Instructions to use callgg/image-edit-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use callgg/image-edit-decoder 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("callgg/image-edit-decoder", 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 text_encoder/model.safetensors from callgg/image-edit-decoder: direct link, hf CLI and curl.
- Browser
- Download file 16.6 GB
-
https://huggingface.co/callgg/image-edit-decoder/resolve/main/text_encoder/model.safetensors
- Command line
-
hf download hf://callgg/image-edit-decoder/text_encoder/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/callgg/image-edit-decoder/resolve/main/text_encoder/model.safetensors
16.6 GB
- Xet hash:
- 7dc87a9c61db8168b119859940bce41cf3f737784e8c43d8e71a9f9720fa4051
- Size of remote file:
- 16.6 GB
- SHA256:
- cfafd739459bc86257397259f612a9aee88e5b98e85b5c0d0d1717e898b3463a
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