Artwork Creation Guide

This tutorial will guide you through implementing AI-powered artwork creation using TeleAgent's artwork generation system.

Overview

We'll create a system that: 1. Generates AI artwork using DALL-E 2. Implements artwork critique and refinement 3. Manages artwork storage and retrieval 4. Handles style customization 5. Provides quality control

Prerequisites

Completed Basic Bot Setup
OpenAI API key for DALL-E
IPFS or similar storage solution
Basic understanding of image generation

Project Structure

artwork_system/
├── generators/
│   ├── artist.py
│   ├── critic.py
│   └── dalle_client.py
├── storage/
│   ├── ipfs_storage.py
│   └── metadata.py
├── styles/
│   ├── style_manager.py
│   └── templates.py
└── main.py

Step 1: Creative Artist Setup

# generators/artist.py
from teleAgent.models.agent_model.artwork_creation import CreativeArtistAgent
from teleAgent.models.agent_model.artwork_creation.dalle_draw import DalleDrawer

class ArtworkCreator:
    def __init__(self, config: dict):
        self.artist = CreativeArtistAgent(
            character_profile=config["profile"],
            dalle_config=config["dalle"],
            llm_config=config["llm"]
        )
        self.drawer = DalleDrawer(config["dalle"])

    async def create_artwork(self, prompt: str, style: dict = None) -> dict:
        """Generate artwork from prompt with optional style"""
        try:
            # Enhance prompt with artistic elements
            enhanced_prompt = await self.artist.enhance_prompt(
                prompt,
                style=style
            )

            # Generate initial image
            artwork = await self.drawer.generate_image(enhanced_prompt)

            # Get artwork critique
            critique = await self.artist.critique_artwork(artwork)

            # Refine if needed
            if critique["score"] < 0.8:
                artwork = await self._refine_artwork(
                    artwork,
                    critique["suggestions"]
                )

            return {
                "image_url": artwork["url"],
                "prompt": enhanced_prompt,
                "metadata": artwork["metadata"],
                "critique": critique
            }
        except Exception as e:
            logger.error(f"Artwork creation failed: {e}")
            raise

Step 2: Artwork Critic Implementation

# generators/critic.py
class ArtworkCritic:
    def __init__(self, config: dict):
        self.llm_config = config["llm"]
        self.quality_threshold = config["quality_threshold"]

    async def evaluate_artwork(self, artwork: dict) -> dict:
        """Evaluate artwork quality and provide feedback"""
        try:
            # Analyze composition
            composition_score = await self._analyze_composition(artwork)

            # Analyze style consistency
            style_score = await self._analyze_style(artwork)

            # Analyze technical quality
            technical_score = await self._analyze_technical_quality(artwork)

            # Calculate overall score
            overall_score = (
                composition_score * 0.4 +
                style_score * 0.3 +
                technical_score * 0.3
            )

            # Generate improvement suggestions
            suggestions = await self._generate_suggestions(
                artwork,
                {
                    "composition": composition_score,
                    "style": style_score,
                    "technical": technical_score
                }
            )

            return {
                "score": overall_score,
                "composition_score": composition_score,
                "style_score": style_score,
                "technical_score": technical_score,
                "suggestions": suggestions
            }
        except Exception as e:
            logger.error(f"Artwork evaluation failed: {e}")
            raise

Step 3: Style Management

# styles/style_manager.py
class StyleManager:
    def __init__(self):
        self.available_styles = {
            "impressionist": {
                "brush_strokes": "visible, loose",
                "color_palette": "vibrant",
                "lighting": "natural, outdoor"
            },
            "minimalist": {
                "composition": "simple, clean",
                "color_palette": "limited",
                "details": "essential only"
            },
            "fantasy": {
                "elements": "magical, surreal",
                "color_palette": "ethereal",
                "atmosphere": "mystical"
            }
        }

    async def apply_style(self, prompt: str, style_name: str) -> str:
        """Apply selected style to prompt"""
        if style_name not in self.available_styles:
            raise ValueError(f"Unknown style: {style_name}")

        style = self.available_styles[style_name]

        # Enhance prompt with style elements
        enhanced_prompt = f"{prompt}, in {style_name} style, "
        enhanced_prompt += f"with {style['brush_strokes']} brush strokes, "
        enhanced_prompt += f"using a {style['color_palette']} color palette"

        return enhanced_prompt

Step 4: Storage Implementation

# storage/ipfs_storage.py
from ipfs_client import IPFSClient

class ArtworkStorage:
    def __init__(self, config: dict):
        self.ipfs = IPFSClient(config["ipfs_endpoint"])
        self.metadata_store = {}

    async def store_artwork(self, artwork: dict) -> str:
        """Store artwork and metadata in IPFS"""
        try:
            # Store image
            image_cid = await self.ipfs.add_file(artwork["image"])

            # Prepare metadata
            metadata = {
                "title": artwork.get("title", "Untitled"),
                "description": artwork["prompt"],
                "image": f"ipfs://{image_cid}",
                "attributes": {
                    "style": artwork.get("style", "default"),
                    "creation_date": datetime.now().isoformat(),
                    "critique_score": artwork["critique"]["score"]
                }
            }

            # Store metadata
            metadata_cid = await self.ipfs.add_json(metadata)

            # Update local store
            self.metadata_store[metadata_cid] = metadata

            return {
                "image_cid": image_cid,
                "metadata_cid": metadata_cid
            }
        except Exception as e:
            logger.error(f"Storage failed: {e}")
            raise

Step 5: Command Implementation

# main.py
class ArtworkCommands:
    def __init__(self, client: TelegramClient):
        self.client = client
        self.creator = ArtworkCreator(config)
        self.storage = ArtworkStorage(config)
        self.style_manager = StyleManager()

    async def setup_handlers(self):
        @self.client.on_command("create_artwork")
        async def handle_create_artwork(message):
            try:
                # Parse command
                args = message.text.split(maxsplit=2)
                if len(args) < 2:
                    raise ValueError(
                        "Usage: /create_artwork [style] <prompt>"
                    )

                style = args[1] if len(args) == 3 else "default"
                prompt = args[-1]

                # Send processing message
                status_message = await self.client.send_message(
                    chat_id=message.chat.id,
                    text="🎨 Creating your artwork..."
                )

                # Generate artwork
                artwork = await self.creator.create_artwork(
                    prompt,
                    style=style
                )

                # Store artwork
                storage_info = await self.storage.store_artwork(artwork)

                # Send result
                await self.client.send_photo(
                    chat_id=message.chat.id,
                    photo=artwork["image_url"],
                    caption=(
                        f"✨ Artwork created!\n"
                        f"Style: {style}\n"
                        f"Quality score: {artwork['critique']['score']:.2f}\n"
                        f"IPFS: ipfs://{storage_info['image_cid']}"
                    )
                )

            except Exception as e:
                await self.client.send_message(
                    chat_id=message.chat.id,
                    text=f"❌ Failed to create artwork: {str(e)}"
                )

Best Practices

Quality Control ```python async def ensure_quality(artwork: dict) -> bool: """Verify artwork meets quality standards""" # Check resolution if not meets_resolution_requirements(artwork["image"]): return False

# Check style consistency if not verify_style_consistency(artwork): return False

# Check content safety if not verify_content_safety(artwork): return False

return True ```
Resource Management ```python class ResourceManager: def init(self): self.api_calls = 0 self.last_reset = time.time()

async def check_resources(self): """Check and manage API usage""" # Reset counter if needed if time.time() - self.last_reset >= 3600: self.api_calls = 0 self.last_reset = time.time()
```
   # Check limits
   if self.api_calls >= MAX_API_CALLS:
       raise ResourceLimitError("API call limit reached")

   self.api_calls += 1
```
```
Error Recovery python async def handle_generation_error(error: Exception, context: dict): """Handle artwork generation errors""" if isinstance(error, APIError): # Retry with different parameters return await retry_with_fallback(context) elif isinstance(error, QualityError): # Try regenerating with enhanced prompt return await regenerate_with_improvements(context) else: raise

Testing

# test_artwork.py
@pytest.mark.asyncio
async def test_artwork_creation():
    # Setup
    creator = ArtworkCreator(test_config)

    # Test basic creation
    artwork = await creator.create_artwork(
        "A serene mountain landscape"
    )
    assert artwork["critique"]["score"] >= 0.8

    # Test style application
    styled_artwork = await creator.create_artwork(
        "A serene mountain landscape",
        style="impressionist"
    )
    assert "impressionist" in styled_artwork["prompt"].lower()

Troubleshooting

Generation Issues
Check API key validity
Verify prompt formatting
Monitor rate limits
Quality Issues
Review critique scores
Check style consistency
Verify image resolution
Storage Issues
Check IPFS connection
Verify metadata format
Monitor storage space

Next Steps

Implement advanced features:
Style transfer
Image editing
Animation support
Add enhancement options:
Resolution upscaling
Color correction
Detail enhancement
Explore integrations:
Social media sharing
NFT creation
Print-on-demand services