Instructions to use SherryXTChen/LatentDiffusionDINOv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SherryXTChen/LatentDiffusionDINOv2 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("SherryXTChen/LatentDiffusionDINOv2", dtype=torch.bfloat16, device_map="cuda") 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
metadata
datasets:
- timbrooks/instructpix2pix-clip-filtered
- SherryXTChen/InstructCLIP-InstructPix2Pix-Data
language:
- en
license: apache-2.0
pipeline_tag: image-to-image
library_name: diffusers
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
This model has been pushed to the Hub using the PytorchModelHubMixin integration: The model is based on the paper Instruct-CLIP: Improving Instruction-Guided Image Editing with Automated Data Refinement Using Contrastive Learning. GitHub: https://github.com/SherryXTChen/Instruct-CLIP.git
- Library:
torch==2.4.0 torchvision==0.19.0 diffusers==0.30.3 transformers==4.45.2 - Docs: See our repo for more information.