{"id":1628,"date":"2026-03-23T07:35:42","date_gmt":"2026-03-23T07:35:42","guid":{"rendered":"https:\/\/wiro.ai\/blog\/?p=1628"},"modified":"2026-09-27T18:11:59","modified_gmt":"2026-09-27T18:11:59","slug":"dreamomni2-6-multi-image-before-and-after-edits","status":"publish","type":"post","link":"https:\/\/wiro.ai\/blog\/dreamomni2-6-multi-image-before-and-after-edits\/","title":{"rendered":"DreamOmni2: 6 Multi-Image Before and After Edits"},"content":{"rendered":"

DreamOmni2 multi-image editing<\/strong> is the point of this six-case test: can one model keep a source image recognizable while borrowing a person, garment, pattern, style, or setting from one or more reference images? The outputs below are the original, unretouched results. This is not a pixel-matched benchmark. It is a practical check of subject retention, attribute transfer, and whether the new elements sit naturally in the scene.<\/p>\n

What DreamOmni2 is built to do<\/h2>\n

DreamOmni2 on Wiro<\/a> accepts an input-image list and a text instruction. Its documentation exposes two controls: steps<\/code> (default 25) and guidance scale<\/code> (default 3.5). The saved post records the input images and prompts, but not the historical task payloads, so it would be misleading to claim those defaults were confirmed for every output. The same applies to speed and price: the Wiro documentation includes a separate example task that completed in 6 seconds at a total cost of $0.00351; it is not a measured runtime or cost for any image below.<\/p>\n

The ordering matters. The model maker’s editing instructions place the image to edit first, then reference images. That convention makes these prompts easier to read: image 1 supplies the scene or main subject; later images supply the garment, person, pattern, or visual treatment. For a closer look at the underlying model, see the DreamOmni2 Hugging Face repository<\/a>, the official project page<\/a>, and the paper<\/a>.<\/p>\n

Test rules and parameters<\/h2>\n