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# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Union, Tuple, List, Callable, Dict
import torch
import torch.nn.functional as nnf
import numpy as np
import abc
from . import ptp_utils
from . import seq_aligner
from transformers import CLIPTextModel, CLIPTokenizer
pretrained_model_path = "checkpoints/CompVis/stable-diffusion-v1-4/"
ldm_stable = None
tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
LOW_RESOURCE = False
NUM_DIFFUSION_STEPS = 50
GUIDANCE_SCALE = 7.5
MAX_NUM_WORDS = 77
device = torch.device('cuda')
class LocalBlend:
def __call__(self, x_t, attention_store):
k = 1
maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, MAX_NUM_WORDS) for item in maps]
maps = torch.cat(maps, dim=1)
maps = (maps * self.alpha_layers).sum(-1).mean(1)
mask = nnf.max_pool2d(maps, (k * 2 + 1, k * 2 +1), (1, 1), padding=(k, k))
mask = nnf.interpolate(mask, size=(x_t.shape[2:]))
mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
mask = mask.gt(self.threshold)
mask = (mask[:1] + mask[1:]).float()
x_t = x_t[:1] + mask * (x_t - x_t[:1])
return x_t
# def __init__(self, prompts: List[str], words: [List[List[str]]], threshold=.3):
def __init__(self, prompts: List[str], words: List[List[str]], threshold=.3):
alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, MAX_NUM_WORDS)
for i, (prompt, words_) in enumerate(zip(prompts, words)):
if type(words_) is str:
words_ = [words_]
for word in words_:
ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
alpha_layers[i, :, :, :, :, ind] = 1
self.alpha_layers = alpha_layers.to(device)
self.threshold = threshold
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 self.num_att_layers if LOW_RESOURCE else 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):
if self.cur_att_layer >= self.num_uncond_att_layers:
if LOW_RESOURCE:
attn = self.forward(attn, is_cross, place_in_unet)
else:
h = attn.shape[0]
attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet)
self.cur_att_layer += 1
if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
self.cur_att_layer = 0
self.cur_step += 1
self.between_steps()
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 EmptyControl(AttentionControl):
def forward (self, attn, is_cross: bool, place_in_unet: str):
return attn
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] <= 32 ** 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 / self.cur_step for item in self.attention_store[key]] for key in self.attention_store}
return average_attention
def reset(self):
super(AttentionStore, self).reset()
self.step_store = self.get_empty_store()
self.attention_store = {}
def __init__(self):
super(AttentionStore, self).__init__()
self.step_store = self.get_empty_store()
self.attention_store = {}
class AttentionControlEdit(AttentionStore, abc.ABC):
def step_callback(self, x_t):
if self.local_blend is not None:
x_t = self.local_blend(x_t, self.attention_store)
return x_t
def replace_self_attention(self, attn_base, att_replace):
if att_replace.shape[2] <= 16 ** 2:
return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
else:
return att_replace
@abc.abstractmethod
def replace_cross_attention(self, attn_base, att_replace):
raise NotImplementedError
def forward(self, attn, is_cross: bool, place_in_unet: str):
super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
h = attn.shape[0] // (self.batch_size)
attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
attn_base, attn_repalce = attn[0], attn[1:]
if is_cross:
alpha_words = self.cross_replace_alpha[self.cur_step]
attn_repalce_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (1 - alpha_words) * attn_repalce
attn[1:] = attn_repalce_new
else:
attn[1:] = self.replace_self_attention(attn_base, attn_repalce)
attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
return attn
def __init__(self, prompts, num_steps: int,
cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
self_replace_steps: Union[float, Tuple[float, float]],
local_blend: Optional[LocalBlend]):
super(AttentionControlEdit, self).__init__()
self.batch_size = len(prompts)
self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device)
if type(self_replace_steps) is float:
self_replace_steps = 0, self_replace_steps
self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
self.local_blend = local_blend
class AttentionReplace(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper)
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
local_blend: Optional[LocalBlend] = None):
super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device)
class AttentionRefine(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
return attn_replace
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
local_blend: Optional[LocalBlend] = None):
super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer)
self.mapper, alphas = self.mapper.to(device), alphas.to(device)
self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
class AttentionReweight(AttentionControlEdit):
def replace_cross_attention(self, attn_base, att_replace):
if self.prev_controller is not None:
attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
return attn_replace
def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, equalizer,
local_blend: Optional[LocalBlend] = None, controller: Optional[AttentionControlEdit] = None):
super(AttentionReweight, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
self.equalizer = equalizer.to(device)
self.prev_controller = controller
def get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float],
Tuple[float, ...]]):
if type(word_select) is int or type(word_select) is str:
word_select = (word_select,)
equalizer = torch.ones(len(values), 77)
values = torch.tensor(values, dtype=torch.float32)
for word in word_select:
inds = ptp_utils.get_word_inds(text, word, tokenizer)
equalizer[:, inds] = values
return equalizer
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