import os import json import numpy from PIL import Image from typing import Tuple, List import torch from min_dalle.load_params import load_dalle_bart_flax_params from min_dalle.text_tokenizer import TextTokenizer from min_dalle.min_dalle_flax import generate_image_tokens_flax from min_dalle.min_dalle_torch import ( generate_image_tokens_torch, detokenize_torch ) def load_dalle_bart_metadata(path: str) -> Tuple[dict, dict, List[str]]: print("parsing metadata from {}".format(path)) 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 tokenize_text( 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 generate_image_from_text( text: str, is_mega: bool = False, is_torch: bool = False, seed: int = 0, image_token_count: int = 256 ) -> Image.Image: model_name = 'mega' if is_mega else 'mini' model_path = './pretrained/dalle_bart_{}'.format(model_name) config, vocab, merges = load_dalle_bart_metadata(model_path) text_tokens = tokenize_text(text, config, vocab, merges) params_dalle_bart = load_dalle_bart_flax_params(model_path) if is_torch: image_tokens = generate_image_tokens_torch( text_tokens = text_tokens, seed = seed, config = config, params = params_dalle_bart, image_token_count = image_token_count ) if image_token_count == config['image_length']: image = detokenize_torch(image_tokens) return Image.fromarray(image) else: image_tokens = generate_image_tokens_flax( text_tokens = text_tokens, seed = seed, config = config, params = params_dalle_bart, ) image = detokenize_torch(torch.tensor(image_tokens)) return Image.fromarray(image)