first commit

image_from_text.py

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+import os
+import json
+import numpy
+import torch
+from PIL import Image
+from typing import Tuple, List
+
+from text_tokenizer import TextTokenizer
+from models.vqgan_detokenizer import VQGanDetokenizer
+from load_params import load_vqgan_torch_params
+
+
+def load_dalle_bart_metadata(path: str) -> Tuple[dict, dict, List[str]]:
+    print("loading model")
+    for f in ['config.json', 'flax_model.msgpack', 'vocab.json', 'merges.txt']:
+        assert(os.path.exists(os.path.join(path, f)))
+    with open(path + '/config.json', 'r') as f: 
+        config = json.load(f)
+    with open(path + '/vocab.json') as f:
+        vocab = json.load(f)
+    with open(path + '/merges.txt') as f:
+        merges = f.read().split("\n")[1:-1]
+    return config, vocab, merges
+
+
+def ascii_from_image(image: Image.Image, size: int) -> str:
+    rgb_pixels = image.resize((size, int(0.55 * size))).convert('L').getdata()
+    chars = list('.,;/IOX')
+    chars = [chars[i * len(chars) // 256] for i in rgb_pixels]
+    chars = [chars[i * size: (i + 1) * size] for i in range(size // 2)]
+    return '\n'.join(''.join(row) for row in chars)
+
+
+def save_image(image: numpy.ndarray, path: str) -> Image.Image:
+    if os.path.isdir(path):
+        path = os.path.join(path, 'generated.png')
+    elif not path.endswith('.png'):
+        path += '.png'
+    print("saving image to", path)
+    image: Image.Image = Image.fromarray(numpy.asarray(image))
+    image.save(path)
+    return image
+
+
+def tokenize(
+    text: str, 
+    config: dict,
+    vocab: dict,
+    merges: List[str]
+) -> numpy.ndarray:
+    print("tokenizing text")
+    tokens = TextTokenizer(vocab, merges)(text)
+    print("text tokens", tokens)
+    text_tokens = numpy.ones((2, config['max_text_length']), dtype=numpy.int32)
+    text_tokens[0, :len(tokens)] = tokens
+    text_tokens[1, :2] = [tokens[0], tokens[-1]]
+    return text_tokens
+
+
+def detokenize_torch(
+    image_tokens: numpy.ndarray, 
+    model_path: str
+) -> numpy.ndarray:
+    print("detokenizing image")
+    params = load_vqgan_torch_params(model_path)
+    detokenizer = VQGanDetokenizer()
+    detokenizer.load_state_dict(params)
+    image_tokens = torch.tensor(image_tokens).to(torch.long)
+    image = detokenizer.forward(image_tokens).to(torch.uint8)
+    return image.detach().numpy()