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 image_2.png from maxspire/trained-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 1.15 MB
-
https://huggingface.co/maxspire/trained-sd3-lora/resolve/main/image_2.png
- Command line
-
hf download hf://maxspire/trained-sd3-lora/image_2.png
-
curl -L -o image_2.png https://huggingface.co/maxspire/trained-sd3-lora/resolve/main/image_2.png
1.15 MB

- Xet hash:
- 50da6e42774c0bac388029d8545a6cd018d6a7819db7669dcbaf4811eb24119d
- Size of remote file:
- 1.15 MB
- SHA256:
- 28ab8d7a1e6e43ea609bee71d87e75a87065226da4fc92f220e7e8ddcaed8916
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