Hyperparameters Are All You Need: Using Five-Step Inference for an Original Diffusion Model to Generate Images Comparable to the Latest Distillation Model
Abstract
The diffusion probability model is a state-of-the-art generative model that generates an image by applying a neural network iteratively. Moreover, this generation process is regarded as an algorithm solving a diffusion ordinary differential equation (ODE) or stochastic differential equation (SDE). Based on the analysis of the truncation error of the diffusion ODE and SDE, our study proposes a training-free algorithm that generates high-quality 512 x 512 and 1024 x 1024 images in eight steps, with flexible guidance scales. To the best of my knowledge, our algorithm is the first one that samples a 1024 x 1024 resolution image in 8 steps with an FID performance comparable to that of the latest distillation model, but without additional training. Meanwhile, our algorithm can also generate a 512 x 512 image in 8 steps, and its FID performance is better than the inference result using state-of-the-art ODE solver DMP++ 2m in 20 steps. The result of our algorithm in generating high-quality 512 x 512 images and 1024 x 1024 images with five-step and six-step inference is also comparable to the latest distillation model. Moreover, unlike most distillation algorithms, which achieve state-of-the-art FID performance by fixing the sampling guidance scale, and which sometimes cannot improve their performance by adding inference steps, our algorithm uses a flexible guidance scale on classifier-free guidance sampling. The increase in inference steps enhances its FID performance. Additionally,…
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