Loom: Diffusion-Transformer for Interleaved Generation

• URL: http://arxiv.org/abs/2512.18254v1
• Date: Sat, 20 Dec 2025 07:33:59 GMT
• Title: Loom: Diffusion-Transformer for Interleaved Generation
• Authors: Mingcheng Ye, Jiaming Liu, Yiren Song,
• Abstract summary: Interleaved text-image generation aims to jointly produce coherent visual frames and aligned textual descriptions within a single sequence.<n>We present Loom, a unified diffusion-transformer framework for interleaved text-image generation.
• Score: 17.092197559386463
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Interleaved text-image generation aims to jointly produce coherent visual frames and aligned textual descriptions within a single sequence, enabling tasks such as style transfer, compositional synthesis, and procedural tutorials. We present Loom, a unified diffusion-transformer framework for interleaved text-image generation. Loom extends the Bagel unified model via full-parameter fine-tuning and an interleaved architecture that alternates textual and visual embeddings for multi-condition reasoning and sequential planning. A language planning strategy first decomposes a user instruction into stepwise prompts and frame embeddings, which guide temporally consistent synthesis. For each frame, Loom conditions on a small set of sampled prior frames together with the global textual context, rather than concatenating all history, yielding controllable and efficient long-horizon generation. Across style transfer, compositional generation, and tutorial-like procedures, Loom delivers superior compositionality, temporal coherence, and text-image alignment. Experiments demonstrate that Loom substantially outperforms the open-source baseline Anole, achieving an average gain of 2.6 points (on a 5-point scale) across temporal and semantic metrics in text-to-interleaved tasks. We also curate a 50K interleaved tutorial dataset and demonstrate strong improvements over unified and diffusion editing baselines.

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