Flex.2-preview
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Flex.2 Preview
Overview :
Flex.2 is currently the most flexible text-to-image diffusion model, featuring built-in inpainting and general control capabilities. It is an open-source project, community-supported, and aims to democratize artificial intelligence. Flex.2 has 800 million parameters, supports 512 token length input, and is compliant with the OSI's Apache 2.0 license. This model can provide powerful support in many creative projects. Users can continuously improve the model through feedback, driving technological progress.
Target Users :
This product is suitable for artists and developers who wish to explore image generation and modification in depth. Whether you're a professional designer or an AI enthusiast, Flex.2's powerful features allow you to create imaginative visual works. Its open-source nature allows for easy integration into your own projects, providing great flexibility and customization.
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Use Cases
Use Flex.2 to generate illustrations and artwork.
Use the built-in inpainting function to modify existing images.
Generate customized character designs based on user-provided pose images.
Features
800 million parameters, capable of generating high-quality images.
Built-in inpainting function for easy image editing and modification.
Supports general control input, including pose, line art, and depth input.
Supports custom fine-tuning; users can adjust model performance as needed.
Compatible with tools such as ComfyUI and Diffusers, simplifying the usage process.
Supports various conditional generation, enhancing creative flexibility.
Community-driven project, encouraging user feedback and contributions.
Open-source, promoting research and development in artificial intelligence.
How to Use
Download the Flex.2-preview model file.
Install the required dependencies, such as torch and diffusers.
Import the relevant libraries and load the model in Python.
Prepare the input image and control image.
Call the model to generate a new image, setting the relevant parameters.
Save the generated image for later use or sharing.
Adjust the generation parameters based on feedback for better results.
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