t2i-adapter-sdxl-canny
Maintainer: adirik
39
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The t2i-adapter-sdxl-canny model is a text-to-image diffusion model that allows users to modify images using canny edge detection. It is an implementation of the T2I-Adapter-SDXL model developed by TencentARC and the diffuser team. The model is maintained by adirik and is available on Replicate.
Similar models maintained by adirik include t2i-adapter-sdxl-sketch, t2i-adapter-sdxl-lineart, and t2i-adapter-sdxl-depth-midas, which allow users to modify images using sketches, line art, and depth maps, respectively. Another similar model, t2i-adapter-sdxl-sketch, is maintained by alaradirik.
Model inputs and outputs
The t2i-adapter-sdxl-canny model takes an input image and a text prompt, and generates a modified image based on the prompt and the canny edge representation of the input image. The model also allows users to customize various parameters, such as the number of samples, the guidance scale, and the number of inference steps.
Inputs
- Image: The input image to be modified.
- Prompt: The text prompt describing the desired output image.
- Scheduler: The scheduler to use for the diffusion process.
- Num Samples: The number of output images to generate.
- Random Seed: A random seed for reproducibility.
- Guidance Scale: The scale to match the prompt.
- Negative Prompt: Specify things to not see in the output.
- Num Inference Steps: The number of diffusion steps.
- Adapter Conditioning Scale: The conditioning scale for the adapter.
- Adapter Conditioning Factor: The factor to scale the image by.
Outputs
- An array of generated image URIs.
Capabilities
The t2i-adapter-sdxl-canny model can be used to modify input images in various ways, such as adding or removing elements, changing the style or composition, or applying artistic effects. The model leverages the canny edge representation of the input image to guide the generation process, allowing for more precise and controllable modifications.
What can I use it for?
The t2i-adapter-sdxl-canny model can be used for a variety of creative and artistic applications, such as photo editing, digital art, and image generation. It could be particularly useful for tasks that involve modifying or enhancing existing images, such as product visualization, architectural rendering, or character design.
Things to try
One interesting thing to try with the t2i-adapter-sdxl-canny model is to experiment with different combinations of the input parameters, such as the guidance scale, the number of inference steps, and the adapter conditioning scale. This can help you find the optimal settings for your specific use case and achieve more compelling results.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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