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If you're just tuning in, names Asher.
I got this cool elk cat sona that loves to vibe and smoke weed and is super cool!
I really love talking to new peeps so consider friending me on discord don't be shy
AsherAnomaly#0869
Holiday freebies! Thank you all for tuning in <3 Characters are their respective owners
Good night おやすみ 🌙
Thanks for tuning in today💖
今日もありがとう💕
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#おはようVtuber #新人Vtuber
#Vtuber #VTuberUprising #ENVtuber
Thanks for tuning into the stream y'all! The VOD for this will be up next Tuesday. Our next stream will be on Dec 29th:
A fresh run of SPARK THE ELECTRIC JESTER 3!
See you then!
#vocaloid
i redid the tuning of my no. 7 cover for the third time in a row lol here's a preview
there were a lotta spots that bothered me so much + i removed len from the duet since imo fukase does it a lot more justice in my tuning :")
ust: sam coles
I've been having so much fun playing with this! Just adjusting and fine tuning as we go 💖💜 So in love with this lil model!!! https://t.co/oH7lneujnL
i got popy and wanted to see her auto tuning
original - aya hirano
https://t.co/CJqXAJoa7E
↑Colab で動かすStable Diffusion (Fintuning Model Ver 1.0)
個人がファインチューニングしたその方々独自のモデルが試せます
モデル提供いただいた方
@8co28
様
@nikaidomasaki
様
@p1atdev_art
様
ありがとうございます
#stablediffusion
#WaifuDiffusion
#AIart
ACertainlyを手元のデータで再度clip_skip=2でfine tuningした。bs 32/5e-6/3,000stepでまあまあ学習できた感じ。ただbs 32/1e-6/4,000stepのでりだモデルのfine tuningよりも学習が反映されにくい気がする。
1枚目からACeratainly、ACertrainly+FT、でりだ、でりだ+FTの順。
ACertainlyをfine tuningしたんだけどclip_skipの指定を忘れてしまった……。
今のところでりだモデルに比べると同じ設定では追加学習が効きにくい印象。また手が下手なのと画風が違うのでたしかにNAI系ではなさそうな感じ。
@MuseumofCrypto @nft_paris @joynxyz "Tuning in to the Lost Frequencies of Home"
So, I've been tuning into the #friendlockeviolet as they've been happening, and then I got *really* tempted to draw any sort of fanart for it. It took me a while to give in, but these are the results of when I did. Anyway, I drew them because I think they're neat.
@SaltyDKArtRT
Good morning, #survivalists!
Have you been tuning into our #lofi #YouTube channel for all your background music needs? If you haven't, you should! We have almost 8 hours of LoFi music 🎧 New videos in the making. Check: https://t.co/RYeuhBps5w
#AnimeNFT #ZAF #NFT #NFTCommunity
anyways thank yall for tuning into my stream!!! I really appreciate the support ❤️
Textual inversion is memory-efficient and fast, but cannot reconstruct with high fidelity,
Dreambooth has high-fidelity, but heavy and overfits unless you have large set of prior preservation.
Can you take the best of both worlds, by Pivotal Tuning in low-rank space? Yes! [1/n]