Instructions to use Remade-AI/kissing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Remade-AI/kissing 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Remade-AI/kissing") prompt = "A man and a woman wearing jackets and hats are standing close together in the snowy mountains. The man is standing facing the woman, then they engage in k144ing kissing." 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:
- 9540cab1caa779518d0cc047efb8d10a3fdcd1396786349ac2cb7b38d902bf16
- Size of remote file:
- 645 kB
- SHA256:
- d242cf6cfcd1f33d5ae265344755da769d54cd2e0de944112dd77231389b53aa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.