Search Results for author:
Found 9 papers, 6 papers with code
Nexus-Gen: A Unified Model for Image Understanding, Generation, and Editing
To bridge this gap, we present Nexus-Gen, a unified model that synergizes the language reasoning capabilities of LLMs with the image synthesis power of diffusion models.
Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs
The Contrastive Language-Image Pre-training (CLIP) framework has become a widely used approach for multimodal representation learning, particularly in image-text retrieval and clustering.
MinMo: A Multimodal Large Language Model for Seamless Voice Interaction
Previous models for voice interactions are categorized as native and aligned.
EliGen: Entity-Level Controlled Image Generation with Regional Attention
Recent advancements in diffusion models have significantly advanced text-to-image generation, yet global text prompts alone remain insufficient for achieving fine-grained control over individual entities within an image.
ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction
This enables the synthesis model to directly produce aesthetically pleasing images without any extra computational cost.
Minimum Tuning to Unlock Long Output from LLMs with High Quality Data as the Key
As large language models rapidly evolve to support longer context, there is a notable disparity in their capability to generate output at greater lengths.
SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning
With support of over $300+$ LLMs and $50+$ MLLMs, SWIFT stands as the open-source framework that provide the most comprehensive support for fine-tuning large models.
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models
Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content
In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input.
