Best 10 AI image restoration Tools of 2025

FLUX.1-dev-Controlnet-Inpainting-Beta is an image restoration model developed by Alibaba's creative team, featuring significant improvements in image restoration. It supports direct processing and generation at 1024x1024 resolution without the need for additional upscaling, providing higher quality and more detailed outputs. The model has been fine-tuned to capture and reproduce more details in the restoration areas, with enhanced prompt explanations that allow for more precise control over the generated content.

FLUX-Controlnet-Inpainting is an image restoration tool released by the Alibaba Mama Creative Team, based on the FLUX.1-dev model. This tool utilizes deep learning technology to restore images, filling in missing sections and suitable for image editing and enhancement. It performs best at a resolution of 768x768, providing high-quality image restoration results. Currently, the tool is in the alpha testing phase, with future updates planned.

Diffuse to Choose is a diffusion-based image repair model primarily designed for virtual try-on scenarios. It can preserve the details of the reference item while repairing the image and perform accurate semantic operations. By directly integrating the detail feature of the reference image into the latent feature map of the main diffusion model and combining it with perceptual loss to further preserve the details of the reference item, the model achieves a good balance between fast inference and high-fidelity details.

The ControlNet-HandRefiner-pruned model is a fp16 version resulting from pruning and compression of the HandRefiner model, enabling faster hand image restoration. This model employs a diffusion model for conditional image completion, allowing for precise restoration of missing or malformed parts in hand images. The model boasts a high compression rate and fast inference speed, making it ideal for high-quality hand image restoration in resource-constrained environments.

CodeFormer is a Transformer-based prediction network for image decimation recovery. By learning discrete codebooks and decoders, it reduces the uncertainty of recovery mapping and generates high-quality faces. It exhibits excellent robustness against degradation and is applicable to both synthetic and real datasets.

Lama Cleaner is a free, open-source AI image repair tool based on state-of-the-art AI models. It can remove any unwanted objects, blemishes, or people from images, and it can also erase and replace any objects in the image. The tool supports CPU, GPU, and M1/2, and offers a variety of SOTA AI models to choose from.

RealFill is a generative image inpainting model that can fill in missing areas of images using a small number of reference images from the same scene, generating visually consistent content with the original scene. RealFill creates personalized generative models by fine-tuning a pre-trained image inpainting diffusion model on both reference and target images. The model not only retains good image priors but also learns the content, illumination, and style of the input image. Subsequently, the fine-tuned model is used to fill in the missing regions of the target image through a standard diffusion sampling process. RealFill has been evaluated on a new image inpainting benchmark containing diverse complex scenes, demonstrating significantly superior performance compared to existing methods.

GFPGAN is a practical facial restoration algorithm that can be used to repair old photos or generate faces. It produces higher quality results with more details, which can be useful for facial recognition. The model runs on Nvidia T4 GPU hardware, and predictions are typically completed within 17 seconds. If GFPGAN is helpful to you, please give the Github Repo a like and recommend it to your friends.

Inst-Inpaint is an image restoration algorithm that can estimate and remove objects to be deleted based on natural language input. This product offers the GQA-Inpaint dataset and a novel restoration framework called Inst-Inpaint, capable of removing objects from images based on textual prompts. The product provides various GAN and diffusion baselines and conducts experiments on both synthetic and real-world image datasets. It utilizes different evaluation metrics to measure the quality and accuracy of the models, demonstrating significant quantitative and qualitative improvements.

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