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The RealisticOutpainter model is designed to "outpaint" or expand an input image while maintaining the quality and characteristics of the original photo with stable diffusion and ControlNet. It takes in various parameters such as the image URL, the number of steps, the painting direction, and a series of prompts and negative prompts to guide the image generation process. The model also supports setting denoising strength and overlaying the original image. It outputs an expansion of the original image according to the set parameters and instructions. The image is returned as a web-hosted URL, and other data such as prompts used are returned within an 'info1' payload.

Use cases

The AI model, realisticoutpainter, is suitable for a variety of applications in the creative industries owing to its ability to enhance images with elements such as RAW photo quality, 8k ultra-high resolution, soft lighting, high quality, film grain, and specific camera models such as Fujifilm XT3. Its unique outpainting feature can be beneficial for photographers and digital artists who want to extend their images in width, maintaining stable diffusion in the process. For instance, this can be useful in post-production of movies or TV shows where editors need to create wider scenes without losing original image quality. The model also has a negative prompt function which speeds up the process of avoiding unwanted elements like poor anatomical images or distorted figures. This can be applied in the field of medical imaging, animation, and gaming to control and manipulate the aesthetics of an image. The model's denoising strength capability could find practical usage in surveillance technology, where crisp clear images are of utmost importance. Lastly, the output of this AI model can be incorporated into AI-powered mobile applications for personal use by photo-enthusiasts for enhancing their image portfolio.



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Avg run time

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Summary of this model and related resources.

Model NameRealisticoutpainter
outpaint with stable diffusion and ControlNet
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided


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