fixed an issue with argument parser
This commit is contained in:
Brett Kuprel
parent
e7001f063c
commit
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11
README.md
11
README.md
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@ -4,27 +4,26 @@ This is a minimal implementation of [DALL·E Mini](https://github.com/borisdayma
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### Setup
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Run `sh setup.sh` to install dependencies and download pretrained models. The only required dependencies are `flax` and `torch`. In the bash script, GitHub LFS is used to download the VQGan detokenizer and the Weight & Biases python package is used to download the DALL·E Mini and DALL·E Mega transformer models. You can also download those files manually by visting the links in the bash script.
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Run `sh setup.sh` to install dependencies and download pretrained models. The only required dependencies are `flax` and `torch`. In the bash script, GitHub LFS is used to download the VQGan detokenizer and the Weight & Biases python package is used to download the DALL·E Mini and DALL·E Mega transformer models. Those files can also be downloaded manually by visting the links in the bash script.
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### Run
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Here are some examples
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```
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python3 image_from_text.py --text='alien life' --seed=7
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python image_from_text.py --seed=7 --text='alien life'
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```
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```
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python3 image_from_text.py --mega --seed=4 \
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--text='a comfy chair that looks like an avocado'
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python image_from_text.py --mega --seed=4 --text='a comfy chair that looks like an avocado'
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```
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```
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python3 image_from_text.py --mega --seed=100 \
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--text='court sketch of godzilla on trial'
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python image_from_text.py --mega --seed=100 --text='court sketch of godzilla on trial'
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```
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@ -1,66 +1,21 @@
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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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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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from min_dalle.generate_image import generate_image_from_text
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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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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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parser.add_argument('--mega', action='store_true')
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parser.add_argument('--no-mega', dest='mega', action='store_false')
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parser.set_defaults(mega=False)
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parser.add_argument('--torch', action='store_true')
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parser.add_argument('--no-torch', dest='torch', action='store_false')
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parser.set_defaults(torch=False)
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parser.add_argument('--text', type=str)
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parser.add_argument('--seed', type=int, default=0)
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parser.add_argument('--image_path', type=str, default='generated')
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parser.add_argument('--image_token_count', type=int, default=256) # for debugging
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def ascii_from_image(image: Image.Image, size: int) -> str:
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@ -71,59 +26,29 @@ def ascii_from_image(image: Image.Image, size: int) -> str:
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return '\n'.join(''.join(row) for row in chars)
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def save_image(image: numpy.ndarray, path: str) -> Image.Image:
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def save_image(image: Image.Image, path: str):
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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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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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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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print(args)
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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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image = generate_image_from_text(
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text = args.text,
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is_mega = args.mega,
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is_torch = args.torch,
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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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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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if image != None:
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save_image(image, args.image_path)
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print(ascii_from_image(image, size=128))
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77
min_dalle/generate_image.py
Normal file
77
min_dalle/generate_image.py
Normal file
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@ -0,0 +1,77 @@
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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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