Instructions to use mit-han-lab/dc-ae-f32c32-in-1.0-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mit-han-lab/dc-ae-f32c32-in-1.0-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mit-han-lab/dc-ae-f32c32-in-1.0-diffusers", 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 assets/dc_ae_diffusion_demo.gif from mit-han-lab/dc-ae-f32c32-in-1.0-diffusers: direct link, hf CLI and curl.
- Browser
- Download file 2.63 MB
-
https://huggingface.co/mit-han-lab/dc-ae-f32c32-in-1.0-diffusers/resolve/main/assets/dc_ae_diffusion_demo.gif
- Command line
-
hf download hf://mit-han-lab/dc-ae-f32c32-in-1.0-diffusers/assets/dc_ae_diffusion_demo.gif
-
curl -L -o dc_ae_diffusion_demo.gif https://huggingface.co/mit-han-lab/dc-ae-f32c32-in-1.0-diffusers/resolve/main/assets/dc_ae_diffusion_demo.gif
2.63 MB

- Xet hash:
- d9a6fac61b080bda4e642681bdf0caf4a1b0e8c28addf48027627a44482271b1
- Size of remote file:
- 2.63 MB
- SHA256:
- 5b3860b826dd126845fb2406e91bad3d122aee3b4e54550b75b9ea11fbf31e3a
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