a Ad @stdZddlZddgZddgZejdgejdZejd gejdZd d Z d d Z dddZ ddZ GdddZ dS)a1RNG imitiating torch cuda randn on CPU. You are welcome. Usage: ``` g = Generator(seed=0) print(g.randn(shape=(3, 4))) ``` Expected output: ``` [[-0.92466259 -0.42534415 -2.6438457 0.14518388] [-0.12086647 -0.57972564 -0.62285122 -0.32838709] [-1.07454231 -0.36314407 -1.67105067 2.26550497]] ``` NlS$lW =lyn<l.vg"=dtypegϖZ!>cCs|tjddddS)z:Converts (N,) np.uint64 array into (2, N) np.unit32 array.r)viewnpuint32reshape transpose)xr :/var/www/html/stable-diffusion-webui/modules/rng_philox.pyr sr cCst|dtjtd}t|dtjtd}|d|dA|dA|d<|d|d<|d|dA|dA|d<|d|d<dS)z:A single round of the Philox 4x32 random number generator.rrrN)r astyperuint64philox_m)counterkeyv1v2r r r philox4_round s  r cCsRt|dD]6}t|||dtd|d<|dtd|d<q t|||S)aGenerates 32-bit random numbers using the Philox 4x32 random number generator. Parameters: counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation). key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed). rounds (int): The number of rounds to perform. Returns: numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers. rr)rangerphilox_w)rrrounds_r r r philox4_32,s   rcCsN|ttd}|ttd}tdt|}|t|}|tjS)uXReturns just the first out of two numbers generated by Box–Muller transform algorithm.rg) two_pow32_invtwo_pow32_inv_2pirsqrtlogsinrfloat32)r yuvsr1r r r box_mullerBs r)c@s eZdZdZddZddZdS) GeneratorzHRNG that produces same outputs as torch.randn(..., device='cuda') on CPUcCs||_d|_dS)Nr)seedoffset)selfr+r r r__init__PszGenerator.__init__cCsd}|D] }||9}qtjd|ftjd}|j|d<tj|tjd|d<|jd7_tj|tjd}||jt|}t ||}t |d|d |S)zGenerate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform.rrrr) rzerosr r,arangeemptyrfillr+rr)r )r-shapenr rrgr r rrandnTs    zGenerator.randnN)__name__ __module__ __qualname____doc__r.r7r r r rr*Msr*)r)r;numpyrrrarrayr#rrr rrr)r*r r r rs