Dreamomni2

What is Dreamomni2

DreamOmni2 is a unified open-source multimodal AI model designed for instruction-based image editing and generation. It supports both text and image instructions, handles abstract attributes and concrete objects, and aims to outperform commercial models in various benchmarks. It is built upon Flux Kontext and Qwen2.5-VL.

How to use Dreamomni2

1. Install DreamOmni2 & Dependencies: Clone the repository, install requirements, and download model weights from Hugging Face.
2. Prepare Source & Reference Images: Gather your source image for editing and reference images for desired attributes or objects. DreamOmni2 supports multiple reference images.
3. Craft Multimodal Instructions: Combine text descriptions with reference images. For editing, specify the source image first. DreamOmni2's architecture handles multi-image inputs without confusion.
4. Run DreamOmni2 Editing or Generation: Execute inference scripts for editing or generation tasks using the crafted multimodal instructions.
5. Review & Iterate: Evaluate the results for consistency and quality. Adjust instructions and references to refine the output.
6. Deploy & Share: Export final images for use. The open-source license permits commercial applications.

Features of Dreamomni2

• Unified Multimodal AI: Supports both text and image instructions for editing and generation.
• Abstract Attribute Handling: Capable of understanding and manipulating concepts like texture, material, and style through image references.
• Concrete Object Editing: Precise editing of specific objects within an image.
• Superior Identity & Pose Consistency: Maintains character identity and pose accurately during edits.
• Open-Source: Model weights, training code, and datasets are available on GitHub and Hugging Face.
• Commercial Use License: Allows for commercial applications of generated images.
• Multi-Image Input: Processes multiple reference images without pixel confusion.
• High Resolution Support: Works with JPG, PNG, and WebP formats, with optimal results from 1024x1024 or higher resolution images.
• Local Deployment: Can be run locally on a CUDA-capable GPU.

Use Cases of Dreamomni2

• Fashion & E-commerce: Transferring fabric textures and materials to product photos.
• Portrait Photography: Applying hairstyles and makeup styles from reference images.
• Digital Art: Applying artistic styles and color palettes from reference artworks.
• Architectural Visualization: Editing building materials, lighting, and surface finishes.
• Automotive Design: Changing vehicle paint colors and finishes with metallic, matte, or custom references.
• Food Photography: Enhancing plating, garnishing, and styling using reference images.
• Brand & Marketing: Maintaining brand consistency across images using brand guidelines and style references.
• Photo Restoration: Improving image quality and texture in older photographs.

Pricing

DreamOmni2 offers several plans based on monthly credits and features:

• Creator: $2/month (billed yearly at $24). Includes 500 credits/month, dual-reference instructions, attribute sliders, prompt recipes, 2K exports, and community access.
• Pro: $20/month (billed yearly at $240). Includes 3,000 credits/month, unlimited edits, 4K exports, layered PSD/TIFF, batch pipelines, commercial license, and team seats.
• Studio: $50/month (billed yearly at $600). Includes 15,000 credits/month, batch pipelines, multi-canvas editing, asset versioning, 8K exports, dedicated GPU lanes, and studio-level support.
• Credits: A pay-as-you-go option is also available.

FAQ

• What is DreamOmni2? DreamOmni2 is a unified open-source multimodal instruction-based image editing and generation model that supports text and image instructions, handles abstract attributes and concrete objects, and outperforms commercial models in benchmarks. It's built on Flux Kontext and Qwen2.5-VL.
• How does DreamOmni2 multimodal editing work? It accepts text and reference images. For editing, provide a source image and reference images for attributes (texture, material, style) or objects. Its encoding scheme processes multi-image inputs without confusion.
• Is DreamOmni2 free and open-source? Yes, model weights, training code, and datasets are fully open-source, allowing free use for research or commercial applications under its license.
• What makes DreamOmni2 better than GPT-4o? DreamOmni2 surpasses GPT-4o and commercial models in abstract attribute generation and multimodal instruction support, achieving a higher success rate in concrete object editing with better consistency. It is also open-source, unlike GPT-4o.
• Can I use DreamOmni2 for commercial projects? Yes, its open-source license permits commercial use for various applications like product photography, design, and portrait editing.
• What image formats does DreamOmni2 support? It accepts JPG, PNG, and WebP formats for source and reference images, and outputs PNG files with full quality preservation.
• What are abstract attributes in DreamOmni2? These are visual concepts like materials, textures, makeup styles, design patterns, or artistic styles that are easier to reference via images than describe with words alone.
• Can I run DreamOmni2 locally? Yes, you can download the model weights and run inference locally on a GPU. It requires a CUDA-capable GPU with sufficient VRAM.
• How do I craft effective multimodal instructions? Combine clear text instructions with relevant reference images. Describe the desired change and provide images showing target attributes or objects. The model understands complex multimodal instructions.
• Where can I learn more about DreamOmni2? Information is available in the technical paper on arXiv, the GitHub repository, Reddit, X (formerly Twitter), and YouTube tutorials.