stable-diffusion-inpainting

Maintainer: stability-ai

Total Score

17.1K

Last updated 6/19/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

stable-diffusion-inpainting is a model created by Stability AI that can fill in masked parts of images using the Stable Diffusion text-to-image model. It is built on top of the Diffusers Stable Diffusion v2 model and can be used to edit and manipulate images in a variety of ways. This model is similar to other inpainting models like GFPGAN, which focuses on face restoration, and Real-ESRGAN, which can enhance the resolution of images.

Model inputs and outputs

The stable-diffusion-inpainting model takes in an initial image, a mask indicating which parts of the image to inpaint, and a prompt describing the desired output. It then generates a new image with the masked areas filled in based on the given prompt. The model can produce multiple output images based on a single input.

Inputs

  • Prompt: A text description of the desired output image.
  • Image: The initial image to be inpainted.
  • Mask: A black and white image used to indicate which parts of the input image should be inpainted.
  • Seed: An optional random seed to control the generated output.
  • Scheduler: The scheduling algorithm to use during the diffusion process.
  • Guidance Scale: A value controlling the trade-off between following the prompt and staying close to the original image.
  • Negative Prompt: A text description of things to avoid in the generated image.
  • Num Inference Steps: The number of denoising steps to perform during the diffusion process.
  • Disable Safety Checker: An option to disable the safety checker, which can be useful for certain applications.

Outputs

  • Image(s): One or more new images with the masked areas filled in based on the provided prompt.

Capabilities

The stable-diffusion-inpainting model can be used to edit and manipulate images in a variety of ways. For example, you could use it to remove unwanted objects or people from a photograph, or to fill in missing parts of an image. The model can also be used to generate entirely new images based on a text prompt, similar to other text-to-image models like Kandinsky 2.2.

What can I use it for?

The stable-diffusion-inpainting model can be useful for a variety of applications, such as:

  • Photo editing: Removing unwanted elements, fixing blemishes, or enhancing photos.
  • Creative projects: Generating new images based on text prompts or combining elements from different images.
  • Content generation: Producing visuals for articles, social media posts, or other digital content.
  • Prototype creation: Quickly mocking up designs or visualizing concepts.

Companies could potentially monetize this model by offering image editing and manipulation services, or by incorporating it into creative tools or content generation platforms.

Things to try

One interesting thing to try with the stable-diffusion-inpainting model is to use it to remove or replace specific elements in an image, such as a person or object. You could then generate a new image that fills in the masked area based on the prompt, creating a seamless edit. Another idea is to use the model to combine elements from different images, such as placing a castle in a forest scene or adding a dragon to a cityscape.



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

AI model preview image

stable-diffusion

stability-ai

Total Score

108.1K

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Developed by Stability AI, it is an impressive AI model that can create stunning visuals from simple text prompts. The model has several versions, with each newer version being trained for longer and producing higher-quality images than the previous ones. The main advantage of Stable Diffusion is its ability to generate highly detailed and realistic images from a wide range of textual descriptions. This makes it a powerful tool for creative applications, allowing users to visualize their ideas and concepts in a photorealistic way. The model has been trained on a large and diverse dataset, enabling it to handle a broad spectrum of subjects and styles. Model inputs and outputs Inputs Prompt**: The text prompt that describes the desired image. This can be a simple description or a more detailed, creative prompt. Seed**: An optional random seed value to control the randomness of the image generation process. Width and Height**: The desired dimensions of the generated image, which must be multiples of 64. Scheduler**: The algorithm used to generate the image, with options like DPMSolverMultistep. Num Outputs**: The number of images to generate (up to 4). Guidance Scale**: The scale for classifier-free guidance, which controls the trade-off between image quality and faithfulness to the input prompt. Negative Prompt**: Text that specifies things the model should avoid including in the generated image. Num Inference Steps**: The number of denoising steps to perform during the image generation process. Outputs Array of image URLs**: The generated images are returned as an array of URLs pointing to the created images. Capabilities Stable Diffusion is capable of generating a wide variety of photorealistic images from text prompts. It can create images of people, animals, landscapes, architecture, and more, with a high level of detail and accuracy. The model is particularly skilled at rendering complex scenes and capturing the essence of the input prompt. One of the key strengths of Stable Diffusion is its ability to handle diverse prompts, from simple descriptions to more creative and imaginative ideas. The model can generate images of fantastical creatures, surreal landscapes, and even abstract concepts with impressive results. What can I use it for? Stable Diffusion can be used for a variety of creative applications, such as: Visualizing ideas and concepts for art, design, or storytelling Generating images for use in marketing, advertising, or social media Aiding in the development of games, movies, or other visual media Exploring and experimenting with new ideas and artistic styles The model's versatility and high-quality output make it a valuable tool for anyone looking to bring their ideas to life through visual art. By combining the power of AI with human creativity, Stable Diffusion opens up new possibilities for visual expression and innovation. Things to try One interesting aspect of Stable Diffusion is its ability to generate images with a high level of detail and realism. Users can experiment with prompts that combine specific elements, such as "a steam-powered robot exploring a lush, alien jungle," to see how the model handles complex and imaginative scenes. Additionally, the model's support for different image sizes and resolutions allows users to explore the limits of its capabilities. By generating images at various scales, users can see how the model handles the level of detail and complexity required for different use cases, such as high-resolution artwork or smaller social media graphics. Overall, Stable Diffusion is a powerful and versatile AI model that offers endless possibilities for creative expression and exploration. By experimenting with different prompts, settings, and output formats, users can unlock the full potential of this cutting-edge text-to-image technology.

Read more

Updated Invalid Date

AI model preview image

stable-diffusion-img2img

stability-ai

Total Score

934

The stable-diffusion-img2img model, developed by Stability AI, is an AI model that can generate new images by using an existing input image as a starting point. This model builds upon the capabilities of the Stable Diffusion model, which is a powerful text-to-image generation system. The stable-diffusion-img2img model introduces the ability to use an existing image as a starting point, allowing for the creation of image variations and transformations. Model inputs and outputs The stable-diffusion-img2img model takes several inputs, including a prompting text, an initial image, and various settings that control the output generation process. The model then generates one or more new images that reflect the input prompt and build upon the provided image. Inputs Prompt**: A text description that guides the image generation process. Image**: An initial image that the model will use as a starting point. Seed**: A random seed value that can be used to control the randomness of the output. Scheduler**: The algorithm used to control the image generation process. Guidance Scale**: A value that controls the influence of the input prompt on the output image. Negative Prompt**: A text description that specifies what the model should avoid generating. Prompt Strength**: A value that controls the balance between the input image and the input prompt. Number of Inference Steps**: The number of steps the model takes to generate the output image. Outputs Generated Images**: One or more new images that reflect the input prompt and build upon the provided image. Capabilities The stable-diffusion-img2img model can be used to generate a wide variety of image variations and transformations. By starting with an existing image, the model can create new versions of the image that incorporate different elements, styles, or visual themes. This can be useful for tasks like image editing, photo manipulation, and creative exploration. What can I use it for? The stable-diffusion-img2img model can be useful for a variety of creative and practical applications. For example, you could use it to generate variations of product images for e-commerce, create unique artwork for your personal or professional projects, or explore new visual ideas and concepts. The model's ability to work with existing images also makes it a useful tool for tasks like image inpainting, where you can fill in missing or damaged parts of an image. Things to try One interesting aspect of the stable-diffusion-img2img model is its ability to preserve the overall structure and depth information of the input image while generating new variations. This can be particularly useful for applications that require maintaining the spatial relationships and 3D characteristics of the original image, such as product visualization or architectural design. You could experiment with using different input images and prompts to see how the model handles various types of visual information and produces new, compelling results.

Read more

Updated Invalid Date

AI model preview image

stable-diffusion-inpainting

andreasjansson

Total Score

1.5K

stable-diffusion-inpainting is a Cog model that implements the Stable Diffusion Inpainting checkpoint. It is developed by andreasjansson and based on the Stable Diffusion model, which is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The inpainting model has the additional capability of filling in masked parts of images. Similar models include stable-diffusion-wip, another inpainting model from the same developer, stable-diffusion-inpainting from Stability AI, the original stable-diffusion model, and stable-diffusion-v2-inpainting from a different developer. Model inputs and outputs The stable-diffusion-inpainting model takes several inputs to guide the image generation process: Inputs Prompt**: The text prompt to describe the desired image. Image**: The input image to be inpainted. Mask**: A black and white image used as a mask, where white pixels indicate the areas to be inpainted. Invert Mask**: An option to invert the mask, so black pixels are inpainted and white pixels are preserved. Num Outputs**: The number of images to generate (up to 4). Guidance Scale**: The scale used for classifier-free guidance, which controls the trade-off between sample quality and sample diversity. Negative Prompt**: Text prompts to guide the model away from certain content. Num Inference Steps**: The number of denoising steps performed during image generation. Outputs Output Images**: The generated images, which are the result of inpainting the input image based on the provided prompt and mask. Capabilities The stable-diffusion-inpainting model can be used to fill in masked or corrupted parts of images based on a text prompt. This can be useful for tasks like image editing, object removal, and content-aware image manipulation. The model is able to generate photo-realistic images while preserving the overall structure and context of the original image. What can I use it for? The stable-diffusion-inpainting model is intended for research purposes, such as understanding the limitations and biases of generative models, generating artworks and designs, and developing educational or creative tools. It should not be used to intentionally create or disseminate images that are harmful, offensive, or propagate stereotypes. Things to try One interesting thing to try with the stable-diffusion-inpainting model is to use it to remove unwanted objects or people from an image, and then have the model generate new content to fill in the resulting empty space. This can be a powerful tool for image editing and content-aware manipulation. You can also experiment with different prompts and mask configurations to see how the model responds and generates new content.

Read more

Updated Invalid Date

AI model preview image

stable-diffusion-wip

andreasjansson

Total Score

13

stable-diffusion-wip is an experimental inpainting model based on the popular Stable Diffusion AI. This model allows you to take an existing image and fill in masked regions with new content generated by the model. It is developed by andreasjansson, who has also created other Stable Diffusion-based models like stable-diffusion-animation. Unlike the production-ready stable-diffusion-inpainting model, this is a work-in-progress version with experimental features. Model inputs and outputs stable-diffusion-wip takes in a variety of inputs to control the inpainting process, including an initial image, a mask image, a text prompt, and various parameters to adjust the output. The model then generates one or more new images based on the provided inputs. Inputs Prompt**: The text prompt that describes the content you want the model to generate. Init Image**: The initial image that you want the model to generate variations of. Mask**: A black and white image used to define the regions of the init image that should be inpainted. Seed**: A random seed value to control the stochastic output of the model. Width/Height**: The desired dimensions of the output image. Num Outputs**: The number of images to generate. Guidance Scale**: A parameter that controls the strength of the text prompt in the generation process. Prompt Strength**: A parameter that controls how much the init image should be preserved in the output. Num Inference Steps**: The number of denoising steps to use during the generation process. Outputs Output Images**: One or more images generated by the model based on the provided inputs. Capabilities stable-diffusion-wip is capable of generating photorealistic images based on a text prompt, while using an existing image as a starting point. The model can fill in masked regions of the image with new content that matches the overall style and composition. This can be useful for tasks like object removal, image editing, and creative visual generation. What can I use it for? With stable-diffusion-wip, you can experiment with inpainting and image editing tasks. For example, you could use it to remove unwanted objects from a photograph, fill in missing parts of an image, or generate new variations of an existing artwork. The model's capabilities can be particularly useful for creative professionals, such as digital artists, designers, and photographers, who are looking to enhance and manipulate their visual content. Things to try One interesting thing to try with stable-diffusion-wip is to experiment with the prompt strength parameter. By adjusting this value, you can control the balance between preserving the original image and generating new content. Lower prompt strength values will result in output that is closer to the init image, while higher values will lead to more dramatic changes. This can be a useful technique for gradually transitioning an image towards a desired style or composition.

Read more

Updated Invalid Date