Seedream 4.5 / 5.0 Pro
ByteDance's Seedream generates and edits images from up to ten reference photos, holding identity and composition steady across a full shoot.
Picking Seedream as the model for a generation.
What Seedream does
Seedream generates photorealistic, cinematic images from a text prompt, or composites up to ten reference images — products, faces, style references — into a single new image while preserving how each element should actually look together.
On AutorunX it's the default engine for two jobs that depend on that consistency: Product Shots, where the same item needs to look identical across a set of marketing photos, and Storyboard, where a sequence of frames needs to hold a consistent character, set, and style from panel to panel.
Key features
Ten-image multi-reference compositing
Feed up to ten reference images in one request — products, faces, style boards — and Seedream reconciles their depth, perspective, and lighting into one coherent output instead of a visible paste-up.
Exact identity preservation
Carries a person's face, a product's exact shape and branding, or a set's layout across multiple generations, which is what makes it the default for sequential Storyboard frames.
Strong text rendering
Produces legible, correctly spelled on-image text, including non-Latin scripts, more reliably than most diffusion-only models.
Unified generation and editing
Text-to-image synthesis and image editing run through the same architecture, so there's no quality drop-off when a job moves from a fresh generation to an edit of an existing shot.
4K-capable output
Scales up to a 4K-capable resolution tier, enough for large-format product photography and print-adjacent storyboard frames.
How Seedream works
Seedream handles text-to-image generation and image editing inside one unified architecture rather than routing edit requests through a separate adapter model. That unification is what lets it accept up to ten reference images in a single request and hold consistent identity, lighting, and perspective across all of them — a product swapped between shots, a face carried across a sequence, a brand's visual style applied consistently.
The multi-reference pipeline is doing real compositing work under the hood: it reads depth, perspective, and lighting cues from each reference and reconciles them into a single coherent output, rather than simply pasting elements together. That's what makes it suitable for sequential work like storyboards, where the same character or set needs to persist believably from frame to frame.
Seedream also has a particular strength in typography — it renders legible, correctly spelled text (including non-Latin scripts) inside generated images more reliably than many competing models, and output scales up to a 4K-capable resolution tier.
What people use Seedream for
Consistent product photo sets
Generate a full set of marketing shots for one product — different angles, backgrounds, staging — that all show the exact same item.
Sequential storyboard frames
Build a multi-panel storyboard where the same character, wardrobe, and set persist believably across every frame.
Brand-consistent campaign assets
Feed brand style references alongside a product photo so every generated asset shares the same visual language.
Multi-element composites
Combine a product, a model, and a background reference into one believable shot rather than shooting and compositing manually.
Who built Seedream
ByteDance / BytePlus
www.byteplus.comByteDance is the Chinese technology company behind TikTok and Douyin. Seedream is developed by ByteDance's Seed research team and made available to developers internationally through BytePlus, ByteDance's overseas cloud and AI services arm.
How to use Seedream on AutorunX
Seedream is the default model for Product Shots in Image Lab.
Open Product Shots or Storyboard in Image Lab
Seedream is the default model for both — head to whichever service fits the job.
Set up your input
Write your prompt and upload up to ten reference images — product photos, faces, or style boards — for the composite you need.
Confirm Seedream in the model picker
Seedream 4.5 or 5.0 Pro is pre-selected by default; open the picker if you want to compare it against another model.
Generate
Run the generation and check that identity and style held consistent across every reference before moving to the next shot.
An example generation rendered with Seedream.
Credit usage
Billed per image from your shared AutorunX credit wallet.
Tips for better results with Seedream
Order your references deliberately
Put the reference you most need preserved exactly (the product, the face) first — it tends to anchor the composite more strongly than later references.
Keep reference images clean
Use well-lit, uncluttered reference photos; Seedream reads depth and lighting cues from them, and noisy references produce noisier composites.
Use 5.0 Pro for sequences, 4.5 for singles
Reach for 5.0 Pro when a job needs frame-to-frame consistency (Storyboard); 4.5 is the leaner default for one-off Product Shots.
Spell out exact copy for on-image text
Quote the exact string you want rendered — Seedream's text rendering is strong when told precisely what to draw, less so when left to infer wording.
Seedream — frequently asked questions
Related models
Nano Banana
Google's Gemini-family image model, nicknamed Nano Banana for its viral photorealistic edits, generates and edits images up to a 4K-capable tier.
Image GenerationQwen-Image-Edit
Alibaba's open-weight 20B image editor, self-hosted by AutorunX as AX-QWN, preserves identity through fine-grained edits at near-zero marginal cost.
Image GenerationGPT Image 2
OpenAI's natively multimodal image model — the default engine behind AI Influencer.
Ready to create with Seedream?
Sign up for AutorunX to get 200 free credits across every lab, including Product Shots.