This is a simplified guide to an AI model called T2i-Adapter-Sdxl-Depth-Midas 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-depth-midas model is a text-to-image diffusion model that allows users to modify images using depth maps. It is an implementation of the T2I-Adapter-SDXL model, developed by TencentARC and the Diffuser team. This model is part of a series of similar models created by adirik, including t2i-adapter-sdxl-sketch, t2i-adapter-sdxl-lineart, and t2i-adapter-sdxl-openpose, each with their own unique capabilities.
Model inputs and outputs
The t2i-adapter-sdxl-depth-midas model takes several inputs, including an image, a prompt, a scheduler, the number of samples to generate, a random seed, a guidance scale, a negative prompt, the number of inference steps, an adapter conditioning scale, and an adapter conditioning factor. The model then generates an array of output images based on the provided inputs.
Inputs
- Image: The input image to be modified.
- Prompt: The text prompt that describes 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
- Output: An array of generated output images.
Capabilities
The t2i-adapter-sdxl-depth-midas mod...
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