Satın alma olmayan hesaplar için: 2 kredinin üzerindeki çalıştırmalar bir yükleme gerektirir.
Yeni misiniz?
Google ile kaydolduğunuzda 10 ücretsiz kredi ($0,10)
Kaydolduktan 24 saat sonra kullanılabilir, günde 5 çalıştırmaya kadar ve çalıştırma başına en fazla 2 kredi. Diğer oturum açma yöntemleri kredi olmadan başlar.
02
Examples
Real outputs from the public examples of this model on Replicate, with the prompt and settings that produced them. They were not generated live on this page.
Prompt
A sports car is driving very fast along a beach at sunset
Uzunluk: 0:05
Settings
aspect_ratio
16:9
resolution
480p
num_frames
81
frames_per_second
16
Prompt
A sports car is driving very fast along a beach at sunset
Uzunluk: 0:05
Settings
aspect_ratio
16:9
resolution
480p
num_frames
81
frames_per_second
16
Prompt
A sports car is driving very fast along a beach at sunset
Uzunluk: 0:05
Settings
aspect_ratio
16:9
resolution
480p
num_frames
81
frames_per_second
16
Örnek İstemler
Railwail kataloğundan örnekler. Bunlar bu sayfada canlı olarak oluşturulmadı.
Quick
Uzunluk: 0:05
Cat playing with yarn on wooden floor
03
Wan 2.2 Text-to-Video Hakkında
Özet23 Eylül 2026 itibariyle
Wan 2.2 Text-to-Video, Alibaba (Wan) tarafından Video Oluşturma kategorisinde geliştirilen bir modeldir. Railwail üzerinde Wan 2.2 Text-to-Video maliyeti $0,060 video başına. Ayarlara bağlı olarak fiyat $0,060/video ile $0,12/video arasında değişir. Desteklenen en boy oranları: 16:9 ve 9:16.
Arka plan
Alibaba (Tongyi Wanxiang Lab) hakkında
Kuruluş yılı 1999 · Hangzhou, China
Alibaba's Tongyi Lab in Hangzhou runs the Qwen LLM family and the Wanxiang generative-media family. After Wan 2.0 (mid-2024) and Wan 2.1 (early 2025), the team released Wan 2.2 in 2025 as the next-generation open-weight video model. Wan 2.2 ships as purpose-tuned variants for Text-to-Video, Image-to-Video and Audio/A2V. Wan 2.2 Text-to-Video is the flagship pure-text-conditioned variant and replaces Wan 2.1 T2V-14B as the principal open-weight text-to-video reference for the Chinese research community. The Wan team consistently rank near the top of open-model VBench leaderboards and ship reproducible training code under a permissive Wan-series licence.
Diffusion Transformer (DiT) with 3D causal VAE; MoE-style scaling
Wan 2.2 Text-to-Video is a Diffusion Transformer operating on a 3D causal Wan-VAE latent. Wan 2.2 introduces architectural refinements over Wan 2.1: improved 3D Rotary Position Embeddings, larger attention windows, and (in the flagship) a Mixture-of-Experts feed-forward design that routes tokens to specialist experts. Text conditioning uses a Qwen-family multilingual encoder with strong Chinese-English capability. The denoiser is trained with Flow Matching on a curated multi-million-clip multilingual video corpus with synthetic dense bilingual captions. Native generation is 5 seconds at 720p / 24 fps (with 1080p extensions). The training recipe and weights are open-source on Hugging Face and GitHub under the Wan-series permissive licence, designed to enable broad commercial and research use.
Parametreler
14 billion (flagship); smaller variants available
Yetenekler
Open-weight text-to-video flagship at 14B parameters (smaller variants available)
Bilingual Chinese/English prompts via Qwen-based text encoder
MoE-style scaling and improved 3D RoPE in flagship variant
Permissive Wan-series licence for research and commercial use
Top-tier results on VBench among open-weight models
Active community ecosystem (LoRAs, fine-tunes, ComfyUI nodes)
Reproducible training recipe and code
Best for: open-source video pipelines, research, on-prem creative tooling, branded fine-tunes.
Eğitim ve lisans
Curated multi-million-clip multilingual video corpus filtered for aesthetics, motion and caption quality, with dense bilingual captions; specifics documented in Wan technical materials.
Lisans: Open weights under an Apache-style permissive licence (Wan-series release).
Güvenlik testleri: Data-level filtering and recommended NSFW classifier for deployment; Alibaba-hosted endpoints follow Chinese CAC algorithmic-content rules.
Bilinen sınırlamalar
Native duration 5 seconds
No native audio
High VRAM requirements for the 14B flagship
Closed leaders (Veo 3, Sora 2, Kling v3) still ahead on absolute fidelity
Resolution capped at 720p natively (1080p only in extended modes)
curl https://railwail.com/api/v1/videos/generations \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "wan-t2v",
"prompt": "A slow drone shot over a misty pine forest at sunrise"
}'
# Response: {"job_id": "...", "status": "queued", ...}
# Poll until status is completed, failed or cancelled:
curl https://railwail.com/api/v1/jobs/JOB_ID \
-H "Authorization: Bearer $RAILWAIL_API_KEY"
import os
import time
import requests
API = "https://railwail.com/api/v1"
headers = {"Authorization": f"Bearer {os.environ['RAILWAIL_API_KEY']}"}
job = requests.post(
f"{API}/videos/generations",
headers=headers,
json={
"model": "wan-t2v",
"prompt": "A slow drone shot over a misty pine forest at sunrise",
},
).json()
while True:
status = requests.get(f"{API}/jobs/{job['job_id']}", headers=headers).json()
if status["status"] in ("completed", "failed", "cancelled"):
break
time.sleep(5)
print(status["status"], status.get("output_url"))
const API = "https://railwail.com/api/v1";
const headers = {
Authorization: `Bearer ${process.env.RAILWAIL_API_KEY}`,
"Content-Type": "application/json",
};
const job = await fetch(`${API}/videos/generations`, {
method: "POST",
headers,
body: JSON.stringify({
model: "wan-t2v",
prompt: "A slow drone shot over a misty pine forest at sunrise"
}),
}).then((r) => r.json());
let status;
do {
await new Promise((r) => setTimeout(r, 5000));
status = await fetch(`${API}/jobs/${job.job_id}`, { headers }).then((r) => r.json());
} while (!["completed", "failed", "cancelled"].includes(status.status));
console.log(status.status, status.output_url);
Wan 2.2 Text-to-Video, Alibaba (Wan) tarafından Video Oluşturma kategorisinde oluşturulan bir modeldir. Railwail üzerinden Railwail API'si aracılığıyla bir API anahtarı ile çağırabilirsiniz.
Wan 2.2 Text-to-Video Railwail'de ne kadar maliyetlidir?
Railwail üzerinde Wan 2.2 Text-to-Video maliyeti $0,060 video başına. Ayarlara bağlı olarak fiyat $0,060/video ile $0,12/video arasında değişir. Fiyat çalıştırma başlamadan önce belli olur. Kullanım ön ödemeli kredilerden ödenir; 1 kredi $0,01 değerindedir.
Wan 2.2 Text-to-Video hangi ayarları destekler?
Giriş şemasına göre Wan 2.2 Text-to-Video şu parametreleri bilir: prompt ve aspect_ratio (16:9 veya 9:16).
Wan 2.2 Text-to-Video ne kadar hızlıdır?
Railwail'de Wan 2.2 Text-to-Video için henüz çalıştırma süresi belirtmek için yeterli ölçülen çalıştırma yoktur. Bu, giriş, ayarlar ve sağlayıcıdaki yüke bağlıdır.
Wan 2.2 Text-to-Video, Google Veo 3.1'dan daha iyi midir?
Bu göreve bağlıdır. Wan 2.2 Text-to-Video (Alibaba (Wan)) ve Google Veo 3.1 (Google DeepMind) her ikisi de Video Oluşturma kategorisinde modellerdir. Karşılaştırma sayfası fiyatlarını ve özelliklerini yan yana gösterir.
Wan 2.2 Text-to-Video'yi API aracılığıyla nasıl kullanırım?
Bir Railwail API anahtarı oluşturun ve isteğinizi wan-t2v model kimliğiyle gönderin. curl, Python ve JavaScript için kod örnekleri bu sayfanın API bölümündedir.
MiniMax Hailuo 02 on Replicate. Text-to-video and image-to-video producing 6s or 10s clips at 768p standard or 1080p pro. Known for accurate real-world physics and stable motion.
