Diffusion models have caused a revolution in text-to-image generation, offering remarkable quality and creativity. Nonetheless, it’s price noting that their multi-step sampling procedure is recognized for its sluggishness, often demanding quite a few inference steps to attain desirable outcomes. On this paper, the authors introduce an progressive one-step generative model derived from the open-source Stable Diffusion (SD) model. 

They found that a simple try to distil SD led to finish failure as a result of a big issue: the suboptimal coupling of noise and pictures, which greatly hindered the distillation process. To beat this challenge, the researchers turned to , a recent advancement in generative models that comes with probabilistic flows. Rectified Flow incorporates a novel technique called reflow, which steadily straightens the trajectory of probability flows. 

This, in turn, reduces the transport cost between the noise distribution and the image distribution. This improvement in coupling greatly facilitates the distillation process, addressing the initial problem. The above image demonstrates the working of Instaflow.

Utilization of a one-step diffusion-based text-to-image generator is evidenced by an FID (Fréchet Inception Distance) rating of 23.3 on the MS COCO 2017-5k dataset, which represents a considerable improvement over the previous state-of-the-art technique often known as progressive distillation (37.2 → 23.3 in FID). Moreover, by employing an expanded network featuring 1.7 billion parameters, the researchers have managed to boost the FID even further, achieving a rating of twenty-two.4. This one-step model is known as “InstaFlow.”

On the MS COCO 2014-30k dataset, InstaFlow demonstrates exceptional performance with an FID of 13.1 in only 0.09 seconds, making it one of the best performer within the ≤ 0.1-second category. This outperforms the recent StyleGAN-T model (13.9 in 0.1 second). Notably, the training of InstaFlow is achieved with a comparatively low computational cost of only 199 A100 GPU days.

Based on these results, researchers have proposed the next contributions:

• Improving One-Step SD: The training of the 2-Rectified Flow model didn’t fully converge, investing 75.2 A100 GPU days. This is simply a fraction of the training cost of the unique SD (6250 A100 GPU days). By scaling up the dataset, model size, and training duration, researchers consider the performance of one-step SD will improve significantly. 
• One-Step ControlNet: By applying our pipeline to coach ControlNet models, it is feasible to get one-step ControlNets able to generating controllable contents inside milliseconds. 
• Personalization for One-Step Models: By fine-tuning SD with the training objective of diffusion models and LORA, users can customize the pre-trained SD to generate specific content and styles.
• Neural Network Structure for One-Step Generation: With the advancement of making one-step SD models using text-conditioned reflow and distillation, several intriguing directions arise: 

(1) exploring alternative one-step structures, similar to successful architectures utilized in  GANs, that might potentially surpass the U-Net by way of quality and efficiency; 

(2) leveraging techniques like pruning, quantization, and other approaches for constructing efficient neural networks to make one-step generation more computationally reasonably priced while minimizing potential degradation in quality.

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