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87 lines
3.2 KiB
87 lines
3.2 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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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): |
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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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self.model_params = load_dalle_bart_flax_params(self.model_path) |
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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 = 32 if self.is_mega else 16, |
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embed_count = 2048 if self.is_mega else 1024, |
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glu_embed_count = 4096 if self.is_mega else 2730, |
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text_token_count = 64, |
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text_vocab_count = 50272 if self.is_mega else 50264, |
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layer_count = 24 if self.is_mega else 12 |
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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 = 256, |
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image_vocab_count = 16415 if self.is_mega else 16384, |
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attention_head_count = 32 if self.is_mega else 16, |
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embed_count = 2048 if self.is_mega else 1024, |
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glu_embed_count = 4096 if self.is_mega else 2730, |
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layer_count = 24 if self.is_mega else 12, |
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start_token = 16415 if self.is_mega else 16384 |
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) |
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def init_detokenizer(self): |
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print("initializing VQGanDetokenizer") |
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params = load_vqgan_torch_params('./pretrained/vqgan') |
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self.detokenizer = VQGanDetokenizer() |
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self.detokenizer.load_state_dict(params) |
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del params |
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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 |