Spaces:
Running
Running
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8" /> | |
| <meta name="viewport" content="width=device-width, initial-scale=1" /> | |
| <title>Wan 2.2 — open large-scale video generative models</title> | |
| <meta name="description" content="Reference for Wan 2.2, the open video generation model family from Alibaba's Tongyi Lab: architecture, released checkpoints, and hosted API usage." /> | |
| <link rel="canonical" href="https://wavespeed.ai/collections/wan-2-2?utm_source=huggingface&utm_medium=space&utm_campaign=wan2_2" /> | |
| <meta property="og:type" content="website" /> | |
| <meta property="og:title" content="Wan 2.2 — open large-scale video generative models" /> | |
| <meta property="og:description" content="Reference for Wan 2.2, the open video generation model family from Alibaba's Tongyi Lab: architecture, released checkpoints, and hosted API usage." /> | |
| <meta property="og:url" content="https://wavespeed.ai/collections/wan-2-2" /> | |
| <meta name="twitter:card" content="summary_large_image" /> | |
| <link rel="stylesheet" href="style.css" /> | |
| </head> | |
| <body> | |
| <header class="site-header"> | |
| <div class="wrap"> | |
| <a class="brand" href="https://wavespeed.ai?utm_source=huggingface&utm_medium=space&utm_campaign=wan2_2" target="_blank" rel="noopener"> | |
| <span class="brand-mark" aria-hidden="true"></span> | |
| <span>WaveSpeed AI</span> | |
| </a> | |
| <nav class="header-nav"> | |
| <a class="jump opt" href="#architecture">Architecture</a> | |
| <a class="jump opt" href="#checkpoints">Checkpoints</a> | |
| <a class="jump" href="#run">Run it</a> | |
| <a class="jump" href="#resources">Resources</a> | |
| <a href="https://wavespeed.ai/collections/wan-2-2?utm_source=huggingface&utm_medium=space&utm_campaign=wan2_2" target="_blank" rel="noopener">wavespeed.ai ↗</a> | |
| </nav> | |
| </div> | |
| </header> | |
| <main class="wrap"> | |
| <div class="hero"> | |
| <p class="eyebrow">Alibaba · Tongyi Lab</p> | |
| <h1>Wan 2.2</h1> | |
| <p class="lede">An open family of large-scale video generative models covering text-to-video, image-to-video and a compact hybrid text/image-to-video checkpoint. Weights are published on Hugging Face under Apache-2.0.</p> | |
| <ul class="meta"> | |
| <li><b>Developer</b> Alibaba Tongyi Lab</li> | |
| <li><b>Task</b> text-to-video · image-to-video</li> | |
| <li><b>License</b> Apache-2.0</li> | |
| <li><b>Released</b> July 2025</li> | |
| </ul> | |
| </div> | |
| <section id="architecture"> | |
| <h2>What changed in 2.2</h2> | |
| <p class="section-note">Wan 2.2 revises the 2.1 architecture in three places. The claims below are the authors' own, taken from the model cards and technical report.</p> | |
| <div class="grid"> | |
| <div class="card"> | |
| <h3>Mixture-of-experts denoiser</h3> | |
| <p>The denoising trajectory is split across specialised expert models rather than one monolithic network, which raises total parameter count without a matching rise in per-step inference cost.</p> | |
| </div> | |
| <div class="card"> | |
| <h3>Curated aesthetic supervision</h3> | |
| <p>Training data carries explicit labels for lighting, composition, contrast and colour tone, so cinematographic attributes can be steered from the prompt instead of emerging by chance.</p> | |
| </div> | |
| <div class="card"> | |
| <h3>Larger motion corpus</h3> | |
| <p>The authors report training on 65.6% more images and 83.2% more video than Wan 2.1, aimed primarily at motion fidelity and prompt adherence.</p> | |
| </div> | |
| <div class="card"> | |
| <h3>High-compression VAE</h3> | |
| <p>The TI2V-5B checkpoint pairs with a Wan2.2-VAE at a 16×16×4 compression ratio, which is what makes 720p/24fps generation practical at that model size.</p> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="checkpoints"> | |
| <h2>Released checkpoints</h2> | |
| <p class="section-note">All weights are on the Hugging Face Hub under the Wan-AI organisation.</p> | |
| <div class="table-scroll"> | |
| <table> | |
| <thead><tr><th>Checkpoint</th><th>Task</th><th>Params</th><th>Weights</th></tr></thead> | |
| <tbody> | |
| <tr><td><code>Wan2.2-T2V-A14B</code></td><td>Text-to-video</td><td>14B (MoE)</td><td><a href="https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B" target="_blank" rel="noopener">base</a> · <a href="https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers" target="_blank" rel="noopener">diffusers</a></td></tr> | |
| <tr><td><code>Wan2.2-I2V-A14B</code></td><td>Image-to-video</td><td>14B (MoE)</td><td><a href="https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B" target="_blank" rel="noopener">base</a> · <a href="https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers" target="_blank" rel="noopener">diffusers</a></td></tr> | |
| <tr><td><code>Wan2.2-TI2V-5B</code></td><td>Text + image-to-video, 720p/24fps</td><td>5B</td><td><a href="https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B" target="_blank" rel="noopener">base</a> · <a href="https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers" target="_blank" rel="noopener">diffusers</a></td></tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| </section> | |
| <section id="run"> | |
| <h2>Run it</h2> | |
| <p class="section-note">If you would rather not provision GPUs, the same checkpoints are served as a hosted endpoint. Available variants: <code>wan-2.2/t2v-480p</code>, <code>t2v-720p</code>, <code>i2v-480p</code> and <code>i2v-720p</code>.</p> | |
| <div class="code"> | |
| <div class="code-tabs" role="tablist"> | |
| <button type="button" role="tab" aria-selected="true" data-panel="run-0">cURL</button> | |
| <button type="button" role="tab" aria-selected="false" data-panel="run-1">Python</button> | |
| <button type="button" role="tab" aria-selected="false" data-panel="run-2">JavaScript</button> | |
| </div> | |
| <pre id="run-0" role="tabpanel"><code># 1. submit the job | |
| curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/t2v-480p" \ | |
| -H "Authorization: Bearer $WAVESPEED_API_KEY" \ | |
| -H "Content-Type: application/json" \ | |
| -d '{ | |
| "prompt": "A paper boat drifting down a rain-slicked gutter at dusk, shallow depth of field, warm street lights", | |
| "duration": 5, | |
| "enable_sync_mode": false | |
| }' | |
| # -> {"code": 200, "data": {"id": "<request-id>", "status": "created", ...}} | |
| # 2. poll until status is "completed" | |
| curl "https://api.wavespeed.ai/api/v3/predictions/<request-id>/result" \ | |
| -H "Authorization: Bearer $WAVESPEED_API_KEY" | |
| # -> {"code": 200, "data": {"status": "completed", "outputs": ["https://..."]}}</code></pre> | |
| <pre id="run-1" role="tabpanel" hidden><code>import os, time, requests | |
| API = "https://api.wavespeed.ai/api/v3" | |
| KEY = os.environ["WAVESPEED_API_KEY"] | |
| HEADERS = {"Authorization": f"Bearer {KEY}"} | |
| # submit | |
| res = requests.post( | |
| f"{API}/wavespeed-ai/wan-2.2/t2v-480p", | |
| headers={**HEADERS, "Content-Type": "application/json"}, | |
| json={ | |
| "prompt": "A paper boat drifting down a rain-slicked gutter at dusk, shallow depth of field, warm street lights", | |
| "duration": 5, | |
| "enable_sync_mode": false | |
| }, | |
| timeout=30, | |
| ) | |
| res.raise_for_status() | |
| request_id = res.json()["data"]["id"] | |
| # poll | |
| while True: | |
| data = requests.get( | |
| f"{API}/predictions/{request_id}/result", | |
| headers=HEADERS, | |
| timeout=30, | |
| ).json()["data"] | |
| if data["status"] == "completed": | |
| print(data["outputs"][0]) | |
| break | |
| if data["status"] == "failed": | |
| raise RuntimeError(data.get("error", "generation failed")) | |
| time.sleep(1.5)</code></pre> | |
| <pre id="run-2" role="tabpanel" hidden><code>const API = "https://api.wavespeed.ai/api/v3"; | |
| const KEY = process.env.WAVESPEED_API_KEY; | |
| const headers = { Authorization: `Bearer ${KEY}` }; | |
| // submit | |
| const submit = await fetch(`${API}/wavespeed-ai/wan-2.2/t2v-480p`, { | |
| method: "POST", | |
| headers: { ...headers, "Content-Type": "application/json" }, | |
| body: JSON.stringify({ | |
| "prompt": "A paper boat drifting down a rain-slicked gutter at dusk, shallow depth of field, warm street lights", | |
| "duration": 5, | |
| "enable_sync_mode": false | |
| }), | |
| }); | |
| const { data: { id } } = await submit.json(); | |
| // poll | |
| for (;;) { | |
| const res = await fetch(`${API}/predictions/${id}/result`, { headers }); | |
| const { data } = await res.json(); | |
| if (data.status === "completed") { | |
| console.log(data.outputs[0]); | |
| break; | |
| } | |
| if (data.status === "failed") throw new Error(data.error ?? "generation failed"); | |
| await new Promise((r) => setTimeout(r, 1500)); | |
| }</code></pre> | |
| </div> | |
| <div class="callout"><p>Requests are asynchronous: <code>POST</code> returns a request id, then you poll <code>/predictions/<id>/result</code> until <code>status</code> is <code>completed</code>. Set <code>enable_sync_mode: true</code> to have the call block and return outputs directly.</p><p>API keys are created in the <a href="https://wavespeed.ai/dashboard?utm_source=huggingface&utm_medium=space&utm_campaign=wan2_2" target="_blank" rel="noopener">WaveSpeed dashboard</a>.</p></div> | |
| <div class="btn-row"> | |
| <a class="btn" href="https://wavespeed.ai/collections/wan-2-2?utm_source=huggingface&utm_medium=space&utm_campaign=wan2_2" target="_blank" rel="noopener">Open Wan 2.2 on WaveSpeed</a> | |
| <a class="btn secondary" href="https://wavespeed.ai/docs?utm_source=huggingface&utm_medium=space&utm_campaign=wan2_2" target="_blank" rel="noopener">API reference</a> | |
| </div> | |
| </section> | |
| <section id="resources"> | |
| <h2>Resources</h2> | |
| <ul class="links"> | |
| <li><a href="https://huggingface.co/Wan-AI" target="_blank" rel="noopener"><span>Wan-AI on Hugging Face</span><span class="host">huggingface.co</span></a></li> | |
| <li><a href="https://github.com/Wan-Video/Wan2.2" target="_blank" rel="noopener"><span>Wan2.2 on GitHub</span><span class="host">github.com</span></a></li> | |
| <li><a href="https://wavespeed.ai/collections/wan-2-2?utm_source=huggingface&utm_medium=space&utm_campaign=wan2_2" target="_blank" rel="noopener"><span>Hosted endpoints</span><span class="host">wavespeed.ai</span></a></li> | |
| <li><a href="https://wavespeed.ai/docs?utm_source=huggingface&utm_medium=space&utm_campaign=wan2_2" target="_blank" rel="noopener"><span>WaveSpeed API docs</span><span class="host">wavespeed.ai</span></a></li> | |
| </ul> | |
| </section> | |
| </main> | |
| <footer class="site-footer"> | |
| <div class="wrap"> | |
| <p>This page is a model reference maintained by WaveSpeed AI. The model itself is developed and released by its respective authors; trademarks belong to them. WaveSpeed AI provides hosted inference for it.</p> | |
| <div class="footer-links"> | |
| <a href="https://wavespeed.ai?utm_source=huggingface&utm_medium=space&utm_campaign=wan2_2" target="_blank" rel="noopener">WaveSpeed AI</a> | |
| <a href="https://wavespeed.ai/docs?utm_source=huggingface&utm_medium=space&utm_campaign=wan2_2" target="_blank" rel="noopener">Docs</a> | |
| <a href="https://huggingface.co/wavespeed" target="_blank" rel="noopener">Hugging Face</a> | |
| </div> | |
| </div> | |
| </footer> | |
| <script src="tabs.js"></script> | |
| </body> | |
| </html> | |