JIA-Lab-research/DreamOmni2

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1978 stars · 173 forks observed · 2026-08-28

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

DreamOmni2 is the official PyTorch implementation of a CVPR 2026 Highlight model for multimodal instruction-based image editing and generation. It supports subject-driven generation referencing concrete objects or abstract attributes, and editing guided by both text instructions and reference images.

Use cases

• edit images with text and reference image instructions
• generate images of a subject with consistent identity and pose
• transfer abstract attributes like style, texture, or makeup to images
• run instruction-based image editing locally
• benchmark multimodal editing models with DreamOmni2Bench
• integrate image editing into ComfyUI workflows

When to choose

• you need open-source instruction-based editing that accepts reference images
• you want subject-driven generation with strong identity and pose consistency
• you need to reproduce or build on a published research model
• you want to reference abstract attributes like artistic style or material

When to avoid

• you need a polished end-user photo editor with a GUI
• you lack a GPU or can't run large diffusion models
• you need lightweight or real-time image editing
• you need a commercially supported product rather than research code

Facets

library · maturity active

image-processing machine-learning deep-learning image-processing artificial-intelligence computer-vision python image-editing image-generation diffusion-model instruction-based-editing subject-driven-generation multimodal research-model comfyui gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="JIA-Lab-research/DreamOmni2")