159 lines
4.3 KiB
Python
159 lines
4.3 KiB
Python
import numpy
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import torch
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from torch import Tensor
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import argparse
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from typing import Dict
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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_torch import DalleBartEncoderTorch
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from models.dalle_bart_decoder_torch import DalleBartDecoderTorch
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from load_params import (
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load_dalle_bart_flax_params,
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convert_dalle_bart_torch_from_flax_params
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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_token_count',
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help='image tokens to sample',
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type=int,
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default=256
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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_torch(
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text_tokens: numpy.ndarray,
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config: dict,
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params: dict
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) -> Tensor:
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print("loading torch encoder")
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encoder = DalleBartEncoderTorch(
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layer_count = config['encoder_layers'],
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embed_count = config['d_model'],
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attention_head_count = config['encoder_attention_heads'],
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text_vocab_count = config['encoder_vocab_size'],
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text_token_count = config['max_text_length'],
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glu_embed_count = config['encoder_ffn_dim']
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)
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encoder_params = convert_dalle_bart_torch_from_flax_params(
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params.pop('encoder'),
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layer_count=config['encoder_layers'],
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is_encoder=True
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)
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encoder.load_state_dict(encoder_params, strict=False)
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del encoder_params
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print("encoding text tokens")
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text_tokens = torch.tensor(text_tokens).to(torch.long)
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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_torch(
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text_tokens: Tensor,
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encoder_state: Tensor,
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config: dict,
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seed: int,
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params: dict,
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image_token_count: int
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) -> Tensor:
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print("loading torch decoder")
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decoder = DalleBartDecoderTorch(
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image_vocab_size = config['image_vocab_size'],
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image_token_count = config['image_length'],
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sample_token_count = image_token_count,
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embed_count = config['d_model'],
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attention_head_count = config['decoder_attention_heads'],
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glu_embed_count = config['decoder_ffn_dim'],
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layer_count = config['decoder_layers'],
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batch_count = 2,
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start_token = config['decoder_start_token_id'],
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is_verbose = True
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)
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decoder_params = convert_dalle_bart_torch_from_flax_params(
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params.pop('decoder'),
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layer_count=config['decoder_layers'],
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is_encoder=False
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)
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decoder.load_state_dict(decoder_params, strict=False)
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del decoder_params
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print("sampling image tokens")
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torch.manual_seed(seed)
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text_tokens = torch.tensor(text_tokens).to(torch.long)
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image_tokens = decoder.forward(text_tokens, encoder_state)
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return image_tokens
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def generate_image_tokens_torch(
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text: str,
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seed: int,
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image_token_count: 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_torch(text_tokens, config, params_dalle_bart)
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image_tokens = decode_torch(
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text_tokens,
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encoder_state,
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config, seed, params_dalle_bart,
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image_token_count
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)
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return image_tokens.detach().numpy()
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if __name__ == '__main__':
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args = parser.parse_args()
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image_tokens = generate_image_tokens_torch(
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args.text,
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args.seed,
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args.image_token_count,
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args.dalle_bart_path
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)
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if args.image_token_count < 256:
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print("image tokens", list(image_tokens, ))
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else:
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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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