model_bbox/MIGC/migc/migc_pipeline.py

import glob
import random
import time
from typing import Any, Callable, Dict, List, Optional, Union
# import moxing as mox
import numpy as np
import torch
from diffusers.loaders import TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.attention_processor import Attention
from diffusers.pipelines.stable_diffusion import (
    StableDiffusionPipeline,
    StableDiffusionPipelineOutput,
    StableDiffusionSafetyChecker,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import logging
from PIL import Image, ImageDraw, ImageFont
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
import inspect
import os
import math
import torch.nn as nn
import torch.nn.functional as F
# from utils import load_utils
import argparse
import yaml
import cv2
import math
from migc.migc_arch import MIGC, NaiveFuser
from scipy.ndimage import uniform_filter, gaussian_filter

logger = logging.get_logger(__name__)

class AttentionStore:
    @staticmethod
    def get_empty_store():
        return {"down": [], "mid": [], "up": []}

    def __call__(self, attn, is_cross: bool, place_in_unet: str):
        if is_cross:
            if attn.shape[1] in self.attn_res:
                self.step_store[place_in_unet].append(attn)

        self.cur_att_layer += 1
        if self.cur_att_layer == self.num_att_layers:
            self.cur_att_layer = 0
            self.between_steps()

    def between_steps(self):
        self.attention_store = self.step_store
        self.step_store = self.get_empty_store()

    def maps(self, block_type: str):
        return self.attention_store[block_type]

    def reset(self):
        self.cur_att_layer = 0
        self.step_store = self.get_empty_store()
        self.attention_store = {}

    def __init__(self, attn_res=[64*64, 32*32, 16*16, 8*8]):
        """
        Initialize an empty AttentionStore :param step_index: used to visualize only a specific step in the diffusion
        process
        """
        self.num_att_layers = -1
        self.cur_att_layer = 0
        self.step_store = self.get_empty_store()
        self.attention_store = {}
        self.curr_step_index = 0
        self.attn_res = attn_res


def get_sup_mask(mask_list):
    or_mask = np.zeros_like(mask_list[0])
    for mask in mask_list:
        or_mask += mask
    or_mask[or_mask >= 1] = 1
    sup_mask = 1 - or_mask
    return sup_mask


class MIGCProcessor(nn.Module):
    def __init__(self, config, attnstore, place_in_unet):
        super().__init__()
        self.attnstore = attnstore
        self.place_in_unet = place_in_unet
        self.not_use_migc = config['not_use_migc']
        self.naive_fuser = NaiveFuser()        
        self.embedding = {}
        if not self.not_use_migc:
            self.migc = MIGC(config['C'])

    def __call__(
            self,
            attn: Attention,
            hidden_states,
            encoder_hidden_states=None,
            attention_mask=None,
            prompt_nums=[],
            bboxes=[],
            ith=None,
            embeds_pooler=None,
            timestep=None,
            height=512,
            width=512,
            MIGCsteps=20,
            NaiveFuserSteps=-1,
            ca_scale=None,
            ea_scale=None,
            sac_scale=None,
            use_sa_preserve=False,
            sa_preserve=False,
    ):
        batch_size, sequence_length, _ = hidden_states.shape
        assert(batch_size == 2, "We currently only implement sampling with batch_size=1, \
               and we will implement sampling with batch_size=N as soon as possible.")
        attention_mask = attn.prepare_attention_mask(
            attention_mask, sequence_length, batch_size
        )
        
        instance_num = len(bboxes[0])

        if ith > MIGCsteps:
            not_use_migc = True
        else:
            not_use_migc = self.not_use_migc
        is_vanilla_cross = (not_use_migc and ith > NaiveFuserSteps)
        if instance_num == 0:
            is_vanilla_cross = True

        is_cross = encoder_hidden_states is not None
        
        ori_hidden_states = hidden_states.clone()

        # Only Need Negative Prompt and Global Prompt.
        if is_cross and is_vanilla_cross:
            encoder_hidden_states = encoder_hidden_states[:2, ...]

        # In this case, we need to use MIGC or naive_fuser, so we copy the hidden_states_cond (instance_num+1) times for QKV
        if is_cross and not is_vanilla_cross:
            hidden_states_uncond = hidden_states[[0], ...]
            hidden_states_cond = hidden_states[[1], ...].repeat(instance_num + 1, 1, 1)
            hidden_states = torch.cat([hidden_states_uncond, hidden_states_cond])

        # QKV Operation of Vanilla Self-Attention or Cross-Attention
        query = attn.to_q(hidden_states)
        
        if (
            not is_cross
            and use_sa_preserve
            and timestep.item() in self.embedding
            and self.place_in_unet == "up"
        ):
            hidden_states = torch.cat((hidden_states, torch.from_numpy(self.embedding[timestep.item()]).to(hidden_states.device)), dim=1)

        if not is_cross and sa_preserve and self.place_in_unet == "up":
            self.embedding[timestep.item()] = ori_hidden_states.cpu().numpy()

        encoder_hidden_states = (
            encoder_hidden_states
            if encoder_hidden_states is not None
            else hidden_states
        )
        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)
        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)
        attention_probs = attn.get_attention_scores(query, key, attention_mask)  # 48 4096 77
        self.attnstore(attention_probs, is_cross, self.place_in_unet)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)
        hidden_states = attn.to_out[0](hidden_states)
        hidden_states = attn.to_out[1](hidden_states)

        ###### Self-Attention Results ######
        if not is_cross:  
            return hidden_states

        ###### Vanilla Cross-Attention Results ######
        if is_vanilla_cross:
            return hidden_states
        
        ###### Cross-Attention with MIGC ######
        assert (not is_vanilla_cross)
        # hidden_states: torch.Size([1+1+instance_num, HW, C]), the first 1 is the uncond ca output, the second 1 is the global ca output.
        hidden_states_uncond = hidden_states[[0], ...]  # torch.Size([1, HW, C])
        cond_ca_output = hidden_states[1: , ...].unsqueeze(0)  # torch.Size([1, 1+instance_num, 5, 64, 1280])
        guidance_masks = []
        in_box = []
        # Construct Instance Guidance Mask
        for bbox in bboxes[0]:  
            guidance_mask = np.zeros((height, width))
            w_min = int(width * bbox[0])
            w_max = int(width * bbox[2])
            h_min = int(height * bbox[1])
            h_max = int(height * bbox[3])
            guidance_mask[h_min: h_max, w_min: w_max] = 1.0
            guidance_masks.append(guidance_mask[None, ...])
            in_box.append([bbox[0], bbox[2], bbox[1], bbox[3]])
        
        # Construct Background Guidance Mask
        sup_mask = get_sup_mask(guidance_masks)
        supplement_mask = torch.from_numpy(sup_mask[None, ...])
        supplement_mask = F.interpolate(supplement_mask, (height//8, width//8), mode='bilinear').float()
        supplement_mask = supplement_mask.to(hidden_states.device)  # (1, 1, H, W)
        
        guidance_masks = np.concatenate(guidance_masks, axis=0)
        guidance_masks = guidance_masks[None, ...]
        guidance_masks = torch.from_numpy(guidance_masks).float().to(cond_ca_output.device)
        guidance_masks = F.interpolate(guidance_masks, (height//8, width//8), mode='bilinear')  # (1, instance_num, H, W)

        in_box = torch.from_numpy(np.array(in_box))[None, ...].float().to(cond_ca_output.device)  # (1, instance_num, 4)

        other_info = {}
        other_info['image_token'] = hidden_states_cond[None, ...]
        other_info['context'] = encoder_hidden_states[1:, ...]
        other_info['box'] = in_box
        other_info['context_pooler'] =embeds_pooler  # (instance_num, 1, 768)
        other_info['supplement_mask'] = supplement_mask
        other_info['attn2'] = None
        other_info['attn'] = attn
        other_info['height'] = height
        other_info['width'] = width
        other_info['ca_scale'] = ca_scale
        other_info['ea_scale'] = ea_scale
        other_info['sac_scale'] = sac_scale

        if not not_use_migc:
            hidden_states_cond, fuser_info = self.migc(cond_ca_output,
                                            guidance_masks,
                                            other_info=other_info,
                                            return_fuser_info=True)
        else:
            hidden_states_cond, fuser_info = self.naive_fuser(cond_ca_output,
                                            guidance_masks,
                                            other_info=other_info,
                                            return_fuser_info=True)
        hidden_states_cond = hidden_states_cond.squeeze(1)

        hidden_states = torch.cat([hidden_states_uncond, hidden_states_cond])
        return hidden_states


class StableDiffusionMIGCPipeline(StableDiffusionPipeline):
    def __init__(
            self,
            vae: AutoencoderKL,
            text_encoder: CLIPTextModel,
            tokenizer: CLIPTokenizer,
            unet: UNet2DConditionModel,
            scheduler: KarrasDiffusionSchedulers,
            safety_checker: StableDiffusionSafetyChecker,
            feature_extractor: CLIPImageProcessor,
            image_encoder: CLIPVisionModelWithProjection = None,
            requires_safety_checker: bool = True,
    ):
        # Get the parameter signature of the parent class constructor
        parent_init_signature = inspect.signature(super().__init__)
        parent_init_params = parent_init_signature.parameters
        
        # Dynamically build a parameter dictionary based on the parameters of the parent class constructor
        init_kwargs = {
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "unet": unet,
            "scheduler": scheduler,
            "safety_checker": safety_checker,
            "feature_extractor": feature_extractor,
            "requires_safety_checker": requires_safety_checker
        }
        if 'image_encoder' in parent_init_params.items():
            init_kwargs['image_encoder'] = image_encoder
        super().__init__(**init_kwargs)
        
        self.instance_set = set()
        self.embedding = {}

    def _encode_prompt(
            self,
            prompts,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt=None,
            prompt_embeds: Optional[torch.FloatTensor] = None,
            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
             prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            device: (`torch.device`):
                torch device
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`):
                whether to use classifier free guidance or not
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
        """
        if prompts is not None and isinstance(prompts, str):
            batch_size = 1
        elif prompts is not None and isinstance(prompts, list):
            batch_size = len(prompts)
        else:
            batch_size = prompt_embeds.shape[0]

        prompt_embeds_none_flag = (prompt_embeds is None)
        prompt_embeds_list = []
        embeds_pooler_list = []
        for prompt in prompts:
            if prompt_embeds_none_flag:
                # textual inversion: procecss multi-vector tokens if necessary
                if isinstance(self, TextualInversionLoaderMixin):
                    prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

                text_inputs = self.tokenizer(
                    prompt,
                    padding="max_length",
                    max_length=self.tokenizer.model_max_length,
                    truncation=True,
                    return_tensors="pt",
                )
                text_input_ids = text_inputs.input_ids
                untruncated_ids = self.tokenizer(
                    prompt, padding="longest", return_tensors="pt"
                ).input_ids

                if untruncated_ids.shape[-1] >= text_input_ids.shape[
                    -1
                ] and not torch.equal(text_input_ids, untruncated_ids):
                    removed_text = self.tokenizer.batch_decode(
                        untruncated_ids[:, self.tokenizer.model_max_length - 1: -1]
                    )
                    logger.warning(
                        "The following part of your input was truncated because CLIP can only handle sequences up to"
                        f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                    )

                if (
                        hasattr(self.text_encoder.config, "use_attention_mask")
                        and self.text_encoder.config.use_attention_mask
                ):
                    attention_mask = text_inputs.attention_mask.to(device)
                else:
                    attention_mask = None

                prompt_embeds = self.text_encoder(
                    text_input_ids.to(device),
                    attention_mask=attention_mask,
                )
                embeds_pooler = prompt_embeds.pooler_output
                prompt_embeds = prompt_embeds[0]

            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
            embeds_pooler = embeds_pooler.to(dtype=self.text_encoder.dtype, device=device)

            bs_embed, seq_len, _ = prompt_embeds.shape
            # duplicate text embeddings for each generation per prompt, using mps friendly method
            prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
            embeds_pooler = embeds_pooler.repeat(1, num_images_per_prompt)
            prompt_embeds = prompt_embeds.view(
                bs_embed * num_images_per_prompt, seq_len, -1
            )
            embeds_pooler = embeds_pooler.view(
                bs_embed * num_images_per_prompt, -1
            )
            prompt_embeds_list.append(prompt_embeds)
            embeds_pooler_list.append(embeds_pooler)
        prompt_embeds = torch.cat(prompt_embeds_list, dim=0)
        embeds_pooler = torch.cat(embeds_pooler_list, dim=0)
        # negative_prompt_embeds: (prompt_nums[0]+prompt_nums[1]+...prompt_nums[n], token_num, token_channel), <class 'torch.Tensor'>

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                negative_prompt = "worst quality, low quality, bad anatomy"
            uncond_tokens = [negative_prompt] * batch_size

            # textual inversion: procecss multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )

            if (
                    hasattr(self.text_encoder.config, "use_attention_mask")
                    and self.text_encoder.config.use_attention_mask
            ):
                attention_mask = uncond_input.attention_mask.to(device)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(
                dtype=self.text_encoder.dtype, device=device
            )

            negative_prompt_embeds = negative_prompt_embeds.repeat(
                1, num_images_per_prompt, 1
            )
            negative_prompt_embeds = negative_prompt_embeds.view(
                batch_size * num_images_per_prompt, seq_len, -1
            )
            # negative_prompt_embeds: (len(prompt_nums), token_num, token_channel), <class 'torch.Tensor'>

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            final_prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        return final_prompt_embeds, prompt_embeds, embeds_pooler[:, None, :]

    def check_inputs(
            self,
            prompt,
            token_indices,
            bboxes,
            height,
            width,
            callback_steps,
            negative_prompt=None,
            prompt_embeds=None,
            negative_prompt_embeds=None,
    ):
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(
                f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
            )

        if (callback_steps is None) or (
                callback_steps is not None
                and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (
                not isinstance(prompt, str) and not isinstance(prompt, list)
        ):
            raise ValueError(
                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
            )

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

        if token_indices is not None:
            if isinstance(token_indices, list):
                if isinstance(token_indices[0], list):
                    if isinstance(token_indices[0][0], list):
                        token_indices_batch_size = len(token_indices)
                    elif isinstance(token_indices[0][0], int):
                        token_indices_batch_size = 1
                    else:
                        raise TypeError(
                            "`token_indices` must be a list of lists of integers or a list of integers."
                        )
                else:
                    raise TypeError(
                        "`token_indices` must be a list of lists of integers or a list of integers."
                    )
            else:
                raise TypeError(
                    "`token_indices` must be a list of lists of integers or a list of integers."
                )

        if bboxes is not None:
            if isinstance(bboxes, list):
                if isinstance(bboxes[0], list):
                    if (
                            isinstance(bboxes[0][0], list)
                            and len(bboxes[0][0]) == 4
                            and all(isinstance(x, float) for x in bboxes[0][0])
                    ):
                        bboxes_batch_size = len(bboxes)
                    elif (
                            isinstance(bboxes[0], list)
                            and len(bboxes[0]) == 4
                            and all(isinstance(x, float) for x in bboxes[0])
                    ):
                        bboxes_batch_size = 1
                    else:
                        print(isinstance(bboxes[0], list), len(bboxes[0]))
                        raise TypeError(
                            "`bboxes` must be a list of lists of list with four floats or a list of tuples with four floats."
                        )
                else:
                    print(isinstance(bboxes[0], list), len(bboxes[0]))
                    raise TypeError(
                        "`bboxes` must be a list of lists of list with four floats or a list of tuples with four floats."
                    )
            else:
                print(isinstance(bboxes[0], list), len(bboxes[0]))
                raise TypeError(
                    "`bboxes` must be a list of lists of list with four floats or a list of tuples with four floats."
                )

        if prompt is not None and isinstance(prompt, str):
            prompt_batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            prompt_batch_size = len(prompt)
        elif prompt_embeds is not None:
            prompt_batch_size = prompt_embeds.shape[0]

        if token_indices_batch_size != prompt_batch_size:
            raise ValueError(
                f"token indices batch size must be same as prompt batch size. token indices batch size: {token_indices_batch_size}, prompt batch size: {prompt_batch_size}"
            )

        if bboxes_batch_size != prompt_batch_size:
            raise ValueError(
                f"bbox batch size must be same as prompt batch size. bbox batch size: {bboxes_batch_size}, prompt batch size: {prompt_batch_size}"
            )

    def get_indices(self, prompt: str) -> Dict[str, int]:
        """Utility function to list the indices of the tokens you wish to alte"""
        ids = self.tokenizer(prompt).input_ids
        indices = {
            i: tok
            for tok, i in zip(
                self.tokenizer.convert_ids_to_tokens(ids), range(len(ids))
            )
        }
        return indices

    @staticmethod
    def draw_box(pil_img: Image, bboxes: List[List[float]]) -> Image:
        """Utility function to draw bbox on the image"""
        width, height = pil_img.size
        draw = ImageDraw.Draw(pil_img)

        for obj_box in bboxes:
            x_min, y_min, x_max, y_max = (
                obj_box[0] * width,
                obj_box[1] * height,
                obj_box[2] * width,
                obj_box[3] * height,
            )
            draw.rectangle(
                [int(x_min), int(y_min), int(x_max), int(y_max)],
                outline="red",
                width=4,
            )

        return pil_img


    @staticmethod
    def draw_box_desc(pil_img: Image, bboxes: List[List[float]], prompt: List[str]) -> Image:
        """Utility function to draw bbox on the image"""
        color_list = ['red', 'blue', 'yellow', 'purple', 'green', 'black', 'brown', 'orange', 'white', 'gray']
        width, height = pil_img.size
        draw = ImageDraw.Draw(pil_img)
        font_folder = os.path.dirname(os.path.dirname(__file__))
        font_path = os.path.join(font_folder, 'Rainbow-Party-2.ttf')
        font = ImageFont.truetype(font_path, 30)

        for box_id in range(len(bboxes)):
            obj_box = bboxes[box_id]
            text = prompt[box_id]
            fill = 'black'
            for color in prompt[box_id].split(' '):
                if color in color_list:
                    fill = color
            text = text.split(',')[0]
            x_min, y_min, x_max, y_max = (
                obj_box[0] * width,
                obj_box[1] * height,
                obj_box[2] * width,
                obj_box[3] * height,
            )
            draw.rectangle(
                [int(x_min), int(y_min), int(x_max), int(y_max)],
                outline=fill,
                width=4,
            )
            draw.text((int(x_min), int(y_min)), text, fill=fill, font=font)

        return pil_img


    @torch.no_grad()
    def __call__(
            self,
            prompt: List[List[str]] = None,
            bboxes: List[List[List[float]]] = None,
            height: Optional[int] = None,
            width: Optional[int] = None,
            num_inference_steps: int = 50,
            guidance_scale: float = 7.5,
            negative_prompt: Optional[Union[str, List[str]]] = None,
            num_images_per_prompt: Optional[int] = 1,
            eta: float = 0.0,
            generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
            latents: Optional[torch.FloatTensor] = None,
            prompt_embeds: Optional[torch.FloatTensor] = None,
            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
            output_type: Optional[str] = "pil",
            return_dict: bool = True,
            callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
            callback_steps: int = 1,
            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
            MIGCsteps=20,
            NaiveFuserSteps=-1,
            ca_scale=None,
            ea_scale=None,
            sac_scale=None,
            aug_phase_with_and=False,
            sa_preserve=False,
            use_sa_preserve=False,
            clear_set=False,
            GUI_progress=None
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            token_indices (Union[List[List[List[int]]], List[List[int]]], optional):
                The list of the indexes in the prompt to layout. Defaults to None.
            bboxes (Union[List[List[List[float]]], List[List[float]]], optional):
                The bounding boxes of the indexes to maintain layout in the image. Defaults to None.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
            max_guidance_iter (`int`, *optional*, defaults to `10`):
                The maximum number of iterations for the layout guidance on attention maps in diffusion mode.
            max_guidance_iter_per_step (`int`, *optional*, defaults to `5`):
                The maximum number of iterations to run during each time step for layout guidance.
            scale_factor (`int`, *optional*, defaults to `50`):
                The scale factor used to update the latents during optimization.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """
        def aug_phase_with_and_function(phase, instance_num):
            instance_num = min(instance_num, 7)
            copy_phase = [phase] * instance_num
            phase = ', and '.join(copy_phase)
            return phase

        if aug_phase_with_and:
            instance_num = len(prompt[0]) - 1
            for i in range(1, len(prompt[0])):
                prompt[0][i] = aug_phase_with_and_function(prompt[0][i],
                                                            instance_num)
        # 0. Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        prompt_nums = [0] * len(prompt)
        for i, _ in enumerate(prompt):
            prompt_nums[i] = len(_)

        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        prompt_embeds, cond_prompt_embeds, embeds_pooler = self._encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )
        # print(prompt_embeds.shape)  3 77 768

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        
        if clear_set:
            self.instance_set = set()
            self.embedding = {}

        now_set = set()
        for i in range(len(bboxes[0])):
            now_set.add((tuple(bboxes[0][i]), prompt[0][i + 1]))

        mask_set = (now_set | self.instance_set) - (now_set & self.instance_set)
        self.instance_set = now_set

        guidance_mask = np.full((4, height // 8, width // 8), 1.0)
                
        for bbox, _ in mask_set:
            w_min = max(0, int(width * bbox[0] // 8) - 5)
            w_max = min(width, int(width * bbox[2] // 8) + 5)
            h_min = max(0, int(height * bbox[1] // 8) - 5)
            h_max = min(height, int(height * bbox[3] // 8) + 5)
            guidance_mask[:, h_min:h_max, w_min:w_max] = 0
        
        kernal_size = 5
        guidance_mask = uniform_filter(
            guidance_mask, axes = (1, 2), size = kernal_size
        )
        
        guidance_mask = torch.from_numpy(guidance_mask).to(self.device).unsqueeze(0)

        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if GUI_progress is not None:
                    GUI_progress[0] = int((i + 1) / len(timesteps) * 100)
                # expand the latents if we are doing classifier free guidance
                latent_model_input = (
                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                )
                latent_model_input = self.scheduler.scale_model_input(
                    latent_model_input, t
                )

                # predict the noise residual
                cross_attention_kwargs = {'prompt_nums': prompt_nums,
                                          'bboxes': bboxes,
                                          'ith': i,
                                          'embeds_pooler': embeds_pooler,
                                          'timestep': t,
                                          'height': height,
                                          'width': width,
                                          'MIGCsteps': MIGCsteps,
                                          'NaiveFuserSteps': NaiveFuserSteps,
                                          'ca_scale': ca_scale,
                                          'ea_scale': ea_scale,
                                          'sac_scale': sac_scale,
                                          'sa_preserve': sa_preserve,
                                          'use_sa_preserve': use_sa_preserve}
                
                self.unet.eval()
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                ).sample

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (
                            noise_pred_text - noise_pred_uncond
                    )

                step_output = self.scheduler.step(
                    noise_pred, t, latents, **extra_step_kwargs
                )
                latents = step_output.prev_sample

                ori_input = latents.detach().clone()
                if use_sa_preserve and i in self.embedding:
                    latents = (
                            latents * (1.0 - guidance_mask)
                            + torch.from_numpy(self.embedding[i]).to(latents.device) * guidance_mask
                        ).float()
                
                if sa_preserve:
                    self.embedding[i] = ori_input.cpu().numpy()
        
                # call the callback, if provided
                if i == len(timesteps) - 1 or (
                        (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
                ):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        if output_type == "latent":
            image = latents
        elif output_type == "pil":
            # 8. Post-processing
            image = self.decode_latents(latents)
            image = self.numpy_to_pil(image)
        else:
            # 8. Post-processing
            image = self.decode_latents(latents)

        # Offload last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image, None)

        return StableDiffusionPipelineOutput(
            images=image, nsfw_content_detected=None
        )