyUyMG1heF9zZXF1ZW5jZV9sZW5ndGglM0QyNTYlMkMlMjBudW1faW5mZXJlbmNlX3N0ZXBzJTNENCkuaW1hZ2VzJTVCMCU1RA==",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronFluxPipeline

pipe = NeuronFluxPipeline.from_pretrained(<span class="hljs-string">&quot;flux_schnell_neuron_1024_tp8&quot;</span>)
prompt = <span class="hljs-string">&quot;A cat holding a sign that says hello world&quot;</span>
out = pipe(prompt, max_sequence_length=<span class="hljs-number">256</span>, num_inference_steps=<span class="hljs-number">4</span>).images[<span class="hljs-number">0</span>]`,wrap:!1}}),V=new q({props:{title:"NeuronFluxPipeline",local:"optimum.neuron.NeuronFluxPipeline",headingTag:"h2"}}),S=new Ie({props:{name:"class optimum.neuron.NeuronFluxPipeline",anchor:"optimum.neuron.NeuronFluxPipeline",parameters:[{name:"config",val:": dict[str, typing.Any]"},{name:"configs",val:": dict[str, 'PretrainedConfig']"},{name:"neuron_configs",val:": dict[str, 'NeuronDefaultConfig']"},{name:"data_parallel_mode",val:": typing.Literal['none', 'unet', 'transformer', 'all']"},{name:"scheduler",val:": diffusers.schedulers.scheduling_utils.SchedulerMixin | None"},{name:"vae_decoder",val:": torch.jit._script.ScriptModule | NeuronModelVaeDecoder"},{name:"text_encoder",val:": torch.jit._script.ScriptModule | NeuronModelTextEncoder | None = None"},{name:"text_encoder_2",val:": torch.jit._script.ScriptModule | NeuronModelTextEncoder | None = None"},{name:"unet",val:": torch.jit._script.ScriptModule | NeuronModelUnet | None = None"},{name:"transformer",val:": torch.jit._script.ScriptModule | NeuronModelTransformer | None = None"},{name:"vae_encoder",val:": torch.jit._script.ScriptModule | NeuronModelVaeEncoder | None = None"},{name:"image_encoder",val:": torch.jit._script.ScriptModule | None = None"},{name:"safety_checker",val:": torch.jit._script.ScriptModule | None = None"},{name:"tokenizer",val:": transformers.models.clip.tokenization_clip.CLIPTokenizer | transformers.models.t5.tokenization_t5.T5Tokenizer | None = None"},{name:"tokenizer_2",val:": transformers.models.clip.tokenization_clip.CLIPTokenizer | None = None"},{name:"feature_extractor",val:": transformers.models.clip.feature_extraction_clip.CLIPFeatureExtractor | None = None"},{name:"controlnet",val:": torch.jit._script.ScriptModule | list[torch.jit._script.ScriptModule]| NeuronControlNetModel | NeuronMultiControlNetModel | None = None"},{name:"requires_aesthetics_score",val:": bool = False"},{name:"force_zeros_for_empty_prompt",val:": bool = True"},{name:"add_watermarker",val:": bool | None = None"},{name:"model_save_dir",val:": str | pathlib.Path | tempfile.TemporaryDirectory | None = None"},{name:"model_and_config_save_paths",val:": dict[str, tuple[str, pathlib.Path]] | None = None"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.3.0/optimum/neuron/modeling_diffusion.py#L1603"}}),A=new Ie({props:{name:"__call__",anchor:"optimum.neuron.NeuronFluxPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.3.0/optimum/neuron/modeling_diffusion.py#L1084"}}),{c(){i=o("meta"),j=s(),f=o("p"),Q=s(),p(T.$$.fragment),Y=s(),J=o("p"),J.textContent=ge,P=s(),p(g.$$.fragment),D=s(),p(b.$$.fragment),K=s(),C=o("ul"),C.innerHTML=Ue,O=s(),p($.$$.fragment),ee=s(),x=o("ul"),x.innerHTML=je,te=s(),p(v.$$.fragment),le=s(),p(N.$$.fragment),ne=s(),I=o("ul"),I.innerHTML=Te,se=s(),p(X.$$.fragment),ae=s(),h=o("img"),ie=s(),p(Z.$$.fragment),oe=s(),B=o("ul"),B.innerHTML=be,re=s(),p(G.$$.fragment),pe=s(),p(F.$$.fragment),me=s(),w=o("img"),ue=s(),p(V.$$.fragment),ce=s(),W=o("p"),W.textContent=$e,Me=s(),_=o("div"),p(S.$$.fragment),we=s(),L=o("div"),p(A.$$.fragment),de=s(),E=o("p"),E.innerHTML=xe,ye=s(),H=o("p"),this.h()},l(e){const 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