Image-to-Image
Diffusers
StableDiffusionInstructPix2PixPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use instruction-tuning-sd/scratch-low-level-img-proc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use instruction-tuning-sd/scratch-low-level-img-proc 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("instruction-tuning-sd/scratch-low-level-img-proc", torch_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
- Xet hash:
- 1efdc7d4d23221aedbb36d3d3cd84ad900b9cc138cedcfa1f3566cd488365597
- Size of remote file:
- 246 MB
- SHA256:
- 6a34f30098988d85dc0fb0fc272a842ebcf552e2ebc6ce4adbcf3695d08e8a90
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.