controlnet-union-sdxl-1.0
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Controlnet Union Sdxl 1.0
Overview :
ControlNet++ is a novel network design based on the ControlNet architecture, supporting 10+ control types for text-to-image generation. It can produce high-resolution images comparable to Midjourney's visual quality. Two new modules extend the original ControlNet, enabling the use of different image conditions with the same network parameters, and supporting multi-condition inputs without increasing computational burden. This open-source model aims to empower more individuals to leverage the convenience of image generation and editing.
Target Users :
ControlNet++ is tailored for designers, image editors, and AI enthusiasts seeking precise control over the image generation process to create high-quality visual content. It offers diverse control options and high-resolution image generation capabilities, making it ideal for professionals requiring intricate image editing and creative exploration.
Total Visits: 29.7M
Top Region: US(17.94%)
Website Views : 79.8K
Use Cases
A designer uses Openpose + Depth functionality to create character images with a sense of depth.
An editor leverages the Canny functionality to extract image edges for creative editing.
AI enthusiasts use the Scribble functionality to rapidly generate sketches and refine them further.
Features
Supports 10+ control types, including Openpose, Depth, Canny, etc.
Generates high-resolution images based on text conditions.
Supports different image conditions using the same network parameters.
Supports multi-condition inputs without adding extra computation or parameters.
Enables designers to perform detailed image editing.
Open-source community, facilitating contributions and usage.
How to Use
1. Visit the ControlNet++ model page.
2. Choose your desired control type, such as Openpose or Depth.
3. Input text conditions describing the desired features of the generated image.
4. Optionally add additional control conditions as needed.
5. Click generate and wait for the AI to process and output the image.
6. Refine or utilize the generated image based on the results.
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