[{"data":1,"prerenderedAt":824},["ShallowReactive",2],{"blog-post-what-is-wan-2-2,zh-tw":3},{"id":4,"title":5,"body":6,"description":813,"extension":814,"meta":815,"navigation":819,"path":820,"seo":821,"stem":822,"__hash__":823},"blog/blog/what-is-wan-2-2/zh-TW.md","理解 Wan2.2：AI 影片創作的次世代",{"type":7,"value":8,"toc":780},"minimark",[9,14,18,22,26,29,32,35,38,41,44,47,50,53,56,59,82,85,89,103,106,120,123,137,141,144,147,183,186,205,208,262,269,272,275,306,309,337,340,343,346,350,402,408,426,430,433,480,483,486,490,520,588,591,594,608,660,663,666,669,689,692,695,715,718,721,724,727,730,733,736,750,753,756,770,776],[10,11,13],"h1",{"id":12},"探索-wan22革命性的-ai-影片生成技術","探索 Wan2.2：革命性的 AI 影片生成技術",[15,16,17],"p",{},"人工智慧影片製作的格局隨著 Wan2.2 的到來而發生了變革，它代表了生成式影片技術的重大躍進。這個尖端平台匯集了創新的架構設計和增強的功能，重新定義了我們對 AI 驅動內容創作的方法。",[19,20,21],"h2",{"id":21},"核心技術創新",[23,24,25],"h3",{"id":25},"先進的專家架構系統",[15,27,28],{},"Wan2.2 實現了專為影片生成工作流設計的複雜混合專家（MoE）框架。這個智能系統採用專門的專家網路來處理影片創建過程的不同階段，在保持運算效率的同時有效地將模型容量翻倍。",[23,30,31],{"id":31},"專業級視覺品質",[15,33,34],{},"平台整合了精心策劃的美學資料集，具有包括照明、構圖、對比度級別和色彩分級在內的電影攝影元素的全面註釋。這種增強使用戶能夠精確控制視覺風格，創建具有可自訂藝術特徵的內容。",[23,36,37],{"id":37},"增強的動作合成",[15,39,40],{},"基於大量資料集擴展——圖像內容增加 65.6%，影片材料增長 83.2%——Wan2.2 在多項評估指標上展現出在動作生成、語義理解和美學品質方面的卓越性能。",[23,42,43],{"id":43},"優化的高解析度處理",[15,45,46],{},"平台採用精簡的 50 億參數模型，使用先進的 Wan2.2-VAE 壓縮系統，實現了驚人的 16×16×4 壓縮比。該模型在 720P 解析度下以 24fps 性能提供文字轉影片和圖像轉影片生成，使包括 RTX 4090 顯卡在內的消費級硬體可以使用。",[19,48,49],{"id":49},"模型規格和性能",[15,51,52],{},"我們的旗艦 T2V-A14B 模型支援在 480P 和 720P 解析度下創建 5 秒影片。採用 MoE 架構構建，它提供卓越的影片生成品質，在我們專有的 Wan-Bench 2.0 評估框架上的多項評估標準中超越了領先的商業解決方案。",[19,54,55],{"id":55},"最新發展",[23,57,58],{"id":58},"最新更新",[60,61,62,70,76],"ul",{},[63,64,65,69],"li",{},[66,67,68],"strong",{},"2025年7月28日","：發布了 Wan2.2 的綜合推理代碼和模型權重",[63,71,72,75],{},[66,73,74],{},"社群整合","：ComfyUI 和 Diffusers 相容性的持續開發",[63,77,78,81],{},[66,79,80],{},"多平台支援","：針對各種硬體配置的增強部署選項",[23,83,84],{"id":84},"開發路線圖",[86,87,88],"h4",{"id":88},"文字轉影片能力",[60,90,91,94,97,100],{},[63,92,93],{},"✅ A14B 和 14B 模型的多 GPU 推理實現",[63,95,96],{},"✅ 完整的模型檢查點可用",[63,98,99],{},"🔄 ComfyUI 插件整合",[63,101,102],{},"🔄 Diffusers 框架相容性",[86,104,105],{"id":105},"圖像轉影片功能",[60,107,108,111,114,117],{},[63,109,110],{},"✅ A14B 模型的多 GPU 推理支援",[63,112,113],{},"✅ 模型檢查點可存取",[63,115,116],{},"🔄 ComfyUI 整合進行中",[63,118,119],{},"🔄 Diffusers 支援開發",[86,121,122],{"id":122},"混合文字-圖像轉影片",[60,124,125,128,131,134],{},[63,126,127],{},"✅ 5B 模型的多 GPU 推理",[63,129,130],{},"✅ 檢查點可用性",[63,132,133],{},"🔄 ComfyUI 相容性",[63,135,136],{},"🔄 Diffusers 整合",[19,138,140],{"id":139},"wan22-入門","Wan2.2 入門",[23,142,143],{"id":143},"系統要求和設定",[15,145,146],{},"首先複製專案儲存庫：",[148,149,154],"pre",{"className":150,"code":151,"language":152,"meta":153,"style":153},"language-bash shiki shiki-themes github-light github-dark","git clone https://github.com/Wan-Video/Wan2.2.git\ncd Wan2.2\n","bash","",[155,156,157,173],"code",{"__ignoreMap":153},[158,159,162,166,170],"span",{"class":160,"line":161},"line",1,[158,163,165],{"class":164},"sScJk","git",[158,167,169],{"class":168},"sZZnC"," clone",[158,171,172],{"class":168}," https://github.com/Wan-Video/Wan2.2.git\n",[158,174,176,180],{"class":160,"line":175},2,[158,177,179],{"class":178},"sj4cs","cd",[158,181,182],{"class":168}," Wan2.2\n",[15,184,185],{},"安裝必要的相依性（需要 PyTorch 2.4.0 或更高版本）：",[148,187,189],{"className":150,"code":188,"language":152,"meta":153,"style":153},"pip install -r requirements.txt\n",[155,190,191],{"__ignoreMap":153},[158,192,193,196,199,202],{"class":160,"line":161},[158,194,195],{"class":164},"pip",[158,197,198],{"class":168}," install",[158,200,201],{"class":178}," -r",[158,203,204],{"class":168}," requirements.txt\n",[23,206,207],{"id":207},"可用的模型變體",[209,210,211,227],"table",{},[212,213,214],"thead",{},[215,216,217,221,224],"tr",{},[218,219,220],"th",{},"模型類型",[218,222,223],{},"儲存庫連結",[218,225,226],{},"功能",[228,229,230,242,252],"tbody",{},[215,231,232,236,239],{},[233,234,235],"td",{},"T2V-A14B",[233,237,238],{},"🤗 Huggingface 🤖 ModelScope",[233,240,241],{},"文字轉影片 MoE 架構，支援 480P 和 720P",[215,243,244,247,249],{},[233,245,246],{},"I2V-A14B",[233,248,238],{},[233,250,251],{},"圖像轉影片 MoE 架構，支援 480P 和 720P",[215,253,254,257,259],{},[233,255,256],{},"TI2V-5B",[233,258,238],{},[233,260,261],{},"高壓縮 VAE，雙重 T2V+I2V 功能，720P 能力",[15,263,264,265,268],{},"💡 ",[66,266,267],{},"注意","：TI2V-5B 模型提供優化性能的 24 FPS 720P 影片生成。",[23,270,271],{"id":271},"模型安裝",[15,273,274],{},"使用 Hugging Face CLI：",[148,276,278],{"className":150,"code":277,"language":152,"meta":153,"style":153},"pip install \"huggingface_hub[cli]\"\nhuggingface-cli download Wan-AI/Wan2.2-T2V-A14B --local-dir ./Wan2.2-T2V-A14B\n",[155,279,280,289],{"__ignoreMap":153},[158,281,282,284,286],{"class":160,"line":161},[158,283,195],{"class":164},[158,285,198],{"class":168},[158,287,288],{"class":168}," \"huggingface_hub[cli]\"\n",[158,290,291,294,297,300,303],{"class":160,"line":175},[158,292,293],{"class":164},"huggingface-cli",[158,295,296],{"class":168}," download",[158,298,299],{"class":168}," Wan-AI/Wan2.2-T2V-A14B",[158,301,302],{"class":178}," --local-dir",[158,304,305],{"class":168}," ./Wan2.2-T2V-A14B\n",[15,307,308],{},"使用 ModelScope CLI：",[148,310,312],{"className":150,"code":311,"language":152,"meta":153,"style":153},"pip install modelscope\nmodelscope download Wan-AI/Wan2.2-T2V-A14B --local_dir ./Wan2.2-T2V-A14B\n",[155,313,314,323],{"__ignoreMap":153},[158,315,316,318,320],{"class":160,"line":161},[158,317,195],{"class":164},[158,319,198],{"class":168},[158,321,322],{"class":168}," modelscope\n",[158,324,325,328,330,332,335],{"class":160,"line":175},[158,326,327],{"class":164},"modelscope",[158,329,296],{"class":168},[158,331,299],{"class":168},[158,333,334],{"class":178}," --local_dir",[158,336,305],{"class":168},[19,338,339],{"id":339},"影片生成工作流程",[23,341,342],{"id":342},"基礎文字轉影片生成",[15,344,345],{},"平台支援 Wan2.2-T2V-A14B 模型在多種解析度下同時創建影片。",[86,347,349],{"id":348},"單-gpu-實現","單 GPU 實現",[148,351,353],{"className":150,"code":352,"language":152,"meta":153,"style":153},"python generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --offload_model True --convert_model_dtype --prompt \"兩隻風格化的貓穿著彩色拳擊裝備，在明亮的舞台燈光下參與激烈比賽的動態場景。\"\n",[155,354,355],{"__ignoreMap":153},[158,356,357,360,363,366,369,372,375,378,381,384,387,390,393,396,399],{"class":160,"line":161},[158,358,359],{"class":164},"python",[158,361,362],{"class":168}," generate.py",[158,364,365],{"class":178}," --task",[158,367,368],{"class":168}," t2v-A14B",[158,370,371],{"class":178}," --size",[158,373,374],{"class":168}," 1280",[158,376,377],{"class":178},"*",[158,379,380],{"class":168},"720",[158,382,383],{"class":178}," --ckpt_dir",[158,385,386],{"class":168}," ./Wan2.2-T2V-A14B",[158,388,389],{"class":178}," --offload_model",[158,391,392],{"class":168}," True",[158,394,395],{"class":178}," --convert_model_dtype",[158,397,398],{"class":178}," --prompt",[158,400,401],{"class":168}," \"兩隻風格化的貓穿著彩色拳擊裝備，在明亮的舞台燈光下參與激烈比賽的動態場景。\"\n",[15,403,264,404,407],{},[66,405,406],{},"硬體要求","：建議最小 80GB VRAM",[15,409,264,410,413,414,417,418,421,422,425],{},[66,411,412],{},"記憶體優化","：使用 ",[155,415,416],{},"--offload_model True","、",[155,419,420],{},"--convert_model_dtype"," 和 ",[155,423,424],{},"--t5_cpu"," 標誌來減少 GPU 記憶體消耗",[86,427,429],{"id":428},"使用-fsdp-deepspeed-的分散式處理","使用 FSDP + DeepSpeed 的分散式處理",[15,431,432],{},"使用 PyTorch FSDP 和 DeepSpeed Ulysses 獲得增強性能：",[148,434,436],{"className":150,"code":435,"language":152,"meta":153,"style":153},"torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt \"兩隻風格化的貓穿著彩色拳擊裝備，在明亮的舞台燈光下參與激烈比賽的動態場景。\"\n",[155,437,438],{"__ignoreMap":153},[158,439,440,443,446,448,450,452,454,456,458,460,462,464,467,470,473,476,478],{"class":160,"line":161},[158,441,442],{"class":164},"torchrun",[158,444,445],{"class":178}," --nproc_per_node=8",[158,447,362],{"class":168},[158,449,365],{"class":178},[158,451,368],{"class":168},[158,453,371],{"class":178},[158,455,374],{"class":168},[158,457,377],{"class":178},[158,459,380],{"class":168},[158,461,383],{"class":178},[158,463,386],{"class":168},[158,465,466],{"class":178}," --dit_fsdp",[158,468,469],{"class":178}," --t5_fsdp",[158,471,472],{"class":178}," --ulysses_size",[158,474,475],{"class":178}," 8",[158,477,398],{"class":178},[158,479,401],{"class":168},[23,481,482],{"id":482},"進階提示增強",[15,484,485],{},"為了獲得卓越的影片品質，我們建議透過兩種主要方法使用提示增強功能：",[86,487,489],{"id":488},"透過-dashscope-api-的雲端增強","透過 Dashscope API 的雲端增強",[491,492,493,496,503,517],"ol",{},[63,494,495],{},"從官方入口網站獲取 Dashscope API 金鑰",[63,497,498,499,502],{},"配置 ",[155,500,501],{},"DASH_API_KEY"," 環境變數",[63,504,505,506,509,510,516],{},"對於國際用戶，將 ",[155,507,508],{},"DASH_API_URL"," 設定為 '",[511,512,513],"a",{"href":513,"rel":514},"https://dashscope-intl.aliyuncs.com/api/v1",[515],"nofollow","'",[63,518,519],{},"使用提示增強執行：",[148,521,523],{"className":150,"code":522,"language":152,"meta":153,"style":153},"DASH_API_KEY=你的金鑰 torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt \"動態拳擊貓場景\" --use_prompt_extend --prompt_extend_method 'dashscope' --prompt_extend_target_lang 'zh-TW'\n",[155,524,525],{"__ignoreMap":153},[158,526,527,530,534,537,540,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,573,576,579,582,585],{"class":160,"line":161},[158,528,501],{"class":529},"sVt8B",[158,531,533],{"class":532},"szBVR","=",[158,535,536],{"class":168},"你的金鑰",[158,538,539],{"class":164}," torchrun",[158,541,445],{"class":178},[158,543,362],{"class":168},[158,545,365],{"class":178},[158,547,368],{"class":168},[158,549,371],{"class":178},[158,551,374],{"class":168},[158,553,377],{"class":178},[158,555,380],{"class":168},[158,557,383],{"class":178},[158,559,386],{"class":168},[158,561,466],{"class":178},[158,563,469],{"class":178},[158,565,472],{"class":178},[158,567,475],{"class":178},[158,569,398],{"class":178},[158,571,572],{"class":168}," \"動態拳擊貓場景\"",[158,574,575],{"class":178}," --use_prompt_extend",[158,577,578],{"class":178}," --prompt_extend_method",[158,580,581],{"class":168}," 'dashscope'",[158,583,584],{"class":178}," --prompt_extend_target_lang",[158,586,587],{"class":168}," 'zh-TW'\n",[86,589,590],{"id":590},"本地模型增強",[15,592,593],{},"根據可用的 GPU 記憶體使用本地 Qwen 模型進行提示增強：",[60,595,596,602],{},[63,597,598,601],{},[66,599,600],{},"文字轉影片","：Qwen2.5-14B-Instruct、Qwen2.5-7B-Instruct 或 Qwen2.5-3B-Instruct",[63,603,604,607],{},[66,605,606],{},"圖像轉影片","：Qwen2.5-VL-7B-Instruct 或 Qwen2.5-VL-3B-Instruct",[148,609,611],{"className":150,"code":610,"language":152,"meta":153,"style":153},"torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt \"動態拳擊貓場景\" --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'zh-TW'\n",[155,612,613],{"__ignoreMap":153},[158,614,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,656,658],{"class":160,"line":161},[158,616,442],{"class":164},[158,618,445],{"class":178},[158,620,362],{"class":168},[158,622,365],{"class":178},[158,624,368],{"class":168},[158,626,371],{"class":178},[158,628,374],{"class":168},[158,630,377],{"class":178},[158,632,380],{"class":168},[158,634,383],{"class":178},[158,636,386],{"class":168},[158,638,466],{"class":178},[158,640,469],{"class":178},[158,642,472],{"class":178},[158,644,475],{"class":178},[158,646,398],{"class":178},[158,648,572],{"class":168},[158,650,575],{"class":178},[158,652,578],{"class":178},[158,654,655],{"class":168}," 'local_qwen'",[158,657,584],{"class":178},[158,659,587],{"class":168},[19,661,662],{"id":662},"技術架構深度解析",[23,664,665],{"id":665},"混合專家實現",[15,667,668],{},"Wan2.2 的 MoE 架構代表了影片生成的革命性方法，具有：",[60,670,671,677,683],{},[63,672,673,676],{},[66,674,675],{},"雙專家設計","：高雜訊專家用於初始佈局階段，低雜訊專家用於細節精煉",[63,678,679,682],{},[66,680,681],{},"智慧切換","：基於信雜比（SNR）閾值的自動轉換",[63,684,685,688],{},[66,686,687],{},"高效資源使用","：總共 270 億參數，每個推理步驟只有 140 億參數活躍",[23,690,691],{"id":691},"高壓縮影片技術",[15,693,694],{},"TI2V-5B 模型透過以下方式實現卓越的效率：",[60,696,697,703,709],{},[63,698,699,702],{},[66,700,701],{},"先進的 VAE 壓縮","：4×16×16 壓縮比加上額外的分塊",[63,704,705,708],{},[66,706,707],{},"統一框架","：支援文字轉影片和圖像轉影片任務的單一模型",[63,710,711,714],{},[66,712,713],{},"消費級硬體相容性","：在 RTX 4090 上不到 9 分鐘生成 720P 影片",[19,716,717],{"id":717},"性能基準",[15,719,720],{},"Wan2.2 在 Wan-Bench 2.0 基準的多個評估維度上相比領先的商業模型展現出卓越性能，為開源影片生成技術建立了新標準。",[19,722,723],{"id":723},"社群和支援",[23,725,726],{"id":726},"開源承諾",[15,728,729],{},"所有模型都在 Apache 2.0 授權條款下發布，在維護負責任使用指引的同時確保廣泛的可存取性。用戶在遵守道德使用標準的同時保留對生成內容的完整權利。",[23,731,732],{"id":732},"社群參與",[15,734,735],{},"透過 Discord 和微信頻道加入我們不斷壯大的社群：",[60,737,738,741,744,747],{},[63,739,740],{},"技術支援和討論",[63,742,743],{},"合作機會",[63,745,746],{},"最新更新和公告",[63,748,749],{},"社群創作展示",[19,751,752],{"id":752},"未來方向",[15,754,755],{},"Wan2.2 專案繼續發展，正在進行的研究包括：",[60,757,758,761,764,767],{},[63,759,760],{},"增強的動作合成能力",[63,762,763],{},"改進的運算效率",[63,765,766],{},"擴展的平台整合",[63,768,769],{},"先進的美學控制功能",[15,771,772],{},[773,774,775],"em",{},"本文提供了 Wan2.2 功能和實現的概覽。有關詳細的技術文檔和最新更新，請訪問我們的官方儲存庫和社群頻道。",[777,778,779],"style",{},"html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: 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