DreamO
Generate high-fidelity images with DreamO, an advanced model supporting diverse inputs & facial fidelity. Ideal for artists & creators. Try it now!
English
English
DreamO is an advanced image customization model designed to improve the fidelity and flexibility of image generation. The framework combines VAE feature coding and is suitable for a variety of inputs, especially in terms of retention of character identities. Supports consumer-grade GPUs, with 8-bit quantization and CPU offload functions, and is adapted to different hardware environments. The continuous update of this model has made some progress in solving the problems of oversaturation and facial plasticity, aiming to provide users with a better image generation experience.
Demand population:
"This product is suitable for researchers, art creators and designers in the field of image generation and editing. Through its high fidelity and flexibility, users can generate personalized images that meet creative and commercial needs."
Example of usage scenarios:
Use DreamO to generate personalized artwork.
Create virtual try-on effects for e-commerce products.
Generate creative avatars and images on social media.
Product Features:
Supports various input forms such as characters, objects and animals, and enhances the flexibility of image generation.
Focusing on facial recognition improves the fidelity of facial features.
It supports virtual trial-on function and can simulate the matching effect of multiple clothing.
Compatible with multi-condition inputs to generate more creative images.
Accelerate inference with Turbo LoRA to improve generation efficiency.
It provides two online and local demonstration methods to facilitate user experience.
Compatible with consumer-grade GPUs, reduces hardware requirements and is convenient for widespread applications.
Online trials can be performed on HuggingFace to facilitate developers to test.
Tutorials for use:
Visit DreamO 's GitHub page.
Clone the code base and create a new conda environment.
Install the required dependency package.
Run the provided demo and select the input conditions for image generation.
Adjust the boot ratio as needed to optimize the output effect.
