thedeoxen/refcontrol-flux-kontext-dataset

Flux Kontext RefControl Dataset

This dataset was created for training Flux Kontext RefControl LoRAs.
It provides paired data of control maps (depth, pose, lineart, canny) and their corresponding results for reference-guided training.

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📂 Dataset Structure

dataset/
│
├── depth/
│   ├── control/   # depth maps
│   └── result/    # corresponding images
│
├── pose/
│   ├── control/   # pose skeletons / keypoints
│   └── result/    # corresponding images
│
├── lineart/
│   ├── control/   # lineart outlines
│   └── result/    # corresponding images
│
├── canny/
│   ├── control/   # canny edge maps
│   └── result/    # corresponding images

• Files in control and result share the same names.
  Example: depth/control/0001.png ↔ depth/result/0001.png

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🎯 Purpose

The dataset is designed to train LoRAs that:

• Preserve identity (faces, style, object details).
• Follow control signals: depth, pose, lineart, or canny edges.
• Enable consistent and controllable generation with Flux Kontext models.

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📸 Data Source & Attribution

All images were sourced from Pexels under the CC0 license.
For this dataset, we carefully selected photo series where:

• The object or person remained the same,
• But pose, position, or composition changed across the sequence.

This approach ensures strong consistency for reference-based training while enabling meaningful variation for control tasks.

🙏 A huge thank you to the talented photographers on Pexels for sharing their work openly and making this dataset possible.

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🔗 Related LoRAs

• Depth Reference Fusion LoRA
  Trigger: redepthkontext
  Preserves identity while following a depth map.

• Reference Pose LoRA
  Trigger: refcontrolpose
  Uses a pose skeleton to transfer style/identity to a new pose.

• Reference Lineart LoRA
  Trigger: refcontrollineart
  Controls proportions with lineart while keeping style.

• Reference Canny LoRA
  Trigger: refcontrolcanny
  Uses canny edges for precise contour control.

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