589 lines
21 KiB
Python
589 lines
21 KiB
Python
from inspect import isfunction
|
|
import math
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torch import nn, einsum
|
|
from einops import rearrange, repeat
|
|
from typing import Optional, Any
|
|
|
|
from ldm.modules.diffusionmodules.util import checkpoint
|
|
from .sub_quadratic_attention import efficient_dot_product_attention
|
|
|
|
import model_management
|
|
|
|
try:
|
|
import xformers
|
|
import xformers.ops
|
|
XFORMERS_IS_AVAILBLE = True
|
|
except:
|
|
XFORMERS_IS_AVAILBLE = False
|
|
|
|
# CrossAttn precision handling
|
|
import os
|
|
_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
|
|
|
|
try:
|
|
OOM_EXCEPTION = torch.cuda.OutOfMemoryError
|
|
except:
|
|
OOM_EXCEPTION = Exception
|
|
|
|
def exists(val):
|
|
return val is not None
|
|
|
|
|
|
def uniq(arr):
|
|
return{el: True for el in arr}.keys()
|
|
|
|
|
|
def default(val, d):
|
|
if exists(val):
|
|
return val
|
|
return d() if isfunction(d) else d
|
|
|
|
|
|
def max_neg_value(t):
|
|
return -torch.finfo(t.dtype).max
|
|
|
|
|
|
def init_(tensor):
|
|
dim = tensor.shape[-1]
|
|
std = 1 / math.sqrt(dim)
|
|
tensor.uniform_(-std, std)
|
|
return tensor
|
|
|
|
|
|
# feedforward
|
|
class GEGLU(nn.Module):
|
|
def __init__(self, dim_in, dim_out):
|
|
super().__init__()
|
|
self.proj = nn.Linear(dim_in, dim_out * 2)
|
|
|
|
def forward(self, x):
|
|
x, gate = self.proj(x).chunk(2, dim=-1)
|
|
return x * F.gelu(gate)
|
|
|
|
|
|
class FeedForward(nn.Module):
|
|
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
|
|
super().__init__()
|
|
inner_dim = int(dim * mult)
|
|
dim_out = default(dim_out, dim)
|
|
project_in = nn.Sequential(
|
|
nn.Linear(dim, inner_dim),
|
|
nn.GELU()
|
|
) if not glu else GEGLU(dim, inner_dim)
|
|
|
|
self.net = nn.Sequential(
|
|
project_in,
|
|
nn.Dropout(dropout),
|
|
nn.Linear(inner_dim, dim_out)
|
|
)
|
|
|
|
def forward(self, x):
|
|
return self.net(x)
|
|
|
|
|
|
def zero_module(module):
|
|
"""
|
|
Zero out the parameters of a module and return it.
|
|
"""
|
|
for p in module.parameters():
|
|
p.detach().zero_()
|
|
return module
|
|
|
|
|
|
def Normalize(in_channels):
|
|
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
|
|
|
|
|
|
class SpatialSelfAttention(nn.Module):
|
|
def __init__(self, in_channels):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
|
|
self.norm = Normalize(in_channels)
|
|
self.q = torch.nn.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.k = torch.nn.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.v = torch.nn.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
self.proj_out = torch.nn.Conv2d(in_channels,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
|
|
def forward(self, x):
|
|
h_ = x
|
|
h_ = self.norm(h_)
|
|
q = self.q(h_)
|
|
k = self.k(h_)
|
|
v = self.v(h_)
|
|
|
|
# compute attention
|
|
b,c,h,w = q.shape
|
|
q = rearrange(q, 'b c h w -> b (h w) c')
|
|
k = rearrange(k, 'b c h w -> b c (h w)')
|
|
w_ = torch.einsum('bij,bjk->bik', q, k)
|
|
|
|
w_ = w_ * (int(c)**(-0.5))
|
|
w_ = torch.nn.functional.softmax(w_, dim=2)
|
|
|
|
# attend to values
|
|
v = rearrange(v, 'b c h w -> b c (h w)')
|
|
w_ = rearrange(w_, 'b i j -> b j i')
|
|
h_ = torch.einsum('bij,bjk->bik', v, w_)
|
|
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
|
|
h_ = self.proj_out(h_)
|
|
|
|
return x+h_
|
|
|
|
|
|
class CrossAttentionBirchSan(nn.Module):
|
|
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
|
|
super().__init__()
|
|
inner_dim = dim_head * heads
|
|
context_dim = default(context_dim, query_dim)
|
|
|
|
self.scale = dim_head ** -0.5
|
|
self.heads = heads
|
|
|
|
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
|
|
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
|
|
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
|
|
|
|
self.to_out = nn.Sequential(
|
|
nn.Linear(inner_dim, query_dim),
|
|
nn.Dropout(dropout)
|
|
)
|
|
|
|
def forward(self, x, context=None, mask=None):
|
|
h = self.heads
|
|
|
|
query = self.to_q(x)
|
|
context = default(context, x)
|
|
key = self.to_k(context)
|
|
value = self.to_v(context)
|
|
del context, x
|
|
|
|
query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
|
|
key_t = key.transpose(1,2).unflatten(1, (self.heads, -1)).flatten(end_dim=1)
|
|
del key
|
|
value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
|
|
|
|
dtype = query.dtype
|
|
upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
|
|
if upcast_attention:
|
|
bytes_per_token = torch.finfo(torch.float32).bits//8
|
|
else:
|
|
bytes_per_token = torch.finfo(query.dtype).bits//8
|
|
batch_x_heads, q_tokens, _ = query.shape
|
|
_, _, k_tokens = key_t.shape
|
|
qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
|
|
|
|
mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
|
|
|
|
chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD
|
|
|
|
kv_chunk_size_min = None
|
|
|
|
#not sure at all about the math here
|
|
#TODO: tweak this
|
|
if mem_free_total > 8192 * 1024 * 1024 * 1.3:
|
|
query_chunk_size_x = 1024 * 4
|
|
elif mem_free_total > 4096 * 1024 * 1024 * 1.3:
|
|
query_chunk_size_x = 1024 * 2
|
|
else:
|
|
query_chunk_size_x = 1024
|
|
kv_chunk_size_min_x = None
|
|
kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024
|
|
if kv_chunk_size_x < 1024:
|
|
kv_chunk_size_x = None
|
|
|
|
if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
|
|
# the big matmul fits into our memory limit; do everything in 1 chunk,
|
|
# i.e. send it down the unchunked fast-path
|
|
query_chunk_size = q_tokens
|
|
kv_chunk_size = k_tokens
|
|
else:
|
|
query_chunk_size = query_chunk_size_x
|
|
kv_chunk_size = kv_chunk_size_x
|
|
kv_chunk_size_min = kv_chunk_size_min_x
|
|
|
|
hidden_states = efficient_dot_product_attention(
|
|
query,
|
|
key_t,
|
|
value,
|
|
query_chunk_size=query_chunk_size,
|
|
kv_chunk_size=kv_chunk_size,
|
|
kv_chunk_size_min=kv_chunk_size_min,
|
|
use_checkpoint=self.training,
|
|
upcast_attention=upcast_attention,
|
|
)
|
|
|
|
hidden_states = hidden_states.to(dtype)
|
|
|
|
hidden_states = hidden_states.unflatten(0, (-1, self.heads)).transpose(1,2).flatten(start_dim=2)
|
|
|
|
out_proj, dropout = self.to_out
|
|
hidden_states = out_proj(hidden_states)
|
|
hidden_states = dropout(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class CrossAttentionDoggettx(nn.Module):
|
|
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
|
|
super().__init__()
|
|
inner_dim = dim_head * heads
|
|
context_dim = default(context_dim, query_dim)
|
|
|
|
self.scale = dim_head ** -0.5
|
|
self.heads = heads
|
|
|
|
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
|
|
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
|
|
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
|
|
|
|
self.to_out = nn.Sequential(
|
|
nn.Linear(inner_dim, query_dim),
|
|
nn.Dropout(dropout)
|
|
)
|
|
|
|
def forward(self, x, context=None, mask=None):
|
|
h = self.heads
|
|
|
|
q_in = self.to_q(x)
|
|
context = default(context, x)
|
|
k_in = self.to_k(context)
|
|
v_in = self.to_v(context)
|
|
del context, x
|
|
|
|
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
|
del q_in, k_in, v_in
|
|
|
|
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
|
|
|
|
mem_free_total = model_management.get_free_memory(q.device)
|
|
|
|
gb = 1024 ** 3
|
|
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
|
|
modifier = 3 if q.element_size() == 2 else 2.5
|
|
mem_required = tensor_size * modifier
|
|
steps = 1
|
|
|
|
|
|
if mem_required > mem_free_total:
|
|
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
|
|
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
|
|
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
|
|
|
|
if steps > 64:
|
|
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
|
|
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
|
|
f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
|
|
|
|
# print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
|
|
first_op_done = False
|
|
cleared_cache = False
|
|
while True:
|
|
try:
|
|
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
|
for i in range(0, q.shape[1], slice_size):
|
|
end = i + slice_size
|
|
if _ATTN_PRECISION =="fp32":
|
|
with torch.autocast(enabled=False, device_type = 'cuda'):
|
|
s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * self.scale
|
|
else:
|
|
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
|
|
first_op_done = True
|
|
|
|
s2 = s1.softmax(dim=-1)
|
|
del s1
|
|
|
|
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
|
|
del s2
|
|
break
|
|
except OOM_EXCEPTION as e:
|
|
if first_op_done == False:
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
if cleared_cache == False:
|
|
cleared_cache = True
|
|
print("out of memory error, emptying cache and trying again")
|
|
continue
|
|
steps *= 2
|
|
if steps > 64:
|
|
raise e
|
|
print("out of memory error, increasing steps and trying again", steps)
|
|
else:
|
|
raise e
|
|
|
|
del q, k, v
|
|
|
|
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
|
del r1
|
|
|
|
return self.to_out(r2)
|
|
|
|
class CrossAttention(nn.Module):
|
|
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
|
|
super().__init__()
|
|
inner_dim = dim_head * heads
|
|
context_dim = default(context_dim, query_dim)
|
|
|
|
self.scale = dim_head ** -0.5
|
|
self.heads = heads
|
|
|
|
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
|
|
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
|
|
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
|
|
|
|
self.to_out = nn.Sequential(
|
|
nn.Linear(inner_dim, query_dim),
|
|
nn.Dropout(dropout)
|
|
)
|
|
|
|
def forward(self, x, context=None, mask=None):
|
|
h = self.heads
|
|
|
|
q = self.to_q(x)
|
|
context = default(context, x)
|
|
k = self.to_k(context)
|
|
v = self.to_v(context)
|
|
|
|
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
|
|
|
# force cast to fp32 to avoid overflowing
|
|
if _ATTN_PRECISION =="fp32":
|
|
with torch.autocast(enabled=False, device_type = 'cuda'):
|
|
q, k = q.float(), k.float()
|
|
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
|
else:
|
|
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
|
|
|
del q, k
|
|
|
|
if exists(mask):
|
|
mask = rearrange(mask, 'b ... -> b (...)')
|
|
max_neg_value = -torch.finfo(sim.dtype).max
|
|
mask = repeat(mask, 'b j -> (b h) () j', h=h)
|
|
sim.masked_fill_(~mask, max_neg_value)
|
|
|
|
# attention, what we cannot get enough of
|
|
sim = sim.softmax(dim=-1)
|
|
|
|
out = einsum('b i j, b j d -> b i d', sim, v)
|
|
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
|
|
return self.to_out(out)
|
|
|
|
class MemoryEfficientCrossAttention(nn.Module):
|
|
# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
|
|
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
|
|
super().__init__()
|
|
print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
|
|
f"{heads} heads.")
|
|
inner_dim = dim_head * heads
|
|
context_dim = default(context_dim, query_dim)
|
|
|
|
self.heads = heads
|
|
self.dim_head = dim_head
|
|
|
|
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
|
|
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
|
|
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
|
|
|
|
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
|
|
self.attention_op: Optional[Any] = None
|
|
|
|
def forward(self, x, context=None, mask=None):
|
|
q = self.to_q(x)
|
|
context = default(context, x)
|
|
k = self.to_k(context)
|
|
v = self.to_v(context)
|
|
|
|
b, _, _ = q.shape
|
|
q, k, v = map(
|
|
lambda t: t.unsqueeze(3)
|
|
.reshape(b, t.shape[1], self.heads, self.dim_head)
|
|
.permute(0, 2, 1, 3)
|
|
.reshape(b * self.heads, t.shape[1], self.dim_head)
|
|
.contiguous(),
|
|
(q, k, v),
|
|
)
|
|
|
|
# actually compute the attention, what we cannot get enough of
|
|
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
|
|
|
|
if exists(mask):
|
|
raise NotImplementedError
|
|
out = (
|
|
out.unsqueeze(0)
|
|
.reshape(b, self.heads, out.shape[1], self.dim_head)
|
|
.permute(0, 2, 1, 3)
|
|
.reshape(b, out.shape[1], self.heads * self.dim_head)
|
|
)
|
|
return self.to_out(out)
|
|
|
|
class CrossAttentionPytorch(nn.Module):
|
|
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
|
|
super().__init__()
|
|
inner_dim = dim_head * heads
|
|
context_dim = default(context_dim, query_dim)
|
|
|
|
self.heads = heads
|
|
self.dim_head = dim_head
|
|
|
|
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
|
|
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
|
|
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
|
|
|
|
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
|
|
self.attention_op: Optional[Any] = None
|
|
|
|
def forward(self, x, context=None, mask=None):
|
|
q = self.to_q(x)
|
|
context = default(context, x)
|
|
k = self.to_k(context)
|
|
v = self.to_v(context)
|
|
|
|
b, _, _ = q.shape
|
|
q, k, v = map(
|
|
lambda t: t.unsqueeze(3)
|
|
.reshape(b, t.shape[1], self.heads, self.dim_head)
|
|
.permute(0, 2, 1, 3)
|
|
.reshape(b * self.heads, t.shape[1], self.dim_head)
|
|
.contiguous(),
|
|
(q, k, v),
|
|
)
|
|
|
|
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
|
|
|
|
if exists(mask):
|
|
raise NotImplementedError
|
|
out = (
|
|
out.unsqueeze(0)
|
|
.reshape(b, self.heads, out.shape[1], self.dim_head)
|
|
.permute(0, 2, 1, 3)
|
|
.reshape(b, out.shape[1], self.heads * self.dim_head)
|
|
)
|
|
|
|
return self.to_out(out)
|
|
|
|
import sys
|
|
if XFORMERS_IS_AVAILBLE == False or "--disable-xformers" in sys.argv:
|
|
if "--use-split-cross-attention" in sys.argv:
|
|
print("Using split optimization for cross attention")
|
|
CrossAttention = CrossAttentionDoggettx
|
|
else:
|
|
if "--use-pytorch-cross-attention" in sys.argv:
|
|
print("Using pytorch cross attention")
|
|
torch.backends.cuda.enable_math_sdp(False)
|
|
CrossAttention = CrossAttentionPytorch
|
|
else:
|
|
print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
|
|
CrossAttention = CrossAttentionBirchSan
|
|
else:
|
|
print("Using xformers cross attention")
|
|
CrossAttention = MemoryEfficientCrossAttention
|
|
|
|
class BasicTransformerBlock(nn.Module):
|
|
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
|
|
disable_self_attn=False):
|
|
super().__init__()
|
|
self.disable_self_attn = disable_self_attn
|
|
self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
|
|
context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn
|
|
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
|
|
self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
|
|
heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
|
|
self.norm1 = nn.LayerNorm(dim)
|
|
self.norm2 = nn.LayerNorm(dim)
|
|
self.norm3 = nn.LayerNorm(dim)
|
|
self.checkpoint = checkpoint
|
|
|
|
def forward(self, x, context=None):
|
|
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
|
|
|
|
def _forward(self, x, context=None):
|
|
x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
|
|
x = self.attn2(self.norm2(x), context=context) + x
|
|
x = self.ff(self.norm3(x)) + x
|
|
return x
|
|
|
|
|
|
class SpatialTransformer(nn.Module):
|
|
"""
|
|
Transformer block for image-like data.
|
|
First, project the input (aka embedding)
|
|
and reshape to b, t, d.
|
|
Then apply standard transformer action.
|
|
Finally, reshape to image
|
|
NEW: use_linear for more efficiency instead of the 1x1 convs
|
|
"""
|
|
def __init__(self, in_channels, n_heads, d_head,
|
|
depth=1, dropout=0., context_dim=None,
|
|
disable_self_attn=False, use_linear=False,
|
|
use_checkpoint=True):
|
|
super().__init__()
|
|
if exists(context_dim) and not isinstance(context_dim, list):
|
|
context_dim = [context_dim]
|
|
self.in_channels = in_channels
|
|
inner_dim = n_heads * d_head
|
|
self.norm = Normalize(in_channels)
|
|
if not use_linear:
|
|
self.proj_in = nn.Conv2d(in_channels,
|
|
inner_dim,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0)
|
|
else:
|
|
self.proj_in = nn.Linear(in_channels, inner_dim)
|
|
|
|
self.transformer_blocks = nn.ModuleList(
|
|
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
|
|
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
|
|
for d in range(depth)]
|
|
)
|
|
if not use_linear:
|
|
self.proj_out = zero_module(nn.Conv2d(inner_dim,
|
|
in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0))
|
|
else:
|
|
self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
|
|
self.use_linear = use_linear
|
|
|
|
def forward(self, x, context=None):
|
|
# note: if no context is given, cross-attention defaults to self-attention
|
|
if not isinstance(context, list):
|
|
context = [context]
|
|
b, c, h, w = x.shape
|
|
x_in = x
|
|
x = self.norm(x)
|
|
if not self.use_linear:
|
|
x = self.proj_in(x)
|
|
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
|
|
if self.use_linear:
|
|
x = self.proj_in(x)
|
|
for i, block in enumerate(self.transformer_blocks):
|
|
x = block(x, context=context[i])
|
|
if self.use_linear:
|
|
x = self.proj_out(x)
|
|
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
|
|
if not self.use_linear:
|
|
x = self.proj_out(x)
|
|
return x + x_in
|
|
|