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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.
Længde: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Input
Prompt
she looks up
Længde: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Input
Prompt
she looks up, and then looks back
Længde: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Eksempelprompts
Eksempler fra Railwail-kataloget. De blev ikke genereret live på denne side.
Animate
Længde: 0:05
Subject turns head and smiles
03
Om Wan 2.2 Image-to-Video
Kort sagtFra 23. september 2026
Wan 2.2 Image-to-Video er en model af Alibaba (Wan) i kategorien Videogenerering. På Railwail koster Wan 2.2 Image-to-Video 0,060 US$ pr. video. Afhængigt af indstillingerne ligger prisen mellem 0,060 US$/video og 0,174 US$/video.
Baggrund
Om Alibaba (Tongyi Wanxiang Lab)
Grundlagt 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.
Parametre
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.
Træning og 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).
Sikkerhedstests: Data-level filtering and recommended NSFW classifier for deployment; Alibaba-hosted endpoints follow Chinese CAC algorithmic-content rules.
Kendte begrænsninger
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 er en model fra Alibaba (Wan) i kategorien Videogenerering. På Railwail kan du kalde den med en API-nøgle via Railwail API.
Hvad koster Wan 2.2 Image-to-Video på Railwail?
På Railwail koster Wan 2.2 Image-to-Video 0,060 US$ pr. video. Afhængigt af indstillingerne ligger prisen mellem 0,060 US$/video og 0,174 US$/video. Prisen kendes før kørslen starter. Forbrug betales fra forudbetalte credits; 1 credit svarer til 0,01 US$.
Hvilke indstillinger understøtter Wan 2.2 Image-to-Video?
Ifølge dens inputskema kender Wan 2.2 Image-to-Video disse parametre: prompt, start_image og end_image.
Hvor hurtig er Wan 2.2 Image-to-Video?
Der er endnu ikke nok målte kørsler af Wan 2.2 Image-to-Video på Railwail til at angive en udførelsestid. Det afhænger af inputtet, indstillingerne og belastningen hos provideren.
Er Wan 2.2 Image-to-Video bedre end Google Veo 3.1?
Det afhænger af opgaven. Wan 2.2 Image-to-Video (Alibaba (Wan)) og Google Veo 3.1 (Google DeepMind) er begge modeller i kategorien Videogenerering. Sammenligningssiden viser deres priser og specifikationer side om side.
Kan Wan 2.2 Image-to-Video bruge et billede som input?
Ja. Send billedet i parameteren start_image.
Hvordan bruger jeg Wan 2.2 Image-to-Video via API'en?
Opret en Railwail API-nøgle og send din anmodning med model-ID'en wan-i2v. Kodeeksempler til curl, Python og JavaScript findes i API-sektionen på denne side.
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.
