Update README.md

README.md

@@ -9,8 +9,9 @@ Colab Notebook for Data Generation: https://colab.research.google.com/drive/1I2I
 
 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.
+2. The finetuning code was adopted directly from the Huggingface Diffusers library - https://github.com/huggingface/diffusers/tree/main/examples/text_to_image.
+3. Link to our GitHub code: https://github.com/Hritikbansal/generative-robustness/tree/main/sd_finetune
+4. The complete set of finetuning arguments are present here - https://docs.google.com/document/d/17ggIdEuhAS0rhX7gIFp2q6H0JjkpERYFkCLTO_MtdgY/edit?usp=sharing
 
 
 Post-finetuning, we repeatedly sample the data from the generative model to generate 1.3M training and 50K validation images.