app.py

import os
os.system('pip install git+https://github.com/huggingface/transformers --upgrade')

import gradio as gr
from transformers import ImageGPTFeatureExtractor, ImageGPTForCausalLM
import torch
import requests
from PIL import Image
import os 
import matplotlib.pyplot as plt

feature_extractor = ImageGPTFeatureExtractor.from_pretrained("openai/imagegpt-small")
model = ImageGPTForCausalLM.from_pretrained("openai/imagegpt-small")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# load image examples
urls = ['https://assetsnffrgf-a.akamaihd.net/assets/m/502013285/univ/art/502013285_univ_sqr_xl.jpg']
for idx, url in enumerate(urls):
  image = Image.open(requests.get(url, stream=True).raw)
  image.save(f"image_{idx}.png")

def process_image(image):
    # prepare 8 images, shape (8, 1024)
    encoding = feature_extractor([image for _ in range(8)], return_tensors="pt")

    # create primers
    samples = encoding.pixel_values.numpy()
    n_px_crop = 16
    primers = samples.reshape(-1,n_px*n_px)[:,:n_px_crop*n_px] # crop top n_px_crop rows. These will be the conditioning tokens
    
    # generate (no beam search)
    context = np.concatenate((np.full((batch_size, 1), model.config.vocab_size - 1), primers), axis=1)
    context = torch.tensor(context).to(device)
    output = model.generate(input_ids=context, max_length=n_px*n_px + 1, temperature=1.0, do_sample=True, top_k=40)

    # decode back to images
    samples = output[:,1:].cpu().detach().numpy()
    samples_img = [np.reshape(np.rint(127.5 * (clusters[s] + 1.0)), [n_px, n_px, 3]).astype(np.uint8) for s in samples] # convert color cluster tokens back to pixels
    
    # save as list of files
    completions = []
    output_dir = '.'
    for i in range(len(samples_img)):
      fname = os.path.join(output_dir, "completion" + str(i) + ".png")
      plt.imsave(fname=fname, arr=samples_img[i], format='png')
      completions.append(fname)

    return completions

title = "Interactive demo: ImageGPT"
description = "Demo for OpenAI's ImageGPT: Generative Pretraining from Pixels. To use it, simply upload an image or use the example image below and click 'submit'. Results will show up in a few seconds."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.10282'>ImageGPT: Generative Pretraining from Pixels</a> | <a href='https://openai.com/blog/image-gpt/'>Official blog</a></p>"
examples =[["image_0.png"]]

iface = gr.Interface(fn=process_image, 
                     inputs=gr.inputs.Image(type="pil"), 
                     outputs=[gr.outputs.Image(type='file', label=f'completion_{i}') for i in range(len(samples_img))],
                     title=title,
                     description=description,
                     article=article,
                     examples=examples)
iface.launch(debug=True)