Text-to-image diffusion models have dominated generative tasks by producing high-quality outcomes. Recently, image-to-image transformation tasks have been guided by diffusion models with external image conditions. However, the iterative and time-consuming nature of diffusion models limits their practical use. Recent research proposes distillation techniques to speed up sampling and condense the models. A single-stage distillation method is presented, which outperforms previous techniques in visual quality and quantitative performance. The distillation technique is also parameter-efficient and can be integrated with existing tuning techniques. Overall, this research overcomes limitations of diffusion models in text-to-image generation.
Researchers from Google and John Hopkins University Reveal a Faster and More Efficient Distillation Method for Text-to-Image Generation: Overcoming Diffusion Model Limitations
Text-to-image diffusion models trained on large-scale data have been dominating generative tasks. However, these models often require many iterations and a long sampling period to produce high-quality results, making them less practical for real-world applications.
Recent research has focused on speeding up the sampling process using distillation techniques. These techniques significantly reduce the sampling steps required while maintaining generative performance. They can also be used to condense large-scale text-to-image diffusion models that have already been trained.
By applying distillation methods, a single-stage distillation process can be used to extract a conditional diffusion model from an unconditional diffusion model. This approach eliminates the need for the original text-to-image data and avoids compromising the diffusion prior in the pre-trained model.
The distilled model produced through this process can forecast high-quality results in a much shorter sampling period, making it more practical for various conditional tasks. Experimental data shows that this distilled model performs better than earlier distillation techniques in terms of visual quality and quantitative performance.
Furthermore, this distillation method offers a parameter-efficient mechanism for conditional generation. By adding a few more learnable parameters, it can convert and speed up an unconditional diffusion model for conditional tasks. This new paradigm has increased the usefulness of several conditional tasks.
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