C:\Practice\min_bild_ai.exe

MIN_BILD_AI (2024-2026)

Progressive GAN, 47M params, 7843 images, GTX 1660 Super

Self-trained from scratch on my whole camera roll, photographs of my life rather than curated pictures of work. Two attractors: dark dense forms and bright flat compositions. The failure became a portrait. 858 Python files, 12 repos.

Training progression: first noise to refined
Epoch 1
Epoch 1
Pure noise. Nothing learned yet.
Epoch 25
Epoch 25
Light and dark begin to separate.
Form
Form
Dense forms emerge, the first attractor.
Progressive
Progressive
Resolution grows layer by layer.
Detail
Detail
Texture and edges resolve.
Full res
Full res
The dark dense attractor holds.
Epoch 60
Epoch 60
Refining, composition settles.
Epoch 80
Epoch 80
The bright flat attractor.
Resolution ladder: 4x4 to 256x256
4x4
4x4
The seed grid.
16x16
16x16
Blocks of tone.
64px
64 px
Forms cohere.
128px
128 px
Detail arrives.
256px
256 px
Final resolution.
The GAN in motion
FramesGenerated frames in sequence, the training's own progression.
Gallery: outputs across the training
StyleGAN epoch 307 StyleGAN2 epoch 80 StyleGAN samples Output Output Genesis Form overnight ACGAN ACGAN early Conditional Progressive epoch 1 Progressive epoch 5 Progressive epoch 25 Progressive continued Simple GAN Seed 42 Seed 123 Seed 666 Output Output Output
Nadeschda Barenje, Stockholm
Konstfack 2026