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Found 5 papers, 5 papers with code
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Rectified flow is a recent generative model formulation that connects data and noise in a straight line.
Adversarial Diffusion Distillation
We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that efficiently samples large-scale foundational image diffusion models in just 1-4 steps while maintaining high image quality.
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
We then explore the impact of finetuning our base model on high-quality data and train a text-to-video model that is competitive with closed-source video generation.
High-Resolution Image Synthesis with Latent Diffusion Models
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond.
Ranked #2 on
Layout-to-Image Generation
on COCO-Stuff 256x256
Unsupervised Part-Based Disentangling of Object Shape and Appearance
Large intra-class variation is the result of changes in multiple object characteristics.
Ranked #3 on
Unsupervised Human Pose Estimation
on Human3.6M
