Instruct CLIP

🚀 InstructCLIP: Improving Instruction-Guided Image Editing with Automated Data Refinement Using Contrastive Learning (CVPR 2025)

This project presents InstructCLIP, a model that enhances instruction - guided image editing through automated data refinement using contrastive learning, offering advanced capabilities in image processing.

🚀 Quick Start

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.

Arxiv | [Image Editing Model](https://huggingface.co/SherryXTChen/InstructCLIP - InstructPix2Pix) | [Data Refinement Model](https://huggingface.co/SherryXTChen/Instruct - CLIP) | [Data](https://huggingface.co/datasets/SherryXTChen/InstructCLIP - InstructPix2Pix - Data)

✨ Features

📦 Installation

pip install -r requirements.txt

💻 Usage Examples

Basic Usage

from PIL import Image
import torch
from torchvision import transforms

from model import InstructCLIP
from utils import get_sd_components, normalize

parser = argparse.ArgumentParser(description="Simple example of estimating edit instruction from image pair")
parser.add_argument(
    "--pretrained_instructclip_name_or_path",
    type=str,
    default="SherryXTChen/Instruct - CLIP",
    help=(
        "instructclip pretrained checkpoints"
    ),
)
parser.add_argument(
    "--pretrained_model_name_or_path",
    type=str,
    default="runwayml/stable - diffusion - v1 - 5",
    help=(
        "sd pretrained checkpoints"
    ),
)
parser.add_argument(
    "--input_path",
    type=str,
    default="assets/1_input.jpg",
    help=(
        "Input image path"
    )
)
parser.add_argument(
    "--output_path",
    type=str,
    default="assets/1_output.jpg",
    help=(
        "Output image path"
    )
)
args = parser.parse_args()
device = "cuda"
    
# load model for edit instruction estimation
model = InstructCLIP.from_pretrained("SherryXTChen/Instruct - CLIP")
model = model.to(device).eval()

# load model to preprocess/encode image to latent space
tokenizer, _, vae, _, _ = get_sd_components(args, device, torch.float32)

# prepare image input
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5]),
])
image_list = [args.input_path, args.output_path]
image_list = [
    transform(Image.open(f).resize((512, 512))).unsqueeze(0).to(device) 
    for f in image_list
]

with torch.no_grad():
    image_list = [vae.encode(x).latent_dist.sample() * vae.config.scaling_factor for x in image_list]
    
    # get image feature
    zero_timesteps = torch.zeros_like(torch.tensor([0])).to(device) 
    img_feat = model.get_image_features(
        inp=image_list[0], out=image_list[1], inp_t=zero_timesteps, out_t=zero_timesteps)
    img_feat = normalize(img_feat)
    
    # get edit instruction
    pred_instruct_input_ids = model.text_decoder.infer(img_feat[:1])[0]
    pred_instruct = tokenizer.decode(pred_instruct_input_ids, skip_special_tokens=True)
    print(pred_instruct)  # as a 3 d sculpture

📄 License

@misc{chen2025instructclipimprovinginstructionguidedimage,
      title={Instruct - CLIP: Improving Instruction - Guided Image Editing with Automated Data Refinement Using Contrastive Learning}, 
      author={Sherry X. Chen and Misha Sra and Pradeep Sen},
      year={2025},
      eprint={2503.18406},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.18406}, 
}

📋 Information Table