Instructions to use jyp96/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jyp96/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("jyp96/trained-sd3-lora") prompt = "A photo of t@# dog in a jungle" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download image_0.png from jyp96/trained-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 1.29 MB
-
https://huggingface.co/jyp96/trained-sd3-lora/resolve/main/image_0.png
- Command line
-
hf download hf://jyp96/trained-sd3-lora/image_0.png
-
curl -L -o image_0.png https://huggingface.co/jyp96/trained-sd3-lora/resolve/main/image_0.png
1.29 MB

- Xet hash:
- 0cb7f20a3b33bed42a9e5786cd76e7515a69dd674cb91c3ec67599d74a54f08b
- Size of remote file:
- 1.29 MB
- SHA256:
- 3ba4e2f34ce5a12ba99dd403d15d12b3d982fc741377a6b8f480db8b151f4a63
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