Researchers train a text-to-image model in 24 hours using diffusion models and perceptual losses.
Researchers combined various architectural and training tricks for diffusion models, including x-prediction formulation, patch size of 32, and 256-dimensional bottleneck, to train a text-to-image model in 24 hours. They used perceptual losses, such as DINO-based perceptual loss, and token routing with TREAD to improve performance. The code is open-sourced on Github.
Developers can leverage the open-sourced code to reproduce and modify the text-to-image model, exploring the use of perceptual losses and token routing to improve performance. This can lead to faster training times and better image quality.
Try implementing the x-prediction formulation and perceptual losses in your own text-to-image model to see if it improves performance.
Tags
Also today
Signals by role
Also today
Tools mentioned