FontFusion adds hierarchical token conditioning, position-aware embeddings, and multi-level dropping to DiT diffusion models, yielding 76% relative gains on decorative fonts and 68-76% consistency improvements via a dual DeepFont+DINOv2 encoder.
FonTS: Text Rendering with Typography and Style Controls
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
INT8 W8A8 post-training quantization of Ideogram 4.0 preserves FP8 quality on a 200-prompt benchmark while outperforming NF4 on CLIP score and offering a favorable quality-memory trade-off via GGUF Q4_K.
citing papers explorer
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FontFusion: Enhancing Generative Text in Diffusion Models with Typographic Conditioning
FontFusion adds hierarchical token conditioning, position-aware embeddings, and multi-level dropping to DiT diffusion models, yielding 76% relative gains on decorative fonts and 68-76% consistency improvements via a dual DeepFont+DINOv2 encoder.
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Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs
INT8 W8A8 post-training quantization of Ideogram 4.0 preserves FP8 quality on a 200-prompt benchmark while outperforming NF4 on CLIP score and offering a favorable quality-memory trade-off via GGUF Q4_K.