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InstructDiffusion: A Generalist Modeling Interface for Vision Tasks

Zigang Geng, Binxin Yang, Tiankai Hang, Chen Li, Shuyang Gu, Ting Zhang, Jianmin Bao, Zheng Zhang, Han Hu, Dong Chen, Baining Guo

arXiv:2309.03895v1cs.CV

TL;DR

Computer vision tasks have heterogeneous outputs and methodologies, motivating a unified interface. InstructDiffusion casts them as instruction-driven image manipulation using diffusion and flexible pixel-space outputs, and reports broad task coverage, joint-training generalization, unseen-task handling, and improved performance on unseen datasets.

  • Problem

    Computer vision tasks use diverse output formats and methodologies, making it difficult to find a uniform representation across tasks.

  • Method

    InstructDiffusion treats vision tasks as human-intuitive instructional image editing, encoding masks and keypoints into 3-channel images for diffusion-based generation.

  • Results

    The framework achieves good performance across individual tasks, benefits from joint training, handles unseen tasks, and outperforms previous methods on unseen datasets.

  • Takeaways & Limitations

    InstructDiffusion provides a generalist modeling interface for diverse vision tasks through flexible, interactive pixel-space outputs.

  • Takeaways & Limitations

    Image-editing quantitative comparisons can be constrained because unchanged images may receive higher scores than meaningful edits.

Abstract

from arXiv · show

We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge and pre-define the output space (e.g., categories and coordinates) for each vision task, we cast diverse vision tasks into a human-intuitive image-manipulating process whose output space is a flexible and interactive pixel space. Concretely, the model is built upon the diffusion process and is trained to predict pixels according to user instructions, such as encircling the man's left shoulder in red or applying a blue mask to the left car. InstructDiffusion could handle a variety of vision tasks, including understanding tasks (such as segmentation and keypoint detection) and generative tasks (such as editing and enhancement). It even exhibits the ability to handle unseen tasks and outperforms prior methods on novel datasets. This represents a significant step towards a generalist modeling interface for vision tasks, advancing artificial general intelligence in the field of computer vision.

1. Introduction

InstructDiffusion seeks to unify diverse computer vision tasks through human-intuitive image manipulation rather than task-specific output formats. It uses diffusion-based pixel prediction and reports strong task performance, improved generalization from joint training, and handling of unseen tasks.

  • Computer vision tasks use diverse outputs, including coordinates, binary masks, images, and categories, complicating uniform representation.
  • InstructDiffusion treats computer vision tasks as instructional image editing, expressing operations such as coloring segmented objects or marking detected keypoints.
  • Masks and keypoints are encoded into 3-channel images so a diffusion model can process varied understanding tasks through one representation.
  • The model is trained uniformly across diverse tasks, with a new image-editing dataset, and joint multi-task training can enhance generalization versus individual models.
  • The model handles unseen tasks such as image detection and classification and performs better than previous methods on datasets unseen during training.

2. Related Work

Related work seeks general-purpose vision models through language-like or image-resembling interfaces. InstructDiffusion extends image-based unification with explicit natural-language instructions and semantic image editing.

  • Vision-language foundation models commonly map visual inputs to language outputs, supporting broad visual-language understanding through text prediction.
  • Vision generalist models face the challenge of unifying diverse and complex task outputs, with language-like and image-resembling interfaces as major directions.
  • Image-resembling approaches use the image itself as a natural interface, but prior instruction-based methods may infer task intention implicitly.
  • InstructDiffusion introduces more explicit instruction alignment and extends image-resembling generation to semantic image editing.

3. Method

InstructDiffusion unifies vision tasks as instructional image editing, generating target images from control instructions and source images. Its framework combines flexible pixel-space outputs, detailed instructions, diffusion training, and diverse editing data.

  • The framework formulates segmentation, keypoint detection, synthesis, and editing as human-intuitive image manipulation in flexible pixel space.
  • Its primary output formats are 3-channel RGB images, binary masks, and keypoints, covering recognition, editing, and enhancement tasks.
  • Instructional Image Editing generates a target image t_i from a source image s_i according to control instruction c_i.
  • Detailed instructions describe specific operations and entities, such as masking a particular dog, marking an object component, sharpening an image, or removing a watermark.
  • Instruction diversity is expanded by manually writing ten instructions per task, using GPT-4 to rewrite them, and randomly selecting one during training.
  • The training data combines public task datasets with a 159,000-pair Image Editing in the Wild dataset covering diverse entities and semantic granularity.
  • The diffusion framework uses Stable Diffusion v1.5, adapts captions for masks and keypoint marks, and fine-tunes the model for instructional image generation.
  • Human alignment uses subject selections among alternative edited outputs to create a 1,000-image instruction-tuning dataset for further fine-tuning.

4. Experiments

Experiments evaluate InstructDiffusion across keypoint detection and related vision settings, including close-set and unseen-dataset generalization. The model uses diverse task training and produces instruction-following visual outputs.

  • Experimental settings: Training samples cover keypoint detection, semantic and referring segmentation, image enhancement, and image editing.The training set combines multiple datasets and instruction-source/target image triplets.
  • Keypoint detection: Evaluation includes close-set COCO validation and open-set generalization on HumanArt and AP-10K.AP-10K tests animal keypoints despite training on human keypoint datasets.
  • Keypoint detection: The model marks requested keypoints with colored circles, including car, animal, and referring-person examples.The instructions specify both target locations and circle colors.
  • Segmentation: Segmentation outputs recolor specified object pixels while preserving the appearance of the remaining pixels.Examples include cats, neutrophils, the Oriental Pearl Tower, and shadows.
  • Keypoint detection: InstructDiffusion outperforms Unified-IO and Painter across all evaluated keypoint-detection datasets.The largest gains are reported on HumanArt and AP-10K, although specialized keypoint methods retain higher localization accuracy.

4.3. Segmentation

Segmentation is evaluated for referring and semantic settings, emphasizing performance on both close-set and open-set datasets. The model uses text instructions to assign colors to semantic regions.

  • Evaluation settings: Evaluation covers referring segmentation on multiple datasets and semantic segmentation on additional open-vocabulary datasets.The listed evaluation includes RefCOCO variants, ADE20K, Pascal Context, and Pascal VOC.
  • Referring segmentation: Table 3 reports referring-segmentation results using cIoU, with U and G denoting the UMD and Google splits.The table highlights the best-performing generalist models.
  • Semantic segmentation: Table 4 reports semantic-segmentation results using mcIoU.The comparison includes specialized and generalist models, with best-performing generalist models highlighted.
  • Referring segmentation: InstructDiffusion largely outperforms Unified-IO across almost all referring-segmentation datasets.The paper reports an unexpectedly significant improvement on RefClef.
  • Semantic segmentation: The model surpasses specialized models on COCO-Stuff and remains comparable on open-set datasets, with a substantial improvement on VOC.Among generalist models, it outperforms competitors except Painter on ADE-150K.
  • Instruction interface: Text instructions establish the color for each semantic category, supporting the reported segmentation performance and visual examples.Figure 4 illustrates visual referring-segmentation examples.

4.4. Image Enhancement

Image enhancement experiments evaluate deblurring, denoising, and watermark removal using standard benchmarks and PSNR. Visual results show instruction-following sharpening, denoising, and watermark removal in real-world images.

  • Evaluation: Evaluation uses GoPro, SIDD, and CLWD for deblurring, denoising, and watermark removal, respectively.The standard PSNR metric measures difference from the ground-truth image.
  • Tasks: InstructDiffusion applies to low-level vision tasks including image deblurring, denoising, and watermark removal.These tasks are presented as part of the model’s low-level vision applicability.
  • Evaluation: Table 5 reports enhancement results using PSNR, with parenthesized values representing VAE reconstruction upper bounds.Editing results in the same table use CLIP-Sim/AP instead.
  • Evaluation: The VAE-reconstructed ground-truth PSNR serves as an upper bound for the model’s enhancement results.This upper-bound calculation feeds the ground-truth image through the VAE before measuring PSNR.
  • Qualitative results: In-the-wild visual results show images effectively processed according to instructions for sharpening, denoising, and watermark removal.Figure 5 provides qualitative evidence of real-world applicability.

4.5. Image Editing

InstructDiffusion is evaluated on instruction-guided image editing using a new 1,000-sample benchmark and visual comparisons with competitive baselines. The model performs additions, removals, and replacements while preserving background details, though the quantitative metrics have important limitations.

  • Benchmark and metrics: A 1,000-sample benchmark covers replacement, removal, and addition editing scenarios using source images, captions, instructions, and target captions.The benchmark evaluates editing quality with CLIP-Sim and Aesthetic Predictor’s Score.
  • Quantitative results: Our model achieves superior CLIP-Sim to Instruct-Pix2Pix and on-par results with MagicBrush under joint training.The comparison is reported in Table 5 against specific image-editing models.
  • Limitations of quantitative evaluation: CLIP-Sim and aesthetic quality alone may reward images that remain nearly unchanged, so background consistency and manipulation accuracy also matter.A model can receive a higher score despite failing to make substantial edits.
  • Visual comparisons: Visual comparisons include InstructPix2Pix, MagicBrush, EDICT, and Null-text Inversion as competitive instruction-guided editing baselines.Figure 6 presents the input and each method’s result from left to right.
  • Visual comparisons: The model successfully adds, removes, and replaces elements according to prompts while maintaining background integrity and intricate details.Examples include eliminating magnets and stickers, converting a truck into a train, and transforming a cat’s style.

4.6. The Benefit of Highly Detailed Instruction

The paper argues that detailed, element-specific instructions support generalization better than coarse task labels. Replacing them with simple indicators produces extremely poor results on new keypoints and object categories.

  • Instruction design: Detailed instructions describe the meanings of individual elements, whereas simple task indicators such as “semantic segmentation” and “keypoint detection” provide only coarse guidance.The authors contrast their detailed descriptions with prior unified models that treat language primarily as a task indicator.
  • Ablation result: Simple instructions perform extremely poorly, especially for new keypoint types or novel object categories.The ablation supports the paper’s hypothesis that generalization depends on understanding specific elements rather than memorizing complete instructions.
  • Ablation result: Detailed instructions provide greater flexibility and adaptability in open-domain settings than fixed task indicators.This conclusion follows directly from the reported instruction ablation.

4.7. The Benefit of Multi-task Training

Multi-task training improves InstructDiffusion’s generalization on unseen datasets and also benefits image editing. The paper additionally examines editing quality and human-alignment effects in related experiments.

  • Open-domain generalization: Joint training performs significantly better than a segmentation-only model across four unseen datasets in open-domain testing.The datasets are RefClef, ADE-847, PC-459, and ADE-150.
  • Editing benefit: Joint training with other tasks enables more precise editing of which objects require modification than single-task training.Figure 9 illustrates this effect for the instruction “put a hat on the leopard,” potentially benefiting from referring segmentation.
  • Open-domain generalization: The multi-task experiment compares single-task and jointly trained models on four unseen test datasets.The comparison is designed to measure generalization beyond the segmentation training data.
  • Human alignment: Figure 10 evaluates how further human-alignment fine-tuning changes CLIP-Sim over training epochs.The metric reaches its peak after approximately 10 epochs.

4.8. The Benefit of Human Alignment

Human-alignment fine-tuning raises CLIP-Sim from 29.6 to 29.9 over approximately 10 epochs, despite using only 1,000 samples.

  • Fine-tuning progress: 29.6 to 29.9: CLIP-Sim increases over approximately 10 epochs of human-alignment fine-tuning.The evaluation tracks image-text alignment during the subsequent fine-tuning phase.
  • Fine-tuning progress: The improvement occurs with a filtered human-alignment dataset containing only 1,000 samples.The paper emphasizes the small dataset size when discussing the observed gain.

4.9. Generalization Capability to Unseen Tasks

InstructDiffusion generalizes beyond its training tasks, handling detection, classification, and face alignment through instruction-based image manipulation. This capability extends even to animal-face alignment.

  • The model handles unseen tasks including detection, classification, and fine-grained face alignment.Figure 11 specifically illustrates these unseen-task applications.
  • Detection and classification use referring-segmentation-like prompts to derive bounding boxes and verify class labels.Bounding boxes are obtained from the marked region’s top, bottom, left, and right boundaries.
  • Face alignment is performed by instructing the model to encircle facial regions such as the nose or right ear.
  • The same face-alignment approach performs admirably on animal faces, extending beyond the initial training scope.

5. Discussion and conclusion

InstructDiffusion unifies vision tasks as instruction-driven image generation using RGB images, binary masks, and keypoints. The authors report good task performance, stronger generalization from joint training, and handling of unseen tasks, while identifying broader representation and learning improvements as future work.

  • InstructDiffusion aligns computer vision tasks with human instructions through a unified framework.It treats all vision tasks as image generation and focuses on RGB images, binary masks, and keypoints.
  • The approach achieves good performance on individual tasks, while joint multi-task training enhances generalization ability.
  • The model handles tasks unseen during training and outperforms previous methods on unseen datasets.
  • Future work targets more diverse unified output representations and self-supervised or unsupervised learning for improved generalization.The proposed learning direction would leverage large-scale unlabeled data for training and adaptation.
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