1roOt/qr-code-illusions-30k

🔮 QR Code Illusions 30k

A high-fidelity, verified paired dataset of 30,000 artistic QR Code illusions, complete with multi-decoder verification and quantitative robustness scoring for training ControlNet and Flow-Matching models.

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🌟 Overview

The QR Code Illusions 30k dataset is designed to train conditional diffusion models (ControlNet, ControlNet-LLLite, LoRA, and Flow-Matching MMDiT adapters) capable of embedding scannable QR codes into aesthetic artwork.

Unlike synthetic or naive QR blends, each sample in this dataset represents a fine-tuned optical illusion where:

1. The QR code is seamlessly embedded into architectural structures, natural foliage, lighting highlights, and artistic textures.
2. Visual aesthetics are preserved without harsh black/white square artifacts.
3. Every image is rigorously validated across multiple real-world QR decoders (pyzbar, OpenCV WeChat QR) and tested against physical distortions (rotation, perspective shift, blur, compression).

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🎨 Featured Samples

Here are four representative examples selected directly from the dataset:

Sample ID	Condition Signal	Original QR	Generated Illusion (Try scanning with your phone!)	Metadata & Prompt Details
#31 Elf Archer				Class: B (Balanced) Style: Cinematic Film Schedule: schedule_K (str=1.35) Prompt: an elf archer, cinematic shot, 35mm film photography, anamorphic lens, dramatic lighting, depth of field, distressed wood grain, weathered timber, rustic textures, dappled sunlight...
#48 Log Cabin				Class: C (Artistic) Style: Watercolor Schedule: schedule_K (str=1.35) Prompt: a cozy log cabin nestled at the edge of a snow-covered pine forest, delicate watercolor painting, fluid wet-on-wet washes, raw paper texture, intricate mosaic tile work, high-contrast chiaroscuro shadows...
#50 Anime Cafe				Class: B (Balanced) Style: Anime Art Schedule: schedule_K (str=1.35) Prompt: a cozy anime cafe, masterpiece anime artwork, vibrant colors, clean lineart, Makoto Shinkai style, high quality animation still, dense foliage, layered autumn leaves, chiaroscuro shadows...
#54 Enchanted Glade				Class: B (Balanced) Style: Dark Fantasy Schedule: schedule_F (str=0.92) Prompt: an enchanted glade, dark fantasy aesthetic, somber tone, grim atmosphere, moody lighting, desaturated shadows, dense foliage, layered autumn leaves, dappled sunlight casting organic shadows...

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📊 Dataset Structure & Statistics

Splits (Zero Matrix Leakage)

To prevent model memorization, the dataset was partitioned using QR matrix hash bucketing ensuring no identical QR layout appears across train and evaluation sets:

• train: 23,988 pairs
• validation: 3,021 pairs
• test: 1,486 pairs
• hard_test: 1,505 pairs (high-difficulty scanning scenarios)
• Total: 30,000 paired samples

Quality Tiers

• Class A (10,564 pairs): Highly scannable under standard and adverse conditions; excellent visual fidelity.
• Class B (5,576 pairs): Reliable scan under standard conditions; enhanced artistic subtlety.
• Class C (13,860 pairs): High artistic immersion and visual blending (useful for contrast and texture pre-training).
• Note: Corrupt and unreadable Class D samples have been strictly removed.

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📋 Schema & Metadata Fields

Each sample contains:

Field	Type	Description
id	string	Unique sample identifier (e.g. sample_00000031)
image	Image	Target generated artwork (768×768 PNG)
condition	Image	Condition signal with gray/black/white pattern (768×768 PNG)
original_qr	Image	Ground-truth binary QR code
mask	Image	Finder and alignment pattern region mask
prompt	string	Detailed generation prompt
style	string	Visual style (cinematic, anime, watercolor, dark_fantasy, architectural, etc.)
prompt_type	string	Category (character, urban, nature, fantasy, sci_fi, etc.)
quality_class	string	Tier (A, B, C)
robustness_score	float32	Normalized score [0.0, 1.0] across physical distortions
is_decoded	bool	Whether the QR code decoded successfully
decoder_count	int32	Number of distinct decoders confirming the payload
schedule_name	string	Control schedule used (schedule_M, schedule_F, schedule_K, etc.)
controlnet_strength	float32	Applied control strength
qr_version	int32	QR matrix version (Version 1 to 10)
qr_ec_level	string	Error correction level (L, M, Q, H)
payload	string	Embedded URL payload
seed	int64	PRNG seed for reproducibility

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🚀 Quickstart & Usage

1. Load with Hugging Face datasets

from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset("1roOt/qr-code-illusions-30k")

# Access train sample
sample = dataset["train"][0]
image = sample["image"]          # PIL.Image
condition = sample["condition"]  # PIL.Image
prompt = sample["prompt"]        # str

print(f"Loaded: {sample['id']} | Style: {sample['style']} | Class: {sample['quality_class']}")

2. Filter for High-Scannability Samples (Class A only)

# Filter for reliable scannability training
class_a_train = dataset["train"].filter(lambda x: x["quality_class"] == "A")
print(f"Number of Class A training samples: {len(class_a_train)}")

3. PyTorch DataLoader Example for ControlNet Training

from torch.utils.data import DataLoader
from torchvision import transforms

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])

def collate_fn(batch):
    images = torch.stack([transform(b["image"]) for b in batch])
    conditions = torch.stack([transform(b["condition"]) for b in batch])
    prompts = [b["prompt"] for b in batch]
    return {"images": images, "conditions": conditions, "prompts": prompts}

loader = DataLoader(dataset["train"], batch_size=4, shuffle=True, collate_fn=collate_fn)

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⚖️ License & Attribution

• Generated Images: Base generations derived from Stable Diffusion architectures under the CreativeML OpenRAIL-M license.
• QR Synthesis: Generated algorithmically via open-source tools under MIT / BSD licenses.
• Dataset & Metadata: Released under OpenRAIL / Creative Commons Attribution 4.0 (CC BY 4.0).