AI Multi-image Composer with GLMImage

A collage preserves separate frames. A coherent composition makes subjects appear to share one camera, one space and one moment. That requires more than background removal: the subjects need compatible scale, perspective, light direction, color temperature, depth, contact shadows and believable overlap. This AI multi-image composer gives GLMImage an explicit integration brief so the result aims for a unified scene rather than a board of unrelated cutouts.

The workflow is intentionally separate from generic image-to-image editing. It requires at least two references because the business job is combining sources. You may upload up to four when the final scene needs an additional person, product, location or style cue. If you leave the prompt empty, the built-in action combines the primary subjects into a balanced natural setting. A custom prompt lets you assign roles, choose the environment and state which identities or product details must remain stable.

Upload order provides a simple plan. Image one is the primary subject and image two is the secondary subject. Images three and four are supporting references. They might supply another subject, a location, a prop arrangement or visual style. In the prompt, describe what to take from each image. For example: preserve the woman from image one and the bicycle from image two; use image three only for the riverside setting and morning color.

Explicit roles reduce identity mixing and accidental copying. A style reference should not donate its people or objects. A location reference should not replace the wardrobe of the main subject. A product reference should preserve shape, materials and packaging text rather than merely contribute its color. The more different the sources are, the more important this role language becomes. Think like a compositor preparing a shot list rather than a user asking the model to “mix these.”

First decide physical relationships: who stands where, which object is held, what sits in front, how far the camera is and where the horizon falls. Then describe the location and light. Only after the scene is plausible should you add palette or rendering style. This order helps GLMImage reconcile geometry before applying aesthetics. If a person should hold a product, state which hand, approximate product scale and whether the label faces the camera.

Choose references with compatible viewpoints when possible. A straight-on product photograph combines more naturally with an eye-level portrait than with an overhead scene. Similar resolution and clear subject boundaries also help. The AI multi-image composer can bridge meaningful differences, but it cannot recover details that do not exist in a tiny or heavily obscured source. Use the clearest authorized image available for every identity or product that matters.

The default 1216 × 832 landscape format gives multiple subjects room to breathe and works for campaign scenes, editorial pairs, product bundles and environmental portraits. Square is useful for compact social compositions, while portrait can stack subjects or place a full-length person with a product. The first reference also informs the initial ratio when it uploads, and you can deliberately choose another valid format before generation.

Custom dimensions must remain between 256 and 1536 pixels and divisible by 32. When changing ratio, describe how the scene should expand or crop. Ask for clean copy space if the image will become an advertisement, but do not overload a four-subject scene with several text blocks. Complex layouts benefit from one clear focal relationship. Additional marketing text can remain live and accessible in the final design rather than being permanently baked into the generated image.

Campaign teams can combine a photographed product with a concept location before scheduling a full shoot. Retail teams can visualize bundles using separate pack shots. Creators can place two authorized portraits into a shared editorial scene. Entertainment teams can test character interactions and key art. Interior teams can place a furniture reference into a room direction. Social teams can remix approved brand assets into a fresh composition while retaining recognizable product truth.

This workflow is especially useful during preproduction because it exposes composition problems early. A creative director can compare subject scale, visual balance and background choice before budgets are committed. Final commercial output still needs review. Ensure every source is licensed for the intended use, verify faces and products against their references, and avoid using the tool to fabricate misleading associations between real people, events or endorsements.

For the strongest result, fix a clear role for every upload before you generate. Decide which reference sets the lighting, which one supplies the subject identity, and which one only defines a palette or surface — then state those roles in the prompt so the model does not have to guess. Keeping the role assignment consistent across iterations makes the composition predictable and the feedback loop fast: when something drifts, you know exactly which reference to adjust instead of re-describing the whole scene.

If one subject disappears, simplify the scene and restate that both primary and secondary subjects are required. If identities blend, describe each subject with a distinct position and interaction, and remove style references until the identities are stable. If scale feels wrong, provide a concrete physical relationship such as “the bottle is 20 centimeters tall on the table beside the seated person.” If lighting conflicts, choose one reference as the lighting authority.

Inspect occlusion and contact closely. Hands should wrap around held objects, feet should meet the ground and cast shadows, and foreground elements should overlap in a physically logical order. Compare logos, packaging and facial features to every source. A near-successful composition should be refined with a short targeted instruction, not a complete prompt rewrite. Keeping the upload order and role structure stable makes the correction easier for both the model and your team to understand.