Semantic Line Diffusion: Character-Consistent Line Art from text-annotated Storyboards

Generating clean line art from conti, rough storyboard sketchesused in webtoon production, remains a labor-intensive process requiringsubstantial artistic expertise. Conti sketches provide only coarse geom-etry and sparse semantic cues, making it difficult to recover detailedline art while preserving consistent character identity across sequentialpanels. Existing sketch-to-image or diffusion-based translation modelsprocess images independently and therefore struggle to maintain cross-panel identity coherence. We present PanelDiff, a diffusion transformerframework for conti-to-line-art generation that explicitly models cross-panel character consistency. PanelDiff integrates three components. Amulti-reference character conditioning module encodes character labelsand multiple line-art exemplars into compact identity tokens for robustidentity guidance under ambiguous conti inputs. A memory-augmentedrepresentation bank accumulates character-aware features from previ-ously generated panels, while a similarity interpreter dynamically re-trieves relevant entries for the current panel. A panel-aware diffusiontransformer then jointly attends to conti structure, textual descriptions,identity tokens, and retrieved memory features to produce coherent line-art sequences. To support this task, we construct a conti–line-art datasetwith character identity annotations and sequential panel structures. Ex-periments show that PanelDiff improves line-art fidelity, identity preser-vation, and cross-panel consistency over strong baselines, and ablationstudies verify the contribution of each component. The code and datasetare available online (huggingface) ⋆ .