MIT researchers present reboot sampling to improve generative processes

Deep generative models based on differential equations have recently emerged as powerful modeling tools for high-dimensional data in fields ranging from image synthesis to biology. These models solve differential equations iteratively in reverse, ultimately transforming a basic distribution (such as a Gaussian in diffusion models) into a complicated data distribution.

Studies have classified previous samplers that can model these reversible processes into two types:

1. ODEsamplers those whose evolution is deterministic after the initial randomization
2. SDE samplers whose generation trajectories are stochastic.

Several publications provide evidence that these samplers exhibit benefits in various settings. The smaller binning errors produced by ODE solvers allow usable sample quality even at larger step sizes. However, the quality of their offspring levels off quickly. On the other hand, SDE improves the quality in the large NFE regime, but at the cost of more time spent on sampling.

Taking inspiration from this, the MIT researchers developed a new sampling technique called Restart, which combines the benefits of ODE and SDE. The reset sampling algorithm consists of K iterations of two subroutines in a fixed amount of time: a forward reset process that introduces a large amount of noise, effectively “restarting” the original backward process, and a backward reset process. back which runs the ODE backwards.

The reset algorithm decouples randomness and drifts, and the amount of noise added in the direct reset process is much larger than the small single-step noise interspersed with drifts in previous SDEs, increasing the twitch effect on accumulated errors. The constriction effect introduced at each reset iteration is reinforced by cycling K times back and forth. The reset can reduce discretization errors and achieve step sizes similar to ODE thanks to its deterministic reverse processes. In reality, the reset interval is often placed at the end of the simulation, where the accumulated error is greatest, to take full advantage of contraction effects. Also, multiple restart periods are used for more difficult activities to reduce initial errors.

Experimental results show that, on various NFEs, data sets, and pretrained models, Restart outperforms state-of-the-art ODE and SDE solvers in quality and speed. Notably, on CIFAR-10 with VP, Restart achieves 10x speedup compared to previous best-performing SDEs, and on ImageNet 64×64 with EDM, 2x speedup and outperforms ODE solvers in regime small NFE.

The researchers also apply Restart to a Stable Diffusion model pretrained on 512 x 512 LAION images to translate text to images. Restart improves upon previous samples by striking a better balance between text and image alignment/visual quality (as assessed by CLIP/Aesthetic scores) and diversity (as measured by FID score) with variable orientation strength without sorter.

To fully harness the potential of the Restart framework, the team plans to build a more moral method in the future to automatically select the appropriate hyperparameters for Restart based on error analysis of the models.

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