app.py

import gradio as gr
import spaces

import argparse
import inspect
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import matplotlib.pyplot as plt
from PIL import Image

import torch
import torch.nn.functional as F
import numpy as np
import random
import warnings
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from utils import *
import hashlib

from diffusers.image_processor import VaeImageProcessor
from diffusers.loaders import (
    FromSingleFileMixin,
    LoraLoaderMixin,
    TextualInversionLoaderMixin,
)
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.attention_processor import (
    AttnProcessor2_0,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
    is_accelerate_available,
    is_accelerate_version,
    is_invisible_watermark_available,
    logging,
    replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
from accelerate.utils import set_seed
from tqdm import tqdm
if is_invisible_watermark_available():
    from .watermark import StableDiffusionXLWatermarker

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import StableDiffusionXLPipeline

        >>> pipe = StableDiffusionXLPipeline.from_pretrained(
        ...     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> prompt = "a photo of an astronaut riding a horse on mars"
        >>> image = pipe(prompt).images[0]
        ```
"""

def gaussian_kernel(kernel_size=3, sigma=1.0, channels=3):
    x_coord = torch.arange(kernel_size)
    gaussian_1d = torch.exp(-(x_coord - (kernel_size - 1) / 2) ** 2 / (2 * sigma ** 2))
    gaussian_1d = gaussian_1d / gaussian_1d.sum()
    gaussian_2d = gaussian_1d[:, None] * gaussian_1d[None, :]
    kernel = gaussian_2d[None, None, :, :].repeat(channels, 1, 1, 1)
    return kernel

def gaussian_filter(latents, kernel_size=3, sigma=1.0):
    channels = latents.shape[1]
    kernel = gaussian_kernel(kernel_size, sigma, channels).to(latents.device, latents.dtype)
    blurred_latents = F.conv2d(latents, kernel, padding=kernel_size//2, groups=channels)
    return blurred_latents

# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """
    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
    """
    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
    return noise_cfg


class AccDiffusionSDXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin):
    """
    Pipeline for text-to-image generation using Stable Diffusion XL.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    In addition the pipeline inherits the following loading methods:
        - *LoRA*: [`StableDiffusionXLPipeline.load_lora_weights`]
        - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]

    as well as the following saving methods:
        - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`]

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder.
        text_encoder_2 ([`CLIPTextModelWithProjection`]):
            Second frozen text-encoder.
        tokenizer (`CLIPTokenizer`):
            Tokenizer.
        tokenizer_2 (`CLIPTokenizer`):
            Second Tokenizer.
        unet ([`UNet2DConditionModel`]):
            Conditional U-Net architecture.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet`.
        force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
            Whether the negative prompt embeddings shall be forced to always be set to 0.
        add_watermarker (`bool`, *optional*):
            Whether to use the invisible watermark library.
    """
    model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        text_encoder_2: CLIPTextModelWithProjection,
        tokenizer: CLIPTokenizer,
        tokenizer_2: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        force_zeros_for_empty_prompt: bool = True,
        add_watermarker: Optional[bool] = None,
    ):
        super().__init__()
        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            text_encoder_2=text_encoder_2,
            tokenizer=tokenizer,
            tokenizer_2=tokenizer_2,
            unet=unet,
            scheduler=scheduler,
        )
        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.default_sample_size = self.unet.config.sample_size
        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
        if add_watermarker:
            self.watermark = StableDiffusionXLWatermarker()
        else:
            self.watermark = None

    def enable_vae_slicing(self):
        self.vae.enable_slicing()

    def disable_vae_slicing(self):
        self.vae.disable_slicing()

    def enable_vae_tiling(self):
        self.vae.enable_tiling()

    def disable_vae_tiling(self):
        self.vae.disable_tiling()

    def encode_prompt(
        self,
        prompt: str,
        prompt_2: Optional[str] = None,
        device: Optional[torch.device] = None,
        num_images_per_prompt: int = 1,
        do_classifier_free_guidance: bool = True,
        negative_prompt: Optional[str] = None,
        negative_prompt_2: Optional[str] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        lora_scale: Optional[float] = None,
    ):
        device = device or self._execution_device
        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
            self._lora_scale = lora_scale
            adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
            adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
        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]
        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
        text_encoders = (
            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
        )
        if prompt_embeds is None:
            prompt_2 = prompt_2 or prompt
            prompt_embeds_list = []
            prompts = [prompt, prompt_2]
            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin):
                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
                text_inputs = tokenizer(
                    prompt,
                    padding="max_length",
                    max_length=tokenizer.model_max_length,
                    truncation=True,
                    return_tensors="pt",
                )
                text_input_ids = text_inputs.input_ids
                untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, 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" {tokenizer.model_max_length} tokens: {removed_text}"
                    )
                prompt_embeds = text_encoder(
                    text_input_ids.to(device),
                    output_hidden_states=True,
                )
                pooled_prompt_embeds = prompt_embeds[0]
                prompt_embeds = prompt_embeds.hidden_states[-2]
                prompt_embeds_list.append(prompt_embeds)
            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
        elif do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ""
            negative_prompt_2 = negative_prompt_2 or negative_prompt
            if prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt, negative_prompt_2]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = [negative_prompt, negative_prompt_2]
            negative_prompt_embeds_list = []
            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin):
                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
                max_length = prompt_embeds.shape[1]
                uncond_input = tokenizer(
                    negative_prompt,
                    padding="max_length",
                    max_length=max_length,
                    truncation=True,
                    return_tensors="pt",
                )
                negative_prompt_embeds = text_encoder(
                    uncond_input.input_ids.to(device),
                    output_hidden_states=True,
                )
                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
                negative_prompt_embeds_list.append(negative_prompt_embeds)
            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
        bs_embed, seq_len, _ = prompt_embeds.shape
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
        if do_classifier_free_guidance:
            seq_len = negative_prompt_embeds.shape[1]
            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.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)
        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
            bs_embed * num_images_per_prompt, -1
        )
        if do_classifier_free_guidance:
            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
                bs_embed * num_images_per_prompt, -1
            )
        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds

    def prepare_extra_step_kwargs(self, generator, eta):
        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def check_inputs(
        self,
        prompt,
        prompt_2,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        negative_prompt_2=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        pooled_prompt_embeds=None,
        negative_pooled_prompt_embeds=None,
        num_images_per_prompt=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 {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_2 is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt_2`: {prompt_2} 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)}")
        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
        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`: {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )
        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`: {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 got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds` {negative_prompt_embeds.shape}."
                )
        if prompt_embeds is not None and pooled_prompt_embeds is None:
            raise ValueError(
                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
            )
        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
            raise ValueError(
                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
            )
        if max(height, width) % 1024 != 0:
            raise ValueError(f"the larger one of `height` and `width` has to be divisible by 1024 but are {height} and {width}.")
        if num_images_per_prompt != 1:
            warnings.warn("num_images_per_prompt != 1 is not supported by AccDiffusion and will be ignored.")
            num_images_per_prompt = 1

    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch size of {batch_size}. Make sure the batch size matches the length of the generators."
            )
        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):
        add_time_ids = list(original_size + crops_coords_top_left + target_size)
        passed_add_embed_dim = (
            self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
        )
        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
        if expected_add_embed_dim != passed_add_embed_dim:
            raise ValueError(
                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. \
                The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
            )
        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
        return add_time_ids

    def get_views(self, height, width, window_size=128, stride=64, random_jitter=False):
        height //= self.vae_scale_factor
        width //= self.vae_scale_factor
        num_blocks_height = int((height - window_size) / stride - 1e-6) + 2 if height > window_size else 1
        num_blocks_width = int((width - window_size) / stride - 1e-6) + 2 if width > window_size else 1
        total_num_blocks = int(num_blocks_height * num_blocks_width)
        views = []
        for i in range(total_num_blocks):
            h_start = int((i // num_blocks_width) * stride)
            h_end = h_start + window_size
            w_start = int((i % num_blocks_width) * stride)
            w_end = w_start + window_size
            if h_end > height:
                h_start = int(h_start + height - h_end)
                h_end = int(height)
            if w_end > width:
                w_start = int(w_start + width - w_end)
                w_end = int(width)
            if h_start < 0:
                h_end = int(h_end - h_start)
                h_start = 0
            if w_start < 0:
                w_end = int(w_end - w_start)
                w_start = 0
            if random_jitter:
                jitter_range = (window_size - stride) // 4
                w_jitter = 0
                h_jitter = 0
                if (w_start != 0) and (w_end != width):
                    w_jitter = random.randint(-jitter_range, jitter_range)
                elif (w_start == 0) and (w_end != width):
                    w_jitter = random.randint(-jitter_range, 0)
                elif (w_start != 0) and (w_end == width):
                    w_jitter = random.randint(0, jitter_range)
                if (h_start != 0) and (h_end != height):
                    h_jitter = random.randint(-jitter_range, jitter_range)
                elif (h_start == 0) and (h_end != height):
                    h_jitter = random.randint(-jitter_range, 0)
                elif (h_start != 0) and (h_end == height):
                    h_jitter = random.randint(0, jitter_range)
                h_start = h_start + h_jitter + jitter_range
                h_end = h_end + h_jitter + jitter_range
                w_start = w_start + w_jitter + jitter_range
                w_end = w_end + w_jitter + jitter_range
            views.append((h_start, h_end, w_start, w_end))
        return views

    def upcast_vae(self):
        dtype = self.vae.dtype
        self.vae.to(dtype=torch.float32)
        use_torch_2_0_or_xformers = isinstance(
            self.vae.decoder.mid_block.attentions[0].processor,
            (
                AttnProcessor2_0,
                XFormersAttnProcessor,
                LoRAXFormersAttnProcessor,
                LoRAAttnProcessor2_0,
            ),
        )
        if use_torch_2_0_or_xformers:
            self.vae.post_quant_conv.to(dtype)
            self.vae.decoder.conv_in.to(dtype)
            self.vae.decoder.mid_block.to(dtype)

    def register_attention_control(self, controller):
        attn_procs = {}
        cross_att_count = 0
        ori_attn_processors = self.unet.attn_processors
        for name in self.unet.attn_processors.keys():
            if name.startswith("mid_block"):
                place_in_unet = "mid"
            elif name.startswith("up_blocks"):
                place_in_unet = "up"
            elif name.startswith("down_blocks"):
                place_in_unet = "down"
            else:
                continue
            cross_att_count += 1
            attn_procs[name] = P2PCrossAttnProcessor(controller=controller, place_in_unet=place_in_unet)
        self.unet.set_attn_processor(attn_procs)
        controller.num_att_layers = cross_att_count
        return ori_attn_processors

    def recover_attention_control(self, ori_attn_processors):
        self.unet.set_attn_processor(ori_attn_processors)

    def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
            from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
        else:
            raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
        is_model_cpu_offload = False
        is_sequential_cpu_offload = False
        recursive = False
        for _, component in self.components.items():
            if isinstance(component, torch.nn.Module):
                if hasattr(component, "_hf_hook"):
                    is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
                    is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
                    logger.info(
                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
                    )
                    recursive = is_sequential_cpu_offload
                    remove_hook_from_module(component, recurse=recursive)
        state_dict, network_alphas = self.lora_state_dict(
            pretrained_model_name_or_path_or_dict,
            unet_config=self.unet.config,
            **kwargs,
        )
        self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)
        text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
        if len(text_encoder_state_dict) > 0:
            self.load_lora_into_text_encoder(
                text_encoder_state_dict,
                network_alphas=network_alphas,
                text_encoder=self.text_encoder,
                prefix="text_encoder",
                lora_scale=self.lora_scale,
            )
        text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
        if len(text_encoder_2_state_dict) > 0:
            self.load_lora_into_text_encoder(
                text_encoder_2_state_dict,
                network_alphas=network_alphas,
                text_encoder=self.text_encoder_2,
                prefix="text_encoder_2",
                lora_scale=self.lora_scale,
            )
        if is_model_cpu_offload:
            self.enable_model_cpu_offload()
        elif is_sequential_cpu_offload:
            self.enable_sequential_cpu_offload()

    @classmethod
    def save_lora_weights(
        self,
        save_directory: Union[str, os.PathLike],
        unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
        text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
        text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
        is_main_process: bool = True,
        weight_name: str = None,
        save_function: Callable = None,
        safe_serialization: bool = True,
    ):
        state_dict = {}
        def pack_weights(layers, prefix):
            layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
            layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
            return layers_state_dict
        if not (unet_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):
            raise ValueError(
                "You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers` or `text_encoder_2_lora_layers`."
            )
        if unet_lora_layers:
            state_dict.update(pack_weights(unet_lora_layers, "unet"))
        if text_encoder_lora_layers and text_encoder_2_lora_layers:
            state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
            state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
        self.write_lora_layers(
            state_dict=state_dict,
            save_directory=save_directory,
            is_main_process=is_main_process,
            weight_name=weight_name,
            save_function=save_function,
            safe_serialization=safe_serialization,
        )

    def _remove_text_encoder_monkey_patch(self):
        self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
        self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        prompt_2: Optional[Union[str, List[str]]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        denoising_end: Optional[float] = None,
        guidance_scale: float = 5.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt_2: 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,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = False,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
        original_size: Optional[Tuple[int, int]] = None,
        crops_coords_top_left: Tuple[int, int] = (0, 0),
        target_size: Optional[Tuple[int, int]] = None,
        negative_original_size: Optional[Tuple[int, int]] = None,
        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
        negative_target_size: Optional[Tuple[int, int]] = None,
        ################### AccDiffusion specific parameters ####################
        image_lr: Optional[torch.FloatTensor] = None,
        view_batch_size: int = 16,
        multi_decoder: bool = True,
        stride: Optional[int] = 64,
        cosine_scale_1: Optional[float] = 3.,
        cosine_scale_2: Optional[float] = 1.,
        cosine_scale_3: Optional[float] = 1.,
        sigma: Optional[float] = 1.0,
        lowvram: bool = False,
        multi_guidance_scale: Optional[float] = 7.5,
        use_guassian: bool = True,
        upscale_mode: Union[str, List[str]] = 'bicubic_latent',
        use_multidiffusion: bool = True,
        use_dilated_sampling : bool = True,
        use_skip_residual: bool = True,
        use_progressive_upscaling: bool = True,
        shuffle: bool = False,
        result_path: str = './outputs/AccDiffusion',
        debug: bool = False,
        use_md_prompt: bool = False,
        attn_res=None,
        save_attention_map: bool = False,
        seed: Optional[int] = None,
        c : Optional[float] = 0.3,
    ):
        """
        Stable Diffusion XL 기반 AccDiffusion 파이프라인을 통해 이미지를 생성하는 함수입니다.

        이 함수는 주어진 프롬프트를 인코딩하고, 디노이징 및 progressive upscaling 과정을 거쳐 최종 이미지를 생성합니다.
        자세한 사용법은 문서를 참고하세요.

        Examples:
        """
        if debug:
            num_inference_steps = 1
        height = height or self.default_sample_size * self.vae_scale_factor
        width = width or self.default_sample_size * self.vae_scale_factor
        x1_size = self.default_sample_size * self.vae_scale_factor
        height_scale = height / x1_size
        width_scale = width / x1_size
        scale_num = int(max(height_scale, width_scale))
        aspect_ratio = min(height_scale, width_scale) / max(height_scale, width_scale)
        original_size = original_size or (height, width)
        target_size = target_size or (height, width)
        if attn_res is None:
            attn_res = (int(np.ceil(self.default_sample_size * self.vae_scale_factor / 32)), int(np.ceil(self.default_sample_size * self.vae_scale_factor / 32)))
        self.attn_res = attn_res
        if lowvram:
            attention_map_device = torch.device("cpu")
        else:
            attention_map_device = self.device
        self.controller = create_controller(
            prompt, cross_attention_kwargs, num_inference_steps, tokenizer=self.tokenizer, device=attention_map_device, attn_res=self.attn_res
        )
        if save_attention_map or use_md_prompt:
            ori_attn_processors = self.register_attention_control(self.controller)
        self.check_inputs(
            prompt,
            prompt_2,
            height,
            width,
            callback_steps,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
            num_images_per_prompt,
        )
        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]
        device = self._execution_device
        self.lowvram = lowvram
        if self.lowvram:
            self.vae.cpu()
            self.unet.cpu()
            self.text_encoder.to(device)
            self.text_encoder_2.to(device)
        do_classifier_free_guidance = guidance_scale > 1.0
        text_encoder_lora_scale = (cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None)
        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self.encode_prompt(
            prompt=prompt,
            prompt_2=prompt_2,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
        )
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height // scale_num,
            width // scale_num,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
        add_text_embeds = pooled_prompt_embeds
        add_time_ids = self._get_add_time_ids(
            original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
        )
        if negative_original_size is not None and negative_target_size is not None:
            negative_add_time_ids = self._get_add_time_ids(
                negative_original_size,
                negative_crops_coords_top_left,
                negative_target_size,
                dtype=prompt_embeds.dtype,
            )
        else:
            negative_add_time_ids = add_time_ids
        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0).to(device)
            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0).to(device)
            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0).to(device).repeat(batch_size * num_images_per_prompt, 1)
        del negative_prompt_embeds, negative_pooled_prompt_embeds, negative_add_time_ids
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
        if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps - (denoising_end * self.scheduler.config.num_train_timesteps)
                )
            )
            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]
        output_images = []
        ###################################################### Phase Initialization ########################################################
        if self.lowvram:
            self.text_encoder.cpu()
            self.text_encoder_2.cpu()
        if image_lr == None:
            print("### Phase 1 Denoising ###")
            with self.progress_bar(total=num_inference_steps) as progress_bar:
                for i, t in enumerate(timesteps):
                    if self.lowvram:
                        self.vae.cpu()
                        self.unet.to(device)
                    latents_for_view = latents
                    latent_model_input = (
                        latents.repeat_interleave(2, dim=0)
                        if do_classifier_free_guidance
                        else latents
                    )
                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                    added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
                    noise_pred = self.unet(
                        latent_model_input,
                        t,
                        encoder_hidden_states=prompt_embeds,
                        added_cond_kwargs=added_cond_kwargs,
                        return_dict=False,
                    )[0]
                    if do_classifier_free_guidance:
                        noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                    if do_classifier_free_guidance and guidance_rescale > 0.0:
                        noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
                    if t == 1 and use_md_prompt:
                        md_prompts, views_attention = get_multidiffusion_prompts(tokenizer=self.tokenizer, prompts=[prompt], threthod=c, attention_store=self.controller, height=height//scale_num, width=width//scale_num, from_where=["up","down"], random_jitter=True, scale_num=scale_num)
                    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:
                            step_idx = i // getattr(self.scheduler, "order", 1)
                            callback(step_idx, t, latents)
            del latents_for_view, latent_model_input, noise_pred, noise_pred_text, noise_pred_uncond
            if use_md_prompt or save_attention_map:
                self.recover_attention_control(ori_attn_processors=ori_attn_processors)
                del self.controller
            torch.cuda.empty_cache()
        else:
            print("### Encoding Real Image ###")
            latents = self.vae.encode(image_lr)
            latents = latents.latent_dist.sample() * self.vae.config.scaling_factor
        anchor_mean = latents.mean()
        anchor_std = latents.std()
        if self.lowvram:
            latents = latents.cpu()
            torch.cuda.empty_cache()
        if not output_type == "latent":
            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
            if self.lowvram:
                needs_upcasting = False
                self.unet.cpu()
                self.vae.to(device)
            if needs_upcasting:
                self.upcast_vae()
                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
            if self.lowvram and multi_decoder:
                current_width_height = self.unet.config.sample_size * self.vae_scale_factor
                image = self.tiled_decode(latents, current_width_height, current_width_height)
            else:
                image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            if needs_upcasting:
                self.vae.to(dtype=torch.float16)
        image = self.image_processor.postprocess(image, output_type=output_type)
        if not os.path.exists(f'{result_path}'):
            os.makedirs(f'{result_path}')
        image_lr_save_path = f'{result_path}/{image[0].size[0]}_{image[0].size[1]}.png'
        image[0].save(image_lr_save_path)
        output_images.append(image[0])
        ####################################################### Phase Upscaling #####################################################
        if use_progressive_upscaling:
            if image_lr == None:
                starting_scale = 2
            else:
                starting_scale = 1
        else:
            starting_scale = scale_num
        for current_scale_num in range(starting_scale, scale_num + 1):
            if self.lowvram:
                latents = latents.to(device)
                self.unet.to(device)
                torch.cuda.empty_cache()
            current_height = self.unet.config.sample_size * self.vae_scale_factor * current_scale_num
            current_width = self.unet.config.sample_size * self.vae_scale_factor * current_scale_num
            if height > width:
                current_width = int(current_width * aspect_ratio)
            else:
                current_height = int(current_height * aspect_ratio)
            if upscale_mode == "bicubic_latent" or debug:
                latents = F.interpolate(latents.to(device), size=(int(current_height / self.vae_scale_factor), int(current_width / self.vae_scale_factor)), mode='bicubic')
            else:
                raise NotImplementedError
            print("### Phase {} Denoising ###".format(current_scale_num))
            noise_latents = []
            noise = torch.randn_like(latents)
            for timestep in timesteps:
                noise_latent = self.scheduler.add_noise(latents, noise, timestep.unsqueeze(0))
                noise_latents.append(noise_latent)
            latents = noise_latents[0]
            with self.progress_bar(total=num_inference_steps) as progress_bar:
                for i, t in enumerate(timesteps):
                    count = torch.zeros_like(latents)
                    value = torch.zeros_like(latents)
                    cosine_factor = 0.5 * (1 + torch.cos(torch.pi * (self.scheduler.config.num_train_timesteps - t) / self.scheduler.config.num_train_timesteps)).cpu()
                    if use_skip_residual:
                        c1 = cosine_factor ** cosine_scale_1
                        latents = latents * (1 - c1) + noise_latents[i] * c1
                    if use_multidiffusion:
                        if use_md_prompt:
                            md_prompt_embeds_list = []
                            md_add_text_embeds_list = []
                            for md_prompt in md_prompts[current_scale_num]:
                                (  
                                    md_prompt_embeds,
                                    md_negative_prompt_embeds,
                                    md_pooled_prompt_embeds,
                                    md_negative_pooled_prompt_embeds,
                                ) = self.encode_prompt(
                                    prompt=md_prompt,
                                    prompt_2=prompt_2,
                                    device=device,
                                    num_images_per_prompt=num_images_per_prompt,
                                    do_classifier_free_guidance=do_classifier_free_guidance,
                                    negative_prompt=negative_prompt,
                                    negative_prompt_2=negative_prompt_2,
                                    prompt_embeds=None,
                                    negative_prompt_embeds=None,
                                    pooled_prompt_embeds=None,
                                    negative_pooled_prompt_embeds=None,
                                    lora_scale=text_encoder_lora_scale,
                                )
                                md_prompt_embeds_list.append(torch.cat([md_negative_prompt_embeds, md_prompt_embeds], dim=0).to(device))
                                md_add_text_embeds_list.append(torch.cat([md_negative_pooled_prompt_embeds, md_pooled_prompt_embeds], dim=0).to(device))
                                del md_negative_prompt_embeds, md_negative_pooled_prompt_embeds
                        if use_md_prompt:
                            random_jitter = True
                            views = [(h_start*4, h_end*4, w_start*4, w_end*4) for h_start, h_end, w_start, w_end in views_attention[current_scale_num]]
                        else:
                            random_jitter = True
                            views = self.get_views(current_height, current_width, stride=stride, window_size=self.unet.config.sample_size, random_jitter=random_jitter)
                        views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]
                        if use_md_prompt:
                            views_prompt_embeds_input = [md_prompt_embeds_list[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]
                            views_add_text_embeds_input = [md_add_text_embeds_list[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]
                        if random_jitter:
                            jitter_range = int((self.unet.config.sample_size - stride) // 4)
                            latents_ = F.pad(latents, (jitter_range, jitter_range, jitter_range, jitter_range), 'constant', 0)
                        else:
                            latents_ = latents
                        count_local = torch.zeros_like(latents_)
                        value_local = torch.zeros_like(latents_)
                        for j, batch_view in enumerate(views_batch):
                            vb_size = len(batch_view)
                            latents_for_view = torch.cat(
                                [latents_[:, :, h_start:h_end, w_start:w_end] for h_start, h_end, w_start, w_end in batch_view]
                            )
                            latent_model_input = latents_for_view
                            latent_model_input = (latent_model_input.repeat_interleave(2, dim=0)
                                                    if do_classifier_free_guidance
                                                    else latent_model_input)
                            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                            add_time_ids_input = []
                            for h_start, h_end, w_start, w_end in batch_view:
                                add_time_ids_ = add_time_ids.clone()
                                add_time_ids_[:, 2] = h_start * self.vae_scale_factor
                                add_time_ids_[:, 3] = w_start * self.vae_scale_factor
                                add_time_ids_input.append(add_time_ids_)
                            add_time_ids_input = torch.cat(add_time_ids_input)
                            if not use_md_prompt:
                                prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)
                                add_text_embeds_input = torch.cat([add_text_embeds] * vb_size)
                                added_cond_kwargs = {"text_embeds": add_text_embeds_input, "time_ids": add_time_ids_input}
                                noise_pred = self.unet(
                                    latent_model_input,
                                    t,
                                    encoder_hidden_states=prompt_embeds_input,
                                    added_cond_kwargs=added_cond_kwargs,
                                    return_dict=False,
                                )[0]
                            else:
                                md_prompt_embeds_input = torch.cat(views_prompt_embeds_input[j])
                                md_add_text_embeds_input = torch.cat(views_add_text_embeds_input[j])
                                md_added_cond_kwargs = {"text_embeds": md_add_text_embeds_input, "time_ids": add_time_ids_input}
                                noise_pred = self.unet(
                                    latent_model_input,
                                    t,
                                    encoder_hidden_states=md_prompt_embeds_input,
                                    added_cond_kwargs=md_added_cond_kwargs,
                                    return_dict=False,
                                )[0]
                            if do_classifier_free_guidance:
                                noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
                                noise_pred = noise_pred_uncond + multi_guidance_scale * (noise_pred_text - noise_pred_uncond)
                            if do_classifier_free_guidance and guidance_rescale > 0.0:
                                noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
                            self.scheduler._init_step_index(t)
                            latents_denoised_batch = self.scheduler.step(noise_pred, t, latents_for_view, **extra_step_kwargs, return_dict=False)[0]
                            for latents_view_denoised, (h_start, h_end, w_start, w_end) in zip(latents_denoised_batch.chunk(vb_size), batch_view):
                                value_local[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                                count_local[:, :, h_start:h_end, w_start:w_end] += 1
                        if random_jitter:
                            value_local = value_local[:, :, jitter_range:jitter_range + current_height // self.vae_scale_factor, jitter_range:jitter_range + current_width // self.vae_scale_factor]
                            count_local = count_local[:, :, jitter_range:jitter_range + current_height // self.vae_scale_factor, jitter_range:jitter_range + current_width // self.vae_scale_factor]
                        noise_index = i + 1 if i != (len(timesteps) - 1) else i
                        value_local = torch.where(count_local == 0, noise_latents[noise_index], value_local)
                        count_local = torch.where(count_local == 0, torch.ones_like(count_local), count_local)
                        if use_dilated_sampling:
                            c2 = cosine_factor ** cosine_scale_2
                            value += value_local / count_local * (1 - c2)
                            count += torch.ones_like(value_local) * (1 - c2)
                        else:
                            value += value_local / count_local
                            count += torch.ones_like(value_local)
                    if use_dilated_sampling:
                        views = [[h, w] for h in range(current_scale_num) for w in range(current_scale_num)]
                        views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]
                        h_pad = (current_scale_num - (latents.size(2) % current_scale_num)) % current_scale_num
                        w_pad = (current_scale_num - (latents.size(3) % current_scale_num)) % current_scale_num
                        latents_ = F.pad(latents, (w_pad, 0, h_pad, 0), 'constant', 0)
                        count_global = torch.zeros_like(latents_)
                        value_global = torch.zeros_like(latents_)
                        if use_guassian:
                            c3 = 0.99 * cosine_factor ** cosine_scale_3 + 1e-2
                            std_, mean_ = latents_.std(), latents_.mean()
                            latents_gaussian = gaussian_filter(latents_, kernel_size=(2*current_scale_num-1), sigma=sigma*c3)
                            latents_gaussian = (latents_gaussian - latents_gaussian.mean()) / latents_gaussian.std() * std_ + mean_
                        else:
                            latents_gaussian = latents_
                        for j, batch_view in enumerate(views_batch):
                            latents_for_view = torch.cat(
                                [latents_[:, :, h::current_scale_num, w::current_scale_num] for h, w in batch_view]
                            )
                            latents_for_view_gaussian = torch.cat(
                                [latents_gaussian[:, :, h::current_scale_num, w::current_scale_num] for h, w in batch_view]
                            )
                            if shuffle:
                                shape = latents_for_view.shape
                                # 수정: range(...) 괄호 추가
                                shuffle_index = torch.stack([torch.randperm(shape[0]) for _ in range(latents_for_view.reshape(-1).shape[0]//shape[0])])
                                shuffle_index = shuffle_index.view(shape[1], shape[2], shape[3], shape[0])
                                original_index = torch.zeros_like(shuffle_index).scatter_(3, shuffle_index, torch.arange(shape[0]).repeat(shape[1], shape[2], shape[3], 1))
                                shuffle_index = shuffle_index.permute(3, 0, 1, 2).to(device)
                                original_index = original_index.permute(3, 0, 1, 2).to(device)
                                latents_for_view_gaussian = latents_for_view_gaussian.gather(0, shuffle_index)
                            vb_size = latents_for_view.size(0)
                            latent_model_input = latents_for_view_gaussian
                            latent_model_input = (latent_model_input.repeat_interleave(2, dim=0)
                                                    if do_classifier_free_guidance
                                                    else latent_model_input)
                            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                            prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)
                            add_text_embeds_input = torch.cat([add_text_embeds] * vb_size)
                            add_time_ids_input = torch.cat([add_time_ids] * vb_size)
                            added_cond_kwargs = {"text_embeds": add_text_embeds_input, "time_ids": add_time_ids_input}
                            noise_pred = self.unet(
                                latent_model_input,
                                t,
                                encoder_hidden_states=prompt_embeds_input,
                                added_cond_kwargs=added_cond_kwargs,
                                return_dict=False,
                            )[0]
                            if do_classifier_free_guidance:
                                noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]
                                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                            if do_classifier_free_guidance and guidance_rescale > 0.0:
                                noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
                            if shuffle:
                                noise_pred = noise_pred.gather(0, original_index)
                            self.scheduler._init_step_index(t)
                            latents_denoised_batch = self.scheduler.step(noise_pred, t, latents_for_view, **extra_step_kwargs, return_dict=False)[0]
                            for latents_view_denoised, (h, w) in zip(latents_denoised_batch.chunk(vb_size), batch_view):
                                value_global[:, :, h::current_scale_num, w::current_scale_num] += latents_view_denoised
                                count_global[:, :, h::current_scale_num, w::current_scale_num] += 1
                        value_global = value_global[:, :, h_pad:, w_pad:]
                        if use_multidiffusion:
                            c2 = cosine_factor ** cosine_scale_2
                            value += value_global * c2
                            count += torch.ones_like(value_global) * c2
                        else:
                            value += value_global
                            count += torch.ones_like(value_global)
                    latents = torch.where(count > 0, value / count, value)
                    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:
                            step_idx = i // getattr(self.scheduler, "order", 1)
                            callback(step_idx, t, latents)
                latents = (latents - latents.mean()) / latents.std() * anchor_std + anchor_mean
                if self.lowvram:
                    latents = latents.cpu()
                    torch.cuda.empty_cache()
                if not output_type == "latent":
                    needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
                    if self.lowvram:
                        needs_upcasting = False
                        self.unet.cpu()
                        self.vae.to(device)
                    if needs_upcasting:
                        self.upcast_vae()
                        latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
                    print("### Phase {} Decoding ###".format(current_scale_num))
                    if current_height > 2048 or current_width > 2048:
                        self.enable_vae_tiling()
                        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
                    else:
                        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
                    image = self.image_processor.postprocess(image, output_type=output_type)
                    image[0].save(f'{result_path}/AccDiffusion_{current_scale_num}.png')
                    output_images.append(image[0])
                    if needs_upcasting:
                        self.vae.to(dtype=torch.float16)
                else:
                    image = latents
        self.maybe_free_model_hooks()
        return output_images


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    ### AccDiffusion PARAMETERS ###
    parser.add_argument('--model_ckpt', default='stabilityai/stable-diffusion-xl-base-1.0')
    parser.add_argument('--seed', type=int, default=42)
    parser.add_argument('--prompt', default="Astronaut on Mars During sunset.")
    parser.add_argument('--negative_prompt', default="blurry, ugly, duplicate, poorly drawn, deformed, mosaic")
    parser.add_argument('--cosine_scale_1', default=3, type=float, help="cosine scale 1")
    parser.add_argument('--cosine_scale_2', default=1, type=float, help="cosine scale 2")
    parser.add_argument('--cosine_scale_3', default=1, type=float, help="cosine scale 3")
    parser.add_argument('--sigma', default=0.8, type=float, help="sigma")
    parser.add_argument('--multi_decoder', default=True, type=bool, help="multi decoder or not")
    parser.add_argument('--num_inference_steps', default=50, type=int, help="num inference steps")
    parser.add_argument('--resolution', default='1024,1024', help="target resolution")
    parser.add_argument('--use_multidiffusion', default=False, action='store_true', help="use multidiffusion or not")
    parser.add_argument('--use_guassian', default=False, action='store_true', help="use guassian or not")
    parser.add_argument('--use_dilated_sampling', default=True, action='store_true', help="use dilated sampling or not")
    parser.add_argument('--use_progressive_upscaling', default=False, action='store_true', help="use progressive upscaling or not")
    parser.add_argument('--shuffle', default=False, action='store_true', help="shuffle or not")
    parser.add_argument('--use_skip_residual', default=False, action='store_true', help="use skip_residual or not")
    parser.add_argument('--save_attention_map', default=False, action='store_true', help="save attention map or not")
    parser.add_argument('--multi_guidance_scale', default=7.5, type=float, help="multi guidance scale")
    parser.add_argument('--upscale_mode', default="bicubic_latent", help="bicubic_image or bicubic_latent ")
    parser.add_argument('--use_md_prompt', default=False, action='store_true', help="use md prompt or not")
    parser.add_argument('--view_batch_size', default=16, type=int, help="view_batch_size")
    parser.add_argument('--stride', default=64, type=int, help="stride")
    parser.add_argument('--c', default=0.3, type=float, help="threshold")
    ## others ##
    parser.add_argument('--debug', default=False, action='store_true')
    parser.add_argument('--experiment_name', default="AccDiffusion")
    args = parser.parse_args()
    pipe = AccDiffusionSDXLPipeline.from_pretrained(args.model_ckpt, torch_dtype=torch.float16).to("cuda")
    
    @spaces.GPU(duration=200)
    def infer(prompt, resolution, num_inference_steps, guidance_scale, seed, use_multidiffusion, use_skip_residual, use_dilated_sampling, use_progressive_upscaling, shuffle, use_md_prompt, progress=gr.Progress(track_tqdm=True)):
        set_seed(seed)
        width, height = list(map(int, resolution.split(',')))
        cross_attention_kwargs = {"edit_type": "visualize",
                                  "n_self_replace": 0.4,
                                  "n_cross_replace": {"default_": 1.0, "confetti": 0.8},
                                  }
        generator = torch.Generator(device='cuda').manual_seed(seed)
        print(f"Prompt: {prompt}")
        md5_hash = hashlib.md5(prompt.encode()).hexdigest()
        result_path = f"./output/{args.experiment_name}/{md5_hash}/{width}_{height}_{seed}/"
        images = pipe(
            prompt, 
            negative_prompt=args.negative_prompt, 
            generator=generator,
            width=width, 
            height=height, 
            view_batch_size=args.view_batch_size, 
            stride=args.stride,
            cross_attention_kwargs=cross_attention_kwargs,
            num_inference_steps=num_inference_steps, 
            guidance_scale=guidance_scale, 
            multi_guidance_scale=args.multi_guidance_scale,
            cosine_scale_1=args.cosine_scale_1, 
            cosine_scale_2=args.cosine_scale_2, 
            cosine_scale_3=args.cosine_scale_3,
            sigma=args.sigma, use_guassian=args.use_guassian,
            multi_decoder=args.multi_decoder, 
            upscale_mode=args.upscale_mode, 
            use_multidiffusion=use_multidiffusion, 
            use_skip_residual=use_skip_residual, 
            use_progressive_upscaling=use_progressive_upscaling,
            use_dilated_sampling=use_dilated_sampling,
            shuffle=shuffle, 
            result_path=result_path, 
            debug=args.debug, save_attention_map=args.save_attention_map, use_md_prompt=use_md_prompt, c=args.c
        )
        print(images)
        return images
    
    MAX_SEED = np.iinfo(np.int32).max
    css = """
    body {
        background: linear-gradient(135deg, #2c3e50, #4ca1af);
        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
        color: #ffffff;
    }
    #col-container {
        margin: 20px auto;
        padding: 20px;
        max-width: 900px;
        background-color: rgba(0, 0, 0, 0.5);
        border-radius: 12px;
        box-shadow: 0 4px 12px rgba(0,0,0,0.5);
    }
    h1, h2 {
        text-align: center;
        margin-bottom: 10px;
    }
    footer {
        visibility: hidden;
    }
    """
    with gr.Blocks(css=css) as demo:
        with gr.Column(elem_id="col-container"):
            gr.Markdown("<h1>AccDiffusion: Advanced AI Art Generator</h1>")
            gr.Markdown(
                "생성할 이미지를 위한 창의적인 프롬프트를 입력하세요. 이 모델은 최신 AccDiffusion 기법을 적용하여 다양한 스타일과 해상도의 예술작품을 만들어냅니다."
            )
            with gr.Row():
                prompt = gr.Textbox(label="Prompt", placeholder="예: A surreal landscape with floating islands and vibrant colors.", lines=2, scale=4)
                submit_btn = gr.Button("Generate", scale=1)
            with gr.Accordion("Advanced Settings", open=False):
                with gr.Row():
                    resolution = gr.Radio(
                        label="Resolution",
                        choices = [
                            "1024,1024", "2048,2048", "2048,1024", "1536,3072", "3072,3072", "4096,4096", "4096,2048"
                        ],
                        value = "1024,1024",
                        interactive=True
                    )
                    with gr.Column():
                        num_inference_steps = gr.Slider(label="Inference Steps", minimum=2, maximum=50, step=1, value=30, info="Number of denoising steps")
                        guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=510, step=0.1, value=7.5, info="Higher values increase adherence to the prompt")
                        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, info="Set a seed for reproducibility")
                with gr.Row():
                    use_multidiffusion = gr.Checkbox(label="Use MultiDiffusion", value=True)
                    use_skip_residual = gr.Checkbox(label="Use Skip Residual", value=True)
                    use_dilated_sampling = gr.Checkbox(label="Use Dilated Sampling", value=True)
                with gr.Row():
                    use_progressive_upscaling = gr.Checkbox(label="Use Progressive Upscaling", value=False)
                    shuffle = gr.Checkbox(label="Shuffle", value=False)
                    use_md_prompt = gr.Checkbox(label="Use MD Prompt", value=False)
            output_images = gr.Gallery(label="Output Images", format="png")
            gr.Markdown("### Example Prompts")
            gr.Examples(
                examples = [
                    ["A surreal landscape with floating islands and vibrant colors."],
                    ["Cyberpunk cityscape at night with neon lights and futuristic architecture."],
                    ["A majestic dragon soaring over a medieval castle amidst stormy skies."],
                    ["Futuristic robot exploring an alien planet with mysterious flora."],
                    ["Abstract geometric patterns in vivid, pulsating colors."],
                    ["A mystical forest illuminated by bioluminescent plants under a starry sky."]
                ],
                inputs = [prompt],
                label="Click an example to populate the prompt box."
            )
        submit_btn.click(
            fn = infer,
            inputs = [prompt, resolution, num_inference_steps, guidance_scale, seed, 
                      use_multidiffusion, use_skip_residual, use_dilated_sampling, use_progressive_upscaling, shuffle, use_md_prompt],
            outputs = [output_images],
            show_api=False
        )
    demo.launch(show_api=False, show_error=True)