114 lines
4.5 KiB
Python
114 lines
4.5 KiB
Python
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_and_save_torch_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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from .models.vqgan_detokenizer import VQGanDetokenizer
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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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print("initializing MinDalleTorch")
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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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self.encoder_params_path = os.path.join(self.model_path, 'encoder.pt')
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self.decoder_params_path = os.path.join(self.model_path, 'decoder.pt')
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self.detoker_params_path = os.path.join('pretrained', 'vqgan', 'detoker.pt')
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is_converted = os.path.exists(self.encoder_params_path)
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is_converted &= os.path.exists(self.decoder_params_path)
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is_converted &= os.path.exists(self.detoker_params_path)
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if not is_converted:
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convert_and_save_torch_params(is_mega, 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 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 = torch.load(self.encoder_params_path)
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self.encoder.load_state_dict(params, strict=False)
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del params
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if torch.cuda.is_available(): self.encoder = self.encoder.cuda()
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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 = torch.load(self.decoder_params_path)
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self.decoder.load_state_dict(params, strict=False)
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del params
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if torch.cuda.is_available(): self.decoder = self.decoder.cuda()
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def init_detokenizer(self):
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print("initializing VQGanDetokenizer")
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self.detokenizer = VQGanDetokenizer()
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params = torch.load(self.detoker_params_path)
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self.detokenizer.load_state_dict(params)
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del params
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if torch.cuda.is_available(): 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 |