This is a simplified guide to an AI model called T2i-Adapter-Sdxl-Openpose maintained by Adirik. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
Model overview
The t2i-adapter-sdxl-openpose model is a text-to-image generation model that allows users to modify images using human pose. It is an implementation of the T2I-Adapter-SDXL model, developed by TencentARC and the diffuser team. The model is available through Replicate and can be accessed using the Cog interface.
Similar models created by the same maintainer, adirik, include the t2i-adapter-sdxl-sketch model for modifying images using sketches, and the t2i-adapter-sdxl-lineart model for modifying images using line art. The maintainer has also created the t2i-adapter-sdxl-sketch model with a different creator, alaradirik, as well as the t2i-adapter-sdxl-depth-midas model for modifying images using depth maps.
Model inputs and outputs
The t2i-adapter-sdxl-openpose model takes in an input image, a prompt, and various optional parameters such as the number of samples, guidance scale, and number of inference steps. The output is an array of generated images based on the input prompt and the modifications made using the human pose.
Inputs
- Image: The input image to be modified.
- Prompt: The text prompt describing the desired output.
- 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 guidance scale to match the prompt.
- Negative Prompt: Specifies 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 images based on the input prompt and human pose modifications.
Capabilities
The t2i-adapter-sdxl-openpose model ...
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