import{s as Ce,n as Le,o as Oe}from"../chunks/scheduler.8c3d61f6.js";import{S as Pe,i as qe,g as i,s as n,r as h,A as Ie,h as a,f as r,c as o,j as F,u as f,x as p,k as E,y as t,a as d,v as g,d as _,t as x,w as b}from"../chunks/index.da70eac4.js";import{D as G}from"../chunks/Docstring.6b390b9a.js";import{H as Te,E as Ne}from"../chunks/EditOnGithub.1e64e623.js";function ke(be){let m,K,R,B,M,J,y,ve='<code>FlowMatchEulerDiscreteScheduler</code> is based on the flow-matching sampling introduced in <a href="https://arxiv.org/abs/2403.03206" rel="nofollow">Stable Diffusion 3</a>.',Q,S,Y,s,T,ie,N,we="Euler scheduler.",ae,k,$e=`This model inherits from <a href="/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a> and <a href="/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin">ConfigMixin</a>. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.`,ce,v,C,le,H,De="Forward process in flow-matching",de,w,L,ue,V,Fe="Sets the begin index for the scheduler. This function should be run from pipeline before the inference.",me,$,O,he,z,Ee="Sets the discrete timesteps used for the diffusion chain (to be run before inference).",fe,D,P,pe,A,Me=`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).`,ge,u,q,_e,W,ye=`Stretches and shifts the timestep schedule to ensure it terminates at the configured <code>shift_terminal</code> config
value.`,xe,j,Se=`Reference:
<a href="https://github.com/Lightricks/LTX-Video/blob/a01a171f8fe3d99dce2728d60a73fecf4d4238ae/ltx_video/schedulers/rf.py#L51" rel="nofollow">https://github.com/Lightricks/LTX-Video/blob/a01a171f8fe3d99dce2728d60a73fecf4d4238ae/ltx_video/schedulers/rf.py#L51</a>`,Z,I,ee,X,te;return M=new Te({props:{title:"FlowMatchEulerDiscreteScheduler",local:"flowmatcheulerdiscretescheduler",headingTag:"h1"}}),S=new Te({props:{title:"FlowMatchEulerDiscreteScheduler",local:"diffusers.FlowMatchEulerDiscreteScheduler",headingTag:"h2"}}),T=new G({props:{name:"class diffusers.FlowMatchEulerDiscreteScheduler",anchor:"diffusers.FlowMatchEulerDiscreteScheduler",parameters:[{name:"num_train_timesteps",val:": int = 1000"},{name:"shift",val:": float = 1.0"},{name:"use_dynamic_shifting",val:": bool = False"},{name:"base_shift",val:": typing.Optional[float] = 0.5"},{name:"max_shift",val:": typing.Optional[float] = 1.15"},{name:"base_image_seq_len",val:": typing.Optional[int] = 256"},{name:"max_image_seq_len",val:": typing.Optional[int] = 4096"},{name:"invert_sigmas",val:": bool = False"},{name:"shift_terminal",val:": typing.Optional[float] = None"},{name:"use_karras_sigmas",val:": typing.Optional[bool] = False"},{name:"use_exponential_sigmas",val:": typing.Optional[bool] = False"},{name:"use_beta_sigmas",val:": typing.Optional[bool] = False"},{name:"time_shift_type",val:": str = 'exponential'"}],parametersDescription:[{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.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.FlowMatchEulerDiscreteScheduler.shift",description:`<strong>shift</strong> (<code>float</code>, defaults to 1.0) &#x2014;
The shift value for the timestep schedule.`,name:"shift"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.use_dynamic_shifting",description:`<strong>use_dynamic_shifting</strong> (<code>bool</code>, defaults to False) &#x2014;
Whether to apply timestep shifting on-the-fly based on the image resolution.`,name:"use_dynamic_shifting"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.base_shift",description:`<strong>base_shift</strong> (<code>float</code>, defaults to 0.5) &#x2014;
Value to stabilize image generation. Increasing <code>base_shift</code> reduces variation and image is more consistent
with desired output.`,name:"base_shift"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.max_shift",description:`<strong>max_shift</strong> (<code>float</code>, defaults to 1.15) &#x2014;
Value change allowed to latent vectors. Increasing <code>max_shift</code> encourages more variation and image may be
more exaggerated or stylized.`,name:"max_shift"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.base_image_seq_len",description:`<strong>base_image_seq_len</strong> (<code>int</code>, defaults to 256) &#x2014;
The base image sequence length.`,name:"base_image_seq_len"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.max_image_seq_len",description:`<strong>max_image_seq_len</strong> (<code>int</code>, defaults to 4096) &#x2014;
The maximum image sequence length.`,name:"max_image_seq_len"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.invert_sigmas",description:`<strong>invert_sigmas</strong> (<code>bool</code>, defaults to False) &#x2014;
Whether to invert the sigmas.`,name:"invert_sigmas"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.shift_terminal",description:`<strong>shift_terminal</strong> (<code>float</code>, defaults to None) &#x2014;
The end value of the shifted timestep schedule.`,name:"shift_terminal"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.use_karras_sigmas",description:`<strong>use_karras_sigmas</strong> (<code>bool</code>, defaults to False) &#x2014;
Whether to use Karras sigmas for step sizes in the noise schedule during sampling.`,name:"use_karras_sigmas"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.use_exponential_sigmas",description:`<strong>use_exponential_sigmas</strong> (<code>bool</code>, defaults to False) &#x2014;
Whether to use exponential sigmas for step sizes in the noise schedule during sampling.`,name:"use_exponential_sigmas"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.use_beta_sigmas",description:`<strong>use_beta_sigmas</strong> (<code>bool</code>, defaults to False) &#x2014;
Whether to use beta sigmas for step sizes in the noise schedule during sampling.`,name:"use_beta_sigmas"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.time_shift_type",description:`<strong>time_shift_type</strong> (<code>str</code>, defaults to &#x201C;exponential&#x201D;) &#x2014;
The type of dynamic resolution-dependent timestep shifting to apply. Either &#x201C;exponential&#x201D; or &#x201C;linear&#x201D;.`,name:"time_shift_type"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py#L47"}}),C=new G({props:{name:"scale_noise",anchor:"diffusers.FlowMatchEulerDiscreteScheduler.scale_noise",parameters:[{name:"sample",val:": FloatTensor"},{name:"timestep",val:": typing.Union[float, torch.FloatTensor]"},{name:"noise",val:": typing.Optional[torch.FloatTensor] = None"}],parametersDescription:[{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.scale_noise.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
The input sample.`,name:"sample"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.scale_noise.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/main/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py#L168",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>
`}}),L=new G({props:{name:"set_begin_index",anchor:"diffusers.FlowMatchEulerDiscreteScheduler.set_begin_index",parameters:[{name:"begin_index",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.set_begin_index.begin_index",description:`<strong>begin_index</strong> (<code>int</code>) &#x2014;
The begin index for the scheduler.`,name:"begin_index"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py#L155"}}),O=new G({props:{name:"set_timesteps",anchor:"diffusers.FlowMatchEulerDiscreteScheduler.set_timesteps",parameters:[{name:"num_inference_steps",val:": typing.Optional[int] = None"},{name:"device",val:": typing.Union[str, torch.device] = None"},{name:"sigmas",val:": typing.Optional[typing.List[float]] = None"},{name:"mu",val:": typing.Optional[float] = None"},{name:"timesteps",val:": typing.Optional[typing.List[float]] = None"}],parametersDescription:[{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.set_timesteps.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The number of diffusion steps used when generating samples with a pre-trained model.`,name:"num_inference_steps"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.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"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.set_timesteps.sigmas",description:`<strong>sigmas</strong> (<code>List[float]</code>, <em>optional</em>) &#x2014;
Custom values for sigmas to be used for each diffusion step. If <code>None</code>, the sigmas are computed
automatically.`,name:"sigmas"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.set_timesteps.mu",description:`<strong>mu</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Determines the amount of shifting applied to sigmas when performing resolution-dependent timestep
shifting.`,name:"mu"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.set_timesteps.timesteps",description:`<strong>timesteps</strong> (<code>List[float]</code>, <em>optional</em>) &#x2014;
Custom values for timesteps to be used for each diffusion step. If <code>None</code>, the timesteps are computed
automatically.`,name:"timesteps"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py#L246"}}),P=new G({props:{name:"step",anchor:"diffusers.FlowMatchEulerDiscreteScheduler.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.FlowMatchEulerDiscreteScheduler.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.FlowMatchEulerDiscreteScheduler.step.timestep",description:`<strong>timestep</strong> (<code>float</code>) &#x2014;
The current discrete timestep in the diffusion chain.`,name:"timestep"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.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.FlowMatchEulerDiscreteScheduler.step.s_churn",description:"<strong>s_churn</strong> (<code>float</code>) &#x2014;",name:"s_churn"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.step.s_tmin",description:"<strong>s_tmin</strong>  (<code>float</code>) &#x2014;",name:"s_tmin"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.step.s_tmax",description:"<strong>s_tmax</strong>  (<code>float</code>) &#x2014;",name:"s_tmax"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.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.FlowMatchEulerDiscreteScheduler.step.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
A random number generator.`,name:"generator"},{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.step.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>) &#x2014;
Whether or not to return a
<code>FlowMatchEulerDiscreteSchedulerOutput</code> or tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py#L370",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If return_dict is <code>True</code>,
<code>FlowMatchEulerDiscreteSchedulerOutput</code> 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><code>FlowMatchEulerDiscreteSchedulerOutput</code> or <code>tuple</code></p>
`}}),q=new G({props:{name:"stretch_shift_to_terminal",anchor:"diffusers.FlowMatchEulerDiscreteScheduler.stretch_shift_to_terminal",parameters:[{name:"t",val:": Tensor"}],parametersDescription:[{anchor:"diffusers.FlowMatchEulerDiscreteScheduler.stretch_shift_to_terminal.t",description:`<strong>t</strong> (<code>torch.Tensor</code>) &#x2014;
A tensor of timesteps to be stretched and shifted.`,name:"t"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py#L225",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A tensor of adjusted timesteps such that the final value equals <code>self.config.shift_terminal</code>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
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