Instructions to use hdkkty/MMFace-DiT-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hdkkty/MMFace-DiT-Models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hdkkty/MMFace-DiT-Models", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Transformers
How to use hdkkty/MMFace-DiT-Models with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hdkkty/MMFace-DiT-Models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download stable-diffusion-2-1-base/vae/diffusion_pytorch_model.bin from hdkkty/MMFace-DiT-Models: direct link, hf CLI and curl.
- Browser
- Download file 335 MB
-
https://huggingface.co/hdkkty/MMFace-DiT-Models/resolve/main/stable-diffusion-2-1-base/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://hdkkty/MMFace-DiT-Models/stable-diffusion-2-1-base/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/hdkkty/MMFace-DiT-Models/resolve/main/stable-diffusion-2-1-base/vae/diffusion_pytorch_model.bin
335 MB
- Xet hash:
- 61555a981781251699f4082032aa464594906dfb8d2007f58ba3280d696f710b
- Size of remote file:
- 335 MB
- SHA256:
- 1b4889b6b1d4ce7ae320a02dedaeff1780ad77d415ea0d744b476155c6377ddc
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