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
How were these created?
#4
by drozbay - opened
Could you provide some details about how these loras were created? Were they trained on top of existin Lightx2v loras, or did you use some other technique like merging, extraction, reweighting, etc? If so, which loras were the originals that were modified to produce these?
Thanks!
some soldering and welding, inspect and compare them with other LightX2V LoRAs, and ask your ai assistant to explain the differences between them.
you're welcome!