ptp_utils_new.py

# 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.

### This file contains code from Google, with functions that have been modified where stated.

import numpy as np
import torch
import torch.nn.functional as nnf
from typing import List, Tuple, Dict, Union, Optional
from PIL import Image
import cv2
import abc
import seq_aligner
from typing import Optional, Union, Tuple, List, Dict
from tqdm.notebook import tqdm
from IPython.display import display

device = torch.device('cuda:0')
MAX_NUM_WORDS = 77
LOW_RESOURCE = True

def text_under_image(image: np.ndarray, text: str, text_color: Tuple[int, int, int] = (0, 0, 0)):
    h, w, c = image.shape
    offset = int(h * .2)
    img = np.ones((h + offset, w, c), dtype=np.uint8) * 255
    font = cv2.FONT_HERSHEY_SIMPLEX
    # font = ImageFont.truetype("/usr/share/fonts/truetype/noto/NotoMono-Regular.ttf", font_size)
    img[:h] = image
    textsize = cv2.getTextSize(text, font, 1, 2)[0]
    text_x, text_y = (w - textsize[0]) // 2, h + offset - textsize[1] // 2
    cv2.putText(img, text, (text_x, text_y ), font, 1, text_color, 2)
    return img


def view_images(images, num_rows=1, offset_ratio=0.02):
    if type(images) is list:
        num_empty = len(images) % num_rows
    elif images.ndim == 4:
        num_empty = images.shape[0] % num_rows
    else:
        images = [images]
        num_empty = 0

    empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
    images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
    num_items = len(images)

    h, w, c = images[0].shape
    offset = int(h * offset_ratio)
    num_cols = num_items // num_rows
    image_ = np.ones((h * num_rows + offset * (num_rows - 1),
                      w * num_cols + offset * (num_cols - 1), 3), dtype=np.uint8) * 255
    for i in range(num_rows):
        for j in range(num_cols):
            image_[i * (h + offset): i * (h + offset) + h:, j * (w + offset): j * (w + offset) + w] = images[
                i * num_cols + j]

    pil_img = Image.fromarray(image_)
    display(pil_img)


def diffusion_step(model, controller, latents, control, context, t, guidance_scale, generator=None):
    # Interfaces with the diffusers library to perform diffusion steps.
    # This function was modified to include ControlNet and to remove unnecessary parts.
    if control is not None:
        width, height = control.size
        image = model.prepare_image(
                        image=control,
                        width=width,
                        height=height,
                        batch_size=1,
                        num_images_per_prompt=1,
                        device = torch.device('cuda:0'),
                        dtype=model.controlnet.dtype,
                        do_classifier_free_guidance=False,
                        guess_mode=False,
                    )
        down_block_res_samples, mid_block_res_sample = model.controlnet(
                        latents,
                        t,
                        encoder_hidden_states=context[1],
                        controlnet_cond=image,
                        conditioning_scale=model.cond_scale,
                        guess_mode=False,
                        return_dict=False,
                    )
        noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0],
                    down_block_additional_residuals=down_block_res_samples,
                    mid_block_additional_residual=mid_block_res_sample)["sample"]
        noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1],
                    down_block_additional_residuals=down_block_res_samples,
                    mid_block_additional_residual=mid_block_res_sample)["sample"]
    else:
        noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
        noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
    noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
    latents = model.scheduler.step(noise_pred, t, latents, generator=generator)["prev_sample"]
    if controller is not None:
        latents = controller.step_callback(latents)
    return latents


def latent2image(vae, latents):
    with torch.no_grad():
        latents = 1 / 0.18215 * latents
        image = vae.decode(latents)['sample']
        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).numpy()
        image = (image * 255).astype(np.uint8)
    return image

def image2latent(vae, image):
    with torch.no_grad():
        if type(image) is Image:
            image = np.array(image)
        if type(image) is torch.Tensor and image.dim() == 4:
            latents = image
        else:
            image = torch.from_numpy(image).float() / 127.5 - 1
            image = image.permute(2, 0, 1).unsqueeze(0).to(vae.device)
            latents = vae.encode(image)['latent_dist'].mean
            latents = latents * 0.18215
    torch.cuda.empty_cache()
    return latents

def init_latent(latent, model, height, width, generator):
    if latent is None:
        latent = torch.randn(
            (1, model.unet.in_channels, height // 8, width // 8),
            generator=generator,
        )
    latents = latent.expand(1,  model.unet.in_channels, height // 8, width // 8).to(model.device)
    return latent, latents


@torch.no_grad()
def text2image_ldm_stable(
    model,
    prompt,
    latent: Optional[torch.FloatTensor] = None,
    control = None,
    precalced: bool = False,
    controller = None,
    switch_percent: int = 0,
    num_inference_steps: int = 20,
    guidance_scale: float = 8.0,
    generator: Optional[torch.Generator] = None,
    logging: bool = False,
):
    # This function was modified to interface with liquidnoise.py. There is additional functionality.

    if type(prompt) is str:
        prompt = [prompt]
    if controller is not None:
        register_attention_control(model, controller)
    height = width = 512
    batch_size = len(prompt)

    text_input = model.tokenizer(
        prompt,
        padding="max_length",
        max_length=model.tokenizer.model_max_length,
        truncation=True,
        return_tensors="pt",
    )
    text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
    max_length = text_input.input_ids.shape[-1]
    uncond_input = model.tokenizer(
        [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
    )
    uncond_embeddings = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
    
    context = [uncond_embeddings, text_embeddings]
    if not LOW_RESOURCE:
        context = torch.cat(context)
    latent, latents = init_latent(latent, model, height, width, generator)
    already_switched = False
    means = []
    stds = []
    curr_control = control
    model.scheduler.set_timesteps(num_inference_steps)
    precalced_latent = None
    count = 0
    for t in tqdm(model.scheduler.timesteps):
        percentage = (count / num_inference_steps) * 100
        if precalced:
            if percentage < switch_percent:
                count += 1
                continue
        if percentage >= switch_percent and not already_switched:
            precalced_latent = latents
            already_switched = True

        latents = diffusion_step(model, controller, latents, curr_control, context, t, guidance_scale, generator)
        if logging:
            means.append(float(latents.mean().cpu()))
            stds.append(float(latents.std().cpu()))

        count += 1
    
    image = latent2image(model.vae, latents)
    if not logging:
        return image, precalced_latent, latents
    else:
        return image, means, stds

def register_attention_control(model, controller):
    def ca_forward(self, place_in_unet):
        to_out = self.to_out
        if type(to_out) is torch.nn.modules.container.ModuleList:
            to_out = self.to_out[0]
        else:
            to_out = self.to_out
        
        def forward(hidden_states, encoder_hidden_states=None, attention_mask=None,temb=None,):
            is_cross = encoder_hidden_states is not None
            
            residual = hidden_states

            if self.spatial_norm is not None:
                hidden_states = self.spatial_norm(hidden_states, temb)

            input_ndim = hidden_states.ndim

            if input_ndim == 4:
                batch_size, channel, height, width = hidden_states.shape
                hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

            batch_size, sequence_length, _ = (
                hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
            )
            attention_mask = self.prepare_attention_mask(attention_mask, sequence_length, batch_size)

            if self.group_norm is not None:
                hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

            query = self.to_q(hidden_states)

            if encoder_hidden_states is None:
                encoder_hidden_states = hidden_states
            elif self.norm_cross:
                encoder_hidden_states = self.norm_encoder_hidden_states(encoder_hidden_states)

            key = self.to_k(encoder_hidden_states)
            value = self.to_v(encoder_hidden_states)

            query = self.head_to_batch_dim(query)
            key = self.head_to_batch_dim(key)
            value = self.head_to_batch_dim(value)

            attention_probs = self.get_attention_scores(query, key, attention_mask)
            attention_probs = controller(attention_probs, is_cross, place_in_unet)

            hidden_states = torch.bmm(attention_probs, value)
            hidden_states = self.batch_to_head_dim(hidden_states)

            # linear proj
            hidden_states = to_out(hidden_states)

            if input_ndim == 4:
                hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

            if self.residual_connection:
                hidden_states = hidden_states + residual

            hidden_states = hidden_states / self.rescale_output_factor

            return hidden_states

        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__ == 'Attention':
            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.unet.named_children()
    for net in sub_nets:
        if "down" in net[0]:
            cross_att_count += register_recr(net[1], 0, "down")
        elif "up" in net[0]:
            cross_att_count += register_recr(net[1], 0, "up")
        elif "mid" in net[0]:
            cross_att_count += register_recr(net[1], 0, "mid")

    controller.num_att_layers = cross_att_count

    
def get_word_inds(text: str, word_place: int, tokenizer):
    split_text = text.split(" ")
    if type(word_place) is str:
        word_place = [i for i, word in enumerate(split_text) if word_place == word]
    elif type(word_place) is int:
        word_place = [word_place]
    out = []
    if len(word_place) > 0:
        words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
        cur_len, ptr = 0, 0

        for i in range(len(words_encode)):
            cur_len += len(words_encode[i])
            if ptr in word_place:
                out.append(i + 1)
            if cur_len >= len(split_text[ptr]):
                ptr += 1
                cur_len = 0
    return np.array(out)


def update_alpha_time_word(alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int,
                           word_inds: Optional[torch.Tensor]=None):
    if type(bounds) is float:
        bounds = 0, bounds
    start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
    if word_inds is None:
        word_inds = torch.arange(alpha.shape[2])
    alpha[: start, prompt_ind, word_inds] = 0
    alpha[start: end, prompt_ind, word_inds] = 1
    alpha[end:, prompt_ind, word_inds] = 0
    return alpha


def get_time_words_attention_alpha(prompt, num_steps,
                                   cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]],
                                   tokenizer, max_num_words=77):
    if type(cross_replace_steps) is not dict:
        cross_replace_steps = {"default_": cross_replace_steps}
    if "default_" not in cross_replace_steps:
        cross_replace_steps["default_"] = (0., 1.)
    alpha_time_words = torch.zeros(num_steps + 1, len(prompt) - 1, max_num_words)
    for i in range(len(prompt) - 1):
        alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"],
                                                  i)
    for key, item in cross_replace_steps.items():
        if key != "default_":
             inds = [get_word_inds(prompt[i], key, tokenizer) for i in range(1, len(prompt))]
             for i, ind in enumerate(inds):
                 if len(ind) > 0:
                    alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)
    alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompt) - 1, 1, 1, max_num_words)
    return alpha_time_words


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, prompt: List[str], words, model, threshold=.3):
        alpha_layers = torch.zeros(len(prompt),  1, 1, 1, 1, MAX_NUM_WORDS)
        for i, (prompt, words_) in enumerate(zip(prompt, words)):
            if type(words_) is str:
                words_ = [words_]
            for word in words_:
                ind = get_word_inds(prompt, word, model.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):
        self.last_step_store = self.step_store.copy()
        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 get_last_attention(self):
        last_attention = {key: [item for item in self.last_step_store[key]] for key in self.last_step_store}
        return last_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, prompt, 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], model):
        super(AttentionControlEdit, self).__init__()
        self.batch_size = len(prompt)
        self.cross_replace_alpha = get_time_words_attention_alpha(prompt, num_steps, cross_replace_steps, model.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, prompt, num_steps: int, cross_replace_steps: float, self_replace_steps: float, model,
                 local_blend: Optional[LocalBlend] = None):
        super(AttentionReplace, self).__init__(prompt, num_steps, cross_replace_steps, self_replace_steps, local_blend, model)
        self.mapper = seq_aligner.get_replacement_mapper(prompt, model.tokenizer).to(device)

class AttentionPermute(AttentionControlEdit):
    # This class is not created by Google.
    # It was newly created to roll the attention maps utilising Google's method (for experimentation purposes).

    def replace_cross_attention(self, attn_base, att_replace, prompt, NUM_DIFFUSION_STEPS=20, roll_frac=4):
        if self.cur_step > 0.25*NUM_DIFFUSION_STEPS:
            res = int(np.sqrt(attn_base.shape[1]))
            num_pixels = res ** 2
            frac = res/roll_frac
            cross_map = attn_base.reshape(len(prompt), -1, res, res, attn_base.shape[-1])[0]
            if res >= roll_frac:
                for i in range(res):
                    cross_map[:, :, i, :] = cross_map[:, :, (i+int(frac))%res, :]
            else:
                for i in range(res):
                    cross_map[:, :, i, :] = (1-frac)*cross_map[:, :, i, :] + frac*cross_map[:, :, (i+1)%res, :]
            permuted_attn = cross_map.reshape(attn_base.shape[0], num_pixels, attn_base.shape[-1])
            return permuted_attn
        else:
            return attn_base


      
    def __init__(self, prompt, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
                 local_blend: Optional[LocalBlend] = None):
        super(AttentionPermute, self).__init__(prompt, num_steps, cross_replace_steps, self_replace_steps, local_blend)
        

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, prompt, num_steps: int, cross_replace_steps: float, self_replace_steps: float, model,
                 local_blend: Optional[LocalBlend] = None):
        super(AttentionRefine, self).__init__(prompt, num_steps, cross_replace_steps, self_replace_steps, local_blend)
        self.mapper, alphas = seq_aligner.get_refinement_mapper(prompt, model.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, prompt, 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__(prompt, 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, ...]], model):
    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 = get_word_inds(text, word, model.tokenizer)
        equalizer[:, inds] = values
    return equalizer

def aggregate_attention(attention_store: AttentionStore, res: int, from_where: List[str], prompt, is_cross: bool, select: int, last):
    out = []
    if last:
        attention_maps = attention_store.get_last_attention()
    else:
        attention_maps = attention_store.get_average_attention()
    num_pixels = res ** 2
    for location in from_where:
        for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
            if item.shape[1] == num_pixels:
                cross_maps = item.reshape(len(prompt), -1, res, res, item.shape[-1])[select]
                out.append(cross_maps)
    out = torch.cat(out, dim=0)
    out = out.sum(0) / out.shape[0]
    return out.cpu()


def show_cross_attention(attention_store: AttentionStore, res: int, from_where: List[str], prompt, model, last=False, select: int = 0):
    if type(prompt) is str:
        prompt = [prompt]
    tokens = model.tokenizer.encode(prompt[select])
    decoder = model.tokenizer.decode
    attention_maps = aggregate_attention(attention_store, res, from_where, prompt, True, select, last)
    images = []
    for i in range(len(tokens)):
        image = attention_maps[:, :, i]
        image = 255 * image / image.max()
        image = image.unsqueeze(-1).expand(*image.shape, 3)
        image = image.numpy().astype(np.uint8)
        image = np.array(Image.fromarray(image).resize((256, 256), Image.NEAREST))
        image = text_under_image(image, decoder(int(tokens[i])))
        images.append(image)
    view_images(np.stack(images, axis=0))
    
def show_self_attention_comp(attention_store: AttentionStore, res: int, from_where: List[str], prompt,
                        max_com=10, select: int = 0):
    if type(prompt) is str:
        prompt = [prompt]
    attention_maps = aggregate_attention(attention_store, res, from_where, prompt, False, select, False).numpy().reshape((res ** 2, res ** 2))
    u, s, vh = np.linalg.svd(attention_maps - np.mean(attention_maps, axis=1, keepdims=True))
    images = []
    for i in range(max_com):
        image = vh[i].reshape(res, res)
        image = image - image.min()
        image = 255 * image / image.max()
        image = np.repeat(np.expand_dims(image, axis=2), 3, axis=2).astype(np.uint8)
        image = Image.fromarray(image).resize((256, 256), Image.NEAREST)
        image = np.array(image)
        images.append(image)
    view_images(np.concatenate(images, axis=1))