CONFLUX: A Latent Diffusion Model for 3D Chest CT Synthesis with Reinforcement Learning Post-Training
Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and…
Read the full articleYou might also wanna read
ArtiFixer: Using Auto-Regressive Diffusion Models to Improve 3D Reconstruction Quality
ArtiFixer: Enhancing and Extending 3D Reconstruction with Auto-Regressive Diffusion Models
ArtiFixer: Using Auto-Regressive Diffusion Models to Improve 3D Reconstruction Quality
ArtiFixer: Enhancing and Extending 3D Reconstruction with Auto-Regressive Diffusion Models
OrbitQuant: Efficient Quantization for Diffusion Transformers
OrbitQuant is introduced, a method for post-training quantization of diffusion transformers without the need for calibration data. The techn
Training-Free Single-Image Diffusion Model Achieves Fast, High-Quality Generation
We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- ma
arxiv.org·1mo ago
Terrain Diffusion: A Diffusion-Based Framework for Infinite, Real-Time Terrain Generation
For decades, procedural worlds have been built on procedural noise functions such as Perlin noise, which are fast and infinite, yet fundamen
arxiv.org·7mo ago
[ICML 2026] High-accuracy sampling for diffusion models and log-concave distributions
Authors: Fan Chen, Sinho Chewi, Constantinos Daskalakis, Alexander Rakhlin
DDN: Discrete Distribution Networks - A Novel Generative Model for Image Reconstruction
Novel Generative Model with Simple Principles and Unique Properties
discrete-distribution-networks.github.io·9mo ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.