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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.
Input
Prompt
Close-up shot of an elderly sailor wearing a yellow raincoat, seated on the deck of a catamaran, slowly puffing on a pipe. His cat lies quietly beside him with eyes closed, enjoying the calm. The warm glow of the setting sun bathes the scene, with gentle waves lapping against the hull and a few seabirds circling slowly above. The camera slowly pushes in, capturing this peaceful and harmonious moment.
Długość: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Input
Prompt
she looks up
Długość: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Input
Prompt
she looks up, and then looks back
Długość: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Przykładowe prompty
Przykłady z katalogu Railwail. Nie zostały wygenerowane na żywo na tej stronie.
Animate
Długość: 0:05
Subject turns head and smiles
03
O Wan 2.2 Image-to-Video
Krótko mówiącStan na 23 września 2026
Wan 2.2 Image-to-Video to model opracowany przez Alibaba (Wan) w kategorii Generowanie wideo. W serwisie Railwail Wan 2.2 Image-to-Video kosztuje 0,060 USD za wideo. W zależności od ustawień cena wynosi od 0,060 USD/wideo do 0,174 USD/wideo.
Tło
O Alibaba (Tongyi Wanxiang Lab)
Założona 1999 · Hangzhou, China
Alibaba's Tongyi Lab runs the Qwen LLM family and the Wanxiang generative-media family (image and video). 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 two purpose-tuned variants -- Image-to-Video (Wan-I2V) and Text-to-Video (Wan-T2V) -- alongside an audio/A2V variant. Wan 2.2 Image-to-Video is positioned for animating a user-supplied first frame with strong identity preservation and rich motion, and remains under the permissive Wan-series licence that enables broad commercial and research use.
Diffusion Transformer (DiT) with image-conditioning adapters and 3D causal VAE
Wan 2.2 Image-to-Video is a Diffusion Transformer operating on a 3D causal Wan-VAE latent. The model is initialised from a Wan 2.2 base and fine-tuned for image-to-video with dedicated conditioning adapters that inject features from the user-provided first frame at multiple resolutions of the DiT, ensuring strong identity preservation and continuity. Text conditioning uses a Qwen-family multilingual encoder with strong Chinese-English capability. Native generation is 5 seconds at 720p / 24 fps (with 1080p in extended modes). Wan 2.2's MoE-style scaling and improved 3D RoPE enable better motion physics and reduced identity drift relative to Wan 2.1. The training recipe includes a curriculum mixing image-to-image, image-to-short-video and image-to-long-video data with synthetic dense captions. The release is open-weight on Hugging Face and GitHub under the Wan-series permissive licence.
Parametry
14 billion (I2V flagship); smaller variants available
Możliwości
Open-weight image-to-video model with strong identity preservation
Bilingual Chinese/English prompts via Qwen-based text encoder
Identity-preserving conditioning on a user first frame
Permissive Wan-series licence for research and commercial use
Active community ecosystem on Hugging Face / ComfyUI
Compatible with LoRA fine-tunes and reference adapters
Reproducible training recipe documented in technical reports
Best for: open-source image animation pipelines, e-commerce product motion, character animation.
Trening i licencja
Curated multi-million-clip multilingual video corpus with paired first-frame conditioning data and dense bilingual captions; specifics documented in the Wan 2.2 technical materials.
Licencja: Open weights under an Apache-style permissive licence (Wan-series release).
Testy bezpieczeństwa: Data-level filtering and recommended NSFW classifier for deployment; Alibaba-hosted endpoints follow Chinese CAC algorithmic-content rules.
Znane ograniczenia
Native duration 5 seconds
Identity can drift on long extensions
No native audio
High VRAM requirements for the 14B flagship
Closed leaders (Veo 3, Sora 2, Kling v3) still ahead on absolute fidelity
curl https://railwail.com/api/v1/videos/generations \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "wan-i2v",
"prompt": "A slow drone shot over a misty pine forest at sunrise",
"start_image": "https://example.com/first-frame.jpg"
}'
# 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-i2v",
"prompt": "A slow drone shot over a misty pine forest at sunrise",
"start_image": "https://example.com/first-frame.jpg",
},
).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-i2v",
prompt: "A slow drone shot over a misty pine forest at sunrise",
start_image: "https://example.com/first-frame.jpg"
}),
}).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 Image-to-Video to model opracowany przez Alibaba (Wan) w kategorii Generowanie wideo. W serwisie Railwail możesz go wywołać za pomocą klucza API poprzez API Railwail.
Ile kosztuje Wan 2.2 Image-to-Video w serwisie Railwail?
W serwisie Railwail Wan 2.2 Image-to-Video kosztuje 0,060 USD za wideo. W zależności od ustawień cena wynosi od 0,060 USD/wideo do 0,174 USD/wideo. Cena jest znana przed rozpoczęciem przebiegu. Użycie jest opłacane z przedpłaconych kredytów; 1 kredyt równa się 0,01 USD.
Jakie ustawienia obsługuje Wan 2.2 Image-to-Video?
Zgodnie ze schematem wejściowym, Wan 2.2 Image-to-Video obsługuje te parametry: prompt, start_image i end_image.
Jak szybki jest Wan 2.2 Image-to-Video?
Dla Wan 2.2 Image-to-Video jest jeszcze zbyt mało zmierzonych przebiegów w serwisie Railwail, aby podać czas przebiegu. Zależy to od wejścia, ustawień i obciążenia u dostawcy.
Czy Wan 2.2 Image-to-Video jest lepszy niż Google Veo 3.1?
To zależy od zadania. Wan 2.2 Image-to-Video (Alibaba (Wan)) i Google Veo 3.1 (Google DeepMind) to oba modele z kategorii Generowanie wideo. Strona porównania pokazuje ich ceny i specyfikacje obok siebie.
Czy Wan 2.2 Image-to-Video może użyć obrazu jako szablonu?
Tak. Wyślij obraz w parametrze start_image.
Jak używać Wan 2.2 Image-to-Video przez API?
Utwórz klucz API Railwail i wyślij swoje żądanie z ID modelu wan-i2v. Przykłady kodu dla curl, Python i JavaScript znajdują się w sekcji API na tej stronie.
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.
