Instructions to use Obeisswenger/FrameDiffuser-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Obeisswenger/FrameDiffuser-Models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Obeisswenger/FrameDiffuser-Models", 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
Download CitySample/controlnet/diffusion_pytorch_model.safetensors from Obeisswenger/FrameDiffuser-Models: direct link, hf CLI and curl.
- Browser
- Download file 1.45 GB
-
https://huggingface.co/Obeisswenger/FrameDiffuser-Models/resolve/main/CitySample/controlnet/diffusion_pytorch_model.safetensors
- Command line
-
hf download hf://Obeisswenger/FrameDiffuser-Models/CitySample/controlnet/diffusion_pytorch_model.safetensors
-
curl -L -o diffusion_pytorch_model.safetensors https://huggingface.co/Obeisswenger/FrameDiffuser-Models/resolve/main/CitySample/controlnet/diffusion_pytorch_model.safetensors
1.45 GB
- Xet hash:
- 244ac88ec828a69bca01a6588b37ce3cea2d4b6aef9ad3c48611abb1fa9697e7
- Size of remote file:
- 1.45 GB
- SHA256:
- b6e021c75742619bb69149076dc3109c8d94d16dce0d540a5d7b80754576998f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.