DreamO
Advanced AI Image Customization Framework
Experience DreamO - ByteDance's unified framework for high-fidelity AI image customization. Featuring IP adaptation, ID preservation, virtual try-on, and style transfer all in one powerful system.
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Experience the power of unified AI image customization with DreamO's advanced features
What is DreamO?
DreamO is an advanced text-to-image AI model developed by ByteDance, focusing on high-fidelity image customization. It operates within a unified framework, integrating multiple tasks that traditionally require separate models.
Built on a Diffusion Transformer (DiT) architecture, DreamO represents a significant advancement over older UNet-style diffusion models, offering more powerful and scalable image generation capabilities.
Unified Framework
Combines multiple AI tasks in one system for seamless image customization
Advanced Architecture
Built on Diffusion Transformer technology for superior performance
Features of DreamO
IP Adaptation
Preserves the appearance of characters, objects, or animals, ensuring consistency across generated images.
ID Preservation
Maintains facial identity across diverse images, perfect for personalized content creation and consistency.
Virtual Try-On
Add clothing items like tops, bottoms, glasses, or hats for virtual fashion applications and styling.
Style Transfer
Apply artistic styles to transform images into various visual aesthetics like Renaissance paintings.
Technical Innovations in DreamO
Feature Routing Constraint
Prevents input entanglement, ensuring each task is processed independently yet cohesively.
VAE-Based Encoding
Enhances character preservation, maintaining detail and fidelity in generated images.
Multi-Condition Support
Allows simultaneous application of ID, IP, and try-on conditions for expanded creativity.
How to Use DreamO
Online Demo
Try DreamO instantly without any installation through our online demo.
Visit the Hugging Face Space
Upload your image or use examples
Enter your text prompt
Generate and download results
Local Setup
Install DreamO locally for full control and customization.
git clone https://github.com/bytedance/DreamO
conda create --name dreamo python=3.10
conda activate dreamo
pip install -r requirements.txtHardware Requirements for DreamO
Recommended
24GB+ VRAM GPU
Full performance with int8 quantization
Minimum
16GB VRAM GPU
With CPU offloading support
macOS
M1/M2/M3/M4 Chips
Uses MPS with int8 for memory efficiency
Frequently Asked Questions about DreamO
What makes DreamO different from other AI image generators?
DreamO offers a unified framework that combines IP adaptation, ID preservation, virtual try-on, and style transfer in one system. Unlike other models that require separate tools for each task, DreamO handles multiple customization tasks simultaneously with advanced feature routing to prevent conflicts.
Can I use DreamO for commercial projects?
Yes! DreamO is released under the Apache 2.0 license, which allows for both personal and commercial use. You can modify, distribute, and use DreamO in commercial applications without restrictions.
What are the minimum hardware requirements for DreamO?
DreamO requires a GPU with at least 16GB VRAM for optimal performance. However, it supports CPU offloading for lower-end GPUs and works on macOS M1/M2/M3/M4 chips with MPS acceleration. For limited memory, you can use int8 quantization.
How does DreamO handle multiple conditions simultaneously?
DreamO uses innovative feature routing constraints and VAE-based feature encoding to handle multiple conditions (ID, IP, try-on, style) without entanglement. This allows you to combine different customization tasks in a single generation process.
Can I integrate DreamO with ComfyUI?
Yes! There's a native ComfyUI implementation available at the ComfyUI-DreamO GitHub repository, allowing you to use DreamO within your existing ComfyUI workflows.
What is the inference speed of DreamO?
DreamO uses an accelerated FLUX LoRA variant (FLUX-turbo) by default, reducing inference steps to just 12 steps for faster processing. You can adjust the guidance scale and disable turbo mode for different quality/speed trade-offs.
How do I troubleshoot common issues like limb distortion?
For issues like limb distortion or poor text rendering, try increasing the guidance scale. For glossy or over-saturated images, lower the guidance scale. You can also adjust parameters specific to your task (IP, ID, Try-On, Style).
Is there community support for DreamO?
Yes! DreamO has an active community on GitHub with ongoing discussions and support. You can also contact the developers directly: Yanze Wu and Chong Mou for technical assistance.
What file formats does DreamO support?
DreamO supports standard image formats including PNG, JPG, and WEBP for input images. The model can generate high-quality outputs in various formats suitable for different applications.
Can DreamO work offline?
Yes! Once you've installed DreamO locally and downloaded the model weights, you can run it completely offline. This is particularly useful for privacy-sensitive projects or when working without internet access.
DreamO Resources & Links
GitHub Repository
Access the complete DreamO source code, model weights, and documentation.
Hugging Face Demo
Try DreamO online without installation on Hugging Face Spaces.
Official Website
Visit the official DreamO website for latest updates and information.
ComfyUI Integration
Native ComfyUI implementation for seamless workflow integration.
Developer Contact
Contact Yanze Wu, lead developer of DreamO, for technical support.
Technical Article
Read detailed technical analysis and use cases on Medium.
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