Dreamomni2

DreamOmni2 is an advanced, open-source multimodal AI model designed for instruction-based image editing and generation. Its primary purpose is to transform images with superior precision and consistency by unifying text prompts with visual guidance from reference images. This allows users to orchestrate complex visual changes, from manipulating concrete objects to referencing abstract attributes like texture, material, and style.

The core value proposition of DreamOmni2 lies in its ability to deliver commercial-grade quality, surpassing models like GPT-4o and Qwen-Edit in multimodal tasks. It excels particularly in abstract attribute editing and maintaining identity consistency, making it a powerful tool for professional visual workflows. By accepting up to two reference images, DreamOmni2 enables users to blend styles and preserve subject identity with high fidelity.

The target audience includes professional creators, digital artists, e-commerce businesses, architectural visualizers, and researchers who require precise, instruction-based control over image content. Key benefits include production-ready quality, superior control over abstract visual elements, and the flexibility of an open-source model with full weights and training code available for local deployment and customization.

Features

• Unified Multimodal Instruction: Combines text prompts with up to two reference images to guide both image editing and generation tasks, offering a level of control beyond text-only models.
• Abstract Attribute Editing: Excels at manipulating abstract visual concepts like material, texture, style, makeup, and lighting by referencing them from input images.
• Superior Identity Consistency: Delivers best-in-class identity and pose preservation among open-source models during subject-driven generation and portrait editing.
• Concrete Object Editing: Achieves high accuracy (0.6585 success rate) in precise object replacement and modification while maintaining pixel-perfect consistency in non-edited areas.
• Open-Source Model: Provides full model weights and training code, allowing for local deployment and customization by researchers and developers.
• Multi-Image Index Encoding: Utilizes an index encoding method to handle multi-image input (source and references) without pixel confusion, ensuring accurate instruction following.

How to Use

DreamOmni2 is designed for instruction-based image editing and generation, following a clear multimodal workflow:

1. Install DreamOmni2 & Dependencies: Clone the DreamOmni2 repository and install the necessary requirements. Download the model weights from Hugging Face, as DreamOmni2 runs on Flux Kontext and Qwen2.5-VL foundations.
2. Prepare Source & Reference Images: Gather your primary source image (for editing) and up to two reference images. Reference images should contain the abstract attributes (e.g., texture, style, hairstyle) or concrete objects you wish to transfer or modify. Supported formats are PNG, JPG, JPEG, or WEBP, up to 10 MB each.
3. Craft Multimodal Instructions: Write a combined text prompt and image instructions. For editing tasks, specify the source image first. The text prompt should clearly articulate the desired change, referencing the visual elements provided in the reference images.
4. Run DreamOmni2 Editing or Generation: Execute the inference scripts for your chosen task (editing or generation). The model processes the multimodal instructions and delivers the output image.
5. Review & Iterate: Examine the generated or edited image. If necessary, refine your text prompt or adjust the reference images and run the process again to achieve the desired production-ready results.

Use Cases

• Product Image Editing with Style Transfer: E-commerce businesses can use DreamOmni2 to transfer specific fabric textures, materials, or patterns from a reference image onto a product photo, maintaining garment structure while applying new finishes for marketing materials.
• Portrait Editing with Hairstyle/Makeup Reference: Photographers and beauty professionals can apply complex hairstyles, makeup styles, or artistic styles from a reference image onto a portrait, ensuring superior identity and pose consistency that text-only instructions cannot achieve.
• Architectural and Interior Design Visualization: Designers can transform room aesthetics or building exteriors by referencing design styles, materials (e.g., wood, stone), or lighting atmospheres from images, maintaining spatial consistency during the transformation.
• Photo Restoration and Enhancement: Users can enhance and restore old or low-quality photos by referencing a target quality, texture, or lighting condition from a high-quality image, improving image quality while preserving the original content and subject identity.
• Automotive and Product Design Visualization: Designers can change vehicle paint colors, finishes (metallic, matte), or product surface finishes using material references, preserving the product's shape while accurately applying new textures and lighting attributes.

FAQ

What is DreamOmni2's core advantage over commercial models like GPT-4o?

DreamOmni2's core advantage is its unified multimodal instruction support, which allows users to reference abstract attributes like material, texture, and style using up to two reference images alongside text instructions. This capability delivers superior identity consistency and editing precision, especially in complex tasks like style transfer and material editing, where commercial models often struggle with visual references.

Is DreamOmni2 an open-source model?

Yes, DreamOmni2 is an open-source multimodal AI model. The creators provide full model weights and training code, allowing researchers and developers to deploy it locally and integrate it into custom workflows. It runs on Flux Kontext and Qwen2.5-VL foundations.

What types of files can I upload as reference images?

Users can upload up to two reference images to steer DreamOmni2's edit or generation. The supported file formats are PNG, JPG, JPEG, or WEBP, with a maximum file size of 10 MB each.

How does DreamOmni2 ensure consistency in subject-driven generation?

DreamOmni2 is benchmarked to deliver the best results among open-source models for identity and pose consistency during subject-driven generation. It uses advanced index encoding to handle multi-image input without pixel confusion, ensuring that the identity of the subject is preserved even when applying complex visual changes or styles from reference images.

Can DreamOmni2 perform precise concrete object editing?

Yes, DreamOmni2 excels at concrete object editing, such as object replacement and modification. Benchmarks show it achieves a 0.6585 success rate with "pixel-perfect consistency" in non-edited areas, indicating high accuracy and precision in manipulating specific objects within an image.