How to prompt Seedream 5.0

How to prompt Seedream 5.0 – Replicate blog

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How to prompt Seedream 5.0

Posted February 24, 2026 by shridharathi

Try Seedream 5.0 on Replicate Run Seedream 5.0

ByteDance’s Seedream line has been on a tear. We spent a bunch of time throwing prompts at it. Here’s what we found.

Aesthetics

Before we get into the meat, let’s talk about how the images actually look. Seedream 5.0 produces genuinely beautiful output — the kind of images where you zoom in and the details hold up.

A color film-inspired portrait of a young man looking to the side with a shallow depth of field that blurs the surrounding elements, drawing attention to his eye. The fine grain and cast suggest a high ISO film stock, while the wide aperture lens creates a motion blur effect, enhancing the candid and natural documentary style.

The model understands photographic language at a deep level. You can reference specific film stocks, lens characteristics, and lighting setups, and it responds with images that feel like they came from that exact equipment.

A woman standing in a Tokyo alleyway at dusk, neon signs reflecting off wet pavement. Shot on expired Kodak Portra 800, pushed two stops. The tungsten light from a ramen shop spills warm orange across her face while the neon casts cool cyan highlights on her hair. Visible grain, halation around the light sources, slightly lifted blacks. She’s mid-step, caught between two worlds of color.

It’s not just portraits. Landscapes, still lifes, architectural photography — the model handles all of them with a level of taste that feels intentional rather than generic.

Aerial photograph of Iceland’s glacial rivers meeting volcanic black sand, creating abstract branching patterns that look like veins or lightning. Taken from 3000 feet during golden hour, the water channels glow turquoise against the obsidian sand. The scale is impossible to determine — it could be a microscope image of capillaries or a satellite photo of a delta. Large format camera, extreme sharpness, no horizon line.

Still life of a half-eaten pomegranate on a rough limestone surface, lit by a single shaft of afternoon light from a high window. Renaissance chiaroscuro lighting — the seeds glisten like rubies against the deep shadow. A few seeds have rolled across the stone, leaving tiny red trails. The mood is somewhere between Caravaggio and a modern food editorial.

An elderly fisherman mending nets on a weathered wooden dock at dawn. His hands are the focal point — scarred, sun-darkened, moving with practiced precision through the pale blue nylon mesh. Behind him, the sea is a soft gradient from pewter to rose gold. Shot on a Leica M with a 50mm Summilux wide open, the bokeh turns the harbor lights into perfect circles. The image has the quiet dignity of a Sebastião Salgado portrait.

A brutalist concrete building reflected in a perfectly still puddle after rain, creating a Rorschach-like symmetry. The building’s geometric facade — repeating rectangular windows and raw concrete — doubles into an abstract pattern. A single figure with a red umbrella walks along the edge, the only color in an otherwise monochrome scene. Overcast sky, flat diffused light, architectural photography with a tilt-shift lens effect on the edges.

Example-based editing

This is the feature that’s hardest to explain and most fun to use.

Instead of trying to describe a complex edit in words, you show the model what you want. Give it a before/after pair — Image 1 and Image 2 — and then a third image. The model figures out what changed between the first two and applies the same transformation to the third.

Here’s an example. We start with a plain white ceramic mug, then show the model what that mug looks like with Japanese kintsugi gold-crack repair (without any textual description). Then we give it a completely different object — a ceramic vase — and ask it to apply the same transformation:

Reference the change from Image 1 to Image 2, apply the same operation to Image 3

Image 1

Image 2

Image 3

Result

The model learned “add gold-filled cracks in a kintsugi pattern” from the mug pair, then applied the same treatment to the vase — without us ever having to describe what kintsugi looks like in words.

This works for all kinds of transformations:

Material swaps : Show wood → marble on one object, apply to another

Scene changes : Show day → night in one photo, apply to a completely different location

Style transfers : Show a photograph converted to a woodblock print, apply the same artistic transformation to new scenes

Style transfer

The power here is that you don’t need to figure out the right words to describe “that exact Hiroshige color palette.” You show it.

Reimagine this scene as a traditional Japanese Ukiyo-e woodblock print in the style of Hiroshige — flat perspective, bold outlines, limited color palette of indigo, vermillion, and ochre.

Input

Output

Transform the color grading to match the following — saturated teal shadows, warm amber highlights, soft diffusion.

Input

Output

Logical reasoning

Most image models treat your prompt as a bag of keywords. Seedream 5.0 actually reasons through what you’re asking.

A Rube Goldberg machine: a marble rolls down a wooden ramp, hits a row of dominoes, the last domino pulls a string that tips a watering can, the water fills a small cup on a balance scale, which lowers and pulls a lever that rings a tiny brass bell. Every component casts physically correct shadows. The marble is mid-roll. The machine sits on a drafting table with visible grid paper underneath. Cross-hatching style, like a patent drawing brought to life.

It extends to understanding physical objects at a mechanical level:

An antique pocket watch disassembled and laid out on black velvet in an exploded-view arrangement. Every gear, spring, escapement wheel, balance cock, and jewel bearing is visible and correctly positioned relative to where it would sit in the assembled movement. The mainspring is partially uncoiled. Tiny labels in copperplate script identify each component. Museum conservation photography style.

Multi-step reasoning with image inputs

Give the model two images and a complex instruction, and it can reason through multi-step operations. Here we give it a mixed bouquet and three empty vases, and ask it to sort the flowers by type:

Classify the flowers from Image 1 by variety and arrange them separately into the three vases from Image 2. Roses in the first vase,…