Instructions to use sushmar7/trained-sd3-lora1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sushmar7/trained-sd3-lora1 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("sushmar7/trained-sd3-lora1") prompt = "A photo of sks person smiling" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2000/optimizer.bin from sushmar7/trained-sd3-lora1: direct link, hf CLI and curl.
- Browser
- Download file 19.1 MB
-
https://huggingface.co/sushmar7/trained-sd3-lora1/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://sushmar7/trained-sd3-lora1/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/sushmar7/trained-sd3-lora1/resolve/main/checkpoint-2000/optimizer.bin
19.1 MB
- Xet hash:
- 5a34de412b6c715fc87d9223eb06f018bb0dcfc5842a8c55019b09075cdcec9b
- Size of remote file:
- 19.1 MB
- SHA256:
- eea23d3d248a92deb3d20ccecd49a34b58f1dd0a3b6958d26a00aa2cc4ff9ef2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.