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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.
Duración: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Input
Prompt
she looks up
Duración: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Input
Prompt
she looks up, and then looks back
Duración: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Prompts de ejemplo
Ejemplos del catálogo de Railwail. No fueron generados en vivo en esta página.
Animate
Duración: 0:05
Subject turns head and smiles
03
Acerca de Wan 2.2 Image-to-Video
ResumenA fecha de 23 de septiembre de 2026
Wan 2.2 Image-to-Video es un modelo de Alibaba (Wan) en la categoría Generación de vídeos. En Railwail, Wan 2.2 Image-to-Video cuesta 0,060 US$ por vídeo. Según la configuración, el precio oscila entre 0,060 US$/vídeo y 0,174 US$/vídeo.
Fondo
Acerca de Alibaba (Tongyi Wanxiang Lab)
Fundado en 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.
Parámetros
14 billion (I2V flagship); smaller variants available
Capacidades
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.
Entrenamiento y licencia
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.
Licencia: Open weights under an Apache-style permissive licence (Wan-series release).
Pruebas de seguridad: Data-level filtering and recommended NSFW classifier for deployment; Alibaba-hosted endpoints follow Chinese CAC algorithmic-content rules.
Limitaciones conocidas
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 es un modelo de Alibaba (Wan) en la categoría Generación de vídeos. En Railwail puedes llamarlo con una clave API a través de la API de Railwail.
¿Cuánto cuesta Wan 2.2 Image-to-Video en Railwail?
En Railwail, Wan 2.2 Image-to-Video cuesta 0,060 US$ por vídeo. Según la configuración, el precio oscila entre 0,060 US$/vídeo y 0,174 US$/vídeo. El precio se conoce antes de que comience la ejecución. El uso se paga con créditos prepagados; 1 crédito equivale a 0,01 US$.
¿Qué configuraciones admite Wan 2.2 Image-to-Video?
Según su esquema de entrada, Wan 2.2 Image-to-Video conoce estos parámetros: prompt, start_image y end_image.
¿Qué velocidad tiene Wan 2.2 Image-to-Video?
Aún no hay suficientes ejecuciones medidas de Wan 2.2 Image-to-Video en Railwail para indicar un tiempo de ejecución. Depende de la entrada, la configuración y la carga en el proveedor.
¿Es Wan 2.2 Image-to-Video mejor que Google Veo 3.1?
Depende de la tarea. Wan 2.2 Image-to-Video (Alibaba (Wan)) y Google Veo 3.1 (Google DeepMind) son ambos modelos en la categoría Generación de vídeos. La página de comparación muestra sus precios y especificaciones lado a lado.
¿Puede Wan 2.2 Image-to-Video usar una imagen como entrada?
Sí. Envía la imagen en el parámetro start_image.
¿Cómo uso Wan 2.2 Image-to-Video a través de la API?
Crea una clave API de Railwail y envía tu solicitud con el ID de modelo wan-i2v. Los ejemplos de código para curl, Python y JavaScript están en la sección API de esta página.
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.
