Instructions to use aggr8/PixEdit-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aggr8/PixEdit-v1 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("aggr8/PixEdit-v1", 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
Commit History
-
Improve model card
Verified signatureThis PR ensures the model can be found at https://huggingface.co/models?pipeline_tag=image-to-image&sort=trending. It also links to the Github repository and project page.a1eccb5
-
Updated README.md
Verified signaturefb5a8aa
-
Uploaded checkpoint!
Verified signature5b83c69 -
initial commit
Verified signature9f6d73c