What matters when building vision-language models?
Paper
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2405.02246
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Published
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99
Multi-Modal Model
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MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data
Paper • 2406.18790 • Published • 33 -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 118 -
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
Paper • 2408.12528 • Published • 50 -
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Paper • 2408.11039 • Published • 56 -
xGen-MM (BLIP-3): A Family of Open Large Multimodal Models
Paper • 2408.08872 • Published • 97 -
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
Paper • 2407.01392 • Published • 39 -
IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning using Instruct Prompts
Paper • 2408.03209 • Published • 21 -
Qwen/Qwen2-VL-7B-Instruct
Image-Text-to-Text • Updated • 1.61M • • 832 -
THUDM/cogvlm2-llama3-chat-19B
Text Generation • Updated • 132k • 201 -
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
Paper • 2408.15998 • Published • 83 -
OmniGen: Unified Image Generation
Paper • 2409.11340 • Published • 108 -
PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions
Paper • 2409.15278 • Published • 22