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AI Image Resolution Calculator

Smart calculation of optimal resolution for AI image generation, supporting DALL-E, Midjourney, Stable Diffusion and other mainstream models

Calculator Interface

Interactive calculator will be available soon

Features

  • ✓ Supports DALL-E 3, Midjourney V6, Stable Diffusion XL and other mainstream image generation models
  • ✓ Auto-calculate optimal resolution combinations for different aspect ratios
  • ✓ Smart estimation of generation time and API costs for different resolutions
  • ✓ Support custom resolutions with auto-adaptation to model limits
  • ✓ Provide image quality vs cost optimization recommendations

How to Use

  1. Select AI image generation model (DALL-E 3, Midjourney, etc.)
  2. Input target aspect ratio or specific pixel dimensions
  3. Choose generation quantity and quality level
  4. View recommended resolution, estimated cost and time

FAQ

What is the optimal resolution for AI image generation?

Different models have different optimal resolutions: DALL-E 3 supports 1024×1024(square), 1792×1024(landscape), 1024×1792(portrait); Midjourney V6 defaults to 1024×1024, supports up to 2048×2048; Stable Diffusion XL recommends 1024×1024. Using non-native resolutions may reduce quality.

Is higher resolution always better quality?

Not necessarily. Each model has a native training resolution; exceeding it may cause repetitive patterns or blurriness. It's recommended to stay within the model's native resolution range and use upscale post-processing for detail enhancement — more cost-effective.

Is there a big cost difference between resolutions?

Significant difference. For DALL-E 3: 1024×1024 standard quality is $0.040/image, 1792×1024 HD quality is $0.080/image — double the cost. Choosing appropriate resolution for batch generation can significantly reduce costs.

How to choose the right aspect ratio?

Choose by use case: social media avatars use 1:1 square; web banners use 16:9 or 2:1 landscape; phone wallpapers use 9:16 portrait; product images use 4:3 or 3:4. Choosing ratios close to the model's native proportion reduces distortion.

How to optimize costs for batch generation?

Recommendations: 1) Iterate ideas quickly with low resolution first; 2) Use high resolution for final refinement; 3) Use native model resolutions to avoid extra processing; 4) Batch API calls for discounts; 5) Consider open-source model local deployment for long-term cost reduction.