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Found 9 papers, 4 papers with code
Video Editing via Factorized Diffusion Distillation
We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data.
Emu Edit: Precise Image Editing via Recognition and Generation Tasks
Lastly, to facilitate a more rigorous and informed assessment of instructable image editing models, we release a new challenging and versatile benchmark that includes seven different image editing tasks.
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs.
Ranked #2 on
Text-to-Image Generation
on MS COCO
Text-To-4D Dynamic Scene Generation
We present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions.
KNN-Diffusion: Image Generation via Large-Scale Retrieval
Recent text-to-image models have achieved impressive results.
Ranked #34 on
Text-to-Image Generation
on MS COCO
Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors
Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains.
Ranked #20 on
Text-to-Image Generation
on MS COCO
(using extra training data)
Locally Shifted Attention With Early Global Integration
The separation of the attention layer into local and global counterparts allows for a low computational cost in the number of patches, while still supporting data-dependent localization already at the first layer, as opposed to the static positioning in other visual transformers.
Local-Global Shifting Vision Transformers
Due to the expensive quadratic cost of the attention mechanism, either a large patch size is used, resulting in coarse-grained global interactions, or alternatively, attention is applied only on a local region of the image at the expense of long-range interactions.
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection
We demonstrate the superiority of our method on both the one-shot and few-shot settings, on the datasets of Paris, CIFAR10, MNIST and FashionMNIST as well as in the setting of defect detection on MVTec.
