Instructclip InstructPix2Pix

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

This model enhances instruction - guided image editing through automated data refinement using contrastive learning, offering new possibilities for image - to - image transformation.

🚀 Quick Start

The model is based on the paper Instruct - CLIP: Improving Instruction - Guided Image Editing with Automated Data Refinement Using Contrastive Learning. GitHub: https://github.com/SherryXTChen/Instruct - CLIP.git

💻 Usage Examples

Basic Usage

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)
pipe.load_lora_weights("SherryXTChen/InstructCLIP-InstructPix2Pix")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

url = "https://raw.githubusercontent.com/SherryXTChen/Instruct-CLIP/refs/heads/main/assets/1_input.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 = "as a 3 d sculpture"
images = pipe(prompt, image=image, num_inference_steps=20).images
images[0].save("output.jpg")

📄 License

This project is licensed under the apache - 2.0 license.

📋 Information Table