Instructions to use xin0920/trained-sd3-saenewdatak96class4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xin0920/trained-sd3-saenewdatak96class4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("xin0920/trained-sd3-saenewdatak96class4", 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 checkpoint-2500/optimizer.bin from xin0920/trained-sd3-saenewdatak96class4: direct link, hf CLI and curl.
- Browser
- Download file 7.08 GB
-
https://huggingface.co/xin0920/trained-sd3-saenewdatak96class4/resolve/main/checkpoint-2500/optimizer.bin
- Command line
-
hf download hf://xin0920/trained-sd3-saenewdatak96class4/checkpoint-2500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/xin0920/trained-sd3-saenewdatak96class4/resolve/main/checkpoint-2500/optimizer.bin
7.08 GB
- Xet hash:
- fd0ec99c69beb4e4b17e14e4f4d7dc809e2286ae5cbcf09ad976192389065ab9
- Size of remote file:
- 7.08 GB
- SHA256:
- 0ae299210c3e60a7c3263f50e454587c953601e54bd27fcfc3ad0fad94070305
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.