5 papers
DifFRACT: Diffusion Feature Reconstruction and Attribution for Circuit Tracing
Artyom Mazur, Nina Konovalova, Aibek Alanov
Mechanistic interpretability seeks to explain neural network behavior by decomposing model computations into interpretable features and circuits. While transcoder-based circuit tra…
SHIFT: Steering Hidden Intermediates in Flow Transformers
Nina Konovalova, Andrey Kuznetsov, Aibek Alanov
Diffusion models have become leading approaches for high-fidelity image generation. Recent DiT-based diffusion models, in particular, achieve strong prompt adherence while producin…
Heeding the Inner Voice: Aligning ControlNet Training via Intermediate Features Feedback
Nina Konovalova, Maxim Nikolaev, Andrey Kuznetsov +1
Despite significant progress in text-to-image diffusion models, achieving precise spatial control over generated outputs remains challenging. ControlNet addresses this by introduci…
DepthART: Monocular Depth Estimation as Autoregressive Refinement Task
Bulat Gabdullin, Nina Konovalova, Nikolay Patakin +2
Monocular depth estimation has seen significant advances through discriminative approaches, yet their performance remains constrained by the limitations of training datasets. While…
A3D: Does Diffusion Dream about 3D Alignment?
Savva Ignatyev, Nina Konovalova, Daniil Selikhanovych +9
We tackle the problem of text-driven 3D generation from a geometry alignment perspective. Given a set of text prompts, we aim to generate a collection of objects with semantically…