The image pipeline behind an AI virtual try-on platform

It combines a photo of a person with a photo of a garment and generates an image of that person wearing it. In Dressr, pose estimation and mask generation work out where the garment should sit, then an SDXL-style inpainting workflow in ComfyUI renders the fit, drape and proportions so the result looks like a real photo.

Yes. Each swap can be scoped to the full outfit, the top and bottom, or the front and back. That control is what makes the tool practical for a real catalogue, where you often need to change one item while keeping the rest of the look, rather than replacing everything every time.

Dressr produces swaps at up to 4K. Users choose a quick tier for fast styling results or a pro tier for higher quality and more control over clothes, pose, background and camera angle. A finished image can also be upscaled later, without running the whole swap again.

Yes. The platform has eight workflows built around one asset: pro and quick clothes swap, fashion modelling, product modelling, video generation, pose generation, camera angle change and product marketing. A single garment can become catalogue shots, product page images, campaign visuals and short videos inside the same system.

Consistency is treated as a product requirement, not a bonus. The pipeline aims for photoreal, studio-style output and keeps garments coherent across different models and variations. Post-generation tools change pose or angle from the existing result instead of regenerating from scratch, which helps the same item look the same everywhere.

Yes. Python services manage job queues, retries and logging, so many looks can be processed in one run without someone watching each image. That turns a per-image tool into something a merchandising team can schedule around, which matters when a new collection needs hundreds of product and campaign assets.