Text-to-Image
Diffusers
PyTorch
gcode
cnc
plotter
polargraph
stable-diffusion
text-to-gcode
diffusion
Instructions to use twarner/dcode-sd-gcode-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use twarner/dcode-sd-gcode-v3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("twarner/dcode-sd-gcode-v3", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| { | |
| "sd_model_id": "runwayml/stable-diffusion-v1-5", | |
| "num_inference_steps": 20, | |
| "gcode_decoder": { | |
| "latent_channels": 4, | |
| "latent_size": 64, | |
| "hidden_size": 1024, | |
| "num_layers": 12, | |
| "num_heads": 16, | |
| "vocab_size": 1714, | |
| "max_seq_len": 2048, | |
| "ffn_mult": 4 | |
| } | |
| } |