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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.
Längd: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Input
Prompt
she looks up
Längd: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Input
Prompt
she looks up, and then looks back
Längd: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Exempelprompts
Exempel från Railwail-katalogen. De genererades inte live på denna sida.
Animate
Längd: 0:05
Subject turns head and smiles
03
Om Wan 2.2 Image-to-Video
Kort sagtFrån och med 23 september 2026
Wan 2.2 Image-to-Video är en modell av Alibaba (Wan) i kategorin Videogenerering. På Railwail kostar Wan 2.2 Image-to-Video 0,060 US$ per video. Beroende på inställningarna varierar priset mellan 0,060 US$/video och 0,174 US$/video.
Bakgrund
Om Alibaba (Tongyi Wanxiang Lab)
Grundat 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.
Parameter
14 billion (I2V flagship); smaller variants available
Funktioner
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.
Träning & licens
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.
Licens: Open weights under an Apache-style permissive licence (Wan-series release).
Säkerhetstestning: Data-level filtering and recommended NSFW classifier for deployment; Alibaba-hosted endpoints follow Chinese CAC algorithmic-content rules.
Kända begränsningar
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 är en modell av Alibaba (Wan) i kategorin Videogenerering. På Railwail kan du anropa den med en API-nyckel via Railwail API.
Vad kostar Wan 2.2 Image-to-Video på Railwail?
På Railwail kostar Wan 2.2 Image-to-Video 0,060 US$ per video. Beroende på inställningarna varierar priset mellan 0,060 US$/video och 0,174 US$/video. Priset är känt innan körningen startar. Användningen betalas från förbetald kredit; 1 kredit motsvarar 0,01 US$.
Vilka inställningar stöder Wan 2.2 Image-to-Video?
Enligt sitt inmatningsschema känner Wan 2.2 Image-to-Video till dessa parametrar: prompt, start_image och end_image.
Hur snabb är Wan 2.2 Image-to-Video?
Det finns ännu inte tillräckligt många uppmätta körningar av Wan 2.2 Image-to-Video på Railwail för att ange en körningstid. Det beror på inmatningen, inställningarna och belastningen hos leverantören.
Är Wan 2.2 Image-to-Video bättre än Google Veo 3.1?
Det beror på uppgiften. Wan 2.2 Image-to-Video (Alibaba (Wan)) och Google Veo 3.1 (Google DeepMind) är båda modeller i kategorin Videogenerering. Jämförelsesidan visar deras priser och specifikationer sida vid sida.
Kan Wan 2.2 Image-to-Video använda en bild som inmatning?
Ja. Skicka bilden i parametern start_image.
Hur använder jag Wan 2.2 Image-to-Video via API:et?
Skapa en Railwail API-nyckel och skicka din begäran med modell-ID:t wan-i2v. Kodexempel för curl, Python och JavaScript finns i API-avsnittet på denna sida.
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.
