modelslab-image-editing

Name: modelslab-image-editing
Author: modelslab

github.com/modelslab创意与媒体1391 次安装36 周前更新

代码仓库

关于 modelslab-image-editing

modelslab-image-editing 是通过 API 请求调用 ModelsLab 图像编辑端点的技能，涵盖背景和物体移除、放大、扩图、创建蒙版及指令式编辑。

主要功能

01移除图像背景
02将图像放大 2 倍或 4 倍
03通过扩图延伸图像边缘
04移除指定物体
05创建物体蒙版
06使用 Qwen 指令编辑图像

适用场景

01移除背景并放大商品照片
02清理房地产照片中的多余物体
03扩展图形以适配不同宽高比
04提升头像分辨率

安装

复制下面的命令，在终端中运行。

$npx skills add https://github.com/modelslab/skills --skill modelslab-image-editing

安装后在 Claude Code 中输入/modelslab-image-editing确认已加载。

详细介绍

ModelsLab 图像编辑

由 AI 驱动的专业图像编辑工具，包括背景移除、放大、扩图和对象处理。

何时使用此技能

移除图像背景
放大图像（2x、4x 超分辨率）
将图像扩展到边界之外（扩图）
移除图像中不需要的对象
创建专业的产品照片
提升图像质量
使用 AI 辅助编辑图像

可用端点

背景移除

POST https://modelslab.com/api/v6/image_editing/removebg

超分辨率（放大）

POST https://modelslab.com/api/v6/image_editing/super_resolution

扩图

POST https://modelslab.com/api/v6/image_editing/outpainting

对象移除

POST https://modelslab.com/api/v6/image_editing/object_removal

蒙版创建器

POST https://modelslab.com/api/v6/image_editing/create_mask

Qwen 图像编辑（AI 编辑）

POST https://modelslab.com/api/v6/image_editing/qwen_edit

背景移除

import requests

def remove_background(image_url, api_key):
    """Remove background from an image.

    Args:
        image_url: URL of the image
        api_key: Your ModelsLab API key

    Returns:
        URL of image with transparent background
    """
    response = requests.post(
        "https://modelslab.com/api/v6/image_editing/removebg",
        json={
            "key": api_key,
            "image": image_url
        }
    )

    data = response.json()

    if data["status"] == "success":
        return data["output"][0]
    else:
        raise Exception(f"Error: {data.get('message', 'Unknown error')}")

# Usage
result = remove_background(
    "https://example.com/product-photo.jpg",
    "your_api_key"
)
print(f"Image without background: {result}")

超分辨率（放大）

def upscale_image(image_url, api_key, scale=4):
    """Upscale an image to higher resolution.

    Args:
        image_url: URL of the image to upscale
        scale: Upscale factor (2 or 4)

    Returns:
        URL of upscaled high-resolution image
    """
    response = requests.post(
        "https://modelslab.com/api/v6/image_editing/super_resolution",
        json={
            "key": api_key,
            "image": image_url,
            "scale": scale  # 2x or 4x
        }
    )

    data = response.json()

    if data["status"] == "success":
        return data["output"][0]
    else:
        raise Exception(data.get("message"))

# Upscale to 4x resolution
hd_image = upscale_image(
    "https://example.com/low-res.jpg",
    "your_api_key",
    scale=4
)
print(f"HD image: {hd_image}")

扩图（扩展图像）

def extend_image(image_url, prompt, api_key, width=1024, height=768):
    """Extend an image beyond its original boundaries.

    Args:
        image_url: Original image URL
        prompt: Description of how to extend the image
        width: New width (larger than original)
        height: New height (larger than original)
    """
    response = requests.post(
        "https://modelslab.com/api/v6/image_editing/outpainting",
        json={
            "key": api_key,
            "image": image_url,
            "prompt": prompt,
            "width": width,
            "height": height,
            "num_inference_steps": 30,
            "guidance_scale": 7.5
        }
    )

    data = response.json()

    if data["status"] == "success":
        return data["output"][0]
    elif data["status"] == "processing":
        return poll_result(data["id"], api_key)

# Extend a landscape image
extended = extend_image(
    "https://example.com/landscape.jpg",
    "Continue the mountain landscape with more peaks and valleys",
    "your_api_key",
    width=1024,
    height=768
)

对象移除

def remove_object(image_url, object_name, api_key):
    """Remove a specific object from an image.

    Args:
        image_url: URL of the image
        object_name: Name of object to remove (e.g., "person", "car", "sign")
    """
    response = requests.post(
        "https://modelslab.com/api/v6/image_editing/object_removal",
        json={
            "key": api_key,
            "image": image_url,
            "object_name": object_name
        }
    )

    data = response.json()

    if data["status"] == "success":
        return data["output"][0]
    elif data["status"] == "processing":
        return poll_result(data["id"], api_key)

# Remove person from photo
cleaned = remove_object(
    "https://example.com/photo.jpg",
    "person in background",
    "your_api_key"
)

AI 驱动的图像编辑（Qwen）

def edit_image_with_ai(image_url, edit_instruction, api_key):
    """Edit image using AI instructions.

    Args:
        image_url: URL of the image to edit
        edit_instruction: Natural language editing instruction
    """
    response = requests.post(
        "https://modelslab.com/api/v6/image_editing/qwen_edit",
        json={
            "key": api_key,
            "image": image_url,
            "prompt": edit_instruction,
            "negative_prompt": "low quality, distorted",
            "num_inference_steps": 30,
            "guidance_scale": 7.5
        }
    )

    data = response.json()

    if data["status"] == "success":
        return data["output"][0]
    elif data["status"] == "processing":
        return poll_result(data["id"], api_key)

# AI-powered editing
edited = edit_image_with_ai(
    "https://example.com/portrait.jpg",
    "Make the background more blurred and add warm sunset lighting",
    "your_api_key"
)

创建蒙版

def create_object_mask(image_url, object_description, api_key):
    """Create a mask for a specific object in an image.

    Args:
        image_url: URL of the image
        object_description: What to mask (e.g., "person", "car", "background")

    Returns:
        URL of mask image (white = selected, black = not selected)
    """
    response = requests.post(
        "https://modelslab.com/api/v6/image_editing/create_mask",
        json={
            "key": api_key,
            "image": image_url,
            "object": object_description
        }
    )

    data = response.json()

    if data["status"] == "success":
        return data["output"][0]

# Create mask for person
mask_url = create_object_mask(
    "https://example.com/group-photo.jpg",
    "person in red shirt",
    "your_api_key"
)

轮询辅助工具

import time

def poll_result(request_id, api_key, timeout=300):
    """Poll for async editing results."""
    start_time = time.time()

    while time.time() - start_time < timeout:
        fetch = requests.post(
            f"https://modelslab.com/api/v6/image_editing/fetch/{request_id}",
            json={"key": api_key}
        )
        result = fetch.json()

        if result["status"] == "success":
            return result["output"][0]
        elif result["status"] == "failed":
            raise Exception(result.get("message", "Failed"))

        time.sleep(5)

    raise Exception("Timeout")

常见工作流

电商产品照片

def prepare_product_image(image_url, api_key):
    """Complete product photo workflow."""

    # Step 1: Remove background
    no_bg = remove_background(image_url, api_key)
    print(f"Background removed: {no_bg}")

    # Step 2: Upscale to high resolution
    hd_image = upscale_image(no_bg, api_key, scale=4)
    print(f"Upscaled: {hd_image}")

    return hd_image

product_image = prepare_product_image(
    "https://example.com/product.jpg",
    "your_api_key"
)

照片清理

def cleanup_photo(image_url, unwanted_objects, api_key):
    """Remove multiple unwanted objects from photo."""

    cleaned_image = image_url

    for obj in unwanted_objects:
        cleaned_image = remove_object(cleaned_image, obj, api_key)
        print(f"Removed: {obj}")

    # Upscale final result
    final = upscale_image(cleaned_image, api_key, scale=2)

    return final

result = cleanup_photo(
    "https://example.com/vacation.jpg",
    ["power lines", "trash can", "stranger in background"],
    "your_api_key"
)

扩展和增强

def extend_and_enhance(image_url, extension_prompt, api_key):
    """Extend image and upscale result."""

    # Extend the image
    extended = extend_image(
        image_url,
        extension_prompt,
        api_key,
        width=1920,
        height=1080
    )

    # Upscale for maximum quality
    final = upscale_image(extended, api_key, scale=2)

    return final

最佳实践

1. 背景移除

主体与背景分离清晰时效果最佳
原始图像光线充足会有所帮助
输入质量越高，输出效果越好

2. 超分辨率

适度放大时使用 2x
需要最大程度提升质量时使用 4x
不要放大分辨率已经很高的图像
清晰、对焦准确的图像效果最佳

3. 扩图

在扩展提示词中提供具体描述
与原始图像风格保持一致
扩展时考虑构图
测试不同尺寸

4. 对象移除

明确说明对象的位置
简单背景下效果最佳
复杂场景可能需要多次处理

5. 使用 Webhook 执行批量操作

# Process many images
for image in image_list:
    response = requests.post(
        endpoint,
        json={
            "key": api_key,
            "image": image,
            "webhook": "https://yourserver.com/callback",
            "track_id": f"img_{image_id}"
        }
    )

常见用例

专业头像照

# Remove background and upscale
headshot = remove_background(original, api_key)
hd_headshot = upscale_image(headshot, api_key, scale=4)

房地产照片

# Remove unwanted items
clean_room = remove_object(
    room_photo,
    "personal items and clutter",
    api_key
)

# Extend to show more space
wider_view = extend_image(
    clean_room,
    "Continue the modern living room with more furniture",
    api_key,
    width=1920,
    height=1080
)

社交媒体图形

# Upscale for quality
high_res = upscale_image(graphic, api_key, scale=2)

# Extend to different aspect ratios
instagram_square = extend_image(
    high_res,
    "Extend background",
    api_key,
    width=1080,
    height=1080
)

错误处理

try:
    result = remove_background(image_url, api_key)
    print(f"Success: {result}")
except Exception as e:
    print(f"Editing failed: {e}")
    # Log error, retry, or notify user

资源

API 文档：https://docs.modelslab.com/image-editing/overview
背景移除：https://docs.modelslab.com/image-editing/removebg-createmask
超分辨率：https://docs.modelslab.com/image-editing/super-resolution
获取 API 密钥：https://modelslab.com/dashboard

基本信息

作者: modelslab
仓库: modelslab/skills
分类: 创意与媒体
安装次数: 91
GitHub Stars: 13

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modelslab-image-editing

关于 modelslab-image-editing

主要功能

适用场景

安装

详细介绍

ModelsLab 图像编辑

何时使用此技能

可用端点

背景移除

超分辨率（放大）

扩图

对象移除

蒙版创建器

Qwen 图像编辑（AI 编辑）

背景移除

超分辨率（放大）

扩图（扩展图像）

对象移除

AI 驱动的图像编辑（Qwen）

创建蒙版

轮询辅助工具

常见工作流

电商产品照片

照片清理

扩展和增强

最佳实践

1. 背景移除

2. 超分辨率

3. 扩图

4. 对象移除

5. 使用 Webhook 执行批量操作

常见用例

专业头像照

房地产照片

社交媒体图形

错误处理

资源

相关技能

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