Rohit Gandikota

I'm a PhD student at Northeastern University advised by Prof. David Bau. I'm interested in enumerating and understanding knowledge inside generative image and language models. I also closely work with Prof. Antonio Torralba from MIT. I interned at Adobe Research.

I previously worked at Indian Space Research Organization as an Applied Scientist, where I worked on advancing image sensing capabilites towards better disaster management and monitoring systems using neural networks.

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Research

I'm interested in understanding and enumerating knowledge inside generative models like text-to-image diffusion and language models. Most of my research is about editing and discovering concepts through model editing and interventions.

We propose Diffusion Target (DT) visualization to better understand diffusion models. Our key finding: we can restore the lost diversity in distilled models with out any training (Diversity Distillation). Control mechanisms like sliders and loras can be swapped between base and distilled (Control Distillation)

SliderSpace automatically decomposes diffusion models' visual capabilities into controllable, human-understandable directions from a single text prompt. This framework enables users to explore and discover novel concepts encoded inside any diffusion model

This work presents a new paradigm - "do diffusion models need millions of artworks in training data to actually learn art?". We release a new diffusion model trained completely on art-free data. This model can still mimic art by seeing less than 5 art images.

We show that language models can be used as classifiers and propose an unlearning methods where a language model self-critics its knowledge and guides itself to unlearn. This method is essential to retain the fluent text generation capabilities after unlearning.

This work introduces LoRA adaptors that provide precise control over specific attributes in diffusion model outputs, such as weather, age, or artistic styles. The sliders are trained using a small set of prompts or images to identify low-rank parameter directions, minimizing interference with other attributes. They can be composed and continuously controlled

UCE employs a fast closed-form solution for editing text-to-image diffusion models to address bias, copyright, and offensive content simultaneously without retraining. The method enables scalable and concurrent edits by modifying cross attention weights.

This work presents a method for removing specific visual concepts from text-to-image diffusion models while preserving their overall generative capabilities.

Miscellanea

Website template adopted from Jon Barron's website!