AI fashion imagery and lookbooks

Use a clear garment image and a person reference with the relevant clothing area visible. The try-on tool accepts separate inputs, with options for garment category and photo type. Unobstructed references give you a more useful basis for exploration.

Yes. Develop visual studies around the collection’s mood, setting, lighting and styling references. Compare a few coherent directions, then compose the selected frames into an editorial board. Use that work to make the intended look clear to the team before planning the physical production.

Yes. Use selected try-on results to explore how garments read on a person and develop the collection’s visual story. Compare prints, trims and garment shape with the source before selecting images for layouts. Try-on supports visual exploration; it does not measure physical fit or establish sizing.

Start with shared art-direction references and a clear brief for the collection. Reuse the connected steps that produce a useful treatment, then vary the garment or scene deliberately. Compare the images together and finish recurring type and graphic details in Compositor so the set reads as one collection.

Yes. Build the layouts with selected imagery and editable text in Compositor. Change a style name, replace an image or rearrange the presentation without asking generation to redraw the whole page. Export the compositions for the deck or publishing workflow your team already uses.

Place original marks and editable text in Compositor rather than asking the image generator to draw them. You can update style names, headlines and layout details while retaining the selected imagery.

Yes. Take a selected editorial image into a video step to explore movement and atmosphere, then compose branding and launch details over the result. Check garment appearance across the clip before selecting it. Still and motion studies can share the same creative references instead of developing as unrelated campaigns.