Baoyu Image Gen

Baoyu Image Gen is a community skill for generating images from text prompts using AI models, covering prompt engineering, style control, resolution settings, batch generation, and image post-processing for AI-powered visual content creation.

What Is This?

Overview

Baoyu Image Gen provides patterns for generating images from text descriptions using diffusion models and image generation APIs. It covers prompt engineering that structures text descriptions for optimal image output quality and relevance, style control that directs the visual aesthetic through style modifiers, negative prompts, and model selection, resolution settings that configure output dimensions and aspect ratios for different use cases, batch generation that produces multiple variants and sizes from a single prompt, and post-processing that enhances, crops, and formats generated images for publishing. The skill enables automated visual content production from text input.

Who Should Use This

This skill serves content creators generating visual assets from text descriptions, application developers integrating image generation into products, and marketing teams producing campaign visuals through AI generation.

Why Use It?

Problems It Solves

Stock photography is expensive and often generic for specific content needs. Custom illustration requires design skills and production time that delays content. Generating consistent visual styles across content pieces needs systematic prompt management. Adapting images for multiple platforms requires manual resizing and formatting.

Core Highlights

Prompt builder structures descriptions for optimal generation quality. Style controller applies consistent aesthetic modifiers across generations. Resolution manager handles output sizing for target platforms. Batch processor generates multiple variants from parameterized prompts.

How to Use It?

Basic Usage

from dataclasses\
  import dataclass
import requests

@dataclass
class GenerationConfig:
  prompt: str
  negative_prompt: str = ''
  width: int = 1024
  height: int = 1024
  steps: int = 30
  guidance: float = 7.5
  seed: int = -1

class ImageGenerator:
  STYLES = {
    'photo':
      'photorealistic,'
      + ' high detail,'
      + ' sharp focus',
    'illustration':
      'digital art,'
      + ' clean lines,'
      + ' vibrant colors',
    'flat':
      'flat design,'
      + ' minimal,'
      + ' vector style',
  }

  def __init__(
    self,
    api_url: str,
    api_key: str
  ):
    self.api_url = api_url
    self.api_key = api_key

  def generate(
    self,
    config: GenerationConfig,
    style: str = 'photo'
  ) -> bytes:
    full_prompt = (
      f'{config.prompt}, '
      f'{self.STYLES.get('
      f'style, "")}')

    resp = requests.post(
      self.api_url,
      headers={
        'Authorization':
          f'Bearer '
          f'{self.api_key}'},
      json={
        'prompt':
          full_prompt,
        'negative_prompt':
          config
            .negative_prompt,
        'width':
          config.width,
        'height':
          config.height,
        'steps':
          config.steps,
      })
    resp.raise_for_status()
    return resp.content

Real-World Examples

from pathlib import Path

class BatchGenerator:
  def __init__(
    self,
    generator:\
      ImageGenerator,
    output_dir: str
  ):
    self.gen = generator
    self.output = Path(
      output_dir)
    self.output.mkdir(
      exist_ok=True)

  def generate_variants(
    self,
    base_prompt: str,
    styles: list[str],
    sizes: list[tuple[
      int, int]]
  ) -> list[dict]:
    results = []
    for style in styles:
      for w, h in sizes:
        config =\
          GenerationConfig(
            prompt=\
              base_prompt,
            width=w,
            height=h)
        data = self.gen\
          .generate(
            config, style)

        filename = (
          f'{style}_{w}x{h}'
          f'.png')
        path = self.output\
          / filename
        path.write_bytes(
          data)
        results.append({
          'style': style,
          'size': f'{w}x{h}',
          'path': str(path)})
    return results

Advanced Tips

Use negative prompts to exclude common artifacts like blurry, distorted, and low quality to improve baseline output. Pin specific seeds for reproducible results when iterating on prompt refinements. Combine style modifiers with subject descriptions rather than appending them separately for more coherent outputs.

When to Use It?

Use Cases

Generate hero images for blog articles from content summaries with consistent visual style. Create product mockup images from description text for e-commerce listings. Build a visual content pipeline that produces images in multiple styles and sizes per prompt.

Related Topics

AI image generation, prompt engineering, diffusion models, visual content creation, and image processing.

Important Notes

Requirements

Image generation API endpoint with valid authentication. Sufficient API credits or GPU resources for batch generation. Storage for generated image output files.

Usage Recommendations

Do: iterate on prompts with small batches before running large generation jobs. Include negative prompts to reduce common artifacts in generated output. Use consistent style prefixes across related content for visual cohesion.

Don't: generate images at maximum resolution for thumbnails where smaller sizes suffice. Rely on a single generation without reviewing for accuracy. Use generated images depicting real people for commercial purposes without model licensing review.

Limitations

Generated images may contain visual artifacts or anatomical inaccuracies. Text rendering within generated images is unreliable and often illegible. Generation costs scale linearly with resolution and batch size. Style consistency varies between different seeds and prompts.