A Unified Framework for Image Customization
DreamO is ByteDance's advanced text-to-image AI model that offers unprecedented image customization capabilities with multiple tasks in a unified framework. Generate personalized images with high fidelity and exceptional detail, using IP adaptation, ID preservation, virtual try-on, and style transfer.
Pioneering Image Generation at ByteDance
A Breakthrough in Unified Image Customization Technology
DreamO FrameworkDreamO is an innovative text-to-image generation model developed by ByteDance Research, representing a significant advancement in the field of image customization. Released under the Apache 2.0 license, DreamO introduces a unified framework that addresses multiple image generation tasks simultaneously.
Unlike previous models that require separate specialized solutions for different customization tasks, DreamO integrates IP adaptation, ID preservation, virtual try-on, and style transfer capabilities into a single coherent framework. The model achieves exceptional image fidelity while mitigating conflicts between multiple conditions.
A key innovation in DreamO is its feature routing constraint, which effectively prevents entanglement when handling multiple inputs. This allows users to combine multiple conditions (like ID, IP, and try-on) to generate more creative images with precise control.
DreamO supports a wide range of inputs including characters, objects, animals, facial identity, clothing items (tops, bottoms, glasses, hats), and artistic styles. The model runs on consumer-grade GPUs through techniques like 8-bit quantization and CPU offloading, making advanced AI image generation more accessible to users.
Why Choose DreamO
Unified Image Customization Framework
DreamO introduces a groundbreaking unified framework that handles multiple image customization tasks simultaneously. Unlike previous approaches requiring separate specialized models, DreamO integrates IP adaptation, ID preservation, virtual try-on, and style transfer in one coherent system. This unified approach simplifies implementation while ensuring consistent high-quality results across different customization needs.
High-Fidelity Character Preservation
DreamO excels at preserving character identity with remarkable fidelity. Through VAE-based feature encoding, it achieves higher fidelity than previous adapter methods, with a distinct advantage in preserving character identity across various poses, expressions, and scenarios. This makes DreamO ideal for creating consistent character representations in different creative contexts.
Multi-Condition Generation
DreamO's feature routing constraint effectively prevents entanglement when handling multiple inputs. This breakthrough allows users to combine multiple conditions (like ID, IP, and try-on) to generate more creative images without conflicts between different elements. The model successfully mitigates issues that typically arise when mixing different customization requirements in the same image.
Open-Source Accessibility
Available on GitHub under the Apache 2.0 license, DreamO includes model weights, inference code, and a Gradio-based demo for easy testing. ByteDance has also added support for 8-bit quantization and CPU offload to enable execution on consumer-grade GPUs (16GB or 24GB), making advanced AI image customization more accessible to users with standard hardware.
How to Use DreamO
Setup DreamO Environment
Get started by cloning the DreamO repository from GitHub and setting up your environment. The installation process requires Python 3.10 and compatible dependencies. For optimal performance, a GPU with at least 16GB VRAM is recommended, but DreamO now supports consumer-grade GPUs through 8-bit quantization (--int8) and CPU offloading options (--offload) for systems with limited resources.
Prepare Your Input Images
Select the images you want to use as input conditions for DreamO. For IP tasks, choose clear images of characters, objects, or animals. For ID tasks, use front-facing portraits with good lighting. For Try-On tasks, prepare images of clothing items like tops, bottoms, glasses, or hats with minimal background distraction. For Style tasks, select representative style images that clearly demonstrate your desired artistic approach.
Configure Task Parameters
Choose your desired task (IP, ID, Try-On, Style) or combine multiple tasks for more creative control. Set the appropriate parameters including guidance scale (lower for less saturation, higher for better text generation and limb details), step count (12 steps with Turbo mode is recommended), and your text prompt describing the image you want to generate. Experiment with different prompt combinations to achieve optimal results.
Generate & Refine Images
Run the generation process and evaluate the results. DreamO provides high-fidelity outputs that maintain the key characteristics of your input conditions. If the generated images appear overly glossy or saturated, try lowering the guidance scale. For issues with text generation or limb distortion, consider increasing the guidance scale. You can further refine results by adjusting prompts or trying different combinations of input conditions.
Advanced Features of DreamO
Unified Feature Encoding Architecture
DreamO employs a VAE-based architecture that encodes features into a semantic latent space, enabling high-fidelity preservation of character attributes. This approach significantly outperforms previous adapter methods in maintaining character identity across different scenarios. The innovative design allows DreamO to handle various customization tasks within a single coherent framework, eliminating the need for separate specialized models for different customization requirements.
Multi-Condition Compatibility
Generate images with multiple conditions simultaneously, such as combining ID preservation with virtual try-on or IP adaptation with style transfers. DreamO's feature routing constraint effectively prevents entanglement when handling multiple inputs, allowing for creative combinations without conflicts between different elements. This capability enables users to create highly personalized and unique images that precisely match their creative vision.
Optimized Performance
DreamO runs efficiently on consumer hardware through optimization techniques like 8-bit quantization and CPU offloading. For 24GB GPUs, users can enable 8-bit quantization with the '--int8' flag, while 16GB GPU users can combine quantization with CPU offloading using '--int8 --offload' flags. The model also includes support for the accelerated FLUX LoRA variant (FLUX-turbo) by default, reducing inference to just 12 steps instead of 25+ for faster image generation.
Open-Source Accessibility
Available on GitHub under the Apache 2.0 license, DreamO includes model weights, inference code, and a Gradio-based demo for easy testing. ByteDance provides comprehensive documentation to help developers of all skill levels implement and customize the technology for their specific needs. The open-source nature of DreamO encourages community contributions and innovations that can further extend its capabilities.
Comprehensive Task Support
DreamO excels at four major customization tasks: IP adaptation for preserving the appearance of characters, objects, and animals; ID preservation for maintaining facial identity in diverse images; virtual try-on for adding clothing items to generated images; and style transfer for applying artistic styles. This comprehensive support enables creators to achieve their desired results without switching between different specialized models for each task.
Quality Control Mechanisms
DreamO incorporates several parameters that users can adjust to optimize output quality. The guidance scale can be lowered to reduce over-saturation or increased to improve text generation and limb details. Step count can be adjusted based on speed vs. quality requirements, with the default Turbo mode using 12 steps for efficient generation. These controls allow users to fine-tune the generation process to achieve their desired aesthetic with minimal artifacts.
DreamO by the Numbers
Unified Tasks in the DreamO Framework
VRAM Support with Optimization Options
DreamO Open-Source Under Apache 2.0
Steps for Fast Image Generation with Turbo Mode
See DreamO Demo in Action
Watch how DreamO's unified image customization framework creates high-fidelity personalized images from simple text prompts and reference images. This demonstration showcases DreamO's ability to handle multiple customization tasks simultaneously, including IP adaptation, ID preservation, virtual try-on, and style transfer.
What Users Say About DreamO
DreamO has revolutionized our character design workflow. We can generate consistent character appearances across different poses and scenarios with incredible fidelity. The unified framework allows us to experiment with clothing and style variations without losing character identity. DreamO has cut our design iteration time by 60% while significantly improving quality.
Sarah Johnson
Digital Content Creator
The multi-condition capabilities of DreamO are remarkable. We can combine ID preservation with virtual try-on features to create personalized marketing visuals that perfectly match our brand aesthetic. The optimization for consumer GPUs means our entire design team can use DreamO without requiring specialized hardware. This technology is truly game-changing for our creative process.
Michael Thompson
Creative Director
As a game developer, I use DreamO to rapidly prototype character variations and outfit designs. The high-fidelity IP adaptation makes characters instantly recognizable while allowing for creative flexibility. Being able to run the model with 8-bit quantization on our existing hardware has made advanced AI image generation accessible to our entire indie team.
David Lee
Game Developer
The open-source nature of DreamO has allowed our research team to build specialized applications for assisted design tools. We're creating interfaces that help designers with limited artistic skills to generate professional-quality visuals. ByteDance's documentation and support have been excellent throughout our implementation process.
Emma Rodriguez
UX Research Lead
Frequently Asked Questions About DreamO
DreamO is an advanced text-to-image generation model developed by ByteDance Research that introduces a unified framework for image customization. What sets DreamO apart is its ability to handle multiple customization tasks (IP adaptation, ID preservation, virtual try-on, and style transfer) within a single framework, whereas previous approaches typically required separate specialized models for each task. DreamO achieves higher fidelity character preservation through VAE-based feature encoding and effectively prevents conflicts between multiple conditions thanks to its innovative feature routing constraint.
DreamO supports four main customization tasks: (1) IP Adaptation - preserving the appearance of characters, objects, and animals; (2) ID Preservation - maintaining facial identity while generating diverse images; (3) Virtual Try-On - adding clothing items like tops, bottoms, glasses, and hats to generated images; and (4) Style Transfer - applying artistic styles to generated images. One of DreamO's key strengths is its ability to combine multiple conditions (e.g., ID + Try-On) to create more creative and personalized images with precise control.
For optimal performance, DreamO traditionally requires a GPU with at least 16GB VRAM. However, ByteDance has recently added support for consumer-grade GPUs through two optimization methods: (1) For 24GB GPUs, users can run DreamO with 8-bit quantization using the '--int8' flag; (2) For 16GB GPUs, both 8-bit quantization and CPU offloading can be enabled with the '--int8 --offload' flags. Note that CPU offloading significantly reduces inference speed and should only be used when necessary. The model also supports the accelerated FLUX LoRA variant (FLUX-turbo) by default, reducing inference to 12 steps instead of 25+.
DreamO is available as an open-source project on GitHub under the Apache 2.0 license. You can clone the repository at github.com/bytedance/DreamO, which includes model weights, inference code, and a Gradio-based demo for easy testing. Installation requires Python 3.10 and the dependencies listed in the requirements.txt file. For quick inference, you can also access DreamO through the Hugging Face Space at huggingface.co/spaces/ByteDance/DreamO, which provides a user-friendly interface for generating images without local installation.
DreamO's output quality can be fine-tuned by adjusting several key parameters: (1) Guidance Scale - For overly glossy or over-saturated images, try lowering the guidance scale; for poor text generation or limb distortion, try increasing the guidance scale; (2) Steps - The default setting with Turbo enabled is 12 steps, which provides a good balance of quality and speed; (3) Prompts - Descriptive and specific text prompts help guide the generation process; (4) Task Selection - Choose the appropriate task (IP, ID, Try-On, Style) based on your specific needs, or combine multiple tasks for more creative control. Experimentation with these parameters will help achieve optimal results.
While DreamO represents a significant advancement in image customization, there are some known limitations in the current version: (1) Style consistency is currently less stable compared to other tasks, and style cannot be combined with other conditions in the current release; (2) Some users may encounter over-saturation and 'plastic-face' issues, though the latest model update has significantly mitigated these problems; (3) When using multiple conditions, there can still be occasional conflicts between different elements; (4) For consumer-grade GPUs using quantization and CPU offloading, there may be some quality degradation and significantly slower inference times. The ByteDance team is actively working on addressing these limitations in future releases.