Instructions to use Evan12/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Evan12/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("Evan12/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 Evan12/trained-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 9.6 MB
-
https://huggingface.co/Evan12/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://Evan12/trained-sd3-lora/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Evan12/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
9.6 MB
- Xet hash:
- 65e8fd938a3a576a7e0b735a9df8c8613794c2ca9c82a0b429d7e8b1377520b8
- Size of remote file:
- 9.6 MB
- SHA256:
- 0f645282fbdf9c1fcb065855f6af79d60df6e642d7e05beaffdbfcecff6136d9
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