From 59b6a66958b19c99888eac46f7e456a19d157ebb Mon Sep 17 00:00:00 2001 From: Brett Kuprel Date: Mon, 11 Jul 2022 20:58:00 -0400 Subject: [PATCH] update performance graph --- README.md | 2 +- min_dalle.ipynb | 2 +- performance.png | Bin 97854 -> 16618 bytes replicate_predictor.py | 6 +++--- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 0334b97..3e149fb 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ To generate a 4x4 grid of DALL·E Mega images it takes: - 48 sec with a P100 in Colab - 13 sec with an A100 on Replicate -Here's a more detailed breakdown of total inference time vs number of generated images on an A100: +Here's a more detailed breakdown of performance on an A100:
min-dalle
diff --git a/min_dalle.ipynb b/min_dalle.ipynb index d7b50d8..4e922a0 100644 --- a/min_dalle.ipynb +++ b/min_dalle.ipynb @@ -194,7 +194,7 @@ "text = \"Dali painting of WALL·E\" #@param {type:\"string\"}\n", "intermediate_outputs = True #@param {type:\"boolean\"}\n", "grid_size = 5 #@param {type:\"integer\"}\n", - "temperature = 1 #@param {type:\"slider\", min:0.01, max:3, step:0.01}\n", + "temperature = 2 #@param {type:\"slider\", min:0.01, max:3, step:0.01}\n", "supercondition_factor = 16 #@param {type:\"number\"}\n", "top_k = 256 #@param {type:\"integer\"}\n", "log2_mid_count = 3 if intermediate_outputs else 0\n", diff --git a/performance.png b/performance.png index 42cd6e765f96d42c5f333059a88ee219c141e609..bfceb4da3053a23d49b495357a1ed82911c8f927 100644 GIT binary patch literal 16618 zcmeHudt8!P_peh^y=CQ0H!CxzDK#~T+dk*}1JPxF32L>cAB-oUH-cj8vb$ecU;pL=1U z>0FQ9X$tm~wIdI=pOGOkf2-ahzPw24* zwTVAbOrfxm7(CgcX*@Pm=G>N5mNjqVWQS(6%-&qs7euAvY>S14^5Qx-UhS-V46pBa z?C!G|!#=OP3lOrN%>L)Sad~%MD&65kwQ-_`tv5#{7e~dMRKkA{qj}er7Jq`*Q@xN3 zo?5QMqFW?K5|fvKXdl-RL$qq(!N4r4*h1_ixOh5GZ+5*8ad%#ZOEl!dnQcX?cC+6wM^bnoU}3Qb@Z`fiN+s3c$-w}uRmPz9 z^LSZ#6{M}UXWh|-*3Jymxi>oLk#nDLYP?=M8|79Gj_KqHS6Lp_v zQ*lIjiMUEU6pT|&nd!FFR^~pL#p3UP+kOBck1Ix_moEOr{ z?sVwCy;UQX$(M&3OM)#t;LP;qa1C=8eqv(=&5OW$f(zrb2ajja$F2jY#Xu-|%k^l)sBL?xwtnQJh6%7rlVjfaI0D0V8GJ z_CSMXldv5f0f?4f-y+wl=aUOr%~~G*h>ho=`}6KlDoH-@5Zn#%3Os33HR=V@|(_n7mbfTnlgdpByByX3v2sJv>boW(>W0y z(l(h1>OPTS7Tl!j%@ZtM8eLx}VrdHcX#I*rjT^@tbcf$sP1;GTwcg@dfP;; zm`~F%RbqQo%ABfB)^MI6#w;3Gw|!L7NjIrBrR>8za1EQjLzu3eN{z%u&o=LAwj-F!+v?!0rY0K^RHXx%6Q;mOUI_M+qFS8Vekc95bi# zw1d;yC(2;xyexQqU55xTYO9P6v_7spgxQkbd{}o)D+*7iviU6YTf9mJJM!+ZDwO7{ON=GlSOM{3Y?85~p?pZq;DVsd z&GVIS);!NdK!-GIo1$?SxRn;%H1Wwy0WbLPRaDuz51_wiM-F^HAHhxFSeffCVTWLE zic#tTyum3;l$O;vFj`ZH3xD>=gs*TmLzKn%V)sM)1f3nR{ECa2ThAfRx(T{YD*-SG z3jFR)h@w+z$fL+%!quYQI$pn9CL$R6qG8^I?>KqC&8HcS_vsjX4S&Y&yl&WWS8?|l zDOP=SVYdBUS~44yzhrFFb12lz0~OF7=MAEhIV~dL-*7kOPadBrHO?Mvv}-f+8fJx( zYdS#Oy5}O-W3PAXokfYhDd*jE5E2tn_wj{nI#1@#SwOryR=cYzPSf7Wp`C*f>%b&l z{}@hiYQXCmUslbS2+0ta*K+GcsZ1ylGuPa{wfA*y?)~E60BAL(^!Qe&UKpZn5~4P` z5MmQYvTTv8z_ThKvjbjKzPnz7USSuzHyAjD`9n!D8)5V-r_Zlr%cqNf#(SBOB|Kc3 z$oY_YpHQeP{1d{l1*B~nE_~fGs*hKIbZQ9DdSPAhnL)^4&Q{Rk#e_X#vrH6nI6NU>sYcXiwk(&ROJUm9v)%op8{C zoT%p@;fKdMf?c=cRw?~;e84&8vNna9puA2R{8h%IlNCDkyUsUad_IX;_S{xS4DhM*ARKy;#-yW2oKHsiF${ zogF&rN?`J>i>;M_u9b6mb!!Vy&1S^7DL=ZiDe)Q3p?3*aoE zSvL>)zS_5$9;1qhNL4V6t#c#Cl0clw9{LrEA`5r^SVLUOAJ(?Cp*N$h93z9c!(bSj zsX?zH)-vA6i`R<@IT1g>WUbx&z;4YeRn+z9UXyHRbWuaaM>-tO2eXesT7)B@bQ(7NFm`xPE+YS4) z%A>g!YXT#Q-YF_DXu+;_C1>lba%I+Si~a*QfSE8^tuMB&oPI@#N`>1yne>k4U`M#P z!!M~goT*Dp+f8A7;KI-`Di(6I?CjSvMqbbo7>}UXCue25;{`C5wX?JfBRuG3_$p^4 zeb>hIC;u^7MyrD58hQiEUj9i0eU43JJ(=%A7lBdawdVi)HW727LHqRJ4A$ub!M;kR z5IOxw3xC1m7b0%gs$l2OH!IRri#L8g=0cD2NO9}=FfxQ{(mk47^O4a!>D%tcwI&Ap z9>%F3`G@?jS_Nmj5xLzydC%@`Mw-ly$sTJX%r<=mrTu{fy6|F{!?PB^5`x(;v4CBM z+3mmfnXwLyw=fB&d}2j@q-w%Pb}4P8Hh-b;{)>%sF+A6p2c3LRP0zsrgDrYaT8&%( z@!!`>p_jlu9TO3DUZZ_SRc&x*64o%HT!gfuzmqqoBoLDRAMeS|Gv&*@qSF@RWbdQ_rk z!efTM_vP{{dbD;Ky(96+h}Ks(<>t0_O}gK{jaS?9%V#!G+(+02OUjW;SM+#axwe`* z`EqJLh9W6y)%5U)!5*$ML_K;Rx^C6yIvE_PrPtA-eP-~=r}!wjeojRYS>dBEtMk!wuo?~bhV_FpSUaeI?RXS4oP`TRm6kq(-Qv^3nq`si&K z^1jZn(mCT>e?jk+2kG}krp)i7&RYshFa}k$;ubZh@$A4VRSL_)uJizUFY4#7<)_gJ zz3L7w(5QHu91oA8?n4Wi!UEX-3lLU!(N(nBcb}t3L+EYOqbd{nYRNd#z5kMyV{YtaVos5Ssi9vP@d6Gyl4VK!#%DS?+(LE7!7J~?!U);D4vz_y zmS1mY(s(Lfpssk@gFXb~g@TjDyS2Wy72T}XGAF8uc6!nV&FsV~g6=;iiwxI-+K!>N z@rbb6EbXI7;}=l*GqM0qzB|nK4z>PrK+J>I7T$G-CKg&h|P- zCKNG_erV6^=WNXU2yQMjJ`3Fr4orgE2Ur(-+_$c=md}f7dXq!E7e}2i8}|GX;9Z{* z20ZgeOS)k7>X#}nnD{S(`6YV>lHzl4cxL0bC8 z{caB7<)cN2S?yzxjtW4#O}RV^imG0c#_kqVzc{PQ0_Ed!fQ^&Xs}@2B##dhSKI{FN$6pg9b~6BBN3=IV9IIYwn>$tEV(e^)L2K zv)6sL-7kG3XuUJvAfNq)%VN+tCNa~94%@JHrx3Gm%zAjb#4xQ7*O%7U>dnGsEeyC* ziwlBJYJO{d9pW<6Ons(&VZ>QvNfk%6*L?`^+rB(yvvy_fn@Lv3Q=~udky(2l#;oJ3 zvjxDQkKwLsf!pwjj0Z=^8+UU2Nw%{u3wNPGsCz$uJ)qucExjG$+S5_=E)-N5@sMrg zC_g;H=?@Jl2?@0ZQ0U(Num9qTNwnH7?N1Ua{z`H0O0R=xpD1qkd$P_L^#uq4cS1O; zr^@Cnd}if>sx+hkxFgQT=X|MALzf|e^R>}Dtz&cXtwGp>l^!?ImIbn359J?TMzE(! zYbj!iAGbZPyyBVH;fjLsmppAx$#gBeQR@+1|LsVjIICG%1-KAB?3@YVc{G4bU2Fn3 zrz)FYX-PJ!0#8NchIMkxmVKJT_dSG7?aGlunu+xL2Bhg?*sNtLFo_hBHmty77V6cM z?2eHt(Mz~#$)hD;#@geF-iR(>x1Z;K1o%ZljDX>^?a30^-MA6-#_PZ{5#{Ik4YeLi zo-1%_?C=Ayj%h3Flb5MFByd+tKc|18ug(Paq2uPXOL=Rfb5{%GG=^Z0GJeWBFCI^? 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a/replicate_predictor.py +++ b/replicate_predictor.py @@ -30,11 +30,11 @@ class ReplicatePredictor(BasePredictor): temperature: float = Input( description='A higher temperature results in more variety.', ge=0.01, - le=3, - default=1 + le=10, + default=2 ), ) -> Iterator[Path]: - try: + try: image_stream = self.model.generate_image_stream( text = text, seed = -1,