Text-to-image AI tools transform written descriptions into visual artwork
- Start with tools that offer free tiers to test quality and workflow fit
- Master prompt engineering and tool-specific features for best results
- Image generation quality depends on prompt engineering and model selection
Getting Started with Text-to-Image AI
Text-to-image AI tools transform written descriptions into visual artwork. Whether you're creating concept art, marketing materials, or personal projects, understanding how to use these tools effectively unlocks their full potential.
Step 1: Choose the Right Tool
Different tools excel at different styles and use cases:
- Nano Banana 2.0: Best for professional work requiring 4K quality and character consistency. Supports multi-reference images for maintaining style across generations.
- Seedream 4.5: Fast generation with DiT architecture. Excellent for rapid iteration and maintaining consistency with up to 15 reference images.
- Midjourney: Strong aesthetic quality and artistic style. Active community with extensive prompt libraries. Best for artistic and creative projects.
- Stable Diffusion: Open-source with extensive customization. Runs locally on consumer hardware. Best for users who need control and customization.
- Flux 2 Flex: Fast generation with good quality. Excellent text rendering capabilities. Good balance of speed and quality.
Step 2: Master Prompt Engineering
Effective prompts combine multiple elements:
- Subject: Clearly describe what you want to see. Be specific about objects, people, animals, or scenes.
- Style: Specify artistic style (photorealistic, oil painting, digital art, anime, etc.)
- Composition: Describe camera angle, framing, and layout (close-up, wide shot, portrait orientation, etc.)
- Lighting: Detail lighting conditions (natural light, studio lighting, golden hour, dramatic shadows, etc.)
- Mood: Convey emotional tone (serene, energetic, mysterious, joyful, etc.)
- Technical Details: Include resolution, aspect ratio, and quality specifications when relevant
Step 3: Write Effective Prompts
Example of a basic prompt:
"A cat sitting on a windowsill"
Example of an enhanced prompt:
"Photorealistic portrait of a ginger tabby cat sitting on a sunlit windowsill, soft natural lighting, shallow depth of field, warm color palette, peaceful mood, 4K quality, professional photography style"
The enhanced prompt provides specific details that guide the AI to produce higher-quality, more intentional results.
Step 4: Iterate and Refine
First results often need refinement:
- Generate initial image: Start with your best prompt
- Analyze the output: Identify what works and what doesn't
- Adjust the prompt: Add details for missing elements, remove or modify parts that didn't work
- Use negative prompts: Specify what you don't want (e.g., "no text, no blur, no distortion")
- Generate variations: Most tools offer variation options to explore different interpretations
Step 5: Use Reference Images
Advanced tools like Nano Banana 2.0 and Seedream 4.5 support reference images:
- Style Reference: Upload an image to match its artistic style
- Character Consistency: Use reference images to maintain character appearance across multiple generations
- Composition Reference: Guide the AI to match specific layouts or compositions
- Multi-Reference: Combine multiple reference images for complex style control
Step 6: Optimize Settings
Most tools offer adjustable parameters:
- Aspect Ratio: Choose based on intended use (16:9 for video, 1:1 for social media, 4:3 for print)
- Resolution: Higher resolution for professional work, lower for quick iterations
- Guidance Scale: Controls how closely the model follows your prompt (higher = more adherence)
- Steps: More steps generally mean higher quality but slower generation
- Seed: Use the same seed to reproduce similar results with slight prompt modifications
Advanced Techniques
Prompt Chaining: Break complex scenes into components. Generate background, foreground, and characters separately, then composite them.
Style Transfer: Use reference images to apply specific artistic styles to your prompts. This works particularly well with models that support multi-reference inputs.
Inpainting and Outpainting: Some tools allow you to edit specific parts of generated images or extend them beyond the original frame.
LoRA Integration: For Stable Diffusion and compatible models, use LoRAs (Low-Rank Adaptations) to apply specific styles, characters, or concepts without retraining the entire model.
Common Mistakes to Avoid
- Overly vague prompts: "A nice picture" won't produce good results. Be specific.
- Conflicting instructions: Avoid contradictory style requests (e.g., "photorealistic cartoon")
- Ignoring aspect ratios: Choose the right ratio for your use case to avoid cropping issues
- Skipping iteration: First results are rarely perfect. Plan for multiple generations.
- Not using negative prompts: Specify unwanted elements to reduce generation errors
Workflow Examples
Marketing Material Creation:
- Define brand style and requirements
- Create initial prompt with brand colors, mood, and composition
- Generate multiple variations
- Select best options and refine prompts
- Use reference images to maintain brand consistency
- Export at appropriate resolution for intended use
Character Design Workflow:
- Generate initial character concept with detailed prompt
- Save reference image of chosen character
- Use reference image for subsequent generations to maintain consistency
- Create variations (different poses, expressions, outfits)
- Iterate on details until satisfied
Best Practices
- Start simple: Begin with basic prompts, then add complexity
- Document what works: Keep a library of effective prompts for future use
- Understand model strengths: Different models excel at different styles. Match tool to task.
- Post-process when needed: Generated images can benefit from light editing, color correction, or upscaling
- Respect copyright: Be aware of usage rights and model training data sources
Explore our curated selection of text-to-image AI tools to find the right model for your needs. For technical details on how these models work, see our guide on how AI image generators work.