SigLIP2 is a multilingual vision-language encoder developed by Google, featuring improved semantic understanding, localization, and dense features. It supports zero-shot image classification, enabling direct image classification via text descriptions without requiring additional training. The model excels in multilingual scenarios and is suitable for various vision-language tasks. Key advantages include efficient image-text alignment, support for multiple resolutions and dynamic resolution adjustment, and robust cross-lingual generalization capabilities. SigLIP2 offers a novel solution for multilingual visual tasks, particularly beneficial for scenarios requiring rapid deployment and multilingual support.

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.

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.

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.

BAGEL is a scalable unified multi-modal model that is revolutionizing the way AI interacts with complex systems. The model has dialogue reasoning, image generation, editing, style transfer, navigation, composition, thinking, and other functions, which provide a foundation for generating high-fidelity and realistic images by pretraining on large-scale alternating video and web data.

Aya Vision is an advanced visual model developed by the Cohere For AI team, focusing on multilingual and multimodal tasks and supporting 23 languages. The model significantly improves the performance of visual and text tasks through innovative algorithmic breakthroughs such as synthetic annotation, multilingual data augmentation, and multimodal model fusion. Its main advantages include efficiency (performing well even with limited computing resources) and extensive multilingual support. The release of Aya Vision aims to advance the forefront of multilingual and multimodal research and provide technical support to the global research community.

SigLIP2 is a multilingual vision-language encoder developed by Google, featuring improved semantic understanding, localization, and dense features. It supports zero-shot image classification, enabling direct image classification via text descriptions without requiring additional training. The model excels in multilingual scenarios and is suitable for various vision-language tasks. Key advantages include efficient image-text alignment, support for multiple resolutions and dynamic resolution adjustment, and robust cross-lingual generalization capabilities. SigLIP2 offers a novel solution for multilingual visual tasks, particularly beneficial for scenarios requiring rapid deployment and multilingual support.

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.

Gemini 1.5 Flash is the latest AI model released by the Google DeepMind team. It distills core knowledge and skills from the larger 1.5 Pro model through a distillation process, providing a smaller and more efficient model. This model excels in multi-modal reasoning, long text processing, chat applications, image and video captioning, long document and table data extraction. Its significance lies in providing solutions for applications requiring low latency and low-cost services while maintaining high-quality output.

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.

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.