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HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing
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This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200,000 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3. To ensure its high quality, diverse examples are first collected online, expanded, and then used to create high-quality diptychs featuring input and output images with detailed text prompts, followed by precise alignment ensured through post-processing. In addition, we propose two evaluation metrics, Alignment and Coherence, to quantitatively assess the quality of image edit pairs using GPT-4V. HQ-Edits high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image editing models. For example, an HQ-Edit finetuned InstructPix2Pix can attain state-of-the-art image editing performance, even surpassing those models fine-tuned with human-annotated data. The project page is https://thefllood.github.io/HQEdit_web.
Forward citations
Cited by 8 Pith papers
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LMM4Edit: Benchmarking and Evaluating Multimodal Image Editing with LMMs
A large human-annotated benchmark of AI-edited images (EBench-18K) plus a fine-tuned LMM metric (LMM4Edit) that predicts human preference scores across three dimensions and answers editing-specific questions.
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Making Implicit Preservation Intent Explicit in Conversational Image Editing
Conversational image editors fail to restore temporarily occluded content; ReSpec fixes this by explicitly selecting historical visual references and rewriting instructions to guide restoration.
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Trade-offs in Image Generation: How Do Different Dimensions Interact?
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ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation
A 91K GPT-4o-generated image and editing dataset, and a fine-tuned open model Janus-4o, report improved text-to-image scores and new editing ability.
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Balancing Preservation and Modification: A Region and Semantic Aware Metric for Instruction-Based Image Editing
A region and semantic aware metric for instruction-based image editing, built from LLM parsing plus detection, segmentation, and CLIP directional similarity, reports the highest human alignment among compared metrics.
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JarvisHub: An Open Harness for Canvas-Native Multimodal Creative Agents
JarvisHub open-sources a three-layer canvas-state, protocol-bridge, and agent-runtime harness so multimodal creative agents can inspect and update a shared editable project graph over long workflows.
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SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
A unified multimodal model can improve its own text-to-image generation by using its understanding module as a rewarder in a global-plus-local reward-weighted training loop.
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Towards Efficient Exemplar Based Image Editing with Multimodal VLMs
ReEdit transfers exemplar-based edits to new images by conditioning Stable Diffusion on a LLaVA-written caption plus a CLIP edit-direction vector, with no per-example optimization.
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