import{S as zr,i as Hr,s as jr,e as s,k as c,w as g,t as i,M as Kr,c as o,d as r,m as p,a as n,x as v,h as a,b as d,G as e,g as _,y as S,L as Rr,q as b,o as E,B as D,v as Ur}from"../../../chunks/vendor-hf-doc-builder.js";import{D as z}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as ar}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Br(dr){let I,Pe,x,O,he,H,Je,fe,Ye,ke,oe,Qe,Ae,$,P,me,j,Xe,_e,Ze,Ce,y,et,K,tt,rt,R,st,ot,Le,w,k,ge,U,nt,ve,it,qe,l,B,at,M,dt,G,lt,ct,J,pt,ut,ht,T,ft,Se,mt,_t,be,gt,vt,St,F,bt,Y,Et,Dt,Ee,yt,It,xt,h,ne,$t,wt,De,Mt,Tt,ye,Ft,Ot,Ie,Pt,kt,ie,At,Ct,ae,Lt,qt,de,Nt,Vt,Wt,A,Q,zt,xe,Ht,jt,C,X,Kt,$e,Rt,Ut,L,Z,Bt,we,Gt,Jt,q,ee,Yt,Me,Qt,Xt,N,te,Zt,Te,er,tr,V,re,rr,Fe,sr,or,W,se,nr,Oe,ir,Ne;return H=new ar({}),j=new ar({}),U=new ar({}),B=new z({props:{name:"class diffusers.DEISMultistepScheduler",anchor:"diffusers.DEISMultistepScheduler",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.Optional[numpy.ndarray] = None"},{name:"solver_order",val:": int = 2"},{name:"prediction_type",val:": str = 'epsilon'"},{name:"thresholding",val:": bool = False"},{name:"dynamic_thresholding_ratio",val:": float = 0.995"},{name:"sample_max_value",val:": float = 1.0"},{name:"algorithm_type",val:": str = 'deis'"},{name:"solver_type",val:": str = 'logrho'"},{name:"lower_order_final",val:": bool = True"},{name:"use_karras_sigmas",val:": typing.Optional[bool] = False"},{name:"timestep_spacing",val:": str = 'linspace'"},{name:"steps_offset",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.DEISMultistepScheduler.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.DEISMultistepScheduler.beta_start",description:"<strong>beta_start</strong> (<code>float</code>) &#x2014; the starting <code>beta</code> value of inference.",name:"beta_start"},{anchor:"diffusers.DEISMultistepScheduler.beta_end",description:"<strong>beta_end</strong> (<code>float</code>) &#x2014; the final <code>beta</code> value.",name:"beta_end"},{anchor:"diffusers.DEISMultistepScheduler.beta_schedule",description:`<strong>beta_schedule</strong> (<code>str</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>, <code>scaled_linear</code>, or <code>squaredcos_cap_v2</code>.`,name:"beta_schedule"},{anchor:"diffusers.DEISMultistepScheduler.trained_betas",description:`<strong>trained_betas</strong> (<code>np.ndarray</code>, optional) &#x2014;
option to pass an array of betas directly to the constructor to bypass <code>beta_start</code>, <code>beta_end</code> etc.`,name:"trained_betas"},{anchor:"diffusers.DEISMultistepScheduler.solver_order",description:`<strong>solver_order</strong> (<code>int</code>, default <code>2</code>) &#x2014;
the order of DEIS; can be <code>1</code> or <code>2</code> or <code>3</code>. We recommend to use <code>solver_order=2</code> for guided sampling, and
<code>solver_order=3</code> for unconditional sampling.`,name:"solver_order"},{anchor:"diffusers.DEISMultistepScheduler.prediction_type",description:`<strong>prediction_type</strong> (<code>str</code>, default <code>epsilon</code>) &#x2014;
indicates whether the model predicts the noise (epsilon), or the data / <code>x0</code>. One of <code>epsilon</code>, <code>sample</code>,
or <code>v-prediction</code>.`,name:"prediction_type"},{anchor:"diffusers.DEISMultistepScheduler.thresholding",description:`<strong>thresholding</strong> (<code>bool</code>, default <code>False</code>) &#x2014;
whether to use the &#x201C;dynamic thresholding&#x201D; method (introduced by Imagen, <a href="https://arxiv.org/abs/2205.11487" rel="nofollow">https://arxiv.org/abs/2205.11487</a>).
Note that the thresholding method is unsuitable for latent-space diffusion models (such as
stable-diffusion).`,name:"thresholding"},{anchor:"diffusers.DEISMultistepScheduler.dynamic_thresholding_ratio",description:`<strong>dynamic_thresholding_ratio</strong> (<code>float</code>, default <code>0.995</code>) &#x2014;
the ratio for the dynamic thresholding method. Default is <code>0.995</code>, the same as Imagen
(<a href="https://arxiv.org/abs/2205.11487" rel="nofollow">https://arxiv.org/abs/2205.11487</a>).`,name:"dynamic_thresholding_ratio"},{anchor:"diffusers.DEISMultistepScheduler.sample_max_value",description:`<strong>sample_max_value</strong> (<code>float</code>, default <code>1.0</code>) &#x2014;
the threshold value for dynamic thresholding. Valid only when <code>thresholding=True</code>`,name:"sample_max_value"},{anchor:"diffusers.DEISMultistepScheduler.algorithm_type",description:`<strong>algorithm_type</strong> (<code>str</code>, default <code>deis</code>) &#x2014;
the algorithm type for the solver. current we support multistep deis, we will add other variants of DEIS in
the future`,name:"algorithm_type"},{anchor:"diffusers.DEISMultistepScheduler.lower_order_final",description:`<strong>lower_order_final</strong> (<code>bool</code>, default <code>True</code>) &#x2014;
whether to use lower-order solvers in the final steps. Only valid for &lt; 15 inference steps. We empirically
find this trick can stabilize the sampling of DEIS for steps &lt; 15, especially for steps &lt;= 10.`,name:"lower_order_final"},{anchor:"diffusers.DEISMultistepScheduler.use_karras_sigmas",description:`<strong>use_karras_sigmas</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the
noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence
of noise levels {&#x3C3;i} as defined in Equation (5) of the paper <a href="https://arxiv.org/pdf/2206.00364.pdf" rel="nofollow">https://arxiv.org/pdf/2206.00364.pdf</a>.`,name:"use_karras_sigmas"},{anchor:"diffusers.DEISMultistepScheduler.timestep_spacing",description:`<strong>timestep_spacing</strong> (<code>str</code>, default <code>&quot;linspace&quot;</code>) &#x2014;
The way the timesteps should be scaled. Refer to Table 2. of <a href="https://arxiv.org/abs/2305.08891" rel="nofollow">Common Diffusion Noise Schedules and Sample
Steps are Flawed</a> for more information.`,name:"timestep_spacing"},{anchor:"diffusers.DEISMultistepScheduler.steps_offset",description:`<strong>steps_offset</strong> (<code>int</code>, default <code>0</code>) &#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, as done in
stable diffusion.`,name:"steps_offset"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_deis_multistep.py#L73"}}),Q=new z({props:{name:"convert_model_output",anchor:"diffusers.DEISMultistepScheduler.convert_model_output",parameters:[{name:"model_output",val:": FloatTensor"},{name:"timestep",val:": int"},{name:"sample",val:": FloatTensor"}],parametersDescription:[{anchor:"diffusers.DEISMultistepScheduler.convert_model_output.model_output",description:"<strong>model_output</strong> (<code>torch.FloatTensor</code>) &#x2014; direct output from learned diffusion model.",name:"model_output"},{anchor:"diffusers.DEISMultistepScheduler.convert_model_output.timestep",description:"<strong>timestep</strong> (<code>int</code>) &#x2014; current discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DEISMultistepScheduler.convert_model_output.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
current instance of sample being created by diffusion process.`,name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_deis_multistep.py#L295",returnDescription:`
<p>the converted model output.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),X=new z({props:{name:"deis_first_order_update",anchor:"diffusers.DEISMultistepScheduler.deis_first_order_update",parameters:[{name:"model_output",val:": FloatTensor"},{name:"timestep",val:": int"},{name:"prev_timestep",val:": int"},{name:"sample",val:": FloatTensor"}],parametersDescription:[{anchor:"diffusers.DEISMultistepScheduler.deis_first_order_update.model_output",description:"<strong>model_output</strong> (<code>torch.FloatTensor</code>) &#x2014; direct output from learned diffusion model.",name:"model_output"},{anchor:"diffusers.DEISMultistepScheduler.deis_first_order_update.timestep",description:"<strong>timestep</strong> (<code>int</code>) &#x2014; current discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DEISMultistepScheduler.deis_first_order_update.prev_timestep",description:"<strong>prev_timestep</strong> (<code>int</code>) &#x2014; previous discrete timestep in the diffusion chain.",name:"prev_timestep"},{anchor:"diffusers.DEISMultistepScheduler.deis_first_order_update.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
current instance of sample being created by diffusion process.`,name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_deis_multistep.py#L333",returnDescription:`
<p>the sample tensor at the previous timestep.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),Z=new z({props:{name:"multistep_deis_second_order_update",anchor:"diffusers.DEISMultistepScheduler.multistep_deis_second_order_update",parameters:[{name:"model_output_list",val:": typing.List[torch.FloatTensor]"},{name:"timestep_list",val:": typing.List[int]"},{name:"prev_timestep",val:": int"},{name:"sample",val:": FloatTensor"}],parametersDescription:[{anchor:"diffusers.DEISMultistepScheduler.multistep_deis_second_order_update.model_output_list",description:`<strong>model_output_list</strong> (<code>List[torch.FloatTensor]</code>) &#x2014;
direct outputs from learned diffusion model at current and latter timesteps.`,name:"model_output_list"},{anchor:"diffusers.DEISMultistepScheduler.multistep_deis_second_order_update.timestep",description:"<strong>timestep</strong> (<code>int</code>) &#x2014; current and latter discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DEISMultistepScheduler.multistep_deis_second_order_update.prev_timestep",description:"<strong>prev_timestep</strong> (<code>int</code>) &#x2014; previous discrete timestep in the diffusion chain.",name:"prev_timestep"},{anchor:"diffusers.DEISMultistepScheduler.multistep_deis_second_order_update.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
current instance of sample being created by diffusion process.`,name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_deis_multistep.py#L363",returnDescription:`
<p>the sample tensor at the previous timestep.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),ee=new z({props:{name:"multistep_deis_third_order_update",anchor:"diffusers.DEISMultistepScheduler.multistep_deis_third_order_update",parameters:[{name:"model_output_list",val:": typing.List[torch.FloatTensor]"},{name:"timestep_list",val:": typing.List[int]"},{name:"prev_timestep",val:": int"},{name:"sample",val:": FloatTensor"}],parametersDescription:[{anchor:"diffusers.DEISMultistepScheduler.multistep_deis_third_order_update.model_output_list",description:`<strong>model_output_list</strong> (<code>List[torch.FloatTensor]</code>) &#x2014;
direct outputs from learned diffusion model at current and latter timesteps.`,name:"model_output_list"},{anchor:"diffusers.DEISMultistepScheduler.multistep_deis_third_order_update.timestep",description:"<strong>timestep</strong> (<code>int</code>) &#x2014; current and latter discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DEISMultistepScheduler.multistep_deis_third_order_update.prev_timestep",description:"<strong>prev_timestep</strong> (<code>int</code>) &#x2014; previous discrete timestep in the diffusion chain.",name:"prev_timestep"},{anchor:"diffusers.DEISMultistepScheduler.multistep_deis_third_order_update.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
current instance of sample being created by diffusion process.`,name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_deis_multistep.py#L405",returnDescription:`
<p>the sample tensor at the previous timestep.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),te=new z({props:{name:"scale_model_input",anchor:"diffusers.DEISMultistepScheduler.scale_model_input",parameters:[{name:"sample",val:": FloatTensor"},{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.DEISMultistepScheduler.scale_model_input.sample",description:"<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014; input sample",name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_deis_multistep.py#L530",returnDescription:`
<p>scaled input sample</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),re=new z({props:{name:"set_timesteps",anchor:"diffusers.DEISMultistepScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": int"},{name:"device",val:": typing.Union[str, torch.device] = None"}],parametersDescription:[{anchor:"diffusers.DEISMultistepScheduler.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.DEISMultistepScheduler.set_timesteps.device",description:`<strong>device</strong> (<code>str</code> or <code>torch.device</code>, optional) &#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/v0.19.2/src/diffusers/schedulers/scheduling_deis_multistep.py#L202"}}),se=new z({props:{name:"step",anchor:"diffusers.DEISMultistepScheduler.step",parameters:[{name:"model_output",val:": FloatTensor"},{name:"timestep",val:": int"},{name:"sample",val:": FloatTensor"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.DEISMultistepScheduler.step.model_output",description:"<strong>model_output</strong> (<code>torch.FloatTensor</code>) &#x2014; direct output from learned diffusion model.",name:"model_output"},{anchor:"diffusers.DEISMultistepScheduler.step.timestep",description:"<strong>timestep</strong> (<code>int</code>) &#x2014; current discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DEISMultistepScheduler.step.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
current instance of sample being created by diffusion process.`,name:"sample"},{anchor:"diffusers.DEISMultistepScheduler.step.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>) &#x2014; option for returning tuple rather than SchedulerOutput class",name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/schedulers/scheduling_deis_multistep.py#L462",returnDescription:`
<p><code>~scheduling_utils.SchedulerOutput</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>~scheduling_utils.SchedulerOutput</code> or <code>tuple</code></p>
`}}),{c(){I=s("meta"),Pe=c(),x=s("h1"),O=s("a"),he=s("span"),g(H.$$.fragment),Je=c(),fe=s("span"),Ye=i("DEIS"),ke=c(),oe=s("p"),Qe=i("Fast Sampling of Diffusion Models with Exponential Integrator."),Ae=c(),$=s("h2"),P=s("a"),me=s("span"),g(j.$$.fragment),Xe=c(),_e=s("span"),Ze=i("Overview"),Ce=c(),y=s("p"),et=i("Original paper can be found "),K=s("a"),tt=i("here"),rt=i(". The original implementation can be found "),R=s("a"),st=i("here"),ot=i("."),Le=c(),w=s("h2"),k=s("a"),ge=s("span"),g(U.$$.fragment),nt=c(),ve=s("span"),it=i("DEISMultistepScheduler"),qe=c(),l=s("div"),g(B.$$.fragment),at=c(),M=s("p"),dt=i("DEIS ("),G=s("a"),lt=i("https://arxiv.org/abs/2204.13902"),ct=i(`) is a fast high order solver for diffusion ODEs. We slightly modify the
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reduces to DDIM.`),St=c(),F=s("p"),bt=i("We also support the \u201Cdynamic thresholding\u201D method in Imagen ("),Y=s("a"),Et=i("https://arxiv.org/abs/2205.11487"),Dt=i(`). For pixel-space
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function, such as `),ye=s("code"),Ft=i("num_train_timesteps"),Ot=i(". They can be accessed via "),Ie=s("code"),Pt=i("scheduler.config.num_train_timesteps"),kt=i(`.
`),ie=s("a"),At=i("SchedulerMixin"),Ct=i(" provides general loading and saving functionality via the "),ae=s("a"),Lt=i("SchedulerMixin.save_pretrained()"),qt=i(` and
`),de=s("a"),Nt=i("from_pretrained()"),Vt=i(" functions."),Wt=c(),A=s("div"),g(Q.$$.fragment),zt=c(),xe=s("p"),Ht=i("Convert the model output to the corresponding type that the algorithm DEIS needs."),jt=c(),C=s("div"),g(X.$$.fragment),Kt=c(),$e=s("p"),Rt=i("One step for the first-order DEIS (equivalent to DDIM)."),Ut=c(),L=s("div"),g(Z.$$.fragment),Bt=c(),we=s("p"),Gt=i("One step for the second-order multistep DEIS."),Jt=c(),q=s("div"),g(ee.$$.fragment),Yt=c(),Me=s("p"),Qt=i("One step for the third-order multistep DEIS."),Xt=c(),N=s("div"),g(te.$$.fragment),Zt=c(),Te=s("p"),er=i(`Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
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enjoys closed-form coefficients for exponential multistep update instead of replying on the numerical solver. More
variants of DEIS can be found in `),J=o(ce,"A",{href:!0,rel:!0});var Dr=n(J);pt=a(Dr,"https://github.com/qsh-zh/deis"),Dr.forEach(r),ut=a(ce,"."),ce.forEach(r),ht=p(u),T=o(u,"P",{});var pe=n(T);ft=a(pe,"Currently, we support the log-rho multistep DEIS. We recommend to use "),Se=o(pe,"CODE",{});var yr=n(Se);mt=a(yr,"solver_order=2 / 3"),yr.forEach(r),_t=a(pe," while "),be=o(pe,"CODE",{});var Ir=n(be);gt=a(Ir,"solver_order=1"),Ir.forEach(r),vt=a(pe,`
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