Continuous Adversarial Flow Models
cs.LG, cs.CV
Submitted: 2026-04-13
Updated: 2026-08-25
Terminology
Sources
- BitDance: Scaling Autoregressive Generative Models with Binary Tokens
- Towards Principled Methods for Training Generative Adversarial Networks
- Layer Normalization
- Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
- Flow Matching on General Geometries
- PixelFlow: Pixel-Space Generative Models with Flow
- Training Deep Nets with Sublinear Memory Cost
- SkipSR: Faster Super Resolution with Token Skipping
- Emerging Properties in Unified Multimodal Pretraining
- Seedance 1.0: Exploring the Boundaries of Video Generation Models
- Explaining and Harnessing Adversarial Examples
- Guided Diffusion from Self-Supervised Diffusion Features
- ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
- Scalable GANs with Transformers
- The relativistic discriminator: a key element missing from standard GAN
- Adam: A Method for Stochastic Optimization
- PyTorch Distributed: Experiences on Accelerating Data Parallel Training
- Back to Basics: Let Denoising Generative Models Denoise
- Geometric GAN
- SDXL-Lightning: Progressive Adversarial Diffusion Distillation
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