baaivision/Emu3.5

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1547 stars · 69 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Emu3.5 is BAAI's native multimodal foundation model that jointly predicts next states across vision and language, trained on 10T+ interleaved video-text tokens. The repo provides model weights, inference code (including vLLM offline inference), and tooling for image generation, editing, and interleaved vision-language generation.

Use cases

• generate images from text or any modality input
• edit images with natural language instructions
• generate interleaved image-and-text sequences
• run multimodal model inference with vLLM
• explore world modeling and embodied manipulation scenarios
• benchmark against commercial image generation models

When to choose

• you need an open-weights multimodal model for image generation and editing
• you want interleaved vision-language generation without modality adapters
• you need fast inference via discrete diffusion parallel decoding
• you are researching unified next-token world modeling

When to avoid

• you only need text-only LLM inference
• you lack GPU resources for large model inference
• you need a production-ready hosted service rather than self-run models

Facets

library · maturity active

machine-learning deep-learning llm-inference image-processing llm-training artificial-intelligence large-language-models image-processing machine-learning python multimodal world-model image-generation vision-language-model diffusion any-to-image gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="baaivision/Emu3.5")