Instructions to use BeQuiet94/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BeQuiet94/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("BeQuiet94/trained-sd3-lora") prompt = "a bear in style of lazypod1" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/optimizer.bin from BeQuiet94/trained-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 19.1 MB
-
https://huggingface.co/BeQuiet94/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://BeQuiet94/trained-sd3-lora/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/BeQuiet94/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
19.1 MB
- Xet hash:
- d28031a6e64fb8e8f74fa10581343af7042842a9df80a1dfd71e463d6ee1ae30
- Size of remote file:
- 19.1 MB
- SHA256:
- 59ea2d2f5125014310487d798ff40d6408e7b8e1e3cabdc49a25566f7399b125
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