mirror of
https://github.com/hpcaitech/Open-Sora.git
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adba00f151
* [feature] diffusion natively support video format * [feature] add timestamp embedding * [feature] support learn from raw video * [feature] update sample script
576 lines
20 KiB
Python
576 lines
20 KiB
Python
# 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 math
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from typing import Callable, Optional
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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 timm.models.vision_transformer import Mlp
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class CrossAttention(nn.Module):
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r"""
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A cross attention layer.
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Parameters:
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query_dim (`int`): The number of channels in the query.
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cross_attention_dim (`int`, *optional*):
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The number of channels in the context. If not given, defaults to `query_dim`.
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num_heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention.
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head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
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dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
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bias (`bool`, *optional*, defaults to False):
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Set to `True` for the query, key, and value linear layers to contain a bias parameter.
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"""
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def __init__(
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self,
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query_dim: int,
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cross_attention_dim: Optional[int] = None,
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num_heads: int = 8,
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head_dim: int = 64,
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dropout: float = 0.0,
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bias=False,
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sdpa=True,
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):
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super().__init__()
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self.hidden_size = head_dim * num_heads
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cross_attention_dim = (
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cross_attention_dim if cross_attention_dim is not None else query_dim
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)
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self.scale = head_dim**-0.5
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self.num_heads = num_heads
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self.head_dim = head_dim
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self.sdpa = sdpa
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self.to_q = nn.Linear(query_dim, self.hidden_size, bias=bias)
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self.to_k = nn.Linear(cross_attention_dim, self.hidden_size, bias=bias)
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self.to_v = nn.Linear(cross_attention_dim, self.hidden_size, bias=bias)
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self.to_out = nn.Sequential(
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nn.Linear(self.hidden_size, query_dim), nn.Dropout(dropout)
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)
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def forward(self, hidden_states, context=None, mask=None):
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bsz, q_len, _ = hidden_states.shape
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query = self.to_q(hidden_states)
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context = context if context is not None else hidden_states
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kv_seq_len = context.shape[1]
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key = self.to_k(context)
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value = self.to_v(context)
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# [B, S, H, D]
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query = query.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key = key.view(bsz, kv_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
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value = value.view(bsz, kv_seq_len, self.num_heads, self.head_dim).transpose(
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1, 2
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)
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if mask is not None:
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assert mask.shape == (bsz, 1, q_len, kv_seq_len)
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if self.sdpa:
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attn_output = F.scaled_dot_product_attention(
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query, key, value, attn_mask=mask, scale=self.scale
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)
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else:
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attn_weights = torch.matmul(query, key.transpose(2, 3)) / self.scale
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assert attn_weights.shape == (bsz, self.num_heads, q_len, kv_seq_len)
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if mask is not None:
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attn_weights = attn_weights + mask
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attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
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query.dtype
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)
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attn_output = torch.matmul(attn_weights, value)
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assert attn_output.shape == (bsz, self.num_heads, q_len, self.head_dim)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.to_out(attn_output)
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return attn_output
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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)
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* torch.arange(start=0, end=half, dtype=torch.float32)
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/ 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(
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[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
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)
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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).to(
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self.mlp[0].weight.dtype
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)
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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(
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num_classes + use_cfg_embedding, hidden_size
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)
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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 = (
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torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
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)
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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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class PatchEmbedder(nn.Module):
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"""Patch Embedding Layer for flat 4D video tensors."""
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def __init__(
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self,
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patch_size: int = 16,
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in_chans: int = 3,
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embed_dim: int = 768,
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norm_layer: Optional[Callable] = None,
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bias: bool = True,
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) -> None:
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super().__init__()
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self.patch_size = patch_size
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self.proj = nn.Conv2d(
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in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias
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)
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self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# [B, S, C, P, P] -> [B, S, C*P*P]
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# FIXME: hack diffusion and use view
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x = x.view(*x.shape[:2], -1)
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out = F.linear(
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x, self.proj.weight.view(self.proj.weight.shape[0], -1), self.proj.bias
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)
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out = self.norm(out)
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# [B, S, H]
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return out
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class TextEmbedder(nn.Module):
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def __init__(
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self, in_features: int, embed_dim: int = 768, bias: bool = True
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) -> None:
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super().__init__()
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self.proj = nn.Linear(in_features, embed_dim, bias=bias)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# [B, S, C] -> [B, S, H]
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return self.proj(x)
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class PositionEmbedding(nn.Module):
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def __init__(self, dim: int, max_position_embeddings=262114) -> None:
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super().__init__()
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self._set_pos_embed_cache(max_position_embeddings)
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def _set_pos_embed_cache(self, seq_len: int, device="cpu", dtype=torch.float):
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self.max_seq_len_cached = seq_len
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pos_embed = get_2d_sincos_pos_embed(self.dim, math.ceil(seq_len**0.5))
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pos_embed = torch.from_numpy(pos_embed).to(device=device, dtype=dtype)
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# [S, H]
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self.register_buffer("pos_embed_cache", pos_embed, persistent=False)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# [B, S, H]
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seq_len = x.shape[1]
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if seq_len > self.max_seq_len_cached:
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self._set_pos_embed_cache(seq_len, x.device, x.dtype)
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pos_embed = self.pos_embed_cache[None, :seq_len]
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return pos_embed
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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__(
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self, hidden_size, num_heads, cross_attention_dim, mlp_ratio=4.0, **block_kwargs
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):
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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 = CrossAttention(
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query_dim=hidden_size,
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cross_attention_dim=cross_attention_dim,
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num_heads=num_heads,
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head_dim=hidden_size // num_heads,
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bias=True,
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sdpa=True,
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)
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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(
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in_features=hidden_size,
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hidden_features=mlp_hidden_dim,
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act_layer=approx_gelu,
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drop=0,
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)
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def forward(self, x, context, attention_mask):
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# TODO: use cross attn
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x = x + self.attn(self.norm1(x), context, attention_mask)
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x = x + self.mlp(self.norm2(x))
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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.linear = nn.Linear(
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hidden_size, patch_size * patch_size * out_channels, bias=True
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)
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self.patch_size = patch_size
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def unpatchify(self, x):
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b, s, h = x.shape
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return x.view(b, s, -1, self.patch_size, self.patch_size)
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def forward(self, x):
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x = self.linear(x)
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x = self.unpatchify(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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patch_size=2,
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in_channels=3,
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text_embed_dim=512,
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hidden_size=1152,
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depth=28,
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num_heads=16,
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mlp_ratio=4.0,
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max_num_embeddings=256 * 1024,
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learn_sigma=True,
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):
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super().__init__()
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self.grad_checkpointing = False
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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.video_embedder = PatchEmbedder(
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patch_size, in_channels, hidden_size, bias=True
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)
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self.t_embedder = TimestepEmbedder(text_embed_dim)
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self.pos_embed = PositionEmbedding(hidden_size, max_num_embeddings)
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self.blocks = nn.ModuleList(
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[
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DiTBlock(hidden_size, num_heads, text_embed_dim, mlp_ratio=mlp_ratio)
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for _ in range(depth)
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]
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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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# TODO: update patch embed init
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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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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 _prepare_mask(self, attention_mask: Optional[torch.Tensor], dtype: torch.dtype):
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if attention_mask is not None:
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assert attention_mask.ndim == 4
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attention_mask = attention_mask.to(dtype)
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inverted_mask = 1.0 - attention_mask
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return inverted_mask.masked_fill(
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inverted_mask.to(torch.bool), torch.finfo(dtype).min
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)
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return attention_mask
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def enable_gradient_checkpointing(self):
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self.grad_checkpointing = True
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def disable_gradient_checkpointing(self):
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self.grad_checkpointing = False
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def forward(
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self,
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video_latent_states,
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t,
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text_latent_states=None,
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attention_mask=None,
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):
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"""
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video_latent_states: [B, S, C, P, P]
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"""
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video_latent_states = self.video_embedder(video_latent_states)
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pos_embed = self.pos_embed(video_latent_states)
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video_latent_states = video_latent_states + pos_embed
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t = self.t_embedder(t) # (N, D)
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text_latent_states = text_latent_states + t.unsqueeze(1)
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attention_mask = self._prepare_mask(attention_mask, video_latent_states.dtype)
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for block in self.blocks:
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if self.grad_checkpointing and self.training:
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video_latent_states = torch.utils.checkpoint.checkpoint(
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block, video_latent_states, text_latent_states, attention_mask
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)
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else:
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video_latent_states = block(
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video_latent_states, text_latent_states, attention_mask
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)
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video_latent_states = self.final_layer(video_latent_states)
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return video_latent_states
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def forward_with_cfg(
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self, x, t, text_latent_states, cfg_scale, attention_mask=None
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):
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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(
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combined, t, text_latent_states, attention_mask=attention_mask
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)
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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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c = model_out.shape[2]
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assert c == 2 * self.in_channels
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eps, rest = model_out.chunk(2, dim=2)
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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=2)
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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(grid_size, 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, grid_size])
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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(
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|
[np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0
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|
)
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|
return pos_embed
|
|
|
|
|
|
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
|
|
|
|
|
|
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
|
"""
|
|
embed_dim: output dimension for each position
|
|
pos: a list of positions to be encoded: size (M,)
|
|
out: (M, D)
|
|
"""
|
|
assert embed_dim % 2 == 0
|
|
omega = np.arange(embed_dim // 2, dtype=np.float64)
|
|
omega /= embed_dim / 2.0
|
|
omega = 1.0 / 10000**omega # (D/2,)
|
|
|
|
pos = pos.reshape(-1) # (M,)
|
|
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
|
|
|
|
emb_sin = np.sin(out) # (M, D/2)
|
|
emb_cos = np.cos(out) # (M, D/2)
|
|
|
|
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
|
return emb
|
|
|
|
|
|
#################################################################################
|
|
# 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,
|
|
}
|