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ScribbleEdit: A Benchmark for Scribble-Only Image Editing
Jie Ren, Hao Kang, Kai Guo, Yiding Yang, Bo Liu, Liming Jiang, Qing Yan, Zichuan Liu, Yizhi Song, Yue Xing, Hui Liu, Xin Lu
TL;DR
Scribble-based editing offers lightweight spatial guidance, but existing models struggle to interpret scribble intentions and spatial cues. ScribbleEdit benchmarks this task and SSE adds scribble-semantic soft tokens; the authors report improved intention understanding and editing performance.
Problem
Existing VLM- and LLM-based editing models struggle to interpret scribble intentions, and without dedicated benchmarks their ability to do so remains unclear.
Source: §1 · pp. 1–2Method
ScribbleEdit combines synthetic training data and an intention-classification metric, while SSE projects CLIP scribble embeddings with an MLP and appends them as soft tokens.
Source: §1 · p. 2Results
Existing VLM- and LLM-based models struggle with scribble intentions, while SSE raises intention accuracy from an average of 66% to 93.6%.
Source: §5.2 · p. 7, §6 · p. 8Takeaways & Limitations
ScribbleEdit and SSE provide a foundation for studying intention understanding and controllable editing under scribble-only guidance.
Source: §6 · p. 8The study covers scribble-expressed moving, removing, resizing, and flipping operations.
Source: §3.1 · p. 3
Abstract
from arXiv · showhide
Scribble-based interaction provides a lightweight and intuitive way for users to specify image editing intents in interactive editing tools. However, current image editing models based on VLMs or LLMs struggle to understand and execute edits based solely on scribble inputs. To systematically study this problem, we construct a new benchmark, ScribbleEdit, that evaluates the ability of image editing models to perform image editing conditioned on scribbles. This task requires both a deep understanding of the intention of the scribble and an accurate interpretation of its spatial information. In ScribbleEdit, we design an automated data construction pipeline and introduce a dedicated evaluation protocol that explicitly measures intention alignment. Our analysis reveals that existing VLM/LLM-based editing models fail to accurately capture scribble intentions. To guide future progress on scribble-only image editing, we propose a simple yet effective soft-token baseline, which enhances the model's understanding of scribble semantics and outperforms standard image editing models on our benchmark. Our evaluation and baseline together provide a concrete foundation for assessing and improving the scribble-driven image editing.
1. Introduction
Scribbles can convey spatial editing guidance more directly than text, but the lack of dedicated benchmarks leaves models’ ability to interpret them unclear. ScribbleEdit evaluates scribble-only editing, and SSE uses soft tokens to improve scribble-intention understanding and editing performance.
1. Introduction
- Scribbles convey spatial information with simple strokes and avoid lengthy text input, while text guidance can be spatially imprecise and cumbersome on mobile devices.Without dedicated datasets and benchmarks, the ability of general-purpose vision-language models to interpret image scribbles remains unclear.Source: §1 · p. 1
- ScribbleEdit evaluates scribble-only editing with 23,772 synthetic training pairs, 250 manually labeled evaluation pairs, and a classification metric for intention understanding.The metric complements visual-similarity measures by separating intention understanding from overall editing quality.Source: §1 · p. 2
- Existing editing models often fail to interpret scribble intentions and spatial semantics, producing inaccurate or inconsistent edits.The authors report that improving intention understanding may be more crucial than generation or editing capability.Source: §1 · p. 2
- SSE uses CLIP scribble embeddings, projects them into a semantic space with an MLP, and appends the result to OmniGen2 as a soft token.The design aims to distinguish scribbles representing different editing intentions.Source: §1 · p. 2
- Scribble-only editing is envisioned as a route toward more intuitive, efficient, and accessible visual editing, though misread scribbles can produce undesired edits.ScribbleEdit and SSE provide an initial evaluation framework and method for advancing scribble understanding.Source: §1 · p. 2
2. Related works
Image editing has progressed from domain-specific methods to diffusion and transformer models conditioned on flexible inputs, including language. Scribble-based generation often uses strokes as structural constraints, while scribble-only editing asks models to infer their semantic intent.
2.1. Image editing
- Diffusion and transformer approaches support editing conditioned on text, masks, reference images, or point controls, while multimodal models align user intentions with visual outputs.Earlier pixel-level and GAN approaches supported paired or style-based editing but remained limited to specific domains or rigid operations.Source: §2.1 · pp. 2–3
2.2. Scribble-based generation
- Prior scribble-generation methods use sparse lines or contours for spatial structure, whereas scribble-only editing requires inferring intentions such as movement, resizing, or removal.ScribbleEdit measures this intention understanding, and SSE embeds scribble semantics into generation.Source: §2.2 · p. 3
3. ScribbleEdit
ScribbleEdit benchmarks image editing guided by scribbles, which convey spatial information for geometric edits. It pairs generated training data and manually annotated test samples with metrics for visual quality and intention alignment.
3.1. Problem statement
- Scribbles provide spatial cues for moving, removing, resizing, and flipping objects; the evaluated models receive a fixed system prompt and a scribble-annotated image, optionally with the scribble separately.The setup focuses on VLM- and LLM-based editing models.Source: §3.1 · p. 3
3.2. Data generation
- ScribbleEdit’s pipeline generates candidate images, creates edited image pairs, and adds operation-specific scribbles.It samples 10,000 AnyEdit prompts and uses FLUX.1-dev to synthesize the candidate images; for edited pairs, the original candidate image serves as the after-edit ground truth because edits may contain artifacts.Source: §3.2 · pp. 3–4, Figure 2 · p. 3
- Scribbles combine arrows, lines, circles, and rectangles under composition rules, with randomized variants and transformations to diversify patterns.The pipeline filters images with overly small detected objects and moving samples with excessive source-target overlap; manually annotated test samples include 50 pairs per operation.Source: §3.2 · p. 4, Figure 3 · p. 4
3.3. Metrics
- Evaluation combines edited-region similarity with a classifier-based measure of whether the model performed the scribble’s intended operation.Focused similarity crops generated and ground-truth images to edited-region bounding boxes before computing SSIM or CLIP-sim; intention accuracy uses a multiclass classifier that predicts the operation or an incorrect/no-operation category.Source: §3.3 · pp. 4–5, Figure 4 · p. 4
- The intention classifier generalizes well to manually labeled edited-image test samples, supporting its use as an evaluation metric.The authors report nearly perfect accuracy on the training and validation sets; no specific accuracy values are supplied here.Source: §3.3 · p. 5, Table 1 · p. 5
4. Soft-token Scribble Editing (SSE)
Soft-Token Scribble Editing (SSE) is proposed as a baseline for models that struggle to generalize to scribble inputs. It encodes scribbles as soft tokens and improves differentiation of editing intentions.
4. Soft-token Scribble Editing (SSE)
- SSE improves the model’s ability to distinguish scribble editing intentions and improves overall performance on ScribbleEdit.The paper presents it as a baseline addressing existing models’ difficulty generalizing to scribble-based inputs.Source: §4 · p. 5
- SSE uses a CLIP encoder and MLP to map scribbles into operation-specific semantic embeddings, then appends the embedding as a soft token to the input sequence.The MLP is trained toward five randomly sampled unit vectors, one per operation; the generation module is fine-tuned to condition edits on the soft tokens.Source: §4 · p. 5, Figure 5 · p. 5
5. Experiments
The experiments compare seven existing editing models with two OmniGen2 baselines, then examine performance, intention errors, an editing upper bound, prompt variants, and failure cases.
5.1. Settings
- The experiments evaluate seven representative existing models and two OmniGen2 variants using intention accuracy, focused SSIM, and focused CLIP-sim.The two variants are direct fine-tuning and training under SSE; the benchmark evaluations use its test split.Source: §5.1 · pp. 5–6, §5.2 · p. 6
5.2. Main results
- SSE raises intention accuracy from an average of 66% to 93.6%, while focused SSIM and focused CLIP-sim rise to 0.549 and 0.932, respectively.The comparison is between SSE and direct fine-tuning; Table 2 reports results across five scribble-only operations.Source: Table 2 · p. 6, §5.2 · pp. 6–7
- Among existing models, Nano Banana reaches 32.9% intention accuracy, while most methods achieve only 22–23%, and their focused SSIM scores mostly fall between 0.4 and 0.5.The authors characterize all existing models’ results as unsatisfactory.Source: §5.2 · p. 6
5.3. Analysis of incorrect intention understanding
- Nano Banana often predicts removal, fine-tuned OmniGen2 overpredicts flip, and SSE correctly identifies most input intentions.The analysis also finds that Nano Banana’s random modifications can be classified as removal, whereas fine-tuned OmniGen2 tends to genuinely misinterpret scribbles.Source: §5.3 · p. 7
5.4. The upper bound of editing ability
- SSE performs close to the estimated editing upper bound from models trained on a single operation, which do not need to distinguish scribble intentions.The authors treat single-operation training as an approximation of the upper bound achievable with ScribbleEdit training data.Source: §5.4 · p. 8, Table 3 · p. 8
5.5. Ablations on system textual prompt
- Prompt 3 performs slightly better on most metrics, but the small differences among prompts indicate that prompts alone do little to resolve scribble understanding.The comparison includes prompts that describe scribble meanings and a further version that supplies a scribble-only image.Source: §5.5 · p. 8
5.6. Case study
- Fine-tuned OmniGen2’s observed failures fall into two main types: misinterpreting the scribble intention or failing to modify the image.These categories cover most observed cases.Source: §5.6 · p. 8, Figure 8 · p. 8
6. Conclusion
The conclusion presents ScribbleEdit as a benchmark for scribble-only editing and SSE as a baseline that improves intention understanding and editing performance.
6. Conclusion
- ScribbleEdit is introduced to systematically evaluate image-editing models under scribble-only guidance.The conclusion describes it as a benchmark for this setting.Source: §6 · p. 8
- The evaluation finds that existing VLM- and LLM-based models struggle to interpret scribbles’ semantic and spatial intentions.The conclusion attributes the finding to the benchmark’s evaluation of scribble-only guidance.Source: §6 · p. 8
- The authors present the benchmark and SSE baseline as a foundation for research on intention understanding and controllable visual editing.They hope the work will inspire more intuitive, efficient, and reliable image-editing systems.Source: §6 · pp. 8–9
7. More details
The supplementary details specify OmniGen2 training settings and the textual prompts used to guide scribble-only editing.
7.1. Implementation details.
- The directly fine-tuned, SSE, and upper-bound models are trained for 12,000 steps with a batch size of 8.The implementation follows the setting of the OmniGen2 repository.Source: §7.1 · p. 12
7.2. Prompts.
- The prompts describe flip, removal, move, and resize scribbles, including the visual marks associated with each operation.For example, a circle with a cross indicates removal, while a rectangle and nearby cross indicate moving an object.Source: §7.2 · p. 12
- Prompt 3 adds an image containing only the scribble on a blank background alongside the scribbled image to edit.The prompt text also labels the two images’ roles.Source: §7.2 · p. 12