Print On Demand Sellers: This AI Background Remover Will Change Your Business Forever! (AI Arty)

About this video

RJ Martinez demos AI Arty Image Matting as a background-remover for print-on-demand sellers, testing it on Ideogram images including a lion, a woman with jellyfish, and portraits with detailed hair. He explains that different AI models suit different subjects: Alpha Standard for wedding dresses, glass, water, hair, pets and semi-transparent edges; Alpha Edge V2 to emphasise edge clarity on semi-transparent subjects; an edge-clear model for birds, bicycles, cars, bags, jewellery and furniture; and a solid model for books, phones, shoes, buildings and sofas. He shows switching models when hair is cut off, cleaning up with erase, brush, dodge and burn tools, single export with transparent background, and built-in 1x/2x upscaling and enhancement. He also highlights batch background removal, a timeline that he says can hold over 3,000 pictures, and offline working on your own computer, arguing precise edge removal saves hours he previously spent giving up on complex designs, and that the isolated elements can then be upscaled and reused in t-shirt designs or other images.

Key takeaways

• Choose the AI Arty model by subject type — Alpha Standard for hair, pets and semi-transparent edges, solid model for solid objects — rather than using one setting for everything
• If a model cuts off hair or fine detail, re-run the same image with a different model and compare the edge result
• Use the built-in erase, brush, dodge and burn tools for small touch-ups instead of redoing the whole removal
• Upscale and enhance inside the tool (1x or 2x) before export if the source image looks blurry
• Batch removal, a large timeline, and offline use are presented as the workflow benefits for sellers with many designs

Episode details: published February 5, 2025 · 10:12 long. Summary written by Self Growth Videos from the episode itself. Business results and any income figures mentioned are RJ Martinez’s own claims, shared for education — they are not typical or guaranteed.

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