license: cc-by-4.0
language:
- en
task_categories:
- image-to-image
- image-text-to-image
pretty_name: PaintBench
size_categories:
- 1K<n<10K
tags:
- benchmark
- visual-editing
- image-editing
- pixel-space-generation
- diffusion-models
- evaluation
dataset_info:
- config_name: PaintBench
features:
- name: category
dtype: string
- name: task
dtype: string
- name: mode
dtype: string
- name: visual_condition
dtype: string
- name: problem_id
dtype: int32
- name: instruction
dtype: string
- name: input_image
dtype: image
- name: answer_image
dtype: image
- name: metadata
dtype: string
splits:
- name: test
num_bytes: 42756475
num_examples: 2016
- name: dev
num_bytes: 5801478
num_examples: 280
download_size: 46011937
dataset_size: 48557953
- config_name: TinyGrafixBench
features:
- name: category
dtype: string
- name: task
dtype: string
- name: mode
dtype: string
- name: visual_condition
dtype: string
- name: problem_id
dtype: int32
- name: instruction
dtype: string
- name: input_image
dtype: image
- name: answer_image
dtype: image
- name: metadata
dtype: string
splits:
- name: test
num_bytes: 55530852
num_examples: 600
download_size: 55366623
dataset_size: 55530852
configs:
- config_name: PaintBench
data_files:
- split: test
path: PaintBench/test-*
- split: dev
path: PaintBench/dev-*
- config_name: TinyGrafixBench
data_files:
- split: test
path: TinyGrafixBench/test-*
PaintBench
Deterministic Evaluation of Precise Visual Editing
Anonymized release for double-blind review.
A precise, deterministic visual editing benchmark. Evaluates whether native pixel-space image generation models can execute "MS-Paint-style" edits — geometric transforms, color changes, structural manipulation, and symbolic-reasoning edits — with pixel-level correctness. Every problem is a (input_image, instruction, answer_image) triplet generated programmatically, so the answer is pixel-exact and the answer distribution is known by construction.
This dataset ships two configurations that share the same schema and evaluation pipeline:
| Config | Splits | Problems | Scope |
|---|---|---|---|
PaintBench |
test, dev |
2,016 + 280 | Main benchmark — 20 tasks × 8 visual conditions × 12 problems-per-cell |
TinyGrafixBench |
test |
600 | Chart-edit micro-benchmark — 5 chart types × 4 subtasks × 30 problems |
Quick start
from datasets import load_dataset
# Full PaintBench test set (1,920 scored problems + 96 preservation diagnostic)
ds = load_dataset("PaintBenchICLR2027/PaintBench", "PaintBench", split="test")
print(ds[0]["instruction"])
ds[0]["input_image"].show()
ds[0]["answer_image"].show()
# Stratified 280-problem dev split for fast iteration
dev = load_dataset("PaintBenchICLR2027/PaintBench", "PaintBench", split="dev")
# Filter by visual condition (one of 8 perturbation axes)
n_xhigh = ds.filter(lambda r: r["visual_condition"] == "n_xhigh")
# Filter to scored problems only (exclude the preservation diagnostic)
scored = ds.filter(lambda r: r["task"] != "preservation")
# Chart-edit subset
charts = load_dataset("PaintBenchICLR2027/PaintBench", "TinyGrafixBench", split="test")
Dataset structure
Schema (both configs)
| Column | Type | Description |
|---|---|---|
category |
string | Top-level grouping. PaintBench: one of 4 task categories; TGF: one of 5 chart types. |
task |
string | Canonical task name (e.g. translation, bar_chart_add_bar). |
mode |
string | Subtask / variant within a task, or "default" for tasks with no mode dimension (7 single-mode PaintBench tasks + all TGF tasks). |
visual_condition |
string | One of 8 PaintBench-side perturbation axes, or "" for TGF rows (no visual-condition axis). |
problem_id |
int32 | Index within the (category, task, mode) cell. |
instruction |
string | Natural-language editing instruction. |
input_image |
image | The source image to edit. |
answer_image |
image | The pixel-exact expected output. |
metadata |
string | JSON dump of the per-problem context (seed, scene shapes / colors, condition parameters, etc.). |
PaintBench task categories
| Category | Tasks |
|---|---|
geometric_transformation |
translation, rotation, reflection, scaling, shearing |
structural_manipulation |
construction, removal, copying, border, cropping |
color_change |
recolor, flood_fill, blending, gradient, point_operations |
symbolic_reasoning |
comparison, ordering, pattern, counting, legend |
The preservation task (96 problems, exposed as task=="preservation") is a diagnostic that copies the input as the expected output and is excluded from aggregate scoring.
Visual conditions
Each PaintBench problem is rendered under one of 8 conditions; each varies exactly one axis from baseline:
visual_condition |
What varies | Detail |
|---|---|---|
baseline |
(nothing) | 1024 × 1024 canvas, default palette, default density |
horizontal |
canvas aspect ratio | 1024 × 576 |
vertical |
canvas aspect ratio | 576 × 1024 |
nonstandard |
palette | Non-standard color palette |
striped |
background | Striped (vs. solid) background |
n_med |
scene density | Medium-density scene |
n_high |
scene density | High-density scene |
n_xhigh |
scene density | Extreme-density scene |
Each (visual_condition, task, mode) cell contains 12 / num_modes problems (12 for single-mode tasks, 6 each for two-mode, 4 each for three-mode). Across all 20 scored tasks this is 1,920 problems (240 per visual condition); the dev split picks the slot=0 problem per cell for a 280-problem stratified subsample.
TinyGrafixBench subtasks
| Chart type | Subtasks |
|---|---|
bar_chart |
add_bar, sort_bars, remove_bar, recolor_bar |
heatmap |
add_cell, shift_heatmap, mask_cells, change_colormap |
line_chart |
draw_segments, normalize_series, filter_series, shade_interval |
network |
add_node, swap_nodes, remove_node, recolor_node |
scatter_plot |
draw_best_fit_line, swap_axes, remove_outlier, recolor_class |
The dev split
PaintBench config ships a 280-problem dev split alongside the full 2,016-problem test split. Construction: one problem per (visual_condition, task, mode) cell across the 20 scored tasks (35 task-modes × 8 visual conditions = 280; preservation excluded). The dev split is a strict subset of the test split, generated deterministically (slot=0 of each cell), and is intended for fast model iteration — not as a standalone benchmark. Reproducibility: the same row's (task, mode, visual_condition, problem_id) quadruple uniquely identifies it in both splits.
Evaluation
Per-problem scoring is pixel-comparison-based: for each pixel, compute the CIE76 ΔE between the model's output and the expected answer_image, threshold at multiple ΔE levels (0, 1, ..., 10), and compute IoU and edit / preservation accuracy. Aggregate scores use macro-averaging at the task / category / visual_condition / benchmark levels with task-bootstrap 95% CIs to match the sampling unit of the displayed mean.
License
Released under Creative Commons Attribution 4.0 (CC BY 4.0). All problems and images are generated programmatically — no third-party content.