ultimate-sd-upscale

Maintainer: fewjative

Total Score

105

Last updated 5/21/2024
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

The ultimate-sd-upscale model is a Stable Diffusion-based AI model developed by fewjative that can upscale images while incorporating ControlNet techniques for improved results. It is part of a family of similar models like ultimate-portrait-upscale, high-resolution-controlnet-tile, and controlnet-x-ip-adapter-realistic-vision-v5 that leverage ControlNet to enhance image generation and upscaling capabilities.

Model inputs and outputs

The ultimate-sd-upscale model accepts an input image and various parameters to control the upscaling process, such as the upscale factor, the type of upscaler to use, and settings for the ControlNet tile. The output is an upscaled version of the input image, which can be significantly larger in resolution while maintaining high quality and preserving important details.

Inputs

  • Image: The input image to be upscaled
  • Upscale By: The factor by which the image should be upscaled (e.g., 2x, 4x)
  • Upscaler: The specific upscaler model to use for the upscaling process
  • Use ControlNet Tile: Whether to use ControlNet techniques for the upscaling
  • ControlNet Strength: The strength of the ControlNet influence on the upscaling process
  • Positive Prompt: The textual prompt to guide the upscaling process
  • Negative Prompt: The textual prompt to exclude certain elements from the upscaling process
  • Steps: The number of steps to run the upscaling process
  • Sampler: The specific sampling algorithm to use for the upscaling
  • Scheduler: The specific scheduler to use for the upscaling
  • Denoise: The amount of denoising to apply to the upscaled image
  • Tile Width/Height: The size of the tiles used in the ControlNet-based upscaling
  • Seam Fix Mode/Width/Denoise/Padding/Mask Blur: Parameters to control the stitching of the tiled upscaling process

Outputs

  • Upscaled Image: The final upscaled image, with improved resolution and quality compared to the input.

Capabilities

The ultimate-sd-upscale model can produce high-quality upscaled images by leveraging Stable Diffusion and ControlNet techniques. It can handle a variety of input images and provides a range of parameters to fine-tune the upscaling process, allowing users to achieve their desired results.

What can I use it for?

The ultimate-sd-upscale model can be useful for a variety of applications that require high-resolution images, such as digital art, photography, and content creation. By upscaling low-resolution images, users can create larger, more detailed versions that are suitable for printing, web display, or other purposes. The ControlNet integration also allows for more nuanced control over the upscaling process, enabling users to preserve important details and features in the output.

Things to try

One interesting aspect of the ultimate-sd-upscale model is the ability to use ControlNet techniques to influence the upscaling process. By adjusting the ControlNet strength and other related parameters, users can experiment with different levels of ControlNet integration and observe how it affects the final upscaled image. Additionally, exploring the various upscalers and sampling algorithms can lead to unique results, allowing users to find the optimal combination for their specific needs.



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

ultimate-portrait-upscale

juergengunz

Total Score

18

ultimate-portrait-upscale is a powerful AI model developed by juergengunz that specializes in upscaling and enhancing portrait images. This model builds upon similar tools like high-resolution-controlnet-tile, real-esrgan, gfpgan, and controlnet-tile, offering advanced features for creating stunning, photorealistic portrait upscales. Model inputs and outputs ultimate-portrait-upscale takes in a portrait image and various configuration parameters to fine-tune the upscaling process. It then generates a high-quality, upscaled version of the input image. Inputs Image**: The input portrait image Positive Prompt**: A text description to guide the upscaling process towards a desired aesthetic Negative Prompt**: A text description to avoid certain undesirable elements in the output Upscale By**: The factor by which to upscale the input image Upscaler**: The specific upscaling method to use Seed**: A random seed value to ensure reproducibility Steps**: The number of iterative refinement steps to perform Denoise**: The amount of noise reduction to apply Scheduler**: The algorithm used to schedule the sampling process Sampler Name**: The specific sampling algorithm to use Controlnet Strength**: The strength of the ControlNet guidance Use Controlnet Tile**: Whether to use the ControlNet tile feature Outputs Upscaled Portrait Image**: The high-quality, upscaled version of the input portrait Capabilities ultimate-portrait-upscale is capable of generating stunning, photorealistic upscales of portrait images. It leverages advanced techniques like ControlNet guidance and tile-based processing to maintain sharp details and natural-looking textures, even when significantly increasing the resolution. What can I use it for? This model is a great tool for enhancing portrait photography, creating high-quality assets for design or advertising, and improving the visual quality of AI-generated portraits. It can be particularly useful for businesses or individuals who need to produce professional-grade portrait images for their products, marketing materials, or other applications. Things to try Experiment with different combinations of prompts, upscaling factors, and ControlNet settings to achieve unique and creative results. You can also try applying additional post-processing techniques, such as face correction or style transfer, to further refine the upscaled portraits.

Read more

Updated Invalid Date

AI model preview image

some-upscalers

daanelson

Total Score

21

The some-upscalers model is a collection of 4x ESRGAN upscalers, which are deep learning models designed to enhance the resolution and quality of images. These upscalers were pulled from the Upscale.wiki Model Database and implemented using Cog, a platform for deploying machine learning models as APIs. The model was created by daanelson, who has also developed other AI models like real-esrgan-a100 and real-esrgan-video. Model inputs and outputs The some-upscalers model takes an input image and an optional choice of the specific upscaler model to use. The available models include 4x_UniversalUpscalerV2-Neutral_115000_swaG, which is the default. The model outputs an upscaled version of the input image. Inputs image**: The input image to be upscaled, provided as a URI. model_name**: The specific upscaler model to use for the upscaling, with the default being 4x_UniversalUpscalerV2-Neutral_115000_swaG. Outputs Output**: The upscaled version of the input image, provided as a URI. Capabilities The some-upscalers model can effectively enhance the resolution and quality of input images by a factor of 4x. It can be used to improve the visual clarity and detail of various types of images, such as photographs, illustrations, and digital art. What can I use it for? The some-upscalers model can be useful for a variety of applications, such as: Enhancing the quality of low-resolution images for use in presentations, publications, or social media. Improving the visual clarity of images used in graphic design, web design, or video production. Upscaling and enhancing the resolution of historical or archival images for preservation and digital archiving purposes. Things to try Experiment with the different upscaler models available in the some-upscalers collection to see which one works best for your specific needs. Try upscaling images with varying levels of complexity, such as detailed landscapes, portraits, or digital art, to see how the model performs. You can also try combining the some-upscalers model with other image processing techniques, such as style transfer or color correction, to achieve unique and compelling visual effects.

Read more

Updated Invalid Date

AI model preview image

stable-diffusion-upscaler

jagilley

Total Score

3

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.

Read more

Updated Invalid Date

AI model preview image

real-esrgan

nightmareai

Total Score

45.4K

real-esrgan is a practical image restoration model developed by researchers at the Tencent ARC Lab and Shenzhen Institutes of Advanced Technology. It aims to tackle real-world blind super-resolution, going beyond simply enhancing image quality. Compared to similar models like absolutereality-v1.8.1, instant-id, clarity-upscaler, and reliberate-v3, real-esrgan is specifically focused on restoring real-world images and videos, including those with face regions. Model inputs and outputs real-esrgan takes an input image and outputs an upscaled and enhanced version of that image. The model can handle a variety of input types, including regular images, images with alpha channels, and even grayscale images. The output is a high-quality, visually appealing image that retains important details and features. Inputs Image**: The input image to be upscaled and enhanced. Scale**: The desired scale factor for upscaling the input image, typically between 2x and 4x. Face Enhance**: An optional flag to enable face enhancement using the GFPGAN model. Outputs Output Image**: The restored and upscaled version of the input image. Capabilities real-esrgan is capable of performing high-quality image upscaling and restoration, even on challenging real-world images. It can handle a variety of input types and produces visually appealing results that maintain important details and features. The model can also be used to enhance facial regions in images, thanks to its integration with the GFPGAN model. What can I use it for? real-esrgan can be useful for a variety of applications, such as: Photo Restoration**: Upscale and enhance low-quality or blurry photos to create high-resolution, visually appealing images. Video Enhancement**: Apply real-esrgan to individual frames of a video to improve the overall visual quality and clarity. Anime and Manga Upscaling**: The RealESRGAN_x4plus_anime_6B model is specifically optimized for anime and manga images, producing excellent results. Things to try Some interesting things to try with real-esrgan include: Experiment with different scale factors to find the optimal balance between quality and performance. Combine real-esrgan with other image processing techniques, such as denoising or color correction, to achieve even better results. Explore the model's capabilities on a wide range of input images, from natural photographs to detailed illustrations and paintings. Try the RealESRGAN_x4plus_anime_6B model for enhancing anime and manga-style images, and compare the results to other upscaling solutions.

Read more

Updated Invalid Date