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301 lines
11 KiB
301 lines
11 KiB
import os |
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from PIL import Image |
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import numpy |
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from torch import LongTensor, FloatTensor |
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from math import sqrt |
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import torch |
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import torch.backends.cudnn, torch.backends.cuda |
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import json |
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import requests |
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from typing import Iterator |
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from .text_tokenizer import TextTokenizer |
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from .models import DalleBartEncoder, DalleBartDecoder, VQGanDetokenizer |
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torch.set_grad_enabled(False) |
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torch.set_num_threads(os.cpu_count()) |
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torch.backends.cudnn.enabled = True |
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torch.backends.cudnn.allow_tf32 = True |
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torch.backends.cudnn.benchmark = True |
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torch.backends.cuda.matmul.allow_tf32 = True |
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torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True |
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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 images_from_tokens( |
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self, |
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image_tokens: LongTensor, |
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is_verbose: bool = False |
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) -> FloatTensor: |
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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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return images |
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def grid_from_images(self, images: FloatTensor) -> Image.Image: |
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grid_size = int(sqrt(images.shape[0])) |
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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').numpy()) |
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return image |
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def generate_images_stream( |
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self, |
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text: str, |
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seed: int, |
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image_count: 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[FloatTensor]: |
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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".format(len(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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image_count, |
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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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images = self.images_from_tokens(tokens, is_verbose) |
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yield images |
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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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images_stream = self.generate_images_stream( |
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text, |
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seed, |
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grid_size ** 2, |
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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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for images in images_stream: |
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yield self.grid_from_images(images) |
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def generate_images( |
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self, |
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text: str, |
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seed: int = -1, |
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image_count: 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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) -> FloatTensor: |
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log2_mid_count = 0 |
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images_stream = self.generate_images_stream( |
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text, |
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seed, |
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image_count, |
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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(images_stream) |
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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) |