Instructions to use kmpartner/bk-testcan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kmpartner/bk-testcan with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kmpartner/bk-testcan", 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 log_loss.csv from kmpartner/bk-testcan: direct link, hf CLI and curl.
- Browser
- Download file 110 Bytes
-
https://huggingface.co/kmpartner/bk-testcan/resolve/main/log_loss.csv
- Command line
-
hf download hf://kmpartner/bk-testcan/log_loss.csv
-
curl -L -o log_loss.csv https://huggingface.co/kmpartner/bk-testcan/resolve/main/log_loss.csv
110 Bytes
| epoch,step,global_step,loss_total,loss_sd,loss_kd_output,loss_kd_feat,lr,lamb_sd,lamb_kd_output,lamb_kd_feat | |