Instructions to use xgemstarx/smol_model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xgemstarx/smol_model2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("xgemstarx/smol_model2") prompt = "a photo of xjiminx" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2000/optimizer.bin from xgemstarx/smol_model2: direct link, hf CLI and curl.
- Browser
- Download file 79.2 MB
-
https://huggingface.co/xgemstarx/smol_model2/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://xgemstarx/smol_model2/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/xgemstarx/smol_model2/resolve/main/checkpoint-2000/optimizer.bin
79.2 MB
- Xet hash:
- a0fa5be541aa677e5aed8133fc5c38bba717dce63acb7f6fc8f44f2c0be18bbf
- Size of remote file:
- 79.2 MB
- SHA256:
- 90a5b386d9b83363e29dad326e54b64ca44c9dc63899ae0751dbbd256d2b5756
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.