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Found 12 papers, 8 papers with code
OmniCaptioner: One Captioner to Rule Them All
We propose OmniCaptioner, a versatile visual captioning framework for generating fine-grained textual descriptions across a wide variety of visual domains.
Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
We introduce Lumina-Image 2. 0, an advanced text-to-image generation framework that achieves significant progress compared to previous work, Lumina-Next.
Ranked #6 on
Text-to-Image Generation
on GenEval
LeX-Art: Rethinking Text Generation via Scalable High-Quality Data Synthesis
We introduce LeX-Art, a comprehensive suite for high-quality text-image synthesis that systematically bridges the gap between prompt expressiveness and text rendering fidelity.
I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow Transformers with Projected Flow
Rectified Flow Transformers (RFTs) offer superior training and inference efficiency, making them likely the most viable direction for scaling up diffusion models.
Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining
We present Lumina-mGPT, a family of multimodal autoregressive models capable of various vision and language tasks, particularly excelling in generating flexible photorealistic images from text descriptions.
VEnhancer: Generative Space-Time Enhancement for Video Generation
Given a generated low-quality video, our approach can increase its spatial and temporal resolution simultaneously with arbitrary up-sampling space and time scales through a unified video diffusion model.
Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions.
Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers
Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lacks sufficient implementation details.
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX.
Ranked #14 on
Video Question Answering
on MVBench
Triplet Knowledge Distillation
Distillation then begins in an online manner, and the teacher is only allowed to express solutions within the aforementioned subspace.
Function-Consistent Feature Distillation
The core idea of FCFD is to make teacher and student features not only numerically similar, but more importantly produce similar outputs when fed to the later part of the same network.
Give Me Something to Eat: Referring Expression Comprehension with Commonsense Knowledge
In this case, we need to use commonsense knowledge to identify the objects in the image.
