Instructions to use raghava0450/instruct-pix2pix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use raghava0450/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("raghava0450/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
| license: mit | |
| tags: | |
| - image-to-image | |
| # InstructPix2Pix: Learning to Follow Image Editing Instructions | |
| GitHub: https://github.com/timothybrooks/instruct-pix2pix | |
| <img src='https://instruct-pix2pix.timothybrooks.com/teaser.jpg'/> | |
| ## Example | |
| To use `InstructPix2Pix`, install `diffusers` using `main` for now. The pipeline will be available in the next release | |
| ```bash | |
| pip install diffusers accelerate safetensors transformers | |
| ``` | |
| ```python | |
| import PIL | |
| import requests | |
| import torch | |
| from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler | |
| model_id = "timbrooks/instruct-pix2pix" | |
| pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) | |
| pipe.to("cuda") | |
| 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(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images | |
| images[0] | |
| ``` |