VIBE: Visual Instruction Based Editor
Instruction-based image editing is among the fastest developing areas in generative AI. Over the past year, the field has reached a new level, with dozens of open-source models released alongside highly capable commercial systems. However, only a limited number of open-source approaches currently achieve real-world quality. In addition, diffusion backbones, the dominant choice for these pipelines, are often large and computationally expensive for many deployments and research settings, with widely used variants typically containing 6B to 20B parameters. This paper presents a compact, high-throughput instruction-based image editing pipeline that uses a modern 2B-parameter Qwen3-VL model to guide the editing process and the 1.6B-parameter diffusion model Sana1.5 for image generation. Our design decisions across architecture, data processing, training configuration, and evaluation target low-cost inference and strict source consistency while maintaining high quality across the major edit categories feasible at this scale. Evaluated on the ImgEdit and GEdit benchmarks, the proposed method matches or exceeds the performance of substantially heavier baselines, including models with several times as many parameters and higher inference cost, and is particularly strong on edits that require preserving the input image, such as an attribute adjustment, object removal, background edits, and targeted replacement. The model fits within 24 GB of GPU memory and generates edited images at up to 2K resolution in approximately 4 seconds on an NVIDIA H100 in BF16, without additional inference optimizations or distillation.
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Grigorii Alekseenko
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2026-01-12T10:36:29.469Z
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Grigorii Alekseenko
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Riko0
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Aleksandr Gordeev
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2026-01-12T14:16:49.474Z
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Aleksandr
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grac20101
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Irina Tolstykh
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2026-01-14T12:42:37.939Z
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Irina Tolstykh
iitolstykh
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iitolstykh
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Bulat Suleimanov
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Vladimir Dokholyan
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Georgii Fedorov
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Sergey Yakubson
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Aleksandra Tsybina
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Mikhail Chernyshov
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Maksim Kuprashevich
claimed_verified
2026-01-16T10:34:25.729Z
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Maksim Kuprashevich
WildChlamydia
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WildChlamydia
VIBE: Visual Instruction Based Editor
Instruction-based image editing is among the fastest developing areas in generative AI. Over the past year, the field has reached a new level, with dozens of open-source models released alongside highly capable commercial systems. However, only a limited number of open-source approaches currently achieve real-world quality. In addition, diffusion backbones, the dominant choice for these pipelines, are often large and computationally expensive for many deployments and research settings, with widely used variants typically containing 6B to 20B parameters. This paper presents a compact, high-throughput instruction-based image editing pipeline that uses a modern 2B-parameter Qwen3-VL model to guide the editing process and the 1.6B-parameter diffusion model Sana1.5 for image generation. Our design decisions across architecture, data processing, training configuration, and evaluation target low-cost inference and strict source consistency while maintaining high quality across the major edit categories feasible at this scale. Evaluated on the ImgEdit and GEdit benchmarks, the proposed method matches or exceeds the performance of substantially heavier baselines, including models with several times as many parameters and higher inference cost, and is particularly strong on edits that require preserving the input image, such as an attribute adjustment, object removal, background edits, and targeted replacement. The model fits within 24 GB of GPU memory and generates edited images at up to 2K resolution in approximately 4 seconds on an NVIDIA H100 in BF16, without additional inference optimizations or distillation.
A compact image editing system uses a 2B-parameter model for guidance and a 1.6B-parameter diffusion model to achieve high-quality edits with low computational requirements and strict source consistency.
diffusion models Qwen3-VL Sana1.5 instruction-based image editing image generation source consistency inference efficiency parameter-efficient models ImgEdit benchmark GEdit benchmark