Text to Image
Describe the scene, style, and text. Control ratio, size, and batch count from the same generator.
통합 크리에이티브 이미지 워크스페이스
생성·편집·참조 블렌드를 한 흐름으로. UI 브랜드는 Swift-Image, 1단계 어댑터는 교체 가능.
결과 예시실제 생성은 사용 가능한 provider adapter로 구동됩니다. 공식 Swift-Image 가중치/API/라이선스는 아직 공개되지 않았습니다.
Swift-Image showcase samples are original creative scenes for people, products, posters, spaces, multi-image synthesis, and edits.
Original sample scenes for people, products, posters, spaces, and multi-image synthesis.






Swift-Image is a single creative surface for three tasks: text-to-image, single-image edit, and multi-image reference.
A single creative surface for text-to-image, single-image edit, and multi-image reference.
Describe the scene, style, and text. Control ratio, size, and batch count from the same generator.
Upload one image and rewrite lighting, wardrobe, background, or layout with prompt-led edits.
Attach multiple references to steer identity, product look, or composition without rebuilding the UI.
Model research
Swift-Image research materials describe 6B, 3B, and Turbo variants; they are research notes, not downloadable official releases on this site.
These are paper positioning notes, not downloadable official releases on this site.
Reported research focus on higher-capacity unified generation and editing quality. Not yet publicly available here.
Reported mid-size research variant balancing quality and cost. Treat metrics as research results only.
Reported faster research profile for interactive loops. Availability remains pending public release.
Source framing: reported / research result / not yet publicly available. This product does not claim official Swift-Image weights or partnership.
Swift-Image product copy uses concrete, attributable numbers from the paper and this SaaS credit table.
Swift-Image research claims on this page cite the public preprint; live generation still uses a replaceable provider adapter for everyday swift image work.
"We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing."
"Swift-Image achieves leading aggregate performance among evaluated open-source models with only 6B parameters and 243K GPU training hours."
"For efficient deployment, structural pruning and few-step distillation produce 3B and accelerated variants."
A Swift-Image job moves prompt → reference understanding → generation → result in four steps.
Prompt → reference understanding → generation → result.
Start from text, templates, or a pasted brief.
Optional single or multi-image uploads guide identity and style.
Submit through Nuxt server APIs with the active provider adapter.
Review, retry, or continue in the dashboard workspace.
No. The UI brand is Swift-Image, but official weights, API, and license are not publicly available. Live tasks use a replaceable provider adapter.
Text-to-image, single-image edit, and multi-image reference with aspect ratio and batch controls, plus loading, success, failure, and empty states.
They summarize research positioning from public materials. They are not downloadable model packages on this website.
Phase 1 routes through the existing GPT Image 2 BFF under a Swift-Image display label. The adapter can be swapped server-side later.
Browsing is open. Generation, history, and billing use the shared za-center auth and credits flow.
No. Pricing describes the unified creative workspace and credits. It does not claim purchase of official Swift-Image model rights.