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app.py

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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)