Instructions to use BharathK333/MMFace-DiT-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BharathK333/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("BharathK333/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 BharathK333/MMFace-DiT-Models with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BharathK333/MMFace-DiT-Models", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and library name metadata
#1
by nielsr HF Staff - opened
Hi, I'm Niels, part of the community science team at Hugging Face.
This PR adds pipeline_tag: text-to-image and library_name: diffusers to the model card metadata. These additions will improve the model's discoverability on the Hugging Face Hub and enable automated code snippets for the diffusers library.
I have also ensured that the existing tags and license information are maintained.