Instructions to use d-wang26/mk1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use d-wang26/mk1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("d-wang26/mk1", 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 checkpoint-2000/optimizer.bin from d-wang26/mk1: direct link, hf CLI and curl.
- Browser
- Download file 910 MB
-
https://huggingface.co/d-wang26/mk1/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://d-wang26/mk1/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/d-wang26/mk1/resolve/main/checkpoint-2000/optimizer.bin
910 MB
- Xet hash:
- 9c189e10973a4b713aa6dacc90c953e9bb1ac31d923903d7c8e935b5036c5332
- Size of remote file:
- 910 MB
- SHA256:
- db0c75fedd8ad3d45ffc3d9bfe1d107fd35cc308a2cb678a0dd806b642217173
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.