Instructions to use saddam213/AutoEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use saddam213/AutoEncoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("saddam213/AutoEncoder", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download Kandinsky5Image/diffusion_pytorch_model.safetensors from saddam213/AutoEncoder: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/saddam213/AutoEncoder/resolve/main/Kandinsky5Image/diffusion_pytorch_model.safetensors
- Command line
-
hf download hf://saddam213/AutoEncoder/Kandinsky5Image/diffusion_pytorch_model.safetensors
-
curl -L -o diffusion_pytorch_model.safetensors https://huggingface.co/saddam213/AutoEncoder/resolve/main/Kandinsky5Image/diffusion_pytorch_model.safetensors
168 MB
- Xet hash:
- 793c44c1df2c83b667a52d3d226fad6b12033659868afe5935f9c944207709fc
- Size of remote file:
- 168 MB
- SHA256:
- f58ad0fbb7e0621871b778bf36a6faeb2c46a79adb32b898b60acd1f9560f890
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