Instructions to use xgemstarx/subset_128_step_16384_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xgemstarx/subset_128_step_16384_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_128_step_16384_model") prompt = "a photo of xjiminx" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- bed4b7e9fbb55a78439e7baaba4b51094c1efbbfb14fbd6e26111dce9a18329e
- Size of remote file:
- 79.2 MB
- SHA256:
- 31dc7eeb867208fe10ada47a84794e6fd60b5caaeddb20b75bc2f74b19074be9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.