Instructions to use Remade/Laughing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Remade/Laughing 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-Diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Remade/Laughing") prompt = "A man with dark hair wearing an orange jacket is shown in front of a brown background. He starts by looking into the camera and smiling and then he starts l4a6ing laughing. He's still l4a6ing laughing at the end." input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") output = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Download example_videos/1.mp4 from Remade/Laughing: direct link, hf CLI and curl.
- Browser
- Download file 316 kB
-
https://huggingface.co/Remade/Laughing/resolve/main/example_videos/1.mp4
- Command line
-
hf download hf://Remade/Laughing/example_videos/1.mp4
-
curl -L -o 1.mp4 https://huggingface.co/Remade/Laughing/resolve/main/example_videos/1.mp4
316 kB
- Xet hash:
- 61d175e012d885b331cb617e37c8dbe1edacddc9e949b348ebc7a17eee869019
- Size of remote file:
- 316 kB
- SHA256:
- ab3181f2cabdeeb8e34520e8a14bffa9926ee84c76553316a351f62432185ae3
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