Flex.2-preview

Flex.2 is the most flexible text-to-image diffusion model at present, with built-in redraw and universal control capabilities. It is an open source project supported by the community to promote the democratization of artificial intelligence. Flex.2 has 800 million parameters, supports 512 token length inputs, and is compliant with the OSI-compliant Apache 2.0 license. This model can provide strong support in many creative projects. Users can continuously improve models through feedback and promote technological progress.

Demand population:

"This product is suitable for artists and developers who want to explore in-depth image generation and modification. Whether professional designers or AI enthusiasts, they can create creative visual works through the power of Flex.2. Its open source features allow users to easily integrate into their projects, providing great flexibility and customization space."

Example of usage scenarios:

Generate illustrations and artwork using Flex.2.

Modify existing images with built-in redraw function.

Generate customized character designs based on the user-provided pose maps.

Product Features:

800 million parameters, capable of generating high-quality images.

Built-in redraw function to facilitate users to edit and modify images.

Supports universal control inputs, including posture, line and depth inputs.

Supports custom fine-tuning, and users can adjust model performance according to their needs.

Compatible with tools such as ComfyUI and Diffusers to simplify usage.

Supports multiple conditions to enhance creative flexibility.

Community-driven projects that encourage users to feedback and contribute.

Open source code promotes the research and development of artificial intelligence.

Tutorials for use:

Download Flex.2-preview model file.

Install the required dependency libraries such as torch and diffusers.

Import related libraries in Python and load the model.

Prepare the input image and control the image.

Call the model to generate a new image and set relevant parameters.

Save the generated image for subsequent use or sharing.

Adjust the generated parameters according to feedback for better results.