Instructions to use ompathak/DeepFakeDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ompathak/DeepFakeDetection with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dima806/deepfake_vs_real_image_detection", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ompathak/DeepFakeDetection") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 52c11961b0f5f8dc9896df838aa49ed7fe470fd0b819310d6a313a2a9026a7c2
- Size of remote file:
- 687 MB
- SHA256:
- 51a461ee7ae77dc6763efb4f4cac2053adb1968ff1b8b4f2f9f7ea7bd723da93
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