4 citations · 9 across the 11 of their papers we have counts for
10 papers · 1 filter
T-DIG: Text-Conditioned Diversity for Image Generation
Simran Khanuja, Rohit Gandikota, Gaurav Parmar +1
Text-to-image (T2I) platforms typically return several images per prompt so users can explore different visual interpretations and choose one that matches their intent. However, T2…
Vision-Language Binding in In-Context Image Generation
Chris Ge, Rohit Gandikota, Antonio Torralba +1
In-context image generation models such as FLUX.2 take a text prompt and an optional reference image as visual conditioning for the output. Internally, all three inputs -- text, re…
SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
Rohit Gandikota, Zongze Wu, Richard Zhang +3
We present SliderSpace, a framework for automatically decomposing the visual capabilities of diffusion models into controllable and human-understandable directions. Unlike existing…
Opt-In Art: Learning Art Styles Only from Few Examples
Hui Ren, Joanna Materzynska, Rohit Gandikota +3
We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples. To investigate this, we train a text-to-ima…
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models
Rohit Gandikota, Joanna Materzynska, Tingrui Zhou +2
We present a method to create interpretable concept sliders that enable precise control over attributes in image generations from diffusion models. Our approach identifies a low-ra…
Unified Concept Editing in Diffusion Models
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov +2
Text-to-image models suffer from various safety issues that may limit their suitability for deployment. Previous methods have separately addressed individual issues of bias, copyri…
Erasing Concepts from Diffusion Models
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman +1
Motivated by recent advancements in text-to-image diffusion, we study erasure of specific concepts from the model's weights. While Stable Diffusion has shown promise in producing e…
DC-Art-GAN: Stable Procedural Content Generation using DC-GANs for Digital Art
Rohit Gandikota, Nik Bear Brown
Art is an artistic method of using digital technologies as a part of the generative or creative process. With the advent of digital currency and NFTs (Non-Fungible Token), the dema…
Computer Vision for Autonomous Vehicles
Rohit Gandikota
In this work, we try to implement Image Processing techniques in the area of autonomous vehicles, both indoor and outdoor. The challenges for both are different and the ways to tac…
How You See Me
Rohit Gandikota, Deepak Mishra
Convolution Neural Networks is one of the most powerful tools in the present era of science. There has been a lot of research done to improve their performance and robustness while…