77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
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import os
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import json
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import numpy
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from PIL import Image
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from typing import Tuple, List
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from min_dalle.load_params import load_dalle_bart_flax_params
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from min_dalle.text_tokenizer import TextTokenizer
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from min_dalle.min_dalle_flax import generate_image_tokens_flax
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from min_dalle.min_dalle_torch import (
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generate_image_tokens_torch,
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detokenize_torch
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)
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def load_dalle_bart_metadata(path: str) -> Tuple[dict, dict, List[str]]:
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print("parsing metadata from {}".format(path))
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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 tokenize_text(
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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 generate_image_from_text(
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text: str,
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is_mega: bool = False,
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is_torch: bool = False,
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seed: int = 0,
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image_token_count: int = 256
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) -> Image.Image:
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model_name = 'mega' if is_mega else 'mini'
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model_path = './pretrained/dalle_bart_{}'.format(model_name)
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config, vocab, merges = load_dalle_bart_metadata(model_path)
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text_tokens = tokenize_text(text, config, vocab, merges)
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params_dalle_bart = load_dalle_bart_flax_params(model_path)
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image_tokens = numpy.zeros(config['image_length'])
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if is_torch:
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image_tokens[:image_token_count] = generate_image_tokens_torch(
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text_tokens = text_tokens,
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seed = seed,
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config = config,
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params = params_dalle_bart,
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image_token_count = image_token_count
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)
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else:
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image_tokens[...] = generate_image_tokens_flax(
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text_tokens = text_tokens,
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seed = seed,
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config = config,
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params = params_dalle_bart,
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)
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if image_token_count == config['image_length']:
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image = detokenize_torch(image_tokens)
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return Image.fromarray(image)
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else:
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return None
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