![]() We demonstrate the quality and versatility of our method on numerous inputs from various domains, showcasing a plethora of high quality complex semantic image edits, all within a single unified framework. It produces a text embedding that aligns with both the input image and the target text, while fine-tuning the diffusion model to capture the image-specific appearance. ![]() Our method, which we call "Imagic", leverages a pre-trained text-to-image diffusion model for this task. ![]() It operates on real images, and does not require any additional inputs (such as image masks or additional views of the object). Contrary to previous work, our proposed method requires only a single input image and a target text (the desired edit). each within its single high-resolution natural image provided by the user. We sent light through different types of glass objects and into the camera, which created the lens reflections. We captured these light overlays in studio with a variety of light sources, reflective and refractive glass, and a professional camera. Our method can make a standing dog sit down or jump, cause a bird to spread its wings, etc. This pack of 120 free light overlays includes transparent PNG files for easy, drag-and-drop use. For example, we can change the posture and composition of one or multiple objects inside an image, while preserving its original characteristics. In this paper we demonstrate, for the very first time, the ability to apply complex (e.g., non-rigid) text-guided semantic edits to a single real image. However, most methods are currently either limited to specific editing types (e.g., object overlay, style transfer), or apply to synthetically generated images, or require multiple input images of a common object. Download a PDF of the paper titled Imagic: Text-Based Real Image Editing with Diffusion Models, by Bahjat Kawar and 7 other authors Download PDF Abstract:Text-conditioned image editing has recently attracted considerable interest.
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