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

I was playing with this yesterday - it's trending on huggingface. Worked really well on the few images I tested :) https://huggingface.co/spaces/Kwai-Kolors/Kolors-Virtual-Try-On More on reddit.com

I've seen a few examples of these, and one consistent problem is logos or graphic tees. I guess that's why you're using plain colors. The generation will never be exactly like the graphic in the original image. I guess the real workaround would be to use a displacement map on top of the generated clothing, but I couldn't find any nodes that allow this. More on reddit.com

https://nextry.app/ More on reddit.com