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| import os | |
| os.system('pip install git+https://github.com/huggingface/transformers --upgrade') | |
| import gradio as gr | |
| from transformers import ImageGPTFeatureExtractor, ImageGPTForCausalImageModeling | |
| import torch | |
| import numpy as np | |
| import requests | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| feature_extractor = ImageGPTFeatureExtractor.from_pretrained("openai/imagegpt-medium") | |
| model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-medium") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| # load image examples | |
| urls = ['https://i.imgflip.com/4/4t0m5.jpg', | |
| 'https://cdn.openai.com/image-gpt/completions/igpt-xl-miscellaneous-2-orig.png', | |
| 'https://cdn.openai.com/image-gpt/completions/igpt-xl-miscellaneous-29-orig.png', | |
| 'https://cdn.openai.com/image-gpt/completions/igpt-xl-openai-cooking-0-orig.png' | |
| ] | |
| 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 7 images, shape (7, 1024) | |
| batch_size = 7 | |
| encoding = feature_extractor([image for _ in range(batch_size)], return_tensors="pt") | |
| # create primers | |
| samples = encoding.input_ids.numpy() | |
| n_px = feature_extractor.size | |
| clusters = feature_extractor.clusters | |
| 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 | |
| # get conditioned image (from first primer tensor), padded with black pixels to be 32x32 | |
| primers_img = np.reshape(np.rint(127.5 * (clusters[primers[0]] + 1.0)), [n_px_crop,n_px, 3]).astype(np.uint8) | |
| primers_img = np.pad(primers_img, pad_width=((0,16), (0,0), (0,0)), mode="constant") | |
| # 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 (convert color cluster tokens back to pixels) | |
| 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] | |
| samples_img = [primers_img] + samples_img | |
| # stack images horizontally | |
| row1 = np.hstack(samples_img[:4]) | |
| row2 = np.hstack(samples_img[4:]) | |
| result = np.vstack([row1, row2]) | |
| # return as PIL Image | |
| completion = Image.fromarray(result) | |
| return completion | |
| 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 =[f"image_{idx}.png" for idx in range(len(urls))] | |
| iface = gr.Interface(fn=process_image, | |
| inputs=gr.inputs.Image(type="pil"), | |
| outputs=gr.outputs.Image(type="pil", label="Model input + completions"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| enable_queue=True) | |
| iface.launch(debug=True) |