How to use Seedream 4.5 for AI image generation

Text-to-image needs nothing but a written description and generates a picture from scratch — the same workflow as most image models. Image-to-image is where Seedream 4.5 differentiates itself: feed it one image and an instruction, and it edits that image while preserving what you didn't ask it to change; feed it several images, and it fuses them into one new composition, carrying over a specific subject, prop, or style from each source.

That makes it the right tool whenever an edit needs to look like a continuation of the original rather than a fresh generation that happens to be similar — a product shot that needs a new background but the same product, a portrait that needs a different outfit but the same face, or several separate photos combined into one coherent scene.

Lead with what changes, then, if it's not obvious, name what must not change. "Replace the background with a calm ocean at sunset, keep the bottle, its label, and the lighting exactly as shown" gives the model a clear edit boundary instead of leaving it to guess how much of the original to preserve.

When fusing multiple images, introduce each one's contribution explicitly, the same discipline as any multi-reference tool: describe which image supplies the subject, which supplies a prop, and how they should sit together in the new composition. Seedream 4.5 responds to precise, layered instructions — vague requests like "combine these nicely" leave too much to interpretation.

For text-to-image, build the prompt in the same layered order that works across most models: subject and pose, then style and color palette, then setting and lighting, then camera framing. If you need legible text or a face in the shot, say so plainly and, for text, quote the exact string — this is one of Seedream 4.5's specific strengths, so give it a precise target.

Fewer, cleaner inputs beat many marginal ones. Two or three sharp images that each clearly show one element — the subject, the outfit, the prop — fuse more reliably than a pile of images where several barely contribute anything.

Keep lighting and quality reasonably matched across your inputs. If one source is bright studio photography and another is a dim phone snapshot, the model has to reconcile two different qualities of material in one composition, which is where a fused result starts looking assembled rather than shot as one scene.

If one element of a fusion looks off but the rest is right, don't rewrite the whole prompt — check that specific input image's quality first, then sharpen only the instruction that refers to it.

Edited image drifted from the original: the prompt re-described the scene instead of naming a specific change. State the edit and, if needed, what to preserve explicitly.

Fused elements look like they don't belong in the same shot: input images differ too much in lighting or quality. Replace the weakest source with a cleaner, better-matched image.

Small text or a face came out garbled: this model is strong at both, so garbled output usually means the prompt paraphrased text instead of quoting it exactly, or gave the model too many competing focal points. Quote text strings exactly and keep the composition to one clear subject where faces matter.

Output looks soft or under-detailed for its resolution: check the input image quality on image-to-image tasks — the model can sharpen and restyle, but it can't invent detail that isn't present anywhere in the source.

Reach for Seedream 4.5 when consistency across an edit is the point — swapping a background while keeping a product identical, restyling a portrait while keeping the face, or combining several photos into one scene without it looking composited. For a from-scratch image where nothing needs to be preserved, a lighter, faster text-to-image model may be quicker and cheaper.

Once you have a still image you're happy with, it makes a clean source for an image-to-video model if the next step is animating the shot — generate and refine the picture here, then hand it off for motion.