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