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CVPR 2023. [Paper]Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, Kfir AbermanGoogle Research | Boston University25 Aug 2022 AbstractFine-tunning ์ด๋?๊ธฐ์กด์ ํ์ต๋ ๋ชจ๋ธ(Pretrained model)์ ๊ธฐ๋ฐ์ผ๋ก ์ถ๊ฐ์ ์ผ๋ก ์๋ก์ด ๋ฐ์ดํฐ์ ํ์ต์ ํตํด ๋ชจ๋ธ์ ํ๋ผ๋ฏธํฐ๋ฅผ ๋ฏธ์ธ์กฐ์ ํ๋ ๊ฒ์ด๋ค. Latent Diffusion ๋ชจ๋ธ์์ ํ์ต๊ฐ๋ฅํ ํ๋ผ๋ฏธํฐ์ ์์ญ์ ํฌ๊ฒ ํ ์คํธ ์ธ์ฝ๋์ U-net ์์ญ์ด ์์ผ๋ฉฐ, ๋๋ฆผ๋ถ์ค๋ ๋๊ฐ์ง ํ๋ผ๋ฏธํฐ ๋ชจ๋ ํ์ตํ๋ค. ๋ณธ ๋ ผ๋ฌธ์์๋ text-to-image diffusion model์ "๊ฐ์ธํ"๋ฅผ ์ํ ์๋ก์ด ..
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1. stable diffusion์ dreambooth๋ก ํ์ธํ๋ ์ค train์์ ์๋ฌ๊ฐ ๊ณ์ ๋ฐ์ํ๋ค.2. ์ฒ์์ GPU๋ฅผ A100์ผ๋ก ๋๋ ค์, T4๋ก ๋ฐ๊ฟ์ฃผ๋๊น ํด๊ฒฐ์ด ๋๋๋ฐ ๊ทธ ๋ค์ ํ์ต๋ถํฐ๋ T4์์๋ ๊ณ์ ๊ฐ์ ์๋ฌ ๋ฐ์.3. ํด๊ฒฐํ๋ ค๊ณ ๋ณ ์ง์ ๋คํ๋๋ฐ ์๋ผ์ ๊ณจ๋จธ๋ฆฌ๋ฅผ ์๋ ์ค!4. ๋ค์ ์ฝ๋๋ก ํด๊ฒฐ !pip install "jax[cuda12_local]==0.4.23" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html!pip install diffusers==0.11.1!pip install transformers scipy ftfy accelerate train ์ ์ ์ด๊ฑธ ๋๋ ค์ฃผ๊ณ ,train ์ํค๋ฉด ํ์ต์ด ์..
stable diffusion ๋ชจ๋ธ์ ์ฝ๋ฉ์์ loadํ ๋, peft๊ด๋ จ ์๋ฌ๊ฐ ๋๋ ๊ฒฝ์ฐ !pip install peft %reset ๋ฅผ ํด์ฃผ๋ฉด pipe.to('cuda') ์ ์ค์ ์ฝ์ ๋ ์ค๋ฅ๊ฐ ์ฌ๋ผ์ง๋ค. ๊ทธ๋ผ์ด๋ง . .
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Martin Arjovsky : https://arxiv.org/abs/1701.07875v3 Wasserstein GAN We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debuggi arxiv.org 1. Introduction Unsupervised Learning์ ๋ฐ์ดํฐ (x)์ ํ๋ฅ ๋ถํฌ ( P(x))๋ฅผ..
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๋ฌธ์ : ์ฝ๋: -- ์ฝ๋๋ฅผ ์์ฑํด์ฃผ์ธ์ SELECT DISTINCT B.ID, B.EMAIL, B.FIRST_NAME, B.LAST_NAME FROM SKILLCODES A JOIN DEVELOPERS B ON (A.CODE & B.SKILL_CODE) > 0 WHERE NAME = 'Python' OR NAME = 'C#' ORDER BY B.ID ํ์ด: 1. DISTINCT๋ก ID ์ค๋ณต ์ ๊ฑฐ๋ฅผ ํด์ค์ผ๋จ. 2. CODE๊ฐ์ด 0 ์ด์์ผ ๋
์ฝ๋: -- ์ฝ๋๋ฅผ ์ ๋ ฅํ์ธ์ SELECT A.REST_ID, A.REST_NAME, A.FOOD_TYPE, A.FAVORITES, A.ADDRESS, ROUND(AVG(B.REVIEW_SCORE), 2) AS SCORE FROM REST_INFO A JOIN REST_REVIEW B ON A.REST_ID = B.REST_ID GROUP BY REST_ID HAVING ADDRESS LIKE '์์ธ%' ORDER BY SCORE DESC,FAVORITES DESC ํ์ด: 1. ํ๊ท ๊ฐ ๊ตฌํ ๋ AVG ํจ์ ์ฐ๊ธฐ 2. ROUND(์ซ์, ์๋ฆฟ์) ์ ๋ ฅํ๋ฉด ๋ฐ์ฌ๋ฆผ๊ฐ ๊ตฌํ ์ ์์ 3. LIKE 4. GROUP BY๋ก ๋ฌถ์ ๋๋ HAVING์ ์ ์กฐ๊ฑด ์จ์ฃผ๊ธฐ
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๋ฌธ์ : ์ฝ๋: def solution(numbers): try: input_list = list(map(str, numbers)) input_list.sort(key = lambda x:x*3, reverse=True) answer = '' answer = answer.join(input_list) except: print('์์ธ ๋ฐ์!') return str(int(answer)) ํ์ด: 1. list(map(str, numbers)) 2. sort ํ ๋ key ๋ก ์ ๋ ฌ ๊ธฐ์ค ์ ํ ์ ์์, lambda ์ฌ์ฉ! data_list.sort() data_list.sort(key=lambda x : len(x)) ex1) x[0]๋ฅผ ๊ธฐ์ค์ผ๋ก ์ ๋ ฌํ๊ณ ๊ฐ์ ๊ฒฝ์ฐ x[1]๋ฅผ ๊ธฐ์ค์ผ๋ก ์ ๋ ฌํ๊ธฐ arr = ['a..
์ฝ๋: def solution(brown, yellow): total = brown + yellow # a * b = total for b in range(1,total+1): if total % b == 0: # total / b = a a = total / b if a >= b: # a >= b if 2*a + 2*b == brown + 4: # 2*a + 2*b = brown + 4 return [a,b] ํ์ด: ๊ฐ๋ก๋ฅผ a, ์ธ๋ก๋ฅผ b ๋ผ๊ณ ํ๊ณ ์์ ์ธ์ฐ๊ธฐ
์ฝ๋: -- ์ฝ๋๋ฅผ ์ ๋ ฅํ์ธ์ SELECT A.PRODUCT_ID, PRODUCT_NAME, SUM(PRICE * AMOUNT) AS TOTAL_SALES FROM FOOD_PRODUCT A JOIN FOOD_ORDER B ON A.PRODUCT_ID = B.PRODUCT_ID WHERE PRODUCE_DATE LIKE '2022-05%' GROUP BY PRODUCT_ID ORDER BY TOTAL_SALES DESC, PRODUCT_ID ASC ํ์ด: 1. PRODUCT_ID๋ฅผ ํค๋ก ์กฐ์ธ 2. LIKE๋ก 5์์ธ ๊ฒ๋ค ๊ตฌํ๊ธฐ 3. GROUP BY๋ก PRODUCT_ID๋ผ๋ฆฌ ๋ฌถ์ด์ค์ผํจ!!! 4. SUM(PRICE * AMOUNT) ์ด๋งค์ถ ๊ตฌํ๊ธฐ **!
์ฝ๋: -- ์ฝ๋๋ฅผ ์ ๋ ฅํ์ธ์ SELECT BOARD_ID, WRITER_ID, TITLE, PRICE, CASE WHEN STATUS = 'DONE' THEN '๊ฑฐ๋์๋ฃ' WHEN STATUS = 'SALE' THEN 'ํ๋งค์ค' WHEN STATUS = 'RESERVED' THEN '์์ฝ์ค' END AS 'STATUS' FROM USED_GOODS_BOARD WHERE DATE_FORMAT(CREATED_DATE, '%Y-%m-%d') = '2022-10-05' ORDER BY BOARD_ID DESC ํ์ด: CASE WHEN ํ์ฉ! ***
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