Stable Diffusion 3.5 ControlNets

Stable Diffusion 3.5 ControlNets is a text-to-image AI model provided by Stability AI that supports a variety of control networks (ControlNets), such as Canny edge detection, depth maps, and high-fidelity upsampling. The model is capable of generating high-quality images based on text prompts, and is particularly suitable for scenarios such as illustration, architectural rendering, and 3D asset textures. Its importance lies in its ability to provide finer image control and improve the quality and detail of the generated images. Product background information includes its citation in academia (arxiv:2302.05543), and the Stability Community License it follows. In terms of price, it is free for non-commercial use and commercial use with annual revenue not exceeding US$1 million. If it exceeds, you need to contact the enterprise for permission.

Demand group:

"The target audience is professionals such as illustrators, 3D modelers, game developers, architects and scientific researchers who need high-quality image generation. This product helps them quickly generate images that meet their needs by providing fine image control capabilities. Improve work efficiency while reducing costs."

Example of usage scenario:

Illustrators use Canny control networks to generate illustrations with a specific style and structure.

Architects use depth map control networks to generate architectural renderings.

Game developers use high-fidelity upsampling to increase the resolution of in-game assets.

Product features:

- Supports Canny edge detection control network to guide the structure of generated images.

- Supports depth map control network, generated by DepthFM, suitable for architectural rendering or 3D asset texturing.

- Supports high-fidelity upsampling, which processes input images into blocks to increase resolution.

- Compatible with Stable Diffusion 3.5 Large model, more control network models will be added in the future.

- Follow the Stability Community License, which clarifies the free use conditions for non-commercial and commercial purposes.

- Provides detailed usage guides and code examples to help users get started quickly.

- Emphasize safety and fair use to avoid generating false content or being abused.

Usage tutorial:

1. Install necessary software environments, such as git and Python.

2. Clone the Stable Diffusion 3.5 code base and install the dependencies.

3. Download the required model files and sample images.

4. Select the control network type as needed and preprocess the input image.

5. Use the command line tool to run image generation and enter the control network model path and conditional image path.

6. Adjust ControlNet strength and other parameters for best results.

7. View the resulting image and perform further processing if necessary.