# Wan: Open and Advanced Large-Scale Video Generative Models (technical report)

- id: src_01M569CM8D6VSGWJJF2Q5JT32E
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=reported_by_source, review=unreviewed
- categories: T06 · tags: wan, technical-report, vae, data-curation, evaluation, dit, level:advanced
- updated: 2026-10-18T01:18:01.985Z
- url: https://veyrul.com/sources/src_01M569CM8D6VSGWJJF2Q5JT32E

Source: https://arxiv.org/abs/2503.20314

```json
{
  "archived": null,
  "checked_at": "2026-10-17T00:00:00.000Z",
  "parent_url": null,
  "usefulness": "Primary technical report for the Wan family: new VAE, scalable pre-training, data curation and automated evaluation; 1.3B and 14B sizes supporting up to eight tasks. Needed to ground claims about Wan capabilities in the authors' own methodology.",
  "source_type": "paper",
  "availability": "available",
  "dependencies": [],
  "source_owner": "Team Wan (Alibaba)",
  "next_check_at": "2026-10-24T00:00:00.000Z",
  "successor_url": null,
  "hardware_notes": null,
  "known_limitations": [
    "Abstract only was read; benchmark claims are the authors' own and were not examined."
  ],
  "upstream_updated_at": null
}
```

Evidence claims:
- [upstream_claim] arXiv listing: submitted 2025-03-26 (v1), revised 2025-04-19 (v2); abstract says 14B outperforms open and commercial generators on several benchmarks and the 1.3B runs with about 8.19 GB VRAM on consumer GPUs. · source: https://arxiv.org/abs/2503.20314
- [upstream_claim] Research review verdict at export: reviewed (as of 2026-10-17). Recorded by the research pipeline, not a hub review outcome; evidence.review stays unreviewed until an independent reviewer records one.

Rights:
- documentation: license=unknown hosting=unknown redistribution=unknown modification=unknown training=unknown · arXiv paper license was not read. · evidence: https://arxiv.org/abs/2503.20314

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