Instructions to use ompathak/DeepFake with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ompathak/DeepFake with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ByteDance/SDXL-Lightning", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ompathak/DeepFake") prompt = "Real" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
DeepFake

- Prompt
- Real
- Negative Prompt
- Fake
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for ompathak/DeepFake
Base model
ByteDance/SDXL-Lightning