{"id":2768,"date":"2026-06-06T14:44:34","date_gmt":"2026-06-06T14:44:34","guid":{"rendered":"https:\/\/wiro.ai\/blog\/?p=2768"},"modified":"2026-06-06T14:44:34","modified_gmt":"2026-06-06T14:44:34","slug":"gpt-image-2-custom-layout-tests","status":"publish","type":"post","link":"https:\/\/wiro.ai\/blog\/gpt-image-2-custom-layout-tests\/","title":{"rendered":"GPT Image 2 Custom Guide: 4 Surprising Layout Tests"},"content":{"rendered":"

GPT Image 2 Custom in 4 layout tests<\/h2>\n

GPT Image 2 Custom looks strongest when a prompt needs exact size control, clean framing, and a clear edit instruction. This review looks at four layout tests that matter in real work: contrast-heavy scenes, travel composites, seasonal scene edits, and subject-preserving lifestyle edits.<\/p>\n

The model page exposes the knobs that usually matter on a live job: width and height from 480 to 3840, up to 16 input images, three quality tiers, and PNG, JPEG, or WebP output. The OpenAI image generation guide<\/a> also lays out the broader workflow split. Quick one-shot image jobs fit the Images API. Longer iterative edits fit the Responses API better.<\/p>\n

For context, the closest reference on this site is GPT Image 1.5: 6 Prompt Tests for Text and Edits<\/a>. GPT Image 2 Custom pushes harder on exact dimensions and layout-sensitive edits, so the main question is simple: does that extra control stay stable once the scene gets busy?<\/p>\n\n\n\n\n\n\n\n\n\n
Setting<\/th>\nWhat it means in practice<\/th>\n<\/tr>\n<\/thead>\n
Custom size<\/td>\nEach side can run from 480 to 3840 pixels, which is wide enough for banners, cards, and cropped social assets.<\/td>\n<\/tr>\n
Reference images<\/td>\nUp to 16 inputs can guide an edit, which helps when the goal is consistency instead of a fresh scene.<\/td>\n<\/tr>\n
Quality<\/td>\nLow, medium, and high give room to trade speed for cleaner detail.<\/td>\n<\/tr>\n
Formats<\/td>\nPNG, JPEG, and WebP cover most publishing and product pipelines.<\/td>\n<\/tr>\n
Batch size<\/td>\nOne to ten outputs keep it usable for both fast checks and wider prompt sweeps.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Test 1: noir street scene<\/h2>\n
\n GPT Image 2 Custom noir street scene layout test
Prompt: Black-and-white night street scene with a woman in a trench coat, wet pavement, and a HOTEL sign.<\/figcaption><\/figure>\n

This frame asks for a lot at once. Hard shadows, bright sign text, glossy pavement, and a human subject can pull an image apart fast. GPT Image 2 Custom keeps the composition steady. The light direction feels locked in. Reflections stay believable instead of turning into random glare. The sign also stays readable enough to support the scene instead of distracting from it.<\/p>\n

The weak spot is the usual one for image models: tiny typography still sits on thin ice. Large letters work. Small decorative type would be riskier. For poster layouts, landing page heroes, and editorial headers, this result is strong. For dense packaging copy, it still needs human review.<\/p>\n

Test 2: travel portrait with a background swap<\/h2>\n
\n Travel portrait generated in GPT Image 2 Custom with a clean coastal layout
Prompt: Sunset travel portrait with a Greek-island feel, bright light, and a clean lifestyle-ad composition.<\/figcaption><\/figure>\n

This is where GPT Image 2 Custom starts to justify its name. The scene has a clear ad-style layout, but it does not feel stiff. The subject remains the anchor, while the coast, buildings, and sunset color sit in the right order behind her. That matters for teams that need editable campaign art in exact aspect ratios.<\/p>\n

Skin tone and fabric lighting also stay coherent with the new environment. That is a bigger deal than it sounds. Many fast image tools can swap a background, then forget to rematch the light on the subject. This sample avoids that mistake. It reads like one captured frame, not a cut-and-paste composite.<\/p>\n

Test 3: winter conversion with scene discipline<\/h2>\n
\n GPT Image 2 Custom winter edit with bench, footprints, and balanced composition
Prompt: Snowy park landscape with a bench, fresh footprints, and a quiet winter atmosphere.<\/figcaption><\/figure>\n

Seasonal edits expose whether a model can follow constraints without rewriting the whole image. In this test, the bench stays the anchor. Snow lands across the ground and branches in a way that feels consistent. The footprints also help. They give the image a natural path through the frame and make the edit feel planned instead of generic.<\/p>\n

This result also shows good restraint. The model does not oversaturate the snow or push the whole frame into fake blue tones. That makes GPT Image 2 Custom a safer option for before-and-after content, retail scene updates, and quick seasonal creative refreshes. It changes the mood without breaking the layout.<\/p>\n

Test 4: lifestyle edit with subject consistency<\/h2>\n
\n GPT Image 2 Custom lifestyle edit with consistent subject pose and beach scene
Prompt: Beach sunset portrait of a woman walking barefoot with a book in a lifestyle scene.<\/figcaption><\/figure>\n

The last test is quieter, but it is useful. Lifestyle work falls apart when a model loses the subject’s identity, shifts the body shape, or muddies the gesture. Here, the figure still feels natural in motion. The book, hair, and sunset light all support the same story. The image works for blog covers, product campaigns, and social cards that need room for text on one side.<\/p>\n

There is still a limit. Fine detail in small accessories and tiny text does soften. That is not unique to this model, but it does shape the best use cases. GPT Image 2 Custom is more reliable for strong shapes, broad scene edits, and clear headline space than for dense label design.<\/p>\n

Where GPT Image 2 Custom works best<\/h2>\n