min-dalle-test/min_dalle/min_dalle.py

import os
from PIL import Image
from matplotlib.pyplot import grid
import numpy
from torch import LongTensor, FloatTensor
from math import sqrt
import torch
import json
import requests
from typing import Iterator
torch.set_grad_enabled(False)
torch.set_num_threads(os.cpu_count())

from .text_tokenizer import TextTokenizer
from .models import DalleBartEncoder, DalleBartDecoder, VQGanDetokenizer

MIN_DALLE_REPO = 'https://huggingface.co/kuprel/min-dalle/resolve/main/'


class MinDalle:
    def __init__(
        self,
        models_root: str = 'pretrained',
        dtype: torch.dtype = torch.float32,
        is_mega: bool = True, 
        is_reusable: bool = True,
        is_verbose = True
    ):
        self.is_mega = is_mega
        self.is_reusable = is_reusable
        self.dtype = dtype
        self.is_verbose = is_verbose
        self.text_token_count = 64
        self.layer_count = 24 if is_mega else 12
        self.attention_head_count = 32 if is_mega else 16
        self.embed_count = 2048 if is_mega else 1024
        self.glu_embed_count = 4096 if is_mega else 2730
        self.text_vocab_count = 50272 if is_mega else 50264
        self.image_vocab_count = 16415 if is_mega else 16384

        model_name = 'dalle_bart_{}'.format('mega' if is_mega else 'mini')
        dalle_path = os.path.join(models_root, model_name)
        vqgan_path = os.path.join(models_root, 'vqgan')
        if not os.path.exists(dalle_path): os.makedirs(dalle_path)
        if not os.path.exists(vqgan_path): os.makedirs(vqgan_path)
        self.vocab_path = os.path.join(dalle_path, 'vocab.json')
        self.merges_path = os.path.join(dalle_path, 'merges.txt')
        self.encoder_params_path = os.path.join(dalle_path, 'encoder.pt')
        self.decoder_params_path = os.path.join(dalle_path, 'decoder.pt')
        self.detoker_params_path = os.path.join(vqgan_path, 'detoker.pt')

        self.init_tokenizer()
        if is_reusable:
            self.init_encoder()
            self.init_decoder()
            self.init_detokenizer()


    def download_tokenizer(self):
        if self.is_verbose: print("downloading tokenizer params")
        suffix = '' if self.is_mega else '_mini'
        vocab = requests.get(MIN_DALLE_REPO + 'vocab{}.json'.format(suffix))
        merges = requests.get(MIN_DALLE_REPO + 'merges{}.txt'.format(suffix))
        with open(self.vocab_path, 'wb') as f: f.write(vocab.content)
        with open(self.merges_path, 'wb') as f: f.write(merges.content)


    def download_encoder(self):
        if self.is_verbose: print("downloading encoder params")
        suffix = '' if self.is_mega else '_mini'
        params = requests.get(MIN_DALLE_REPO + 'encoder{}.pt'.format(suffix))
        with open(self.encoder_params_path, 'wb') as f: f.write(params.content)


    def download_decoder(self):
        if self.is_verbose: print("downloading decoder params")
        suffix = '' if self.is_mega else '_mini'
        params = requests.get(MIN_DALLE_REPO + 'decoder{}.pt'.format(suffix))
        with open(self.decoder_params_path, 'wb') as f: f.write(params.content)
    

    def download_detokenizer(self):
        if self.is_verbose: print("downloading detokenizer params")
        params = requests.get(MIN_DALLE_REPO + 'detoker.pt')
        with open(self.detoker_params_path, 'wb') as f: f.write(params.content)


    def init_tokenizer(self):
        is_downloaded = os.path.exists(self.vocab_path)
        is_downloaded &= os.path.exists(self.merges_path)
        if not is_downloaded: self.download_tokenizer()
        if self.is_verbose: print("intializing TextTokenizer")
        with open(self.vocab_path, 'r', encoding='utf8') as f:
            vocab = json.load(f)
        with open(self.merges_path, 'r', encoding='utf8') as f:
            merges = f.read().split("\n")[1:-1]
        self.tokenizer = TextTokenizer(vocab, merges)


    def init_encoder(self):
        is_downloaded = os.path.exists(self.encoder_params_path)
        if not is_downloaded: self.download_encoder()
        if self.is_verbose: print("initializing DalleBartEncoder")
        self.encoder = DalleBartEncoder(
            attention_head_count = self.attention_head_count,
            embed_count = self.embed_count,
            glu_embed_count = self.glu_embed_count,
            text_token_count = self.text_token_count,
            text_vocab_count = self.text_vocab_count,
            layer_count = self.layer_count
        ).to(self.dtype).eval()
        params = torch.load(self.encoder_params_path)
        self.encoder.load_state_dict(params, strict=False)
        del params
        if torch.cuda.is_available(): self.encoder = self.encoder.cuda()


    def init_decoder(self):
        is_downloaded = os.path.exists(self.decoder_params_path)
        if not is_downloaded: self.download_decoder()
        if self.is_verbose: print("initializing DalleBartDecoder")
        self.decoder = DalleBartDecoder(
            image_vocab_count = self.image_vocab_count,
            attention_head_count = self.attention_head_count,
            embed_count = self.embed_count,
            glu_embed_count = self.glu_embed_count,
            layer_count = self.layer_count,
            start_token = self.image_vocab_count
        ).to(self.dtype).eval()
        params = torch.load(self.decoder_params_path)
        self.decoder.load_state_dict(params, strict=False)
        del params
        if torch.cuda.is_available(): self.decoder = self.decoder.cuda()


    def init_detokenizer(self):
        is_downloaded = os.path.exists(self.detoker_params_path)
        if not is_downloaded: self.download_detokenizer()
        if self.is_verbose: print("initializing VQGanDetokenizer")
        self.detokenizer = VQGanDetokenizer().eval()
        params = torch.load(self.detoker_params_path)
        self.detokenizer.load_state_dict(params)
        del params
        if torch.cuda.is_available(): self.detokenizer = self.detokenizer.cuda()


    def images_from_tokens(
        self,
        image_tokens: LongTensor,
        is_verbose: bool = False
    ) -> FloatTensor:
        if not self.is_reusable: del self.decoder
        if torch.cuda.is_available(): torch.cuda.empty_cache()
        if not self.is_reusable: self.init_detokenizer()
        if is_verbose: print("detokenizing image")
        images = self.detokenizer.forward(image_tokens).to(torch.uint8)
        if not self.is_reusable: del self.detokenizer
        return images


    def grid_from_images(self, images: FloatTensor) -> Image.Image:
        grid_size = int(sqrt(images.shape[0]))
        images = images.reshape([grid_size] * 2 + list(images.shape[1:]))
        image = images.flatten(1, 2).transpose(0, 1).flatten(1, 2)
        image = Image.fromarray(image.to('cpu').detach().numpy())
        return image


    def generate_images_stream(
        self, 
        text: str, 
        seed: int,
        image_count: int,
        log2_mid_count: int,
        log2_k: int = 6,
        log2_supercondition_factor: int = 3,
        is_verbose: bool = False
    ) -> Iterator[FloatTensor]:
        assert(log2_mid_count in range(5))
        if is_verbose: print("tokenizing text")
        tokens = self.tokenizer.tokenize(text, is_verbose=is_verbose)
        if len(tokens) > self.text_token_count: 
            tokens = tokens[:self.text_token_count]
        if is_verbose: print("text tokens", tokens)
        text_tokens = numpy.ones((2, 64), dtype=numpy.int32)
        text_tokens[0, :2] = [tokens[0], tokens[-1]]
        text_tokens[1, :len(tokens)] = tokens

        text_tokens = torch.tensor(text_tokens).to(torch.long)
        if torch.cuda.is_available(): text_tokens = text_tokens.cuda()

        if not self.is_reusable: self.init_encoder()
        if is_verbose: print("encoding text tokens")
        with torch.cuda.amp.autocast(dtype=self.dtype):
            encoder_state = self.encoder.forward(text_tokens)
        if not self.is_reusable: del self.encoder
        if torch.cuda.is_available(): torch.cuda.empty_cache()

        if not self.is_reusable: self.init_decoder()

        with torch.cuda.amp.autocast(dtype=self.dtype):
            encoder_state, attention_mask, attention_state, image_tokens = ( 
                self.decoder.decode_initial(
                    seed, 
                    image_count, 
                    text_tokens, 
                    encoder_state
                )
            )

        row_count = 16
        for row_index in range(row_count):
            if is_verbose: 
                print('sampling row {} of {}'.format(row_index + 1, row_count))
            with torch.cuda.amp.autocast(dtype=self.dtype):
                attention_state, image_tokens = self.decoder.decode_row(
                    row_index,
                    log2_k,
                    log2_supercondition_factor,
                    encoder_state,
                    attention_mask,
                    attention_state,
                    image_tokens
                )
            with torch.cuda.amp.autocast(dtype=torch.float32):
                if ((row_index + 1) * (2 ** log2_mid_count)) % row_count == 0:
                    tokens = image_tokens[:, 1:]
                    images = self.images_from_tokens(tokens, is_verbose)
                    yield images


    def generate_image_stream(
        self, 
        text: str, 
        seed: int,
        grid_size: int,
        log2_mid_count: int,
        log2_k: int = 6,
        log2_supercondition_factor: int = 3,
        is_verbose: bool = False
    ) -> Iterator[Image.Image]:
        images_stream = self.generate_images_stream(
            text, 
            seed,
            grid_size ** 2,
            log2_mid_count,
            log2_k,
            log2_supercondition_factor,
            is_verbose
        )
        for images in images_stream:
            yield self.grid_from_images(images)


    def generate_images(
        self, 
        text: str,
        seed: int = -1,
        image_count: int = 1,
        log2_k: int = 6,
        log2_supercondition_factor: int = 3,
        is_verbose: bool = False
    ) -> FloatTensor:
        log2_mid_count = 0
        images_stream = self.generate_images_stream(
            text,
            seed,
            image_count,
            log2_mid_count,
            log2_k,
            log2_supercondition_factor,
            is_verbose
        )
        return next(images_stream)


    def generate_image(
        self, 
        text: str,
        seed: int = -1,
        grid_size: int = 1,
        log2_k: int = 6,
        log2_supercondition_factor: int = 3,
        is_verbose: bool = False
    ) -> Image.Image:
        log2_mid_count = 0
        image_stream = self.generate_image_stream(
            text,
            seed,
            grid_size,
            log2_mid_count,
            log2_k,
            log2_supercondition_factor,
            is_verbose
        )
        return next(image_stream)