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126 lines
3.4 KiB
126 lines
3.4 KiB
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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from load_params import load_dalle_bart_flax_params |
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from 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 models.dalle_bart_encoder_flax import DalleBartEncoderFlax |
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from models.dalle_bart_decoder_flax import DalleBartDecoderFlax |
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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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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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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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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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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)) |