atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video

gambar-ke-video

TURBO

Wan 2.2 Turbo Spicy Infinite Image-to-Video API by Atlas Cloud

atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video

Infinite-image-to-video

Image-to-video model for segmented prompt video generation with stable motion and 30fps workflow post-processing.

INPUT

Memuat konfigurasi parameter...

OUTPUT

Menunggu

Video yang dihasilkan akan muncul di sini

Konfigurasikan pengaturan Anda dan klik Jalankan untuk memulai

Permintaan Anda akan dikenakan biaya $0.02 per eksekusi. Dengan $10 Anda dapat menjalankan model ini sekitar 500 kali.

Berikut yang dapat Anda lakukan selanjutnya:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameter

Contoh kode
import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video",
    "prompt": "A beautiful sunset over the ocean with gentle waves",
    "width": 512,
    "height": 512,
    "duration": 3,
    "fps": 24,
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated video:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

video_url = check_status()

Instalasi

Instal paket yang diperlukan untuk bahasa pemrograman Anda.

bash

pip install requests

Autentikasi

Semua permintaan API memerlukan autentikasi melalui API key. Anda bisa mendapatkan API key dari dasbor Atlas Cloud.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Jaga keamanan API key Anda

Jangan pernah mengekspos API key Anda di kode sisi klien atau repositori publik. Gunakan variabel lingkungan atau proxy backend sebagai gantinya.

Kirim permintaan

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Kirim Permintaan

Kirim permintaan pembuatan asinkron. API mengembalikan prediction ID yang dapat Anda gunakan untuk memeriksa status dan mengambil hasil.

POST/api/v1/model/generateVideo

Isi Permintaan

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video",
    "input": {
        "prompt": "A beautiful sunset over the ocean with gentle waves"
    }
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")

Respons

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Periksa Status

Polling prediction endpoint untuk memeriksa status permintaan Anda saat ini.

GET/api/v1/model/prediction/{prediction_id}

Contoh Polling

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Nilai Status

processingPermintaan masih diproses.

completedPembuatan selesai. Output tersedia.

succeededPembuatan berhasil. Output tersedia.

failedPembuatan gagal. Periksa field error.

Respons Selesai

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Unggah File

Unggah file ke penyimpanan Atlas Cloud dan dapatkan URL yang dapat Anda gunakan dalam permintaan API Anda. Gunakan multipart/form-data untuk mengunggah.

POST/api/v1/model/uploadMedia

Contoh Unggah

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Respons

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

Parameter berikut diterima di isi permintaan.

Total: 0Wajib: 0Opsional: 0

Tidak ada parameter yang tersedia.

Contoh Isi Permintaan

json

{
  "model": "atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video"
}

Output Schema

API mengembalikan respons prediction dengan URL output yang dihasilkan.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for video generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

Contoh Respons

json

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills mengintegrasikan 300+ model AI langsung ke asisten pengkodean AI Anda. Satu perintah untuk menginstal, lalu gunakan bahasa alami untuk menghasilkan gambar, video, dan mengobrol dengan LLM.

Klien yang Didukung

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ klien yang didukung

Instalasi

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Atur API Key

Dapatkan API key dari dasbor Atlas Cloud dan atur sebagai variabel lingkungan.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

Kemampuan

Setelah diinstal, Anda dapat menggunakan bahasa alami di asisten AI Anda untuk mengakses semua model Atlas Cloud.

Pembuatan GambarBuat gambar dengan model seperti Nano Banana 2, Z-Image, dan lainnya.

Pembuatan VideoBuat video dari teks atau gambar dengan Kling, Vidu, Veo, dll.

Obrolan LLMMengobrol dengan Qwen, DeepSeek, dan model bahasa besar lainnya.

Unggah MediaUnggah file lokal untuk pengeditan gambar dan alur kerja gambar-ke-video.

Pelajari lebih lanjut

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server menghubungkan IDE Anda dengan 300+ model AI melalui Model Context Protocol. Berfungsi dengan klien apa pun yang kompatibel dengan MCP.

Klien yang Didukung

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ klien yang didukung

Instalasi

bash

npx -y atlascloud-mcp

Konfigurasi

Tambahkan konfigurasi berikut ke file pengaturan MCP di IDE Anda.

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Alat yang Tersedia

atlas_generate_imageBuat gambar dari prompt teks.

atlas_generate_videoBuat video dari teks atau gambar.

atlas_chatMengobrol dengan model bahasa besar.

atlas_list_modelsJelajahi 300+ model AI yang tersedia.

atlas_quick_generatePembuatan konten satu langkah dengan pemilihan model otomatis.

atlas_upload_mediaUnggah file lokal untuk alur kerja API.

Pelajari lebih lanjut

github.com/AtlasCloudAI/mcp-server

Schema API

Schema tidak tersedia

Silakan masuk untuk melihat riwayat permintaan

Anda perlu masuk untuk mengakses riwayat permintaan model Anda.

Masuk

Wan 2.2 Turbo Spicy Infinite Image-to-Video

Model Overview

Field	Description
Model Name	`atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video`
Model Type	Advanced Image-to-Video Generation
Core Architecture	Mixture-of-Experts (MoE)
Active Parameters	14B
Variant	Base
Tuning	Spicy-tuned post-processing pipeline (adult-oriented)

Wan 2.2 Turbo Spicy Infinite Image-to-Video is an enhanced image-to-video model built on the Wan 2.2 foundation. Inheriting the Mixture-of-Experts (MoE) architecture and cinematic-level aesthetics of the original Wan series, this variant introduces two breakthroughs — inference acceleration and infinite-length generation — and ships with a spicy-tuned post-processing pipeline for adult-oriented creative work.

Key Features & Innovations

1. Ultra-Fast Inference: 4-Step Distillation with RCM

To address the high latency typical of large-scale models, we apply specialized sampling optimization and knowledge distillation:

RCM (Refined Consistency Model) Sampler — a more efficient ODE solver that significantly improves single-step sampling quality.
4-Step Distillation — denoising steps are compressed to 4 steps through multi-stage distillation, enabling cinematic-grade generation at a fraction of the original cost and unlocking low-latency interaction.

2. Infinite-Length Generation: Anchor-Frame Autoregressive Architecture

A targeted retraining gives the model an advanced temporal extension mechanism that breaks the duration limits of traditional video models:

Anchor-Frame Evolution — automatically extracts key "anchor frames" during generation as global temporal references.
Dual-Frame Constraint (Anchor + Last Frame) — combines the structural consistency of the global anchor frame with the motion continuity of the previous frame to construct video sequences autoregressively.
Semantic Stability — subject identity, scene details, and lighting stay consistent across multi-minute outputs, suppressing semantic drift and logical collapse.

3. Cinematic-Level Aesthetics (Inherited)

The model retains the curated training foundation of Wan 2.2:

Precise Control — detailed labels for lighting, composition, and color tone.
Complex Motion — superior generation of realistic, fluid motion across diverse semantics.

Why Infinite?

Most image-to-video models lock you into a single short clip (5–10 s). Infinite extends that into a controlled multi-segment clip — output duration equals prompt_count × duration_per_segment, up to 6 prompts x 5 s. Direct each segment with its own prompt; the API returns one server-stitched 30 fps MP4.

Prompts	Per-segment	Total output
1	5 s	5 s
3	5 s	15 s
6	5 s	30 s

60-second Quickstart

curl -X POST https://api.atlascloud.ai/api/v1/model/generateVideo \
  -H "Authorization: Bearer $APIKEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video",
    "image": "https://static.atlascloud.ai/media/images/db548fe3bd5cafa4ef7e0141d69c8566.jpeg",
    "prompt": [
      "She turns slowly toward the camera, golden hour light hitting her face.",
      "She walks forward through the wheat field, hand brushing the tops.",
      "Close-up: a single tear catches the sun as she smiles."
    ],
    "duration": 5,
    "resolution": "720p"
  }'

Returns one MP4 — segments are stitched server-side at 30 fps.

Base vs LoRA — which one?

	Base (this model)	LoRA variant
Model name	`atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video`	`…/infinite-image-to-video-lora`
Price (480 p, per second)	$0.020	$0.026 (+30 %)
Best for	Standard runs, fast iteration, bulk drafts	Higher fidelity, fine-grained control
Recommended for	Pre-production, A/B prompts	Final renders

Switch the variant by changing model only — all other fields are identical.

Request Fields

Field	Type	Required	Notes
`model`	string	✅	`atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video`
`image`	string (URL)	✅	Source frame; jpg/png
`prompt`	string[]	✅	Must be a JSON array. Plain string is rejected.
`duration`	number	✅	Fixed at 5 s per segment.
`resolution`	string	optional	`480p`, `720p`, or `1080p`. Defaults to `720p`.
`seed`	number	optional	`-1` for random

Pricing — at a glance

price = $0.020 × max(1, prompt_count) × max(5, duration_seconds) × resolution_factor
                                                                     480p → 1
                                                                     720p → 2
                                                                     1080p → 3

Common combos:

Prompts	Duration	Resolution	Total
1	5 s	480 p	$0.10
1	5 s	720 p	$0.20
1	5 s	1080 p	$0.30
3	5 s	720 p	$0.60
6	5 s	720 p	$1.20
6	5 s	1080 p	$1.80

Output Spec

Format: MP4 (H.264)
Frame rate: 30 fps (post-processed)
Resolution: 480 p / 720 p / 1080 p tiers, aspect-ratio preserving
Audio: none

Intended Use & Applications

Cinematic Long-Take Production — high-fidelity, consistent long-duration shots without manual stitching.
Low-Latency Interactive Content — leverage 4-step distillation for live broadcasts and AI-driven interactive installations.
Advanced Image-to-Video (I2V) — transform a static image into infinite, naturally moving visual scrolls via anchor-frame technology.
Professional Pre-visualization — minutes-long dynamic storyboards that compress pre-production time.

Content Policy

This model is tuned for adult-oriented (NSFW) generation. By calling it you confirm:

All depicted subjects are 18 +.
You hold the rights to the source image.
You will not generate content depicting real, identifiable people without their explicit consent.

Violations may result in account suspension.

Limitations

prompt must be a JSON array, never a plain string.
While anchor-frame technology suppresses cross-segment drift, it does not fully eliminate it — long prompts sharing fine identity details across many segments may still show minor variation.
480 p generates ~2× faster than 720 p; use 480 p for drafts.

LoRA variant: atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video-lora
Non-spicy alias: atlascloud/wan-2.2-turbo/infinite-image-to-video

Note: This model is designed to empower the creative community. Users are expected to follow AI ethical guidelines and copyright regulations.