Brett Kuprel
2 years ago
parent
18e6a9852f
commit
e7001f063c
8 changed files with 182 additions and 344 deletions
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import argparse |
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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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|
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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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|
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|
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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'--text', |
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help='text to generate image from', |
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type=str |
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) |
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parser.add_argument( |
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'--seed', |
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help='random seed', |
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type=int, |
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default=0 |
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) |
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parser.add_argument( |
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'--mega', |
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help='use larger dalle mega model', |
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action=argparse.BooleanOptionalAction |
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) |
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parser.add_argument( |
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'--torch', |
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help='use torch transformers', |
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action=argparse.BooleanOptionalAction |
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) |
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parser.add_argument( |
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'--image_path', |
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help='path to save generated image', |
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type=str, |
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default='generated.png' |
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) |
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parser.add_argument( |
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'--image_token_count', |
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help='number of image tokens to generate (for debugging)', |
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type=int, |
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default=256 |
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) |
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|
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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("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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|
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|
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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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|
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|
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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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|
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|
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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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|
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|
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if __name__ == '__main__': |
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args = parser.parse_args() |
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model_name = 'mega' if args.mega == True 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(args.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 args.torch == True: |
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image_tokens[:args.image_token_count] = generate_image_tokens_torch( |
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text_tokens = text_tokens, |
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seed = args.seed, |
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config = config, |
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params = params_dalle_bart, |
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image_token_count = args.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 = args.seed, |
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config = config, |
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params = params_dalle_bart, |
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) |
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|
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if args.image_token_count == config['image_length']: |
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image = detokenize_torch(image_tokens) |
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image = save_image(image, args.image_path) |
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print(ascii_from_image(image, size=128)) |
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@ -1,126 +0,0 @@ |
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import jax |
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from jax import numpy as jnp |
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import numpy |
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import argparse |
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|
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from min_dalle.load_params import load_dalle_bart_flax_params |
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from min_dalle.image_from_text import ( |
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load_dalle_bart_metadata, |
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tokenize, |
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detokenize_torch, |
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save_image, |
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ascii_from_image |
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) |
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from min_dalle.models.dalle_bart_encoder_flax import DalleBartEncoderFlax |
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from min_dalle.models.dalle_bart_decoder_flax import DalleBartDecoderFlax |
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|
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|
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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'--text', |
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help='text to generate image from', |
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type=str |
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) |
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parser.add_argument( |
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'--seed', |
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help='random seed', |
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type=int, |
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default=0 |
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) |
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parser.add_argument( |
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'--image_path', |
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help='generated image path', |
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type=str, |
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default='generated.png' |
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) |
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parser.add_argument( |
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'--dalle_bart_path', |
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help='pretraied dalle bart path', |
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type=str, |
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default='./pretrained/dalle_bart_mini' |
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) |
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parser.add_argument( |
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'--vqgan_path', |
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help='pretraied vqgan path', |
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type=str, |
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default='./pretrained/vqgan' |
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) |
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|
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def encode_flax( |
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text_tokens: numpy.ndarray, |
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config: dict, |
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params: dict |
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) -> jnp.ndarray: |
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print("loading flax encoder") |
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encoder: DalleBartEncoderFlax = DalleBartEncoderFlax( |
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attention_head_count = config['encoder_attention_heads'], |
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embed_count = config['d_model'], |
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glu_embed_count = config['encoder_ffn_dim'], |
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text_token_count = config['max_text_length'], |
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text_vocab_count = config['encoder_vocab_size'], |
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layer_count = config['encoder_layers'] |
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).bind({'params': params.pop('encoder')}) |
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print("encoding text tokens") |
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encoder_state = encoder(text_tokens) |
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del encoder |
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return encoder_state |
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|
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def decode_flax( |
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text_tokens: jnp.ndarray, |
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encoder_state: jnp.ndarray, |
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config: dict, |
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seed: int, |
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params: dict |
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) -> jnp.ndarray: |
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print("loading flax decoder") |
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decoder = DalleBartDecoderFlax( |
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image_token_count = config['image_length'], |
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text_token_count = config['max_text_length'], |
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image_vocab_count = config['image_vocab_size'], |
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attention_head_count = config['decoder_attention_heads'], |
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embed_count = config['d_model'], |
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glu_embed_count = config['decoder_ffn_dim'], |
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layer_count = config['decoder_layers'], |
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start_token = config['decoder_start_token_id'] |
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) |
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print("sampling image tokens") |
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image_tokens = decoder.sample_image_tokens( |
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text_tokens, |
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encoder_state, |
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jax.random.PRNGKey(seed), |
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params.pop('decoder') |
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) |
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del decoder |
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return image_tokens |
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|
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def generate_image_tokens_flax( |
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text: str, |
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seed: int, |
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dalle_bart_path: str |
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) -> numpy.ndarray: |
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config, vocab, merges = load_dalle_bart_metadata(dalle_bart_path) |
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text_tokens = tokenize(text, config, vocab, merges) |
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params_dalle_bart = load_dalle_bart_flax_params(dalle_bart_path) |
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encoder_state = encode_flax(text_tokens, config, params_dalle_bart) |
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image_tokens = decode_flax( |
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text_tokens, |
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encoder_state, |
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config, seed, |
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params_dalle_bart |
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) |
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return numpy.array(image_tokens) |
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|
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if __name__ == '__main__': |
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args = parser.parse_args() |
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image_tokens = generate_image_tokens_flax( |
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args.text, |
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args.seed, |
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args.dalle_bart_path |
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) |
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print("image tokens", list(image_tokens)) |
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image = detokenize_torch(image_tokens, args.vqgan_path) |
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image = save_image(image, args.image_path) |
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print(ascii_from_image(image, size=128)) |
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@ -1,70 +0,0 @@ |
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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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|
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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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|
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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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|
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|
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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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|
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|
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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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|
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|
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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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|
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|
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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() |
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