min-dalle-test/min_dalle/generate_image.py

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2022-06-27 20:49:42 +00:00
import os
import json
import numpy
from PIL import Image
from typing import Tuple, List
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)
image_tokens = numpy.zeros(config['image_length'])
if is_torch:
image_tokens[:image_token_count] = generate_image_tokens_torch(
text_tokens = text_tokens,
seed = seed,
config = config,
params = params_dalle_bart,
image_token_count = image_token_count
)
else:
image_tokens[...] = generate_image_tokens_flax(
text_tokens = text_tokens,
seed = seed,
config = config,
params = params_dalle_bart,
)
if image_token_count == config['image_length']:
image = detokenize_torch(image_tokens)
return Image.fromarray(image)
else:
return None