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Running
on
Zero
File size: 6,240 Bytes
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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
@property
def num_uncond_att_layers(self):
# return self.num_att_layers if self.low_resource else 0
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):
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):
@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] <= 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
@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
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)
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