ControlNet
Maintainer: lllyasviel
3.5K
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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
ControlNet is a neural network structure developed by Lvmin Zhang and Maneesh Agrawala to control diffusion models by adding extra conditions. It allows large diffusion models like Stable Diffusion to be augmented with various types of conditional inputs like edge maps, segmentation maps, keypoints, and more. This can enrich the methods to control large diffusion models and facilitate related applications.
The maintainer, lllyasviel, has released 14 different ControlNet checkpoints, each trained on Stable Diffusion v1-5 with a different type of conditioning. These include models for canny edge detection, depth estimation, line art generation, pose estimation, and more. The checkpoints allow users to guide the generation process with these auxiliary inputs, resulting in images that adhere to the specified conditions.
Model inputs and outputs
Inputs
- Conditioning image: An image that provides additional guidance to the model, such as edges, depth, segmentation, poses, etc. The type of conditioning image depends on the specific
ControlNetcheckpoint being used.
Outputs
- Generated image: The image generated by the diffusion model, guided by the provided conditioning image.
Capabilities
ControlNet enables fine-grained control over the output of large diffusion models like Stable Diffusion. By incorporating specific visual conditions, users can generate images that adhere to the desired constraints, such as having a particular edge structure, depth map, or pose arrangement. This can be useful for a variety of applications, from product design to creative art generation.
What can I use it for?
The ControlNet models can be used in a wide range of applications that require precise control over the generated imagery. Some potential use cases include:
- Product design: Generating product renderings based on 3D models or sketches
- Architectural visualization: Creating photorealistic architectural scenes from floor plans or massing models
- Creative art generation: Producing unique artworks by combining diffusion with specific visual elements
- Illustration and comics: Generating illustrations or comic panels with desired line art, poses, or color palettes
- Educational tools: Creating custom training datasets or visualization aids for computer vision tasks
Things to try
One interesting aspect of ControlNet is the ability to combine multiple conditioning inputs to guide the generation process. For example, you could use a depth map and a segmentation map together to create a more detailed and coherent output. Additionally, experimenting with the conditioning scales and the balance between the text prompt and the visual input can lead to unique and unexpected results.
Another area to explore is the potential of ControlNet to enable interactive, iterative image generation. By allowing users to gradually refine the conditioning images, the model can be guided towards a desired output in an incremental fashion, similar to how artists work.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
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ControlNet-v1-1
3.3K
ControlNet-v1-1 is a powerful AI model developed by Lvmin Zhang that enables conditional control over text-to-image diffusion models like Stable Diffusion. This model builds upon the original ControlNet by adding new capabilities and improving existing ones. The key innovation of ControlNet is its ability to accept additional input conditions beyond just text prompts, such as edge maps, depth maps, segmentation, and more. This allows users to guide the image generation process in very specific ways, unlocking a wide range of creative possibilities. For example, the control_v11p_sd15_canny model is trained to generate images conditioned on canny edge detection, while the control_v11p_sd15_openpose model is trained on human pose estimation. Model inputs and outputs Inputs Condition Image**: An auxiliary image that provides additional guidance for the text-to-image generation process. This could be an edge map, depth map, segmentation, or other type of conditioning image. Text Prompt**: A natural language description of the desired output image. Outputs Generated Image**: The final output image generated by the model based on the text prompt and condition image. Capabilities ControlNet-v1-1 is highly versatile, allowing users to leverage a wide range of conditioning inputs to guide the image generation process. This enables fine-grained control over the output, enabling everything from realistic scene generation to stylized and abstract art. The model has also been trained on a diverse dataset, allowing it to handle a broad range of subject matter and styles. What can I use it for? ControlNet-v1-1 opens up many creative possibilities for users. Artists and designers can use it to generate custom illustrations, concept art, and product visualizations by providing targeted conditioning inputs. Developers can integrate it into applications that require image generation, such as virtual world builders, game assets, and interactive experiences. Researchers may also find it useful for exploring new frontiers in conditional image synthesis. Things to try One interesting thing to try with ControlNet-v1-1 is experimenting with different types of conditioning inputs. For example, you could start with a simple line drawing and see how the model generates a detailed, realistic image. Or you could try providing a depth map or surface normal map to guide the model towards generating a 3D-like scene. The possibilities are endless, and the model's flexibility allows for a wide range of creative exploration.
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control_v11p_sd15_openpose
72
The control_v11p_sd15_openpose model is a version of the ControlNet model developed by Lvmin Zhang and Maneesh Agrawala. ControlNet is a neural network structure that allows for adding extra conditions to control diffusion models like Stable Diffusion. This specific checkpoint is conditioned on openpose images, which can be used to generate images by providing the model with an openpose image as input. The ControlNet v1.1 model is the successor to the original ControlNet v1.0 model, and this checkpoint is a conversion of the original checkpoint into the diffusers format. It can be used in combination with Stable Diffusion models like runwayml/stable-diffusion-v1-5. Model inputs and outputs Inputs Control image**: An openpose image that provides the model with a structure to guide the image generation. Initial image**: An optional starting image that the model can use as a reference. Text prompt**: A text description that the model uses to generate the final image. Outputs Generated image**: The final output image generated by the model based on the provided inputs. Capabilities The control_v11p_sd15_openpose model can generate images by using an openpose image as a structural guide. This allows for creating images that follow a specific pose or layout, while still generating the visual details based on the text prompt. The model is capable of producing high-quality, photorealistic images when used in combination with Stable Diffusion. What can I use it for? The control_v11p_sd15_openpose model can be useful for a variety of applications, such as: Generating images of people in specific poses or positions, like dance moves, martial arts techniques, or sports actions. Creating illustrations or concept art that follows a predetermined layout or composition. Enhancing the realism and coherence of images generated from text prompts by providing a structural guide. Things to try One interesting thing to try with the control_v11p_sd15_openpose model is experimenting with the balance between the guidance from the openpose image and the text prompt. By adjusting the controlnet_conditioning_scale parameter, you can control how much influence the openpose image has on the final output. Lower values will result in images that are more closely aligned with the text prompt, while higher values will prioritize the structural guidance from the openpose image. Additionally, you can try using different initial images as a starting point and see how the model combines the openpose structure, text prompt, and initial image to generate the final output.
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sd-controlnet-canny
147
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.
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sd-controlnet-openpose
110
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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