Search Results for author:
Found 5 papers, 4 papers with code
Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions
We demonstrate that these directions can be used to augment the prompt text input with fine-grained control over attributes of specific subjects in a compositional manner (control over multiple attributes of a single subject) without having to adapt the diffusion model.
DepthFM: Fast Monocular Depth Estimation with Flow Matching
Due to the generative nature of our approach, our model reliably predicts the confidence of its depth estimates.
ZigMa: A DiT-style Zigzag Mamba Diffusion Model
The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures.
Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers
We present the Hourglass Diffusion Transformer (HDiT), an image generative model that exhibits linear scaling with pixel count, supporting training at high-resolution (e. g. $1024 \times 1024$) directly in pixel-space.
Deeper Convolutional Neural Networks and Broad Augmentation Policies Improve Performance in Musical Key Estimation
In recent years, complex convolutional neural network architectures such as the Inception architecture have been shown to offer significant improvements over previous architectures in image classification.
Ranked #1 on
Key Detection
on Giantsteps
(using extra training data)
