The finegrain-object-cutter is an image editing tool based on the Hugging Face Spaces platform that utilizes advanced machine learning technology for fine-grained object cutting in images. Its main advantages are high precision and user-friendliness, allowing users to accomplish complex editing tasks with simple operations. It is particularly well-suited for designers and developers needing detailed image processing and can be widely applied in fields such as image editing, augmented reality, and virtual reality.

Diffree is a text-guided image restoration model capable of adding new objects to images based on text descriptions while maintaining background consistency, spatial appropriateness, and the quality and relevance of the objects. This model was trained on the OABench dataset, utilizing a stable diffusion model along with an additional mask prediction module, enabling it to uniquely predict the locations of new objects for text-guided object addition.

ViTMatte is an image segmentation system based on a pretrained pure vision transformer (Plain Vision Transformers, ViTs). It optimizes the balance between performance and computational efficiency by utilizing a hybrid attention mechanism and convolutional neck, and introduces a detail capture module to supplement the detailed information required for segmentation. ViTMatte is the first work to harness the potential of ViT in the field of image segmentation through simple adaptation, inheriting the advantages of ViT in terms of pretraining strategy, concise architecture design, and flexible inference strategy. In the Composition-1k and Distinctions-646, the most commonly used image segmentation benchmark tests, ViTMatte achieves state-of-the-art performance and surpasses previous works significantly.

Magic Insert is an innovative image editing technique that allows users to drag and drop image subjects of any style into another style's target image, achieving style-aware and realistic insertion. This technique formally defines the problem of style-aware drag-and-drop by addressing the two subproblems of style-aware personalization and realistic object insertion in stylized images and proposes a method to solve it. Magic Insert's method significantly outperforms traditional image inpainting techniques. Moreover, it provides a dataset called SubjectPlop to facilitate the evaluation and future development of this field.

InstaDrag is a rapid and high-quality image editing technology based on drag-and-drop, which is trained using information from videos, allowing for pixel-level control in approximately 1 second. By eliminating time-consuming operations such as gradient descent, the technology improves the speed and accuracy of editing. This technology can be widely applied in the field of image editing.

MGIE (Multimodal Large Language Model Guided Editing) is an open-source technology by Apple that leverages Multimodal Large Language Models (MLLMs) to generate image editing instructions. Through end-to-end training, it captures visual imagination and executes image processing operations, making image editing more intelligent and intuitive.

InstructIR accepts images and human-written instructions as input, performing integrated image restoration through a single neural model. It has achieved state-of-the-art results across multiple restoration tasks, including image denoising, rain removal, defogging, deblurring, and low-light image enhancement. ?? Get started with the demonstration tutorial. Visit our GitHub for more information. Disclaimer: Please note that this is not a product, and you may notice certain limitations. This demonstration requires input of images with certain degradations (blur, noise, rain, low light, fog) and a prompt indicating the operation to be performed. The application may crash if input high-resolution images (2K, 4K) are used due to GPU memory limitations. The model is primarily trained on synthetic data, which may result in suboptimal performance on real-world complex images. However, it performs surprisingly well on real-world foggy and low-light images. You can also try general image enhancement prompts (e.g., 'polish this image', 'enhance color') to see how it improves color clarity.

TIP-Editor is an accurate 3D editor that supports text and image prompts. Users can precisely control the appearance and position of the editing area through text and image prompts as well as 3D bounding boxes. It employs a step-by-step 2D personalization strategy to better learn the representations of existing scenes and reference images. Precise appearance control is achieved through local editing. TIP-Editor utilizes explicit and flexible 3D Gaussian splatting as its 3D representation, allowing for local editing while keeping the background unchanged. Extensive experiments have proven that TIP-Editor accurately edits within specified bounding box areas based on text and image prompts, exhibiting superior qualitative and quantitative editing quality and alignment with prompts compared to baselines.

Tugaigai is an online AI image text editor that allows users to easily modify, add, or delete text on images using artificial intelligence technology. Users do not need to have professional image editing skills or use complex software; they can simply click on the text within the image to make edits. Tugaigai supports various image formats like JPG, PNG, and JPEG, and can handle images up to 2560x2560 in size. The product features a user-friendly interface, straightforward operation, and is accessible on both computer and mobile devices, making it ideal for users needing quick text edits. Tugaigai provides a free service and emphasizes user data security.

The finegrain-object-cutter is an image editing tool based on the Hugging Face Spaces platform that utilizes advanced machine learning technology for fine-grained object cutting in images. Its main advantages are high precision and user-friendliness, allowing users to accomplish complex editing tasks with simple operations. It is particularly well-suited for designers and developers needing detailed image processing and can be widely applied in fields such as image editing, augmented reality, and virtual reality.

Alchemist is a technology that utilizes a pre-trained text-to-image model and synthesized data to allow users to edit the material properties of objects in images. It achieves parameterized control over specific material properties, such as roughness, metallicity, base color saturation, and transparency, through fine-tuning on synthesized datasets. The main advantages of this technology include the ability to alter object properties while maintaining the geometric shape and lighting of the image, and even realistically filling in backgrounds, hidden internal structures, and refraction effects when objects become transparent.

Diffree is a text-guided image restoration model capable of adding new objects to images based on text descriptions while maintaining background consistency, spatial appropriateness, and the quality and relevance of the objects. This model was trained on the OABench dataset, utilizing a stable diffusion model along with an additional mask prediction module, enabling it to uniquely predict the locations of new objects for text-guided object addition.

ViTMatte is an image segmentation system based on a pretrained pure vision transformer (Plain Vision Transformers, ViTs). It optimizes the balance between performance and computational efficiency by utilizing a hybrid attention mechanism and convolutional neck, and introduces a detail capture module to supplement the detailed information required for segmentation. ViTMatte is the first work to harness the potential of ViT in the field of image segmentation through simple adaptation, inheriting the advantages of ViT in terms of pretraining strategy, concise architecture design, and flexible inference strategy. In the Composition-1k and Distinctions-646, the most commonly used image segmentation benchmark tests, ViTMatte achieves state-of-the-art performance and surpasses previous works significantly.

Magic Insert is an innovative image editing technique that allows users to drag and drop image subjects of any style into another style's target image, achieving style-aware and realistic insertion. This technique formally defines the problem of style-aware drag-and-drop by addressing the two subproblems of style-aware personalization and realistic object insertion in stylized images and proposes a method to solve it. Magic Insert's method significantly outperforms traditional image inpainting techniques. Moreover, it provides a dataset called SubjectPlop to facilitate the evaluation and future development of this field.

InstaDrag is a rapid and high-quality image editing technology based on drag-and-drop, which is trained using information from videos, allowing for pixel-level control in approximately 1 second. By eliminating time-consuming operations such as gradient descent, the technology improves the speed and accuracy of editing. This technology can be widely applied in the field of image editing.

MGIE (Multimodal Large Language Model Guided Editing) is an open-source technology by Apple that leverages Multimodal Large Language Models (MLLMs) to generate image editing instructions. Through end-to-end training, it captures visual imagination and executes image processing operations, making image editing more intelligent and intuitive.