Instructions to use lilquail/Qwanima-vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lilquail/Qwanima-vae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lilquail/Qwanima-vae", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download comparison/preview.png from lilquail/Qwanima-vae: direct link, hf CLI and curl.
- Browser
- Download file 535 kB
-
https://huggingface.co/lilquail/Qwanima-vae/resolve/main/comparison/preview.png
- Command line
-
hf download hf://lilquail/Qwanima-vae/comparison/preview.png
-
curl -L -o preview.png https://huggingface.co/lilquail/Qwanima-vae/resolve/main/comparison/preview.png
535 kB

- Xet hash:
- e68a16c96e03713038f0bf834b2b15080db50b657cbf10142c12231104bd0e67
- Size of remote file:
- 535 kB
- SHA256:
- 2163dcaada016e4b1132afc12b3bea95498a23fd84cf242fa71b909239e86953
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