Instructions to use Remade-AI/angry-face with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Remade-AI/angry-face with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.1-I2V-14B-480P,Wan-AI/Wan2.1-I2V-14B-480P-Diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Remade-AI/angry-face") prompt = "The man begins with a neutral expression. His expression changes to 4ngr23 angry face, and he starts yelling. He then throws his arms up while making the 4ngr23 angry face." input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") image = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- Xet hash:
- 5f7285b22fd6cfae5ef1c054df55631301685403fc83e16aca4156d1cd7f4f77
- Size of remote file:
- 359 MB
- SHA256:
- 5c1345180ed79eadd88b2b95a9b5da9b782568c1f21d9bd310f63a3321084ac1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.