Instructions to use xin0920/trained-sd3-saenewdata with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xin0920/trained-sd3-saenewdata 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-saenewdata", 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-4/optimizer.bin from xin0920/trained-sd3-saenewdata: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/xin0920/trained-sd3-saenewdata/resolve/main/checkpoint-4/optimizer.bin
- Command line
-
hf download hf://xin0920/trained-sd3-saenewdata/checkpoint-4/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/xin0920/trained-sd3-saenewdata/resolve/main/checkpoint-4/optimizer.bin
1.33 GB
- Xet hash:
- 81095f7b7b336d0bdc71aca6df21850a9682b8b20683dace713e850d4074e24a
- Size of remote file:
- 1.33 GB
- SHA256:
- 29bf03394ffa989edb7f14992876843e89aa33b2a94d26d879b88a5ecb4f4b0f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.