latent-consistency-model
Maintainer: fofr
920
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | No paper link provided |
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Model overview
The latent-consistency-model is a powerful AI model developed by fofr that offers super-fast image generation at 0.6s per image. It combines several key capabilities, including img2img, large batching, and Canny controlnet support. This model can be seen as a refinement and extension of similar models like sdxl-controlnet-lora and instant-id-multicontrolnet, which also leverage ControlNet technology for enhanced image generation.
Model inputs and outputs
The latent-consistency-model accepts a variety of inputs, including a prompt, image, width, height, number of images, guidance scale, and various ControlNet-related parameters. The model's outputs are an array of generated image URLs.
Inputs
- Prompt: The text prompt that describes the desired image
- Image: An input image for img2img
- Width: The width of the output image
- Height: The height of the output image
- Num Images: The number of images to generate per prompt
- Guidance Scale: The scale for classifier-free guidance
- Control Image: An image for ControlNet conditioning
- Prompt Strength: The strength of the prompt when using img2img
- Sizing Strategy: How to resize images, such as by width/height or based on input/control image
- LCM Origin Steps: The number of steps for the LCM origin
- Canny Low Threshold: The low threshold for the Canny edge detector
- Num Inference Steps: The number of denoising steps
- Canny High Threshold: The high threshold for the Canny edge detector
- Control Guidance Start: The start of the ControlNet guidance
- Control Guidance End: The end of the ControlNet guidance
- Controlnet Conditioning Scale: The scale for ControlNet conditioning
Outputs
- An array of URLs for the generated images
Capabilities
The latent-consistency-model is capable of generating high-quality images at a lightning-fast pace, making it an excellent choice for applications that require real-time or batch image generation. Its integration of ControlNet technology allows for enhanced control over the generated images, enabling users to influence the final output using various conditioning parameters.
What can I use it for?
The latent-consistency-model can be used in a variety of applications, such as:
- Rapid prototyping and content creation for designers, artists, and marketing teams
- Generative art projects that require quick turnaround times
- Integration into web applications or mobile apps that need to generate images on the fly
- Exploration of different artistic styles and visual concepts through the use of ControlNet conditioning
Things to try
One interesting aspect of the latent-consistency-model is its ability to generate images with a high degree of consistency, even when using different input parameters. This can be especially useful for creating cohesive visual styles or generating variations on a theme. Experiment with different prompts, image inputs, and ControlNet settings to see how the model responds and explore the possibilities for your specific use case.
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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