The field of diffusion models is moving towards more efficient and effective control mechanisms, with a focus on reducing computational demands and improving generation speeds. Researchers are exploring novel architectures and parallelization schemes to achieve these goals. One of the key directions is the development of unidirectional information flow and probabilistic frameworks for analyzing and improving diffusion synchronization. These advancements have the potential to enable the training of larger and more complex models, while also making high-quality text-to-image generation more accessible. Notable papers in this area include:

• UniCon, which introduces a unidirectional flow from the diffusion network to the adapter, allowing for reduced computational demands and increased training speed.
• PCM, which proposes a new parallelization scheme that significantly reduces the number of generation steps in Picard iteration, achieving up to a 2.71x speedup over sequential sampling.
• Scaling Down Text Encoders of Text-to-Image Diffusion Models, which demonstrates that smaller text encoders can generate images of comparable quality to those produced by larger models, while being 50 times smaller in size.