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README.md

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+---
+license: mit
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+  - gcode
+  - cnc
+  - plotter
+  - polargraph
+  - stable-diffusion
+  - text-to-gcode
+  - diffusion
+base_model: runwayml/stable-diffusion-v1-5
+datasets:
+  - twarner/dcode-imagenet-sketch
+---
+
+# dcode: Text-to-Gcode Diffusion Model
+
+An end-to-end diffusion model that converts **text prompts directly into G-code** for CNC machines, plotters, and polargraph drawing robots.
+
+## Overview
+
+dcode is a fine-tuned Stable Diffusion model with a custom G-code decoder head. It takes a text description (e.g., "a sketch of a horse") and outputs machine-executable G-code.
+
+| Component | Description |
+|-----------|-------------|
+| Base Model | [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) |
+| Decoder | 200M param transformer (12 layers, 1024 hidden, 16 heads) |
+| Tokenizer | Custom BPE tokenizer for G-code |
+| Training Data | [dcode-imagenet-sketch](https://huggingface.co/datasets/twarner/dcode-imagenet-sketch) |
+
+## Architecture
+
+```
+Text Prompt
+    ↓
+[CLIP Text Encoder] ← frozen
+    ↓
+[UNet Diffusion] ← frozen
+    ↓
+Latent (4×64×64)
+    ↓
+[CNN Projector] ← trained
+    ↓
+[Transformer Decoder] ← trained
+    ↓
+G-code Tokens
+    ↓
+G-code Text
+```
+
+## Usage
+
+### With Diffusers
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline
+from huggingface_hub import hf_hub_download
+from transformers import PreTrainedTokenizerFast
+
+# Load components
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    torch_dtype=torch.float16
+).to("cuda")
+
+# Download decoder weights
+weights = hf_hub_download("twarner/dcode-sd-gcode-v3", "pytorch_model.bin")
+tokenizer_path = hf_hub_download("twarner/dcode-sd-gcode-v3", "gcode_tokenizer/tokenizer.json")
+
+# Load custom gcode tokenizer
+gcode_tokenizer = PreTrainedTokenizerFast(tokenizer_file=tokenizer_path)
+
+# Generate latent from text
+with torch.no_grad():
+    latent = pipe("a sketch of a horse", output_type="latent").images
+
+# ... decode with GcodeDecoderV3 (see repo for full inference code)
+```
+
+### Interactive Demo
+
+Try the model live: **[huggingface.co/spaces/twarner/dcode](https://huggingface.co/spaces/twarner/dcode)**
+
+## Training
+
+- **Dataset**: 50,000 ImageNet-Sketch images → 200,000 G-code files
+- **Hardware**: 8× NVIDIA H100 80GB
+- **Epochs**: 50
+- **Batch Size**: 256 effective (32 × 8 GPUs)
+- **Learning Rate**: 1e-4 with cosine schedule
+- **Regularization**: Label smoothing (0.1), weight decay (0.05)
+
+## G-code Output
+
+The model generates G-code compatible with:
+- Polargraph/drawbot machines
+- Pen plotters
+- Any G-code compatible CNC
+
+Example output:
+```gcode
+G21 ; mm
+G90 ; absolute
+M280 P0 S90 ; pen up
+G28 ; home
+
+G0 X-200.00 Y100.00 F1000
+M280 P0 S40 ; pen down
+G1 X-180.00 Y120.00 F500
+G1 X-160.00 Y115.00 F500
+...
+```
+
+## Machine Specs
+
+Default work area (configurable):
+- Width: 841mm
+- Height: 1189mm (A0 paper)
+- Pen servo: 40° down, 90° up
+
+## Project
+
+Full project documentation, hardware build guide, and source code:
+
+**🔗 [teddywarner.org/Projects/Polargraph/#dcode](https://teddywarner.org/Projects/Polargraph/#dcode)**
+
+**GitHub**: [github.com/Twarner491/dcode](https://github.com/Twarner491/dcode)
+
+## Citation
+
+```bibtex
+@misc{dcode2024,
+  author = {Teddy Warner},
+  title = {dcode: Text-to-Gcode Diffusion Model},
+  year = {2024},
+  url = {https://teddywarner.org/Projects/Polargraph/#dcode}
+}
+```
+
+## License
+
+MIT License

config.json

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     "hidden_size": 1024,
     "num_layers": 12,
     "num_heads": 16,
-    "vocab_size": 1865,
+    "vocab_size": 1714,
     "max_seq_len": 2048,
     "ffn_mult": 4
   }

gcode_tokenizer/tokenizer_config.json

@@ -35,10 +35,10 @@
     "4": {
       "content": "<newline>",
       "lstrip": false,
-      "normalized": true,
+      "normalized": false,
       "rstrip": false,
       "single_word": false,
-      "special": false
+      "special": true
     }
   },
   "bos_token": "<s>",

pytorch_model.bin

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