Text-to-image synthesis technology is gaining popularity because of its ability to convert text descriptions into visual content. This has great applications in different sectors, from digital art to practical design. But the challenge is to balance high-quality image creation with speed.
One popular method, known as Large Latent Diffusion, is praised for its ability to generate detailed images. However, it takes up a lot of computing power and time. To address this issue, researchers at Segmind and Hugging Face developed a technique called Progressive Knowledge Distillation.
This technique targets the Stable Diffusion XL model, specifically making it smaller while maintaining its image generation capabilities. This involves carefully removing specific layers, guided by layer-level losses to identify and retain essential features.
The researchers found that they could eliminate specific layers and blocks of the model structure without affecting image quality. This resulted in two streamlined versions: Segmind Stable Diffusion and Segmind-Vega, which were more efficient while closely mimicking the original model’s outputs.
In summary, this research offers a solution to the computational efficiency challenge in text-to-image models. Progressive Knowledge Distillation reduces model size without losing image quality, paving the way for potential applications in other large-scale AI models.
For more information on this research, check out the paper and project page. Follow Marktechpost on Twitter and join their ML SubReddit, Facebook Community, Discord Channel, and LinkedIn Group. And don’t forget to join their Telegram Channel.