optimum-neuron/v0.2.0.dev4/en/_app/immutable/nodes/23.aaad99fa.js

import{s as ve,n as Ce,o as We}from"../chunks/scheduler.85c25b89.js";import{S as Ze,i as $e,g as o,s,r as i,A as Ge,h as r,f as n,c as a,j as ce,u as p,x as f,k as de,y as fe,a as l,v as m,d as u,t as c,w as d}from"../chunks/index.c9bcf812.js";import{D as Xe}from"../chunks/Docstring.af42ec0f.js";import{C as Me}from"../chunks/CodeBlock.c004bd26.js";import{H as S}from"../chunks/index.9790a2b6.js";function Ve(he){let M,F,R,k,h,E,b,z,g,be='Latent Consistency Models (LCMs) were proposed in <a href="https://huggingface.co/papers/2310.04378" rel="nofollow">Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao</a>. LCMs enable inference with fewer steps on any pre-trained LDMs, including Stable Diffusion and SDXL.',Q,_,ge="In <code>optimum-neuron</code>, you can:",H,w,_e="<li>Use the class <code>NeuronLatentConsistencyModelPipeline</code> to compile and run inference of LCMs distilled from Stable Diffusion (SD) models.</li> <li>And continue to use the class <code>NeuronStableDiffusionXLPipeline</code> for LCMs distilled from SDXL models.</li>",Y,j,we='Here are examples to compile the LCMs of Stable Diffusion ( <a href="https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7" rel="nofollow">SimianLuo/LCM_Dreamshaper_v7</a> ) and Stable Diffusion XL( <a href="https://huggingface.co/latent-consistency/lcm-sdxl" rel="nofollow">latent-consistency/lcm-sdxl</a> ), and then run inference on AWS Inferentia 2 :',D,T,A,J,P,U,K,N,O,X,ee,v,te,C,je="Now we can generate images from text prompts on Inf2 using the pre-compiled model:",ne,W,Te="<li>LCM of Stable Diffusion</li>",le,Z,se,$,Je="<li>LCM of Stable Diffusion XL</li>",ae,G,oe,V,ie,y,I,ye,x,L,re,B,Ue='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 🤗 !',pe,q,me;return h=new S({props:{title:"Latent Consistency Models",local:"latent-consistency-models",headingTag:"h1"}}),b=new S({props:{title:"Overview",local:"overview",headingTag:"h2"}}),T=new S({props:{title:"Export to Neuron",local:"export-to-neuron",headingTag:"h2"}}),J=new S({props:{title:"LCM of Stable Diffusion",local:"lcm-of-stable-diffusion",headingTag:"h3"}}),U=new Me({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronLatentConsistencyModelPipeline

model_id = <span class="hljs-string">&quot;SimianLuo/LCM_Dreamshaper_v7&quot;</span>
num_images_per_prompt = <span class="hljs-number">1</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">768</span>, <span class="hljs-string">&quot;width&quot;</span>: <span class="hljs-number">768</span>, <span class="hljs-string">&quot;num_images_per_prompt&quot;</span>: num_images_per_prompt}
compiler_args = {<span class="hljs-string">&quot;auto_cast&quot;</span>: <span class="hljs-string">&quot;matmul&quot;</span>, <span class="hljs-string">&quot;auto_cast_type&quot;</span>: <span class="hljs-string">&quot;bf16&quot;</span>}

stable_diffusion = NeuronLatentConsistencyModelPipeline.from_pretrained(
    model_id, export=<span class="hljs-literal">True</span>, **compiler_args, **input_shapes
)
save_directory = <span class="hljs-string">&quot;lcm_sd_neuron/&quot;</span>
stable_diffusion.save_pretrained(save_directory)

<span class="hljs-comment"># Push to hub</span>
stable_diffusion.push_to_hub(save_directory, repository_id=<span class="hljs-string">&quot;my-neuron-repo&quot;</span>)  <span class="hljs-comment"># Replace with your repo id, eg. &quot;Jingya/LCM_Dreamshaper_v7_neuronx&quot;</span>`,wrap:!1}}),N=new S({props:{title:"LCM of Stable Diffusion XL",local:"lcm-of-stable-diffusion-xl",headingTag:"h3"}}),X=new Me({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronStableDiffusionXLPipeline

model_id = <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>
unet_id = <span class="hljs-string">&quot;latent-consistency/lcm-sdxl&quot;</span>
num_images_per_prompt = <span class="hljs-number">1</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>, <span class="hljs-string">&quot;num_images_per_prompt&quot;</span>: num_images_per_prompt}
compiler_args = {<span class="hljs-string">&quot;auto_cast&quot;</span>: <span class="hljs-string">&quot;matmul&quot;</span>, <span class="hljs-string">&quot;auto_cast_type&quot;</span>: <span class="hljs-string">&quot;bf16&quot;</span>}

stable_diffusion = NeuronStableDiffusionXLPipeline.from_pretrained(
    model_id, unet_id=unet_id, export=<span class="hljs-literal">True</span>, **compiler_args, **input_shapes
)
save_directory = <span class="hljs-string">&quot;lcm_sdxl_neuron/&quot;</span>
stable_diffusion.save_pretrained(save_directory)

<span class="hljs-comment"># Push to hub</span>
stable_diffusion.push_to_hub(save_directory, repository_id=<span class="hljs-string">&quot;my-neuron-repo&quot;</span>)   <span class="hljs-comment"># Replace with your repo id, eg. &quot;Jingya/lcm-sdxl-neuronx&quot;</span>`,wrap:!1}}),v=new S({props:{title:"Text-to-Image",local:"text-to-image",headingTag:"h2"}}),Z=new Me({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronLatentConsistencyModelPipeline

pipe = NeuronLatentConsistencyModelPipeline.from_pretrained(<span class="hljs-string">&quot;Jingya/LCM_Dreamshaper_v7_neuronx&quot;</span>)
prompts = [<span class="hljs-string">&quot;Self-portrait oil painting, a beautiful cyborg with golden hair, 8k&quot;</span>] * <span class="hljs-number">2</span>

images = pipe(prompt=prompts, num_inference_steps=<span class="hljs-number">4</span>, guidance_scale=<span class="hljs-number">8.0</span>).images`,wrap:!1}}),G=new Me({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronStableDiffusionXLPipeline

pipe = NeuronStableDiffusionXLPipeline.from_pretrained(<span class="hljs-string">&quot;Jingya/lcm-sdxl-neuronx&quot;</span>)
prompts = [<span class="hljs-string">&quot;a close-up picture of an old man standing in the rain&quot;</span>] * <span class="hljs-number">2</span>

images = pipe(prompt=prompts, num_inference_steps=<span class="hljs-number">4</span>, guidance_scale=<span class="hljs-number">8.0</span>).images`,wrap:!1}}),V=new S({props:{title:"NeuronLatentConsistencyModelPipeline",local:"optimum.neuron.NeuronLatentConsistencyModelPipeline",headingTag:"h2"}}),I=new Xe({props:{name:"class optimum.neuron.NeuronLatentConsistencyModelPipeline",anchor:"optimum.neuron.NeuronLatentConsistencyModelPipeline",parameters:[{name:"config",val:": typing.Dict[str, typing.Any]"},{name:"configs",val:": typing.Dict[str, ForwardRef('PretrainedConfig')]"},{name:"neuron_configs",val:": typing.Dict[str, ForwardRef('NeuronDefaultConfig')]"},{name:"data_parallel_mode",val:": typing.Literal['none', 'unet', 'transformer', 'all']"},{name:"scheduler",val:": typing.Optional[diffusers.schedulers.scheduling_utils.SchedulerMixin]"},{name:"vae_decoder",val:": typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelVaeDecoder')]"},{name:"text_encoder",val:": typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelTextEncoder'), NoneType] = None"},{name:"text_encoder_2",val:": typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelTextEncoder'), NoneType] = None"},{name:"unet",val:": typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelUnet'), NoneType] = None"},{name:"transformer",val:": typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelTransformer'), NoneType] = None"},{name:"vae_encoder",val:": typing.Union[torch.jit._script.ScriptModule, ForwardRef('NeuronModelVaeEncoder'), NoneType] = None"},{name:"image_encoder",val:": typing.Optional[torch.jit._script.ScriptModule] = None"},{name:"safety_checker",val:": typing.Optional[torch.jit._script.ScriptModule] = None"},{name:"tokenizer",val:": typing.Union[transformers.models.clip.tokenization_clip.CLIPTokenizer, transformers.utils.dummy_sentencepiece_objects.T5Tokenizer, NoneType] = None"},{name:"tokenizer_2",val:": typing.Optional[transformers.models.clip.tokenization_clip.CLIPTokenizer] = None"},{name:"feature_extractor",val:": typing.Optional[transformers.models.clip.feature_extraction_clip.CLIPFeatureExtractor] = None"},{name:"controlnet",val:": typing.Union[torch.jit._script.ScriptModule, typing.List[torch.jit._script.ScriptModule], ForwardRef('NeuronControlNetModel'), ForwardRef('NeuronMultiControlNetModel'), NoneType] = None"},{name:"requires_aesthetics_score",val:": bool = False"},{name:"force_zeros_for_empty_prompt",val:": bool = True"},{name:"add_watermarker",val:": typing.Optional[bool] = None"},{name:"model_save_dir",val:": typing.Union[str, pathlib.Path, tempfile.TemporaryDirectory, NoneType] = None"},{name:"model_and_config_save_paths",val:": typing.Optional[typing.Dict[str, typing.Tuple[str, pathlib.Path]]] = None"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.2.0.dev4/optimum/neuron/modeling_diffusion.py#L1589"}}),L=new Xe({props:{name:"__call__",anchor:"optimum.neuron.NeuronLatentConsistencyModelPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.2.0.dev4/optimum/neuron/modeling_diffusion.py#L1165"}}),{c(){M=o("meta"),F=s(),R=o("p"),k=s(),i(h.$$.fragment),E=s(),i(b.$$.fragment),z=s(),g=o("p"),g.innerHTML=be,Q=s(),_=o("p"),_.innerHTML=ge,H=s(),w=o("ul"),w.innerHTML=_e,Y=s(),j=o("p"),j.innerHTML=we,D=s(),i(T.$$.fragment),A=s(),i(J.$$.fragment),P=s(),i(U.$$.fragment),K=s(),i(N.$$.fragment),O=s(),i(X.$$.fragment),ee=s(),i(v.$$.fragment),te=s(),C=o("p"),C.textContent=je,ne=s(),W=o("ul"),W.innerHTML=Te,le=s(),i(Z.$$.fragment),se=s(),$=o("ul"),$.innerHTML=Je,ae=s(),i(G.$$.fragment),oe=s(),i(V.$$.fragment),ie=s(),y=o("div"),i(I.$$.fragment),ye=s(),x=o("div"),i(L.$$.fragment),re=s(),B=o("p"),B.innerHTML=Ue,pe=s(),q=o("p"),this.h()},l(e){const 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