ControlNet-Models-For-Core-ML
Maintainer: coreml-community
56
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The ControlNet-Models-For-Core-ML is a collection of ControlNet models converted to the Apple CoreML format by the coreml-community maintainer. ControlNet is a neural network structure that allows controlling pretrained large diffusion models like Stable Diffusion by adding extra conditioning inputs. These CoreML models are specifically designed for use with Swift apps like MOCHI DIFFUSION or the SwiftCLI, and are not compatible with Python-based Diffusers pipelines.
The models in this repository include both "Original" and "Split-Einsum" versions, all built for Stable Diffusion v1.5. They feature various conditioning inputs such as Canny edge detection, Midas depth estimation, HED edge detection, MLSD line detection, surface normal estimation, OpenPose pose detection, scribbles, and semantic segmentation. These conditioning inputs can be used to guide and control the image generation process.
Model inputs and outputs
Inputs
- Conditioning Image: An image that provides additional input information to guide the image generation process, such as edge maps, depth maps, poses, etc.
- Text Prompt: A text description that specifies the desired output image.
Outputs
- Generated Image: The final output image generated by the model, based on the provided text prompt and conditioning image.
Capabilities
The ControlNet-Models-For-Core-ML models excel at generating images that adhere to specific visual constraints or guidelines, such as incorporating detailed edge information, depth cues, or semantic segmentation. This allows for more precise control over the generated imagery, enabling users to create images that closely match their desired visual characteristics.
What can I use it for?
These ControlNet models are well-suited for various creative and artistic applications, such as generating concept art, illustrations, or visualizations that require a high degree of control over the output. Developers of Swift apps focused on image generation or manipulation can leverage these models to offer users more advanced capabilities beyond standard text-to-image generation.
Things to try
Experiment with different conditioning inputs and prompts to see how the models respond. Try using edge maps, depth information, or pose data to guide the generation of specific types of images, such as architectural renderings, character designs, or product visualizations. Additionally, explore the differences between the "Original" and "Split-Einsum" versions to see how they impact the quality and performance of the generated outputs.
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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