# Cross-Modal Generation

LLaDA-V is a vision-language model based on the diffusion model, outperforming other diffusion multimodal large language models in performance.

This model is a GGUF-format conversion of Qwen2.5-VL-7B-Instruct, supporting multimodal tasks and applicable to image and text interaction processing.

Llama 3.2-Vision is a multimodal large language model developed by Meta, supporting image and text input with text output, excelling in visual recognition, image reasoning, image captioning, and visual question answering tasks.

A multimodal model supporting interleaved text-image input/output, based on Chameleon 7B model with enhanced image generation capabilities through the Align-Anything framework

4M is a 'any-to-any' foundational model training framework that achieves multimodal expansion through tokenization and masking techniques

4M is an 'any-to-any' foundational model training framework extended to multiple modalities through tokenization and masking techniques

High-resolution text-to-image generation model based on latent diffusion, achieving efficient image synthesis through latent space manipulation