MIRA: Multimodal Iterative Reasoning Agent for Image Editing
Instruction-guided image editing offers an intuitive way for users to edit images with natural language. However, diffusion-based editing models often struggle to accurately interpret complex user instructions, especially those involving compositional relationships, contextual cues, or referring expressions, leading to edits that drift semantically or fail to reflect the intended changes. We tackle this problem by proposing MIRA (Multimodal Iterative Reasoning Agent), a lightweight, plug-and-play multimodal reasoning agent that performs editing through an iterative perception-reasoning-action loop, effectively simulating multi-turn human-model interaction processes. Instead of issuing a single prompt or static plan, MIRA predicts atomic edit instructions step by step, using visual feedback to make its decisions. Our 150K multimodal tool-use dataset, MIRA-Editing, combined with a two-stage SFT + GRPO training pipeline, enables MIRA to perform reasoning and editing over complex editing instructions. When paired with open-source image editing models such as Flux.1-Kontext, Step1X-Edit, and Qwen-Image-Edit, MIRA significantly improves both semantic consistency and perceptual quality, achieving performance comparable to or exceeding proprietary systems such as GPT-Image and Nano-Banana.
6928a2681f3fae537858b4a4
Ziyun Zeng
claimed_verified
2025-11-28T13:48:50.315Z
65e6a9a4c799256ec5f7ef77
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ziyun zeng
zengziyun
user
zengziyun
6928a2681f3fae537858b4a5
Hang Hua
claimed_verified
2025-11-28T13:48:48.434Z
6855a23082b55fe44a8d0f14
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/4IJaLsm6W1lRK3y2FMh8u.png
Hang
hhua1
user
hhua1
6928a2681f3fae537858b4a6
Jiebo Luo
MIRA: Multimodal Iterative Reasoning Agent for Image Editing
Instruction-guided image editing offers an intuitive way for users to edit images with natural language. However, diffusion-based editing models often struggle to accurately interpret complex user instructions, especially those involving compositional relationships, contextual cues, or referring expressions, leading to edits that drift semantically or fail to reflect the intended changes. We tackle this problem by proposing MIRA (Multimodal Iterative Reasoning Agent), a lightweight, plug-and-play multimodal reasoning agent that performs editing through an iterative perception-reasoning-action loop, effectively simulating multi-turn human-model interaction processes. Instead of issuing a single prompt or static plan, MIRA predicts atomic edit instructions step by step, using visual feedback to make its decisions. Our 150K multimodal tool-use dataset, MIRA-Editing, combined with a two-stage SFT + GRPO training pipeline, enables MIRA to perform reasoning and editing over complex editing instructions. When paired with open-source image editing models such as Flux.1-Kontext, Step1X-Edit, and Qwen-Image-Edit, MIRA significantly improves both semantic consistency and perceptual quality, achieving performance comparable to or exceeding proprietary systems such as GPT-Image and Nano-Banana.
MIRA, a multimodal reasoning agent, enhances diffusion-based image editing by iteratively interpreting complex instructions, improving both semantic consistency and perceptual quality.
diffusion-based editing models multimodal reasoning agent iterative perception-reasoning-action loop atomic edit instructions visual feedback multimodal tool-use dataset MIRA-Editing two-stage SFT + GRPO training pipeline semantic consistency perceptual quality Flux.1-Kontext Step1X-Edit Qwen-Image-Edit GPT-Image Nano-Banana