Search Results for author:
Found 13 papers, 8 papers with code
SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer
This paper presents SANA-1. 5, a linear Diffusion Transformer for efficient scaling in text-to-image generation.
HART: Efficient Visual Generation with Hybrid Autoregressive Transformer
To address these challenges, we present the hybrid tokenizer, which decomposes the continuous latents from the autoencoder into two components: discrete tokens representing the big picture and continuous tokens representing the residual components that cannot be represented by the discrete tokens.
Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models
With these designs, we improve the autoencoder's spatial compression ratio up to 128 while maintaining the reconstruction quality.
Ranked #12 on
Image Generation
on ImageNet 512x512
SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers
We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096$\times$4096 resolution.
PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
In this paper, we introduce PixArt-\Sigma, a Diffusion Transformer model~(DiT) capable of directly generating images at 4K resolution.
Ranked #5 on
Image Generation
on TextAtlasEval
PIXART-δ: Fast and Controllable Image Generation with Latent Consistency Models
As a state-of-the-art, open-source image generation model, PIXART-{\delta} offers a promising alternative to the Stable Diffusion family of models, contributing significantly to text-to-image synthesis.
A Survey of Reasoning with Foundation Models
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation
Motivated by the inherent redundancy of 3D compared to 2D, we propose FastDiT-3D, a novel masked diffusion transformer tailored for efficient 3D point cloud generation, which greatly reduces training costs.
PixArt-$α$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
We hope PIXART-$\alpha$ will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.
MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation
These queries are then processed iteratively by a BEV-Evolving decoder, which selectively aggregates deep features from either LiDAR, cameras, or both modalities.
DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving
In addition, we propose a new task, end-to-end motion and accident prediction, which can be used to directly evaluate the accident prediction ability for different autonomous driving algorithms.
ARKitTrack: A New Diverse Dataset for Tracking Using Mobile RGB-D Data
Compared with traditional RGB-only visual tracking, few datasets have been constructed for RGB-D tracking.
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation
These queries are then processed iteratively by a BEV-Evolving decoder, which selectively aggregates deep features from either LiDAR, cameras, or both modalities.
