min-dalle-test/min_dalle/min_dalle_flax.py

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2022-06-27 16:43:47 +00:00
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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class MinDalleFlax(MinDalleBase):
def __init__(self, is_mega: bool, is_expendable: bool = False):
super().__init__(is_mega)
self.is_expendable = is_expendable
print("initializing MinDalleFlax")
if not is_expendable:
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')})
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']
)
def generate_image(self, text: str, seed: int) -> Image.Image:
text_tokens = self.tokenize_text(text)
if self.is_expendable: self.init_encoder()
print("encoding text tokens")
encoder_state = self.encoder(text_tokens)
if self.is_expendable: del self.encoder
if self.is_expendable:
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
)
if self.is_expendable: del self.decoder
image_tokens = torch.tensor(numpy.array(image_tokens))
if self.is_expendable: self.init_detokenizer()
print("detokenizing image")
image = self.detokenizer.forward(image_tokens).to(torch.uint8)
if self.is_expendable: del self.detokenizer
image = Image.fromarray(image.to('cpu').detach().numpy())
return image