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from omegaconf import OmegaConf
import torch as th
import torch
import math
import abc
from torch import nn, einsum
from einops import rearrange, repeat
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from transformers import CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextConfig, CLIPTextModel, CLIPTextTransformer#, _expand_mask
from inspect import isfunction
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 register_attention_control(model, controller):
def ca_forward(self, place_in_unet):
def forward(x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
is_cross = context is not None
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))
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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
attn = sim.softmax(dim=-1)
attn2 = rearrange(attn, '(b h) k c -> h b k c', h=h).mean(0)
controller(attn2, is_cross, place_in_unet)
out = einsum('b i j, b j d -> b i d', attn, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
return self.to_out(out)
return forward
class DummyController:
def __call__(self, *args):
return args[0]
def __init__(self):
self.num_att_layers = 0
if controller is None:
controller = DummyController()
def register_recr(net_, count, place_in_unet):
if net_.__class__.__name__ == 'CrossAttention':
net_.forward = ca_forward(net_, place_in_unet)
return count + 1
elif hasattr(net_, 'children'):
for net__ in net_.children():
count = register_recr(net__, count, place_in_unet)
return count
cross_att_count = 0
sub_nets = model.diffusion_model.named_children()
for net in sub_nets:
if "input_blocks" in net[0]:
cross_att_count += register_recr(net[1], 0, "down")
elif "output_blocks" in net[0]:
cross_att_count += register_recr(net[1], 0, "up")
elif "middle_block" in net[0]:
cross_att_count += register_recr(net[1], 0, "mid")
controller.num_att_layers = cross_att_count
class AttentionControl(abc.ABC):
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
@property
def num_uncond_att_layers(self):
return 0
@abc.abstractmethod
def forward (self, attn, is_cross: bool, place_in_unet: str):
raise NotImplementedError
def __call__(self, attn, is_cross: bool, place_in_unet: str):
attn = self.forward(attn, is_cross, place_in_unet)
return attn
def reset(self):
self.cur_step = 0
self.cur_att_layer = 0
def __init__(self):
self.cur_step = 0
self.num_att_layers = -1
self.cur_att_layer = 0
class AttentionStore(AttentionControl):
@staticmethod
def get_empty_store():
return {"down_cross": [], "mid_cross": [], "up_cross": [],
"down_self": [], "mid_self": [], "up_self": []}
def forward(self, attn, is_cross: bool, place_in_unet: str):
key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
if attn.shape[1] <= (self.max_size) ** 2: # avoid memory overhead
self.step_store[key].append(attn)
return attn
def between_steps(self):
if len(self.attention_store) == 0:
self.attention_store = self.step_store
else:
for key in self.attention_store:
for i in range(len(self.attention_store[key])):
self.attention_store[key][i] += self.step_store[key][i]
self.step_store = self.get_empty_store()
def get_average_attention(self):
average_attention = {key: [item for item in self.step_store[key]] for key in self.step_store}
return average_attention
def reset(self):
super(AttentionStore, self).reset()
self.step_store = self.get_empty_store()
self.attention_store = {}
def __init__(self, base_size=64, max_size=None):
super(AttentionStore, self).__init__()
self.step_store = self.get_empty_store()
self.attention_store = {}
self.base_size = base_size
if max_size is None:
self.max_size = self.base_size // 2
else:
self.max_size = max_size
def register_hier_output(model):
self = model.diffusion_model
from ldm.modules.diffusionmodules.util import checkpoint, timestep_embedding
def forward(x, timesteps=None, context=None, y=None,**kwargs):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param context: conditioning plugged in via crossattn
:param y: an [N] Tensor of labels, if class-conditional.
:return: an [N x C x ...] Tensor of outputs.
"""
assert (y is not None) == (
self.num_classes is not None
), "must specify y if and only if the model is class-conditional"
hs = []
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb)
if self.num_classes is not None:
assert y.shape == (x.shape[0],)
emb = emb + self.label_emb(y)
h = x.type(self.dtype)
for module in self.input_blocks:
# import pdb; pdb.set_trace()
if context.shape[1]==2:
h = module(h, emb, context[:,0,:].unsqueeze(1))
else:
h = module(h, emb, context)
hs.append(h)
if context.shape[1]==2:
h = self.middle_block(h, emb, context[:,0,:].unsqueeze(1))
else:
h = self.middle_block(h, emb, context)
out_list = []
for i_out, module in enumerate(self.output_blocks):
h = th.cat([h, hs.pop()], dim=1)
if context.shape[1]==2:
h = module(h, emb, context[:,1,:].unsqueeze(1))
else:
h = module(h, emb, context)
if i_out in [1, 4, 7]:
out_list.append(h)
h = h.type(x.dtype)
out_list.append(h)
return out_list
self.forward = forward
class UNetWrapper(nn.Module):
def __init__(self, unet, use_attn=True, base_size=512, max_attn_size=None, attn_selector='up_cross+down_cross') -> None:
super().__init__()
self.unet = unet
self.attention_store = AttentionStore(base_size=base_size // 8, max_size=max_attn_size)
self.size16 = base_size // 32
self.size32 = base_size // 16
self.size64 = base_size // 8
self.use_attn = use_attn
if self.use_attn:
register_attention_control(unet, self.attention_store)
register_hier_output(unet)
self.attn_selector = attn_selector.split('+')
def forward(self, *args, **kwargs):
if self.use_attn:
self.attention_store.reset()
out_list = self.unet(*args, **kwargs)
if self.use_attn:
avg_attn = self.attention_store.get_average_attention()
attn16, attn32, attn64 = self.process_attn(avg_attn)
out_list[1] = torch.cat([out_list[1], attn16], dim=1)
out_list[2] = torch.cat([out_list[2], attn32], dim=1)
if attn64 is not None:
out_list[3] = torch.cat([out_list[3], attn64], dim=1)
return out_list[::-1]
def process_attn(self, avg_attn):
attns = {self.size16: [], self.size32: [], self.size64: []}
for k in self.attn_selector:
for up_attn in avg_attn[k]:
size = int(math.sqrt(up_attn.shape[1]))
attns[size].append(rearrange(up_attn, 'b (h w) c -> b c h w', h=size))
attn16 = torch.stack(attns[self.size16]).mean(0)
attn32 = torch.stack(attns[self.size32]).mean(0)
if len(attns[self.size64]) > 0:
attn64 = torch.stack(attns[self.size64]).mean(0)
else:
attn64 = None
return attn16, attn32, attn64
class TextAdapter(nn.Module):
def __init__(self, text_dim=768, hidden_dim=None):
super().__init__()
if hidden_dim is None:
hidden_dim = text_dim
self.fc = nn.Sequential(
nn.Linear(text_dim, hidden_dim),
nn.GELU(),
nn.Linear(hidden_dim, text_dim)
)
def forward(self, latents, texts, gamma):
n_class, channel = texts.shape
bs = latents.shape[0]
texts_after = self.fc(texts)
texts = texts + gamma * texts_after
texts = repeat(texts, 'n c -> b n c', b=bs)
return texts
class TextAdapterRefer(nn.Module):
def __init__(self, text_dim=768):
super().__init__()
self.fc = nn.Sequential(
nn.Linear(text_dim, text_dim),
nn.GELU(),
nn.Linear(text_dim, text_dim)
)
def forward(self, latents, texts, gamma):
texts_after = self.fc(texts)
texts = texts + gamma * texts_after
return texts
class TextAdapterDepth(nn.Module):
def __init__(self, text_dim=768):
super().__init__()
self.fc = nn.Sequential(
nn.Linear(text_dim, text_dim),
nn.GELU(),
nn.Linear(text_dim, text_dim)
)
def forward(self, latents, texts, gamma):
# use the gamma to blend
n_sen, channel = texts.shape
bs = latents.shape[0]
texts_after = self.fc(texts)
texts = texts + gamma * texts_after
texts = repeat(texts, 'n c -> n b c', b=1)
return texts
class FrozenCLIPEmbedder(nn.Module):
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, pool=True):
super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.transformer = CLIPTextModel.from_pretrained(version)
self.device = device
self.max_length = max_length
self.freeze()
self.pool = pool
def freeze(self):
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
outputs = self.transformer(input_ids=tokens)
if self.pool:
z = outputs.pooler_output
else:
z = outputs.last_hidden_state
return z
def encode(self, text):
return self(text)
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