min-dalle-test/min_dalle/min_dalle_flax.py

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import jax
import numpy
from PIL import Image
import torch
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from .min_dalle_base import MinDalleBase
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from .models.dalle_bart_encoder_flax import DalleBartEncoderFlax
from .models.dalle_bart_decoder_flax import DalleBartDecoderFlax
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from .models.vqgan_detokenizer import VQGanDetokenizer
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from .load_params import load_dalle_bart_flax_params, load_vqgan_torch_params
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class MinDalleFlax(MinDalleBase):
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def __init__(self, is_mega: bool, is_reusable: bool = True):
super().__init__(is_mega)
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self.is_reusable = is_reusable
print("initializing MinDalleFlax")
self.model_params = load_dalle_bart_flax_params(self.model_path)
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if is_reusable:
self.init_encoder()
self.init_decoder()
self.init_detokenizer()
def init_encoder(self):
print("initializing DalleBartEncoderFlax")
self.encoder: DalleBartEncoderFlax = DalleBartEncoderFlax(
attention_head_count = self.config['encoder_attention_heads'],
embed_count = self.config['d_model'],
glu_embed_count = self.config['encoder_ffn_dim'],
text_token_count = self.config['max_text_length'],
text_vocab_count = self.config['encoder_vocab_size'],
layer_count = self.config['encoder_layers']
).bind({'params': self.model_params.pop('encoder')})
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def init_decoder(self):
print("initializing DalleBartDecoderFlax")
self.decoder = DalleBartDecoderFlax(
image_token_count = self.config['image_length'],
text_token_count = self.config['max_text_length'],
image_vocab_count = self.config['image_vocab_size'],
attention_head_count = self.config['decoder_attention_heads'],
embed_count = self.config['d_model'],
glu_embed_count = self.config['decoder_ffn_dim'],
layer_count = self.config['decoder_layers'],
start_token = self.config['decoder_start_token_id']
)
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def init_detokenizer(self):
print("initializing VQGanDetokenizer")
params = load_vqgan_torch_params('./pretrained/vqgan')
self.detokenizer = VQGanDetokenizer()
self.detokenizer.load_state_dict(params)
del params
def generate_image(self, text: str, seed: int) -> Image.Image:
text_tokens = self.tokenize_text(text)
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if not self.is_reusable: self.init_encoder()
print("encoding text tokens")
encoder_state = self.encoder(text_tokens)
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if not self.is_reusable: del self.encoder
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if not self.is_reusable:
self.init_decoder()
params = self.model_params.pop('decoder')
else:
params = self.model_params['decoder']
print("sampling image tokens")
image_tokens = self.decoder.sample_image_tokens(
text_tokens,
encoder_state,
jax.random.PRNGKey(seed),
params
)
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if not self.is_reusable: del self.decoder
image_tokens = torch.tensor(numpy.array(image_tokens))
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if not self.is_reusable: self.init_detokenizer()
print("detokenizing image")
image = self.detokenizer.forward(image_tokens).to(torch.uint8)
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if not self.is_reusable: del self.detokenizer
image = Image.fromarray(image.to('cpu').detach().numpy())
return image