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Text-to-image diffusion model is a popular paradigm that synthesizes personalized images by providing a text prompt and a random Gaussian noise.
Matrix perturbation theory
GW Stewart · 1990
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Microsoft coco: Common objects in context, 2015
Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár · 2015
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Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2018
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Scaling laws for neural language models, 2020
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing, 2021
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Clipscore: A reference-free evaluation metric for image captioning, 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Pseudo numerical methods for diffusion models on manifolds, 2022
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao · 2022
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Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Photorealistic text-to-image diffusion models with deep language understanding, 2022
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Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models, 2023
Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or · 2023
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Geneval: An object-focused framework for evaluating text-to-image alignment, 2023
Dhruba Ghosh, Hanna Hajishirzi, and Ludwig Schmidt · 2023
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Susung Hong, Gyuseong Lee, Wooseok Jang, and Seungryong Kim · 2023
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A noise is worth diffusion guidance, 2024
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