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_01.mp4 from Ashmotv/transformation_lora: direct link, hf CLI and curl.
- Browser
- Download file 24.3 MB
-
https://huggingface.co/Ashmotv/transformation_lora/resolve/main/samples/sample_01.mp4
- Command line
-
hf download hf://Ashmotv/transformation_lora/samples/sample_01.mp4
-
curl -L -o sample_01.mp4 https://huggingface.co/Ashmotv/transformation_lora/resolve/main/samples/sample_01.mp4
24.3 MB
- Xet hash:
- ea59e52cdefa38ddb60cad88ce370dafb2d19d6b8a5497681e35ed418b7e078c
- Size of remote file:
- 24.3 MB
- SHA256:
- ac2119b5dfa1b6f28a783df20cb973d9bb7bf855faf5cf719f57a693ce50a9dd
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