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 checkpoint-1000/optimizer.bin from cremepuff/trained-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 19.1 MB
-
https://huggingface.co/cremepuff/trained-sd3-lora/resolve/main/checkpoint-1000/optimizer.bin
- Command line
-
hf download hf://cremepuff/trained-sd3-lora/checkpoint-1000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/cremepuff/trained-sd3-lora/resolve/main/checkpoint-1000/optimizer.bin
19.1 MB
- Xet hash:
- 5d15bab23ae74571df83b0eada5761838cc598d24b4e689263b301d6b20a3f21
- Size of remote file:
- 19.1 MB
- SHA256:
- be8edef570feb1a45e37d8e0a6e2345e4ba281bb420010f39b83e2725d2934dc
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