Instructions to use maxspire/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use maxspire/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("maxspire/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 maxspire/trained-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 9.6 MB
-
https://huggingface.co/maxspire/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://maxspire/trained-sd3-lora/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/maxspire/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
9.6 MB
- Xet hash:
- c22b0d3d401a8dfd8fbdb8f56eddbb1e79e61bd9164aaf893c4edc22d453cd17
- Size of remote file:
- 9.6 MB
- SHA256:
- 2f0e1529cb2ee3a5ed505747dd99076d846858552f585008bfa3859ae50321b4
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