dcode

Text prompt → polargraph gcode via Stable Diffusion + trained decoder.

Live Demo

HuggingFace Space | Model | Dataset

Setup

uv venv --python 3.12
uv pip install -e ".[dev,ray]"
huggingface-cli login

Quick Start

Train SD-Gcode v3 (recommended)

Single GPU:

uv run --active dcode train-sd-gcode-v3 \
  -m data/processed/captioned.json \
  -e 20 \
  -b 16 \
  --grad-accum 2 \
  --lr 3e-4

Multi-GPU (2x/4x/8x H100s):

torchrun --nproc_per_node=4 \
  -m dcode.train_sd_gcode_v3 \
  data/processed/captioned.json

Inference

uv run --active dcode infer-sd-gcode "line drawing of a horse" \
  -m checkpoints/sd_gcode_v3/final \
  -o horse.gcode

Training Options

uv run --active dcode train-sd-gcode-v3 --help

Key options:

• --epochs: More epochs for larger model (default: 20)
• --batch-size: Per-GPU batch (16 fits H100 80GB)
• --grad-accum: Gradient accumulation steps
• --lr: Learning rate (3e-4 with warmup works well)
• --max-len: Max gcode tokens (default: 2048)
• --text-latents: Generate text-derived latents for alignment (slower but better)
• --num-gpus: Number of GPUs (auto-detect if not set)

Architecture (v3)

Text → SD Text Encoder → SD UNet (diffusion) → Latent (4×64×64)
                                                    ↓
                                           CNN Projector → 16 memory tokens
                                                    ↓
                                           Transformer Decoder (12 layers, 1024-dim)
                                                    ↓
                                           Gcode Tokens → Gcode

• SD Components: Frozen (pretrained)
• Decoder: Trained (~200M params)
• Tokenizer: Custom gcode-specific (8192 vocab, preserves newlines)

Dataset Generation

# Download art images from Kaggle
uv run --active python scripts/download_data.py

# Generate gcode from images (5 algorithms)
uv run --active python scripts/generate_dataset.py

# Add BLIP captions
uv run --active dcode caption -m data/processed/manifest.json -o data/processed/captioned.json

Upload to HuggingFace

export HF_TOKEN="your_token"

# Upload v3 model
uv run --active python scripts/upload_to_hub.py sd-gcode-v3

# Upload dataset
uv run --active python scripts/upload_to_hub.py dataset

Legacy Models

For text-to-text models (Flan-T5, GPT-2):

uv run --active dcode train -m data/processed/captioned.json --model flan-t5-base -e 10
uv run --active dcode infer "drawing of a goat" -m checkpoints/flan-t5-base_seed42/final -o goat.gcode

Machine Config

configs/machine.json - polargraph limits:

• Work area: 841×1189mm (A0)
• X: -420.5 to 420.5 mm
• Y: -594.5 to 594.5 mm
• Pen servo: 40° (down) to 90° (up)

License

MIT