instruct pix2pix

Introduction

InstructPix2Pix is a model designed to follow image editing instructions, enabling users to transform images based on textual prompts. It leverages the capabilities of the StableDiffusionInstructPix2PixPipeline to perform image-to-image transformations.

Architecture

The model operates within the diffusers library, utilizing the StableDiffusionInstructPix2PixPipeline. It incorporates key components such as EulerAncestralDiscreteScheduler for managing the diffusion process and operates with torch for computations, specifically using torch.float16 for efficient execution on compatible hardware like GPUs.

Training

The training details of InstructPix2Pix are not explicitly provided in the documentation. However, it is built upon the principles of stable diffusion, a method that combines deep learning and diffusion techniques to iteratively refine images based on input instructions.

Guide: Running Locally

To run the InstructPix2Pix model locally, follow these steps:

1. Install Required Libraries:
   Use the following command to install necessary libraries:

   pip install diffusers accelerate safetensors transformers
   

2. Import Libraries:
   Ensure you have the required Python libraries imported in your script:

   import PIL
   import requests
   import torch
   from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
   

3. Set Up the Model:
   Load the pre-trained model and configure it:

   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)
   

4. Download and Use an Example Image:
   Download an example image to apply transformations:

   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)
   

5. Apply Image Transformation:
   Use a prompt to modify the image:

   prompt = "turn him into cyborg"
   images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images
   images[0]
   

Cloud GPU Suggestion

For optimal performance, especially when handling large models or datasets, consider using cloud GPUs such as those offered by Google Cloud or AWS. These services provide scalable and powerful GPU resources that can significantly speed up the processing.

License

This project is licensed under the MIT License, allowing for flexibility in usage, modification, and distribution.