instant-id-multicontrolnet

Maintainer: tgohblio

128

Last updated 7/1/2024

Property	Value
Model Link	View on Replicate
API Spec	View on Replicate
Github Link	View on Github
Paper Link	View on Arxiv

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Model overview

The instant-id-multicontrolnet model is an extension of the popular InstantID model, developed by the Replicate creator tgohblio. This model leverages the power of ControlNets to provide advanced image generation capabilities, allowing users to create realistic images of people with customizable features and settings.

The model builds upon the foundation of the InstantID model, which is known for its ability to generate highly realistic images of real people. The instant-id-multicontrolnet model adds additional capabilities, such as the ability to control various aspects of the generated image through the use of ControlNets. This includes features like pose, canny edges, depth maps, and more.

Model inputs and outputs

The instant-id-multicontrolnet model accepts a variety of inputs, including an image of a face, a reference pose image, and a text prompt. The model then generates a new image based on these inputs, adhering to the specified parameters and settings.

Inputs

face_image_path: The path to an input image of a face
pose_image_path: The path to a reference pose image
prompt: The text prompt describing the desired image
negative_prompt: The text prompt describing the aspects to be avoided in the generated image
model: The SDXL image model to be used
enable_fast_mode: A toggle to enable or disable SDXL-Lightning fast inference
lightning_steps: The number of denoising steps to use for SDXL-Lightning
scheduler: The scheduler algorithm to be used
width: The width of the output image
height: The height of the output image
adapter_strength_ratio: The scale for the IP adapter
identitynet_strength_ratio: The scale for the ControlNet conditioning
pose: A toggle to enable or disable the use of the ControlNet pose model
pose_strength: The scale for pose conditioning
canny: A toggle to enable or disable the use of the ControlNet canny edge model
canny_strength: The scale for canny edge conditioning
depth_map: A toggle to enable or disable the use of the ControlNet depth model
depth_strength: The scale for depth map conditioning
num_steps: The number of denoising steps
guidance_scale: The scale for classifier-free guidance
seed: The RNG seed number
safety_checker: A toggle to enable or disable the NSFW filter

Outputs

The generated image, represented as a URI.

Capabilities

The instant-id-multicontrolnet model is capable of generating highly realistic images of people, with the added ability to control various aspects of the image through the use of ControlNets. This allows users to create images that closely match their desired specifications, such as a specific pose, facial features, or environmental context.

What can I use it for?

The instant-id-multicontrolnet model can be used for a variety of applications, such as:

Content creation: Generating realistic images of people for use in various media, such as social media, advertising, or film/TV productions.
Character design: Creating custom character designs for use in video games, animations, or other creative projects.
Virtual photography: Capturing unique and compelling images of virtual subjects for artistic or commercial purposes.
Personalization: Generating personalized images based on user preferences and inputs, such as profile pictures or avatars.

Things to try

One interesting aspect of the instant-id-multicontrolnet model is its ability to blend multiple ControlNet modalities, such as pose, canny edges, and depth maps, to create more complex and nuanced images. By experimenting with different combinations of these inputs, users can discover unique and unexpected visual outcomes.

Another interesting feature is the model's "fast mode" option, which enables SDXL-Lightning for faster inference times. This can be useful for rapid prototyping or real-time applications, although it may come at the cost of some image quality. Comparing the results of the fast mode to the standard mode can provide insights into the trade-offs between speed and fidelity.

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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