ChArtist generates pictorial charts via a Diffusion Transformer using skeleton-based spatial control and reference-image subject control, supported by a new 30,000-triplet dataset and data accuracy metric.
Ex- ploring clip for assessing the look and feel of images
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
SMFSR achieves state-of-the-art perceptual quality among one-step diffusion-based real-world super-resolution methods by preserving noise-started generation via LR-conditioned SplitMeanFlow and GAN refinement.
FlowSR reformulates SR as a rectified flow and applies consistency distillation with HR regularization plus fast-slow scheduling to enable single-step high-quality super-resolution.
citing papers explorer
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ChArtist: Generating Pictorial Charts with Unified Spatial and Subject Control
ChArtist generates pictorial charts via a Diffusion Transformer using skeleton-based spatial control and reference-image subject control, supported by a new 30,000-triplet dataset and data accuracy metric.
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Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement
SMFSR achieves state-of-the-art perceptual quality among one-step diffusion-based real-world super-resolution methods by preserving noise-started generation via LR-conditioned SplitMeanFlow and GAN refinement.
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Fast Image Super-Resolution via Consistency Rectified Flow
FlowSR reformulates SR as a rectified flow and applies consistency distillation with HR regularization plus fast-slow scheduling to enable single-step high-quality super-resolution.