Skip to main content
Generate and edit images from text prompts, sketches, or existing images with creative control. Perfect for creative content generation, marketing and advertising, product visualization, and artistic expression.
Image generation example showing AI-generated images from text prompts with creative control
Image-to-Image
Text-to-Image
Image-Inpainting
Image-Inpainting
Style Transfer
Style Transfer

​
Example Usage

For best results, we recommend using the Structured Outputs API to get responses in a structured and validated data format.

​
Text-to-Image

Generate images from text descriptions with creative control over style, composition, and details. Generate an image of a cat flying through the sky and clouds, with the background of a green city with river 
from vlmrun.client import VLMRun

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

# Generate the image
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": "Generate an image of a modern building as a cyberpunk-style futuristic structure with neon lights and holographic elements"
        }
    ]
)

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

​
Image-to-Image

Transform existing images by applying new styles, enhancing details, or changing specific elements while preserving the original structure.
Image-to-Image example showing AI-generated images combining objects from two separate images
Reference Dog Image
Reference Dog Image
Dog flying through space
More examples of image-to-image
from vlmrun.client import VLMRun

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

# Generate the image
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Transform the image of my dog on the right into a flying dog with superman cape, with majestic background"},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/dog-cat.jpg", "detail": "auto"}}
            ]
        }
    ]
)

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

​
Image Inpainting

Fill in missing areas, remove unwanted objects, or add new elements to existing images seamlessly.
Image inpainting example showing AI-generated images combining objects from two separate images
Reference Dog Image
Reference Dog Image
Reference Car Image
Reference Car Image
from vlmrun.client import VLMRun

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

# Generate the image
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Given two images: the first with a dog in front holding a yellow ball, inpaint her driving the car shown in the second image."},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/dogs.jpg", "detail": "auto"}},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg", "detail": "auto"}}
            ]
        }
    ]
)

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

​
Style Transfer

Apply artistic styles from reference images to transform the visual appearance while preserving content. In the example below, we apply the Van Gogh’s “Starry Night” painting style to a photo of a city skyline at night.
Example of image style transfer
Reference Van Gogh Image
Reference Van Gogh Image
Reference City Image
Reference City Image
from vlmrun.client import VLMRun

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

# Generate the image
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Given two images: the first with a Van Gogh painting, and the second with a city skyline at night, apply the Van Gogh painting style to the city skyline."},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.generation/starry-night.jpg", "detail": "auto"}},
                {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.generation/sf-golden-gate.jpg", "detail": "auto"}}
            ]
        }
    ]
)

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

​
FAQ

  • Photorealistic: Ultra-realistic images with fine details
  • High Detail: Ultra-realistic images with fine details
  • Natural Lighting: Realistic lighting and shadows
  • Professional Quality: Suitable for commercial and professional use
  • Multiple Subjects: People, objects, landscapes, architecture
  • Be Specific: Include details about style, composition, lighting, and mood
  • Use Keywords: Include relevant art terms, techniques, and descriptors
  • Reference Styles: Mention specific artists, art movements, or visual styles
  • Technical Details: Specify resolution, quality, and output format needs