Instructions to use DaveLoay/Riffusion_FineTuning_Tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DaveLoay/Riffusion_FineTuning_Tutorial with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DaveLoay/Riffusion_FineTuning_Tutorial", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-4000/unet/diffusion_pytorch_model.bin from DaveLoay/Riffusion_FineTuning_Tutorial: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/DaveLoay/Riffusion_FineTuning_Tutorial/resolve/main/checkpoint-4000/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://DaveLoay/Riffusion_FineTuning_Tutorial/checkpoint-4000/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/DaveLoay/Riffusion_FineTuning_Tutorial/resolve/main/checkpoint-4000/unet/diffusion_pytorch_model.bin
3.44 GB
- Xet hash:
- f0a2316e801044ac075f8b7056e2ca396f81ad1bcf6da5f67b3c249821da4f59
- Size of remote file:
- 3.44 GB
- SHA256:
- 28a6c10eb359a0f5cdd9e11a0861539034758bdf4531d38867f38dcc2a7b32de
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