stable-diffusion-upscaler

Maintainer: jagilley

Total Score

3

Last updated 6/9/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

stable-diffusion-upscaler is an AI model developed by Replicate creator jagilley that can upscale images using the Stable Diffusion model. This model builds upon the capabilities of the Stable Diffusion model, which can generate photo-realistic images from text prompts. The stable-diffusion-upscaler model can take an existing image and intelligently upscale it, increasing the resolution and detail while preserving the original content.

Model inputs and outputs

The stable-diffusion-upscaler model takes a variety of inputs that allow users to customize the upscaling process. These include the image to be upscaled, a scaling factor, the number of sampling steps, and optional prompts to guide the upscaling. The model then outputs an upscaled version of the input image.

Inputs

  • image: The image to be upscaled
  • scale: The factor by which to scale the image
  • steps: The number of steps to take in the diffusion process
  • prompt: An optional text prompt to guide the upscaling
  • decoder: The decoder model to use
  • sampler: The sampling algorithm to use
  • tol_scale: The tolerance scale for the upscaling
  • batch_size: The batch size for processing
  • num_samples: The number of samples to generate
  • guidance_scale: The scale factor for guidance
  • noise_aug_type: The type of noise augmentation to apply
  • noise_aug_level: The level of noise augmentation

Outputs

  • Output: The upscaled version of the input image

Capabilities

The stable-diffusion-upscaler model can take existing images and intelligently upscale them, increasing the resolution and detail while preserving the original content. This can be useful for a variety of applications, such as enhancing low-quality images, generating high-resolution versions of artwork or illustrations, or improving the visual quality of images for use in presentations, websites, or other media.

What can I use it for?

The stable-diffusion-upscaler model can be used in a variety of creative and practical applications. For example, you could use it to upscale and enhance low-resolution images, create high-quality versions of digital artwork or illustrations, or improve the visual quality of images for use in presentations, websites, or other media. Additionally, the model's ability to intelligently upscale images while preserving the original content could be useful in fields such as photography, video production, or digital design.

Things to try

One interesting aspect of the stable-diffusion-upscaler model is its ability to use text prompts to guide the upscaling process. By providing a relevant prompt, you can subtly influence the way the model upscales the image, potentially creating more visually appealing or relevant results. For example, you could try upscaling a landscape image with a prompt like "a lush, detailed forest scene" to see how the model incorporates that guidance into the upscaled output.

Another interesting aspect of the model is its use of different decoders and samplers. By experimenting with these settings, you can potentially achieve different visual styles or levels of detail in the upscaled images. For example, you could try using the "finetuned_840k" decoder and the "k_dpm_adaptive" sampler to see how that combination affects the upscaling results.



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_diffusion2_upscaling

arielreplicate

Total Score

7

The stable_diffusion2_upscaling model is an image super-resolution model based on the Stable Diffusion 2 architecture. It can be used to upscale low-resolution images by a factor of 4, preserving important details and producing high-quality, photorealistic results. This model is similar to other Stable Diffusion-based models like Stable Diffusion, Stable Diffusion Upscaler, and Stable Diffusion x4 Upscaler, but is specifically focused on the high-resolution upscaling task. Model inputs and outputs The stable_diffusion2_upscaling model takes a low-resolution image as input and outputs a high-resolution version of the same image, upscaled by a factor of 4. The model is designed to preserve important details and maintain a photorealistic appearance in the upscaled output. Inputs input_image**: The low-resolution image to be upscaled, provided as a URI. ddim_steps**: The number of denoising steps to use during the upscaling process, with a default of 50 and a range of 2 to 250. ddim_eta**: The upscale factor, with a default of 0 and a range of 0 to 1. seed**: An integer seed value to control the randomness of the upscaling process. Outputs Output**: An array of one or more high-resolution images, represented as URIs. Capabilities The stable_diffusion2_upscaling model can take low-resolution images and significantly increase their resolution while preserving important details and maintaining a photorealistic appearance. This can be useful for tasks such as enhancing product images, upscaling old photographs, or creating high-quality visualizations from low-res sources. What can I use it for? The stable_diffusion2_upscaling model can be used in a variety of applications that require high-resolution images, such as: E-commerce**: Upscaling product images to improve the visual appeal and detail for customers. Photography**: Enhancing old or low-quality photographs to create high-quality prints and digital assets. Graphic design**: Generating high-resolution images for use in designs, presentations, or marketing materials. Video production**: Upscaling low-res footage or animation frames to improve visual quality. Things to try Some interesting things to try with the stable_diffusion2_upscaling model include: Experimenting with different ddim_steps and ddim_eta values to find the optimal balance between speed and quality. Applying the model to a variety of image types, from natural scenes to abstract art, to see how it handles different visual styles. Combining the upscaling model with other Stable Diffusion models, such as the Stable Diffusion Inpainting or Stable Diffusion Img2Img models, to create even more powerful image generation and manipulation workflows.

Read more

Updated Invalid Date

AI model preview image

stable-diffusion-x4-upscaler

lucataco

Total Score

5

The stable-diffusion-x4-upscaler is an AI model developed by Stability AI and maintained by lucataco. It is an implementation of the Stable Diffusion x4 upscaler model, which can be used to enhance the resolution of images. This model is similar to other Stable Diffusion-based models like stable-diffusion-inpainting, dreamshaper-xl-lightning, and pasd-magnify in its use of the Stable Diffusion framework. Model inputs and outputs The stable-diffusion-x4-upscaler model takes in a grayscale input image and a text prompt, and outputs an upscaled image. The input image can be scaled by a factor of up to 4, and the text prompt can be used to guide the upscaling process. Inputs Image**: A grayscale input image Scale**: The factor to scale the image by, with a default of 4 Prompt**: A text prompt to guide the upscaling process, with a default of "A white cat" Outputs Output**: The upscaled image Capabilities The stable-diffusion-x4-upscaler model can be used to enhance the resolution of images while preserving the content and style of the original image. It can be particularly useful for tasks like enlarging low-resolution images or generating high-quality images from sketches or low-quality source material. What can I use it for? The stable-diffusion-x4-upscaler model can be used for a variety of image-related tasks, such as creating high-quality images for marketing materials, enhancing the resolution of family photos, or generating concept art for games and animations. The model's ability to preserve the content and style of the original image makes it a versatile tool for creative projects. Additionally, the model's maintainer, lucataco, has developed other Stable Diffusion-based models like dreamshaper-xl-lightning and pasd-magnify that may be of interest for similar use cases. Things to try One interesting aspect of the stable-diffusion-x4-upscaler model is its ability to generate high-quality images from low-resolution input. This can be particularly useful for tasks like restoring old photographs or creating high-quality images from sketches or low-quality source material. Additionally, experimenting with different text prompts can result in unique and creative upscaled images, allowing users to explore the model's capabilities in generating content-aware image enhancements.

Read more

Updated Invalid Date

AI model preview image

stable-diffusion-depth2img

jagilley

Total Score

53

The stable-diffusion-depth2img model, created by maintainer jagilley, allows users to generate variations of an image while preserving its shape and depth. This model can be particularly useful for tasks such as image editing, creative content generation, and scene manipulation. It builds upon the capabilities of the well-known stable-diffusion model, which is a powerful latent text-to-image diffusion model. Model inputs and outputs The stable-diffusion-depth2img model takes a variety of inputs, including a prompt, input image, depth image, and various configuration parameters such as the number of outputs, guidance scale, and number of inference steps. These inputs allow users to customize the image generation process and achieve the desired results. Inputs Prompt**: The text prompt that guides the image generation process. Input Image**: The starting image that will be used as the basis for the variations. Depth Image**: An optional depth map that specifies the depth of each pixel in the input image. Number of Outputs**: The number of images to generate. Guidance Scale**: The scale for classifier-free guidance, which controls the balance between image quality and adherence to the text prompt. Negative Prompt**: Keywords to exclude from the resulting image. Prompt Strength**: The strength of the text prompt when providing the input image. Number of Inference Steps**: The number of denoising steps to perform, which affects the quality of the generated images. Outputs Generated Images**: The model outputs an array of image URLs, representing the variations of the input image. Capabilities The stable-diffusion-depth2img model can be used to create unique and visually appealing image variations that maintain the shape and depth of the original input. This can be particularly useful for tasks such as scene manipulation, character design, and abstract art generation. The model's ability to leverage depth information sets it apart from the standard stable-diffusion model, allowing for more nuanced and realistic image variations. What can I use it for? The stable-diffusion-depth2img model can be utilized in a variety of creative and practical applications. For example, you could use it to generate a series of fantasy landscape images with subtle variations, or to create a collection of stylized character portraits with unique depth and lighting effects. Additionally, the model could be employed in the creation of visual assets for video games, film, or even product design. Its versatility and ability to preserve shape and depth make it a valuable tool for professionals and hobbyists alike. Things to try One interesting experiment with the stable-diffusion-depth2img model would be to explore its capabilities in generating images that combine realistic elements with more abstract or surreal components. By leveraging the depth information and playing with the various input parameters, users could potentially create visually striking and thought-provoking artworks. Additionally, the model could be used in conjunction with other Stable Diffusion-based models, such as the stable-diffusion-upscaler or the controlnet-depth2img model, to further enhance the image generation process and create even more compelling results.

Read more

Updated Invalid Date