\n\nInput A: person and pose to preserve.<\/figcaption><\/figure>\nInput B: clothing reference.<\/figcaption><\/figure>\n<\/td>\n\nPrompt: Replace the suit in the first image with the clothes in the second image.<\/figcaption><\/figure>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nThis is the cleanest practical editing case in the set. The output has to preserve the first subject’s face, pose, framing, and lighting while taking the outfit from the second image. It shows why a reference image beats a long text description for garments: cut, fabric, and small design cues travel together. Pick DreamOmni2 for a guided wardrobe concept when source identity and pose matter more than pixel-perfect catalog accuracy.<\/p>\n
2) Two-character composition: a generation-style edit<\/h2>\n \n\n\nInputs<\/th>\n Output<\/th>\n<\/tr>\n<\/thead>\n \n\n\nInput A: left character.<\/figcaption><\/figure>\nInput B: right character.<\/figcaption><\/figure>\n<\/td>\n\nPrompt: In the scene, the character from the first image stands on the left, and the character from the second image stands on the right. They are shaking hands against the backdrop of a spaceship interior.<\/figcaption><\/figure>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nThis result asks for more than an edit. It creates a new scene, assigns left and right positions, and gives the characters an interaction. The output demonstrates that the references can serve as subject anchors while the prompt supplies a setting and action. This is the right mode when an existing photo does not need strict preservation and the goal is a new composition built around two known subjects.<\/p>\n
3) Pattern transfer: the curved-surface check<\/h2>\n \n\n\nInputs<\/th>\n Output<\/th>\n<\/tr>\n<\/thead>\n \n\n\nInput A: car to retain.<\/figcaption><\/figure>\nInput B: pattern reference.<\/figcaption><\/figure>\n<\/td>\n\nPrompt: Make the car in the first image have the same pattern as the mouse in the second image.<\/figcaption><\/figure>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nThe car makes this a tougher test than a flat product mockup. The transferred pattern needs to follow panels, curves, and reflections without turning into a pasted texture. The output is useful as a concept image because it clearly carries the second image’s visual motif onto the first image’s vehicle. For brand-pattern exploration, give the model a clean subject photo and a distinct reference. For production vehicle graphics, treat the result as art direction rather than final manufacturing artwork.<\/p>\n
4) Three-image merge: subjects plus style<\/h2>\n \n\n\nInputs<\/th>\n Output<\/th>\n<\/tr>\n<\/thead>\n \n\n\nInput A: cat.<\/figcaption><\/figure>\nInput B: dog.<\/figcaption><\/figure>\nInput C: style reference.<\/figcaption><\/figure>\n<\/td>\n\nPrompt: The cat from Image 1 and the dog from Image 2 are sitting side by side, with the background inside a car. The style of the image is the same as in Image 3.<\/figcaption><\/figure>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nThis is the hardest test in the article. DreamOmni2 must retain two different subjects, place them in a new environment, and borrow an abstract look from a third image. The result shows a coherent single frame instead of three unrelated references. Pick this approach for moodboards, character concepts, or story beats where subject identity and look need to arrive in one run. Use fewer references when strict source fidelity is the priority.<\/p>\n
5) Style transfer: preserve the scene, change the visual language<\/h2>\n \n\n\nInputs<\/th>\n Output<\/th>\n<\/tr>\n<\/thead>\n \n\n\nInput A: scene content.<\/figcaption><\/figure>\nInput B: visual style.<\/figcaption><\/figure>\n<\/td>\n\nPrompt: Replace the first image have the same image style as the second image.<\/figcaption><\/figure>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nThe result tests whether the model can separate content from treatment. The output should still read as the first image, while palette, line work, texture, or atmosphere move toward the second. That is a better fit than a text-only style prompt when the reference has details that are hard to name. Choose this route for a fast art-direction pass. Use a single local edit instead if only one object needs changing.<\/p>\n
6) Local car repaint: control the smallest change<\/h2>\n \n\n\nBefore<\/th>\n After<\/th>\n<\/tr>\n<\/thead>\n \n\n\nBefore.<\/figcaption><\/figure>\n<\/td>\n\nPrompt: Change the car paint to matte black and add a thin red racing stripe. Keep the scene and camera angle the same.<\/figcaption><\/figure>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nThe final output narrows the request to paint and a thin stripe. It is a good test of restraint: the scene and camera angle should remain stable while the object changes. This is the mode to pick for a single visible alteration, such as a colorway, material, or small product detail. State what must stay unchanged in the instruction, then inspect edges, reflections, and nearby objects before using the result.<\/p>\n
When to choose DreamOmni2 image edits<\/h2>\n Choose DreamOmni2 when the reference image carries the information that words cannot describe well: a specific outfit, texture, pattern, subject, lighting feel, or illustration style. Use one source and one reference for the tightest edit. Use two or three references when assembling a new image matters more than exact preservation. The six outputs make the tradeoff clear: the model is strongest as a visual-relationship tool, not a replacement for manual retouching where every pixel must match.<\/p>\n
For another reference-led editing workflow, see Seedream V5 Lite image editing tests<\/a>, FireRed Image Edit vs Seedream V5 Lite<\/a>, and GPT Image 1.5 text and edit tests<\/a>. Then try DreamOmni2 on Wiro<\/a> with a clear base image, a purposeful reference, and a prompt that names both the intended change and the parts that must stay put.<\/p>\n","protected":false},"excerpt":{"rendered":"DreamOmni2: 6 Before\/After Image Edits DreamOmni2 image edits were tested here as a reference-driven workflow, not as a prompt-only filter. Each test…<\/p>\n","protected":false},"author":4,"featured_media":1499,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56],"tags":[128,61,60],"class_list":["post-1497","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-before-after","tag-dreamomni2","tag-image-editing","tag-image-to-image"],"_links":{"self":[{"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/posts\/1497","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/comments?post=1497"}],"version-history":[{"count":2,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/posts\/1497\/revisions"}],"predecessor-version":[{"id":4195,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/posts\/1497\/revisions\/4195"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/media\/1499"}],"wp:attachment":[{"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/media?parent=1497"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/categories?post=1497"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wiro.ai\/blog\/wp-json\/wp\/v2\/tags?post=1497"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"Person](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-01-1.jpg\")
![\"Clothing](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-01-2.jpg\")
![\"DreamOmni2](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-out-01.png\")
![\"First](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-02-1.jpg\")
![\"Second](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-02-2.jpg\")
![\"DreamOmni2](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-out-02.png\")
![\"Car](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-03-1.jpg\")
![\"Pattern](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-03-2.jpg\")
![\"DreamOmni2](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-out-03.png\")
![\"Cat](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-04-1.jpg\")
![\"Dog](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-04-2.jpg\")
![\"Style](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-04-3.jpg\")
![\"DreamOmni2](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-out-04.png\")
![\"Source](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-05-1.jpg\")
![\"Style](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-input-05-2.jpg\")
![\"DreamOmni2](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-out-05.png\")
![\"DreamOmni2](\"https:\/\/wiro.ai\/blog\/wp-content\/uploads\/2026\/03\/dreamomni2-out-06.png\")