DDPM na DDIM Samples
DDPM na DDIM bụ ụzọ abụọ iji mee mgbanwe mgbanwe nke ụdị mgbasa ozi, na-atụgharị mkpọtụ enweghị usoro ka ọ bụrụ ihe onyonyo site na nzọụkwụ.
Nchịkọta
DDPM is the original stochastic recipe; DDIM is a faster, deterministic shortcut that produces comparable images in far fewer steps.
Ime miri emi
A zụrụ ụdị mgbasa ozi site na iji nwayọọ nwayọọ na-agbakwunye ụda Gaussian na onyonyo, wee mụta ịkọ mkpọtụ ahụ. Sampling na-atụgharị nke a. DDPM (Denoising Diffusion Probabilistic Models, Ho et al. 2020) na-aga azụ site na ọkwa mkpọtụ ọ bụla, na-agbakwunye ụda mkpọtụ ọhụrụ na nzọụkwụ ọ bụla, yabụ na ọ na-achọkarị narị otu narị na otu puku nzọụkwụ. DDIM (Denoising Diffusion implicit Models, Song et al. 2021) na-ejigharị otu netwọk a zụrụ azụ mana na-agbaso nke na-abụghị nke Markvian, ọnọdụ deterministic. Site n'ịhapụ enweghị usoro ịgbanye, DDIM nwere ike ịwụgharị ọtụtụ oge ma ka na-ada na onyonyo dị elu na usoro 10-50. N'ihi na DDIM bụ mkpebi siri ike, otu mkpọtụ mmalite ahụ na-ewepụta otu foto mgbe niile, na-eme ka mmekọrịta dị nro na mmụgharị.
Nghọta nka nka
Ndị samplers abụọ na-eji netwọk na-ebu amụma mkpọtụ epsilon agbakwunyere na onyonyo na timesstep t. Mmelite DDPM na-ewepụ ụdị amụma ahụ gbagoro agbagoro wee gbakwụnye mkpọtụ mgbanwe sitere na azụ. DDIM na-edegharị mmelite ahụ ka ọ buru ụzọ tụọ onyonyo dị ọcha x0, wee megharịa ya gaa n'ihu n'ọzọ (obere) na-esote na-enweghị okwu stochastic. Otu paramita eta na-agwakọta abụọ ahụ: eta=1 na-eweghachi DDPM, eta=0 na-enye DDIM zuru oke.
Mmetụta atụmatụ
Ọsọ na ọnụ ọgụgụ
Visual AI nwere ike megharịa nyocha, nchọpụta na mkpado ọrụ n'ọtụtụ.
Mee nhọrọ
Otu ndị na-emepụta ihe nwere ike imepụta echiche ngwa ngwa site na ngbanwe akwụkwọ ntuziaka ole na ole.
Team na usoro ọrụ
Ọrụ nwere ike iji onyonyo na akara vidiyo siri ike ịhazi.
Ọdịnihu nke DDPM na DDIM Samplers
Nnyocha Sampler na-agba ọsọ n'ọgbọ otu-ma ọ bụ ole na ole. Ndị na-edozi ODE dị elu dị ka DPM-Solver na DPM-Solver ++ ewepụlarị nlele mma n'okpuru usoro 20, ebe usoro distillation (distillation na-aga n'ihu, ụdị agbanwe agbanwe, nkwụsi ike latent) mpikota onu ụdị n'ime 1-4 nzọụkwụ generators. Na-atụ anya DDPM/DDIM ka ọ ga-anọgide na-eche echiche ka usoro mmepụta na-adabere na distilled na ndị na-edozi ihe na-eme mgbanwe maka ihe oyiyi oge na vidiyo na ngwaike ndị ahịa.
Mmejuputa n'ezie n'ụwa
Ọgbọ onyonyo Stable Diffusion, ebe a na-enye DDIM ka ọ bụrụ ihe nlere ngwa ngwa maka ederede gaa na onyonyo n'ime ngwaọrụ dị ka Automatic1111 na ComfyUI.
Pipeline nka enwere ike imepụtagharị nke na-eji DDIM nwere mkpebi na-edozi mkpụrụ na-enweghị usoro ka otu ngwa ngwa na mkpụrụ ahụ na-emeghachi onyonyo ahụ mgbe niile.
Mkpakọrịta oghere dị nro n'etiti onyonyo abụọ maka morphing animations, nke DDIM siri na-ekpebi maapụ site na mkpọtụ gaa na mmepụta ga-ekwe omume.
Ntugharị ihe okike ngwa ngwa ebe ndị na-emepụta na-eji nlele DDIM nzọụkwụ 20 iji nyochaa echiche tupu ha emee iji nwayọọ nwayọọ na-egosipụta nzọụkwụ zuru oke.
Ihe ize ndụ & okporo ụzọ nche
Ikike onyonyo na nkwenye nwere ike bụrụ ihe egwu dị n'iwu ma ọ bụrụ na edoghị anya.
Ọrụ nlereanya nwere ike ịdịgasị iche n'ofe ọkụ, igwe mmadụ, na gburugburu.
Enwere ike ghara ịhụ ihe dị mma ma ọ bụrụ na enyochaghị oke ntụkwasị obi.
Map mmejuputa
Kọwaa ụkpụrụ nnabata maka nkenke, icheta, na ụgwọ njehie.
Nwalee na data dabara na ọnọdụ mmepụta n'ezie.
Tinye nyocha mmadụ maka obere obi ike ma ọ bụ amụma mmetụta dị elu.
Sochie ihe nlere anya wee megharịa ka emechara mgbanwe igwefoto ma ọ bụ dataset.
Nọgide na-eme nchọpụta
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Ntuziaka na-esote
Ebe ọdịda Fréchet
Ajụjụ a na-ajụkarị
What is DDPM and DDIM Samplers?
DDPM na DDIM bụ ụzọ abụọ iji mee mgbanwe mgbanwe nke ụdị mgbasa ozi, na-atụgharị mkpọtụ enweghị usoro ka ọ bụrụ ihe onyonyo site na nzọụkwụ. DDPM bụ uzommeputa stochastic mbụ; DDIM bụ ụzọ mkpirisi dị ngwa ngwa, nke na-ewepụta onyonyo atụnyere n'usoro dị obere.
Kedu uru bụ isi bara uru nke DDIM karịa DDPM?
DDIM na-agbaso usoro mkpebi siri ike, nke na-abụghị nke Markvian nke na-ahapụ ya ka ọ mafe ọtụtụ oge, na-emepụta onyonyo dị mma n'ihe dịka 10-50 nzọụkwụ kama ịbụ narị otu narị.
Kedu ihe netwọkụ akwara nke ụdị mgbasa ozi na-amụta n'ezie ịkọ amụma n'oge ọzụzụ?
A zụrụ netwọk ahụ ịkọ amụma mkpọtụ Gaussian (epsilon) agbakwunyere na oge ọ bụla, nke ma DDPM na DDIM na-eji mebie usoro ahụ.
Kedu ihe kpatara DDIM nwere ike isi mepụta otu ihe oyiyi ahụ site na otu mkpọtụ mmalite oge ọ bụla?
DDIM na-atụba okwu mkpọtụ stochastic na mmelite ya, na-eme ka ụzọ si na mkpọtụ gaa na onyonyo nke ọma na ya mere enwere ike imepụtagharị ya.
Na DDIM, kedu uru paramita eta na-eweghachi omume DDPM mbụ stochastic?
Paramita eta na-ejikọta n'etiti ndị samplers abụọ: eta = 1 na-enye DDPM stochasticity zuru oke, ebe eta = 0 na-enye DDIM zuru oke.
Ihe dị ka nzọụkwụ ole ka DDPM mbụ na-achọkarị maka nlele dị elu?
DDPM na-aga azụ site na ọkwa mkpọtụ ọ bụla na-agbakwunye enweghị usoro, yabụ ọ na-achọkarị n'usoro nke narị otu narị ruo otu puku nzọụkwụ ntụgharị.