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_08.mp4 from Ashmotv/transformation_lora: direct link, hf CLI and curl.
- Browser
- Download file 1.5 MB
-
https://huggingface.co/Ashmotv/transformation_lora/resolve/main/samples/sample_08.mp4
- Command line
-
hf download hf://Ashmotv/transformation_lora/samples/sample_08.mp4
-
curl -L -o sample_08.mp4 https://huggingface.co/Ashmotv/transformation_lora/resolve/main/samples/sample_08.mp4
1.5 MB
- Xet hash:
- 3ea2feabeb0c37e5613397a38d819a99f7ac0cd59cb37d92bc9ae70338d11474
- Size of remote file:
- 1.5 MB
- SHA256:
- 78674ccbe852bd23b06d39749b85bfe1ffcce6fa9e491544a5d284ea157312ed
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