Instructions to use memj/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use memj/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("memj/trained-sd3-lora", dtype=torch.bfloat16, device_map="cuda") prompt = "A photo of sks dog in a bucket" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/optimizer.bin from memj/trained-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 9.6 MB
-
https://huggingface.co/memj/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://memj/trained-sd3-lora/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/memj/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
9.6 MB
- Xet hash:
- 27c796fbf7067cb5044b84383a5bb461cb8a5395f1d6ee2964782cd07d4028c5
- Size of remote file:
- 9.6 MB
- SHA256:
- 398cf179ada6b2a2b9f0877312da6260c0083d17b02d48daa7b7e337a3057cdd
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