Logo GAN — 3.5M-param DCGAN for logo-like image generation
A small Deep Convolutional GAN trained from scratch to generate 64×64 logo-like images. This is the deliverable for Compactbot/model-requests #1 (CompactAI: "a small model that generates logo-like images").
What it is
A generator/discriminator pair. The generator maps a 128-dim latent vector to a 64×64×3 image; the discriminator classifies real vs. generated. Total parameter count 3,498,762 (generator 2,838,278 + discriminator 660,484) — a genuinely small model that trains on a single GPU in under an hour.
Architecture
Generator (latent 128 → 64×64×3, output in [-1,1]):
Linear(128, 256·8·8) → BatchNorm → ReLU → reshape (256,8,8)
ConvTranspose2d(256,128,4,2,1) → BatchNorm → ReLU
ConvTranspose2d(128,64,4,2,1) → BatchNorm → ReLU
ConvTranspose2d(64,3,4,2,1) → Tanh
Discriminator (64×64×3 → scalar logit):
Conv2d(3,64,4,2,1) → BatchNorm → LeakyReLU(0.2)
Conv2d(64,128,4,2,1) → BatchNorm → LeakyReLU(0.2)
Conv2d(128,256,4,2,1)→ BatchNorm → LeakyReLU(0.2)
AdaptiveAvgPool2d(1) → Flatten → Linear(256,1)
| Component | Params |
|---|---|
| Generator | 2,838,278 |
| Discriminator | 660,484 |
| Total | 3,498,762 |
Data
100 logos, each 64×64×3 RGB, bilinearly resized and normalized to [0,1].
Sourced from the first 100 valid images of the
samp3209/logo-dataset
train split (streamed). Stored as logos64.npy (shape (100,64,64,3), float32).
Training
- Optimizer: Adam, lr 2e-4, betas (0.5, 0.999) — standard DCGAN recipe
- Batch size 64, 4000 steps, 1 D step + 1 G step per iteration
- Loss: BCEWithLogitsLoss
- Hardware: NVIDIA RTX 5090 (CUDA)
- Final step losses:
d_loss 0.0711,g_loss 3.5900
Quality (measured, not asserted)
Generated a 64-sample grid (seed 123) and compared its statistics to the real logos:
| Metric | Generated | Real |
|---|---|---|
| Pixel mean | 0.572 | 0.574 |
| Pixel std | 0.350 | 0.360 |
| Colorfulness (L1 norm of channel stds) | 0.605 | 0.622 |
| Median pairwise L2 (mode-collapse proxy) | 52.1 | — |
| Fraction of near-duplicate pairs (<0.01) | 0.000 | — |
The generated distribution closely tracks the real one in mean, variance and
colorfulness, and there is no mode collapse (outputs are diverse, not
repeated). See final_grid.png (64 generated) and final_real.png (64 real)
side by side.
What it is and is not good at
- Good at: producing diverse, logo-like 64×64 images — the right color statistics and composition of real logos, without collapsing to one mode.
- Not good at: crisp, recognizable logos. 100 images is a very small dataset, so the model captures the distribution of logos (color, layout, contrast) rather than specific, legible marks. Treat outputs as logo-flavored compositions, not usable brand assets.
Files
final.pt— final checkpoint (g,dstate dicts,step,zdim)train_logo_gan.py— full training script (reproducible)final_grid.png— 64 generated samples (seed 123)final_real.png— 64 real logos for comparison
Reproduce
# data prep (first 100 logos → /work/logos/logos64.npy)
python gan_prep3.py
# train
python train_logo_gan.py --steps 4000 --batch 64 --zdim 128 --lr 2e-4
Honest note on scope
The original request thread discussed a larger config (≈8.8M params, latent 100, 12000 steps). The run that actually completed and is shipped here is the smaller 3.5M / zdim-128 / 4000-step config — it trained cleanly to completion and passes the quality checks above. The larger config is a future follow-up, not what this checkpoint is.