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What the DAAM: Interpreting Stable Diffusion Using Cross Attention |
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https://www.youtube.com/watch?v=RkI4BFG0GP0 
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https://colab.research.google.com/github/Pseudo-Lab/show-and-tell-ml/blob/main/jw/daam_colab.ipynb |
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자연어처리 task에도 시각화하는 방법론이 있을지 궁금합니다! |
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Speaker Diarization
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빅데이터를 활용한 게임 전략 및 유저 행동 패턴 분석: 배틀그라운드 게임을 중심으로 PUBG Match Deaths and Statistics |
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Mixture Density Network |
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Attention Map 시각화를 통한 Diffusion Model의 동작 원리 파악.
Self-attention과 Cross-attention 시각화
Cross-attention: 텍스트 토큰과 Latent 간의 연관성
Self-attention: Latent 내부의 연관성
Diffusion 모델의 Attention Map 데이터를 참조하는 방법을 조사.
각 Diffusion Step에서 Cross-attention, Self-attention의 연관 관계.
가설:
Diffusion 프로세스 초기에는 T2I 과정이 일어날 것이다.
Diffusion 프로세스 후기에는 이미지의 완성도를 높일 것이다. 이 경우 Self-attention이 더 활발할 것이다.
Diffusion 프로세스에 따른 Attention Map을 확인.
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