Papers › MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis

MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis

8 Feb 2024CVPR 2024 1arXiv:2402.05408archive 2025-07-28

Dewei Zhou, You Li, Fan Ma, Xiaoting Zhang, Yi Yang

We present a Multi-Instance Generation (MIG) task, simultaneously generating multiple instances with diverse controls in one image. Given a set of predefined coordinates and their corresponding descriptions, the task is to ensure that generated instances are accurately at the designated locations and that all instances' attributes adhere to their corresponding description. This broadens the scope of current research on Single-instance generation, elevating it to a more versatile and practical dimension. Inspired by the idea of divide and conquer, we introduce an innovative approach named Multi-Instance Generation Controller (MIGC) to address the challenges of the MIG task. Initially, we break down the MIG task into several subtasks, each involving the shading of a single instance. To ensure precise shading for each instance, we introduce an instance enhancement attention mechanism. Lastly, we aggregate all the shaded instances to provide the necessary information for accurately generating multiple instances in stable diffusion (SD). To evaluate how well generation models perform on the MIG task, we provide a COCO-MIG benchmark along with an evaluation pipeline. Extensive experiments were conducted on the proposed COCO-MIG benchmark, as well as on various commonly used benchmarks. The evaluation results illustrate the exceptional control capabilities of our model in terms of quantity, position, attribute, and interaction. Code and demos will be released at https://migcproject.github.io/.

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Code

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limuloo/migc officialmentioned in papermentioned on GitHubpytorch report

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1ran · our draft was wrong
7ran
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CBAM limuloo/migc/migc/migc_arch.py official repository ran licence not identified · pointer only · 7e1368435195acfc · report
ChannelGate limuloo/migc/migc/migc_arch.py official repository ran · metamorphic tier: invariant licence not identified · pointer only · 41c4729230248845 · report
CrossAttention limuloo/migc/migc/migc_arch.py official repository ran · metamorphic tier: invariant fingerprinted licence not identified · pointer only · c14fdb4777f76063 · report
LayoutAttention limuloo/migc/migc/migc_arch.py official repository ran licence not identified · pointer only · cf57795be166055f · report
PositionNet limuloo/migc/migc/migc_arch.py official repository ran fingerprinted licence not identified · pointer only · 3545cb044aae219f · report
SAC limuloo/migc/migc/migc_arch.py official repository ran fingerprinted licence not identified · pointer only · b94c1c2a13728953 · report
SpatialGate limuloo/migc/migc/migc_arch.py official repository ran · metamorphic tier: invariant fingerprinted licence not identified · pointer only · dfdca8dedf1fb371 · report
MIGC limuloo/migc/migc/migc_arch.py official repository unverified licence not identified · pointer only · 32bdc62bc54343cd · report
logsumexp_2d identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · 7abfa32a2f1d7424 · report

Tasks

AttributeConditional Text-to-Image SynthesisImage Generation

Datasets

Introduced by this paper, per the archive.

COCO-MIG

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Conditional Text-to-Image Synthesis COCO-MIG MIGC instance success rate 0.66 #1 of 5 Archive leaderboard report
Conditional Text-to-Image Synthesis COCO-MIG MIGC mIoU 0.56 #1 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

DiffusionSET

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