E-commerce-Grade Product Photography Quality: gpt-image-2.5-flare Image Generation Plan

Using a skincare serum bottle as an example, this demonstrates how to use gpt-image-2.5-flare to write photographic quality into prompts for e-commerce product hero images: use material terms (frosted glass, brushed metal, matte paper box) to lock in surface reflections; use lighting terms (large softbox key light, right-side rim light, baseboard reflection fill light) to control the shape of highlights; use lens terms (85mm macro, shallow depth of field, front-facing three-quarter viewpoint) to constrain perspective and blur; finally, add the relationship between background and floor to avoid messy shadows. This model family is relatively stable in lighting and material rendering and in following complex visual instructions, and the Flare variant is better suited to batch image generation in terms of latency, making it suitable for rapid iteration on hero image prototypes, detail page visuals, and advertising assets. The work also provides a reference image as a composition baseline, passed in via image_urls, to observe with the same prompt how stably the model redraws materials while preserving the subject's form and layout.