Why Do ComfyUI and A1111 Generate Different Images with the Same Seed?

Why Do ComfyUI and A1111 Generate Different Images with the Same Seed?

When using ComfyUI and Automatic1111 WebUI (A1111), you might notice that even with the same seed value, the generated images differ. This is primarily due to two key factors: noise generation method and prompt weight handling.

Noise Generation Differences

ComfyUI's Approach

  • Generates noise on the CPU
  • Advantage: Better seed reproducibility across different hardware configurations
  • Produces completely different noise patterns from GPU-based UIs like A1111

A1111's Approach

  • Generates noise on the GPU
  • May have slight variations across different hardware
  • Produces fundamentally different noise patterns from ComfyUI

Performance Impact

  • No performance difference between CPU and GPU noise generation
  • Each approach has its benefits, with ComfyUI prioritizing consistency

Prompt Weight Handling

ComfyUI's Method

  • Uses raw weight values
  • No weight normalization
  • More precise reflection of user-set weights

A1111's Method

  • Normalizes weights
  • Automatically adjusts relative strengths of prompts

Comparison Example

Consider this prompt:

(masterpiece:1.2) (best:1.3) (quality:1.4) girl

A1111 actually processes it as (normalized weights):

(masterpiece:0.98) (best:1.06) (quality:1.14) (girl:0.81)

While in ComfyUI:

  • Original weight values are maintained
  • No automatic adjustment
  • Provides more direct weight control

Practical Application Tips

  1. Weight Setting

    • Be more cautious with weights in ComfyUI
    • Consider balance between weight values
    • Avoid excessive weight values
  2. Cross-Platform Migration

    • Adjust weights when moving from A1111 to ComfyUI
    • Experiment to find appropriate weight ranges
  3. Best Practices

    • Document effective weight combinations
    • Build your own weight reference system
    • Fine-tune based on results

Other Differences

Beyond these main factors, ComfyUI and A1111 differ in:

  • Sampler implementation
  • Model loading mechanisms
  • Workflow control
  • Intermediate result handling

Important Notes

  • Differences don't indicate superiority
  • Choose the platform that fits your workflow
  • Understanding differences helps better control generation results

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