Upload min_dalle.py

min_dalle/min_dalle.py

@@ -0,0 +1,291 @@
+import os
+from PIL import Image
+import numpy
+from torch import LongTensor, FloatTensor
+import torch
+import torch.backends.cudnn, torch.backends.cuda
+import json
+import requests
+from typing import Iterator
+from .text_tokenizer import TextTokenizer
+from .models import DalleBartEncoder, DalleBartDecoder, VQGanDetokenizer
+import streamlit as st
+
+torch.set_grad_enabled(False)
+torch.set_num_threads(os.cpu_count())
+torch.backends.cudnn.enabled = True
+torch.backends.cudnn.allow_tf32 = True
+
+MIN_DALLE_REPO = 'https://huggingface.co/kuprel/min-dalle/resolve/main/'
+IMAGE_TOKEN_COUNT = 256
+
+
+class MinDalle:
+    def __init__(
+        self,
+        models_root: str = 'pretrained',
+        dtype: torch.dtype = torch.float32,
+        device: str = None,
+        is_mega: bool = True, 
+        is_reusable: bool = True,
+        is_verbose = True
+    ):
+        if device == None:
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        if is_verbose: print("using device", device)
+        self.device = device
+        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'
+        _ = requests.get(MIN_DALLE_REPO + 'config.json') # trigger HF download
+        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,
+            device=self.device
+        ).to(self.dtype).eval()
+        params = torch.load(self.encoder_params_path)
+        self.encoder.load_state_dict(params, strict=False)
+        del params
+        self.encoder = self.encoder.to(device=self.device)
+
+
+    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,
+            device=self.device
+        ).to(self.dtype).eval()
+        params = torch.load(self.decoder_params_path)
+        self.decoder.load_state_dict(params, strict=False)
+        del params
+        self.decoder = self.decoder.to(device=self.device)
+
+
+    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
+        self.detokenizer = self.detokenizer.to(device=self.device)
+
+
+    def image_grid_from_tokens(
+        self,
+        image_tokens: LongTensor,
+        is_seamless: bool,
+        is_verbose: bool = False
+    ) -> FloatTensor:
+        if not self.is_reusable: del self.decoder
+        torch.cuda.empty_cache()
+        if not self.is_reusable: self.init_detokenizer()
+        if is_verbose: print("detokenizing image")
+        images = self.detokenizer.forward(is_seamless, image_tokens)
+        if not self.is_reusable: del self.detokenizer
+        return images
+
+
+    def generate_raw_image_stream(
+        self, 
+        text: str, 
+        seed: int,
+        grid_size: int,
+        progressive_outputs: bool = False,
+        is_seamless: bool = False,
+        temperature: float = 1,
+        top_k: int = 256,
+        supercondition_factor: int = 16,
+        is_verbose: bool = False
+    ) -> Iterator[FloatTensor]:
+        image_count = grid_size ** 2
+        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".format(len(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, 
+            dtype=torch.long, 
+            device=self.device
+        )
+
+        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
+        torch.cuda.empty_cache()
+
+        if not self.is_reusable: self.init_decoder()
+
+        with torch.cuda.amp.autocast(dtype=self.dtype):
+            expanded_indices = [0] * image_count + [1] * image_count
+            text_tokens = text_tokens[expanded_indices]
+            encoder_state = encoder_state[expanded_indices]
+            attention_mask = text_tokens.not_equal(1)
+            attention_state = torch.zeros(
+                size=(
+                    self.layer_count,
+                    image_count * 4,
+                    IMAGE_TOKEN_COUNT,
+                    self.embed_count
+                ), 
+                device=self.device
+            )
+            image_tokens = torch.full(
+                (IMAGE_TOKEN_COUNT + 1, image_count), 
+                self.image_vocab_count,
+                dtype=torch.long,
+                device=self.device
+            )
+            
+            if seed > 0: torch.manual_seed(seed)
+
+        token_indices = torch.arange(IMAGE_TOKEN_COUNT, device=self.device)
+        settings = torch.tensor(
+            [temperature, top_k, supercondition_factor], 
+            dtype=torch.float32,
+            device=self.device
+        )
+        for i in range(IMAGE_TOKEN_COUNT):
+            if(st.session_state.page != 0):
+                break
+            st.session_state.bar.progress(i/IMAGE_TOKEN_COUNT)
+
+            torch.cuda.empty_cache()                
+            with torch.cuda.amp.autocast(dtype=self.dtype):
+                image_tokens[i + 1], attention_state = self.decoder.forward(
+                    settings=settings,
+                    attention_mask=attention_mask,
+                    encoder_state=encoder_state,
+                    attention_state=attention_state,
+                    prev_tokens=image_tokens[i],
+                    token_index=token_indices[[i]]
+                )
+
+            with torch.cuda.amp.autocast(dtype=torch.float32):
+                if ((i + 1) % 32 == 0 and progressive_outputs) or i + 1 == 256:
+                    yield self.image_grid_from_tokens(
+                        image_tokens=image_tokens[1:].T,
+                        is_seamless=is_seamless,
+                        is_verbose=is_verbose
+                    )
+
+    def generate_image_stream(self, *args, **kwargs) -> Iterator[Image.Image]:
+        image_stream = self.generate_raw_image_stream(*args, **kwargs)
+        for image in image_stream:
+            image = image.to(torch.uint8).to('cpu').numpy()
+            yield Image.fromarray(image)
+
+
+    def generate_images_stream(self, *args, **kwargs) -> Iterator[FloatTensor]:
+        image_stream = self.generate_raw_image_stream(*args, **kwargs)
+        for image in image_stream:
+            grid_size = kwargs['grid_size']
+            image = image.view([grid_size * 256, grid_size, 256, 3])
+            image = image.transpose(1, 0)
+            image = image.reshape([grid_size ** 2, 2 ** 8, 2 ** 8, 3])
+            yield image
+
+
+    def generate_image(self, *args, **kwargs) -> Image.Image:
+        image_stream = self.generate_image_stream(
+            *args, **kwargs, 
+            progressive_outputs=False
+        )
+        return next(image_stream)
+
+
+    def generate_images(self, *args, **kwargs) -> Image.Image:
+        images_stream = self.generate_images_stream(
+            *args, **kwargs, 
+            progressive_outputs=False
+        )
+        return next(images_stream)