Instructions to use SZOH/trained-sd3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SZOH/trained-sd3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stable-diffusion-3.5-medium", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("SZOH/trained-sd3") 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 SZOH/trained-sd3: direct link, hf CLI and curl.
- Browser
- Download file 19.1 MB
-
https://huggingface.co/SZOH/trained-sd3/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://SZOH/trained-sd3/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/SZOH/trained-sd3/resolve/main/checkpoint-500/optimizer.bin
19.1 MB
- Xet hash:
- 85b5fc8a6673883e523d5509aed2105e3cf94b67e081870093226320e031859e
- Size of remote file:
- 19.1 MB
- SHA256:
- e019fb755f91aad104fbb982737ee32696ece2a5b38974b6ddf89c7217056267
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