Google-research
Models by this creator
maxim
457
MAXIM is a powerful AI model developed by the Google Research team that excels at a variety of image processing tasks, including denoising, deblurring, deraining, dehazing, and enhancement. Unlike traditional convolutional neural networks, MAXIM utilizes a novel multi-axis MLP architecture that allows it to efficiently process images and produce high-quality results. Compared to similar models like stable-diffusion, MAXIM is specifically designed for image restoration and enhancement tasks, rather than generative tasks like text-to-image synthesis. It also differs from models like GFPGAN and Codeformer, which focus on face restoration, by having a broader scope that encompasses a variety of image processing applications. Model inputs and outputs MAXIM takes in an input image and produces a processed output image. The model is capable of handling a wide range of image resolutions and can be applied to both natural and synthetic images. Inputs Image**: An input image, which can be a noisy, blurry, rainy, hazy, or low-light image. Outputs Image**: The processed output image, with the desired enhancement or restoration applied. Capabilities MAXIM has demonstrated state-of-the-art performance on a variety of image processing benchmarks, including denoising, deblurring, deraining, dehazing, and enhancement. Its multi-axis MLP architecture allows it to effectively capture both local and global image features, resulting in high-quality outputs. What can I use it for? MAXIM can be utilized in numerous applications that require image restoration or enhancement, such as: Photography and videography**: Improving the quality of images or videos captured in challenging conditions, such as low light, motion blur, or inclement weather. Surveillance and security**: Enhancing the clarity and details of surveillance footage to aid in identification and analysis. Medical imaging**: Improving the quality of medical images, such as CT scans or MRI, to aid in diagnosis and treatment. Artistic and creative applications**: Utilizing MAXIM to enhance or manipulate images for artistic or creative purposes. Things to try With MAXIM, you can experiment with a variety of image processing tasks, such as: Denoising images captured in low-light conditions Deblurring images affected by camera shake or motion Removing rain or haze from outdoor scenes Enhancing the details and contrast of underexposed or washed-out images Combining MAXIM with other AI models, such as BLIP or LLAVA-13B, to create more advanced image processing pipelines. The versatility of MAXIM makes it a valuable tool for a wide range of image-related applications and tasks.
Updated 9/20/2024
frame-interpolation
260
The frame-interpolation model, developed by the Google Research team, is a high-quality frame interpolation neural network that can transform near-duplicate photos into slow-motion footage. It uses a unified single-network approach without relying on additional pre-trained networks like optical flow or depth estimation, yet achieves state-of-the-art results. The model is trainable from frame triplets alone and uses a multi-scale feature extractor with shared convolution weights across scales. The frame-interpolation model is similar to the FILM: Frame Interpolation for Large Motion model, which also focuses on frame interpolation for large scene motion. Other related models include stable-diffusion, a latent text-to-image diffusion model, video-to-frames and frames-to-video, which split a video into frames and convert frames to a video, respectively, and lcm-animation, a fast animation model using a latent consistency model. Model inputs and outputs The frame-interpolation model takes two input frames and the number of times to interpolate between them. The output is a URI pointing to the interpolated frames, including the input frames, with the number of output frames determined by the "Times To Interpolate" parameter. Inputs Frame1**: The first input frame Frame2**: The second input frame Times To Interpolate**: Controls the number of times the frame interpolator is invoked. When set to 1, the output will be the sub-frame at t=0.5; when set to > 1, the output will be an interpolation video with (2^times_to_interpolate + 1) frames, at 30 fps. Outputs Output**: A URI pointing to the interpolated frames, including the input frames. Capabilities The frame-interpolation model can transform near-duplicate photos into slow-motion footage that looks as if it was shot with a video camera. It is capable of handling large scene motion and achieving state-of-the-art results without relying on additional pre-trained networks. What can I use it for? The frame-interpolation model can be used to create high-quality slow-motion videos from a set of near-duplicate photos. This can be particularly useful for capturing dynamic scenes or events where a video camera was not available. The model's ability to handle large scene motion makes it well-suited for a variety of applications, such as creating cinematic-quality videos, enhancing surveillance footage, or generating visual effects for film and video production. Things to try With the frame-interpolation model, you can experiment with different levels of interpolation by adjusting the "Times To Interpolate" parameter. This allows you to control the number of in-between frames generated, enabling you to create slow-motion footage with varying degrees of smoothness and detail. Additionally, you can try the model on a variety of input image pairs to see how it handles different types of motion and scene complexity.
Updated 9/20/2024