Image-to-Image
Diffusers
lora
template:diffusion-lora
text-to-video
image-to-video
video-to-video
lightx2v
Instructions to use rzgar/Bernini-R-LightX2V-4step-loras with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rzgar/Bernini-R-LightX2V-4step-loras with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ByteDance/Bernini-R", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("rzgar/Bernini-R-LightX2V-4step-loras") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
The video editing speed is as slow as before.
#2
by ifuckumum - opened
Yes, he hasn't improved his video editing skills, but he can guarantee better results with 2+2 steps than before.
Video editing is a heavy task, especially with high FPS videos (25-60). Even a 5-second clip can take forever and eat VRAM.
Check the screenshot : untouched 25 fps took 280 seconds on my hardware. Dropping it to 8 fps cut that down to 49 seconds. Edit fast, then use frame interpolation after to get 16 or 25 fps back.
