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
- Xet hash:
- 520949ad5babb4ab3e7a86e1506836a0be764fe881baa8b32acd876cf6eee8db
- Size of remote file:
- 2.81 GB
- SHA256:
- 530516a85ff658469b9f36c87261c95d57e2e9de34d13a63b9a8d298f6295f12
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.