Instructions to use haopt/scflow_t2i with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use haopt/scflow_t2i with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("haopt/scflow_t2i", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
metadata
base_model:
- XCLiu/instaflow_0_9B_from_sd_1_5
Official PyTorch models of "Self-Corrected Flow Distillation for Consistent One-Step and Few-Step Text-to-Image Generation" (AAAI 2025)
Quan Dao*12β β Β· β
Hao Phung*13β β Β· β
Trung Dao1 β Β· β
Dimitris N. Metaxas2 β Β· β
Anh Tran1
1VinAI Research β 2Rutgers University β 3Cornell University
[Paper] ββ [Code]
*Equal contribution β β Work done while at VinAI Research
1VinAI Research β 2Rutgers University β 3Cornell University
[Paper] ββ [Code]
*Equal contribution β β Work done while at VinAI Research
Model details
We present a distilled Text-to-Image (T2I) model that supports both few-step and single-step generation. Distilled from XCLiu/instaflow_0_9B_from_sd_1_5, our model achieves an FID of 11.91 for 1-NFE generation on the COCO2014 benchmark.
Please CITE our paper and give us a :star: whenever this repository is used to help produce published results or incorporated into other software.
@inproceedings{dao2025scflow,
title = {Self-Corrected Flow Distillation for Consistent One-Step and Few-Step Text-to-Image Generation},
author = {Quan Dao and Hao Phung and Trung Dao and Dimitris Metaxas and Anh Tran},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
year = {2025}
}