| --- |
| 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 |
|
|
| <b><em>Deterministic Evaluation of Precise Visual Editing</em></b> |
|
|
| *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 |
|
|
| ```python |
| 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)](https://creativecommons.org/licenses/by/4.0/). All problems and images are generated programmatically — no third-party content. |
| |
