import{s as xe,n as De,o as $e}from"../chunks/scheduler.182ea377.js";import{S as Ee,i as Se,g as a,s as o,r as g,A as ye,h as d,f as t,c as i,j as L,u as _,x as P,k as q,y as s,a as c,v,d as b,t as x,w as D}from"../chunks/index.abf12888.js";import{D as U}from"../chunks/Docstring.93f6f462.js";import{H as ue}from"../chunks/Heading.16916d63.js";function Te(pe){let u,G,V,K,$,j,E,fe='The Euler scheduler (Algorithm 2) is from the <a href="https://huggingface.co/papers/2206.00364" rel="nofollow">Elucidating the Design Space of Diffusion-Based Generative Models</a> paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original <a href="https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51" rel="nofollow">k-diffusion</a> implementation by <a href="https://github.com/crowsonkb/" rel="nofollow">Katherine Crowson</a>.',R,S,W,n,y,re,M,he="Euler scheduler.",se,k,me=`This model inherits from <a href="/docs/diffusers/v0.23.0/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a> and <a href="/docs/diffusers/v0.23.0/en/api/configuration#diffusers.ConfigMixin">ConfigMixin</a>. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.`,ne,f,T,oe,H,ge=`Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep. Scales the denoising model input by <code>(sigma**2 + 1) ** 0.5</code> to match the Euler algorithm.`,ie,h,w,ae,I,_e="Sets the discrete timesteps used for the diffusion chain (to be run before inference).",de,m,C,ce,A,ve=`Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise).`,B,O,Y,p,F,le,N,be="Output class for the scheduler’s <code>step</code> function output.",J,z,Q;return $=new ue({props:{title:"EulerDiscreteScheduler",local:"eulerdiscretescheduler",headingTag:"h1"}}),S=new ue({props:{title:"EulerDiscreteScheduler",local:"diffusers.EulerDiscreteScheduler",headingTag:"h2"}}),y=new U({props:{name:"class diffusers.EulerDiscreteScheduler",anchor:"diffusers.EulerDiscreteScheduler",parameters:[{name:"num_train_timesteps",val:": int = 1000"},{name:"beta_start",val:": float = 0.0001"},{name:"beta_end",val:": float = 0.02"},{name:"beta_schedule",val:": str = 'linear'"},{name:"trained_betas",val:": typing.Union[numpy.ndarray, typing.List[float], NoneType] = None"},{name:"prediction_type",val:": str = 'epsilon'"},{name:"interpolation_type",val:": str = 'linear'"},{name:"use_karras_sigmas",val:": typing.Optional[bool] = False"},{name:"timestep_spacing",val:": str = 'linspace'"},{name:"steps_offset",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.EulerDiscreteScheduler.num_train_timesteps",description:`<strong>num_train_timesteps</strong> (<code>int</code>, defaults to 1000) &#x2014;
The number of diffusion steps to train the model.`,name:"num_train_timesteps"},{anchor:"diffusers.EulerDiscreteScheduler.beta_start",description:`<strong>beta_start</strong> (<code>float</code>, defaults to 0.0001) &#x2014;
The starting <code>beta</code> value of inference.`,name:"beta_start"},{anchor:"diffusers.EulerDiscreteScheduler.beta_end",description:`<strong>beta_end</strong> (<code>float</code>, defaults to 0.02) &#x2014;
The final <code>beta</code> value.`,name:"beta_end"},{anchor:"diffusers.EulerDiscreteScheduler.beta_schedule",description:`<strong>beta_schedule</strong> (<code>str</code>, defaults to <code>&quot;linear&quot;</code>) &#x2014;
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
<code>linear</code> or <code>scaled_linear</code>.`,name:"beta_schedule"},{anchor:"diffusers.EulerDiscreteScheduler.trained_betas",description:`<strong>trained_betas</strong> (<code>np.ndarray</code>, <em>optional</em>) &#x2014;
Pass an array of betas directly to the constructor to bypass <code>beta_start</code> and <code>beta_end</code>.`,name:"trained_betas"},{anchor:"diffusers.EulerDiscreteScheduler.prediction_type",description:`<strong>prediction_type</strong> (<code>str</code>, defaults to <code>epsilon</code>, <em>optional</em>) &#x2014;
Prediction type of the scheduler function; can be <code>epsilon</code> (predicts the noise of the diffusion process),
<code>sample</code> (directly predicts the noisy sample<code>) or </code>v_prediction\` (see section 2.4 of <a href="https://imagen.research.google/video/paper.pdf" rel="nofollow">Imagen
Video</a> paper).`,name:"prediction_type"},{anchor:"diffusers.EulerDiscreteScheduler.interpolation_type(str,",description:`<strong>interpolation_type(<code>str</code>,</strong> defaults to <code>&quot;linear&quot;</code>, <em>optional</em>) &#x2014;
The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be on of
<code>&quot;linear&quot;</code> or <code>&quot;log_linear&quot;</code>.`,name:"interpolation_type(str,"},{anchor:"diffusers.EulerDiscreteScheduler.use_karras_sigmas",description:`<strong>use_karras_sigmas</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If <code>True</code>,
the sigmas are determined according to a sequence of noise levels {&#x3C3;i}.`,name:"use_karras_sigmas"},{anchor:"diffusers.EulerDiscreteScheduler.timestep_spacing",description:`<strong>timestep_spacing</strong> (<code>str</code>, defaults to <code>&quot;linspace&quot;</code>) &#x2014;
The way the timesteps should be scaled. Refer to Table 2 of the <a href="https://huggingface.co/papers/2305.08891" rel="nofollow">Common Diffusion Noise Schedules and
Sample Steps are Flawed</a> for more information.`,name:"timestep_spacing"},{anchor:"diffusers.EulerDiscreteScheduler.steps_offset",description:`<strong>steps_offset</strong> (<code>int</code>, defaults to 0) &#x2014;
An offset added to the inference steps. You can use a combination of <code>offset=1</code> and
<code>set_alpha_to_one=False</code> to make the last step use step 0 for the previous alpha product like in Stable
Diffusion.`,name:"steps_offset"}],source:"https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_euler_discrete.py#L95"}}),T=new U({props:{name:"scale_model_input",anchor:"diffusers.EulerDiscreteScheduler.scale_model_input",parameters:[{name:"sample",val:": FloatTensor"},{name:"timestep",val:": typing.Union[float, torch.FloatTensor]"}],parametersDescription:[{anchor:"diffusers.EulerDiscreteScheduler.scale_model_input.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
The input sample.`,name:"sample"},{anchor:"diffusers.EulerDiscreteScheduler.scale_model_input.timestep",description:`<strong>timestep</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The current timestep in the diffusion chain.`,name:"timestep"}],source:"https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_euler_discrete.py#L196",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A scaled input sample.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.FloatTensor</code></p>
`}}),w=new U({props:{name:"set_timesteps",anchor:"diffusers.EulerDiscreteScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": int"},{name:"device",val:": typing.Union[str, torch.device] = None"}],parametersDescription:[{anchor:"diffusers.EulerDiscreteScheduler.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.EulerDiscreteScheduler.set_timesteps.device",description:`<strong>device</strong> (<code>str</code> or <code>torch.device</code>, <em>optional</em>) &#x2014;
The device to which the timesteps should be moved to. If <code>None</code>, the timesteps are not moved.`,name:"device"}],source:"https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_euler_discrete.py#L222"}}),C=new U({props:{name:"step",anchor:"diffusers.EulerDiscreteScheduler.step",parameters:[{name:"model_output",val:": FloatTensor"},{name:"timestep",val:": typing.Union[float, torch.FloatTensor]"},{name:"sample",val:": FloatTensor"},{name:"s_churn",val:": float = 0.0"},{name:"s_tmin",val:": float = 0.0"},{name:"s_tmax",val:": float = inf"},{name:"s_noise",val:": float = 1.0"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.EulerDiscreteScheduler.step.model_output",description:`<strong>model_output</strong> (<code>torch.FloatTensor</code>) &#x2014;
The direct output from learned diffusion model.`,name:"model_output"},{anchor:"diffusers.EulerDiscreteScheduler.step.timestep",description:`<strong>timestep</strong> (<code>float</code>) &#x2014;
The current discrete timestep in the diffusion chain.`,name:"timestep"},{anchor:"diffusers.EulerDiscreteScheduler.step.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
A current instance of a sample created by the diffusion process.`,name:"sample"},{anchor:"diffusers.EulerDiscreteScheduler.step.s_churn",description:"<strong>s_churn</strong> (<code>float</code>) &#x2014;",name:"s_churn"},{anchor:"diffusers.EulerDiscreteScheduler.step.s_tmin",description:"<strong>s_tmin</strong>  (<code>float</code>) &#x2014;",name:"s_tmin"},{anchor:"diffusers.EulerDiscreteScheduler.step.s_tmax",description:"<strong>s_tmax</strong>  (<code>float</code>) &#x2014;",name:"s_tmax"},{anchor:"diffusers.EulerDiscreteScheduler.step.s_noise",description:`<strong>s_noise</strong> (<code>float</code>, defaults to 1.0) &#x2014;
Scaling factor for noise added to the sample.`,name:"s_noise"},{anchor:"diffusers.EulerDiscreteScheduler.step.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
A random number generator.`,name:"generator"},{anchor:"diffusers.EulerDiscreteScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/v0.23.0/en/api/schedulers/euler#diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput">EulerDiscreteSchedulerOutput</a> or
tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_euler_discrete.py#L333",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If return_dict is <code>True</code>, <a
  href="/docs/diffusers/v0.23.0/en/api/schedulers/euler#diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput"
>EulerDiscreteSchedulerOutput</a> is
returned, otherwise a tuple is returned where the first element is the sample tensor.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/v0.23.0/en/api/schedulers/euler#diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput"
>EulerDiscreteSchedulerOutput</a> or <code>tuple</code></p>
`}}),O=new ue({props:{title:"EulerDiscreteSchedulerOutput",local:"diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput",headingTag:"h2"}}),F=new U({props:{name:"class diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput",anchor:"diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput",parameters:[{name:"prev_sample",val:": FloatTensor"},{name:"pred_original_sample",val:": typing.Optional[torch.FloatTensor] = None"}],parametersDescription:[{anchor:"diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput.prev_sample",description:`<strong>prev_sample</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code> for images) &#x2014;
Computed sample <code>(x_{t-1})</code> of previous timestep. <code>prev_sample</code> should be used as next model input in the
denoising loop.`,name:"prev_sample"},{anchor:"diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput.pred_original_sample",description:`<strong>pred_original_sample</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code> for images) &#x2014;
The predicted denoised sample <code>(x_{0})</code> based on the model output from the current timestep.
<code>pred_original_sample</code> can be used to preview progress or for guidance.`,name:"pred_original_sample"}],source:"https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_euler_discrete.py#L33"}}),{c(){u=a("meta"),G=o(),V=a("p"),K=o(),g($.$$.fragment),j=o(),E=a("p"),E.innerHTML=fe,R=o(),g(S.$$.fragment),W=o(),n=a("div"),g(y.$$.fragment),re=o(),M=a("p"),M.textContent=he,se=o(),k=a("p"),k.innerHTML=me,ne=o(),f=a("div"),g(T.$$.fragment),oe=o(),H=a("p"),H.innerHTML=ge,ie=o(),h=a("div"),g(w.$$.fragment),ae=o(),I=a("p"),I.textContent=_e,de=o(),m=a("div"),g(C.$$.fragment),ce=o(),A=a("p"),A.textContent=ve,B=o(),g(O.$$.fragment),Y=o(),p=a("div"),g(F.$$.fragment),le=o(),N=a("p"),N.innerHTML=be,J=o(),z=a("p"),this.h()},l(e){const r=ye("svelte-u9bgzb",document.head);u=d(r,"META",{name:!0,content:!0}),r.forEach(t),G=i(e),V=d(e,"P",{}),L(V).forEach(t),K=i(e),_($.$$.fragment,e),j=i(e),E=d(e,"P",{"data-svelte-h":!0}),P(E)!=="svelte-1gv4s4b"&&(E.innerHTML=fe),R=i(e),_(S.$$.fragment,e),W=i(e),n=d(e,"DIV",{class:!0});var l=L(n);_(y.$$.fragment,l),re=i(l),M=d(l,"P",{"data-svelte-h":!0}),P(M)!=="svelte-rqsn3u"&&(M.textContent=he),se=i(l),k=d(l,"P",{"data-svelte-h":!0}),P(k)!=="svelte-1aejr5d"&&(k.innerHTML=me),ne=i(l),f=d(l,"DIV",{class:!0});var X=L(f);_(T.$$.fragment,X),oe=i(X),H=d(X,"P",{"data-svelte-h":!0}),P(H)!=="svelte-1hew89w"&&(H.innerHTML=ge),X.forEach(t),ie=i(l),h=d(l,"DIV",{class:!0});var Z=L(h);_(w.$$.fragment,Z),ae=i(Z),I=d(Z,"P",{"data-svelte-h":!0}),P(I)!=="svelte-1vzm9q"&&(I.textContent=_e),Z.forEach(t),de=i(l),m=d(l,"DIV",{class:!0});var ee=L(m);_(C.$$.fragment,ee),ce=i(ee),A=d(ee,"P",{"data-svelte-h":!0}),P(A)!=="svelte-hi84tp"&&(A.textContent=ve),ee.forEach(t),l.forEach(t),B=i(e),_(O.$$.fragment,e),Y=i(e),p=d(e,"DIV",{class:!0});var te=L(p);_(F.$$.fragment,te),le=i(te),N=d(te,"P",{"data-svelte-h":!0}),P(N)!=="svelte-id9kic"&&(N.innerHTML=be),te.forEach(t),J=i(e),z=d(e,"P",{}),L(z).forEach(t),this.h()},h(){q(u,"name","hf:doc:metadata"),q(u,"content",we),q(f,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(h,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(m,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(p,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,r){s(document.head,u),c(e,G,r),c(e,V,r),c(e,K,r),v($,e,r),c(e,j,r),c(e,E,r),c(e,R,r),v(S,e,r),c(e,W,r),c(e,n,r),v(y,n,null),s(n,re),s(n,M),s(n,se),s(n,k),s(n,ne),s(n,f),v(T,f,null),s(f,oe),s(f,H),s(n,ie),s(n,h),v(w,h,null),s(h,ae),s(h,I),s(n,de),s(n,m),v(C,m,null),s(m,ce),s(m,A),c(e,B,r),v(O,e,r),c(e,Y,r),c(e,p,r),v(F,p,null),s(p,le),s(p,N),c(e,J,r),c(e,z,r),Q=!0},p:De,i(e){Q||(b($.$$.fragment,e),b(S.$$.fragment,e),b(y.$$.fragment,e),b(T.$$.fragment,e),b(w.$$.fragment,e),b(C.$$.fragment,e),b(O.$$.fragment,e),b(F.$$.fragment,e),Q=!0)},o(e){x($.$$.fragment,e),x(S.$$.fragment,e),x(y.$$.fragment,e),x(T.$$.fragment,e),x(w.$$.fragment,e),x(C.$$.fragment,e),x(O.$$.fragment,e),x(F.$$.fragment,e),Q=!1},d(e){e&&(t(G),t(V),t(K),t(j),t(E),t(R),t(W),t(n),t(B),t(Y),t(p),t(J),t(z)),t(u),D($,e),D(S,e),D(y),D(T),D(w),D(C),D(O,e),D(F)}}}const we='{"title":"EulerDiscreteScheduler","local":"eulerdiscretescheduler","sections":[{"title":"EulerDiscreteScheduler","local":"diffusers.EulerDiscreteScheduler","sections":[],"depth":2},{"title":"EulerDiscreteSchedulerOutput","local":"diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput","sections":[],"depth":2}],"depth":1}';function Ce(pe){return $e(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class qe extends Ee{constructor(u){super(),Se(this,u,Ce,Te,xe,{})}}export{qe as component};
