70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
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() |