control-lora

Maintainer: stabilityai

Total Score

794

Last updated 5/17/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The control-lora model, developed by Stability AI, is a set of low-rank adaptation (LoRA) models that can be used to add model control to a variety of consumer GPUs in a more efficient and compact way. These Control-LoRA models were created by adding low-rank parameter efficient fine tuning to ControlNet, a popular image control model. The Control-LoRA models come in two variants - Rank 256 and Rank 128 - which significantly reduce the original 4.7GB ControlNet models down to around 738MB and 377MB, respectively. These Control-LoRA models have been trained on a diverse range of image concepts and aspect ratios, making them versatile for various image generation tasks.

The Control-LoRA models include several specialized variants, such as the MiDaS and ClipDrop Depth, Canny Edge, Photograph and Sketch Colorizer, and Revision models. These variants leverage different image processing techniques like depth estimation, edge detection, and CLIP embeddings to guide the image generation process.

Similar models include the sdxl-controlnet-lora, lcm-lora-sdxl, sdxl-controlnet, sdxl-controlnet-depth, and the stable-diffusion-xl-refiner-1.0 models, all of which explore different approaches to incorporating control and refinement into Stable Diffusion models.

Model inputs and outputs

Inputs

  • Image: The Control-LoRA models accept various types of input images, such as depth maps, edge maps, and sketches, to guide the image generation process.
  • Text prompt: The models can be conditioned on text prompts to generate images that match the specified concepts.

Outputs

  • Generated image: The primary output of the Control-LoRA models is a generated image that reflects the input image and text prompt.

Capabilities

The Control-LoRA models excel at generating images with specific visual characteristics controlled by the input image. For example, the Depth-based variant can generate images guided by a grayscale depth map, highlighting variations in proximity. The Canny Edge variant uses the edges from an image to generate the final output. The Colorizer variants can colorize both black and white photographs and sketches. The Revision model uses CLIP embeddings to produce images conceptually similar to the input, allowing for blending of multiple image and text prompts.

What can I use it for?

The Control-LoRA models can be particularly useful for applications that require fine-grained control over the image generation process, such as design, creative tools, and research on generative models. The compact model size and efficient inference also make these models suitable for deployment on a wider range of consumer GPUs, expanding the accessibility of advanced image generation capabilities.

Things to try

One interesting aspect of the Control-LoRA models is their ability to be combined with other LoRA adapters, such as the Papercut LoRA, to generate styled images in just a few inference steps. This opens up possibilities for exploring the synergies between different control mechanisms and stylization techniques in a computationally efficient way.

Additionally, the Control-LoRA models can be used in conjunction with ControlNet and other image-to-image techniques, as demonstrated in the examples provided. Experimenting with different input images, prompts, and inference parameters can lead to a wide range of creative and novel 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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