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_07.mp4 from Ashmotv/transformation_lora: direct link, hf CLI and curl.
- Browser
- Download file 2.54 MB
-
https://huggingface.co/Ashmotv/transformation_lora/resolve/main/samples/sample_07.mp4
- Command line
-
hf download hf://Ashmotv/transformation_lora/samples/sample_07.mp4
-
curl -L -o sample_07.mp4 https://huggingface.co/Ashmotv/transformation_lora/resolve/main/samples/sample_07.mp4
2.54 MB
- Xet hash:
- 0af45cf10050166abb144296b3e420efa74a6d916c69014610b8dc481a9fcbb3
- Size of remote file:
- 2.54 MB
- SHA256:
- 828b74086dbc84032bfa51b027bc530ed859100d30829dc3fb7b3cba897cb685
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