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      "statement": "# SANA/SANA-Sprint: efficient linear diffusion and hybrid few-step integration\n\nIntegrated deep-compression autoencoding, linear diffusion attention, a decoder-only text encoder and Flow-DPM sampling/caption-selection choices for efficient high-resolution synthesis. Later Sprint combines continuous-time consistency and latent adversarial distillation with step-adaptive sampling and interactive ControlNet.\n\n## Scientific scope\n\nCompression, linear attention, diffusion transformers and language encoders are inherited. Delta is the specific combined design and sampler/training study, not invention of linear attention. Original measured resolution/latency comparisons are hardware/configuration dependent. Sprint inherits sCM/LADD/ControlNet. Training-free flow-model conversion does not make the complete distillation training-free. Same family bundled rather than checkpoint counting.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nEnze Xie; Junsong Chen; Junyu Chen; Han Cai; Haotian Tang; Yujun Lin; Zhekai Zhang; Muyang Li; Ligeng Zhu; Yao Lu; Song Han; Shuchen Xue; Yuyang Zhao; Jincheng Yu; Sayak Paul\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers](https://arxiv.org/abs/2410.10629v1)\n- [SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation](https://arxiv.org/abs/2503.09641v1)\n",
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