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Utilizzabile 24 ore dopo l'iscrizione, fino a 5 esecuzioni al giorno e al massimo 2 crediti per esecuzione. Altri metodi di accesso iniziano senza crediti.
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.
Durata: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Input
Prompt
she looks up
Durata: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Input
Prompt
she looks up, and then looks back
Durata: 0:05
Settings
resolution
480p
num_frames
81
frames_per_second
16
Prompt di esempio
Esempi dal catalogo Railwail. Non sono stati generati live su questa pagina.
Animate
Durata: 0:05
Subject turns head and smiles
03
Informazioni su Wan 2.2 Image-to-Video
RiassuntoA partire da 23 settembre 2026
Wan 2.2 Image-to-Video è un modello di Alibaba (Wan) nella categoria Generazione video. Su Railwail, Wan 2.2 Image-to-Video costa 0,060 USD per video. A seconda delle impostazioni, il prezzo varia da 0,060 USD/video a 0,174 USD/video.
Sfondo
Informazioni su Alibaba (Tongyi Wanxiang Lab)
Fondato 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.
Parametri
14 billion (I2V flagship); smaller variants available
CapacitÃ
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.
Addestramento e licenza
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.
Licenza: Open weights under an Apache-style permissive licence (Wan-series release).
Test di sicurezza: Data-level filtering and recommended NSFW classifier for deployment; Alibaba-hosted endpoints follow Chinese CAC algorithmic-content rules.
Limitazioni note
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 è un modello di Alibaba (Wan) nella categoria Generazione video. Su Railwail puoi richiamarlo con una chiave API tramite l'API Railwail.
Quanto costa Wan 2.2 Image-to-Video su Railwail?
Su Railwail, Wan 2.2 Image-to-Video costa 0,060 USD per video. A seconda delle impostazioni, il prezzo varia da 0,060 USD/video a 0,174 USD/video. Il prezzo è noto prima dell'inizio dell'esecuzione. L'utilizzo viene pagato con crediti prepagati; 1 credito equivale a 0,01 USD.
Quali impostazioni supporta Wan 2.2 Image-to-Video?
Secondo il suo schema di input, Wan 2.2 Image-to-Video conosce questi parametri: prompt, start_image e end_image.
Quanto è veloce Wan 2.2 Image-to-Video?
Non ci sono ancora abbastanza esecuzioni misurate di Wan 2.2 Image-to-Video su Railwail per indicare un tempo di esecuzione. Dipende dall'input, dalle impostazioni e dal carico presso il provider.
Wan 2.2 Image-to-Video è migliore di Google Veo 3.1?
Dipende dall'attività . Wan 2.2 Image-to-Video (Alibaba (Wan)) e Google Veo 3.1 (Google DeepMind) sono entrambi modelli nella categoria Generazione video. La pagina di confronto mostra i loro prezzi e le specifiche affiancati.
Wan 2.2 Image-to-Video può usare un'immagine come input?
Sì. Invia l'immagine nel parametro start_image.
Come uso Wan 2.2 Image-to-Video tramite l'API?
Crea una chiave API Railwail e invia la tua richiesta con l'ID modello wan-i2v. Gli esempi di codice per curl, Python e JavaScript sono nella sezione API di questa pagina.
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.
