diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..5a1981d --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +* linguist-vendored +*.py linguist-vendored=false diff --git a/README.md b/README.md index 3f84228..8265af6 100644 --- a/README.md +++ b/README.md @@ -3,27 +3,31 @@ [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb) \ Try Replicate web demo here [![Replicate](https://replicate.com/kuprel/min-dalle/badge)](https://replicate.com/kuprel/min-dalle) -This is a minimal implementation of [DALL·E Mini](https://github.com/borisdayma/dalle-mini). It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch. The only third party dependencies are `numpy` and `torch` for the torch model and `flax` for the flax model. +This is a minimal implementation of [DALL·E Mini](https://github.com/borisdayma/dalle-mini). It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch. The only third party dependencies are numpy, torch, and flax (and optionally wandb to download the models). + +DALL·E Mega inference with PyTorch takes 7.3 seconds in Colab to generate an avocado armchair ### Setup -Run `sh setup.sh` to install dependencies and download pretrained models. The models can also be downloaded manually: +Run `sh setup.sh` to install dependencies and download pretrained models. The models can also be downloaded manually here: [VQGan](https://huggingface.co/dalle-mini/vqgan_imagenet_f16_16384), [DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/artifacts/DalleBart_model/mini-1/v0/files), [DALL·E Mega](https://wandb.ai/dalle-mini/dalle-mini/artifacts/DalleBart_model/mega-1-fp16/v14/files) ### Usage -Use the command line python script `image_from_text.py` to generate images. Here are some examples: +Use the python script `image_from_text.py` to generate images from the command line. Note: the command line script loads the models and parameters each time. To load a model once and generate multiple times, initialize either `MinDalleTorch` or `MinDalleFlax`, then call `generate_image` with some text and a seed. See the colab for an example. + +### Examples ``` -python image_from_text.py --text='alien life' --seed=7 +python image_from_text.py --text='artificial intelligence' --torch ``` -![Alien](examples/alien.png) +![Alien](examples/artificial_intelligence.png) ``` -python image_from_text.py --text='a comfy chair that looks like an avocado' --mega --seed=4 +python image_from_text.py --text='a comfy chair that looks like an avocado' --torch --mega --seed=10 ``` ![Avocado Armchair](examples/avocado_armchair.png) @@ -32,4 +36,4 @@ python image_from_text.py --text='a comfy chair that looks like an avocado' --me python image_from_text.py --text='court sketch of godzilla on trial' --mega --seed=100 ``` -![Godzilla Trial](examples/godzilla_trial.png) +![Godzilla Trial](examples/godzilla_trial.png) \ No newline at end of file diff --git a/examples/alien.png b/examples/alien.png deleted file mode 100644 index cd4c59b..0000000 Binary files a/examples/alien.png and /dev/null differ diff --git a/examples/artificial_intelligence.png b/examples/artificial_intelligence.png new file mode 100644 index 0000000..30b3f43 Binary files /dev/null and b/examples/artificial_intelligence.png differ diff --git a/examples/avocado_armchair.png b/examples/avocado_armchair.png index f270df5..1dd2c87 100644 Binary files a/examples/avocado_armchair.png and b/examples/avocado_armchair.png differ diff --git a/image_from_text.py b/image_from_text.py index a56522d..a0a3bdf 100644 --- a/image_from_text.py +++ b/image_from_text.py @@ -2,8 +2,8 @@ import argparse import os from PIL import Image -from min_dalle.generate_image import generate_image_from_text - +from min_dalle.min_dalle_torch import MinDalleTorch +from min_dalle.min_dalle_flax import MinDalleFlax parser = argparse.ArgumentParser() parser.add_argument('--mega', action='store_true') @@ -12,10 +12,10 @@ parser.set_defaults(mega=False) parser.add_argument('--torch', action='store_true') parser.add_argument('--no-torch', dest='torch', action='store_false') parser.set_defaults(torch=False) -parser.add_argument('--text', type=str) -parser.add_argument('--seed', type=int, default=0) +parser.add_argument('--text', type=str, default='alien life') +parser.add_argument('--seed', type=int, default=7) parser.add_argument('--image_path', type=str, default='generated') -parser.add_argument('--image_token_count', type=int, default=256) # for debugging +parser.add_argument('--sample_token_count', type=int, default=256) # for debugging def ascii_from_image(image: Image.Image, size: int) -> str: @@ -36,19 +36,40 @@ def save_image(image: Image.Image, path: str): return image +def generate_image( + is_torch: bool, + is_mega: bool, + text: str, + seed: int, + image_path: str, + sample_token_count: int +): + if is_torch: + image_generator = MinDalleTorch(is_mega, sample_token_count) + image_tokens = image_generator.generate_image_tokens(text, seed) + + if sample_token_count < image_generator.config['image_length']: + print('image tokens', list(image_tokens.to('cpu').detach().numpy())) + return + else: + image = image_generator.generate_image(text, seed) + + else: + image_generator = MinDalleFlax(is_mega) + image = image_generator.generate_image(text, seed) + + save_image(image, image_path) + print(ascii_from_image(image, size=128)) + + if __name__ == '__main__': args = parser.parse_args() - print(args) - - image = generate_image_from_text( - text = args.text, - is_mega = args.mega, - is_torch = args.torch, - seed = args.seed, - image_token_count = args.image_token_count - ) - - if image != None: - save_image(image, args.image_path) - print(ascii_from_image(image, size=128)) \ No newline at end of file + generate_image( + is_torch=args.torch, + is_mega=args.mega, + text=args.text, + seed=args.seed, + image_path=args.image_path, + sample_token_count=args.sample_token_count + ) \ No newline at end of file diff --git a/min_dalle.ipynb b/min_dalle.ipynb index 9fbfc8c..389353d 100644 --- a/min_dalle.ipynb +++ b/min_dalle.ipynb @@ -25,7 +25,7 @@ "id": "Zl_ZFisFApeh" }, "source": [ - "### Setup" + "### Download models and install dependencies" ] }, { @@ -46,6 +46,50 @@ "! wandb artifact get --root=/content/min-dalle/pretrained/dalle_bart_mega dalle-mini/dalle-mini/mega-1-fp16:v14\n" ] }, + { + "cell_type": "markdown", + "source": [ + "### Load Model\n", + "Note: mega requires the high-RAM runtime type, uncheck it if you're using standard" + ], + "metadata": { + "id": "kViq2dMbGDKt" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "os.chdir('/content/min-dalle')\n", + "from min_dalle.min_dalle_torch import MinDalleTorch\n", + "from min_dalle.min_dalle_flax import MinDalleFlax\n", + "\n", + "mega = True #@param {type:\"boolean\"}\n", + "torch = True #@param {type:\"boolean\"}\n", + "\n", + "model = MinDalleTorch(mega) if torch else MinDalleFlax(mega)\n" + ], + "metadata": { + "id": "8W-L2ICFGFup", + "outputId": "2588b13e-bfe2-41ec-c2de-1cf3d180cb64", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "reading files from pretrained/dalle_bart_mega\n", + "initializing MinDalleTorch\n", + "loading encoder\n", + "loading decoder\n" + ] + } + ] + }, { "cell_type": "markdown", "metadata": { @@ -61,24 +105,27 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 443 + "height": 528 }, "id": "nQ0UG05dA4p2", - "outputId": "b2c98233-d50d-42d6-b858-7676cb011926" + "outputId": "fde0085a-9fc3-40ea-cc90-1bb7a4b56104" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "parsing metadata from ./pretrained/dalle_bart_mini\n", "tokenizing text\n", - "['Ġartificial']\n", - "['Ġintelligence']\n", - "text tokens [0, 6316, 7815, 2]\n", - "loading torch encoder\n", + "['Ġa']\n", + "['Ġcomfy']\n", + "['Ġchair']\n", + "['Ġthat']\n", + "['Ġlooks']\n", + "['Ġlike']\n", + "['Ġan']\n", + "['Ġavocado']\n", + "text tokens [0, 58, 29872, 2408, 766, 4126, 1572, 101, 16632, 2]\n", "encoding text tokens\n", - "loading torch decoder\n", "sampling image tokens\n", "detokenizing image\n" ] @@ -87,23 +134,28 @@ "output_type": "display_data", "data": { "text/plain": [ - "" + "" ], - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 7.38 s, sys: 14 ms, total: 7.39 s\n", + "Wall time: 7.29 s\n" + ] } ], "source": [ - "text = \"artificial intelligence\" #@param {type:\"string\"}\n", - "seed = 0 #@param {type:\"integer\"}\n", - "torch = True #@param {type:\"boolean\"}\n", - "mega = False #@param {type:\"boolean\"}\n", + "%%time\n", "\n", - "import os\n", - "os.chdir('/content/min-dalle')\n", - "from min_dalle.generate_image import generate_image_from_text\n", - "image = generate_image_from_text(text, seed=seed, is_torch=torch, is_mega=mega)\n", + "text = \"a comfy chair that looks like an avocado\" #@param {type:\"string\"}\n", + "seed = 10 #@param {type:\"integer\"}\n", + "\n", + "image = model.generate_image(text, seed)\n", "display(image)" ] } @@ -117,7 +169,7 @@ "name": "min-dalle", "provenance": [], "machine_shape": "hm", - "authorship_tag": "ABX9TyMIs89kdBk8iPr64XMI6Tjx", + "authorship_tag": "ABX9TyMD0M+1bhwaISRugJcCWvwm", "include_colab_link": true }, "gpuClass": "standard", diff --git a/min_dalle/generate_image.py b/min_dalle/generate_image.py deleted file mode 100644 index f7f63cb..0000000 --- a/min_dalle/generate_image.py +++ /dev/null @@ -1,78 +0,0 @@ -import os -import json -import numpy -from PIL import Image -from typing import Tuple, List -import torch - -from min_dalle.load_params import load_dalle_bart_flax_params -from min_dalle.text_tokenizer import TextTokenizer -from min_dalle.min_dalle_flax import generate_image_tokens_flax -from min_dalle.min_dalle_torch import ( - generate_image_tokens_torch, - detokenize_torch -) - -def load_dalle_bart_metadata(path: str) -> Tuple[dict, dict, List[str]]: - print("parsing metadata from {}".format(path)) - for f in ['config.json', 'flax_model.msgpack', 'vocab.json', 'merges.txt']: - assert(os.path.exists(os.path.join(path, f))) - with open(path + '/config.json', 'r') as f: - config = json.load(f) - with open(path + '/vocab.json') as f: - vocab = json.load(f) - with open(path + '/merges.txt') as f: - merges = f.read().split("\n")[1:-1] - return config, vocab, merges - - -def tokenize_text( - text: str, - config: dict, - vocab: dict, - merges: List[str] -) -> numpy.ndarray: - print("tokenizing text") - tokens = TextTokenizer(vocab, merges)(text) - print("text tokens", tokens) - text_tokens = numpy.ones((2, config['max_text_length']), dtype=numpy.int32) - text_tokens[0, :len(tokens)] = tokens - text_tokens[1, :2] = [tokens[0], tokens[-1]] - return text_tokens - - -def generate_image_from_text( - text: str, - is_mega: bool = False, - is_torch: bool = False, - seed: int = 0, - image_token_count: int = 256 -) -> Image.Image: - model_name = 'mega' if is_mega else 'mini' - model_path = './pretrained/dalle_bart_{}'.format(model_name) - config, vocab, merges = load_dalle_bart_metadata(model_path) - text_tokens = tokenize_text(text, config, vocab, merges) - params_dalle_bart = load_dalle_bart_flax_params(model_path) - - if is_torch: - image_tokens = generate_image_tokens_torch( - text_tokens = text_tokens, - seed = seed, - config = config, - params = params_dalle_bart, - image_token_count = image_token_count - ) - if image_token_count == config['image_length']: - image = detokenize_torch(image_tokens, is_torch=True) - return Image.fromarray(image) - else: - print(list(image_tokens.to('cpu').detach().numpy())) - else: - image_tokens = generate_image_tokens_flax( - text_tokens = text_tokens, - seed = seed, - config = config, - params = params_dalle_bart, - ) - image = detokenize_torch(torch.tensor(image_tokens), is_torch=False) - return Image.fromarray(image) \ No newline at end of file diff --git a/min_dalle/min_dalle.py b/min_dalle/min_dalle.py new file mode 100644 index 0000000..adaa6f7 --- /dev/null +++ b/min_dalle/min_dalle.py @@ -0,0 +1,43 @@ +import os +import json +import numpy + +from .text_tokenizer import TextTokenizer +from .load_params import load_vqgan_torch_params, load_dalle_bart_flax_params +from .models.vqgan_detokenizer import VQGanDetokenizer + +class MinDalle: + def __init__(self, is_mega: bool): + self.is_mega = is_mega + model_name = 'dalle_bart_{}'.format('mega' if is_mega else 'mini') + model_path = os.path.join('pretrained', model_name) + + print("reading files from {}".format(model_path)) + config_path = os.path.join(model_path, 'config.json') + vocab_path = os.path.join(model_path, 'vocab.json') + merges_path = os.path.join(model_path, 'merges.txt') + + with open(config_path, 'r', encoding='utf8') as f: + self.config = json.load(f) + with open(vocab_path, 'r', encoding='utf8') as f: + vocab = json.load(f) + with open(merges_path, 'r', encoding='utf8') as f: + merges = f.read().split("\n")[1:-1] + + self.model_params = load_dalle_bart_flax_params(model_path) + + self.tokenizer = TextTokenizer(vocab, merges) + self.detokenizer = VQGanDetokenizer() + vqgan_params = load_vqgan_torch_params('./pretrained/vqgan') + self.detokenizer.load_state_dict(vqgan_params) + + + def tokenize_text(self, text: str) -> numpy.ndarray: + print("tokenizing text") + tokens = self.tokenizer.tokenize(text) + print("text tokens", tokens) + text_token_count = self.config['max_text_length'] + text_tokens = numpy.ones((2, text_token_count), dtype=numpy.int32) + text_tokens[0, :len(tokens)] = tokens + text_tokens[1, :2] = [tokens[0], tokens[-1]] + return text_tokens \ No newline at end of file diff --git a/min_dalle/min_dalle_flax.py b/min_dalle/min_dalle_flax.py index 884f271..100d4ab 100644 --- a/min_dalle/min_dalle_flax.py +++ b/min_dalle/min_dalle_flax.py @@ -1,79 +1,58 @@ import jax -from jax import numpy as jnp import numpy +from PIL import Image +import torch +from .min_dalle import MinDalle from .models.dalle_bart_encoder_flax import DalleBartEncoderFlax from .models.dalle_bart_decoder_flax import DalleBartDecoderFlax -def encode_flax( - text_tokens: numpy.ndarray, - config: dict, - params: dict -) -> jnp.ndarray: - print("loading flax encoder") - encoder: DalleBartEncoderFlax = DalleBartEncoderFlax( - attention_head_count = config['encoder_attention_heads'], - embed_count = config['d_model'], - glu_embed_count = config['encoder_ffn_dim'], - text_token_count = config['max_text_length'], - text_vocab_count = config['encoder_vocab_size'], - layer_count = config['encoder_layers'] - ).bind({'params': params.pop('encoder')}) +class MinDalleFlax(MinDalle): + def __init__(self, is_mega: bool): + super().__init__(is_mega) + print("initializing MinDalleFlax") - print("encoding text tokens") - encoder_state = encoder(text_tokens) - del encoder - return encoder_state + print("loading encoder") + self.encoder = DalleBartEncoderFlax( + attention_head_count = self.config['encoder_attention_heads'], + embed_count = self.config['d_model'], + glu_embed_count = self.config['encoder_ffn_dim'], + text_token_count = self.config['max_text_length'], + text_vocab_count = self.config['encoder_vocab_size'], + layer_count = self.config['encoder_layers'] + ).bind({'params': self.model_params.pop('encoder')}) + print("loading decoder") + self.decoder = DalleBartDecoderFlax( + image_token_count = self.config['image_length'], + text_token_count = self.config['max_text_length'], + image_vocab_count = self.config['image_vocab_size'], + attention_head_count = self.config['decoder_attention_heads'], + embed_count = self.config['d_model'], + glu_embed_count = self.config['decoder_ffn_dim'], + layer_count = self.config['decoder_layers'], + start_token = self.config['decoder_start_token_id'] + ) + -def decode_flax( - text_tokens: jnp.ndarray, - encoder_state: jnp.ndarray, - config: dict, - seed: int, - params: dict -) -> jnp.ndarray: - print("loading flax decoder") - decoder = DalleBartDecoderFlax( - image_token_count = config['image_length'], - text_token_count = config['max_text_length'], - image_vocab_count = config['image_vocab_size'], - attention_head_count = config['decoder_attention_heads'], - embed_count = config['d_model'], - glu_embed_count = config['decoder_ffn_dim'], - layer_count = config['decoder_layers'], - start_token = config['decoder_start_token_id'] - ) - print("sampling image tokens") - image_tokens = decoder.sample_image_tokens( - text_tokens, - encoder_state, - jax.random.PRNGKey(seed), - params.pop('decoder') - ) - del decoder - return image_tokens + def generate_image(self, text: str, seed: int) -> Image.Image: + text_tokens = self.tokenize_text(text) + print("encoding text tokens") + encoder_state = self.encoder(text_tokens) -def generate_image_tokens_flax( - text_tokens: numpy.ndarray, - seed: int, - config: dict, - params: dict -) -> numpy.ndarray: - encoder_state = encode_flax( - text_tokens, - config, - params - ) - image_tokens = decode_flax( - text_tokens, - encoder_state, - config, - seed, - params - ) - image_tokens = numpy.array(image_tokens) - print("image tokens", list(image_tokens)) - return image_tokens \ No newline at end of file + print("sampling image tokens") + image_tokens = self.decoder.sample_image_tokens( + text_tokens, + encoder_state, + jax.random.PRNGKey(seed), + self.model_params['decoder'] + ) + + image_tokens = torch.tensor(numpy.array(image_tokens)) + + print("detokenizing image") + image = self.detokenizer.forward(image_tokens).to(torch.uint8) + image = Image.fromarray(image.to('cpu').detach().numpy()) + return image \ No newline at end of file diff --git a/min_dalle/min_dalle_torch.py b/min_dalle/min_dalle_torch.py index 228c601..6bf71af 100644 --- a/min_dalle/min_dalle_torch.py +++ b/min_dalle/min_dalle_torch.py @@ -1,118 +1,83 @@ +from random import sample import numpy import os +from PIL import Image from typing import Dict -from torch import LongTensor, FloatTensor +from torch import LongTensor import torch torch.set_grad_enabled(False) torch.set_num_threads(os.cpu_count()) -from .models.vqgan_detokenizer import VQGanDetokenizer +from .load_params import convert_dalle_bart_torch_from_flax_params +from .min_dalle import MinDalle from .models.dalle_bart_encoder_torch import DalleBartEncoderTorch from .models.dalle_bart_decoder_torch import DalleBartDecoderTorch -from .load_params import ( - load_vqgan_torch_params, - convert_dalle_bart_torch_from_flax_params -) + +class MinDalleTorch(MinDalle): + def __init__(self, is_mega: bool, sample_token_count: int = 256): + super().__init__(is_mega) + print("initializing MinDalleTorch") + + print("loading encoder") + self.encoder = DalleBartEncoderTorch( + layer_count = self.config['encoder_layers'], + embed_count = self.config['d_model'], + attention_head_count = self.config['encoder_attention_heads'], + text_vocab_count = self.config['encoder_vocab_size'], + text_token_count = self.config['max_text_length'], + glu_embed_count = self.config['encoder_ffn_dim'] + ) + encoder_params = convert_dalle_bart_torch_from_flax_params( + self.model_params.pop('encoder'), + layer_count=self.config['encoder_layers'], + is_encoder=True + ) + self.encoder.load_state_dict(encoder_params, strict=False) + + print("loading decoder") + self.decoder = DalleBartDecoderTorch( + image_vocab_size = self.config['image_vocab_size'], + image_token_count = self.config['image_length'], + sample_token_count = sample_token_count, + embed_count = self.config['d_model'], + attention_head_count = self.config['decoder_attention_heads'], + glu_embed_count = self.config['decoder_ffn_dim'], + layer_count = self.config['decoder_layers'], + batch_count = 2, + start_token = self.config['decoder_start_token_id'], + is_verbose = True + ) + decoder_params = convert_dalle_bart_torch_from_flax_params( + self.model_params.pop('decoder'), + layer_count=self.config['decoder_layers'], + is_encoder=False + ) + self.decoder.load_state_dict(decoder_params, strict=False) + + if torch.cuda.is_available(): + self.encoder = self.encoder.cuda() + self.decoder = self.decoder.cuda() + self.detokenizer = self.detokenizer.cuda() -def encode_torch( - text_tokens: LongTensor, - config: dict, - params: dict -) -> FloatTensor: - print("loading torch encoder") - encoder = DalleBartEncoderTorch( - layer_count = config['encoder_layers'], - embed_count = config['d_model'], - attention_head_count = config['encoder_attention_heads'], - text_vocab_count = config['encoder_vocab_size'], - text_token_count = config['max_text_length'], - glu_embed_count = config['encoder_ffn_dim'] - ) - encoder_params = convert_dalle_bart_torch_from_flax_params( - params.pop('encoder'), - layer_count=config['encoder_layers'], - is_encoder=True - ) - encoder.load_state_dict(encoder_params, strict=False) - del encoder_params - if torch.cuda.is_available(): encoder = encoder.cuda() + def generate_image_tokens(self, text: str, seed: int) -> LongTensor: + text_tokens = self.tokenize_text(text) + text_tokens = torch.tensor(text_tokens).to(torch.long) + if torch.cuda.is_available(): text_tokens = text_tokens.cuda() - print("encoding text tokens") - encoder_state = encoder(text_tokens) - del encoder - return encoder_state + print("encoding text tokens") + encoder_state = self.encoder.forward(text_tokens) + print("sampling image tokens") + torch.manual_seed(seed) + image_tokens = self.decoder.forward(text_tokens, encoder_state) + return image_tokens + -def decode_torch( - text_tokens: LongTensor, - encoder_state: FloatTensor, - config: dict, - seed: int, - params: dict, - image_token_count: int -) -> LongTensor: - print("loading torch decoder") - decoder = DalleBartDecoderTorch( - image_vocab_size = config['image_vocab_size'], - image_token_count = config['image_length'], - sample_token_count = image_token_count, - embed_count = config['d_model'], - attention_head_count = config['decoder_attention_heads'], - glu_embed_count = config['decoder_ffn_dim'], - layer_count = config['decoder_layers'], - batch_count = 2, - start_token = config['decoder_start_token_id'], - is_verbose = True - ) - decoder_params = convert_dalle_bart_torch_from_flax_params( - params.pop('decoder'), - layer_count=config['decoder_layers'], - is_encoder=False - ) - decoder.load_state_dict(decoder_params, strict=False) - del decoder_params - if torch.cuda.is_available(): decoder = decoder.cuda() - - print("sampling image tokens") - torch.manual_seed(seed) - image_tokens = decoder.forward(text_tokens, encoder_state) - return image_tokens - - -def generate_image_tokens_torch( - text_tokens: numpy.ndarray, - seed: int, - config: dict, - params: dict, - image_token_count: int -) -> LongTensor: - text_tokens = torch.tensor(text_tokens).to(torch.long) - if torch.cuda.is_available(): text_tokens = text_tokens.cuda() - encoder_state = encode_torch( - text_tokens, - config, - params - ) - image_tokens = decode_torch( - text_tokens, - encoder_state, - config, - seed, - params, - image_token_count - ) - return image_tokens - - -def detokenize_torch(image_tokens: LongTensor, is_torch: bool) -> numpy.ndarray: - print("detokenizing image") - model_path = './pretrained/vqgan' - params = load_vqgan_torch_params(model_path) - detokenizer = VQGanDetokenizer() - detokenizer.load_state_dict(params) - if torch.cuda.is_available() and is_torch: detokenizer = detokenizer.cuda() - image = detokenizer.forward(image_tokens).to(torch.uint8) - del detokenizer, params - return image.to('cpu').detach().numpy() + def generate_image(self, text: str, seed: int) -> Image.Image: + image_tokens = self.generate_image_tokens(text, seed) + print("detokenizing image") + image = self.detokenizer.forward(image_tokens).to(torch.uint8) + image = Image.fromarray(image.to('cpu').detach().numpy()) + return image \ No newline at end of file diff --git a/min_dalle/models/dalle_bart_decoder_flax.py b/min_dalle/models/dalle_bart_decoder_flax.py index caf28ec..fa2d457 100644 --- a/min_dalle/models/dalle_bart_decoder_flax.py +++ b/min_dalle/models/dalle_bart_decoder_flax.py @@ -26,7 +26,8 @@ class DecoderCrossAttentionFlax(AttentionFlax): class DecoderSelfAttentionFlax(AttentionFlax): - def __call__(self, + def __call__( + self, decoder_state: jnp.ndarray, keys_state: jnp.ndarray, values_state: jnp.ndarray, @@ -77,7 +78,8 @@ class DalleBartDecoderLayerFlax(nn.Module): self.glu = GLUFlax(self.embed_count, self.glu_embed_count) @nn.compact - def __call__(self, + def __call__( + self, decoder_state: jnp.ndarray, encoder_state: jnp.ndarray, keys_state: jnp.ndarray, @@ -173,7 +175,8 @@ class DalleBartDecoderFlax(nn.Module): self.final_ln = nn.LayerNorm(use_scale=False) self.lm_head = nn.Dense(self.image_vocab_count + 1, use_bias=False) - def __call__(self, + def __call__( + self, encoder_state: jnp.ndarray, keys_state: jnp.ndarray, values_state: jnp.ndarray, @@ -198,7 +201,8 @@ class DalleBartDecoderFlax(nn.Module): decoder_state = self.lm_head(decoder_state) return decoder_state, keys_state, values_state - def sample_image_tokens(self, + def sample_image_tokens( + self, text_tokens: jnp.ndarray, encoder_state: jnp.ndarray, prng_key: jax.random.PRNGKey, diff --git a/min_dalle/models/dalle_bart_decoder_torch.py b/min_dalle/models/dalle_bart_decoder_torch.py index f4555ab..9957f2b 100644 --- a/min_dalle/models/dalle_bart_decoder_torch.py +++ b/min_dalle/models/dalle_bart_decoder_torch.py @@ -16,40 +16,34 @@ class DecoderCrossAttentionTorch(AttentionTorch): keys = self.k_proj.forward(encoder_state) values = self.v_proj.forward(encoder_state) queries = self.q_proj.forward(decoder_state) - query_shape = queries.shape[:2] + (self.head_count, -1) - key_value_shape = keys.shape[:2] + (self.head_count, -1) - keys = keys.reshape(key_value_shape) - values = values.reshape(key_value_shape) - queries = queries.reshape(query_shape) - queries /= queries.shape[-1] ** 0.5 return super().forward(keys, values, queries, attention_mask) class DecoderSelfAttentionTorch(AttentionTorch): - def forward(self, + def forward( + self, decoder_state: FloatTensor, keys_values: FloatTensor, attention_mask: BoolTensor, token_mask: BoolTensor ) -> Tuple[FloatTensor, FloatTensor]: batch_count = decoder_state.shape[0] - shape = (batch_count, 1) + keys_values.shape[2:] - keys = self.k_proj.forward(decoder_state).view(shape) - values = self.v_proj.forward(decoder_state).view(shape) + keys = self.k_proj.forward(decoder_state) + values = self.v_proj.forward(decoder_state) + queries = self.q_proj.forward(decoder_state) keys_values = torch.where( - token_mask[None, :, None, None], + token_mask[None, :, None], torch.cat([keys, values]), keys_values ) - queries = self.q_proj.forward(decoder_state).reshape(shape) - queries /= queries.shape[-1] ** 0.5 keys, values = keys_values[:batch_count], keys_values[batch_count:] decoder_state = super().forward(keys, values, queries, attention_mask) return decoder_state, keys_values class DecoderLayerTorch(nn.Module): - def __init__(self, + def __init__( + self, image_token_count: int, head_count: int, embed_count: int, @@ -69,7 +63,8 @@ class DecoderLayerTorch(nn.Module): if torch.cuda.is_available(): self.token_indices = self.token_indices.cuda() - def forward(self, + def forward( + self, decoder_state: FloatTensor, encoder_state: FloatTensor, keys_values_state: FloatTensor, @@ -111,7 +106,8 @@ class DecoderLayerTorch(nn.Module): class DalleBartDecoderTorch(nn.Module): - def __init__(self, + def __init__( + self, image_vocab_size: int, image_token_count: int, sample_token_count: int, @@ -146,8 +142,7 @@ class DalleBartDecoderTorch(nn.Module): self.keys_values_state_shape = ( layer_count * 2 * batch_count, image_token_count, - attention_head_count, - embed_count // attention_head_count + embed_count ) self.zero_prob = torch.zeros([1]) self.token_indices = torch.arange(self.sample_token_count) @@ -158,7 +153,8 @@ class DalleBartDecoderTorch(nn.Module): self.start_token = self.start_token.cuda() - def decode_step(self, + def decode_step( + self, text_tokens: LongTensor, encoder_state: FloatTensor, keys_values_state: FloatTensor, @@ -183,7 +179,6 @@ class DalleBartDecoderTorch(nn.Module): token_index[:1] ) keys_values.append(keys_values_layer) - keys_values = torch.cat(keys_values, dim=0) decoder_state = self.final_ln(decoder_state) logits = self.lm_head(decoder_state) a = self.condition_factor @@ -195,10 +190,11 @@ class DalleBartDecoderTorch(nn.Module): self.zero_prob, torch.exp(logits - top_logits[0]) ) - return probs, keys_values + return probs, torch.cat(keys_values) - def forward(self, + def forward( + self, text_tokens: LongTensor, encoder_state: FloatTensor ) -> LongTensor: diff --git a/min_dalle/models/dalle_bart_encoder_flax.py b/min_dalle/models/dalle_bart_encoder_flax.py index 71bbef3..3d159f0 100644 --- a/min_dalle/models/dalle_bart_encoder_flax.py +++ b/min_dalle/models/dalle_bart_encoder_flax.py @@ -34,7 +34,8 @@ class AttentionFlax(nn.Module): self.v_proj = nn.Dense(self.embed_count, use_bias=False) self.out_proj = nn.Dense(self.embed_count, use_bias=False) - def forward(self, + def forward( + self, keys: jnp.ndarray, values: jnp.ndarray, queries: jnp.ndarray, @@ -92,7 +93,8 @@ class DalleBartEncoderLayerFlax(nn.Module): self.glu = GLUFlax(self.embed_count, self.glu_embed_count) @nn.compact - def __call__(self, + def __call__( + self, encoder_state: jnp.ndarray, attention_mask: jnp.ndarray ) -> jnp.ndarray: diff --git a/min_dalle/models/dalle_bart_encoder_torch.py b/min_dalle/models/dalle_bart_encoder_torch.py index 92bf775..296cdec 100644 --- a/min_dalle/models/dalle_bart_encoder_torch.py +++ b/min_dalle/models/dalle_bart_encoder_torch.py @@ -37,12 +37,18 @@ class AttentionTorch(nn.Module): self.one = torch.ones((1, 1)) if torch.cuda.is_available(): self.one = self.one.cuda() - def forward(self, + def forward( + self, keys: FloatTensor, values: FloatTensor, queries: FloatTensor, attention_mask: BoolTensor ) -> FloatTensor: + keys = keys.reshape(keys.shape[:2] + (self.head_count, -1)) + values = values.reshape(values.shape[:2] + (self.head_count, -1)) + queries = queries.reshape(queries.shape[:2] + (self.head_count, -1)) + queries /= queries.shape[-1] ** 0.5 + attention_bias = torch.where( attention_mask, self.one * 0, @@ -72,11 +78,9 @@ class EncoderSelfAttentionTorch(AttentionTorch): encoder_state: FloatTensor, attention_mask: BoolTensor ) -> FloatTensor: - shape_split = encoder_state.shape[:2] + (self.head_count, -1) - keys = self.k_proj.forward(encoder_state).reshape(shape_split) - values = self.v_proj.forward(encoder_state).reshape(shape_split) - queries = self.q_proj.forward(encoder_state).reshape(shape_split) - queries /= queries.shape[-1] ** 0.5 + keys = self.k_proj.forward(encoder_state) + values = self.v_proj.forward(encoder_state) + queries = self.q_proj.forward(encoder_state) return super().forward(keys, values, queries, attention_mask) @@ -105,7 +109,8 @@ class EncoderLayerTorch(nn.Module): class DalleBartEncoderTorch(nn.Module): - def __init__(self, + def __init__( + self, layer_count: int, embed_count: int, attention_head_count: int, diff --git a/min_dalle/text_tokenizer.py b/min_dalle/text_tokenizer.py index 1d601e6..1d06349 100644 --- a/min_dalle/text_tokenizer.py +++ b/min_dalle/text_tokenizer.py @@ -8,7 +8,7 @@ class TextTokenizer: pairs = [tuple(pair.split()) for pair in merges] self.rank_from_pair = dict(zip(pairs, range(len(pairs)))) - def __call__(self, text: str) -> List[int]: + def tokenize(self, text: str) -> List[int]: sep_token = self.token_from_subword[''] cls_token = self.token_from_subword[''] unk_token = self.token_from_subword[''] diff --git a/requirements.txt b/requirements.txt index 0867d5f..d43a781 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ torch -flax==0.4.2 +flax==0.5.2 wandb \ No newline at end of file