import{S as To,i as Io,s as Co,e as a,k as l,w as g,t as s,M as Ao,c as i,d as t,m as d,a as o,x as _,h as r,b as f,G as e,g as h,y as b,q as v,o as D,B as w,v as Uo,L as Go}from"../../../../chunks/vendor-hf-doc-builder.js";import{D as U}from"../../../../chunks/Docstring-hf-doc-builder.js";import{C as ko}from"../../../../chunks/CodeBlock-hf-doc-builder.js";import{I as _n}from"../../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Oo}from"../../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function No(Ut){let y,j,E,M,S;return M=new ko({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwU3RhYmxlRGlmZnVzaW9uUGlwZWxpbmUlMEElMEFwaXBlJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uTERNM0RQaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIySW50ZWwlMkZsZG0zZCUyMiklMEFwaXBlJTIwJTNEJTIwcGlwZS50byglMjJjdWRhJTIyKSUwQSUwQXByb21wdCUyMCUzRCUyMCUyMmElMjBwaG90byUyMG9mJTIwYW4lMjBhc3Ryb25hdXQlMjByaWRpbmclMjBhJTIwaG9yc2UlMjBvbiUyMG1hcnMlMjIlMEFvdXRwdXQlMjAlM0QlMjBwaXBlKHByb21wdCklMEFyZ2JfaW1hZ2UlMkMlMjBkZXB0aF9pbWFnZSUyMCUzRCUyMG91dHB1dC5yZ2IlMkMlMjBvdXRwdXQuZGVwdGg=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableDiffusionLDM3DPipeline.from_pretrained(<span class="hljs-string">&quot;Intel/ldm3d&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>output = pipe(prompt)
<span class="hljs-meta">&gt;&gt;&gt; </span>rgb_image, depth_image = output.rgb, output.depth`}}),{c(){y=a("p"),j=s("Examples:"),E=l(),g(M.$$.fragment)},l(m){y=i(m,"P",{});var k=o(y);j=r(k,"Examples:"),k.forEach(t),E=d(m),_(M.$$.fragment,m)},m(m,k){h(m,y,k),e(y,j),h(m,E,k),b(M,m,k),S=!0},p:Go,i(m){S||(v(M.$$.fragment,m),S=!0)},o(m){D(M.$$.fragment,m),S=!1},d(m){m&&t(y),m&&t(E),w(M,m)}}}function Ro(Ut){let y,j,E,M,S,m,k,He,bn,Gt,B,vn,ae,Dn,wn,Ot,Ie,ie,yn,oe,Mn,En,Nt,se,Qe,Ln,$n,Rt,Z,Ke,$,et,xn,Pn,tt,Sn,kn,Ce,Tn,In,Ae,Cn,An,nt,x,at,re,Un,Gn,it,ot,On,Nn,Ue,Rn,Wn,Ge,jn,Wt,G,F,st,le,Bn,rt,Zn,jt,V,de,Fn,Oe,Vn,qn,zn,lt,Jn,Bt,q,Xn,Ne,Yn,Hn,Zt,fe,Ft,O,z,dt,pe,Qn,ft,Kn,Vt,P,ce,ea,pt,ta,na,J,ue,aa,ct,ia,qt,N,X,ut,he,oa,ht,sa,zt,p,me,ra,Re,la,ge,da,fa,_e,pa,We,ca,ua,ha,mt,ma,ga,R,be,gt,_a,ba,je,va,Da,ve,_t,wa,ya,Be,Ma,Ea,De,bt,La,$a,Ze,xa,Pa,vt,Sa,ka,Dt,we,wt,Ta,Ia,Fe,Ca,Aa,T,ye,Ua,yt,Ga,Oa,Y,Na,H,Me,Ra,Ee,Wa,Mt,ja,Ba,Za,Q,Le,Fa,$e,Va,Et,qa,za,Ja,K,xe,Xa,L,Ya,Lt,Ha,Qa,$t,Ka,ei,xt,ti,ni,Pt,ai,ii,oi,ee,Pe,si,W,ri,St,li,di,kt,fi,pi,ci,I,Se,ui,Tt,hi,mi,It,gi,_i,C,ke,bi,Ct,vi,Di,At,wi,Jt;return m=new _n({}),le=new _n({}),fe=new ko({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvbkxETTNEUGlwZWxpbmUlMEElMEFwaXBlJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uTERNM0RQaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIySW50ZWwlMkZsZG0zZCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionLDM3DPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableDiffusionLDM3DPipeline.from_pretrained(<span class="hljs-string">&quot;Intel/ldm3d&quot;</span>)
prompt =<span class="hljs-string">&quot;A picture of some lemons on a table&quot;</span>
output = pipe(prompt)
rgb_image, depth_image = output.rgb, output.depth
rgb_image[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;lemons_ldm3d_rgb.jpg&quot;</span>)
depth_image[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;lemons_ldm3d_depth.png&quot;</span>)`}}),pe=new _n({}),ce=new U({props:{name:"class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput",anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput",parameters:[{name:"images",val:": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]"},{name:"nsfw_content_detected",val:": typing.Optional[typing.List[bool]]"}],parametersDescription:[{anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput.images",description:`<strong>images</strong> (<code>List[PIL.Image.Image]</code> or <code>np.ndarray</code>) &#x2014;
List of denoised PIL images of length <code>batch_size</code> or numpy array of shape <code>(batch_size, height, width, num_channels)</code>. PIL images or numpy array present the denoised images of the diffusion pipeline.`,name:"images"},{anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput.nsfw_content_detected",description:`<strong>nsfw_content_detected</strong> (<code>List[bool]</code>) &#x2014;
List of flags denoting whether the corresponding generated image likely represents &#x201C;not-safe-for-work&#x201D;
(nsfw) content, or <code>None</code> if safety checking could not be performed.`,name:"nsfw_content_detected"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/stable_diffusion/__init__.py#L22"}}),ue=new U({props:{name:"__call__",anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}]}}),he=new _n({}),me=new U({props:{name:"class diffusers.StableDiffusionLDM3DPipeline",anchor:"diffusers.StableDiffusionLDM3DPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"safety_checker",val:": StableDiffusionSafetyChecker"},{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"requires_safety_checker",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.StableDiffusionLDM3DPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.18.0/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode rgb and depth images to and from latent
representations.`,name:"vae"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
Frozen text-encoder. Stable Diffusion uses the text portion of
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIP</a>, specifically
the <a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a> variant.`,name:"text_encoder"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/v0.18.0/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded rgb and depth latents.',name:"unet"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.18.0/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latents. Can be one of
<a href="/docs/diffusers/v0.18.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.18.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/v0.18.0/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.safety_checker",description:`<strong>safety_checker</strong> (<code>StableDiffusionSafetyChecker</code>) &#x2014;
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the <a href="https://huggingface.co/runwayml/stable-diffusion-v1-5" rel="nofollow">model card</a> for details.`,name:"safety_checker"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<code>CLIPImageProcessor</code>) &#x2014;
Model that extracts features from generated images to be used as inputs for the <code>safety_checker</code>.`,name:"feature_extractor"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py#L77"}}),ye=new U({props:{name:"__call__",anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 49"},{name:"guidance_scale",val:": float = 5.0"},{name:"negative_prompt",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": int = 1"},{name:"cross_attention_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the image generation. If not defined, one has to pass <code>prompt_embeds</code>.
instead.`,name:"prompt"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to self.unet.config.sample_size * self.vae_scale_factor) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to self.unet.config.sample_size * self.vae_scale_factor) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 5.0) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation. If not defined, one has to pass
<code>negative_prompt_embeds</code> instead. Ignored when not using guidance (i.e., ignored if <code>guidance_scale</code> is
less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) in the DDIM paper: <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">https://arxiv.org/abs/2010.02502</a>. Only applies to
<a href="/docs/diffusers/v0.18.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
to make generation deterministic.`,name:"generator"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting. If not
provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input
argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
The output format of the generate image. Choose between
<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/v0.18.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that will be called every <code>callback_steps</code> steps during inference. The function will be
called with the following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function will be called. If not specified, the callback will be
called at every step.`,name:"callback_steps"},{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.cross_attention_kwargs",description:`<strong>cross_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined under
<code>self.processor</code> in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py" rel="nofollow">diffusers.cross_attention</a>.`,name:"cross_attention_kwargs"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py#L522",returnDescription:`
<p><a
  href="/docs/diffusers/v0.18.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> if <code>return_dict</code> is True, otherwise a <code>tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of </code>bool<code>s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the </code>safety_checker\`.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.18.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> or <code>tuple</code></p>
`}}),Y=new Oo({props:{anchor:"diffusers.StableDiffusionLDM3DPipeline.__call__.example",$$slots:{default:[No]},$$scope:{ctx:Ut}}}),Me=new U({props:{name:"disable_vae_slicing",anchor:"diffusers.StableDiffusionLDM3DPipeline.disable_vae_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py#L171"}}),Le=new U({props:{name:"disable_vae_tiling",anchor:"diffusers.StableDiffusionLDM3DPipeline.disable_vae_tiling",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py#L189"}}),xe=new U({props:{name:"enable_model_cpu_offload",anchor:"diffusers.StableDiffusionLDM3DPipeline.enable_model_cpu_offload",parameters:[{name:"gpu_id",val:" = 0"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py#L222"}}),Pe=new U({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.StableDiffusionLDM3DPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:" = 0"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py#L196"}}),Se=new U({props:{name:"enable_vae_slicing",anchor:"diffusers.StableDiffusionLDM3DPipeline.enable_vae_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py#L161"}}),ke=new U({props:{name:"enable_vae_tiling",anchor:"diffusers.StableDiffusionLDM3DPipeline.enable_vae_tiling",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py#L179"}}),{c(){y=a("meta"),j=l(),E=a("h1"),M=a("a"),S=a("span"),g(m.$$.fragment),k=l(),He=a("span"),bn=s("LDM3D"),Gt=l(),B=a("p"),vn=s("LDM3D was proposed in "),ae=a("a"),Dn=s("LDM3D: Latent Diffusion Model for 3D"),wn=s(` by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, Vasudev Lal
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