ModelsLab Image Generation

Generate stunning AI images using ModelsLab's extensive library of models and powerful generation endpoints.

When to Use This Skill

• Generate images from text descriptions
• Transform or modify existing images
• Fill in masked parts of images (inpainting)
• Use ControlNet for precise control
• Need fast generation (Realtime API)
• Need highest quality (Community/FLUX models)
• Create product images, marketing graphics, or artwork

Available APIs (v7)

1. Text to Image

• POST https://modelslab.com/api/v7/images/text-to-image
• 50,000+ models available

2. Image to Image

• POST https://modelslab.com/api/v7/images/image-to-image
• Transform existing images with a prompt

3. Inpainting

• POST https://modelslab.com/api/v7/images/inpaint
• Fill masked regions of an image

4. Fetch Result

• POST https://modelslab.com/api/v7/images/fetch/{id}
• Poll for async generation results

Note: v6 endpoints (/api/v6/images/text2img, etc.) still work but v7 is the current version.

Discovering Image Models

# Search image models by name
modelslab models search --feature imagen

# Search specific models
modelslab models search --search "flux"
modelslab models search --search "midjourney"

# Get model details
modelslab models detail --id flux

Quick Start: Text to Image

import requests
import time

def generate_image(prompt, api_key, model_id="flux"):
    """Generate an image from text.

    Args:
        prompt: Text description of the image
        api_key: Your ModelsLab API key
        model_id: Model to use (flux, midjourney, sdxl, imagen-3, etc.)
    """
    response = requests.post(
        "https://modelslab.com/api/v7/images/text-to-image",
        json={
            "key": api_key,
            "model_id": model_id,
            "prompt": prompt,
            "negative_prompt": "blurry, low quality, distorted",
            "width": 1024,
            "height": 1024,
            "samples": 1,
            "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)
    else:
        raise Exception(f"Error: {data.get('message')}")

def poll_result(request_id, api_key, timeout=300):
    """Poll for async generation results."""
    start = time.time()
    while time.time() - start < timeout:
        resp = requests.post(
            f"https://modelslab.com/api/v7/images/fetch/{request_id}",
            json={"key": api_key}
        )
        data = resp.json()

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

        time.sleep(5)
    raise Exception("Timeout")

# Usage
image_url = generate_image(
    "A futuristic cityscape at sunset, cyberpunk style, 8k, highly detailed",
    "your_api_key",
    model_id="flux"
)
print(f"Image: {image_url}")

Image to Image Transformation

def transform_image(init_image, prompt, api_key, model_id="flux", strength=0.7):
    """Transform an existing image based on a prompt.

    Args:
        init_image: URL of the input image
        prompt: How to transform the image
        model_id: Model to use (flux models don't need scheduler, SD models do)
        strength: 0.0-1.0, higher = more change
    """
    payload = {
        "key": api_key,
        "model_id": model_id,
        "prompt": prompt,
        "init_image": [init_image],  # v7 expects array
        "strength": strength,
        "width": 512,
        "height": 512
    }

    response = requests.post(
        "https://modelslab.com/api/v7/images/image-to-image",
        json=payload
    )

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

# Transform a photo into a painting
result = transform_image(
    "https://example.com/photo.jpg",
    "An oil painting in Van Gogh style",
    "your_api_key",
    model_id="flux",
    strength=0.8
)

Inpainting (Fill Masked Regions)

def inpaint_image(image_url, mask_url, prompt, api_key, model_id="flux"):
    """Fill in masked parts of an image.

    Args:
        image_url: Original image URL
        mask_url: Mask image URL (white = inpaint, black = keep)
        prompt: What to generate in masked area
        model_id: Model to use
    """
    response = requests.post(
        "https://modelslab.com/api/v7/images/inpaint",
        json={
            "key": api_key,
            "model_id": model_id,
            "init_image": image_url,
            "mask_image": mask_url,
            "prompt": prompt,
            "negative_prompt": "blurry, low quality"
        }
    )

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

# Replace object in image
result = inpaint_image(
    "https://example.com/room.jpg",
    "https://example.com/mask.jpg",
    "A modern red sofa",
    "your_api_key",
    model_id="flux"
)

Popular Model IDs

Premium / Fast

• flux - Flux Dev (fast, high-quality, no scheduler needed)
• imagen-3 - Google Imagen 3 (photorealistic)
• imagen-4 - Google Imagen 4 (latest)

Photorealistic

• realistic-vision-v13 - High-quality realistic images
• deliberate-v2 - Versatile realistic style
• absolute-reality-v1.6 - Ultra-realistic outputs

Artistic/Stylized

• midjourney - MidJourney-style images (most popular)
• dreamshaper-8 - Artistic and versatile
• openjourney - Open-source MidJourney alternative

Anime/Illustration

• anything-v5 - Anime style
• counterfeit-v2.5 - Anime portraits
• meinamix - High-quality anime art

Browse all models: https://modelslab.com/models

Key Parameters

Parameter	Description	Recommended Values
model_id	Model to use (required)	flux, midjourney, sdxl, etc.
prompt	Text description of desired image	Be specific and detailed
negative_prompt	What to avoid	"blurry, low quality, distorted"
width / height	Dimensions (512-1024)	512, 768, 1024
samples	Number of images	1-4
guidance_scale	Prompt adherence	7-8 (balanced), 10-15 (strict)
seed	Reproducibility	null (random) or number
strength	img2img change amount	0.3 (subtle), 0.7 (moderate), 0.9 (heavy)
webhook	Async notification URL	URL string
track_id	Custom tracking identifier	Any string

Best Practices

1. Craft Effective Prompts

Bad: "a car"
Good: "A red Ferrari sports car, studio lighting, highly detailed, 4k, photorealistic"

Include: Subject, style, lighting, quality descriptors

2. Use Negative Prompts

negative_prompt = "blurry, low quality, distorted, deformed, ugly, bad anatomy, extra limbs"

3. Choose Right Model

• flux: Fast, high quality, good default choice
• midjourney: Artistic, stylized outputs
• imagen-3/4: Premium photorealistic (Google)
• sdxl: Versatile base with many fine-tunes

4. Handle Async Operations

# Always check status and poll if needed
if data["status"] == "processing":
    result = poll_result(data["id"], api_key)

5. Use Webhooks for Long Operations

payload = {
    "key": api_key,
    "prompt": "...",
    "webhook": "https://yourserver.com/webhook",
    "track_id": "unique-identifier"
}

Error Handling

try:
    image = generate_image(prompt, api_key)
    print(f"Success: {image}")
except Exception as e:
    print(f"Generation failed: {e}")
    # Log error, retry, or notify user

Resources

• API Documentation: https://docs.modelslab.com/image-generation/overview
• Model Library: https://modelslab.com/models
• Model Selection Guide: https://docs.modelslab.com/guides/model-selection
• Get API Key: https://modelslab.com/dashboard
• Community: https://discord.gg/modelslab

Related Skills

• modelslab-model-discovery - Find and filter models
• modelslab-image-editing - Edit and enhance images
• modelslab-video-generation - Generate videos from images
• modelslab-chat-generation - Chat with LLM models
• modelslab-webhooks - Async operation handling
• modelslab-sdk-usage - Use official SDKs