Creative Generation Agent

Build intelligent agents that generate original creative content across multiple modalities including text, music, images, memes, and podcasts.

Overview

Creative generation combines:

• Content Models: Diffusion models, transformers, GANs
• Prompt Engineering: Guide creative output
• Style Control: Maintain artistic consistency
• Quality Assessment: Evaluate creative output
• Iteration & Refinement: Improve results

Applications

• AI music composition and arrangement
• Automated meme generation
• Podcast script and audio generation
• Creative writing assistance
• Art and image generation
• Video content creation
• Game asset generation

Quick Start

Extract the code examples and utilities from the directories:

• Examples: See examples/ directory for complete implementations:

  • music_generation.py - Music generation and audio synthesis
  • meme_generator.py - Image and text-based meme generation
  • podcast_producer.py - Podcast script and audio production
  • image_generation.py - Diffusion-based image generation
  • style_transfer.py - Neural style transfer

• Utilities: See scripts/ directory for helper modules:

  • creative_quality_assessment.py - Quality evaluation
  • audio_effects.py - Audio effect processing
  • content_moderation.py - Safety and compliance filtering

Music Generation

1. Symbolic Music Generation

Generate music as MIDI/musical notation. See examples/music_generation.py.

Key Classes:

• MusicGenerationAgent - Generates melodies and full compositions
• Methods: generate_melody(), generate_full_composition(), generate_harmony()

Usage:

from examples.music_generation import MusicGenerationAgent

agent = MusicGenerationAgent()
melody = agent.generate_melody(
    seed_notes=[("C4", 1), ("E4", 1), ("G4", 1)],
    length=32,
    temperature=0.8
)
composition = agent.generate_full_composition(style="classical", duration_bars=32)

2. Audio Synthesis

Generate audio waveforms directly. See examples/music_generation.py.

Key Classes:

• AudioSynthesisAgent - Synthesizes audio from MIDI and applies effects

Usage:

from examples.music_generation import AudioSynthesisAgent

synth = AudioSynthesisAgent(sample_rate=44100)
audio = synth.synthesize_from_midi(midi_data, duration_seconds=60)
audio = synth.add_effects(audio, effect_type="reverb")
synth.save_audio(audio, "output.wav")

Meme Generation

See examples/meme_generator.py for complete implementations.

1. Image-Based Meme Generator

Generate memes by applying captions to templates.

Key Classes:

• MemeGenerationAgent - Generates image-based memes with captions
• Methods: generate_meme(), generate_caption(), apply_caption_to_template()

Usage:

from examples.meme_generator import MemeGenerationAgent

agent = MemeGenerationAgent()
meme = agent.generate_meme(topic="AI agents", meme_template="drake")
meme.save("output_meme.png")

2. Text-Based Meme Generator

Generate text-only memes in various formats.

Key Classes:

• TextMemeGenerator - Generates text-based memes
• Methods: generate_text_meme(), generate_joke_meme(), generate_deep_meme()

Usage:

from examples.meme_generator import TextMemeGenerator

generator = TextMemeGenerator()
joke_meme = generator.generate_text_meme(topic="Python programming", format_type="joke")
deep_meme = generator.generate_text_meme(topic="AI", format_type="deep")

Podcast Generation

See examples/podcast_producer.py for complete implementations.

1. Script Generation

Generate podcast scripts with structure and natural conversation flow.

Key Classes:

• PodcastScriptGenerator - Creates scripts from topics
• Methods: generate_episode(), generate_script(), generate_content_segments(), generate_intro(), generate_outro()

Usage:

from examples.podcast_producer import PodcastScriptGenerator

generator = PodcastScriptGenerator()
episode = generator.generate_episode(
    topic="Future of AI",
    duration_minutes=30,
    num_hosts=2
)

print(episode["script"])

2. Audio Production

Convert scripts to audio with text-to-speech and effects.

Key Classes:

• PodcastAudioProducer - Produces audio from podcast scripts
• Methods: produce_podcast(), text_to_speech(), add_background_music(), add_transitions()

Usage:

from examples.podcast_producer import PodcastAudioProducer

producer = PodcastAudioProducer()
audio = producer.produce_podcast(script_text)

Image and Art Generation

See examples/image_generation.py and examples/style_transfer.py.

1. Diffusion Model Integration

Generate images from text prompts using Stable Diffusion or similar models.

Key Classes:

• ImageGenerationAgent - Generates images from text prompts
• Methods: generate_image(), enhance_prompt(), generate_variations()

Usage:

from examples.image_generation import ImageGenerationAgent

agent = ImageGenerationAgent()
image = agent.generate_image(
    prompt="A futuristic city with neon lights",
    style="cyberpunk",
    num_inference_steps=50
)
image.save("generated_image.png")

variations = agent.generate_variations(image, num_variations=4)

2. Style Transfer

Transfer artistic style from one image to another.

Key Classes:

• StyleTransferAgent - Applies style transfer between images
• Methods: transfer_style(), preprocess_image(), postprocess_image()

Usage:

from examples.style_transfer import StyleTransferAgent

agent = StyleTransferAgent()
stylized = agent.transfer_style(
    content_image="photo.jpg",
    style_image="monet_painting.jpg"
)

Quality Assessment

See scripts/creative_quality_assessment.py for complete implementations.

1. Creative Quality Metrics

Evaluate generated content across multiple quality dimensions.

Key Classes:

• CreativeQualityAssessor - Assesses quality of all content types
• Methods: assess_content_quality(), assess_music_quality(), assess_meme_quality(), assess_image_quality()

Usage:

from scripts.creative_quality_assessment import CreativeQualityAssessor

assessor = CreativeQualityAssessor()

# Assess music quality
music_assessment = assessor.assess_content_quality(audio, content_type="music")
print(f"Overall score: {music_assessment['overall_score']}")
print(f"Metrics: {music_assessment['metrics']}")

# Assess meme quality
meme_assessment = assessor.assess_content_quality(meme, content_type="meme")

# Assess image quality
image_assessment = assessor.assess_content_quality(image, content_type="image")

Best Practices

Content Generation

• ✓ Start with clear style/mood specifications
• ✓ Use temperature wisely (0.7-0.9 for creativity, 0.3-0.5 for consistency)
• ✓ Implement iterative refinement
• ✓ Maintain seed values for reproducibility
• ✓ Test with diverse prompts

Quality Control

• ✓ Assess generated content systematically (see creative_quality_assessment.py)
• ✓ Implement human review loops
• ✓ Track quality metrics over time
• ✓ Use feedback to refine models
• ✓ Version different creative styles

Audio Processing

• ✓ Use audio effects wisely (see audio_effects.py)
  • Reverb for spatial depth
  • Compression for dynamic control
  • EQ for frequency balance
  • Fade in/out for smooth transitions
• ✓ Monitor audio levels to prevent clipping
• ✓ Mix multiple tracks appropriately

Content Moderation

• ✓ Filter inappropriate content (see content_moderation.py)
• ✓ Ensure copyright compliance
• ✓ Validate factual accuracy
• ✓ Check for bias in generation
• ✓ Implement safety guidelines
• ✓ Use strict mode for sensitive applications

Implementation Checklist

• Choose content modality (music, images, text, etc.)
• Select generation model/framework
• Implement prompt engineering
• Set up quality assessment metrics
• Create iterative refinement loop
• Build content moderation system
• Test generation across diverse inputs
• Optimize for speed/quality tradeoff
• Implement version control for outputs
• Document prompting strategies

Resources

Music Generation

• Music Transformer: https://magenta.tensorflow.org/
• MuseNet: https://openai.com/blog/musenet/
• music21: https://web.mit.edu/music21/

Image Generation

• Stable Diffusion: https://huggingface.co/runwayml/stable-diffusion-v1-5
• DALL-E: https://openai.com/dall-e/
• Midjourney: https://www.midjourney.com/

Audio Synthesis

• Jukebox: https://openai.com/research/jukebox
• Chirp: https://deepmind.google/discover/blog/chirp-universal-speech-model/

Video Generation

• Runway: https://runwayml.com/
• Pika: https://pika.art/