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-2/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-2/optimizer.bin
- Command line
-
hf download hf://xin0920/trained-sd3-saenewdata/checkpoint-2/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/xin0920/trained-sd3-saenewdata/resolve/main/checkpoint-2/optimizer.bin
1.33 GB
- Xet hash:
- d05b1737a4782a733e41b7e4eaac1fa290f27bafbcbbca36bd85f936ff703ff5
- Size of remote file:
- 1.33 GB
- SHA256:
- a636e0e1cc57754c7a2cbb8177e8cac818bc47be17d313c77b93d1ab2bb254c2
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