Instructions to use timbrooks/instruct-pix2pix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use timbrooks/instruct-pix2pix 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("timbrooks/instruct-pix2pix", 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
Tried to use OpenCl for my AMD
import PIL
import matplotlib
import requests
import pyopencl as cl
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, safety_checker=None)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
url = "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/main/imgs/example.jpg"
def download_image(url):
image = PIL.Image.open(requests.get(url, stream=True).raw)
image = PIL.ImageOps.exif_transpose(image)
image = image.convert("RGB")
return image
image = download_image(url)
prompt = "turn him into cyborg"
images = pipe.run_on_opencl(prompt, image, num_inference_steps=10, image_guidance_scale=1).images
images[0].save("output.jpg")
matplotlib.pyplot.imshow(images[0])
But unfortunately it said it couldn't find the model.safetensors file, and even if it did find my board, it probably wouldn't do the trick. But found it useful for those with AMD cards.