SwiftBrush v2: Make Your One-step Diffusion Model Better Than Its Teacher

VinAI Research

Highlights

In this paper, we aim to enhance the performance of SwiftBrush, a prominent one-step text-to-image diffusion model, to be competitive with its multi-step Stable Diffusion counterpart. Initially, we explore the quality-diversity trade-off between SwiftBrush and SD Turbo: the former excels in image diversity, while the latter excels in image quality. Based on these insights, we add improvements to the model's training approach. Key among these improvements is the optimization of the model's initial setup and the adoption of an advanced training strategy that enhances the model's learning efficiency, including auxialliary loss during the distillation process. Also with further improvements from post-training strategies, we establish a new state-of-the-art one-step diffusion model, achieving an FID of 8.14 and surpassing all GAN-based and multi-step Stable Diffusion models.

Supplementary Video

Qualitative Results

8k wallpaper of a mysterious beautiful kunoichi ninja wearing black, red, and gold jewelry in the streets of a dark snowy town in russia, by artgerm, intricate detail, trending on artstation, 8k, fluid motion, stunning shading, by wlop

A architectural drawing of a new town square for Cambridge England, big traditional musuem with columns, fountain in middle, classical design, traditional design, trees

A meandering river through a picturesque anime countryside, in the style of Makoto Shinkai's breathtaking landscapes, with attention to natural beauty

Abstract art style, abstract painting, pulsating quasar that is sending an enormous amount of energy throughout the universe

A laughing cute grey rabbit with white stripe on the head, piles of gold coins in background, colorful, Disney Picture render, photorealistic

a cute kitty, (extremely detailed CG unity 8k wallpaper), professional majestic impressionism oil painting

Photo of an elderly man from Siberia with a full beard during a cold day. The sunlight beams onto his face, emphasizing the ice that has formed from his breath in his beard. He exudes a feeling of satisfaction. Portra 800. Analog light leak.

Portrait of a woman looking at the camera

Quantitative Results

**Quantitative comparisons** between our method and others on zero-shot MS COCO-2014 benchmark. For multi-step SD models, we report each with the *cfg* that returns the best FID, e.g., *cfg* = 3 for SDv1.5 and *cfg* = 2 for SDv2.1. We also report performance of the teacher model (SDv2.1 with *cfg* = 4.5). ^† denotes reported numbers, ^‡ denotes our rerun based on provided GitHubs. `-' denotes unreported data. Ours* indicates training with additional image regularization.
Method	NFEs	FID↓	CLIP↑	Precision↑	Recall↑
StyleGAN-T^†	1	13.90	-	-	-
GigaGAN^†	1	9.09	-	-	-
SDv1.5 (cfg = 3)^†	25	8.78	0.30	0.59	0.53
SDv2.1 (cfg = 2)^‡	25	9.64	0.31	0.57	0.53
SDv2.1 (cfg = 4.5)^‡	25	12.26	0.33	0.61	0.41
SD Turbo^‡	1	16.10	0.33	0.65	0.35
UFOGen^†	1	12.78	-	-	-
MD-UFOGen^†	1	11.67	-	-	-
HiPA^†	1	13.91	0.31	-	-
InstaFlow-0.9B^‡	1	13.33	0.30	0.53	0.45
DMD^†	1	11.49	0.32	-	-
SwiftBrush	1	15.46	0.30	0.47	0.46
Ours	1	8.77	0.32	0.55	0.53
Ours*	1	8.14	0.32	0.57	0.52