Commita5f66b5

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feature: beta scheduler

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Original file line number	Diff line number	Diff line change
`@@ -2,6 +2,7 @@`
`2`	`2`	`importtorch`
`3`	`3`	`importk_diffusion`
`4`	`4`	`importnumpyasnp`
	`5`	`+fromscipyimportstats`
`5`	`6`
`6`	`7`	`frommodulesimportshared`
`7`	`8`
`@@ -115,6 +116,17 @@ def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):`
`115`	`116`	`returntorch.FloatTensor(sigs).to(device)`
`116`	`117`
`117`	`118`
	`119`	`+defbeta_scheduler(n,sigma_min,sigma_max,inner_model,device):`
	`120`	`+# From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024) """`
	`121`	`+alpha=0.6`
	`122`	`+beta=0.6`
	`123`	`+timesteps=1-np.linspace(0,1,n)`
	`124`	`+timesteps= [stats.beta.ppf(x,alpha,beta)forxintimesteps]`
	`125`	`+sigmas= [sigma_min+ ((x)*(sigma_max-sigma_min))forxintimesteps]+ [0.0]`
	`126`	`+sigmas=torch.FloatTensor(sigmas).to(device)`
	`127`	`+returnsigmas`
	`128`	`+`
	`129`	`+`
`118`	`130`	`schedulers= [`
`119`	`131`	`Scheduler('automatic','Automatic',None),`
`120`	`132`	`Scheduler('uniform','Uniform',uniform,need_inner_model=True),`
`@@ -127,6 +139,7 @@ def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):`
`127`	`139`	`Scheduler('simple','Simple',simple_scheduler,need_inner_model=True),`
`128`	`140`	`Scheduler('normal','Normal',normal_scheduler,need_inner_model=True),`
`129`	`141`	`Scheduler('ddim','DDIM',ddim_scheduler,need_inner_model=True),`
	`142`	`+Scheduler('beta','Beta',beta_scheduler,need_inner_model=True),`
`130`	`143`	`]`
`131`	`144`
`132`	`145`	`schedulers_map= {{x.name:xforxinschedulers},{x.label:xforxinschedulers}}`

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