I wonder how good it is. I have broad shoulders for my height and a long torso. It's not easy to find off the rack clothing that fits me well. Would it be able to detect how clothing will hang on different bodies keeping in mind the size and cut of the clothing? More on reddit.com

can't use it in Europe... Why does google disable so many features for us? More on reddit.com

Google tried to holdback LLM out of fear of the chaos it would unleash. Look where it landed them. So no, they are expected to go all-in on all the AI tools. I read the blog post, lingerie and swimwear is not allowed. It auto screens for CSA, adult content and uploading photos of someone other than yourself is against policy. So the bottom line is, be responsible for who has your full length photos. 🤷🏻‍♀️ More on reddit.com

Here is more information about the project: https://tryondiffusion.github.io/ https://arxiv.org/pdf/2306.08276.pdf TryOnDiffusion generates apparel try-on results with a significant body shape and pose modification, while preserving garment details at 1024×1024 resolution (...) Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person. A key challenge is to synthesize a photorealistic detail-preserving visualization of the garment, while warping the garment to accommodate a significant body pose and shape change across the subjects. Previous methods either focus on garment detail preservation without effective pose and shape variation, or allow try-on with the desired shape and pose but lack garment details. In this paper, we propose a diffusion-based architecture that unifies two UNets (referred to as Parallel-UNet), which allows us to preserve garment details and warp the garment for significant pose and body change in a single network. The key ideas behind Parallel-UNet include: 1) garment is warped implicitly via a cross attention mechanism, 2) garment warp and person blend happen as part of a unified process as opposed to a sequence of two separate tasks. Experimental results indicate that TryOnDiffusion achieves state-of-the-art performance both qualitatively and quantitatively. More on reddit.com