Flux.1-dev Controlnet Upscaler is an image upscaling model hosted on the Hugging Face platform, utilizing advanced deep learning techniques to enhance image resolution while maintaining quality. This model is particularly suited for scenarios requiring lossless upscaling of images, such as image editing, game development, and virtual reality.

StableDelight is an advanced model focused on removing specular reflections from textured surfaces. It builds upon the success of StableNormal, which aims to enhance the stability of monocular normal estimation. StableDelight applies this concept to tackle the challenging task of reflection removal. The training data includes datasets from Hypersim, Lumos, and various specular highlight removal datasets from TSHRNet. Additionally, we integrated multi-scale SSIM loss and random conditional scaling techniques during the diffusion training process to enhance the clarity of single-step diffusion predictions.

FLUX.1-dev-Controlnet-Inpainting-Alpha is an AI image restoration model released by the AlimamaCreative Team, specifically developed to repair and fill in missing or damaged areas of images. This model performs optimally at a resolution of 768x768, delivering high-quality image restoration. As an alpha version, it showcases advanced technology in the field of image restoration and is expected to provide even better performance with further training and optimization.

Flux Latent Detailer is an experimental tool that generates finer details in images through Flux's latent space interpolation technology. This tool enhances image details through multiple processing iterations while attempting to maintain the overall composition and avoid an over-processed appearance. Developers emphasize that this is an experimental project, providing no support and is offered solely for sharing.

AuraSR is a Super-Resolution model based on GAN, which enhances the quality of generated images through image conditional enhancement techniques. The model is implemented as a variant of the GigaGAN paper and utilizes the Torch framework. AuraSR's strength lies in its ability to effectively improve the resolution and quality of images, making it suitable for the field of image processing.

APISR aims to restore and enhance low-quality, low-resolution anime images and video sources from real-world scenarios using various degradation processes. The project supports multiple upsampling factor weights, such as 2x, 4x, and provides a Gradio demo.

SUPIR is a groundbreaking image repair method utilizing the power of generative priors and model expansion. Through multi-modal techniques and advanced generative priors, SUPIR has made significant strides in intelligent and realistic image repair. Model expansion, a key catalyst within SUPIR, has significantly enhanced its capabilities, revealing new potential in image repair. We trained our model on a dataset of 20 million high-resolution, high-quality images, each accompanied by descriptive textual annotations. SUPIR can repair images based on text prompts, broadening its application range and potential. Furthermore, we introduced negative quality prompts to further improve perceived quality. We also developed a repair-guided sampling method to mitigate the fidelity issues encountered in generative repair. Experiments have demonstrated SUPIR's outstanding repair effects and its novel ability to manipulate repairs through text prompts.

An open-source image lossless enlargement tool that leverages artificial intelligence technology to automatically enlarge low-quality images by 4 times their original resolution. Developed as a project for TheFullstack hackathon using the Next.js framework and Replicate API, it boasts numerous advantages including open-source, free-to-use, and user-friendly design. Key features include one-click lossless image enlargement, drag-and-drop upload support, compatibility with various image formats, and generation of high-definition large images. Suitable for a wide range of scenarios requiring image resolution improvement, such as enlarging old photos, enhancing product images for online stores, and magnifying blurry ID photos. The project's code is open-source, allowing for free use and further development.

iPhone Photo [FLUX] is an AI model based on LoRA technology, designed to enhance the realism of images, especially excelling in mimicking iPhone photo effects. It not only enhances the quality of iPhone-captured photos but also adds a natural and realistic visual effect to non-iPhone photos. Developed by Anibaaal and released on October 2, 2024, this model belongs to the Civitai platform. A usage tip suggests setting the intensity to 1, and it boasts over 1000 likes and 375 comments, indicating its popularity.

Flux.1-dev Controlnet Upscaler is an image upscaling model hosted on the Hugging Face platform, utilizing advanced deep learning techniques to enhance image resolution while maintaining quality. This model is particularly suited for scenarios requiring lossless upscaling of images, such as image editing, game development, and virtual reality.

StableDelight is an advanced model focused on removing specular reflections from textured surfaces. It builds upon the success of StableNormal, which aims to enhance the stability of monocular normal estimation. StableDelight applies this concept to tackle the challenging task of reflection removal. The training data includes datasets from Hypersim, Lumos, and various specular highlight removal datasets from TSHRNet. Additionally, we integrated multi-scale SSIM loss and random conditional scaling techniques during the diffusion training process to enhance the clarity of single-step diffusion predictions.

FLUX.1-dev-Controlnet-Inpainting-Alpha is an AI image restoration model released by the AlimamaCreative Team, specifically developed to repair and fill in missing or damaged areas of images. This model performs optimally at a resolution of 768x768, delivering high-quality image restoration. As an alpha version, it showcases advanced technology in the field of image restoration and is expected to provide even better performance with further training and optimization.

Flux Latent Detailer is an experimental tool that generates finer details in images through Flux's latent space interpolation technology. This tool enhances image details through multiple processing iterations while attempting to maintain the overall composition and avoid an over-processed appearance. Developers emphasize that this is an experimental project, providing no support and is offered solely for sharing.

AuraSR is a Super-Resolution model based on GAN, which enhances the quality of generated images through image conditional enhancement techniques. The model is implemented as a variant of the GigaGAN paper and utilizes the Torch framework. AuraSR's strength lies in its ability to effectively improve the resolution and quality of images, making it suitable for the field of image processing.

SwinIR is the official PyTorch implementation of an image restoration model based on Swin Transformer. It achieves state-of-the-art performance on tasks such as classic, lightweight, and real-world image super-resolution, grayscale/color image denoising, and JPEG compression artifact removal. It consists of shallow feature extraction, deep feature extraction, and high-quality image reconstruction, with excellent performance and parameter optimization.

APISR aims to restore and enhance low-quality, low-resolution anime images and video sources from real-world scenarios using various degradation processes. The project supports multiple upsampling factor weights, such as 2x, 4x, and provides a Gradio demo.

SUPIR is a groundbreaking image repair method utilizing the power of generative priors and model expansion. Through multi-modal techniques and advanced generative priors, SUPIR has made significant strides in intelligent and realistic image repair. Model expansion, a key catalyst within SUPIR, has significantly enhanced its capabilities, revealing new potential in image repair. We trained our model on a dataset of 20 million high-resolution, high-quality images, each accompanied by descriptive textual annotations. SUPIR can repair images based on text prompts, broadening its application range and potential. Furthermore, we introduced negative quality prompts to further improve perceived quality. We also developed a repair-guided sampling method to mitigate the fidelity issues encountered in generative repair. Experiments have demonstrated SUPIR's outstanding repair effects and its novel ability to manipulate repairs through text prompts.