Instructions to use lpyy/trained-sd3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lpyy/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("lpyy/trained-sd3-lora") prompt = "a photo of TOK yarn art dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/optimizer.bin from lpyy/trained-sd3-lora: direct link, hf CLI and curl.
- Browser
- Download file 179 MB
-
https://huggingface.co/lpyy/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://lpyy/trained-sd3-lora/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/lpyy/trained-sd3-lora/resolve/main/checkpoint-500/optimizer.bin
179 MB
- Xet hash:
- cf2e137af435a476845e6846a1e72ef6785e5957665256145ff329425ba77a7c
- Size of remote file:
- 179 MB
- SHA256:
- 2a73dde0dd486128bf8c53e9e6ca2726324be85e98d4ba5596837d6ebc439546
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