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‎.gitignore

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# OS specific
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*.DS_Store
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# Python
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/build
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/dist
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__pycache__
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*.ipynb_checkpoints
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*.egg-info
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# Vim
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*.vim
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*.swk
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*.swl
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*.swm
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*.swn
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*.swo
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*.swp

‎LICENSE

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Modified MIT License
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Software Copyright (c) 2021 OpenAI
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We don’t claim ownership of the content you create with the DALL-E discrete VAE, so it is yours to
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do with as you please. We only ask that you use the model responsibly and clearly indicate that it
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was used.
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
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associated documentation files (the "Software"), to deal in the Software without restriction,
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including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
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and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
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subject to the following conditions:
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The above copyright notice and this permission notice shall be included
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in all copies or substantial portions of the Software.
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The above copyright notice and this permission notice need not be included
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with content created by the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
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INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
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BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
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TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
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OR OTHER DEALINGS IN THE SOFTWARE.

‎README.md

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# Overview
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[[Blog]](https://openai.com/blog/dall-e/) [[Paper]](https://arxiv.org/abs/2102.12092) [[Model Card]](model_card.md) [[Usage]](notebooks/usage.ipynb)
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This is the official PyTorch package for the discrete VAE used for DALL·E.
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# Installation
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Before running [the example notebook](notebooks/usage.ipynb), you will need to install the package using
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pip install git+https://github.com/openai/DALL-E.git

‎dall_e/__init__.py

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import io, requests
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import torch
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import torch.nn as nn
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from dall_e.encoder import Encoder
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from dall_e.decoder import Decoder
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from dall_e.utils import map_pixels, unmap_pixels
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def load_model(path: str, device: torch.device = None) -> nn.Module:
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if path.startswith('http://') or path.startswith('https://'):
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resp = requests.get(path)
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resp.raise_for_status()
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with io.BytesIO(resp.content) as buf:
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return torch.load(buf, map_location=device)
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else:
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with open(path, 'rb') as f:
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return torch.load(f, map_location=device)

‎dall_e/decoder.py

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import attr
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from collections import OrderedDict
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from functools import partial
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from dall_e.utils import Conv2d
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@attr.s(eq=False, repr=False)
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class DecoderBlock(nn.Module):
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n_in: int = attr.ib(validator=lambda i, a, x: x >= 1)
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n_out: int = attr.ib(validator=lambda i, a, x: x >= 1 and x % 4 ==0)
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n_layers: int = attr.ib(validator=lambda i, a, x: x >= 1)
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device: torch.device = attr.ib(default=None)
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requires_grad: bool = attr.ib(default=False)
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def __attrs_post_init__(self) -> None:
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super().__init__()
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self.n_hid = self.n_out // 4
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self.post_gain = 1 / (self.n_layers ** 2)
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make_conv = partial(Conv2d, device=self.device, requires_grad=self.requires_grad)
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self.id_path = make_conv(self.n_in, self.n_out, 1) if self.n_in != self.n_out else nn.Identity()
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self.res_path = nn.Sequential(OrderedDict([
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('relu_1', nn.ReLU()),
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('conv_1', make_conv(self.n_in, self.n_hid, 1)),
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('relu_2', nn.ReLU()),
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('conv_2', make_conv(self.n_hid, self.n_hid, 3)),
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('relu_3', nn.ReLU()),
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('conv_3', make_conv(self.n_hid, self.n_hid, 3)),
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('relu_4', nn.ReLU()),
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('conv_4', make_conv(self.n_hid, self.n_out, 3)),]))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.id_path(x) + self.post_gain * self.res_path(x)
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@attr.s(eq=False, repr=False)
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class Decoder(nn.Module):
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group_count: int = 4
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n_init: int = attr.ib(default=128, validator=lambda i, a, x: x >= 8)
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n_hid: int = attr.ib(default=256, validator=lambda i, a, x: x >= 64)
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n_blk_per_group: int = attr.ib(default=2, validator=lambda i, a, x: x >= 1)
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output_channels: int = attr.ib(default=3, validator=lambda i, a, x: x >= 1)
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vocab_size: int = attr.ib(default=8192, validator=lambda i, a, x: x >= 512)
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device: torch.device = attr.ib(default=torch.device('cpu'))
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requires_grad: bool = attr.ib(default=False)
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use_mixed_precision: bool = attr.ib(default=True)
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def __attrs_post_init__(self) -> None:
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super().__init__()
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blk_range = range(self.n_blk_per_group)
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n_layers = self.group_count * self.n_blk_per_group
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make_conv = partial(Conv2d, device=self.device, requires_grad=self.requires_grad)
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make_blk = partial(DecoderBlock, n_layers=n_layers, device=self.device,
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requires_grad=self.requires_grad)
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self.blocks = nn.Sequential(OrderedDict([
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('input', make_conv(self.vocab_size, self.n_init, 1, use_float16=False)),
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('group_1', nn.Sequential(OrderedDict([
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*[(f'block_{i + 1}', make_blk(self.n_init if i == 0 else 8 * self.n_hid, 8 * self.n_hid)) for i in blk_range],
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('upsample', nn.Upsample(scale_factor=2, mode='nearest')),
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]))),
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('group_2', nn.Sequential(OrderedDict([
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*[(f'block_{i + 1}', make_blk(8 * self.n_hid if i == 0 else 4 * self.n_hid, 4 * self.n_hid)) for i in blk_range],
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('upsample', nn.Upsample(scale_factor=2, mode='nearest')),
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]))),
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('group_3', nn.Sequential(OrderedDict([
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*[(f'block_{i + 1}', make_blk(4 * self.n_hid if i == 0 else 2 * self.n_hid, 2 * self.n_hid)) for i in blk_range],
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('upsample', nn.Upsample(scale_factor=2, mode='nearest')),
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]))),
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('group_4', nn.Sequential(OrderedDict([
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*[(f'block_{i + 1}', make_blk(2 * self.n_hid if i == 0 else 1 * self.n_hid, 1 * self.n_hid)) for i in blk_range],
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]))),
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('output', nn.Sequential(OrderedDict([
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('relu', nn.ReLU()),
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('conv', make_conv(1 * self.n_hid, 2 * self.output_channels, 1)),
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]))),
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]))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if len(x.shape) != 4:
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raise ValueError(f'input shape {x.shape} is not 4d')
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if x.shape[1] != self.vocab_size:
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raise ValueError(f'input has {x.shape[1]} channels but model built for {self.vocab_size}')
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if x.dtype != torch.float32:
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raise ValueError('input must have dtype torch.float32')
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return self.blocks(x)

‎dall_e/encoder.py

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import attr
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from collections import OrderedDict
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from functools import partial
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from dall_e.utils import Conv2d
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@attr.s(eq=False, repr=False)
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class EncoderBlock(nn.Module):
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n_in: int = attr.ib(validator=lambda i, a, x: x >= 1)
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n_out: int = attr.ib(validator=lambda i, a, x: x >= 1 and x % 4 ==0)
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n_layers: int = attr.ib(validator=lambda i, a, x: x >= 1)
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device: torch.device = attr.ib(default=None)
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requires_grad: bool = attr.ib(default=False)
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def __attrs_post_init__(self) -> None:
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super().__init__()
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self.n_hid = self.n_out // 4
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self.post_gain = 1 / (self.n_layers ** 2)
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make_conv = partial(Conv2d, device=self.device, requires_grad=self.requires_grad)
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self.id_path = make_conv(self.n_in, self.n_out, 1) if self.n_in != self.n_out else nn.Identity()
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self.res_path = nn.Sequential(OrderedDict([
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('relu_1', nn.ReLU()),
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('conv_1', make_conv(self.n_in, self.n_hid, 3)),
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('relu_2', nn.ReLU()),
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('conv_2', make_conv(self.n_hid, self.n_hid, 3)),
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('relu_3', nn.ReLU()),
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('conv_3', make_conv(self.n_hid, self.n_hid, 3)),
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('relu_4', nn.ReLU()),
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('conv_4', make_conv(self.n_hid, self.n_out, 1)),]))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.id_path(x) + self.post_gain * self.res_path(x)
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@attr.s(eq=False, repr=False)
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class Encoder(nn.Module):
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group_count: int = 4
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n_hid: int = attr.ib(default=256, validator=lambda i, a, x: x >= 64)
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n_blk_per_group: int = attr.ib(default=2, validator=lambda i, a, x: x >= 1)
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input_channels: int = attr.ib(default=3, validator=lambda i, a, x: x >= 1)
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vocab_size: int = attr.ib(default=8192, validator=lambda i, a, x: x >= 512)
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device: torch.device = attr.ib(default=torch.device('cpu'))
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requires_grad: bool = attr.ib(default=False)
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use_mixed_precision: bool = attr.ib(default=True)
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def __attrs_post_init__(self) -> None:
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super().__init__()
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blk_range = range(self.n_blk_per_group)
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n_layers = self.group_count * self.n_blk_per_group
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make_conv = partial(Conv2d, device=self.device, requires_grad=self.requires_grad)
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make_blk = partial(EncoderBlock, n_layers=n_layers, device=self.device,
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requires_grad=self.requires_grad)
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self.blocks = nn.Sequential(OrderedDict([
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('input', make_conv(self.input_channels, 1 * self.n_hid, 7)),
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('group_1', nn.Sequential(OrderedDict([
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*[(f'block_{i + 1}', make_blk(1 * self.n_hid, 1 * self.n_hid)) for i in blk_range],
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('pool', nn.MaxPool2d(kernel_size=2)),
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]))),
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('group_2', nn.Sequential(OrderedDict([
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*[(f'block_{i + 1}', make_blk(1 * self.n_hid if i == 0 else 2 * self.n_hid, 2 * self.n_hid)) for i in blk_range],
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('pool', nn.MaxPool2d(kernel_size=2)),
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]))),
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('group_3', nn.Sequential(OrderedDict([
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*[(f'block_{i + 1}', make_blk(2 * self.n_hid if i == 0 else 4 * self.n_hid, 4 * self.n_hid)) for i in blk_range],
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('pool', nn.MaxPool2d(kernel_size=2)),
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]))),
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('group_4', nn.Sequential(OrderedDict([
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*[(f'block_{i + 1}', make_blk(4 * self.n_hid if i == 0 else 8 * self.n_hid, 8 * self.n_hid)) for i in blk_range],
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]))),
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('output', nn.Sequential(OrderedDict([
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('relu', nn.ReLU()),
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('conv', make_conv(8 * self.n_hid, self.vocab_size, 1, use_float16=False)),
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]))),
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]))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if len(x.shape) != 4:
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raise ValueError(f'input shape {x.shape} is not 4d')
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if x.shape[1] != self.input_channels:
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raise ValueError(f'input has {x.shape[1]} channels but model built for {self.input_channels}')
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if x.dtype != torch.float32:
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raise ValueError('input must have dtype torch.float32')
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return self.blocks(x)

‎dall_e/utils.py

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import attr
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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logit_laplace_eps: float = 0.1
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@attr.s(eq=False)
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class Conv2d(nn.Module):
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n_in: int = attr.ib(validator=lambda i, a, x: x >= 1)
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n_out: int = attr.ib(validator=lambda i, a, x: x >= 1)
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kw: int = attr.ib(validator=lambda i, a, x: x >= 1 and x % 2 == 1)
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use_float16: bool = attr.ib(default=True)
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device: torch.device = attr.ib(default=torch.device('cpu'))
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requires_grad: bool = attr.ib(default=False)
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def __attrs_post_init__(self) -> None:
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super().__init__()
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w = torch.empty((self.n_out, self.n_in, self.kw, self.kw), dtype=torch.float32,
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device=self.device, requires_grad=self.requires_grad)
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w.normal_(std=1 / math.sqrt(self.n_in * self.kw ** 2))
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b = torch.zeros((self.n_out,), dtype=torch.float32, device=self.device,
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requires_grad=self.requires_grad)
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self.w, self.b = nn.Parameter(w), nn.Parameter(b)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if self.use_float16 and 'cuda' in self.w.device.type:
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if x.dtype != torch.float16:
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x = x.half()
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w, b = self.w.half(), self.b.half()
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else:
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if x.dtype != torch.float32:
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x = x.float()
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w, b = self.w, self.b
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return F.conv2d(x, w, b, padding=(self.kw - 1) // 2)
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def map_pixels(x: torch.Tensor) -> torch.Tensor:
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if len(x.shape) != 4:
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raise ValueError('expected input to be 4d')
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if x.dtype != torch.float:
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raise ValueError('expected input to have type float')
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return (1 - 2 * logit_laplace_eps) * x + logit_laplace_eps
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def unmap_pixels(x: torch.Tensor) -> torch.Tensor:
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if len(x.shape) != 4:
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raise ValueError('expected input to be 4d')
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if x.dtype != torch.float:
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raise ValueError('expected input to have type float')
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return torch.clamp((x - logit_laplace_eps) / (1 - 2 * logit_laplace_eps), 0, 1)

‎model_card.md

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# Model Card: DALL·E dVAE
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Following [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993) and [Lessons from
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Archives (Jo & Gebru)](https://arxiv.org/pdf/1912.10389.pdf), we're providing some information about about the discrete
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VAE (dVAE) that was used to train DALL·E.
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## Model Details
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The dVAE was developed by researchers at OpenAI to reduce the memory footprint of the transformer trained on the
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text-to-image generation task. The details involved in training the dVAE are described in [the paper][dalle_paper]. This
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model card describes the first version of the model, released in February 2021. The model consists of a convolutional
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encoder and decoder whose architectures are described [here](dall_e/encoder.py) and [here](dall_e/decoder.py), respectively.
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For questions or comments about the models or the code release, please file a Github issue.
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## Model Use
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### Intended Use
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The model is intended for others to use for training their own generative models.
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### Out-of-Scope Use Cases
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This model is inappropriate for high-fidelity image processing applications. We also do not recommend its use as a
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general-purpose image compressor.
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## Training Data
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The model was trained on publicly available text-image pairs collected from the internet. This data consists partly of
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[Conceptual Captions][cc] and a filtered subset of [YFCC100M][yfcc100m]. We used a subset of the filters described in
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[Sharma et al.][cc_paper] to construct this dataset; further details are described in [our paper][dalle_paper]. We will
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not be releasing the dataset.
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## Performance and Limitations
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The heavy compression from the encoding process results in a noticeable loss of detail in the reconstructed images. This
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renders it inappropriate for applications that require fine-grained details of the image to be preserved.
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[dalle_paper]: https://arxiv.org/abs/2102.12092
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[cc]: https://ai.google.com/research/ConceptualCaptions
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[cc_paper]: https://www.aclweb.org/anthology/P18-1238/
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[yfcc100m]: http://projects.dfki.uni-kl.de/yfcc100m/

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