README.md

---
license: cc-by-4.0
task_categories:
- image-to-image
language:
- en
tags:
- image-editing
- diffusion
- identity-preservation
- synthetic-data
- augmentation
- spatial-reasoning
pretty_name: Identity Preservation Augmentation Dataset for Image Editing
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: '*.jpg'
dataset_info:
  features:
  - name: target_image
    dtype: image
  - name: instruct_prompt
    dtype: string
  - name: reference_image
    dtype: image
  splits:
  - name: train
---

# Identity Preservation Augmentation Dataset for Image Editing

## Overview

This dataset contains algorithmically generated image pairs designed to
teach diffusion-based image editing models **pixel-level identity
preservation** — the ability to keep unchanged regions of an image exactly
intact while applying targeted edits.

Every example consists of a reference image, a target image, and a short
natural-language prompt. The transformation between reference and target is
always a deterministic, rule-based operation (no generative model was used
at any stage). This guarantees perfect ground truth with zero hallucination
and makes the dataset fully reproducible from any source image collection.

The dataset is intended as a **complementary augmentation layer** alongside
semantic edit datasets such as
[molbal/multi_reference_image_editing](https://huggingface.co/datasets/molbal/multi_reference_image_editing).
On its own it teaches preservation and spatial reasoning; combined with
semantic edit data it closes the gap between "follows the instruction" and
"keeps everything else pixel-perfect."

---

## Motivation

Models trained exclusively on semantic edit pairs learn to always apply a
transformation. They have never seen a training signal that says
*"the reference pixels must survive unchanged in the output."* The
consequences are visible at inference time as:

- Face identity drift even on minor edits
- Background detail loss
- Texture and colour shifts outside the edited region

This dataset provides three complementary curriculum signals to fix that:

| Signal | What it teaches |
|---|---|
| **Identity copy** | Full preservation — output must equal reference |
| **Spatial transforms** | Partial preservation with geometric change |
| **Photometric transforms** | Preservation under tone/colour/degradation |

---

## Source Images

All source images are the 10,000 real-world photographs from
[molbal/multi_reference_image_editing](https://huggingface.co/datasets/molbal/multi_reference_image_editing),
which were originally sourced from Pexels with the longest edge capped at
2048 px. No additional imagery was introduced. 

---

## Pair Types

### 1. Identity Copy

| Field | Value |
|---|---|
| Prompt | *(empty string)* |
| Reference | Original image |
| Target | Identical copy of reference |

The prompt is intentionally empty. This forces the model to learn that the
reference latent itself carries the preservation signal rather than any
text instruction. These examples are the strongest anchor for pixel-level
fidelity.

---

### 2. Spatial Transforms

All spatial pairs use the following convention for operations that would
reduce sharpness through interpolation:

> **The target is always the sharp image.** The reference is the
> geometrically transformed (potentially softer) version. This means
> the model always has to produce a sharp output and never has to
> hallucinate detail it was not given.

#### 2a. Pan / Shift

Two overlapping crops from the same image, simulating a camera pan.
The overlapping region (~65–80% of the frame) must be reproduced exactly;
the non-overlapping region must be plausibly completed.

| Slug | Prompt |
|---|---|
| `pan-right` | pan the view to the right |
| `pan-left` | pan the view to the left |
| `pan-down` | pan the view downward |
| `pan-up` | pan the view upward |
| `pan-right-s` | shift the view slightly to the right |
| `pan-left-s` | shift the view slightly to the left |
| `pan-down-s` | shift the view slightly downward |
| `pan-up-s` | shift the view slightly upward |

Shift fractions: 30% (full pan) and 20% (slight shift).
Output dimensions are derived from the crop aspect ratio and snapped to
the nearest 64-pixel boundary. No rotation or distortion is introduced.

#### 2b. Directional Zoom-In

Reference = full image resized to output dimensions (slightly soft).
Target = directional crop resized to output dimensions (sharp).

| Slug | Prompt |
|---|---|
| `zoom-in-center-1x5` | zoom in on the centre |
| `zoom-in-center-2x` | zoom in close on the centre |
| `zoom-in-left` | zoom in on the left side |
| `zoom-in-right` | zoom in on the right side |
| `zoom-in-top` | zoom in on the top part |
| `zoom-in-bottom` | zoom in on the bottom part |

Crop factors: 60% of the relevant axis for directional zooms,
67% and 50% for centre zooms.

#### 2c. Zoom-Out

Reference = centre crop (zoomed-in view the model sees).
Target = full image (always sharp, what the model must produce).

| Slug | Prompt |
|---|---|
| `zoom-out-1x5` | zoom out to reveal more of the scene |
| `zoom-out-2x` | zoom out to show the full scene |

#### 2d. Flip

| Slug | Prompt |
|---|---|
| `flip-h` | mirror the image horizontally |
| `flip-v` | flip the image vertically |

---

### 3. Photometric Transforms

Reference = original image. Target = transformed image.
*(Exception: JPEG pairs — see below.)*

#### 3a. Brightness

| Slug | Prompt | Factor |
|---|---|---|
| `brightness-darker` | darken the image | 0.55 |
| `brightness-brighter` | brighten the image | 1.45 |

#### 3b. Contrast

| Slug | Prompt | Factor |
|---|---|---|
| `contrast-low` | reduce the contrast | 0.45 |
| `contrast-high` | increase the contrast | 1.85 |

#### 3c. Saturation

| Slug | Prompt | Factor |
|---|---|---|
| `saturation-bw` | convert to black and white | 0.0 |
| `saturation-desat` | desaturate the image | 0.35 |
| `saturation-vivid` | make the colors more vivid | 1.85 |

#### 3d. Colour Temperature

| Slug | Prompt |
|---|---|
| `temp-warm` | make the image warmer |
| `temp-cool` | make the image cooler |

Warm: red channel ×1.20, blue ×0.80. Cool: inverse.

#### 3e. Blur

| Slug | Prompt | Radius |
|---|---|---|
| `blur-slight` | add a slight blur | 2 |
| `blur-medium` | blur the image | 5 |
| `blur-heavy` | heavily blur the image | 10 |

#### 3f. Tilt-Shift

A horizontal band (30–70% of image height) is kept sharp; top and bottom
are Gaussian blurred (radius 8) with 30-pixel feathering.

| Slug | Prompt |
|---|---|
| `tiltshift` | apply tilt-shift effect |

#### 3g. Sharpen

Unsharp mask (radius 2, strength 160%, threshold 3).

| Slug | Prompt |
|---|---|
| `sharpen` | sharpen the image |

#### 3h. Noise

Additive Gaussian noise.

| Slug | Prompt | Std |
|---|---|---|
| `noise-slight` | add slight noise to the image | 12 |
| `noise-medium` | add noise to the image | 35 |
| `noise-heavy` | add heavy grain to the image | 65 |

#### 3i. JPEG Compression / Restoration

**Inverted convention:** reference = JPEG-compressed image,
target = original sharp image. Prompt instructs removal of artifacts.

| Slug | Prompt | Quality |
|---|---|---|
| `jpeg-heavy` | remove JPEG compression artifacts | 15 |
| `jpeg-severe` | remove severe JPEG compression artifacts | 5 |

#### 3j. Vignette

Radial darkening mask applied multiplicatively.

| Slug | Prompt | Strength |
|---|---|---|
| `vignette-medium` | add vignette | 0.55 |
| `vignette-heavy` | add strong vignette | 0.80 |

#### 3k. Colour Tint Overlay

RGBA overlay composited onto the image.

| Slug | Prompt | Colour (RGBA) |
|---|---|---|
| `tint-warm-orange` | add a warm orange tint | (255, 200, 150, 75) |
| `tint-cool-blue` | add a cool blue tint | (150, 200, 255, 75) |
| `tint-green` | add a green tint | (200, 255, 200, 60) |
| `tint-dark` | add a dark overlay | (0, 0, 0, 110) |

#### 3l. Film Grain

Multiplicative noise sampled from uniform(0.82, 1.18) per channel per pixel.

| Slug | Prompt |
|---|---|
| `filmgrain` | add film grain |

#### 3m. Pixelate

Nearest-neighbour downsample then upsample.

| Slug | Prompt | Factor |
|---|---|---|
| `pixelate-medium` | pixelate the image | 8 |
| `pixelate-heavy` | heavily pixelate the image | 16 |

---

## File Structure

All files sit in a single flat directory. No subdirectories.