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Found 9 papers, 6 papers with code
DirectorLLM for Human-Centric Video Generation
In this paper, we introduce DirectorLLM, a novel video generation model that employs a large language model (LLM) to orchestrate human poses within videos.
MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model.
Improving Tuning-Free Real Image Editing with Proximal Guidance
Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control.
Diffusion Guided Domain Adaptation of Image Generators
Can a text-to-image diffusion model be used as a training objective for adapting a GAN generator to another domain?
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images.
Ranked #2 on
Image Generation
on ADE-Indoor
Self-Supervised Sketch-to-Image Synthesis
Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which require synthesized images to be both style-consistent and semantically meaningful.
TIME: Text and Image Mutual-Translation Adversarial Networks
Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework.
Sketch-to-Art: Synthesizing Stylized Art Images From Sketches
We propose a new approach for synthesizing fully detailed art-stylized images from sketches.
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload.
Performance Databases Distributed, Parallel, and Cluster Computing Software Engineering
