Instructions to use diff-mining/g3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diff-mining/g3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("diff-mining/g3", 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
Download unet/diffusion_pytorch_model.safetensors from diff-mining/g3: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/diff-mining/g3/resolve/main/unet/diffusion_pytorch_model.safetensors
- Command line
-
hf download hf://diff-mining/g3/unet/diffusion_pytorch_model.safetensors
-
curl -L -o diffusion_pytorch_model.safetensors https://huggingface.co/diff-mining/g3/resolve/main/unet/diffusion_pytorch_model.safetensors
3.44 GB
- Xet hash:
- 62b07ebb7510b870a98f18d98af143d5992cfcbdb001fc6cb118bebde36c521c
- Size of remote file:
- 3.44 GB
- SHA256:
- d98161887c18ab119f1be8e36df99a4491402accde86bd2887809c707c5d064e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.