Jagilley

Models by this creator

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controlnet-scribble

jagilley

Total Score

37.9K

The controlnet-scribble model is a part of the ControlNet suite of AI models 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. The controlnet-scribble model specifically focuses on generating detailed images from scribbled drawings. This sets it apart from other ControlNet models that use different types of input conditions like normal maps, depth maps, or semantic segmentation. Model inputs and outputs The controlnet-scribble model takes several inputs to generate the output image: Inputs Image**: The input scribbled drawing to be used as the control condition. Prompt**: The text prompt describing the desired image. Seed**: A seed value for the random number generator to ensure reproducibility. Eta**: A hyperparameter that controls the noise scale in the DDIM sampling process. Scale**: The guidance scale, which controls the strength of the text prompt. A Prompt**: An additional prompt that is combined with the main prompt. N Prompt**: A negative prompt that specifies undesired elements to exclude from the generated image. Ddim Steps**: The number of sampling steps to use in the DDIM process. Num Samples**: The number of output images to generate. Image Resolution**: The resolution of the generated images. Outputs An array of generated image URLs, with each image corresponding to the provided inputs. Capabilities The controlnet-scribble model can generate detailed images from simple scribbled drawings, allowing users to create complex images with minimal artistic input. This can be particularly useful for non-artists who want to create visually compelling images. The model is able to faithfully interpret the input scribbles and translate them into photorealistic or stylized images, depending on the provided text prompt. What can I use it for? The controlnet-scribble model can be used for a variety of creative and practical applications. Artists and illustrators can use it to quickly generate concept art or sketches, saving time on the initial ideation process. Hobbyists and casual users can experiment with creating unique images from their own scribbles. Businesses may find it useful for generating product visualizations, architectural renderings, or other visuals to support their operations. Things to try One interesting aspect of the controlnet-scribble model is its ability to interpret abstract or minimalist scribbles and transform them into detailed, photorealistic images. Try experimenting with different levels of complexity in your input scribbles to see how the model handles them. You can also play with the various input parameters, such as the guidance scale and negative prompt, to fine-tune the output to your desired aesthetic.

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Updated 6/12/2024

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controlnet-hough

jagilley

Total Score

9.0K

The controlnet-hough model is a Cog implementation of the ControlNet framework, which allows modifying images using M-LSD line detection. It was created by jagilley, the same developer behind similar ControlNet models like controlnet-scribble, controlnet, controlnet-normal, and controlnet-depth2img. These models all leverage the ControlNet framework to condition Stable Diffusion on various input modalities, allowing for fine-grained control over the generated images. Model inputs and outputs The controlnet-hough model takes in an image and a prompt, and outputs a modified image based on the provided input. The key highlight is the ability to use M-LSD (Modified Line Segment Detector) to identify straight lines in the input image and use that as a conditioning signal for the Stable Diffusion model. Inputs image**: The input image to be modified prompt**: The text prompt describing the desired output image seed**: The random seed to use for generation scale**: The guidance scale to use for generation ddim_steps**: The number of steps to use for the DDIM sampler num_samples**: The number of output samples to generate value_threshold**: The threshold to use for the M-LSD line detection distance_threshold**: The distance threshold to use for the M-LSD line detection a_prompt**: The additional prompt to use for generation n_prompt**: The negative prompt to use for generation detect_resolution**: The resolution to use for the M-LSD line detection Outputs Output image(s)**: The modified image(s) generated by the model based on the input image and prompt. Capabilities The controlnet-hough model can be used to modify images by detecting straight lines in the input image and using that as a conditioning signal for Stable Diffusion. This allows for precise control over the structure and geometry of the generated images, as demonstrated in the examples provided in the README. The model can be used to generate images of rooms, buildings, and other scenes with straight line features. What can I use it for? The controlnet-hough model can be useful for a variety of image generation tasks, such as architectural visualization, technical illustration, and creative art. By leveraging the M-LSD line detection, you can generate images that closely match a desired layout or structure, making it a valuable tool for professional and hobbyist designers, artists, and engineers. The model could be used to create realistic renders of buildings, machines, or other engineered systems, or to generate stylized illustrations with a strong focus on geometric forms. Things to try One interesting aspect of the controlnet-hough model is its ability to preserve the structural integrity of the input image while still allowing for creative expression through the text prompt. This could be particularly useful for tasks like image inpainting or object insertion, where you need to maintain the overall composition and perspective of the scene while modifying or adding new elements. You could try using the model to replace specific objects in an image, or to generate new scenes that seamlessly integrate with an existing background. Another interesting direction to explore would be combining the controlnet-hough model with other ControlNet models, such as controlnet-normal or controlnet-depth2img, to create even more sophisticated and nuanced image generations that incorporate multiple conditioning signals.

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Updated 6/12/2024

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controlnet-canny

jagilley

Total Score

802

The controlnet-canny model is a variation of the ControlNet family of AI models developed by Lvmin Zhang and Maneesh Agrawala. ControlNet is a neural network structure that allows diffusion models like Stable Diffusion to be controlled by adding extra conditions. The controlnet-canny model specifically uses Canny edge detection to modify images. This model can be compared to other ControlNet variants like controlnet-hough, controlnet-scribble, and controlnet-seg, each of which uses a different type of conditional input. Model inputs and outputs The controlnet-canny model takes an input image and a text prompt, and generates a new image that combines the content and structure of the input image with the semantics described in the text prompt. The input image is first processed using Canny edge detection, and this edge map is then used as a conditional input to the diffusion model alongside the text prompt. Inputs Image**: The input image to be modified Prompt**: The text prompt describing the desired output image Low Threshold**: The low threshold for Canny edge detection High Threshold**: The high threshold for Canny edge detection Outputs Image**: The generated output image that combines the input image's structure with the prompt's semantics Capabilities The controlnet-canny model can be used to generate images that preserve the structure of the input image while altering the contents to match a text prompt. For example, it can take a photograph of a building and generate an image of that building in a different style or with different objects present, while maintaining the overall shape and layout of the original. This can be useful for tasks like architectural visualization, product design, and creative concept exploration. What can I use it for? The controlnet-canny model and other ControlNet variants can be used for a variety of creative and practical applications. For example, you could use it to generate concept art for a video game, visualize architectural designs, or explore different stylistic interpretations of a photograph. The ability to preserve the structure of an input image while modifying the contents can be particularly valuable for tasks where maintaining certain spatial or geometric properties is important. Things to try One interesting aspect of the controlnet-canny model is its ability to selectively highlight or emphasize certain edges in the input image based on the Canny edge detection parameters. By adjusting the low and high thresholds, you can experiment with different levels of detail and focus in the generated output. This can be useful for emphasizing or de-emphasizing certain structural elements, depending on your desired artistic or design goals.

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Updated 6/12/2024

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controlnet-depth2img

jagilley

Total Score

596

The controlnet-depth2img model is a powerful AI tool created by jagilley that allows users to modify images using depth maps. This model is part of the ControlNet family, which includes similar models like controlnet-scribble, controlnet-normal, and controlnet. The ControlNet models work by adding extra conditions to text-to-image diffusion models, allowing for more precise control over the generated images. Model inputs and outputs The controlnet-depth2img model takes in several inputs, including an image, a prompt, and various parameters to control the generation process. The output is an array of generated images that match the input prompt while preserving the structure of the input image using depth information. Inputs Image**: The input image to be modified. Prompt**: The text prompt that describes the desired output image. Scale**: The guidance scale, which controls the strength of the text prompt. Ddim Steps**: The number of denoising steps to perform during image generation. Seed**: The random seed used for image generation. A Prompt**: An additional prompt that is combined with the main prompt. N Prompt**: A negative prompt that specifies aspects to exclude from the generated image. Detect Resolution**: The resolution used for depth detection. Outputs Output**: An array of generated images that match the input prompt while preserving the structure of the input image using depth information. Capabilities The controlnet-depth2img model is capable of generating detailed images based on a text prompt while preserving the structure of an input image using depth information. This allows for precise control over the generated images, enabling users to create unique and customized content. What can I use it for? The controlnet-depth2img model can be used for a variety of applications, such as: Generating product visualizations or prototypes based on a text description and an existing product image. Creating realistic 3D scenes by combining text prompts with depth information from reference images. Enhancing existing images by modifying their depth-based structure while preserving their overall composition. Experimenting with different artistic styles and compositions by combining text prompts with depth-based image modifications. Things to try One interesting thing to try with the controlnet-depth2img model is to experiment with different depth detection resolutions. The higher the resolution, the more detailed the depth information that the model can use to preserve the structure of the input image. This can lead to more realistic and visually striking generated images, especially for complex scenes or objects. Another thing to try is to combine the controlnet-depth2img model with other ControlNet models, such as controlnet-normal or controlnet-scribble. By leveraging multiple types of conditional inputs, you can create even more sophisticated and nuanced image generations that blend different visual cues and artistic styles.

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Updated 6/12/2024

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controlnet-hed

jagilley

Total Score

425

The controlnet-hed model is a Stable Diffusion-based AI model that allows you to modify images using HED (Holistically-Nested Edge Detection) maps. It is part of the ControlNet family of models developed by Replicate AI researcher jagilley, which also includes similar models like controlnet-hough, controlnet-scribble, and controlnet-normal. These models allow users to control and guide the image generation process by providing additional contextual information like edge maps, line drawings, or depth maps. Model inputs and outputs The controlnet-hed model takes in a prompt, an input image, and various other parameters like guidance scale, steps, and seed. The input image is used to generate an HED map, which is then used as an additional conditioning input to the Stable Diffusion model to produce the final output image. The output is an array of generated images. Inputs input_image**: The input image to use for generating the HED map. prompt**: The text prompt describing the desired image. num_samples**: The number of output images to generate. image_resolution**: The resolution of the output images. ddim_steps**: The number of diffusion steps to use. scale**: The guidance scale to use. seed**: The random seed to use. a_prompt**: An additional prompt to include. n_prompt**: A negative prompt to exclude. detect_resolution**: The resolution to use for HED detection. Outputs Output**: An array of generated image URLs. Capabilities The controlnet-hed model allows you to use HED maps to guide the image generation process. HED maps capture the boundaries and edges in an image, and by using this information, the model can generate images that maintain the structure and details of the input image while still allowing for creative interpretation based on the text prompt. What can I use it for? You can use the controlnet-hed model to generate detailed, high-quality images that maintain the structure and details of an input image while still allowing for creative interpretation. This could be useful for tasks like image recoloring, stylizing, or artistic creation, where you want to preserve the overall composition and details of an image while still allowing the model to generate new and creative content. Things to try One interesting thing to try with the controlnet-hed model is to experiment with different input images and prompts to see how the model uses the HED map to guide the generation process. For example, you could try using a simple line drawing or sketch as the input image and see how the model interprets and expands on that input to generate a more detailed and creative image.

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Updated 6/12/2024

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controlnet-normal

jagilley

Total Score

329

The controlnet-normal model, created by Lvmin Zhang, is a Stable Diffusion-based AI model that allows users to modify images using normal maps. This model is part of the larger ControlNet project, which explores ways to add conditional control to text-to-image diffusion models. The controlnet-normal model is similar to other ControlNet models, such as controlnet-inpaint-test, controlnet_2-1, controlnet_1-1, controlnet-v1-1-multi, and ultimate-portrait-upscale, all of which explore different ways to leverage ControlNet technology. Model inputs and outputs The controlnet-normal model takes an input image and a prompt, and generates a new image based on the input and the prompt. The model uses normal maps, which capture the orientation of surfaces in an image, to guide the image generation process. Inputs Image**: The input image to be modified. Prompt**: The text prompt that describes the desired output image. Eta**: A parameter that controls the amount of noise introduced during the image generation process. Seed**: A seed value used to initialize the random number generator for image generation. Scale**: The guidance scale, which controls the influence of the prompt on the generated image. A Prompt**: An additional prompt that is combined with the original prompt to guide the image generation. N Prompt**: A negative prompt that specifies elements to be avoided in the generated image. Ddim Steps**: The number of steps used in the DDIM sampling algorithm for image generation. Num Samples**: The number of output images to generate. Bg Threshold**: A threshold value used to determine the background area in the normal map (only applicable when the model type is 'normal'). Image Resolution**: The resolution of the generated image. Detect Resolution**: The resolution used for detection (e.g., depth estimation, normal map computation). Outputs Output Images**: The generated images that match the input prompt and image. Capabilities The controlnet-normal model can be used to modify images by leveraging normal maps. This allows users to guide the image generation process and create unique outputs that align with their desired visual style. The model can be particularly useful for tasks like 3D rendering, product visualization, and artistic creation. What can I use it for? The controlnet-normal model can be used for a variety of creative and practical applications. For example, users could generate product visualizations by providing a normal map of a product and a prompt describing the desired appearance. Artists could also use the model to create unique digital art pieces by combining normal maps with their own creative prompts. Things to try One interesting aspect of the controlnet-normal model is its ability to preserve geometric details in the generated images. By using normal maps as a guiding signal, the model can maintain the shape and structure of objects, even when significant changes are made to the appearance or visual style. Users could experiment with this by providing normal maps of different objects or scenes and observing how the model handles the preservation of geometric features.

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Updated 6/12/2024

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controlnet-pose

jagilley

Total Score

169

The controlnet-pose model is a powerful AI tool that allows you to modify images with humans using pose detection. It is part of the ControlNet family of models, which are designed to add conditional control to text-to-image diffusion models like Stable Diffusion. The controlnet-pose model specifically uses human pose detection to guide the image generation process. Similar ControlNet models include controlnet-hough, which uses M-LSD line detection, controlnet, the original ControlNet model, and controlnet-scribble, which generates detailed images from scribbled drawings. The model was created by jagilley, a developer with Replicate. Model inputs and outputs The controlnet-pose model takes several inputs, including an image, a prompt, and various parameters to control the image generation process. The image is used as a reference for the pose detection, while the prompt describes the desired output image. Inputs Image**: The input image that will be used for pose detection. Prompt**: A text description of the desired output image. Seed**: A numerical value used to initialize the random number generator for reproducibility. Scale**: A parameter that controls the amount of guidance from the text prompt. A Prompt**: Additional text to be appended to the main prompt. N Prompt**: A negative prompt that describes undesirable elements to be avoided in the output. DDIM Steps**: The number of diffusion steps to use during the image generation process. Number of Samples**: The number of output images to generate. Low Threshold**: The lower threshold for Canny edge detection. High Threshold**: The upper threshold for Canny edge detection. Image Resolution**: The resolution of the output image. Detect Resolution**: The resolution at which the pose detection is performed. Outputs Array of generated images**: The model outputs an array of generated images based on the provided inputs. Capabilities The controlnet-pose model can be used to generate images that are guided by the detected poses in the input image. This allows you to modify the output image in a way that preserves the structure and composition of the original, while still allowing the text prompt to influence the final result. For example, you could use the controlnet-pose model to generate an image of a "chef in the kitchen" by providing a photo of someone posing as a chef, along with the prompt "chef in the kitchen". The model would then generate a new image that maintains the overall pose and composition of the original photo, but with the details and appearance changed to match the prompt. What can I use it for? The controlnet-pose model can be used in a variety of creative and practical applications. For example, you could use it to: Generate concept art or illustrations**: By providing a reference pose and a prompt, you can create detailed and visually striking images for use in games, films, or other media. Modify product images**: You could use the model to generate images of products in different poses or settings, without having to stage a new photoshoot. Create virtual avatars or characters**: The pose detection capabilities of the model could be used to generate personalized 3D characters or avatars based on user input. Things to try One interesting thing to try with the controlnet-pose model is to experiment with different levels of pose detail in the input image. While the model can work with relatively simple poses, providing more detailed and nuanced poses may result in more interesting and realistic outputs. You could also try combining the controlnet-pose model with other ControlNet models, such as controlnet-normal or controlnet-hed, to add additional layers of control and refinement to the generated images.

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Updated 6/12/2024

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controlnet-seg

jagilley

Total Score

165

The controlnet-seg model is a Cog implementation of the ControlNet framework, which allows for modifying images using semantic segmentation. The ControlNet framework, developed by Lvmin Zhang and Maneesh Agrawala, adds extra conditional control to text-to-image diffusion models like Stable Diffusion. This enables fine-tuning on small datasets without destroying the original model's capabilities. The controlnet-seg model specifically uses semantic segmentation to guide the image generation process. Similar models include controlnet-hough, which uses M-LSD line detection, controlnet, the base ControlNet model, controlnet-scribble, which uses scribble inputs, controlnet-hed, which uses HED maps, and controlnet-normal, which uses normal maps. Model inputs and outputs The controlnet-seg model takes in an image and a text prompt, and generates a new image that combines the input image with the text prompt using semantic segmentation as a guiding condition. The model's inputs and outputs are as follows: Inputs Image**: The input image to be modified Prompt**: The text prompt describing the desired output image Seed**: The random seed used for image generation Guidance scale**: The strength of the text prompt's influence on the output Negative prompt**: A prompt describing what should not be in the output image Detect resolution**: The resolution used for the semantic segmentation detection DDIM steps**: The number of steps used in the DDIM sampling process Outputs Generated images**: The resulting image(s) that combine the input image with the text prompt, guided by the semantic segmentation Capabilities The controlnet-seg model can be used to modify images by leveraging semantic segmentation as a guiding condition. This allows for more precise control over the generated output, enabling users to preserve the structure and content of the input image while transforming it according to the text prompt. What can I use it for? The controlnet-seg model can be used for a variety of creative and practical applications. For example, you could use it to recolor or stylize an existing image, or to generate detailed images from high-level textual descriptions while maintaining the structure of the input. The model could also be fine-tuned on small datasets to create custom image generation models for specific domains or use cases. Things to try One interesting aspect of the controlnet-seg model is its ability to preserve the structure and details of the input image while transforming it according to the text prompt. This could be particularly useful for tasks like image editing, where you want to modify an existing image in a specific way without losing important visual information. You could also experiment with using different input images and prompts to see how the model's output changes, and explore the limits of its capabilities.

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Updated 6/12/2024

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free-vc

jagilley

Total Score

61

The free-vc model is a tool developed by jagilley that allows you to change the voice of spoken text. It can be used to convert the audio of one person's voice to sound like another person's voice. This can be useful for applications like voice over, dubbing, or text-to-speech. The free-vc model is similar in capabilities to other voice conversion models like VoiceConversionWebUI, incredibly-fast-whisper, voicecraft, and styletts2. Model inputs and outputs The free-vc model takes two inputs: a source audio file containing the words that should be spoken, and a reference audio file containing the voice that the resulting audio should have. The model then outputs a new audio file with the source text spoken in the voice of the reference audio. Inputs Source Audio**: The audio file containing the words that should be spoken Reference Audio**: The audio file containing the voice that the resulting audio should have Outputs Output Audio**: The new audio file with the source text spoken in the voice of the reference audio Capabilities The free-vc model can be used to change the voice of any spoken audio, allowing you to convert one person's voice to sound like another. This can be useful for a variety of applications, such as voice over, dubbing, or text-to-speech. What can I use it for? The free-vc model can be used for a variety of applications, such as: Voice Over**: Convert the voice in a video or audio recording to sound like a different person. Dubbing**: Change the voice in a foreign language film or video to match the local language. Text-to-Speech**: Generate audio of text spoken in a specific voice. Things to try Some ideas for things to try with the free-vc model include: Experiment with different source and reference audio files to see how the resulting audio sounds. Try using the model to create a voice over or dub for a short video or audio clip. See if you can use the model to generate text-to-speech audio in a specific voice.

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Updated 6/12/2024

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controlnet

jagilley

Total Score

57

The controlnet model, created by Replicate user jagilley, is a neural network that allows users to modify images using various control conditions, such as edge detection, depth maps, and semantic segmentation. It builds upon the Stable Diffusion text-to-image model, allowing for more precise control over the generated output. The model is designed to be efficient and friendly for fine-tuning, with the ability to preserve the original model's performance while learning new conditions. controlnet can be used alongside similar models like controlnet-scribble, controlnet-normal, controlnet_2-1, and controlnet-inpaint-test to create a wide range of image manipulation capabilities. Model inputs and outputs The controlnet model takes in an input image and a prompt, and generates a modified image that combines the input image's structure with the desired prompt. The model can use various control conditions, such as edge detection, depth maps, and semantic segmentation, to guide the image generation process. Inputs Image**: The input image to be modified. Prompt**: The text prompt describing the desired output image. Model Type**: The type of control condition to use, such as canny edge detection, MLSD line detection, or semantic segmentation. Num Samples**: The number of output images to generate. Image Resolution**: The resolution of the generated output image. Detector Resolution**: The resolution at which the control condition is detected. Various threshold and parameter settings**: Depending on the selected model type, additional parameters may be available to fine-tune the control condition. Outputs Array of generated images**: The modified images that combine the input image's structure with the desired prompt. Capabilities The controlnet model allows users to precisely control the image generation process by incorporating various control conditions. This can be particularly useful for tasks like image editing, artistic creation, and product visualization. For example, you can use the canny edge detection model to generate images that preserve the structure of the input image, or the depth map model to create images with a specific depth perception. What can I use it for? The controlnet model is a versatile tool that can be used for a variety of applications. Some potential use cases include: Image editing**: Use the model to modify existing images by applying various control conditions, such as edge detection or semantic segmentation. Artistic creation**: Leverage the model's control capabilities to create unique and expressive art, combining the input image's structure with desired prompts. Product visualization**: Use the depth map or normal map models to generate realistic product visualizations, helping designers and marketers showcase their products. Scene generation**: The semantic segmentation model can be used to generate images of complex scenes, such as indoor environments or landscapes, by providing a high-level description. Things to try One interesting aspect of the controlnet model is its ability to preserve the structure of the input image while applying the desired control condition. This can be particularly useful for tasks like image inpainting, where you want to modify part of an image while maintaining the overall composition. Another interesting feature is the model's efficiency and ease of fine-tuning. By using the "zero convolution" technique, the model can be trained on small datasets without disrupting the original Stable Diffusion model's performance. This makes the controlnet model a versatile tool for a wide range of image manipulation tasks.

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Updated 6/12/2024