Instructions to use xgemstarx/subset_2_step_2048_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xgemstarx/subset_2_step_2048_model 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/subset_2_step_2048_model") 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/subset_2_step_2048_model: direct link, hf CLI and curl.
- Browser
- Download file 79.2 MB
-
https://huggingface.co/xgemstarx/subset_2_step_2048_model/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://xgemstarx/subset_2_step_2048_model/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/xgemstarx/subset_2_step_2048_model/resolve/main/checkpoint-2000/optimizer.bin
79.2 MB
- Xet hash:
- 785e20f39fd1cc2cb2119ba814208b25b742b2326155d4f571526208325f3698
- Size of remote file:
- 79.2 MB
- SHA256:
- 46c74450c5b32ba0915b3411319c43046509414910576090c372be3bb9e32af1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.