Pico-Banana-400K is a large-scale dataset of ~400K text–image–edit triplets designed to advance research in text-guided image editing.
Each example contains:
- an original image (from Open Images),
- a human-like edit instruction, and
- the edited result generated and verified by the Nano-Banana model.
The dataset spans 35 edit operations across 8 semantic categories, covering diverse transformations—from low-level color adjustments to high-level object, scene, and stylistic edits.
| Feature | Description |
|---|---|
| Total Samples | ~257K single-turn text–image–edit triplets for SFT, ~56K single-turn text-image(positive) - image(negative)-edit for preference learning, and ~72K multi-turn texts-images-edits for multi-turn applications |
| Source | Open Images |
| Edit Operations | 35 across 8 semantic categories |
| Categories | Pixel & Photometric, Object-Level, Scene Composition, Stylistic, Text & Symbol, Human-Centric, Scale & Perspective, Spatial/Layout |
| Image Resolution | 512–1024 px |
| Prompt Generator | Gemini-2.5-Flash |
| Editing Model | Nano-Banana |
| Self-Evaluation | Automated judging pipeline using Gemini-2.5-Pro for edit quality |
Pico-Banana-400K is built using a two-stage multimodal generation pipeline:
- Instruction Generation
Each Open Images sample is passed to Gemini-2.5-Flash, which writes concise, natural-language editing instructions grounded in visible content. We also provide short instructions summarized by Qwen-2.5-Instruct-7B. Example:{ "instruction": "Change the red car to blue." } - Editing + Self-Evaluation The Nano-Banana model performs the edit, then automatically evaluates the result using a structured quality prompt that measures: Instruction Compliance (40%) Editing Realism (25%) Preservation Balance (20%) Technical Quality (15%) Only edits scoring above a strict threshold (~0.7) are labeled as successful, forming the main dataset; the remaining ~56K are retained as failure cases for robustness and preference learning.
Nano-Banana-400K contains ~400K image editing data, covering a wide visual and semantic range drawn from real-world imagery.
| Category | Description | Percentage |
|---|---|---|
| Object-Level Semantic | Add, remove, replace, or relocate objects | 35% |
| Scene Composition & Multi-Subject | Contextual and environmental transformations | 20% |
| Human-Centric | Edits involving clothing, expression, or appearance | 18% |
| Stylistic | Domain and artistic style transfer | 10% |
| Text & Symbol | Edits involving visible text, signs, or symbols | 8% |
| Pixel & Photometric | Brightness, contrast, and tonal adjustments | 5% |
| Scale & Perspective | Zoom, viewpoint, or framing changes | 2% |
| Spatial / Layout | Outpainting, composition, or canvas extension | 2% |
- Single-Turn SFT samples (successful edits): ~257K
- Single-Turn Preference samples (failure cases): ~56K
- Multi-Turn SFT samples (failure cases): ~72K
- Gemini-generated instructions: concise, natural, and image-aware
- Edit coverage: 35 edit types across 8 semantic categories
- Image diversity: includes humans, objects, text-rich scenes, etc from Open Images
Below are representative examples from different categories:
| Category | Example |
|---|---|
| Object-Level | “Replace the red apple with a green one.” |
| Scene Composition | “Add sunlight streaming through the window.” |
| Human-Centric | “Change the person’s expression to smiling.” |
| Text & Symbol | “Uppercase the text on the billboard.” |
| Stylistic | “Convert the image to a Van Gogh painting style.” |
Pico-Banana-400K provides both breadth (diverse edit operations) and depth (quality-controlled multimodal supervision), making it a strong foundation for training and evaluating text-guided image editing models.
Pico-Banana-400K serves as a versatile resource for advancing controllable and instruction-aware image editing.
Beyond single-step editing, the dataset enables multi-turn, conversational editing and reward-based training paradigms.
The Pico-Banana-400K dataset is hosted on Apple’s public CDN.
You can download each component (single-turn, multi-turn, and preference data) using the provided manifest files.
Manifest files: sft link and preference link
Manifest file: multi-turn link
Urls to download source images are provided along with edit instructions in sft link, preference link, and multi-turn link
Pico-Banana-400K is released under the Creative Commons Attribution–NonCommercial–NoDerivatives (CC BY-NC-ND 4.0) license. ✅ Free for research and non-commercial use ❌ Commercial use and derivative redistribution are not permitted 🖼️ Source images follow the Open Images (CC BY 2.0) license By using this dataset, you agree to comply with the terms of both licenses.
If you use 🍌 Pico-Banana-400K in your research, please cite it as follows:
@misc{qian2025picobanana,
title = {Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing},
author = {Yusu Qian and Eli Bocek-Rivele and Liangchen Song and Jiasen Lu and Jialing Tong and Yinfei Yang and Wenze Hu and Zhe Gan},
year = {2025},
note = {Dataset release (preprint / placeholder citation). Paper forthcoming.},
url = {https://github.com/apple/ml-pico-banana-400K},
}