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