513 lines
20 KiB
Python
513 lines
20 KiB
Python
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# GLIDE: https://github.com/openai/glide-text2im
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# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
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# --------------------------------------------------------
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import torch
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import torch.nn as nn
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import numpy as np
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import math
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from timm.models.vision_transformer import Attention, Mlp # PatchEmbed
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from torch.nn import TransformerEncoder, TransformerEncoderLayer
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from .helpers import SinusoidalPosEmb
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class PatchEmbed(nn.Module):
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""" 2D Image to Patch Embedding
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"""
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def __init__(
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self,
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img_size=224,
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patch_size=16,
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in_chans=3,
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embed_dim=768,
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norm_layer=None,
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flatten=True,
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bias=True,
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):
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super().__init__()
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img_size = (img_size, 1)
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patch_size = (patch_size, 1)
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self.img_size = img_size
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self.patch_size = patch_size
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self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
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self.num_patches = self.grid_size[0] * self.grid_size[1]
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self.flatten = flatten
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
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self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
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def forward(self, x):
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B, C, H, W = x.shape
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#_assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
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#_assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
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x = self.proj(x)
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if self.flatten:
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x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
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x = self.norm(x)
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return x
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def modulate(x, shift, scale):
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return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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#################################################################################
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# Embedding Layers for Timesteps and Class Labels #
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#################################################################################
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class TimestepEmbedder(nn.Module):
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"""
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Embeds scalar timesteps into vector representations.
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"""
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def __init__(self, hidden_size, frequency_embedding_size=256):
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super().__init__()
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self.mlp = nn.Sequential(
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nn.Linear(frequency_embedding_size, hidden_size, bias=True),
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nn.SiLU(),
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nn.Linear(hidden_size, hidden_size, bias=True),
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)
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self.frequency_embedding_size = frequency_embedding_size
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@staticmethod
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def timestep_embedding(t, dim, max_period=10000):
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"""
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Create sinusoidal timestep embeddings.
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:param t: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an (N, D) Tensor of positional embeddings.
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"""
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# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
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).to(device=t.device)
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args = t[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
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return embedding
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def forward(self, t):
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t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
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t_emb = self.mlp(t_freq)
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return t_emb
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class LabelEmbedder(nn.Module):
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"""
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Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
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"""
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def __init__(self, num_classes, hidden_size, dropout_prob):
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super().__init__()
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use_cfg_embedding = dropout_prob > 0
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self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
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self.num_classes = num_classes
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self.dropout_prob = dropout_prob
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def token_drop(self, labels, force_drop_ids=None):
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"""
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Drops labels to enable classifier-free guidance.
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"""
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if force_drop_ids is None:
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drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
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else:
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drop_ids = force_drop_ids == 1
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labels = torch.where(drop_ids, self.num_classes, labels)
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return labels
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def forward(self, labels, train, force_drop_ids=None):
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use_dropout = self.dropout_prob > 0
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if (train and use_dropout) or (force_drop_ids is not None):
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labels = self.token_drop(labels, force_drop_ids)
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embeddings = self.embedding_table(labels)
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return embeddings
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#################################################################################
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# Core DiT Model #
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#################################################################################
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class DiTBlock(nn.Module):
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"""
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A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
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"""
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def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
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super().__init__()
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self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
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self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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mlp_hidden_dim = int(hidden_size * mlp_ratio)
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(hidden_size, 6 * hidden_size, bias=True)
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)
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def forward(self, x, c):
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
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x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
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x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
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return x
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class FinalLayer(nn.Module):
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"""
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The final layer of DiT.
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"""
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def __init__(self, hidden_size, patch_size, out_channels):
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super().__init__()
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self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.linear = nn.Linear(hidden_size, patch_size * 1 * out_channels, bias=True)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(hidden_size, 2 * hidden_size, bias=True)
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)
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def forward(self, x, c):
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shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
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x = modulate(self.norm_final(x), shift, scale)
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x = self.linear(x)
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return x
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class DiT(nn.Module):
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"""
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Diffusion model with a Transformer backbone.
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"""
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def __init__(
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self,
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input_size=32,
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patch_size=2,
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in_channels=4,
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hidden_size=384,
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depth=12,
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num_heads=6,
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mlp_ratio=4.0,
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class_dropout_prob=0.1,
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num_classes=1000,
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learn_sigma=False,
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):
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super().__init__()
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self.learn_sigma = learn_sigma
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self.in_channels = in_channels
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self.out_channels = in_channels * 2 if learn_sigma else in_channels
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self.patch_size = patch_size
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self.num_heads = num_heads
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self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
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self.t_embedder = TimestepEmbedder(hidden_size)
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#self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
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num_patches = self.x_embedder.num_patches
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# Will use fixed sin-cos embedding:
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
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self.blocks = nn.ModuleList([
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DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(depth)
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])
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self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
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self.initialize_weights()
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def initialize_weights(self):
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# Initialize transformer layers:
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def _basic_init(module):
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if isinstance(module, nn.Linear):
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torch.nn.init.xavier_uniform_(module.weight)
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if module.bias is not None:
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nn.init.constant_(module.bias, 0)
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self.apply(_basic_init)
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# Initialize (and freeze) pos_embed by sin-cos embedding:
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pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches )) #** 0.5
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#print('pos_embed', pos_embed.shape, self.x_embedder.num_patches)
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self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
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# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
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w = self.x_embedder.proj.weight.data
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nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
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nn.init.constant_(self.x_embedder.proj.bias, 0)
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# Initialize label embedding table:
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#nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
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# Initialize timestep embedding MLP:
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nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
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nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
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# Zero-out adaLN modulation layers in DiT blocks:
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for block in self.blocks:
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nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
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nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
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# Zero-out output layers:
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nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
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nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
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nn.init.constant_(self.final_layer.linear.weight, 0)
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nn.init.constant_(self.final_layer.linear.bias, 0)
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def unpatchify(self, x):
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"""
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x: (N, T, patch_size**2 * C)
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imgs: (N, H, W, C)
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"""
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c = self.out_channels
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p = self.x_embedder.patch_size[0]
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'''h = w = int(x.shape[1] ** 0.5)
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assert h * w == x.shape[1]'''
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h = x.shape[1]
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w = 1
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#print(x.shape)
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x = x.reshape(shape=(x.shape[0], h, w, p, 1, c))
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x = torch.einsum('nhwpqc->nchpwq', x)
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imgs = x.reshape(shape=(x.shape[0], c, h * p, w * 1))
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return imgs
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def forward(self, x, t):
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"""
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Forward pass of DiT.
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x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
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t: (N,) tensor of diffusion timesteps
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y: (N,) tensor of class labels
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"""
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#print('x', x.shape, 'x_embedder', self.x_embedder(x).shape, 'pos_embed', self.pos_embed.shape)
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x = self.x_embedder(x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
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t = self.t_embedder(t) # (N, D)
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#y = self.y_embedder(y, self.training) # (N, D)
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#c = t + y # (N, D)
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c = t
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for block in self.blocks:
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x = block(x, c) # (N, T, D)
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x = self.final_layer(x, c) # (N, T, patch_size ** 2 * out_channels)
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x = self.unpatchify(x) # (N, out_channels, H, W)
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return x
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def forward_with_cfg(self, x, t, y, cfg_scale):
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"""
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Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
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"""
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# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
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half = x[: len(x) // 2]
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combined = torch.cat([half, half], dim=0)
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model_out = self.forward(combined, t, y)
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# For exact reproducibility reasons, we apply classifier-free guidance on only
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# three channels by default. The standard approach to cfg applies it to all channels.
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# This can be done by uncommenting the following line and commenting-out the line following that.
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# eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
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eps, rest = model_out[:, :3], model_out[:, 3:]
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cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
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half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
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eps = torch.cat([half_eps, half_eps], dim=0)
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return torch.cat([eps, rest], dim=1)
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#################################################################################
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# Sine/Cosine Positional Embedding Functions #
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#################################################################################
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# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
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def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
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"""
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grid_size: int of the grid height and width
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return:
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pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
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"""
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grid_h = np.arange(grid_size, dtype=np.float32)
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grid_w = np.arange(1, dtype=np.float32)
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grid = np.meshgrid(grid_w, grid_h) # here w goes first
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grid = np.stack(grid, axis=0)
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grid = grid.reshape([2, 1, grid_size, 1])
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pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
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if cls_token and extra_tokens > 0:
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pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
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return pos_embed
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def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
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assert embed_dim % 2 == 0
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# use half of dimensions to encode grid_h
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emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
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emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
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emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
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return emb
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def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
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"""
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embed_dim: output dimension for each position
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pos: a list of positions to be encoded: size (M,)
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out: (M, D)
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"""
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assert embed_dim % 2 == 0
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omega = np.arange(embed_dim // 2, dtype=np.float64)
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omega /= embed_dim / 2.
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omega = 1. / 10000**omega # (D/2,)
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pos = pos.reshape(-1) # (M,)
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out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
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emb_sin = np.sin(out) # (M, D/2)
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emb_cos = np.cos(out) # (M, D/2)
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emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
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return emb
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def get_emb(sin_inp):
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"""
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Gets a base embedding for one dimension with sin and cos intertwined
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"""
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emb = torch.stack((sin_inp.sin(), sin_inp.cos()), dim=-1)
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|
return torch.flatten(emb, -2, -1)
|
||
|
|
||
|
|
||
|
class PositionalEncoding1D(nn.Module):
|
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|
def __init__(self, channels, dtype_override=None):
|
||
|
"""
|
||
|
:param channels: The last dimension of the tensor you want to apply pos emb to.
|
||
|
:param dtype_override: If set, overrides the dtype of the output embedding.
|
||
|
"""
|
||
|
super(PositionalEncoding1D, self).__init__()
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||
|
self.org_channels = channels
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||
|
channels = int(np.ceil(channels / 2) * 2)
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||
|
inv_freq = 1.0 / (10000 ** (torch.arange(0, channels, 2).float() / channels))
|
||
|
self.register_buffer("inv_freq", inv_freq)
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||
|
self.register_buffer("cached_penc", None, persistent=False)
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||
|
self.channels = channels
|
||
|
self.dtype_override = dtype_override
|
||
|
|
||
|
def forward(self, tensor):
|
||
|
"""
|
||
|
:param tensor: A 3d tensor of size (batch_size, ch, x)
|
||
|
:return: Positional Encoding Matrix of size (batch_size, ch, x)
|
||
|
"""
|
||
|
if len(tensor.shape) != 3:
|
||
|
raise RuntimeError("The input tensor has to be 3d!")
|
||
|
|
||
|
if self.cached_penc is not None and self.cached_penc.shape == tensor.shape:
|
||
|
return self.cached_penc
|
||
|
|
||
|
self.cached_penc = None
|
||
|
batch_size, orig_ch, x = tensor.shape
|
||
|
pos_x = torch.arange(x, device=tensor.device, dtype=self.inv_freq.dtype)
|
||
|
sin_inp_x = torch.einsum("i,j->ij", pos_x, self.inv_freq)
|
||
|
emb_x = get_emb(sin_inp_x)
|
||
|
#print('emb_x', emb_x.shape)
|
||
|
emb = torch.zeros(
|
||
|
(self.channels, x),
|
||
|
device=tensor.device,
|
||
|
dtype=(
|
||
|
self.dtype_override if self.dtype_override is not None else tensor.dtype
|
||
|
),
|
||
|
)
|
||
|
emb[:self.channels, :] = emb_x.permute(1,0)
|
||
|
|
||
|
self.cached_penc = emb[None, :orig_ch, :].repeat(batch_size, 1, 1)
|
||
|
return self.cached_penc
|
||
|
|
||
|
class TransformerModel(nn.Module):
|
||
|
|
||
|
def __init__(self, ntoken, ninp, nhead, nhid, nlayers=6, dropout=0.0):
|
||
|
super(TransformerModel, self).__init__()
|
||
|
|
||
|
self.model_type = 'Transformer'
|
||
|
self.pos_encoder = PositionalEncoding1D(ninp)
|
||
|
encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout, batch_first=True)
|
||
|
self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
|
||
|
#self.encoder = nn.Embedding(ntoken, ninp)
|
||
|
self.ninp = ninp
|
||
|
#self.decoder = nn.Linear(ninp, ntoken)
|
||
|
self.time_mlp = nn.Sequential( # should be removed for Noise and Deterministic Baselines
|
||
|
SinusoidalPosEmb(ninp),
|
||
|
nn.Linear(ninp-1, ninp * 4),
|
||
|
nn.Mish(),
|
||
|
nn.Linear(ninp * 4, ninp),
|
||
|
)
|
||
|
|
||
|
#self.init_weights()
|
||
|
|
||
|
def generate_square_subsequent_mask(self, sz):
|
||
|
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
|
||
|
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
|
||
|
return mask
|
||
|
|
||
|
'''def init_weights(self):
|
||
|
initrange = 0.1
|
||
|
self.encoder.weight.data.uniform_(-initrange, initrange)
|
||
|
self.decoder.bias.data.zero_()
|
||
|
self.decoder.weight.data.uniform_(-initrange, initrange)'''
|
||
|
|
||
|
def forward(self, src, t):
|
||
|
#print('self.ninp', self.ninp)
|
||
|
#src = self.encoder(src) * math.sqrt(self.ninp)
|
||
|
#print('src', src.shape)
|
||
|
|
||
|
#t = self.time_mlp(t).unsqueeze(1)
|
||
|
|
||
|
emb = self.pos_encoder(src)
|
||
|
#time = torch.cat((t,t,t), dim=1)
|
||
|
#print('time', time.shape)
|
||
|
output = self.transformer_encoder(src+emb)
|
||
|
#print('shape after transformer', output.shape)
|
||
|
#output = self.decoder(output)
|
||
|
return output
|
||
|
|
||
|
|
||
|
#################################################################################
|
||
|
# DiT Configs #
|
||
|
#################################################################################
|
||
|
|
||
|
def DiT_XL_2(**kwargs):
|
||
|
return DiT(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs)
|
||
|
|
||
|
def DiT_XL_4(**kwargs):
|
||
|
return DiT(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs)
|
||
|
|
||
|
def DiT_XL_8(**kwargs):
|
||
|
return DiT(depth=28, hidden_size=1152, patch_size=8, num_heads=16, **kwargs)
|
||
|
|
||
|
def DiT_L_2(**kwargs):
|
||
|
return DiT(depth=24, hidden_size=1024, patch_size=2, num_heads=16, **kwargs)
|
||
|
|
||
|
def DiT_L_4(**kwargs):
|
||
|
return DiT(depth=24, hidden_size=1024, patch_size=4, num_heads=16, **kwargs)
|
||
|
|
||
|
def DiT_L_8(**kwargs):
|
||
|
return DiT(depth=24, hidden_size=1024, patch_size=8, num_heads=16, **kwargs)
|
||
|
|
||
|
def DiT_B_2(**kwargs):
|
||
|
return DiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)
|
||
|
|
||
|
def DiT_B_4(**kwargs):
|
||
|
return DiT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)
|
||
|
|
||
|
def DiT_B_8(**kwargs):
|
||
|
return DiT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
|
||
|
|
||
|
def DiT_S_2(**kwargs):
|
||
|
return DiT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
|
||
|
|
||
|
def DiT_S_4(**kwargs):
|
||
|
return DiT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
|
||
|
|
||
|
def DiT_S_8(**kwargs):
|
||
|
return DiT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
|
||
|
|
||
|
|
||
|
DiT_models = {
|
||
|
'DiT-XL/2': DiT_XL_2, 'DiT-XL/4': DiT_XL_4, 'DiT-XL/8': DiT_XL_8,
|
||
|
'DiT-L/2': DiT_L_2, 'DiT-L/4': DiT_L_4, 'DiT-L/8': DiT_L_8,
|
||
|
'DiT-B/2': DiT_B_2, 'DiT-B/4': DiT_B_4, 'DiT-B/8': DiT_B_8,
|
||
|
'DiT-S/2': DiT_S_2, 'DiT-S/4': DiT_S_4, 'DiT-S/8': DiT_S_8,
|
||
|
}
|