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README.md

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+Paper: Leaving Reality to Imagination: Robust Classification via Generated Datasets (https://arxiv.org/abs/2302.02503)
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+Colab Notebook for Data Generation: https://colab.research.google.com/drive/1I2IO8tD_l9JdCRJHOqlAP6ojMPq_BsoR?usp=sharing
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+Finetuning Recipe:
+1. We finetune the Stable Diffusion V1.5 model for 1 epoch on the complete ImageNet-1K training dataset, which contains ~1.3M images. The model was finetuned on a single 24GB A5000 GPU. It took us ~1day to complete the finetuning.
+2. The finetuning code was adopted directly from the Huggingface Diffusers library - https://github.com/huggingface/diffusers/tree/main/examples/text_to_image. Our adopted code is present at XXXX
+3. During finetuning, we (a) do not enable --use_ema, (b) do not use gradient checkpoint, (c) use a lower learning rate = 1e-6, (d) use a 'cosine' learning rate schedule with 0 warmup steps, (e) enable --use_8bit_adam from bitsandbytes.
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+Post-finetuning, we repeatedly sample the data from the generative model to generate 1.3M training and 50K validation images.
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+All the newly generated images from the finetuned Stable Diffusion as well as the pretrained Stable Diffusion are present here - https://drive.google.com/drive/folders/14DJyU_xx018Ir6Cw-mETKw9a0yLtc2NJ?usp=sharing
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+Github Repo for the paper: https://github.com/Hritikbansal/generative-robustness
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+Authors: Hritik Bansal (https://sites.google.com/view/hbansal), Aditya Grover (https://aditya-grover.github.io/)