InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise
  Optimization
Xiefan Guo Jinlin Liu Miaomiao Cui Jiankai Li Hongyu Yang Di Huang
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.