You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
77 lines
2.4 KiB
77 lines
2.4 KiB
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 |