Instructions to use zac/testing-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zac/testing-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("Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("zac/testing-lora") prompt = "John" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") image = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- Xet hash:
- 7e510b9c0e4d480e30d3a6b2dbe3c49047035c0f613932351113807652cf9ce6
- Size of remote file:
- 590 MB
- SHA256:
- cdd611793dbdc74232737ef3e1e93af0430ab7233a7f4cb23c3378b6934b1b3a
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