sd-controlnet-canny
Maintainer: lllyasviel
147
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| Property | Value |
|---|---|
| Model Link | View 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 sd-controlnet-canny model is a version of the ControlNet neural network structure developed by Lvmin Zhang and Maneesh Agrawala. ControlNet is designed to add extra conditional control to large diffusion models like Stable Diffusion. This particular checkpoint is trained to condition the diffusion model on Canny edge detection.
Similar models include controlnet-canny-sdxl-1.0 which is a ControlNet trained on the Stable Diffusion XL base model, and control_v11p_sd15_openpose which uses OpenPose pose detection as the conditioning input.
Model inputs and outputs
Inputs
- Image: The ControlNet model takes an image as input, which is used to condition the Stable Diffusion text-to-image generation.
Outputs
- Generated image: The output of the pipeline is a generated image that combines the text prompt with the Canny edge conditioning provided by the input image.
Capabilities
The sd-controlnet-canny model can be used to generate images that are guided by the edge information in the input image. This allows for more precise control over the generated output compared to using Stable Diffusion alone. By providing a Canny edge map, you can influence the placement and structure of elements in the final image.
What can I use it for?
The sd-controlnet-canny model can be useful for a variety of applications that require more controlled text-to-image generation, such as product visualization, architectural design, technical illustration, and more. The edge conditioning can help ensure the generated images adhere to specific structural requirements.
Things to try
One interesting aspect of the sd-controlnet-canny model is the ability to experiment with different levels of conditioning strength. By adjusting the controlnet_conditioning_scale parameter, you can find the right balance between the text prompt and the Canny edge input. This allows you to fine-tune the generation process to your specific needs.
Additionally, you can try using the model in combination with other ControlNet checkpoints, such as those trained on depth estimation or segmentation, to layer multiple conditioning inputs and create even more precise and tailored text-to-image generations.
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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The sd-controlnet-seg model is a version of the ControlNet, a neural network structure developed by Lvmin Zhang and Maneesh Agrawala to control pretrained diffusion models like Stable Diffusion by adding extra conditioning inputs. This specific checkpoint is trained on ADE20K's semantic segmentation protocol, allowing the model to generate images based on segmentation maps. Model inputs and outputs Inputs Segmentation map**: An image representing segmented regions, usually color-coded, that provides the conditioning input to guide the image generation. Outputs Generated image**: The output image generated by the model based on the provided segmentation map input. Capabilities The sd-controlnet-seg model can generate images based on semantic segmentation maps, allowing for precise control over the layout and composition of the output. This can be useful for applications like scene generation, image manipulation, or content-aware image editing. What can I use it for? The sd-controlnet-seg model can be used in a variety of applications that require generating images from semantic segmentation maps, such as: Scene generation**: Creating realistic scenes by providing a segmentation map as input and letting the model generate the corresponding image. Image manipulation**: Modifying existing images by altering the segmentation map and generating a new image with the desired changes. Content-aware image editing**: Performing tasks like object removal, image inpainting, or image compositing by leveraging the segmentation-based control provided by the model. Things to try One interesting thing to try with the sd-controlnet-seg model is to experiment with different levels of detail in the segmentation map input. By providing more or less granular segmentation information, you can explore how the model adjusts the generated image accordingly, potentially leading to diverse and unexpected results.
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The sd-controlnet-openpose model is a Controlnet, a neural network structure developed by Lvmin Zhang and Maneesh Agrawala to control pretrained large diffusion models like Stable Diffusion by adding extra conditions. This specific checkpoint is conditioned on human pose estimation using OpenPose. Similar Controlnet models have been developed for other conditioning tasks, such as edge detection (sd-controlnet-canny), depth estimation (control_v11f1p_sd15_depth), and semantic segmentation (lllyasviel/sd-controlnet-seg). These models allow for more fine-grained control over the output of Stable Diffusion. Model inputs and outputs Inputs Image**: An image to be used as the conditioning input for the Controlnet. This image should represent the desired human pose. Outputs Image**: A new image generated by Stable Diffusion, conditioned on the input image and the text prompt. Capabilities The sd-controlnet-openpose model can be used to generate images that incorporate specific human poses and body positions. This can be useful for creating illustrations, concept art, or visualizations that require accurate human figures. By providing the model with an image of a desired pose, the generated output can be tailored to match that pose, allowing for more precise control over the final image. What can I use it for? The sd-controlnet-openpose model can be used for a variety of applications that require the integration of human poses and figures, such as: Character design and illustration for games, films, or comics Concept art for choreography, dance, or other movement-based performances Visualizations of athletic or physical activities Medical or scientific illustrations depicting human anatomy and movement Things to try When using the sd-controlnet-openpose model, you can experiment with different input images and prompts to see how the generated output changes. Try providing images with varied human poses, from dynamic action poses to more static, expressive poses. Additionally, you can adjust the controlnet_conditioning_scale parameter to control the influence of the input image on the final output.
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