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
- Xet hash:
- 14196f4a4e9b194a2715eda52d7c159da3752f69b11b5e7ac433fe73ddae2787
- Size of remote file:
- 1.02 MB
- SHA256:
- 186810a46b171e21b7afd3365f5dc42f19dadb7207ab73474262a2d9dcaff459
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