Instructions to use Ashmotv/transformation_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Ashmotv/transformation_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("minimax-h3", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Ashmotv/transformation_lora") prompt = "Tr@nsf0rmation_style" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") output = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Download samples/sample_02.mp4 from Ashmotv/transformation_lora: direct link, hf CLI and curl.
- Browser
- Download file 2.16 MB
-
https://huggingface.co/Ashmotv/transformation_lora/resolve/main/samples/sample_02.mp4
- Command line
-
hf download hf://Ashmotv/transformation_lora/samples/sample_02.mp4
-
curl -L -o sample_02.mp4 https://huggingface.co/Ashmotv/transformation_lora/resolve/main/samples/sample_02.mp4
2.16 MB
- Xet hash:
- 6d31c151cc540a21d3e5179dcf6ea8964df830237dfb0fb7339f047ffc349b49
- Size of remote file:
- 2.16 MB
- SHA256:
- b13150a3093869eb19aeaf1f78435ee50f9bb402eb3896fa373e0bacab96228d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.