import{S as Wn,i as Hn,s as On,e as n,k as d,w as y,t as s,M as Bn,c as o,d as a,m as f,a as i,x as P,h as r,b as l,G as e,g as u,y as D,q as k,o as E,B as $,v as qn,L as Xn}from"../../../chunks/vendor-hf-doc-builder.js";import{T as Qn}from"../../../chunks/Tip-hf-doc-builder.js";import{D as lt}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Yn}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as dt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Kn}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function eo(ye){let h,T,g,_,v,p,w,Z;return{c(){h=n("p"),T=s("While calling this pipeline, it\u2019s possible to specify the "),g=n("code"),_=s("view_batch_size"),v=s(` to have a >1 value.
For some GPUs with high performance, higher a `),p=n("code"),w=s("view_batch_size"),Z=s(`, can speedup the generation
and increase the VRAM usage.`)},l(j){h=o(j,"P",{});var M=i(h);T=r(M,"While calling this pipeline, it\u2019s possible to specify the "),g=o(M,"CODE",{});var x=i(g);_=r(x,"view_batch_size"),x.forEach(a),v=r(M,` to have a >1 value.
For some GPUs with high performance, higher a `),p=o(M,"CODE",{});var I=i(p);w=r(I,"view_batch_size"),I.forEach(a),Z=r(M,`, can speedup the generation
and increase the VRAM usage.`),M.forEach(a)},m(j,M){u(j,h,M),e(h,T),e(h,g),e(g,_),e(h,v),e(h,p),e(p,w),e(h,Z)},d(j){j&&a(h)}}}function to(ye){let h,T,g,_,v;return _=new Yn({props:{code:"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",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> StableDiffusionPanoramaPipeline, DDIMScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>model_ckpt = <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-base&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = StableDiffusionPanoramaPipeline.from_pretrained(
<span class="hljs-meta">... </span>    model_ckpt, scheduler=scheduler, torch_dtype=torch.float16
<span class="hljs-meta">... </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 the dolomites&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(prompt).images[<span class="hljs-number">0</span>]`}}),{c(){h=n("p"),T=s("Examples:"),g=d(),y(_.$$.fragment)},l(p){h=o(p,"P",{});var w=i(h);T=r(w,"Examples:"),w.forEach(a),g=f(p),P(_.$$.fragment,p)},m(p,w){u(p,h,w),e(h,T),u(p,g,w),D(_,p,w),v=!0},p:Xn,i(p){v||(k(_.$$.fragment,p),v=!0)},o(p){E(_.$$.fragment,p),v=!1},d(p){p&&a(h),p&&a(g),$(_,p)}}}function ao(ye){let h,T,g,_,v,p,w,Z,j,M,x,I,je,K,Ut,Ae,Gt,ft,ee,te,Ct,Zt,ct,Pe,jt,pt,De,At,ut,ke,zt,ht,S,Ee,ae,Jt,Vt,Ft,$e,ne,Nt,Rt,Lt,Me,oe,Yt,Wt,Ht,Te,ie,Ot,Bt,mt,A,R,ze,se,qt,Je,Xt,gt,L,Ve,z,Fe,Qt,Kt,Ne,ea,ta,xe,aa,na,Re,J,Le,re,oa,ia,Ye,We,sa,ra,le,de,la,da,_t,V,Y,He,fe,fa,Oe,ca,bt,ce,vt,W,wt,F,H,Be,pe,pa,qe,ua,yt,m,ue,ha,Xe,ma,ga,he,_a,Se,ba,va,wa,U,ya,Qe,Pa,Da,Ke,ka,Ea,et,$a,Ma,Ta,G,me,xa,tt,Sa,Ia,O,Ua,B,ge,Ga,_e,Ca,at,Za,ja,Aa,q,be,za,N,Ja,nt,Va,Fa,ot,Na,Ra,La,C,ve,Ya,it,Wa,Ha,st,Oa,Pt;return p=new dt({}),K=new dt({}),se=new dt({}),fe=new dt({}),ce=new Yn({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPanoramaPipeline, DDIMScheduler

model_ckpt = <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-base&quot;</span>
scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>)
pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, torch_dtype=torch.float16)

pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;a photo of the dolomites&quot;</span>
image = pipe(prompt).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;dolomites.png&quot;</span>)`}}),W=new Qn({props:{$$slots:{default:[eo]},$$scope:{ctx:ye}}}),pe=new dt({}),ue=new lt({props:{name:"class diffusers.StableDiffusionPanoramaPipeline",anchor:"diffusers.StableDiffusionPanoramaPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": DDIMScheduler"},{name:"safety_checker",val:": StableDiffusionSafetyChecker"},{name:"feature_extractor",val:": CLIPImageProcessor"},{name:"requires_safety_checker",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.StableDiffusionPanoramaPipeline.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 images to and from latent representations.`,name:"vae"},{anchor:"diffusers.StableDiffusionPanoramaPipeline.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.StableDiffusionPanoramaPipeline.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.StableDiffusionPanoramaPipeline.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 image latents.',name:"unet"},{anchor:"diffusers.StableDiffusionPanoramaPipeline.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. The original work
on Multi Diffsion used the <a href="/docs/diffusers/v0.18.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.StableDiffusionPanoramaPipeline.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.StableDiffusionPanoramaPipeline.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_panorama.py#L54"}}),me=new lt({props:{name:"__call__",anchor:"diffusers.StableDiffusionPanoramaPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"height",val:": typing.Optional[int] = 512"},{name:"width",val:": typing.Optional[int] = 2048"},{name:"num_inference_steps",val:": int = 50"},{name:"guidance_scale",val:": float = 7.5"},{name:"view_batch_size",val:": int = 1"},{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:": typing.Optional[int] = 1"},{name:"cross_attention_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 512 &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.StableDiffusionPanoramaPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
The width in pixels of the generated image. The width is kept to a high number because the
pipeline is supposed to be used for generating panorama-like images.`,name:"width"},{anchor:"diffusers.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.5) &#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.StableDiffusionPanoramaPipeline.__call__.view_batch_size",description:`<strong>view_batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The batch size to denoise splited views. For some GPUs with high performance, higher view batch size
can speedup the generation and increase the VRAM usage.`,name:"view_batch_size"},{anchor:"diffusers.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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.StableDiffusionPanoramaPipeline.__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_panorama.py#L477",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>
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