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After reading this guide, you will know:

• How to generate images from text prompts.
• How to select different image generation models.
• How to specify image sizes (for supported models).
• How to access and save generated image data (URL or Base64).
• How to integrate image generation with Rails Active Storage.
• Tips for writing effective image prompts.
• How to handle errors during image generation.

Basic Image Generation

The simplest way to generate an image is using the global RubyLLM.paint method:

# Generate an image using the default image model
image = RubyLLM.paint("A photorealistic image of a red panda coding Ruby on a laptop")

# For models returning a URL:
if image.url
  puts "Image URL: #{image.url}"
  # => "https://oaidalleapiprodscus.blob.core.windows.net/..."
end

# For models returning Base64 data (like Imagen):
if image.base64?
  puts "MIME Type: #{image.mime_type}" # => "image/png" or similar
  puts "Data size: ~#{image.data.length} bytes"
end

# Some models revise the prompt for better results
if image.revised_prompt
  puts "Revised Prompt: #{image.revised_prompt}"
  # => "A photorealistic depiction of a red panda intently coding Ruby..."
end

puts "Model Used: #{image.model_id}"

The paint method abstracts the differences between provider APIs.

Choosing Models

By default, RubyLLM uses the model specified in config.default_image_model, but you can specify a different one.

# Explicitly use GPT-Image-1
image_dalle = RubyLLM.paint(
  "Impressionist painting of a Parisian cafe",
  model: "{{ site.models.image_openai }}"
)

# Use Google's Imagen 3
image_imagen = RubyLLM.paint(
  "Cyberpunk city street at night, raining, neon signs",
  model: "{{ site.models.image_google }}"
)

# Use a model not in the registry (useful for custom endpoints)
image_custom = RubyLLM.paint(
  "A sunset over mountains",
  model: "my-custom-image-model",
  provider: :openai,
  assume_model_exists: true
)

You can configure the default model globally:

RubyLLM.configure do |config|
  config.default_image_model = "{{ site.models.default_image }}" # Or another available image model ID
end

Refer to the [Working with Models Guide]({% link _advanced/models.md %}) and the [Available Models Guide]({% link _reference/available-models.md %}) to find image models.

Image Sizes

Some models, like DALL-E 3, allow you to specify the desired image dimensions via the size: argument.

# Standard square (1024x1024 - default for DALL-E 3)
image_square = RubyLLM.paint("a fluffy white cat", size: "1024x1024")

# Wide landscape (1792x1024 for DALL-E 3)
image_landscape = RubyLLM.paint(
  "a panoramic mountain landscape at dawn",
  size: "1792x1024"
)

# Tall portrait (1024x1792 for DALL-E 3)
image_portrait = RubyLLM.paint(
  "a knight standing before a castle gate",
  size: "1024x1792"
)

Not all models support size customization. If a size is specified for a model that doesn't support it (like Google Imagen), RubyLLM may log a debug message indicating the size parameter is ignored. Check the provider's documentation or the [Available Models Guide]({% link _reference/available-models.md %}) for supported sizes. {: .note }

Working with Generated Images

The RubyLLM::Image object provides access to the generated image data and metadata.

Accessing Image Data

• image.url: Returns the URL for providers like OpenAI. nil otherwise.
• image.data: Returns the Base64-encoded image data string for providers like Google (Imagen). nil otherwise.
• image.mime_type: Returns the MIME type (e.g., "image/png", "image/jpeg").
• image.base64?: Returns true if the image data is Base64-encoded, false otherwise.

Saving Images Locally

The save method works regardless of whether the image was delivered via URL or Base64. It fetches the data if necessary and writes it to the specified file path.

# Generate an image
image = RubyLLM.paint("A steampunk mechanical owl")

# Save the image to a local file
begin
  saved_path = image.save("steampunk_owl.png")
  puts "Image saved to #{saved_path}"
rescue => e
  puts "Failed to save image: #{e.message}"
end

Getting Raw Image Blob

The to_blob method returns the raw binary image data (decoded from Base64 or downloaded from URL). This is useful for integration with other libraries or frameworks.

image = RubyLLM.paint("Abstract geometric patterns in pastel colors")
image_blob = image.to_blob

# Now you can use image_blob, e.g., upload to S3, process with MiniMagick, etc.
puts "Image blob size: #{image_blob.bytesize} bytes"

Rails Active Storage Integration

Use to_blob to easily attach generated images to Active Storage attributes.

# In a Rails model or job
class Product < ApplicationRecord
  has_one_attached :generated_image
end

def generate_and_attach_image(product, prompt)
  puts "Generating image for Product #{product.id}..."
  image = RubyLLM.paint(prompt) # Or another model

  filename = "#{product.slug}-#{Time.current.to_i}.png"

  # Use StringIO to provide an IO object to Active Storage
  image_io = StringIO.new(image.to_blob)

  product.generated_image.attach(
    io: image_io,
    filename: filename,
    content_type: image.mime_type || 'image/png' # Use detected MIME type or default
  )

  puts "Image attached successfully."

  # Optionally save metadata
  product.update(
    image_prompt: prompt,
    image_revised_prompt: image.revised_prompt,
    image_model: image.model_id
  )
rescue RubyLLM::Error => e
  puts "Image generation failed: #{e.message}"
  # Handle error appropriately
rescue => e
  puts "Failed to attach image: #{e.message}"
  # Handle attachment error
end

# Usage:
# product = Product.find(1)
# generate_and_attach_image(product, "A sleek, modern logo for 'RubyLLM'")

Prompt Engineering for Images

Crafting effective prompts is key to getting the desired image. Be descriptive!

# Simple prompt - often yields generic results
image1 = RubyLLM.paint("dog")

# Detailed prompt - better results
image2 = RubyLLM.paint(
  "A photorealistic image of a golden retriever puppy playing fetch " \
  "in a sunny park, shallow depth of field, captured with a DSLR camera."
)

# Specify style
image3 = RubyLLM.paint(
  "A majestic mountain range, oil painting in the style of Bob Ross"
)

Tips for Better Prompts:

• Subject: Be specific (e.g., "red panda" vs. "animal").
• Action/Setting: Describe what's happening and where (e.g., "coding on a laptop in a cozy library").
• Style: Specify artistic style ("photorealistic", "watercolor", "pixel art", "impressionist painting", "3D render").
• Details: Add adjectives ("fluffy", "ancient", "glowing", "minimalist").
• Composition: Mention framing ("close-up", "wide angle", "overhead shot").
• Lighting: Describe the light ("soft morning light", "dramatic sunset", "neon glow").
• Mood: Convey the feeling ("serene", "chaotic", "mysterious").

Error Handling

Image generation can fail due to content policy violations, rate limits, or API issues:

begin
  image = RubyLLM.paint("Your prompt here")
  puts "Image URL: #{image.url}"
rescue RubyLLM::BadRequestError => e
  # Often indicates a content policy violation
  puts "Generation failed: #{e.message}"
rescue RubyLLM::Error => e
  puts "Error: #{e.message}"
end

See the [Error Handling Guide]({% link _advanced/error-handling.md %}) for comprehensive error handling strategies.

Content Safety

AI image generation services have content safety filters. Prompts requesting harmful, explicit, or otherwise prohibited content will usually result in a BadRequestError. Avoid generating:

• Violent or hateful imagery.
• Sexually explicit content.
• Images of real people (especially public figures without consent, though policies vary).
• Direct copies of copyrighted characters or artwork.

Performance Considerations

Image generation can take several seconds (typically 5-20 seconds depending on the model and load).

• Use Background Jobs: In web applications, always perform image generation in a background job (like Sidekiq or GoodJob) to avoid blocking web requests.
• Timeouts: Configure appropriate network timeouts in RubyLLM (see [Configuration Guide]({% link _getting_started/configuration.md %})).
• Caching: Store generated images (e.g., using Active Storage, cloud storage) rather than regenerating them frequently if the prompt is the same.

Next Steps

• [Chatting with AI Models]({% link _core_features/chat.md %}): Learn about conversational AI.
• [Embeddings]({% link _core_features/embeddings.md %}): Explore text vector representations.
• [Error Handling]({% link _advanced/error-handling.md %}): Master handling API errors.