Instructions to use lovelyjaban/trained-sd3-lora-test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lovelyjaban/trained-sd3-lora-test2 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("lovelyjaban/trained-sd3-lora-test2") prompt = "A photo of a t-shirt with hmwrks drawing style" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-106500/optimizer.bin from lovelyjaban/trained-sd3-lora-test2: direct link, hf CLI and curl.
- Browser
- Download file 9.6 MB
-
https://huggingface.co/lovelyjaban/trained-sd3-lora-test2/resolve/main/checkpoint-106500/optimizer.bin
- Command line
-
hf download hf://lovelyjaban/trained-sd3-lora-test2/checkpoint-106500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/lovelyjaban/trained-sd3-lora-test2/resolve/main/checkpoint-106500/optimizer.bin
9.6 MB
- Xet hash:
- 6a54f6d6ac354c46470545e0049a76f2b809228eb03b0d1004554844a6c46cba
- Size of remote file:
- 9.6 MB
- SHA256:
- 374e91bfec45bc7f3d73c8be30b72a6fe7e8b34e6b6a69746171f853306a80b2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.