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VLM Run’s Orion agents can leverage various image-editing tools such as cropping, rotating, super-resolution, and de-oldify. These tools are designed to help you enhance image quality, extract specific regions, correct orientation, and restore historical photos with modern AI techniques.
For best results, we recommend using the Structured Outputs API to get responses in a structured and validated data format.

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1. Image Cropping

Extract specific regions or focus on particular subjects within an image. 
Crop the clock to tell the time more clearly.

Example of intelligent cropping to focus on the clock.

from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>", base_url="https://agent.vlm.run/v1")

# Crop image to focus on main subject
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Crop the clock to tell the time more clearly"},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.agent/clock.jpg", "detail": "auto"}}
            ]
        }
    ]
)

print(response.choices[0].message.content)
# >>> {"url": "https://.../cropped.jpg", "label": "clock", "xywh": [0.2, 0.2, 0.6, 0.6]}
While the demonstration uses a single crop, we also support cropping multiple regions at once.

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2. Image Rotation

Correct image orientation or apply creative rotations for better composition. 
Rotate the image 90 degrees clockwise to correct the orientation.
Original image before rotation
Image after 90-degree rotation in the clockwise direction

Example of image rotation by 90 degrees clockwise.

from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>", base_url="https://agent.vlm.run/v1")

# Rotate image to correct orientation
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Rotate this image 90 degrees clockwise"},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/cats.jpg"}}
            ]
        }
    ]
)

print(response.choices[0].message.content)
# >>> {"url": "https://.../rotated.jpg", "angle": 90}

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3. Super-Resolution Enhancement

Upscale images while maintaining quality and adding realistic details. 
Enhance this image using super-resolution to increase its resolution while preserving quality.
Original low-resolution image
Enhanced high-resolution image

Example of super-resolution enhancement showing 4x upscaling with quality preservation.

from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>", base_url="https://agent.vlm.run/v1")

# Apply super-resolution enhancement
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Enhance this image using super-resolution to increase its resolution while preserving quality"},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.agent/vegetables-lo.jpg"}}
            ]
        }
    ]
)

print(response.choices[0].message.content)
# >>> {"url": "https://.../enhanced.jpg"}

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4. De-Oldify (Colorization)

Transform black and white or sepia images into vibrant color photos using AI. 
De-oldify this image so that it's colorized and upsampled.
Original black and white image
Colorized image with realistic colors

Example of AI-powered colorization transforming a vintage black and white photo.

from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>", base_url="https://agent.vlm.run/v1")

# Colorize black and white image
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "De-oldify this image so that it's colorized and upsampled"},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.agent/lunch-skyscraper.jpg"}}
            ]
        }
    ]
)

print(response.choices[0].message.content)
# >>> {"url": "https://.../colorized.jpg"}

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FAQ

  • JPEG/JPG: Most common format with excellent compatibility
  • PNG: Lossless format with transparency support
  • TIFF: High-quality format for professional editing
  • WebP: Modern format with superior compression
  • BMP: Uncompressed bitmap format
  • Quality Preservation: Maintains original image quality in all transformations
  • Rule of Thirds: Align subjects with intersection points for better composition
  • Aspect Ratio: Maintain consistent aspect ratios for professional results
  • Subject Focus: Keep the main subject centered or following composition rules
  • Background Removal: Remove distracting elements while preserving context
  • AI-Powered: Uses advanced neural networks for realistic detail generation
  • Multiple Scales: Supports 2x, 4x, and 8x upscaling with quality preservation
  • Detail Enhancement: Intelligently adds realistic textures and patterns
  • Quality Metrics: Provides confidence scores for enhancement quality
  • Historical Accuracy: Uses context-aware AI to suggest period-appropriate colors
  • Natural Colors: Generates realistic skin tones, clothing, and environmental colors
  • Confidence Scoring: Provides confidence levels for color accuracy
  • Region Analysis: Identifies and colors different regions with appropriate palettes