Instructions to use cremepuff/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cremepuff/trained-sd3-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("cremepuff/trained-sd3-lora") prompt = "some makeup placed on a dressing table in the style <1>" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download image_2.png from cremepuff/trained-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 1.55 MB
-
https://huggingface.co/cremepuff/trained-sd3-lora/resolve/main/image_2.png
- Command line
-
hf download hf://cremepuff/trained-sd3-lora/image_2.png
-
curl -L -o image_2.png https://huggingface.co/cremepuff/trained-sd3-lora/resolve/main/image_2.png
1.55 MB

- Xet hash:
- 89088d2802a9291ec9a118fd5d7117a5ffbee0c3d9018a108788fa204e7309fe
- Size of remote file:
- 1.55 MB
- SHA256:
- cbb728798ab193c5505d299cb10a2cc98bcecca8b76738cfc254fb8e62a8d953
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.