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

图生视频

TURBO

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

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

Infinite-image-to-video-lora

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

输入

正在加载参数配置...

输出

空闲

生成的视频将在这里显示

配置参数后点击运行开始生成

每次运行将花费 $0.026。$10 可运行约 384 次。

你可以继续：

Seedance 2.0 Kling v3 Vidu Wan2.7

参数

代码示例
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-lora",
    "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()

安装

安装所需的依赖包。

bash

pip install requests

认证

所有 API 请求需要通过 API Key 进行认证。您可以在 Atlas Cloud 控制台获取 API Key。

bash

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

HTTP 请求头

python

import os

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

保护好您的 API Key

切勿在客户端代码或公开仓库中暴露您的 API Key。请使用环境变量或后端代理。

提交请求

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())

提交请求

提交一个异步生成请求。API 返回一个 prediction ID，您可以用它来检查状态和获取结果。

POST/api/v1/model/generateVideo

请求体

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-lora",
    "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']}")

响应

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

检查状态

轮询 prediction 端点以检查请求的当前状态。

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

轮询示例

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)

状态值

processing请求仍在处理中。

completed生成完成，输出可用。

succeeded生成成功，输出可用。

failed生成失败，请检查 error 字段。

完成响应

{
  "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"
  }
}

上传文件

将文件上传到 Atlas Cloud 存储，获取可在 API 请求中使用的 URL。使用 multipart/form-data 上传。

POST/api/v1/model/uploadMedia

上传示例

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}")

响应

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

Input Schema

以下参数在请求体中被接受。

总计: 0必填: 0可选: 0

暂无可用参数。

请求体示例

json

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

Output Schema

API 返回包含生成输出 URL 的 prediction 响应。

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

响应示例

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 将 300+ AI 模型直接集成到您的 AI 编程助手中。一条命令安装，即可用自然语言生成图像、视频和与 LLM 对话。

支持的客户端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支持的客户端

安装

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

设置 API Key

从 Atlas Cloud 控制台获取 API Key，并将其设置为环境变量。

bash

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

功能

安装后，您可以在 AI 助手中使用自然语言访问所有 Atlas Cloud 模型。

图像生成使用 Nano Banana 2、Z-Image 等模型生成图像。

视频创作使用 Kling、Vidu、Veo 等模型从文本或图像创建视频。

LLM 对话与 Qwen、DeepSeek 等大语言模型对话。

媒体上传上传本地文件用于图像编辑和图生视频工作流。

MCP Server

Atlas Cloud MCP Server 通过 Model Context Protocol 将您的 IDE 与 300+ AI 模型连接。支持任何兼容 MCP 的客户端。

支持的客户端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支持的客户端

安装

bash

npx -y atlascloud-mcp

配置

将以下配置添加到您的 IDE 的 MCP 设置文件中。

json

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

可用工具

atlas_generate_image从文本提示生成图像。

atlas_generate_video从文本或图像创建视频。

atlas_chat与大语言模型对话。

atlas_list_models浏览 300+ 可用 AI 模型。

atlas_quick_generate一步式内容创建，自动选择最佳模型。

atlas_upload_media上传本地文件用于 API 工作流。

了解更多

github.com/AtlasCloudAI/mcp-server

API Schema

Schema 不可用

请登录以查看请求历史

您需要登录才能访问模型请求历史记录。

Wan 2.2 Turbo Spicy Infinite Image-to-Video — LoRA

Model Overview

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

The LoRA variant of Wan 2.2 Turbo Spicy Infinite Image-to-Video. Same Infinite segmented-prompt mechanic and acceleration stack as the base model, with LoRA-grade fidelity and motion stability for final renders. Built on the Wan 2.2 Mixture-of-Experts (MoE) foundation with a spicy-tuned post-processing pipeline for adult-oriented creative work.

Key Features & Innovations

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

RCM (Refined Consistency Model) Sampler — efficient ODE solver that improves single-step sampling quality.
4-Step Distillation — denoising compressed to 4 steps, enabling cinematic-grade generation at low latency. LoRA inference is ~10–20 % slower than base but stays well within interactive territory.

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

Anchor-Frame Evolution — automatically extracts key "anchor frames" during generation as global temporal references.
Dual-Frame Constraint (Anchor + Last Frame) — combines global structural consistency with motion continuity to construct video sequences autoregressively.
Semantic Stability — LoRA further sharpens identity and detail consistency across multi-minute outputs.

3. Cinematic-Level Aesthetics (Inherited + LoRA-Enhanced)

Precise Control — detailed labels for lighting, composition, color tone.
Complex Motion — fluid motion across diverse semantics.
Fine-Grained Fidelity — LoRA adapter delivers sharper textures, more stable identities, and stylistic depth that the base variant cannot match on its own.

Why Infinite?

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

When to Pick the LoRA Variant

Final renders, not drafts — the quality margin is worth the +30 % price.
Subjects with fine identity details that must stay consistent across segments.
Stylized motion or lighting that the base model under-delivers on.

For early iteration / bulk drafts, use the base: atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video (cheaper, faster).

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-lora",
    "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.

Request Fields

Field	Type	Required	Notes
`model`	string	✅	`atlascloud/wan-2.2-turbo-spicy/infinite-image-to-video-lora`
`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 = $0.026 × 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	$0.13
1	5 s	720 p	$0.26
1	5 s	1080 p	$0.39
3	5 s	720 p	$0.78
6	5 s	720 p	$1.56
6	5 s	1080 p	$2.34

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

Final cinematic renders with cross-segment identity stability.
High-fidelity advertising / pre-visualization that depend on stylistic consistency.
Identity-critical I2V where minor drift would break the narrative.

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.