Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
Magic Eraser lets you erase big unwanted areas from your photos while keeping them crisp and realistic. Magic eraser is based on LaMa—which stands for Resolution-robust Large Mask Inpainting, is a powerhouse for tackling large areas in need of inpainting. This is further enhanced by a high receptive field perceptual loss and the use of expansive training masks, ensuring that every edit is seamless and natural-looking.
Here's how magic eraser helps in AI photo editing:
Erases Big Stuff: Regular Inpainting tools struggle with large areas. But Magic Eraser tackles huge unwanted parts in your picture effortlessly.
Works on High-Res Photos: Even on super clear photos, It can magically remove big things without making the rest blurry.
Generative Fill: Magic eraser can seamlessly fill in missing areas in photos with realistic details.
SDXL Img2Img is used for text-guided image-to-image translation. This model uses the weights from Stable Diffusion to generate new images from an input image using StableDiffusionImg2ImgPipeline from diffusers
This model is capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask
The SDXL model is the official upgrade to the v1.5 model. The model is released as open-source software
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training