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| from typing import Optional, Union, Tuple, List, Callable, Dict | |
| import torch | |
| import torch.nn.functional as nnf | |
| import numpy as np | |
| import abc | |
| import src.prompt_attention.p2p_utils as p2p_utils | |
| import src.prompt_attention.seq_aligner as seq_aligner | |
| class AttentionControl(abc.ABC): | |
| def step_callback(self, x_t): | |
| return x_t | |
| def between_steps(self): | |
| return | |
| def num_uncond_att_layers(self): | |
| # return self.num_att_layers if self.low_resource else 0 | |
| return 0 | |
| 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 self.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, low_resource=False, width=None, height=None): | |
| self.cur_step = 0 | |
| self.num_att_layers = -1 | |
| self.cur_att_layer = 0 | |
| self.low_resource = low_resource | |
| self.width = width | |
| self.height = height | |
| class AttentionStore(AttentionControl): | |
| 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] <= att_size * 64: | |
| return attn | |
| def between_steps(self): | |
| if self.save_global_store: | |
| 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] | |
| else: | |
| self.attention_store = self.step_store | |
| 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, width, height, low_resolution=False, save_global_store=False): | |
| super(AttentionStore, self).__init__(low_resolution, width, height) | |
| self.step_store = self.get_empty_store() | |
| self.attention_store = {} | |
| self.save_global_store = save_global_store | |
| class AttentionControlEdit(AttentionStore, abc.ABC): | |
| 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=None, width=None, height=None, tokenizer=None, device=None): | |
| super(AttentionControlEdit, self).__init__(width, height) | |
| self.batch_size = len(prompts) | |
| self.cross_replace_alpha = p2p_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 | |
| def step_callback(self, x_t): | |
| print("step_callback") | |
| 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] <= self.width * self.height: | |
| return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape) | |
| else: | |
| return att_replace | |
| 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 | |
| class AttentionReplace(AttentionControlEdit): | |
| def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, width, height, | |
| local_blend = None, tokenizer=None, device=None, dtype=None): | |
| super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, width, height, tokenizer=tokenizer, device=device) | |
| self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(dtype=dtype, device=device) | |
| def replace_cross_attention(self, attn_base, att_replace): | |
| return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper) | |