Diffree - An AI texture mapping framework developed by OpenGVLab - AiBoss

What is Diffree?

Diffree, an AI mapping framework developed by OpenGVLab, automatically finds suitable locations in images to add new objects based on user-defined text descriptions. Through intelligent mask prediction, it determines the shape and position of new objects without manual drawing. During the addition process, Diffree prioritizes maintaining consistency with the original image's background, including lighting, tone, and color, ensuring the new objects blend naturally. It is trained using the high-quality OABench dataset to learn how to seamlessly add new elements to images. This technology has broad application potential in fields such as interior design.

Diffree Features

Text-to-image editingUsers can input text descriptions, and Diffree can identify and add corresponding objects to images.
Automatic mask predictionDiffree can automatically predict and determine the position and shape of newly added objects without requiring users to manually draw masks.
Seamless integrationThe newly added object maintains consistency with the background of the original image in terms of lighting, hue, and color, achieving a natural blend.
Background information retainedWhen adding new objects, Diffree preserves the background information of the original image to ensure harmony between the old and new elements.
High-quality image outputBy progressively generating and refining, Diffree outputs high-quality images, making newly added objects appear as if they were already in the image.

Diffree's technical principles

Text description inputUsers provide the system with a text description, clearly indicating the object they want to add to the image, such as "add a cat".
Mask predictionDiffree uses advanced algorithms to predict where a new object should appear and its approximate shape. The process is essentially creating a virtual outline on the image, guiding the AI to understand where the object should be placed.
Step-by-step generationDiffree doesn't simply "paste" objects onto an image; instead, it progressively builds a new image. The process is similar to a jigsaw puzzle, where the AI constructs a new image piece by piece while ensuring that each piece blends seamlessly with the background of the original image.
Background PreservationWhen generating new objects, Diffree places great emphasis on preserving the background information of the original image, including lighting, shadows, and textures, to ensure that the new objects can be seamlessly integrated into the image.
Multi-stage refinementDiffree may employ a multi-stage refinement process to gradually optimize the blending of new objects with the background, thereby enhancing the realism of the final image.
Training on high-quality datasetsDiffree was trained using a high-quality dataset from OABench, which contains a large number of real-world image pairs to help the AI learn how to add new objects while maintaining background consistency.

Diffree's project address

Project official website:https://opengvlab.github.io/Diffree/
GitHubstorehouse:https://github.com/OpenGVLab/Diffree
Hugging Face Model Library:https://huggingface.co/spaces/LiruiZhao/Dif_free
arXiv technical paper:https://arxiv.org/pdf/2407.16982

Application scenarios of Diffree

Interior DesignUsers can add furniture, decorations, and other items to photos of empty rooms and quickly preview the interior design.
e-commerceOnline retailers can use Diffree to add or replace products in product images, showcasing different colors, styles, or accessories.
Game developmentGame designers can quickly add or modify elements in game scenes, improving development efficiency.
Film and video productionPost-production teams can use Diffree to add or modify elements in the scene, saving on special effects production costs.
Advertising CreativityAdvertising designers can easily experiment with different product placements or backgrounds in advertising images to find the best visual effect.