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Visual storytelling often uses nontypical aspect-ratio images like scrollpaintings, comic strips, and panoramas to create an expressive and compellingnarrative. While generative AI has achieved great success and shown thepotential to reshape the creative industry, it remains a challenge to generatecoherent and engaging content with arbitrary size and controllable style,concept, and layout, all of which are essential for visual storytelling. Toovercome the shortcomings of previous methods including repetitive content,style inconsistency, and lack of controllability, we propose MagicScroll, amulti-layered, progressive diffusion-based image generation framework with anovel semantic-aware denoising process. The model enables fine-grained controlover the generated image on object, scene, and background levels with text,image, and layout conditions. We also establish the first benchmark fornontypical aspect-ratio image generation for visual storytelling includingmediums like paintings, comics, and cinematic panoramas, with customizedmetrics for systematic evaluation. Through comparative and ablation studies,MagicScroll showcases promising results in aligning with the narrative text,improving visual coherence, and engaging the audience. We plan to release thecode and benchmark in the hope of a better collaboration between AI researchersand creative practitioners involving visual storytelling.

MagicScroll: Nontypical Aspect-Ratio Image Generation for Visual Storytelling via Multi-Layered Semantic-Aware Denoising