342 lines
12 KiB
Python
342 lines
12 KiB
Python
from inspect import isfunction
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import math
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import torch
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import torch.nn.functional as F
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from torch import nn, einsum
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from einops import rearrange, repeat
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from typing import Optional, Any
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from .diffusionmodules.util import checkpoint
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try:
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import xformers
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import xformers.ops
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XFORMERS_IS_AVAILBLE = True
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except:
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XFORMERS_IS_AVAILBLE = False
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# CrossAttn precision handling
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import os
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_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
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def exists(val):
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return val is not None
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def uniq(arr):
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return{el: True for el in arr}.keys()
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def default(val, d):
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if exists(val):
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return val
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return d() if isfunction(d) else d
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def max_neg_value(t):
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return -torch.finfo(t.dtype).max
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def init_(tensor):
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dim = tensor.shape[-1]
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std = 1 / math.sqrt(dim)
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tensor.uniform_(-std, std)
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return tensor
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# feedforward
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class GEGLU(nn.Module):
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def __init__(self, dim_in, dim_out):
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super().__init__()
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self.proj = nn.Linear(dim_in, dim_out * 2)
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def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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return x * F.gelu(gate)
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class FeedForward(nn.Module):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = default(dim_out, dim)
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project_in = nn.Sequential(
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nn.Linear(dim, inner_dim),
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nn.GELU()
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) if not glu else GEGLU(dim, inner_dim)
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self.net = nn.Sequential(
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project_in,
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nn.Dropout(dropout),
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nn.Linear(inner_dim, dim_out)
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)
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def forward(self, x):
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return self.net(x)
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def zero_module(module):
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"""
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Zero out the parameters of a module and return it.
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"""
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for p in module.parameters():
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p.detach().zero_()
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return module
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def Normalize(in_channels):
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return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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class SpatialSelfAttention(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels)
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self.q = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.k = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.v = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.proj_out = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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def forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b,c,h,w = q.shape
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q = rearrange(q, 'b c h w -> b (h w) c')
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k = rearrange(k, 'b c h w -> b c (h w)')
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w_ = torch.einsum('bij,bjk->bik', q, k)
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w_ = w_ * (int(c)**(-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = rearrange(v, 'b c h w -> b c (h w)')
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w_ = rearrange(w_, 'b i j -> b j i')
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h_ = torch.einsum('bij,bjk->bik', v, w_)
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h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
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h_ = self.proj_out(h_)
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return x+h_
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class CrossAttention(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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self.scale = dim_head ** -0.5
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self.heads = heads
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self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
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self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, query_dim),
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nn.Dropout(dropout)
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)
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def forward(self, x, context=None, mask=None):
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h = self.heads
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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v = self.to_v(context)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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# force cast to fp32 to avoid overflowing
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if _ATTN_PRECISION =="fp32":
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with torch.autocast(enabled=False, device_type = 'cuda'):
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q, k = q.float(), k.float()
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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else:
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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del q, k
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if exists(mask):
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mask = rearrange(mask, 'b ... -> b (...)')
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, 'b j -> (b h) () j', h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# attention, what we cannot get enough of
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sim = sim.softmax(dim=-1)
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out = einsum('b i j, b j d -> b i d', sim, v)
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out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
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return self.to_out(out)
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class MemoryEfficientCrossAttention(nn.Module):
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# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
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super().__init__()
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print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
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f"{heads} heads.")
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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self.heads = heads
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self.dim_head = dim_head
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self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
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self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
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self.attention_op: Optional[Any] = None
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def forward(self, x, context=None, mask=None):
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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v = self.to_v(context)
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b, _, _ = q.shape
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q, k, v = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, t.shape[1], self.heads, self.dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b * self.heads, t.shape[1], self.dim_head)
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.contiguous(),
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(q, k, v),
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)
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# actually compute the attention, what we cannot get enough of
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out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
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if exists(mask):
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raise NotImplementedError
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out = (
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out.unsqueeze(0)
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.reshape(b, self.heads, out.shape[1], self.dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b, out.shape[1], self.heads * self.dim_head)
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)
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return self.to_out(out)
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class BasicTransformerBlock(nn.Module):
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ATTENTION_MODES = {
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"softmax": CrossAttention, # vanilla attention
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"softmax-xformers": MemoryEfficientCrossAttention
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}
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def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
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disable_self_attn=False):
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super().__init__()
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attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
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assert attn_mode in self.ATTENTION_MODES
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attn_cls = self.ATTENTION_MODES[attn_mode]
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self.disable_self_attn = disable_self_attn
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self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
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context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn
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self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
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self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
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heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
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self.norm1 = nn.LayerNorm(dim)
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self.norm2 = nn.LayerNorm(dim)
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self.norm3 = nn.LayerNorm(dim)
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self.checkpoint = checkpoint
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def forward(self, x, context=None):
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return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
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def _forward(self, x, context=None):
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x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
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x = self.attn2(self.norm2(x), context=context) + x
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x = self.ff(self.norm3(x)) + x
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return x
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class SpatialTransformer(nn.Module):
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"""
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Transformer block for image-like data.
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First, project the input (aka embedding)
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and reshape to b, t, d.
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Then apply standard transformer action.
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Finally, reshape to image
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NEW: use_linear for more efficiency instead of the 1x1 convs
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"""
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def __init__(self, in_channels, n_heads, d_head,
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depth=1, dropout=0., context_dim=None,
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disable_self_attn=False, use_linear=False,
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use_checkpoint=True):
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super().__init__()
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if exists(context_dim) and not isinstance(context_dim, list):
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context_dim = [context_dim]
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self.in_channels = in_channels
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inner_dim = n_heads * d_head
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self.norm = Normalize(in_channels)
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if not use_linear:
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self.proj_in = nn.Conv2d(in_channels,
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inner_dim,
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kernel_size=1,
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stride=1,
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padding=0)
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else:
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self.proj_in = nn.Linear(in_channels, inner_dim)
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self.transformer_blocks = nn.ModuleList(
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[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
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disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
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for d in range(depth)]
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)
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if not use_linear:
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self.proj_out = zero_module(nn.Conv2d(inner_dim,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0))
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else:
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self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
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self.use_linear = use_linear
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def forward(self, x, context=None):
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# note: if no context is given, cross-attention defaults to self-attention
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if not isinstance(context, list):
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context = [context]
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b, c, h, w = x.shape
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x_in = x
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x = self.norm(x)
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if not self.use_linear:
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x = self.proj_in(x)
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x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
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if self.use_linear:
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x = self.proj_in(x)
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for i, block in enumerate(self.transformer_blocks):
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x = block(x, context=context[i])
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if self.use_linear:
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x = self.proj_out(x)
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x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
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if not self.use_linear:
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x = self.proj_out(x)
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return x + x_in
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