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README.md

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+# InstaFlow: 2-Rectified Flow fine-tuned from Stable Diffusion v1.5
+
+2-Rectified Flow is a few-step text-to-image generative model fine-tuned from Stabled Diffusion v1.5.
+
+We use text-conditioned reflow as described in [our paper](https://arxiv.org/abs/2309.06380). 
+
+Reflow has interesting theoretical properties. You may check [this ICLR paper](https://arxiv.org/abs/2209.03003) and [this arXiv paper](https://arxiv.org/abs/2209.14577).
+
+## Images Generated from Random Diffusion DB prompts
+
+| 4-Step | 8-Step | 25-Step |
+| --- | --- | --- |
+| ![image/png](https://cdn-uploads.huggingface.co/production/uploads/646b0bbdec9a61e871799339/-6RX-t4ilNPwOy6POaH85.png) | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/646b0bbdec9a61e871799339/YX1hThlsMFXpPLllVgNse.png) | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/646b0bbdec9a61e871799339/uQBM25_BzLhFosXvF7y9S.png) | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/646b0bbdec9a61e871799339/uQBM25_BzLhFosXvF7y9S.png) |
+
+## Usage
+
+Please refer to the [official github repo](https://github.com/gnobitab/InstaFlow).
+
+## Training
+
+Training pipeline:
+1. Reflow (Stage 1): We train the model using the text-conditioned reflow objective with a batch size of 64 for 70,000 iterations. 
+The model is initialized from the pre-trained SD 1.5 weights. (11.2 A100 GPU days)
+2. Reflow (Stage 2):  We continue to train the model using the text-conditioned reflow objective with an increased batch size of 1024 for 25,000 iterations. (64 A100 GPU days)
+
+The final model is **2-Rectified Flow**.
+
+**Total Training Cost:**  It takes 75.2 A100 GPU days to get 2-Rectified Flow.
+
+
+## Evaluation Results - Metrics
+
+The following metrics of 2-Rectified Flow are measured on MS COCO 2017 with 5000 images and 25-step Euler solver.
+
+FID-5k = 21.5, CLIP score = 0.315
+
+## Evaluation Results - Impact of Guidance Scale
+
+We 
+
+## 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}
+}
+```