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107 lines
4.0 KiB
107 lines
4.0 KiB
import os |
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from PIL import Image |
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from typing import Dict |
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from torch import LongTensor |
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import torch |
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torch.set_grad_enabled(False) |
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torch.set_num_threads(os.cpu_count()) |
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from .load_params import convert_dalle_bart_torch_from_flax_params |
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from .min_dalle_base import MinDalleBase |
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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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class MinDalleTorch(MinDalleBase): |
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def __init__( |
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self, |
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is_mega: bool, |
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is_reusable: bool = True, |
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token_count: int = 256 |
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): |
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super().__init__(is_mega) |
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self.is_reusable = is_reusable |
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self.token_count = token_count |
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print("initializing MinDalleTorch") |
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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 DalleBartEncoderTorch") |
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self.encoder = DalleBartEncoderTorch( |
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layer_count = self.config['encoder_layers'], |
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embed_count = self.config['d_model'], |
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attention_head_count = self.config['encoder_attention_heads'], |
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text_vocab_count = self.config['encoder_vocab_size'], |
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text_token_count = self.config['max_text_length'], |
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glu_embed_count = self.config['encoder_ffn_dim'] |
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) |
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params = convert_dalle_bart_torch_from_flax_params( |
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self.model_params.pop('encoder'), |
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layer_count=self.config['encoder_layers'], |
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is_encoder=True |
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) |
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self.encoder.load_state_dict(params, strict=False) |
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if torch.cuda.is_available(): self.encoder = self.encoder.cuda() |
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del params |
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def init_decoder(self): |
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print("initializing DalleBartDecoderTorch") |
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self.decoder = DalleBartDecoderTorch( |
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image_vocab_size = self.config['image_vocab_size'], |
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image_token_count = self.config['image_length'], |
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sample_token_count = self.token_count, |
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embed_count = self.config['d_model'], |
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attention_head_count = self.config['decoder_attention_heads'], |
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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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batch_count = 2, |
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start_token = self.config['decoder_start_token_id'], |
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is_verbose = True |
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) |
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params = convert_dalle_bart_torch_from_flax_params( |
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self.model_params.pop('decoder'), |
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layer_count=self.config['decoder_layers'], |
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is_encoder=False |
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) |
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self.decoder.load_state_dict(params, strict=False) |
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if torch.cuda.is_available(): self.decoder = self.decoder.cuda() |
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del params |
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def init_detokenizer(self): |
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super().init_detokenizer() |
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if torch.cuda.is_available(): |
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self.detokenizer = self.detokenizer.cuda() |
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def generate_image_tokens(self, text: str, seed: int) -> LongTensor: |
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text_tokens = self.tokenize_text(text) |
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text_tokens = torch.tensor(text_tokens).to(torch.long) |
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if torch.cuda.is_available(): text_tokens = text_tokens.cuda() |
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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.forward(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() |
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print("sampling image tokens") |
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torch.manual_seed(seed) |
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image_tokens = self.decoder.forward(text_tokens, encoder_state) |
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if not self.is_reusable: del self.decoder |
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return image_tokens |
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def generate_image(self, text: str, seed: int) -> Image.Image: |
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image_tokens = self.generate_image_tokens(text, seed) |
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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 |