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Unified Text-to-Image, Image Editing & In-Context Generation

One model for three different jobs - generate images from text, edit existing images with simple instructions, or combine multiple reference images into a new scene. All driven by natural-language prompts.

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What it does

OmniGen2 is a single AI model that can do three creative tasks that usually require three separate tools: turn text prompts into images, edit existing images by typing instructions ("change the sky to sunset", "remove the person on the left"), and create new images that combine elements from multiple reference photos ("put the bird from image 1 onto the desk in image 2"). It is a 7B-parameter unified multimodal model accepted at CVPR 2026, built on top of a Qwen-VL-2.5 visual-understanding backbone so it can read images and prompts together. Instead of stitching multiple specialist models into a pipeline, OmniGen2 handles all of these workflows in one place.

Problem it solves

• One model, three workflows – Text-to-image, image editing, and multi-image composition all in a single model — no need to switch between specialized tools
• Edit images with plain English – Describe the change you want ("make the dress red", "add a hat") instead of using masks, layers, or selection tools
• Combine multiple references – Pull a subject from one image and a background or style from another, controlled entirely by text
• Visual understanding built in – Because it inherits Qwen-VL-2.5's understanding ability, the model actually "reads" the input image and instruction together rather than treating the image as a passive reference
• State-of-the-art editing among open models – Strong instruction-guided image editing performance compared to other open-source models
• Reasonable hardware footprint – Runs on a single ~24GB consumer GPU (e.g., RTX 3090) at native settings, with CPU offload available for less-capable machines

Input/Output

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• Input:
  • Text to Image: A plain-language description of the image you want
  • Image Editing: One reference image + a text instruction describing the desired change
  • In-context / Multi-Reference Generation: One or more reference images + a text instruction explaining which elements should come from which image
    • Tip: be explicit — e.g. "Add the bird from image 1 onto the desk in image 2" works much better than "add bird to desk"
  • Recommended input image size: greater than 512 × 512 px for best quality
  • Best prompt language: English </aside>

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• Options:
  • Diffusion steps (30 ~ 50, default 30)
  • More steps = better quality, but slower generation
  • Random seed (-1 for random, or set a number to reproduce the same image)
  • Output width (px) — default 1024
  • Output height (px) — default 1024
  • Advanced Options:
    • Text CFG scale (default 5) — how strictly the output follows the text prompt
    • Image CFG scale (default 2) — how strongly the output stays faithful to the reference image
      • Higher = sticks closer to the reference image's structure and style
      • Lower (~1.5) = gives more weight to the text prompt
      • Recommended: 1.2 – 2.0 for image editing, 2.5 – 3.0 for in-context generation
    • Negative prompt — what you don't want in the image
      • Default: (((deformed))), blurry, over saturation, bad anatomy, disfigured, poorly drawn face, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), fused fingers, messy drawing, broken legs censor, censored, censor_bar </aside>

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• Output: Generated image
  • High-fidelity, aesthetically pleasing output
  • Same model used whether the task is generation, editing, or multi-reference composition </aside>

Accuracy & Speed

Technical Details

Compliance & Provenance