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accdiffusion_sdxl.py

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+# 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 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 *
+
+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=False, 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()
+
+
+    set_seed(args.seed)
+    width,height = list(map(int, args.resolution.split(',')))
+    pipe = AccDiffusionSDXLPipeline.from_pretrained(args.model_ckpt, torch_dtype=torch.float16).to("cuda")
+    generator = torch.Generator(device='cuda')
+    generator = generator.manual_seed(args.seed)
+    cross_attention_kwargs = {"edit_type": "visualize",
+                              "n_self_replace": 0.4,
+                              "n_cross_replace": {"default_": 1.0, "confetti": 0.8},
+                              }
+    
+    if os.path.isfile(args.prompt):
+        with open(args.prompt, "r") as file:
+            prompts = file.read().strip()
+            prompts =  prompts.split("\n")
+    else:
+        prompts = [args.prompt]
+
+    seed = args.seed
+    generator = generator.manual_seed(seed)
+    for prompt in tqdm(prompts):
+        print(f"Prompt: {prompt}")
+        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=args.num_inference_steps, 
+                    guidance_scale = 7.5, 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=args.use_multidiffusion, 
+                    use_skip_residual=args.use_skip_residual, use_progressive_upscaling=args.use_progressive_upscaling,
+                    use_dilated_sampling=args.use_dilated_sampling,
+                    shuffle=args.shuffle, result_path=f"./output/{args.experiment_name}/{prompt}/{width}_{height}_{seed}/", 
+                    debug=args.debug, save_attention_map=args.save_attention_map, use_md_prompt=args.use_md_prompt, c=args.c
+                    )
+
+

requirements.txt

@@ -0,0 +1,13 @@
+diffusers~=0.21.4
+torch~=2.1.0
+scipy~=1.11.3
+omegaconf~=2.3.0
+accelerate~=0.23.0
+transformers~=4.34.0
+tqdm
+einops
+matplotlib
+gradio
+gradio_imageslider
+opencv-python
+torchvision

utils.py

@@ -0,0 +1,885 @@
+from __future__ import annotations
+import os
+import cv2
+import abc
+from typing import Dict, List, Optional, Tuple, Union
+import numpy as np
+import torch
+import torch.nn.functional as F
+from diffusers.models.attention import Attention
+from PIL import Image
+import random
+import matplotlib.pyplot as plt
+import pdb
+import math
+from PIL import Image
+
+
+
+class P2PCrossAttnProcessor:
+    def __init__(self, controller, place_in_unet):
+        super().__init__()
+        self.controller = controller
+        self.place_in_unet = place_in_unet
+
+    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
+        batch_size, sequence_length, _ = hidden_states.shape
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        query = attn.to_q(hidden_states)
+
+        is_cross = encoder_hidden_states is not None
+        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+
+        # one line change
+        self.controller(attention_probs, is_cross, self.place_in_unet)
+
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        return hidden_states
+
+
+def create_controller(
+    prompts: List[str], cross_attention_kwargs: Dict, num_inference_steps: int, tokenizer, device, attn_res
+) -> AttentionControl:
+    edit_type = cross_attention_kwargs.get("edit_type", None)
+    local_blend_words = cross_attention_kwargs.get("local_blend_words", None)
+    equalizer_words = cross_attention_kwargs.get("equalizer_words", None)
+    equalizer_strengths = cross_attention_kwargs.get("equalizer_strengths", None)
+    n_cross_replace = cross_attention_kwargs.get("n_cross_replace", 0.4)
+    n_self_replace = cross_attention_kwargs.get("n_self_replace", 0.4)
+
+    if edit_type == 'visualize':
+        return AttentionStore(device=device)
+
+    # only replace
+    if edit_type == "replace" and local_blend_words is None:
+        return AttentionReplace(
+            prompts, num_inference_steps, n_cross_replace, n_self_replace, tokenizer=tokenizer, device=device, attn_res=attn_res
+        )
+
+    # replace + localblend
+    if edit_type == "replace" and local_blend_words is not None:
+        lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)
+        return AttentionReplace(
+            prompts, num_inference_steps, n_cross_replace, n_self_replace, lb, tokenizer=tokenizer, device=device, attn_res=attn_res
+        )
+
+    # only refine
+    if edit_type == "refine" and local_blend_words is None:
+        return AttentionRefine(
+            prompts, num_inference_steps, n_cross_replace, n_self_replace, tokenizer=tokenizer, device=device, attn_res=attn_res
+        )
+
+    # refine + localblend
+    if edit_type == "refine" and local_blend_words is not None:
+        lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)
+        return AttentionRefine(
+            prompts, num_inference_steps, n_cross_replace, n_self_replace, lb, tokenizer=tokenizer, device=device, attn_res=attn_res
+        )
+
+    # only reweight
+    if edit_type == "reweight" and local_blend_words is None:
+        assert (
+            equalizer_words is not None and equalizer_strengths is not None
+        ), "To use reweight edit, please specify equalizer_words and equalizer_strengths."
+        assert len(equalizer_words) == len(
+            equalizer_strengths
+        ), "equalizer_words and equalizer_strengths must be of same length."
+        equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)
+        return AttentionReweight(
+            prompts,
+            num_inference_steps,
+            n_cross_replace,
+            n_self_replace,
+            tokenizer=tokenizer,
+            device=device,
+            equalizer=equalizer,
+            attn_res=attn_res,
+        )
+
+    # reweight and localblend
+    if edit_type == "reweight" and local_blend_words:
+        assert (
+            equalizer_words is not None and equalizer_strengths is not None
+        ), "To use reweight edit, please specify equalizer_words and equalizer_strengths."
+        assert len(equalizer_words) == len(
+            equalizer_strengths
+        ), "equalizer_words and equalizer_strengths must be of same length."
+        equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)
+        lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)
+        return AttentionReweight(
+            prompts,
+            num_inference_steps,
+            n_cross_replace,
+            n_self_replace,
+            tokenizer=tokenizer,
+            device=device,
+            equalizer=equalizer,
+            attn_res=attn_res,
+            local_blend=lb,
+        )
+
+    raise ValueError(f"Edit type {edit_type} not recognized. Use one of: replace, refine, reweight.")
+
+
+class AttentionControl(abc.ABC):
+    def step_callback(self, x_t):
+        return x_t
+
+    def between_steps(self):
+        return
+
+    @property
+    def num_uncond_att_layers(self):
+        return 0
+
+    @abc.abstractmethod
+    def forward(self, attn, is_cross: bool, place_in_unet: str):
+        raise NotImplementedError
+
+    def __call__(self, attn, is_cross: bool, place_in_unet: str):
+        if self.cur_att_layer >= self.num_uncond_att_layers:
+            h = attn.shape[0]
+            attn[h // 2 :] = self.forward(attn[h // 2 :], is_cross, place_in_unet)
+        self.cur_att_layer += 1
+        if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
+            self.cur_att_layer = 0
+            self.cur_step += 1
+            self.between_steps()
+        return attn
+
+    def reset(self):
+        self.cur_step = 0
+        self.cur_att_layer = 0
+
+    def __init__(self, attn_res=None):
+        self.cur_step = 0
+        self.num_att_layers = -1
+        self.cur_att_layer = 0
+        self.attn_res = attn_res
+
+
+
+class EmptyControl(AttentionControl):
+    def forward(self, attn, is_cross: bool, place_in_unet: str):
+        return attn
+
+
+class AttentionStore(AttentionControl):
+    @staticmethod
+    def get_empty_store():
+        return {"down_cross": [], "mid_cross": [], "up_cross": [], "down_self": [], "mid_self": [], "up_self": []}
+
+    def forward(self, attn, is_cross: bool, place_in_unet: str):
+        key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
+        if attn.shape[1] <= 32**2:  # avoid memory overhead
+            if self.device.type != 'cuda':
+                attn = attn.cpu()
+            self.step_store[key].append(attn)
+        return attn
+
+    def between_steps(self):
+        if len(self.attention_store) == 0:
+            self.attention_store = self.step_store
+        else:
+            for key in self.attention_store:
+                for i in range(len(self.attention_store[key])):
+                    self.attention_store[key][i] += self.step_store[key][i]
+        self.step_store = self.get_empty_store()
+
+    def get_average_attention(self):
+        average_attention = {
+            key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store
+        }
+        return average_attention
+
+    def reset(self):
+        super(AttentionStore, self).reset()
+        self.step_store = self.get_empty_store()
+        self.attention_store = {}
+
+    def __init__(self, attn_res=None, device='cuda'):
+        super(AttentionStore, self).__init__(attn_res)
+        self.step_store = self.get_empty_store()
+        self.attention_store = {}
+        self.device = device
+
+
+
+class LocalBlend:
+    def __call__(self, x_t, attention_store):
+        # note that this code works on the latent level!
+        k = 1
+        # maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]  # These are the numbers because we want to take layers that are 256 x 256, I think this can be changed to something smarter...like, get all attentions where thesecond dim is self.attn_res[0] * self.attn_res[1] in up and down cross.
+        maps = [m for m in attention_store["down_cross"] + attention_store["mid_cross"] +  attention_store["up_cross"] if m.shape[1] == self.attn_res[0] * self.attn_res[1]]
+        maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, self.attn_res[0], self.attn_res[1], self.max_num_words) for item in maps]
+        maps = torch.cat(maps, dim=1)
+        maps = (maps * self.alpha_layers).sum(-1).mean(1) # since alpha_layers is all 0s except where we edit, the product zeroes out all but what we change. Then, the sum adds the values of the original and what we edit. Then, we average across dim=1, which is the number of layers.
+        mask = F.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
+        mask = F.interpolate(mask, size=(x_t.shape[2:]))
+        mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
+        mask = mask.gt(self.threshold)
+
+        mask = mask[:1] + mask[1:]
+        mask = mask.to(torch.float16)
+
+        x_t = x_t[:1] + mask * (x_t - x_t[:1]) # x_t[:1] is the original image. mask*(x_t - x_t[:1]) zeroes out the original image and removes the difference between the original and each image we are generating (mostly just one). Then, it applies the mask on the image. That is, it's only keeping the cells we want to generate.
+        return x_t
+
+    def __init__(
+        self, prompts: List[str], words: [List[List[str]]], tokenizer, device, threshold=0.3, attn_res=None
+    ):
+        self.max_num_words = 77
+        self.attn_res = attn_res
+
+        alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words)
+        for i, (prompt, words_) in enumerate(zip(prompts, words)):
+            if isinstance(words_, str):
+                words_ = [words_]
+            for word in words_:
+                ind = get_word_inds(prompt, word, tokenizer)
+                alpha_layers[i, :, :, :, :, ind] = 1
+        self.alpha_layers = alpha_layers.to(device) # a one-hot vector where the 1s are the words we modify (source and target)
+        self.threshold = threshold
+
+
+class AttentionControlEdit(AttentionStore, abc.ABC):
+    def step_callback(self, x_t):
+        if self.local_blend is not None:
+            x_t = self.local_blend(x_t, self.attention_store)
+        return x_t
+
+    def replace_self_attention(self, attn_base, att_replace):
+        if att_replace.shape[2] <= self.attn_res[0]**2:
+            return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
+        else:
+            return att_replace
+
+    @abc.abstractmethod
+    def replace_cross_attention(self, attn_base, att_replace):
+        raise NotImplementedError
+
+    def forward(self, attn, is_cross: bool, place_in_unet: str):
+        super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
+        if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
+            h = attn.shape[0] // (self.batch_size)
+            attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
+            attn_base, attn_replace = attn[0], attn[1:]
+            if is_cross:
+                alpha_words = self.cross_replace_alpha[self.cur_step]
+                attn_replace_new = (
+                    self.replace_cross_attention(attn_base, attn_replace) * alpha_words
+                    + (1 - alpha_words) * attn_replace
+                )
+                attn[1:] = attn_replace_new
+            else:
+                attn[1:] = self.replace_self_attention(attn_base, attn_replace)
+            attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
+        return attn
+
+    def __init__(
+        self,
+        prompts,
+        num_steps: int,
+        cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
+        self_replace_steps: Union[float, Tuple[float, float]],
+        local_blend: Optional[LocalBlend],
+        tokenizer,
+        device,
+        attn_res=None,
+    ):
+        super(AttentionControlEdit, self).__init__(attn_res=attn_res)
+        # add tokenizer and device here
+
+        self.tokenizer = tokenizer
+        self.device = device
+
+        self.batch_size = len(prompts)
+        self.cross_replace_alpha = get_time_words_attention_alpha(
+            prompts, num_steps, cross_replace_steps, self.tokenizer
+        ).to(self.device)
+        if isinstance(self_replace_steps, float):
+            self_replace_steps = 0, self_replace_steps
+        self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
+        self.local_blend = local_blend
+
+
+class AttentionReplace(AttentionControlEdit):
+    def replace_cross_attention(self, attn_base, att_replace):
+        return torch.einsum("hpw,bwn->bhpn", attn_base, self.mapper)
+
+    def __init__(
+        self,
+        prompts,
+        num_steps: int,
+        cross_replace_steps: float,
+        self_replace_steps: float,
+        local_blend: Optional[LocalBlend] = None,
+        tokenizer=None,
+        device=None,
+        attn_res=None,
+    ):
+        super(AttentionReplace, self).__init__(
+            prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res
+        )
+        self.mapper = get_replacement_mapper(prompts, self.tokenizer).to(self.device)
+
+
+class AttentionRefine(AttentionControlEdit):
+    def replace_cross_attention(self, attn_base, att_replace):
+        attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
+        attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
+        return attn_replace
+
+    def __init__(
+        self,
+        prompts,
+        num_steps: int,
+        cross_replace_steps: float,
+        self_replace_steps: float,
+        local_blend: Optional[LocalBlend] = None,
+        tokenizer=None,
+        device=None,
+        attn_res=None
+    ):
+        super(AttentionRefine, self).__init__(
+            prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res
+        )
+        self.mapper, alphas = get_refinement_mapper(prompts, self.tokenizer)
+        self.mapper, alphas = self.mapper.to(self.device), alphas.to(self.device)
+        self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
+
+
+class AttentionReweight(AttentionControlEdit):
+    def replace_cross_attention(self, attn_base, att_replace):
+        if self.prev_controller is not None:
+            attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
+        attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
+        return attn_replace
+
+    def __init__(
+        self,
+        prompts,
+        num_steps: int,
+        cross_replace_steps: float,
+        self_replace_steps: float,
+        equalizer,
+        local_blend: Optional[LocalBlend] = None,
+        controller: Optional[AttentionControlEdit] = None,
+        tokenizer=None,
+        device=None,
+        attn_res=None,
+    ):
+        super(AttentionReweight, self).__init__(
+            prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res
+        )
+        self.equalizer = equalizer.to(self.device)
+        self.prev_controller = controller
+
+
+### util functions for all Edits
+def update_alpha_time_word(
+    alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int, word_inds: Optional[torch.Tensor] = None
+):
+    if isinstance(bounds, float):
+        bounds = 0, bounds
+    start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
+    if word_inds is None:
+        word_inds = torch.arange(alpha.shape[2])
+    alpha[:start, prompt_ind, word_inds] = 0
+    alpha[start:end, prompt_ind, word_inds] = 1
+    alpha[end:, prompt_ind, word_inds] = 0
+    return alpha
+
+
+def get_time_words_attention_alpha(
+    prompts, num_steps, cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]], tokenizer, max_num_words=77
+):
+    if not isinstance(cross_replace_steps, dict):
+        cross_replace_steps = {"default_": cross_replace_steps}
+    if "default_" not in cross_replace_steps:
+        cross_replace_steps["default_"] = (0.0, 1.0)
+    alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)
+    for i in range(len(prompts) - 1):
+        alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"], i)
+    for key, item in cross_replace_steps.items():
+        if key != "default_":
+            inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]
+            for i, ind in enumerate(inds):
+                if len(ind) > 0:
+                    alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)
+    alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)
+    return alpha_time_words
+
+
+### util functions for LocalBlend and ReplacementEdit
+def get_word_inds(text: str, word_place: int, tokenizer):
+    split_text = text.split(" ")
+    if isinstance(word_place, str):
+        word_place = [i for i, word in enumerate(split_text) if word_place == word]
+    elif isinstance(word_place, int):
+        word_place = [word_place]
+    out = []
+    if len(word_place) > 0:
+        words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
+        cur_len, ptr = 0, 0
+
+        for i in range(len(words_encode)):
+            cur_len += len(words_encode[i])
+            if ptr in word_place:
+                out.append(i + 1)
+            if cur_len >= len(split_text[ptr]):
+                ptr += 1
+                cur_len = 0
+    return np.array(out)
+
+
+### util functions for ReplacementEdit
+def get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):
+    words_x = x.split(" ")
+    words_y = y.split(" ")
+    if len(words_x) != len(words_y):
+        raise ValueError(
+            f"attention replacement edit can only be applied on prompts with the same length"
+            f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words."
+        )
+    inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]
+    inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]
+    inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]
+    mapper = np.zeros((max_len, max_len))
+    i = j = 0
+    cur_inds = 0
+    while i < max_len and j < max_len:
+        if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:
+            inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]
+            if len(inds_source_) == len(inds_target_):
+                mapper[inds_source_, inds_target_] = 1
+            else:
+                ratio = 1 / len(inds_target_)
+                for i_t in inds_target_:
+                    mapper[inds_source_, i_t] = ratio
+            cur_inds += 1
+            i += len(inds_source_)
+            j += len(inds_target_)
+        elif cur_inds < len(inds_source):
+            mapper[i, j] = 1
+            i += 1
+            j += 1
+        else:
+            mapper[j, j] = 1
+            i += 1
+            j += 1
+
+    # return torch.from_numpy(mapper).float()
+    return torch.from_numpy(mapper).to(torch.float16)
+
+
+def get_replacement_mapper(prompts, tokenizer, max_len=77):
+    x_seq = prompts[0]
+    mappers = []
+    for i in range(1, len(prompts)):
+        mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)
+        mappers.append(mapper)
+    return torch.stack(mappers)
+
+
+### util functions for ReweightEdit
+def get_equalizer(
+    text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float], Tuple[float, ...]], tokenizer
+):
+    if isinstance(word_select, (int, str)):
+        word_select = (word_select,)
+    equalizer = torch.ones(len(values), 77)
+    values = torch.tensor(values, dtype=torch.float32)
+    for i, word in enumerate(word_select):
+        inds = get_word_inds(text, word, tokenizer)
+        equalizer[:, inds] = torch.FloatTensor(values[i])
+    return equalizer
+
+
+### util functions for RefinementEdit
+class ScoreParams:
+    def __init__(self, gap, match, mismatch):
+        self.gap = gap
+        self.match = match
+        self.mismatch = mismatch
+
+    def mis_match_char(self, x, y):
+        if x != y:
+            return self.mismatch
+        else:
+            return self.match
+
+
+def get_matrix(size_x, size_y, gap):
+    matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
+    matrix[0, 1:] = (np.arange(size_y) + 1) * gap
+    matrix[1:, 0] = (np.arange(size_x) + 1) * gap
+    return matrix
+
+
+def get_traceback_matrix(size_x, size_y):
+    matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
+    matrix[0, 1:] = 1
+    matrix[1:, 0] = 2
+    matrix[0, 0] = 4
+    return matrix
+
+
+def global_align(x, y, score):
+    matrix = get_matrix(len(x), len(y), score.gap)
+    trace_back = get_traceback_matrix(len(x), len(y))
+    for i in range(1, len(x) + 1):
+        for j in range(1, len(y) + 1):
+            left = matrix[i, j - 1] + score.gap
+            up = matrix[i - 1, j] + score.gap
+            diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])
+            matrix[i, j] = max(left, up, diag)
+            if matrix[i, j] == left:
+                trace_back[i, j] = 1
+            elif matrix[i, j] == up:
+                trace_back[i, j] = 2
+            else:
+                trace_back[i, j] = 3
+    return matrix, trace_back
+
+
+def get_aligned_sequences(x, y, trace_back):
+    x_seq = []
+    y_seq = []
+    i = len(x)
+    j = len(y)
+    mapper_y_to_x = []
+    while i > 0 or j > 0:
+        if trace_back[i, j] == 3:
+            x_seq.append(x[i - 1])
+            y_seq.append(y[j - 1])
+            i = i - 1
+            j = j - 1
+            mapper_y_to_x.append((j, i))
+        elif trace_back[i][j] == 1:
+            x_seq.append("-")
+            y_seq.append(y[j - 1])
+            j = j - 1
+            mapper_y_to_x.append((j, -1))
+        elif trace_back[i][j] == 2:
+            x_seq.append(x[i - 1])
+            y_seq.append("-")
+            i = i - 1
+        elif trace_back[i][j] == 4:
+            break
+    mapper_y_to_x.reverse()
+    return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)
+
+
+def get_mapper(x: str, y: str, tokenizer, max_len=77):
+    x_seq = tokenizer.encode(x)
+    y_seq = tokenizer.encode(y)
+    score = ScoreParams(0, 1, -1)
+    matrix, trace_back = global_align(x_seq, y_seq, score)
+    mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]
+    alphas = torch.ones(max_len)
+    alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()
+    mapper = torch.zeros(max_len, dtype=torch.int64)
+    mapper[: mapper_base.shape[0]] = mapper_base[:, 1]
+    mapper[mapper_base.shape[0] :] = len(y_seq) + torch.arange(max_len - len(y_seq))
+    return mapper, alphas
+
+
+def get_refinement_mapper(prompts, tokenizer, max_len=77):
+    x_seq = prompts[0]
+    mappers, alphas = [], []
+    for i in range(1, len(prompts)):
+        mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)
+        mappers.append(mapper)
+        alphas.append(alpha)
+    return torch.stack(mappers), torch.stack(alphas)
+
+
+
+
+
+def aggregate_attention(prompts, attention_store: AttentionStore, height: int, width: int, from_where: List[str], is_cross: bool, select: int):
+    out = []
+    attention_maps = attention_store.get_average_attention()
+    attention_map_height = height // 32
+    attention_map_width = width // 32
+    num_pixels = attention_map_height * attention_map_width
+    for location in from_where:
+        for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
+            if item.shape[1] == num_pixels:
+                cross_maps = item.reshape(len(prompts), -1, attention_map_width, attention_map_height, item.shape[-1])[select]
+                out.append(cross_maps)
+    out = torch.cat(out, dim=0)
+    out = out.sum(0) / out.shape[0]
+    return out.cpu()
+
+
+def show_cross_attention(tokenizer, prompts, attention_store: AttentionStore, res: int, from_where: List[str], select: int = 0, t=0):
+    tokens = tokenizer.encode(prompts[select])
+    decoder = tokenizer.decode
+    attention_maps = aggregate_attention(prompts, attention_store, res, from_where, True, select)
+    images = []
+    for i in range(len(tokens)):
+        image = attention_maps[:, :, i]
+        image = 255 * image / image.max()
+        image = image.unsqueeze(-1).expand(*image.shape, 3)
+        image = image.numpy().astype(np.uint8)
+        image = np.array(Image.fromarray(image).resize((256, 256)))
+        image = text_under_image(image, decoder(int(tokens[i])))
+        images.append(image)
+
+    view_images(np.stack(images, axis=0), t=t, from_where=from_where)
+    
+
+def show_self_attention_comp(attention_store: AttentionStore, res: int, from_where: List[str],
+                        max_com=10, select: int = 0):
+    attention_maps = aggregate_attention(attention_store, res, from_where, False, select).numpy().reshape((res ** 2, res ** 2))
+    u, s, vh = np.linalg.svd(attention_maps - np.mean(attention_maps, axis=1, keepdims=True))
+    images = []
+    for i in range(max_com):
+        image = vh[i].reshape(res, res)
+        image = image - image.min()
+        image = 255 * image / image.max()
+        image = np.repeat(np.expand_dims(image, axis=2), 3, axis=2).astype(np.uint8)
+        image = Image.fromarray(image).resize((256, 256))
+        image = np.array(image)
+        images.append(image)
+    view_images(np.concatenate(images, axis=1),from_where=from_where)
+
+def view_images(images, num_rows=1, offset_ratio=0.02, t=0, from_where= List[str]):
+    if type(images) is list:
+        num_empty = len(images) % num_rows
+    elif images.ndim == 4:
+        num_empty = images.shape[0] % num_rows
+    else:
+        images = [images]
+        num_empty = 0
+
+    empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
+    images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
+    num_items = len(images)
+
+    h, w, c = images[0].shape
+    offset = int(h * offset_ratio)
+    num_cols = num_items // num_rows
+    image_ = np.ones((h * num_rows + offset * (num_rows - 1),
+                      w * num_cols + offset * (num_cols - 1), 3), dtype=np.uint8) * 255
+    for i in range(num_rows):
+        for j in range(num_cols):
+            image_[i * (h + offset): i * (h + offset) + h:, j * (w + offset): j * (w + offset) + w] = images[
+                i * num_cols + j]
+
+    pil_img = Image.fromarray(image_)
+
+    if len(from_where) > 1:
+        from_where = '_'.join(from_where)
+
+    save_path = f'./visualization/{from_where}'
+    if not os.path.exists(save_path):
+        os.mkdir(save_path)
+    pil_img.save(f"{save_path}/{t}.png")
+
+
+def text_under_image(image: np.ndarray, text: str, text_color: Tuple[int, int, int] = (0, 0, 0)):
+    h, w, c = image.shape
+    offset = int(h * .2)
+    img = np.ones((h + offset, w, c), dtype=np.uint8) * 255
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    img[:h] = image
+    textsize = cv2.getTextSize(text, font, 1, 2)[0]
+    text_x, text_y = (w - textsize[0]) // 2, h + offset - textsize[1] // 2
+    cv2.putText(img, text, (text_x, text_y ), font, 1, text_color, 2)
+    return img
+
+def get_views(height, width, window_size=32, stride=16, random_jitter=False):
+        num_blocks_height = int((height - window_size) / stride - 1e-6) + 2 if height > window_size else 1
+        num_blocks_width = int((width - window_size) / stride - 1e-6) + 2 if width > window_size else 1
+        total_num_blocks = int(num_blocks_height * num_blocks_width)
+        views = []
+        for i in range(total_num_blocks):
+            h_start = int((i // num_blocks_width) * stride)
+            h_end = h_start + window_size
+            w_start = int((i % num_blocks_width) * stride)
+            w_end = w_start + window_size
+
+            if h_end > height:
+                h_start = int(h_start + height - h_end)
+                h_end = int(height)
+            if w_end > width:
+                w_start = int(w_start + width - w_end)
+                w_end = int(width)
+            if h_start < 0:
+                h_end = int(h_end - h_start)
+                h_start = 0
+            if w_start < 0:
+                w_end = int(w_end - w_start)
+                w_start = 0
+            
+            if random_jitter:
+                jitter_range = (window_size - stride) // 4
+                w_jitter = 0
+                h_jitter = 0
+                if (w_start != 0) and (w_end != width):
+                    w_jitter = random.randint(-jitter_range, jitter_range)
+                elif (w_start == 0) and (w_end != width):
+                    w_jitter = random.randint(-jitter_range, 0)
+                elif (w_start != 0) and (w_end == width):
+                    w_jitter = random.randint(0, jitter_range)
+                if (h_start != 0) and (h_end != height):
+                    h_jitter = random.randint(-jitter_range, jitter_range)
+                elif (h_start == 0) and (h_end != height):
+                    h_jitter = random.randint(-jitter_range, 0)
+                elif (h_start != 0) and (h_end == height):
+                    h_jitter = random.randint(0, jitter_range)
+                h_start += (h_jitter + jitter_range)
+                h_end += (h_jitter + jitter_range)
+                w_start += (w_jitter + jitter_range)
+                w_end += (w_jitter + jitter_range)
+
+            views.append((int(h_start), int(h_end), int(w_start), int(w_end)))
+        return views
+
+
+def get_multidiffusion_prompts(tokenizer, prompts, threthod, attention_store:AttentionStore, height:int, width:int, from_where: List[str], scale_num=4, random_jitter=False):
+    tokens = tokenizer.encode(prompts[0])
+    decoder = tokenizer.decode
+
+    # get cross_attention_maps
+    attention_maps = aggregate_attention(prompts, attention_store, height, width, from_where, True, 0)
+
+    # view cross_attention_maps
+    images = []
+    for i in range(len(tokens)):
+        image = attention_maps[:, :, i]
+        image = 255 * image / image.max()
+        image = image.unsqueeze(-1).expand(*image.shape, 3).numpy().astype(np.uint8)
+        image = np.array(Image.fromarray(image).resize((256, 256)))
+        image = text_under_image(image, decoder(int(tokens[i])))
+        images.append(image)
+
+    # get high attention regions
+    masks = []
+
+    for i in range(len(tokens)):
+        attention_map = attention_maps[:, :, i]
+        attention_map = attention_map.to(torch.float32)
+        words = decoder(int(tokens[i]))
+        mask = torch.where(attention_map > attention_map.mean(), 1, 0).numpy().astype(np.uint8)
+        mask = mask * 255
+        # process mask
+        kernel = np.ones((3, 3), np.uint8)
+        eroded_mask = cv2.erode(mask, kernel, iterations=mask.shape[0]//16)
+        dilated_mask = cv2.dilate(eroded_mask, kernel, iterations=mask.shape[0]//16)
+        masks.append(dilated_mask)
+
+    # dict for prompts and views
+    prompt_dict = {}
+    view_dict = {}
+
+    ori_w, ori_h = mask.shape
+    window_size = max(ori_h, ori_w)
+    for scale in range(2, scale_num+1):
+        # current height and width
+        cur_w = ori_w * scale
+        cur_h = ori_h * scale
+        views = get_views(height=cur_h, width=cur_w, window_size=window_size, stride=window_size/2, random_jitter=random_jitter)
+
+        words_in_patch = []
+        for i, mask in enumerate(masks):
+            # skip endoftext and beginof text masks
+            if i == 0 or i == len(masks) - 1:
+                continue
+            # upscale masks
+            mask = cv2.resize(mask, (cur_w, cur_h), interpolation=cv2.INTER_NEAREST)
+            if random_jitter:
+                jitter_range = int((ori_h - ori_h/2) // 4)
+                mask = np.pad(mask, ((jitter_range, jitter_range), (jitter_range, jitter_range)), 'constant', constant_values=(0, 0))
+            
+            word_in_patch =[]
+            word = decoder(int(tokens[i]))
+            for i, view in enumerate(views):
+                h_start, h_end, w_start, w_end = view
+                view_mask = mask[h_start:h_end, w_start:w_end]
+                if (view_mask/255).sum() / (ori_h * ori_w) >= threthod:
+                    word_in_patch.append(word)   # word in patch
+                else:
+                    word_in_patch.append('')     # word not in patch
+            words_in_patch.append(word_in_patch)
+
+        # get prompts for each view
+        result = []
+        prompts_for_each_views = [' '.join(strings) for strings in zip(*words_in_patch)]
+        for prompt in prompts_for_each_views:
+            prompt = prompt.split()
+            result.append(" ".join(prompt))
+        # save prompts and views in each scale
+        prompt_dict[scale] = result
+        view_dict[scale] = views
+    
+    return prompt_dict, view_dict
+
+
+
+
+
+class ScaledAttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __init__(self, processor, test_res, train_res):
+        self.processor = processor
+        self.test_res = test_res
+        self.train_res = train_res
+
+    def __call__(
+            self,
+            attn,
+            hidden_states,
+            encoder_hidden_states=None,
+            attention_mask=None,
+            temb=None,
+    ):
+        input_ndim = hidden_states.ndim
+        # print(f"cross attention: {not encoder_hidden_states is None}")
+        # if encoder_hidden_states is None:
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            sequence_length = height * width
+        else:
+            batch_size, sequence_length, _ = hidden_states.shape
+
+        test_train_ratio = (self.test_res ** 2.0) / (self.train_res ** 2.0)
+        # test_train_ratio = float(self.test_res / self.train_res)
+        # print(f"test_train_ratio: {test_train_ratio}")
+        train_sequence_length = sequence_length / test_train_ratio
+        scale_factor = math.log(sequence_length, train_sequence_length) ** 0.5
+        # else:
+        #     scale_factor = 1
+        # print(f"scale factor: {scale_factor}")
+
+        original_scale = attn.scale
+        attn.scale = attn.scale * scale_factor
+        hidden_states = self.processor(attn, hidden_states, encoder_hidden_states, attention_mask, temb, scale = attn.scale )
+        # hidden_states = super(ScaledAttnProcessor, self).__call__(
+        #     attn, hidden_states, encoder_hidden_states, attention_mask, temb)
+        attn.scale = original_scale
+        return hidden_states
+
+

watermark.py

@@ -0,0 +1,36 @@
+import numpy as np
+import torch
+
+from diffusers.utils import is_invisible_watermark_available
+
+
+if is_invisible_watermark_available():
+    from imwatermark import WatermarkEncoder
+
+
+# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
+WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
+# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
+WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
+
+
+class StableDiffusionXLWatermarker:
+    def __init__(self):
+        self.watermark = WATERMARK_BITS
+        self.encoder = WatermarkEncoder()
+
+        self.encoder.set_watermark("bits", self.watermark)
+
+    def apply_watermark(self, images: torch.FloatTensor):
+        # can't encode images that are smaller than 256
+        if images.shape[-1] < 256:
+            return images
+
+        images = (255 * (images / 2 + 0.5)).cpu().permute(0, 2, 3, 1).float().numpy()
+
+        images = [self.encoder.encode(image, "dwtDct") for image in images]
+
+        images = torch.from_numpy(np.array(images)).permute(0, 3, 1, 2)
+
+        images = torch.clamp(2 * (images / 255 - 0.5), min=-1.0, max=1.0)
+        return images