Uno

Uno

Playground

Uno AI transforms input images into new visuals guided by text prompts, blending reference images with your creative directions for precise, style-aware edits. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

UNO – Universal In‑Context Diffusion Transformer 📸

A powerful subject-driven image synthesis model (developed by ByteDance Research) enabling both single-subject and multi-subject image generation with high consistency and controllability using diffusion transformers.

Implementation ✨

This model leverages a two-stage progressive cross‑modal alignment strategy, combined with Universal Rotary Position Embedding (UnoPE):

1. Stage I: Fine-tune a pretrained T2I (text-to-image) model using generated single-subject in-context data.
2. Stage II: Further train on multi-subject paired data to support scenes with multiple specified subjects. :contentReference[oaicite:1]{index=1}

Highlights:

• Built on Diffusion Transformers (DiT) with FLUX.1-dev backbone
• UnoPE maintains subject identity and reduces confusion across multiple subjects :contentReference[oaicite:2]{index=2}
• Input: 1–4 reference images + text prompt
• Output: synthesized image reflecting consistent subject(s) in context

Key Features

• ✅ High-consistency, multi-subject generation—preserves unique subject traits across images :contentReference[oaicite:3]{index=3}
• 🔁 Single → multi subject scaling via staged training
• 🔧 Controllable layout and reference identity handling
• 📐 Handles varying aspect ratios and resolutions (512–704px+) :contentReference[oaicite:4]{index=4}

Predictions Examples 🌟

• Generating images of the same person in different settings
• Placing multiple consistent products or characters in a single scene
• Virtual try-on and identity-preserving e-commerce renders

# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/uno" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "image_size": "square_hd",
    "num_images": 1,
    "num_inference_steps": 28,
    "guidance_scale": 3.5,
    "output_format": "jpeg"
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"