Search Results for author:
Found 6 papers, 2 papers with code
Towards image compression with perfect realism at ultra-low bitrates
We find that our model leads to reconstructions with state-of-the-art visual quality as measured by FID and KID.
Zero-shot spatial layout conditioning for text-to-image diffusion models
Large-scale text-to-image diffusion models have significantly improved the state of the art in generative image modelling and allow for an intuitive and powerful user interface to drive the image generation process.
Controllable Image Generation via Collage Representations
Our experiments on the OI dataset show that M&Ms outperforms baselines in terms of fine-grained scene controllability while being very competitive in terms of image quality and sample diversity.
Few-shot Semantic Image Synthesis with Class Affinity Transfer
The class affinity matrix is introduced as a first layer to the source model to make it compatible with the target label maps, and the source model is then further finetuned for the target domain.
Unifying conditional and unconditional semantic image synthesis with OCO-GAN
To allow for more control, image synthesis can be conditioned on semantic segmentation maps that instruct the generator the position of objects in the image.
Instance-Conditioned GAN
Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces.
Ranked #1 on
Conditional Image Generation
on ImageNet 64x64
