Instructions to use BreakpointAI/boxnet3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BreakpointAI/boxnet3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BreakpointAI/boxnet3", 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
Download ema/transformer_trainable_ema.pth from BreakpointAI/boxnet3: direct link, hf CLI and curl.
- Browser
- Download file 3.03 GB
-
https://huggingface.co/BreakpointAI/boxnet3/resolve/main/ema/transformer_trainable_ema.pth
- Command line
-
hf download hf://BreakpointAI/boxnet3/ema/transformer_trainable_ema.pth
-
curl -L -o transformer_trainable_ema.pth https://huggingface.co/BreakpointAI/boxnet3/resolve/main/ema/transformer_trainable_ema.pth
3.03 GB
- Xet hash:
- 8f00d8135a3c1ce909dc00d366a3c1fad73a355f2f79267e5f9fcbf496a4b3e5
- Size of remote file:
- 3.03 GB
- SHA256:
- 859b4de007443504ed870e04546a21d6df9cb4a5b1d5d1506c9e93ea36e0f73c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.