Instructions to use neph1/hard_cut_wan_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neph1/hard_cut_wan_lora 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/Wan2.2-I2V-A14B", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("neph1/hard_cut_wan_lora") prompt = "-" 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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
Hard Cut Lora Wan2.2 I2V 14B
Mirror of: https://civitai.com/models/2088559/hard-cut
v1: hard_cut_200_wan_i2v_high.safetensors v2: hard_cut_2_100_wan_i2v_high.safetensors
As much as I love Wan, if it's one thing it's not good at, it's cuts.
This lora is an experiment in greater shot control, by being trained on hard cuts. Most of the clips are also cut on action, fwiw, to allow for a continuous flow.
It's captioned with "wide-angle", "mid-shot" and "close-up". But I think Wan already handles them well.
Prompt format:
[a brief description of your initial shot.]
the camera makes a hard cut to [resulting shot]
[what happens after]
Where's the low noise model? There isn't one. It seems to work well with only the high noise model. I'll still train and test a low noise model, to see if it will improve consistency during cuts.
Play with strength if the scene changes too much. Too low strength will make it a dissolve transition (at least testing seems to imply that).
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Wan-AI/Wan2.2-I2V-A14B