instaflow_0_9B_from_sd_1_5

InstaFlow-0.9B fine-tuned from 2-Rectified Flow

InstaFlow-0.9B is a one-step text-to-image generative model fine-tuned from 2-Rectified Flow.

It is trained with text-conditioned reflow and distillation as described in our paper.

Rectified Flow has interesting theoretical properties. You may check this ICLR paper and this arXiv paper.

512-Resolution Images Generated from InstaFlow-0.9B

Usage

Please refer to the official github repo.

Training

Training pipeline:

1. Distill (Stage 1): Starting from the 2-Rectified Flow checkpoint, we fix the time t=0 for the neural network, and fine-tune it using the distillation objective with a batch size of 1024 for 21,500 iterations. The guidance scale of the teacher model, 2-Rectified Flow, is set to 1.5 and the similarity loss is L2 loss. (54.4 A100 GPU days)
2. Distill (Stage 2): We switch the similarity loss to LPIPS loss, then we continue to train the model using the distillation objective and a batch size of 1024 for another 18,000 iterations. (53.6 A100 GPU days)

The final model is InstaFlow-0.9B.

Total Training Cost: It takes 199.2 A100 GPU days in total (data generation + reflow + distillation) to get InstaFlow-0.9B.

Evaluation Results - Metrics

The following metrics of InstaFlow-0.9B are measured on MS COCO 2017 with 5,000 images and 1-step Euler solver:

FID-5k = 23.4, CLIP score = 0.304

Measured on MS COCO 2014 with 30,000 images and 1-step Euler solver:

FID-30k = 13.1

Citation

@article{liu2023insta,
  title={InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation},
  author={Liu, Xingchao and Zhang, Xiwen and Ma, Jianzhu and Peng, Jian and Liu, Qiang},
  journal={arXiv preprint arXiv:2309.06380},
  year={2023}
}