InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

• URL: http://arxiv.org/abs/2404.04650v1
• Date: Sat, 6 Apr 2024 14:56:59 GMT
• Title: InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization
• Authors: Xiefan Guo, Jinlin Liu, Miaomiao Cui, Jiankai Li, Hongyu Yang, Di Huang,
• Abstract summary: InitNO is a paradigm that refines the initial noise in semantically-faithful images. A strategically crafted noise optimization pipeline is developed to guide the initial noise towards valid regions. Our method, validated through rigorous experimentation, shows a commendable proficiency in generating images in strict accordance with text prompts.
• Score: 27.508861002013358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent strides in the development of diffusion models, exemplified by advancements such as Stable Diffusion, have underscored their remarkable prowess in generating visually compelling images. However, the imperative of achieving a seamless alignment between the generated image and the provided prompt persists as a formidable challenge. This paper traces the root of these difficulties to invalid initial noise, and proposes a solution in the form of Initial Noise Optimization (InitNO), a paradigm that refines this noise. Considering text prompts, not all random noises are effective in synthesizing semantically-faithful images. We design the cross-attention response score and the self-attention conflict score to evaluate the initial noise, bifurcating the initial latent space into valid and invalid sectors. A strategically crafted noise optimization pipeline is developed to guide the initial noise towards valid regions. Our method, validated through rigorous experimentation, shows a commendable proficiency in generating images in strict accordance with text prompts. Our code is available at https://github.com/xiefan-guo/initno.

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