245 lines
9.4 KiB
Python
245 lines
9.4 KiB
Python
import os
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from PIL import Image
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import numpy
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from torch import LongTensor
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import torch
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import json
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import requests
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from typing import Iterator
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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 .text_tokenizer import TextTokenizer
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from .models import DalleBartEncoder, DalleBartDecoder, VQGanDetokenizer
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MIN_DALLE_REPO = 'https://huggingface.co/kuprel/min-dalle/resolve/main/'
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class MinDalle:
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def __init__(
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self,
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models_root: str = 'pretrained',
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dtype: torch.dtype = torch.float32,
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is_mega: bool = True,
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is_reusable: bool = True,
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is_verbose = True
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):
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self.is_mega = is_mega
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self.is_reusable = is_reusable
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self.dtype = dtype
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self.is_verbose = is_verbose
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self.text_token_count = 64
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self.layer_count = 24 if is_mega else 12
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self.attention_head_count = 32 if is_mega else 16
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self.embed_count = 2048 if is_mega else 1024
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self.glu_embed_count = 4096 if is_mega else 2730
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self.text_vocab_count = 50272 if is_mega else 50264
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self.image_vocab_count = 16415 if is_mega else 16384
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model_name = 'dalle_bart_{}'.format('mega' if is_mega else 'mini')
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dalle_path = os.path.join(models_root, model_name)
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vqgan_path = os.path.join(models_root, 'vqgan')
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if not os.path.exists(dalle_path): os.makedirs(dalle_path)
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if not os.path.exists(vqgan_path): os.makedirs(vqgan_path)
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self.vocab_path = os.path.join(dalle_path, 'vocab.json')
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self.merges_path = os.path.join(dalle_path, 'merges.txt')
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self.encoder_params_path = os.path.join(dalle_path, 'encoder.pt')
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self.decoder_params_path = os.path.join(dalle_path, 'decoder.pt')
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self.detoker_params_path = os.path.join(vqgan_path, 'detoker.pt')
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self.init_tokenizer()
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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 download_tokenizer(self):
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if self.is_verbose: print("downloading tokenizer params")
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suffix = '' if self.is_mega else '_mini'
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vocab = requests.get(MIN_DALLE_REPO + 'vocab{}.json'.format(suffix))
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merges = requests.get(MIN_DALLE_REPO + 'merges{}.txt'.format(suffix))
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with open(self.vocab_path, 'wb') as f: f.write(vocab.content)
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with open(self.merges_path, 'wb') as f: f.write(merges.content)
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def download_encoder(self):
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if self.is_verbose: print("downloading encoder params")
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suffix = '' if self.is_mega else '_mini'
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params = requests.get(MIN_DALLE_REPO + 'encoder{}.pt'.format(suffix))
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with open(self.encoder_params_path, 'wb') as f: f.write(params.content)
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def download_decoder(self):
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if self.is_verbose: print("downloading decoder params")
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suffix = '' if self.is_mega else '_mini'
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params = requests.get(MIN_DALLE_REPO + 'decoder{}.pt'.format(suffix))
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with open(self.decoder_params_path, 'wb') as f: f.write(params.content)
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def download_detokenizer(self):
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if self.is_verbose: print("downloading detokenizer params")
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params = requests.get(MIN_DALLE_REPO + 'detoker.pt')
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with open(self.detoker_params_path, 'wb') as f: f.write(params.content)
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def init_tokenizer(self):
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is_downloaded = os.path.exists(self.vocab_path)
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is_downloaded &= os.path.exists(self.merges_path)
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if not is_downloaded: self.download_tokenizer()
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if self.is_verbose: print("intializing TextTokenizer")
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with open(self.vocab_path, 'r', encoding='utf8') as f:
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vocab = json.load(f)
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with open(self.merges_path, 'r', encoding='utf8') as f:
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merges = f.read().split("\n")[1:-1]
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self.tokenizer = TextTokenizer(vocab, merges)
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def init_encoder(self):
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is_downloaded = os.path.exists(self.encoder_params_path)
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if not is_downloaded: self.download_encoder()
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if self.is_verbose: print("initializing DalleBartEncoder")
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self.encoder = DalleBartEncoder(
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attention_head_count = self.attention_head_count,
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embed_count = self.embed_count,
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glu_embed_count = self.glu_embed_count,
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text_token_count = self.text_token_count,
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text_vocab_count = self.text_vocab_count,
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layer_count = self.layer_count
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).to(self.dtype).eval()
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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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is_downloaded = os.path.exists(self.decoder_params_path)
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if not is_downloaded: self.download_decoder()
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if self.is_verbose: print("initializing DalleBartDecoder")
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self.decoder = DalleBartDecoder(
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image_vocab_count = self.image_vocab_count,
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attention_head_count = self.attention_head_count,
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embed_count = self.embed_count,
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glu_embed_count = self.glu_embed_count,
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layer_count = self.layer_count,
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start_token = self.image_vocab_count
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).to(self.dtype).eval()
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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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is_downloaded = os.path.exists(self.detoker_params_path)
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if not is_downloaded: self.download_detokenizer()
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if self.is_verbose: print("initializing VQGanDetokenizer")
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self.detokenizer = VQGanDetokenizer().eval()
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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 image_from_tokens(
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self,
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grid_size: int,
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image_tokens: LongTensor,
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is_verbose: bool = False
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) -> Image.Image:
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if not self.is_reusable: del self.decoder
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if torch.cuda.is_available(): torch.cuda.empty_cache()
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if not self.is_reusable: self.init_detokenizer()
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if is_verbose: print("detokenizing image")
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images = self.detokenizer.forward(image_tokens).to(torch.uint8)
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if not self.is_reusable: del self.detokenizer
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images = images.reshape([grid_size] * 2 + list(images.shape[1:]))
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image = images.flatten(1, 2).transpose(0, 1).flatten(1, 2)
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image = Image.fromarray(image.to('cpu').detach().numpy())
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return image
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def generate_image_stream(
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self,
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text: str,
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seed: int,
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grid_size: int,
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log2_mid_count: int,
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log2_k: int = 6,
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log2_supercondition_factor: int = 3,
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is_verbose: bool = False
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) -> Iterator[Image.Image]:
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assert(log2_mid_count in range(5))
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if is_verbose: print("tokenizing text")
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tokens = self.tokenizer.tokenize(text, is_verbose=is_verbose)
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if len(tokens) > self.text_token_count:
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tokens = tokens[:self.text_token_count]
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if is_verbose: print("text tokens", tokens)
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text_tokens = numpy.ones((2, 64), dtype=numpy.int32)
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text_tokens[0, :2] = [tokens[0], tokens[-1]]
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text_tokens[1, :len(tokens)] = tokens
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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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if is_verbose: print("encoding text tokens")
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with torch.cuda.amp.autocast(dtype=self.dtype):
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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 torch.cuda.is_available(): torch.cuda.empty_cache()
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if not self.is_reusable: self.init_decoder()
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with torch.cuda.amp.autocast(dtype=self.dtype):
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encoder_state, attention_mask, attention_state, image_tokens = (
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self.decoder.decode_initial(
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seed,
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grid_size ** 2,
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text_tokens,
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encoder_state
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)
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)
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row_count = 16
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for row_index in range(row_count):
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if is_verbose:
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print('sampling row {} of {}'.format(row_index + 1, row_count))
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with torch.cuda.amp.autocast(dtype=self.dtype):
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attention_state, image_tokens = self.decoder.decode_row(
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row_index,
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log2_k,
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log2_supercondition_factor,
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encoder_state,
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attention_mask,
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attention_state,
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image_tokens
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)
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with torch.cuda.amp.autocast(dtype=torch.float32):
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if ((row_index + 1) * (2 ** log2_mid_count)) % row_count == 0:
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tokens = image_tokens[:, 1:]
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image = self.image_from_tokens(grid_size, tokens, is_verbose)
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yield image
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def generate_image(
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self,
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text: str,
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seed: int = -1,
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grid_size: int = 1,
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log2_k: int = 6,
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log2_supercondition_factor: int = 3,
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is_verbose: bool = False
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) -> Image.Image:
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log2_mid_count = 0
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image_stream = self.generate_image_stream(
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text,
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seed,
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grid_size,
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log2_mid_count,
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log2_k,
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log2_supercondition_factor,
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is_verbose
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)
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return next(image_stream) |