The Official ImageGen, 4o and Dall-E Megathread

Thanks :cherry_blossom:

I did an analysis, I hope it’s okay .. I took our images:

Analyse-dialog with ChatGPT

Me
”Take a look at these two pictures and tell me which one looks more physically realistic in terms of the broken glass and the distribution of the wine. Divide into: Picture 1 - green background Picture 2 - black background”

GPT


Image 1 – green background

Physical evaluation:

Wine distribution:

Very realistic. The liquid forms thin, connected filaments (ligaments) and drops of varying sizes, as would be expected from a glass breaking quickly + the inertia of the liquid.

Splash direction:

The movement plausibly follows the direction of momentum of the tilting glass: wine spills outwards, not symmetrically, but slightly preferentially in one direction.

Glass breakage:

The glass shards are irregular, vary in size and follow a ballistic dispersion. No exaggerated ‘explosion effect’.

Interaction between glass and wine:

Particularly good: the wine ‘flows around’ the broken edges, with a few drops sticking to the shards → surface tension correctly represented.

Conclusion for image 1:

:backhand_index_pointing_right: Very high physical plausibility for high-speed photography (similar to schlieren/stroboscopic photography).

Image 2 – black background

Physical evaluation:

Wine distribution:

Highly aestheticised. The liquid forms a symmetrical, almost sculptural crown that looks more like staged splash art than a real break.

Splash pattern:

Too uniform, too ‘clean’. In reality, more chaotic fragmentation would be visible.

Glass breakage:

Some of the shards appear too large and too evenly distributed, almost as if deliberately choreographed.

Interaction between glass and wine:

Fewer microdroplets, less viscous distortion → appears like ‘frozen aesthetics’ rather than genuine dynamics.

Conclusion Image 2:

:backhand_index_pointing_right: Physically possible, but highly stylised – more visual drama than real fracture physics.

Overall assessment

:white_check_mark: More physically realistic: Image 1 – green background

:performing_arts: More aesthetically dramatic: Image 2 – black background

Me

“Is Image 1 wild?”

GPT
”Why image 1 is not wild

Chaos yes, but causal: The image shows controlled chaos: liquid follows inertia, broken glass creates irregular shards, nothing looks ‘staged’.

Asymmetry is correct: Real breaks are never clean or symmetrical.

The scale is right: Droplet sizes, filaments and shards are in a plausible relationship to each other.

The timing is realistic: It looks like a real high-speed freeze, not a staged splash.

:backhand_index_pointing_right: In your language:

Image 1 is not wild, but honest.

It’s untamed physics, but not exaggerated.

Another test, thanks @LarisaHaster for your input here :blush:

Your attempt was again more aesthetically oriented, I tried to incorporate physics.

But as you can see, the fine splinters don’t really look like glass. Similar to the ‘wet sand’ effect in a snowstorm.

I guess this is a ‘bias-variance trade-off’ fail.

Analyse
  1. High bias

The model has a strong aesthetic priority:

  • ‘Dramatic breakthrough’
  • ‘Cinematic debris’
  • ‘High-speed impact look’

→ These assumptions dominate the generation.

  1. High variance (untamed)

At the same time, too many concepts are allowed simultaneously:

  • Glass
  • Dust
  • Water
  • Smoke
  • Splinters
  • Particle light

→ No clear decision for a material class.

Sorry here for German :face_with_peeking_eye:

In ML-Sprache Im Bild
Underfitting Physik nicht gelernt
Overfitting Filmästhetik reproduziert
Bias dominiert Symbolik schlägt Kausalität
Varianz unreguliert Material-Matsch

Then I readjusted and got this:

With a more realistic template, the priority in the model shifts:

  • less cinematic bias,
  • less symbolic exaggeration,
  • clearer material separation (glass ≠ dust ≠ liquid),
  • better fragment geometry.

The result immediately appears ‘more correct’, not because it is suddenly physically correct, but because - the aesthetic bias is closer to the real reference.

I guess, it’s not about the “physics engine” or that physics has been better learned - regularisation has been improved :wink:

This is precisely why good references work so well:

They reduce variance before generation instead of smoothing it out afterwards.