import{S as Xo,i as Yo,s as Zo,e as r,k as l,w as g,t as o,M as es,c as s,d as t,m as p,a as i,x as v,h as n,b as d,G as e,g as E,y as _,L as ts,q as S,o as b,B as D,v as rs}from"../../../chunks/vendor-hf-doc-builder.js";import{D as T}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as no}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function os(io){let F,rt,O,k,xe,K,vt,we,_t,ot,C,q,Ee,Q,St,Te,bt,st,y,Dt,X,yt,Pt,Y,Mt,$t,Z,xt,wt,nt,I,V,Fe,ee,Et,Oe,Tt,it,c,te,Ft,Ce,Ot,Ct,W,It,re,Lt,At,oe,Nt,kt,L,qt,Ie,Vt,Wt,Le,zt,Ht,Ut,M,Bt,se,Gt,Jt,Ae,Rt,jt,Ne,Kt,Qt,Xt,u,ge,Yt,Zt,ke,er,tr,qe,rr,or,Ve,sr,nr,ve,ir,ar,_e,dr,lr,Se,pr,cr,hr,P,ne,ur,We,mr,fr,ze,gr,vr,He,_r,Sr,$,ie,br,Ue,Dr,yr,ae,Pr,de,Mr,$r,xr,z,le,wr,Be,Er,Tr,H,pe,Fr,Ge,Or,Cr,U,ce,Ir,Je,Lr,Ar,x,he,Nr,Re,kr,qr,A,Vr,je,Wr,zr,Ke,Hr,Ur,Br,w,ue,Gr,Qe,Jr,Rr,N,jr,Xe,Kr,Qr,Ye,Xr,Yr,Zr,B,me,eo,Ze,to,ro,G,fe,oo,et,so,at;return K=new no({}),Q=new no({}),ee=new no({}),te=new T({props:{name:"class diffusers.DPMSolverSinglestepScheduler",anchor:"diffusers.DPMSolverSinglestepScheduler",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 = 'dpmsolver++'"},{name:"solver_type",val:": str = 'midpoint'"},{name:"lower_order_final",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.DPMSolverSinglestepScheduler.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.DPMSolverSinglestepScheduler.beta_start",description:"<strong>beta_start</strong> (<code>float</code>) &#x2014; the starting <code>beta</code> value of inference.",name:"beta_start"},{anchor:"diffusers.DPMSolverSinglestepScheduler.beta_end",description:"<strong>beta_end</strong> (<code>float</code>) &#x2014; the final <code>beta</code> value.",name:"beta_end"},{anchor:"diffusers.DPMSolverSinglestepScheduler.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.DPMSolverSinglestepScheduler.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.DPMSolverSinglestepScheduler.solver_order",description:`<strong>solver_order</strong> (<code>int</code>, default <code>2</code>) &#x2014;
the order of DPM-Solver; 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.DPMSolverSinglestepScheduler.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.DPMSolverSinglestepScheduler.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>).
For pixel-space diffusion models, you can set both <code>algorithm_type=dpmsolver++</code> and <code>thresholding=True</code> to
use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion
models (such as stable-diffusion).`,name:"thresholding"},{anchor:"diffusers.DPMSolverSinglestepScheduler.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.DPMSolverSinglestepScheduler.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> and
<code>algorithm_type=&quot;dpmsolver++</code>.`,name:"sample_max_value"},{anchor:"diffusers.DPMSolverSinglestepScheduler.algorithm_type",description:`<strong>algorithm_type</strong> (<code>str</code>, default <code>dpmsolver++</code>) &#x2014;
the algorithm type for the solver. Either <code>dpmsolver</code> or <code>dpmsolver++</code>. The <code>dpmsolver</code> type implements the
algorithms in <a href="https://arxiv.org/abs/2206.00927" rel="nofollow">https://arxiv.org/abs/2206.00927</a>, and the <code>dpmsolver++</code> type implements the algorithms in
<a href="https://arxiv.org/abs/2211.01095" rel="nofollow">https://arxiv.org/abs/2211.01095</a>. We recommend to use <code>dpmsolver++</code> with <code>solver_order=2</code> for guided
sampling (e.g. stable-diffusion).`,name:"algorithm_type"},{anchor:"diffusers.DPMSolverSinglestepScheduler.solver_type",description:`<strong>solver_type</strong> (<code>str</code>, default <code>midpoint</code>) &#x2014;
the solver type for the second-order solver. Either <code>midpoint</code> or <code>heun</code>. The solver type slightly affects
the sample quality, especially for small number of steps. We empirically find that <code>midpoint</code> solvers are
slightly better, so we recommend to use the <code>midpoint</code> type.`,name:"solver_type"},{anchor:"diffusers.DPMSolverSinglestepScheduler.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. For singlestep schedulers, we recommend to enable
this to use up all the function evaluations.`,name:"lower_order_final"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py#L57"}}),ne=new T({props:{name:"convert_model_output",anchor:"diffusers.DPMSolverSinglestepScheduler.convert_model_output",parameters:[{name:"model_output",val:": FloatTensor"},{name:"timestep",val:": int"},{name:"sample",val:": FloatTensor"}],parametersDescription:[{anchor:"diffusers.DPMSolverSinglestepScheduler.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.DPMSolverSinglestepScheduler.convert_model_output.timestep",description:"<strong>timestep</strong> (<code>int</code>) &#x2014; current discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DPMSolverSinglestepScheduler.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/main/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py#L240",returnDescription:`
<p>the converted model output.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),ie=new T({props:{name:"dpm_solver_first_order_update",anchor:"diffusers.DPMSolverSinglestepScheduler.dpm_solver_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.DPMSolverSinglestepScheduler.dpm_solver_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.DPMSolverSinglestepScheduler.dpm_solver_first_order_update.timestep",description:"<strong>timestep</strong> (<code>int</code>) &#x2014; current discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DPMSolverSinglestepScheduler.dpm_solver_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.DPMSolverSinglestepScheduler.dpm_solver_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/main/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py#L311",returnDescription:`
<p>the sample tensor at the previous timestep.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),le=new T({props:{name:"get_order_list",anchor:"diffusers.DPMSolverSinglestepScheduler.get_order_list",parameters:[{name:"num_inference_steps",val:": int"}],parametersDescription:[{anchor:"diffusers.DPMSolverSinglestepScheduler.get_order_list.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"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py#L184"}}),pe=new T({props:{name:"scale_model_input",anchor:"diffusers.DPMSolverSinglestepScheduler.scale_model_input",parameters:[{name:"sample",val:": FloatTensor"},{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"diffusers.DPMSolverSinglestepScheduler.scale_model_input.sample",description:"<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014; input sample",name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py#L568",returnDescription:`
<p>scaled input sample</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),ce=new T({props:{name:"set_timesteps",anchor:"diffusers.DPMSolverSinglestepScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": int"},{name:"device",val:": typing.Union[str, torch.device] = None"}],parametersDescription:[{anchor:"diffusers.DPMSolverSinglestepScheduler.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.DPMSolverSinglestepScheduler.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/main/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py#L218"}}),he=new T({props:{name:"singlestep_dpm_solver_second_order_update",anchor:"diffusers.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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/main/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py#L343",returnDescription:`
<p>the sample tensor at the previous timestep.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),ue=new T({props:{name:"singlestep_dpm_solver_third_order_update",anchor:"diffusers.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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/main/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py#L404",returnDescription:`
<p>the sample tensor at the previous timestep.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),me=new T({props:{name:"singlestep_dpm_solver_update",anchor:"diffusers.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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"},{name:"order",val:": int"}],parametersDescription:[{anchor:"diffusers.DPMSolverSinglestepScheduler.singlestep_dpm_solver_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.DPMSolverSinglestepScheduler.singlestep_dpm_solver_update.timestep",description:"<strong>timestep</strong> (<code>int</code>) &#x2014; current and latter discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DPMSolverSinglestepScheduler.singlestep_dpm_solver_update.prev_timestep",description:"<strong>prev_timestep</strong> (<code>int</code>) &#x2014; previous discrete timestep in the diffusion chain.",name:"prev_timestep"},{anchor:"diffusers.DPMSolverSinglestepScheduler.singlestep_dpm_solver_update.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
current instance of sample being created by diffusion process.`,name:"sample"},{anchor:"diffusers.DPMSolverSinglestepScheduler.singlestep_dpm_solver_update.order",description:`<strong>order</strong> (<code>int</code>) &#x2014;
the solver order at this step.`,name:"order"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py#L475",returnDescription:`
<p>the sample tensor at the previous timestep.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),fe=new T({props:{name:"step",anchor:"diffusers.DPMSolverSinglestepScheduler.step",parameters:[{name:"model_output",val:": FloatTensor"},{name:"timestep",val:": int"},{name:"sample",val:": FloatTensor"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.DPMSolverSinglestepScheduler.step.model_output",description:"<strong>model_output</strong> (<code>torch.FloatTensor</code>) &#x2014; direct output from learned diffusion model.",name:"model_output"},{anchor:"diffusers.DPMSolverSinglestepScheduler.step.timestep",description:"<strong>timestep</strong> (<code>int</code>) &#x2014; current discrete timestep in the diffusion chain.",name:"timestep"},{anchor:"diffusers.DPMSolverSinglestepScheduler.step.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
current instance of sample being created by diffusion process.`,name:"sample"},{anchor:"diffusers.DPMSolverSinglestepScheduler.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/main/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py#L512",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(){F=r("meta"),rt=l(),O=r("h1"),k=r("a"),xe=r("span"),g(K.$$.fragment),vt=l(),we=r("span"),_t=o("Singlestep DPM-Solver"),ot=l(),C=r("h2"),q=r("a"),Ee=r("span"),g(Q.$$.fragment),St=l(),Te=r("span"),bt=o("Overview"),st=l(),y=r("p"),Dt=o("Original paper can be found "),X=r("a"),yt=o("here"),Pt=o(" and the "),Y=r("a"),Mt=o("improved version"),$t=o(". The original implementation can be found "),Z=r("a"),xt=o("here"),wt=o("."),nt=l(),I=r("h2"),V=r("a"),Fe=r("span"),g(ee.$$.fragment),Et=l(),Oe=r("span"),Tt=o("DPMSolverSinglestepScheduler"),it=l(),c=r("div"),g(te.$$.fragment),Ft=l(),Ce=r("p"),Ot=o(`DPM-Solver (and the improved version DPM-Solver++) is a fast dedicated high-order solver for diffusion ODEs with
the convergence order guarantee. Empirically, sampling by DPM-Solver with only 20 steps can generate high-quality
samples, and it can generate quite good samples even in only 10 steps.`),Ct=l(),W=r("p"),It=o("For more details, see the original paper: "),re=r("a"),Lt=o("https://arxiv.org/abs/2206.00927"),At=o(" and "),oe=r("a"),Nt=o("https://arxiv.org/abs/2211.01095"),kt=l(),L=r("p"),qt=o(`Currently, we support the singlestep DPM-Solver for both noise prediction models and data prediction models. We
recommend to use `),Ie=r("code"),Vt=o("solver_order=2"),Wt=o(" for guided sampling, and "),Le=r("code"),zt=o("solver_order=3"),Ht=o(" for unconditional sampling."),Ut=l(),M=r("p"),Bt=o("We also support the \u201Cdynamic thresholding\u201D method in Imagen ("),se=r("a"),Gt=o("https://arxiv.org/abs/2205.11487"),Jt=o(`). For pixel-space
diffusion models, you can set both `),Ae=r("code"),Rt=o('algorithm_type="dpmsolver++"'),jt=o(" and "),Ne=r("code"),Kt=o("thresholding=True"),Qt=o(` to use the dynamic
thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as
stable-diffusion).`),Xt=l(),u=r("p"),ge=r("a"),Yt=o("~ConfigMixin"),Zt=o(" takes care of storing all config attributes that are passed in the scheduler\u2019s "),ke=r("code"),er=o("__init__"),tr=o(`
function, such as `),qe=r("code"),rr=o("num_train_timesteps"),or=o(". They can be accessed via "),Ve=r("code"),sr=o("scheduler.config.num_train_timesteps"),nr=o(`.
`),ve=r("a"),ir=o("SchedulerMixin"),ar=o(" provides general loading and saving functionality via the "),_e=r("a"),dr=o("SchedulerMixin.save_pretrained()"),lr=o(` and
`),Se=r("a"),pr=o("from_pretrained()"),cr=o(" functions."),hr=l(),P=r("div"),g(ne.$$.fragment),ur=l(),We=r("p"),mr=o("Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) needs."),fr=l(),ze=r("p"),gr=o(`DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to
discretize an integral of the data prediction model. So we need to first convert the model output to the
corresponding type to match the algorithm.`),vr=l(),He=r("p"),_r=o(`Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or
DPM-Solver++ for both noise prediction model and data prediction model.`),Sr=l(),$=r("div"),g(ie.$$.fragment),br=l(),Ue=r("p"),Dr=o("One step for the first-order DPM-Solver (equivalent to DDIM)."),yr=l(),ae=r("p"),Pr=o("See "),de=r("a"),Mr=o("https://arxiv.org/abs/2206.00927"),$r=o(" for the detailed derivation."),xr=l(),z=r("div"),g(le.$$.fragment),wr=l(),Be=r("p"),Er=o("Computes the solver order at each time step."),Tr=l(),H=r("div"),g(pe.$$.fragment),Fr=l(),Ge=r("p"),Or=o(`Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
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the convergence order guarantee. Empirically, sampling by DPM-Solver with only 20 steps can generate high-quality
samples, and it can generate quite good samples even in only 10 steps.`),bo.forEach(t),Ct=p(h),W=s(h,"P",{});var tt=i(W);It=n(tt,"For more details, see the original paper: "),re=s(tt,"A",{href:!0,rel:!0});var Do=i(re);Lt=n(Do,"https://arxiv.org/abs/2206.00927"),Do.forEach(t),At=n(tt," and "),oe=s(tt,"A",{href:!0,rel:!0});var yo=i(oe);Nt=n(yo,"https://arxiv.org/abs/2211.01095"),yo.forEach(t),tt.forEach(t),kt=p(h),L=s(h,"P",{});var be=i(L);qt=n(be,`Currently, we support the singlestep DPM-Solver for both noise prediction models and data prediction models. We
recommend to use `),Ie=s(be,"CODE",{});var Po=i(Ie);Vt=n(Po,"solver_order=2"),Po.forEach(t),Wt=n(be," for guided sampling, and "),Le=s(be,"CODE",{});var Mo=i(Le);zt=n(Mo,"solver_order=3"),Mo.forEach(t),Ht=n(be," for unconditional sampling."),be.forEach(t),Ut=p(h),M=s(h,"P",{});var R=i(M);Bt=n(R,"We also support the \u201Cdynamic thresholding\u201D method in Imagen ("),se=s(R,"A",{href:!0,rel:!0});var $o=i(se);Gt=n($o,"https://arxiv.org/abs/2205.11487"),$o.forEach(t),Jt=n(R,`). For pixel-space
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`),Se=s(m,"A",{href:!0});var Lo=i(Se);pr=n(Lo,"from_pretrained()"),Lo.forEach(t),cr=n(m," functions."),m.forEach(t),hr=p(h),P=s(h,"DIV",{class:!0});var j=i(P);v(ne.$$.fragment,j),ur=p(j),We=s(j,"P",{});var Ao=i(We);mr=n(Ao,"Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) needs."),Ao.forEach(t),fr=p(j),ze=s(j,"P",{});var No=i(ze);gr=n(No,`DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to
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corresponding type to match the algorithm.`),No.forEach(t),vr=p(j),He=s(j,"P",{});var ko=i(He);_r=n(ko,`Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or
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