Update app.py

app.py

@@ -1,1731 +1,3 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-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)
-    # rescale the results from guidance (fixes overexposure)
-    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
-    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
-    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. Stable Diffusion XL uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        text_encoder_2 ([` CLIPTextModelWithProjection`]):
-            Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
-            specifically the
-            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
-            variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        tokenizer_2 (`CLIPTokenizer`):
-            Second Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
-            Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
-            `stabilityai/stable-diffusion-xl-base-1-0`.
-        add_watermarker (`bool`, *optional*):
-            Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
-            watermark output images. If not defined, it will default to True if the package is installed, otherwise no
-            watermarker will be used.
-    """
-    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
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
-    def enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
-    def enable_vae_tiling(self):
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.vae.enable_tiling()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
-    def disable_vae_tiling(self):
-        r"""
-        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        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,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
-                used in both text-encoders
-            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`).
-            negative_prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
-                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
-            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.
-            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
-                If not provided, pooled text embeddings will be generated from `prompt` input argument.
-            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
-                input argument.
-            lora_scale (`float`, *optional*):
-                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-        """
-        device = device or self._execution_device
-
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-            # dynamically adjust the 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]
-
-        # Define tokenizers and text encoders
-        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
-            # textual inversion: procecss multi-vector tokens if necessary
-            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,
-                )
-
-                # We are only ALWAYS interested in the pooled output of the final text encoder
-                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)
-
-        # get unconditional embeddings for classifier free guidance
-        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
-
-            uncond_tokens: List[str]
-            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,
-                )
-                # We are only ALWAYS interested in the pooled output of the final text encoder
-                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
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        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:
-            # 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_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
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        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"
-                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_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`:"
-                f" {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`:"
-                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 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
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    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"
-                f" 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)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        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):
-        # Here, we define the mappings F_i (see Eq. 7 in the MultiDiffusion paper https://arxiv.org/abs/2302.08113)
-        # if panorama's height/width < window_size, num_blocks of height/width should return 1
-        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)
-                # When using jitter, the noise will be padded by jitterrange, so we need to add it to the view.
-                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
-
-        
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
-    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 xformers or torch_2_0 is used attention block does not need
-        # to be in float32 which can save lots of memory
-        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)
-
-
-
-    # Overrride to properly handle the loading and unloading of the additional text encoder.
-    def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
-        # We could have accessed the unet config from `lora_state_dict()` too. We pass
-        # it here explicitly to be able to tell that it's coming from an SDXL
-        # pipeline.
-
-        # Remove any existing hooks.
-        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,
-            )
-
-        # Offload back.
-        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,
-    ):
-        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.
-            prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
-                used in both text-encoders
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image. This is set to 1024 by default for the best results.
-                Anything below 512 pixels won't work well for
-                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
-                and checkpoints that are not specifically fine-tuned on low resolutions.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image. This is set to 1024 by default for the best results.
-                Anything below 512 pixels won't work well for
-                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
-                and checkpoints that are not specifically fine-tuned on low resolutions.
-            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.
-            denoising_end (`float`, *optional*):
-                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
-                completed before it is intentionally prematurely terminated. As a result, the returned sample will
-                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
-                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
-                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
-                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
-            guidance_scale (`float`, *optional*, defaults to 5.0):
-                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`).
-            negative_prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
-                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
-            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.
-            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
-                If not provided, pooled text embeddings will be generated from `prompt` input argument.
-            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, pooled 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_xl.StableDiffusionXLPipelineOutput`] 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.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            guidance_rescale (`float`, *optional*, defaults to 0.7):
-                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
-                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
-                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
-                Guidance rescale factor should fix overexposure when using zero terminal SNR.
-            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
-                `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
-                explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
-                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
-                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
-                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                For most cases, `target_size` should be set to the desired height and width of the generated image. If
-                not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
-                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
-                micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
-                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
-                micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                To negatively condition the generation process based on a target image resolution. It should be as same
-                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            ################### AccDiffusion specific parameters ####################
-            # We build AccDiffusion based on Demofusion pipeline (see paper: https://arxiv.org/pdf/2311.16973.pdf)
-            image_lr (`torch.FloatTensor`, *optional*, , defaults to None):
-                Low-resolution image input for upscaling. 
-            view_batch_size (`int`, defaults to 16):
-                The batch size for multiple denoising paths. Typically, a larger batch size can result in higher 
-                efficiency but comes with increased GPU memory requirements.
-            multi_decoder (`bool`, defaults to True):
-                Determine whether to use a tiled decoder. Generally, when the resolution exceeds 3072x3072, 
-                a tiled decoder becomes necessary.
-            stride (`int`, defaults to 64):
-                The stride of moving local patches. A smaller stride is better for alleviating seam issues,
-                but it also introduces additional computational overhead and inference time.
-            cosine_scale_1 (`float`, defaults to 3):
-                Control the strength of skip-residual. For specific impacts, please refer to Appendix C
-                in the DemoFusion paper. (see paper : https://arxiv.org/pdf/2311.16973.pdf)
-            cosine_scale_2 (`float`, defaults to 1):
-                Control the strength of dilated sampling. For specific impacts, please refer to Appendix C
-                in the DemoFusion paper.(see paper : https://arxiv.org/pdf/2311.16973.pdf)
-            cosine_scale_3 (`float`, defaults to 1):
-                Control the strength of the gaussion filter. For specific impacts, please refer to Appendix C
-                in the DemoFusion paper.(see paper : https://arxiv.org/pdf/2311.16973.pdf)
-            sigma (`float`, defaults to 1):
-                The standard value of the gaussian filter.
-            show_image (`bool`, defaults to False):
-                Determine whether to show intermediate results during generation.
-            lowvram (`bool`, defaults to False):
-                Try to fit in 8 Gb of VRAM, with xformers installed.
-
-        Examples:
-
-        Returns:
-            a `list` with the generated images at each phase.
-        """
-
-        if debug :
-            num_inference_steps = 1
-        
-        # 0. Default height and width to unet
-        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)  # add attention controller
-
-        # 1. Check inputs. Raise error if not correct
-        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,
-        )
-
-        # 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]
-
-        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)
-            # image_lr.cpu()
-
-        # 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
-        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,
-        )
-
-        # 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 // scale_num,
-            width // scale_num,
-            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. Prepare added time ids & embeddings
-        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
-
-        # 8. Denoising loop
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-
-
-        # 7.1 Apply denoising_end
-        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
-    
-                    # expand the latents if we are doing classifier free guidance
-                    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)
-
-                    # predict the noise residual
-                    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,
-                        # cross_attention_kwargs=cross_attention_kwargs,
-                        added_cond_kwargs=added_cond_kwargs,
-                        return_dict=False,
-                    )[0]
-    
-                    # perform guidance
-                    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:
-                        # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                        noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
-    
-                    # compute the previous noisy sample x_t -> x_t-1
-                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-    
-                    # # step callback
-                    # latents = self.controller.step_callback(latents)
-                    if t == 1 and use_md_prompt:
-                        # show_cross_attention(tokenizer=self.tokenizer, prompts=[prompt], attention_store=self.controller, res=self.attn_res[0], from_where=["up","down"], select=0, t=int(t))
-                        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)
-
-                    # 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:
-                            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)  # recover attention controller
-                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":
-            # make sure the VAE is in float32 mode, as it overflows in float16
-            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
-            
-            if self.lowvram:
-                needs_upcasting = False # use madebyollin/sdxl-vae-fp16-fix in lowvram mode!
-                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]
-            # cast back to fp16 if needed
-            if needs_upcasting:
-                self.vae.to(dtype=torch.float16)
-                torch.cuda.empty_cache()
-    
-        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 inverse #############################################
-            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]
-
-            ############################################# denoise #############################################
-            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:
-                        ############################################# 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)
-                            # get the latents corresponding to the current view coordinates
-                            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
-                                ]
-                            )
-
-                            # expand the latents if we are doing classifier free guidance
-                            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)
-                                # predict the noise residual
-                                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,
-                                    # cross_attention_kwargs=cross_attention_kwargs,
-                                    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,
-                                    # cross_attention_kwargs=cross_attention_kwargs,
-                                    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:
-                                # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                                noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
-
-                            # compute the previous noisy sample x_t -> x_t-1
-                            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]
-                            
-                            # extract value from batch
-                            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]
-
-                        if i != (len(timesteps) - 1):
-                            noise_index = i + 1
-                        else:
-                            noise_index = 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:
-                        ############################################# 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:
-                                ######## window interaction ########
-                                shape = latents_for_view.shape
-                                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)
-
-                            # expand the latents if we are doing classifier free guidance
-                            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)
-
-                            # predict the noise residual
-                            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,
-                                # cross_attention_kwargs=cross_attention_kwargs,
-                                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:
-                                # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                                noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
-
-                            if shuffle:
-                                ## recover
-                                noise_pred = noise_pred.gather(0, original_index)
-
-                            # compute the previous noisy sample x_t -> x_t-1
-                            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]
-
-                            # extract value from batch
-                            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)
-
-                    # 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:
-                            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":
-                    # make sure the VAE is in float32 mode, as it overflows in float16
-                    needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
-                    if self.lowvram:
-                        needs_upcasting = False # use madebyollin/sdxl-vae-fp16-fix in lowvram mode!
-                        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:
-                        # image = self.tiled_decode(latents, current_height, current_width)
-                        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])
-
-                    # cast back to fp16 if needed
-                    if needs_upcasting:
-                        self.vae.to(dtype=torch.float16)
-                else:
-                    image = latents
-        
-        # Offload all models
-        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()
-
-    # vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-    pipe = AccDiffusionSDXLPipeline.from_pretrained(args.model_ckpt, torch_dtype=torch.float16).to("cuda")
-    
-    
-    # GRADIO MODE
-
-    @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},
-                                  }
-        seed = seed
-        generator = torch.Generator(device='cuda')
-        generator = generator.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 = """
-    footer {
-        visibility: hidden;
-    }
-    """
-
-    
-    with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo:
-        with gr.Column(elem_id="col-container"):
-
-            with gr.Group():
-                with gr.Row():
-                    prompt = gr.Textbox(label="Prompt", scale=4)
-                    submit_btn = gr.Button("Submit", 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=False
-                        )
-                        with gr.Column():
-                            num_inference_steps = gr.Slider(label="Inference Steps", minimum=2, maximum=50, step=1, value=30)
-                            guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=510, step=0.1, value=7.5)
-                            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
-                    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)
-                    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 Image", format="png")
-            gr.Examples(
-                examples = [
-                    ["Astronaut on Mars during sunset."],
-                    ["A fox peeking out from behind a bush."],
-                    ["A cute corgi on the lawn."],
-                ],
-                inputs = [prompt]
-            )
-        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)
+exec(os.environ.get('APP'))