sd-controlnet-scribble
Maintainer: lllyasviel
50
🏷️
| Property | Value |
|---|---|
| Run this model | Run 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-scribble model is part of the ControlNet family of AI models developed by Lvmin Zhang and Maneesh Agrawala. ControlNet is a neural network structure that can control diffusion models like Stable Diffusion by adding extra conditioning inputs. This specific checkpoint is conditioned on scribble images, which are hand-drawn monochrome images with white outlines on a black background.
Similar ControlNet models include the sd-controlnet-canny model, which is conditioned on canny edge detection, and the sd-controlnet-seg model, which is conditioned on image segmentation. These models offer different ways to guide and control the output of the Stable Diffusion text-to-image generation model.
Model inputs and outputs
Inputs
- Scribble image: A hand-drawn monochrome image with white outlines on a black background.
- Text prompt: A natural language description of the desired image.
Outputs
- Generated image: The text-to-image generation output, guided and controlled by the provided scribble image.
Capabilities
The sd-controlnet-scribble model can generate images based on a text prompt while using the provided scribble image as a guiding condition. This can be useful for tasks like illustrating a concept, creating stylized artwork, or generating images with a specific artistic style.
What can I use it for?
The sd-controlnet-scribble model can be used for a variety of creative applications, such as:
- Generating illustrations or concept art based on a written description
- Creating stylized or abstract images inspired by hand-drawn scribbles
- Complementing text-based storytelling with visuals
- Experimenting with different artistic styles and techniques
Things to try
One interesting aspect of the sd-controlnet-scribble model is its ability to generate images that closely match the style and composition of the input scribble. You can try providing scribbles with different levels of detail, complexity, and abstraction to see how the model responds and how the generated images vary.
Additionally, you can experiment with combining the scribble condition with different text prompts to explore the interplay between the guiding visual input and the language-based instructions. This can lead to unexpected and creative results, expanding the potential use cases for the model.
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-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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The sd-controlnet-mlsd model is part of the ControlNet family of AI models developed by Lvmin Zhang and Maneesh Agrawala. It is a diffusion-based text-to-image generation model that is conditioned on M-LSD (Multi-Level Straight Line Detector) images. This means the model can generate images based on an input image that contains only white straight lines on a black background. Similar ControlNet models are available that condition on other types of images, such as canny edges, HED soft edges, depth maps, and semantic segmentation. These models allow for precise control over the visual attributes of the generated images. Model inputs and outputs Inputs M-LSD image**: A monochrome image composed only of white straight lines on a black background. Outputs Generated image**: The model outputs a new image based on the provided M-LSD input and the text prompt. Capabilities The sd-controlnet-mlsd model can generate images that adhere to the structural and linear constraints defined by the input M-LSD image. For example, if the input image contains lines representing the outline of a room, the generated image will include those same linear structures while filling in the details based on the text prompt. What can I use it for? The sd-controlnet-mlsd model could be useful for applications that require precise control over the geometric and structural elements of generated images, such as architectural design, technical illustration, or conceptual art. By providing an M-LSD input image, you can guide the model to create images that match a specific visual blueprint or layout. Things to try Try experimenting with different types of M-LSD input images, such as those representing machinery, cityscapes, or abstract patterns. Observe how the generated images reflect the linear structures and shapes defined by the input, while the details are determined by the text prompt. This can lead to interesting and unexpected results that combine your creative vision with the model's capabilities.
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