MIGC++: Advanced Multi-Instance Generation Controller for Image Synthesis
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Abstract (arXiv)
We introduce the Multi-Instance Generation (MIG) task, which focuses on generating multiple instances within a single image, each accurately placed at predefined positions with attributes such as category, color, and shape, strictly following user specifications. MIG faces three main challenges: avoiding attribute leakage between instances, supporting diverse instance descriptions, and maintaining consistency in iterative generation. To address attribute leakage, we propose the Multi-Instance Generation Controller (MIGC). MIGC generates multiple instances through a divide-and-conquer strategy, breaking down multi-instance shading into single-instance tasks with singular attributes, later integrated. To provide more types of instance descriptions, we developed MIGC++. MIGC++ allows attribute control through text & images and position control through boxes & masks. Lastly, we introduced the Consistent-MIG algorithm to enhance the iterative MIG ability of MIGC and MIGC++. This algorithm ensures consistency in unmodified regions during the addition, deletion, or modification of instances, and preserves the identity of instances when their attributes are changed. We introduce the COCO-MIG and Multimodal-MIG benchmarks to evaluate these methods. Extensive experiments on these benchmarks, along with the COCO-Position benchmark and DrawBench, demonstrate that our methods substantially outperform existing techniques, maintaining precise control over aspects including position, attribute, and quantity. Project page: https://github.com/limuloo/MIGC.
Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 77 of its 82 references that are in the map. First posted on arXiv 2 Jul 2024.
Builds on
77 of 82 references.
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50 most cited of 77 All 77 (JSON)
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Citing papers per year: 2024: 9; 2025: 16; 2026: 10.
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- 2026LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts12 Feb 2026
- 2025ContextGen: Contextual Layout Anchoring for Identity-Consistent Multi-Instance Generation13 Oct 2025
- 2025DragFlow: Unleashing DiT Priors with Region Based Supervision for Drag Editing2 Oct 2025
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- 2025MUSE: Multi-Subject Unified Synthesis via Explicit Layout Semantic Expansion20 Aug 2025
- 2025Inversion-DPO: Precise and Efficient Post-Training for Diffusion Models14 Jul 2025
- 2025In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer29 Apr 2025
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- 2026Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation29 May 2026
- 2026From Zero to Detail: A Progressive Spectral Decoupling Paradigm for UHD Image Restoration with New Benchmark17 Apr 2026
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- 2026WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing12 Mar 2026
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