import{S as Vs,i as ks,s as Ls,e as a,k as c,w as E,t as o,M as Ps,c as r,d as t,m as u,a as n,x as A,h as i,b as l,G as e,g,y as $,L as Ns,q as S,o as Q,B as D,v as Fs}from"../../../chunks/vendor-hf-doc-builder.js";import{D as Ue}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as as}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Os(rs){let w,be,T,J,re,q,Oe,ne,Xe,je,y,x,oe,V,ze,ie,Be,Ee,k,He,L,Re,Ae,C,U,le,P,We,ce,Ge,$e,d,N,Ke,ue,Ye,Ze,de,et,tt,p,K,st,at,pe,rt,nt,he,ot,it,fe,lt,ct,Y,ut,dt,Z,pt,ht,ee,ft,mt,_t,te,vt,F,Mt,gt,M,O,wt,X,Tt,me,yt,Ct,It,_e,Jt,xt,h,z,Ut,B,bt,ve,jt,Et,At,Me,$t,St,ge,Qt,Dt,we,qt,Vt,Te,H,kt,ye,Lt,Pt,Nt,I,Ft,Ce,Ot,Xt,Ie,zt,Bt,Ht,b,R,Rt,Je,Wt,Gt,j,W,Kt,G,Yt,xe,Zt,es,Se;return q=new as({}),V=new as({}),P=new as({}),N=new Ue({props:{name:"class diffusers.VQDiffusionScheduler",anchor:"diffusers.VQDiffusionScheduler",parameters:[{name:"num_vec_classes",val:": int"},{name:"num_train_timesteps",val:": int = 100"},{name:"alpha_cum_start",val:": float = 0.99999"},{name:"alpha_cum_end",val:": float = 9e-06"},{name:"gamma_cum_start",val:": float = 9e-06"},{name:"gamma_cum_end",val:": float = 0.99999"}],parametersDescription:[{anchor:"diffusers.VQDiffusionScheduler.num_vec_classes",description:`<strong>num_vec_classes</strong> (<code>int</code>) &#x2014;
The number of classes of the vector embeddings of the latent pixels. Includes the class for the masked
latent pixel.`,name:"num_vec_classes"},{anchor:"diffusers.VQDiffusionScheduler.num_train_timesteps",description:`<strong>num_train_timesteps</strong> (<code>int</code>) &#x2014;
Number of diffusion steps used to train the model.`,name:"num_train_timesteps"},{anchor:"diffusers.VQDiffusionScheduler.alpha_cum_start",description:`<strong>alpha_cum_start</strong> (<code>float</code>) &#x2014;
The starting cumulative alpha value.`,name:"alpha_cum_start"},{anchor:"diffusers.VQDiffusionScheduler.alpha_cum_end",description:`<strong>alpha_cum_end</strong> (<code>float</code>) &#x2014;
The ending cumulative alpha value.`,name:"alpha_cum_end"},{anchor:"diffusers.VQDiffusionScheduler.gamma_cum_start",description:`<strong>gamma_cum_start</strong> (<code>float</code>) &#x2014;
The starting cumulative gamma value.`,name:"gamma_cum_start"},{anchor:"diffusers.VQDiffusionScheduler.gamma_cum_end",description:`<strong>gamma_cum_end</strong> (<code>float</code>) &#x2014;
The ending cumulative gamma value.`,name:"gamma_cum_end"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_vq_diffusion.py#L106"}}),O=new Ue({props:{name:"log_Q_t_transitioning_to_known_class",anchor:"diffusers.VQDiffusionScheduler.log_Q_t_transitioning_to_known_class",parameters:[{name:"t",val:": torch.int32"},{name:"x_t",val:": LongTensor"},{name:"log_onehot_x_t",val:": FloatTensor"},{name:"cumulative",val:": bool"}],parametersDescription:[{anchor:"diffusers.VQDiffusionScheduler.log_Q_t_transitioning_to_known_class.t",description:`<strong>t</strong> (torch.Long) &#x2014;
The timestep that determines which transition matrix is used.`,name:"t"},{anchor:"diffusers.VQDiffusionScheduler.log_Q_t_transitioning_to_known_class.x_t",description:`<strong>x_t</strong> (<code>torch.LongTensor</code> of shape <code>(batch size, num latent pixels)</code>) &#x2014;
The classes of each latent pixel at time <code>t</code>.`,name:"x_t"},{anchor:"diffusers.VQDiffusionScheduler.log_Q_t_transitioning_to_known_class.log_onehot_x_t",description:`<strong>log_onehot_x_t</strong> (<code>torch.FloatTensor</code> of shape <code>(batch size, num classes, num latent pixels)</code>) &#x2014;
The log one-hot vectors of <code>x_t</code>`,name:"log_onehot_x_t"},{anchor:"diffusers.VQDiffusionScheduler.log_Q_t_transitioning_to_known_class.cumulative",description:`<strong>cumulative</strong> (<code>bool</code>) &#x2014;
If cumulative is <code>False</code>, we use the single step transition matrix <code>t-1</code>-&gt;<code>t</code>. If cumulative is <code>True</code>,
we use the cumulative transition matrix <code>0</code>-&gt;<code>t</code>.`,name:"cumulative"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_vq_diffusion.py#L379",returnDescription:`
<p>Each <em>column</em> of the returned matrix is a <em>row</em> of log probabilities of the complete probability
transition matrix.</p>
<p>When non cumulative, returns <code>self.num_classes - 1</code> rows because the initial latent pixel cannot be
masked.</p>
<p>Where:</p>
<ul>
<li><code>q_n</code> is the probability distribution for the forward process of the <code>n</code>th latent pixel.</li>
<li>C_0 is a class of a latent pixel embedding</li>
<li>C_k is the class of the masked latent pixel</li>
</ul>
<p>non-cumulative result (omitting logarithms):</p>

	<CodeBlock 
		code={\`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\`}
		highlighted={\`q<span class="hljs-constructor">_0(<span class="hljs-params">x_t</span> | <span class="hljs-params">x_</span>{<span class="hljs-params">t</span>-1\\} = C_0)</span><span class="hljs-operator"> ... </span>q<span class="hljs-constructor">_n(<span class="hljs-params">x_t</span> | <span class="hljs-params">x_</span>{<span class="hljs-params">t</span>-1\\} = C_0)</span>
          .      .                     .
          .               .            .
          .                      .     .
q<span class="hljs-constructor">_0(<span class="hljs-params">x_t</span> | <span class="hljs-params">x_</span>{<span class="hljs-params">t</span>-1\\} = C_k)</span><span class="hljs-operator"> ... </span>q<span class="hljs-constructor">_n(<span class="hljs-params">x_t</span> | <span class="hljs-params">x_</span>{<span class="hljs-params">t</span>-1\\} = C_k)</span>\`}
	/>
<p>cumulative result (omitting logarithms):</p>

	<CodeBlock 
		code={\`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\`}
		highlighted={\`q<span class="hljs-constructor">_0_cumulative(<span class="hljs-params">x_t</span> | <span class="hljs-params">x_0</span> = C_0)</span><span class="hljs-operator">    ...  </span>q<span class="hljs-constructor">_n_cumulative(<span class="hljs-params">x_t</span> | <span class="hljs-params">x_0</span> = C_0)</span>
          .               .                          .
          .                        .                 .
          .                               .          .
q<span class="hljs-constructor">_0_cumulative(<span class="hljs-params">x_t</span> | <span class="hljs-params">x_0</span> = C_{<span class="hljs-params">k</span>-1\\})</span><span class="hljs-operator"> ... </span>q<span class="hljs-constructor">_n_cumulative(<span class="hljs-params">x_t</span> | <span class="hljs-params">x_0</span> = C_{<span class="hljs-params">k</span>-1\\})</span>\`}
	/>
`,returnType:`
<p><code>torch.FloatTensor</code> of shape <code>(batch size, num classes - 1, num latent pixels)</code></p>
`}}),z=new Ue({props:{name:"q_posterior",anchor:"diffusers.VQDiffusionScheduler.q_posterior",parameters:[{name:"log_p_x_0",val:""},{name:"x_t",val:""},{name:"t",val:""}],parametersDescription:[{anchor:"diffusers.VQDiffusionScheduler.q_posterior.t",description:`<strong>t</strong> (torch.Long) &#x2014;
The timestep that determines which transition matrix is used.`,name:"t"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_vq_diffusion.py#L260",returnDescription:`
<p>The log probabilities for the predicted classes of the image at timestep <code>t-1</code>. I.e. Equation (11).</p>
`,returnType:`
<p><code>torch.FloatTensor</code> of shape <code>(batch size, num classes, num latent pixels)</code></p>
`}}),R=new Ue({props:{name:"set_timesteps",anchor:"diffusers.VQDiffusionScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": int"},{name:"device",val:": typing.Union[str, torch.device] = None"}],parametersDescription:[{anchor:"diffusers.VQDiffusionScheduler.set_timesteps.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>) &#x2014;
the number of diffusion steps used when generating samples with a pre-trained model.`,name:"num_inference_steps"},{anchor:"diffusers.VQDiffusionScheduler.set_timesteps.device",description:`<strong>device</strong> (<code>str</code> or <code>torch.device</code>) &#x2014;
device to place the timesteps and the diffusion process parameters (alpha, beta, gamma) on.`,name:"device"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_vq_diffusion.py#L190"}}),W=new Ue({props:{name:"step",anchor:"diffusers.VQDiffusionScheduler.step",parameters:[{name:"model_output",val:": FloatTensor"},{name:"timestep",val:": torch.int64"},{name:"sample",val:": LongTensor"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.VQDiffusionScheduler.step.t",description:`<strong>t</strong> (<code>torch.long</code>) &#x2014;
The timestep that determines which transition matrices are used.</p>
<p>x_t &#x2014; (<code>torch.LongTensor</code> of shape <code>(batch size, num latent pixels)</code>):
The classes of each latent pixel at time <code>t</code></p>
<p>generator &#x2014; (<code>torch.Generator</code> or None):
RNG for the noise applied to p(x_{t-1} | x_t) before it is sampled from.`,name:"t"},{anchor:"diffusers.VQDiffusionScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>) &#x2014;
option for returning tuple rather than VQDiffusionSchedulerOutput class`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_vq_diffusion.py#L212",returnDescription:`
<p><code>~schedulers.scheduling_utils.VQDiffusionSchedulerOutput</code> if <code>return_dict</code> is True, otherwise a <code>tuple</code>.
When returning a tuple, the first element is the sample tensor.</p>
`,returnType:`
<p><code>~schedulers.scheduling_utils.VQDiffusionSchedulerOutput</code> or <code>tuple</code></p>
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forward probabilities.`),bs.forEach(t),St=u(_),ge=r(_,"P",{});var js=n(ge);Qt=i(js,"Equation (11) stated in terms of forward probabilities via Equation (5):"),js.forEach(t),Dt=u(_),we=r(_,"P",{});var Es=n(we);qt=i(Es,"Where:"),Es.forEach(t),Vt=u(_),Te=r(_,"UL",{});var As=n(Te);H=r(As,"LI",{});var Le=n(H);kt=i(Le,"the sum is over x"),ye=r(Le,"EM",{});var $s=n(ye);Lt=i($s,"0 = {C_0 \u2026 C"),$s.forEach(t),Pt=i(Le,"{k-1}} (classes for x_0)"),Le.forEach(t),As.forEach(t),Nt=u(_),I=r(_,"P",{});var ae=n(I);Ft=i(ae,"p(x"),Ce=r(ae,"EM",{});var Ss=n(Ce);Ot=i(Ss,"{t-1} | x_t) = sum( q(x_t | x"),Ss.forEach(t),Xt=i(ae,"{t-1}) "),Ie=r(ae,"EM",{});var Qs=n(Ie);zt=i(Qs,"q(x_{t-1} | x_0)"),Qs.forEach(t),Bt=i(ae," p(x_0) / q(x_t | x_0) )"),ae.forEach(t),_.forEach(t),Ht=u(f),b=r(f,"DIV",{class:!0});var Pe=n(b);A(R.$$.fragment,Pe),Rt=u(Pe),Je=r(Pe,"P",{});var Ds=n(Je);Wt=i(Ds,"Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference."),Ds.forEach(t),Pe.forEach(t),Gt=u(f),j=r(f,"DIV",{class:!0});var Ne=n(j);A(W.$$.fragment,Ne),Kt=u(Ne),G=r(Ne,"P",{});var Fe=n(G);Yt=i(Fe,`Predict the sample at the previous timestep via the reverse transition distribution i.e. Equation (11). See the
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