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FreeFlux: Understanding and Exploiting Layer-Specific Roles in RoPE-Based MMDiT for Versatile Image Editing

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arxiv 2503.16153 v1 pith:DVTQ4H66 submitted 2025-03-20 cs.CV

Tianyi Wei , Yifan Zhou , Dongdong Chen , Xingang Pan This is my paper · ORCID
classification cs.CV
keywords editingmmditcontentgenerationrope-basedanalysisduringembedding
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The integration of Rotary Position Embedding (RoPE) in Multimodal Diffusion Transformer (MMDiT) has significantly enhanced text-to-image generation quality. However, the fundamental reliance of self-attention layers on positional embedding versus query-key similarity during generation remains an intriguing question. We present the first mechanistic analysis of RoPE-based MMDiT models (e.g., FLUX), introducing an automated probing strategy that disentangles positional information versus content dependencies by strategically manipulating RoPE during generation. Our analysis reveals distinct dependency patterns that do not straightforwardly correlate with depth, offering new insights into the layer-specific roles in RoPE-based MMDiT. Based on these findings, we propose a training-free, task-specific image editing framework that categorizes editing tasks into three types: position-dependent editing (e.g., object addition), content similarity-dependent editing (e.g., non-rigid editing), and region-preserved editing (e.g., background replacement). For each type, we design tailored key-value injection strategies based on the characteristics of the editing task. Extensive qualitative and quantitative evaluations demonstrate that our method outperforms state-of-the-art approaches, particularly in preserving original semantic content and achieving seamless modifications.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Post-hoc scalar scaling of individual DiT block outputs, found by CMA-ES, improves text-to-image quality and cuts inference steps from 30-100 to 10-15.

  3. Prompt Reinjection: Alleviating Prompt Forgetting in Multimodal Diffusion Transformers for Text-to-Image Generation

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    Re-injecting shallow text features into deeper MMDiT blocks counteracts measured 'prompt forgetting' and improves instruction following in SD3, SD3.5, FLUX, and Qwen-Image without retraining.

  4. Phase-Aligned RoPE for Mixed-Resolution Diffusion Transformer

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    Expressing all RoPE positions on the query's grid ('one attention, one scale') plus a small boundary content-exchange step restores mixed-resolution diffusion generation that naive position interpolation destroys.

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    cs.CV 2025-06 conditional novelty 4.0 of 10

    TV-LiVE injects key/value features from a source video into selected layers of a CogVideoX generator to perform object addition and non-rigid video editing without training.

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