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Stable Diffusion 3.5 ControlNets
Overview :
Stable Diffusion 3.5 ControlNets, provided by Stability AI, is a text-to-image AI model that supports various ControlNets, including Canny edge detection, depth maps, and high-fidelity upscaling. This model generates high-quality images based on textual prompts, making it especially suitable for applications in illustration, architectural rendering, and 3D asset texturing. Its significance lies in providing finer control over image generation, enhancing the quality and detail of outputs. Background information includes its citation in academia (arxiv:2302.05543) and adherence to the Stability Community License. In terms of pricing, it is free for non-commercial use and for commercial use with an annual income below $1 million; exceeding this requires contacting for a corporate license.
Target Users :
This product is targeted at professionals who require high-quality image generation, including illustrators, 3D modelers, game developers, architects, and researchers. It aids them in quickly generating images that meet their needs by providing precise image control capabilities, thus enhancing productivity while reducing costs.
Use Cases
An illustrator uses the Canny control network to generate illustrations with specific styles and structures.
An architect employs the depth map control network to create architectural renderings.
A game developer utilizes the high-fidelity upscaling feature to enhance the resolution of in-game assets.
Features
- Supports Canny edge detection control networks for guiding the structural integrity of generated images.
- Supports depth map control networks generated by DepthFM, suitable for architectural rendering or 3D asset texturing.
- Facilitates high-fidelity upscaling by processing input images in segments to enhance resolution.
- Compatible with Stable Diffusion 3.5 Large model, with plans to integrate additional control network models in the future.
- Adheres to the Stability Community License, outlining free usage terms for both non-commercial and commercial purposes.
- Provides detailed usage guidelines and code examples for easy user onboarding.
- Emphasizes safety and responsible use to prevent the generation of false content or misuse.
How to Use
1. Install the necessary software environment, such as Git and Python.
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2. Clone the Stable Diffusion 3.5 repository and install the dependencies.
3. Download the required model files and sample images.
4. Select the type of control network as needed and preprocess the input images.
5. Use the command line tool to run image generation, providing the paths to the control network model and the condition image.
6. Adjust the ControlNet strength and other parameters for optimal results.
7. Review the generated images and perform any necessary post-processing.
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