Instructions to use metazlb/MT-OPSD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use metazlb/MT-OPSD 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("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("metazlb/MT-OPSD") 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
- Local Apps Settings
- Draw Things
Add library_name and paper/code links
#1
by nielsr HF Staff - opened
This PR adds library_name: diffusers to the model card metadata, since the repository contains diffusers-format LoRA checkpoints and the linked training/inference code is built on diffusers and PEFT. This enables the appropriate Diffusers usage snippet on the Hub. It also adds links to the paper and GitHub repository in the model card body.