Instructions to use Remade-AI/Squish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Remade-AI/Squish 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/Squish") prompt = "In the video, a miniature dog is presented. The dog is held in a person's hands. The person then presses on the dog, causing a sq41sh squish effect. The person keeps pressing down on the dog, further showing the sq41sh squish effect." 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
- Draw Things
- Xet hash:
- 224a8429eab3de1e7ed5707c134778ee30c66542a290a3d9313ae3512e167919
- Size of remote file:
- 14.5 MB
- SHA256:
- 67cb381142895e776b29a4c3421935ae304a5e2447413bfee9b6937b80c1d35c
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