Instructions to use xiangzai/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xiangzai/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("xiangzai/trained-sd3-lora") 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 xiangzai/trained-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 19.1 MB
-
https://huggingface.co/xiangzai/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://xiangzai/trained-sd3-lora/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/xiangzai/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
19.1 MB
- Xet hash:
- 7031755725100c292959311ac52d12da7c52724022584957b7d7a6e2e79b1812
- Size of remote file:
- 19.1 MB
- SHA256:
- 940ceea2d2712c9991f17311a2fb9e5f384f644b47f36d362ee48166787e8cd0
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