second

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+venv/
+public/
+*.pem

README.md

@@ -4,7 +4,7 @@ emoji: 💻
 colorFrom: blue
 colorTo: red
 sdk: gradio
-sdk_version: 4.7.1
+sdk_version: 4.8.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,178 @@
+import gradio as gr
+from gradio_imageslider import ImageSlider
+import torch
+from diffusers import DiffusionPipeline, AutoencoderKL
+from PIL import Image
+from torchvision import transforms
+import numpy as np
+import tempfile
+import os
+import uuid
+
+TORCH_COMPILE = os.getenv("TORCH_COMPILE", "0") == "1"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)
+pipe = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    custom_pipeline="pipeline_demofusion_sdxl.py",
+    custom_revision="main",
+    torch_dtype=dtype,
+    variant="fp16",
+    use_safetensors=True,
+    vae=vae,
+)
+pipe = pipe.to(device)
+if TORCH_COMPILE:
+    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+
+
+def load_and_process_image(pil_image):
+    transform = transforms.Compose(
+        [
+            transforms.Resize((1024, 1024)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+        ]
+    )
+    image = transform(pil_image)
+    image = image.unsqueeze(0).half()
+    return image
+
+
+def pad_image(image):
+    w, h = image.size
+    if w == h:
+        return image
+    elif w > h:
+        new_image = Image.new(image.mode, (w, w), (0, 0, 0))
+        pad_w = 0
+        pad_h = (w - h) // 2
+        new_image.paste(image, (0, pad_h))
+        return new_image
+    else:
+        new_image = Image.new(image.mode, (h, h), (0, 0, 0))
+        pad_w = (h - w) // 2
+        pad_h = 0
+        new_image.paste(image, (pad_w, 0))
+        return new_image
+
+
+def predict(
+    input_image,
+    prompt,
+    negative_prompt,
+    seed,
+    scale=2,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if input_image is None:
+        raise gr.Error("Please upload an image.")
+    padded_image = pad_image(input_image).resize((1024, 1024))
+    padded_image.save(f"padded_image+{seed}.jpg")
+    image_lr = load_and_process_image(padded_image).to(device)
+    generator = torch.manual_seed(seed)
+    images = pipe(
+        prompt,
+        negative_prompt=negative_prompt,
+        image_lr=image_lr,
+        width=1024 * scale,
+        height=1024 * scale,
+        view_batch_size=16,
+        stride=64,
+        generator=generator,
+        num_inference_steps=25,
+        guidance_scale=7.5,
+        cosine_scale_1=3,
+        cosine_scale_2=1,
+        cosine_scale_3=1,
+        sigma=0.8,
+        multi_decoder=True,
+        show_image=False,
+        lowvram=True,
+    )
+    images_path = tempfile.mkdtemp()
+    paths = []
+    uuid_name = uuid.uuid4()
+    for i, img in enumerate(images):
+        img.save(images_path + f"/img_{uuid_name}_{img.size[0]}.jpg")
+        paths.append(images_path + f"/img_{uuid_name}_{img.size[0]}.jpg")
+    return (images[0], images[-1]), paths
+
+
+css = """
+#intro{
+    max-width: 100%;
+    text-align: center;
+    margin: 0 auto;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(
+        """# Super Resolution - SDXL
+            ## [DemoFusion](https://github.com/PRIS-CV/DemoFusion)""",
+        elem_id="intro",
+    )
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil", label="Input Image")
+            prompt = gr.Textbox(
+                label="Prompt",
+                info="The prompt is very important to get the desired results. Please try to describe the image as best as you can.",
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                value="blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
+            )
+            scale = gr.Slider(minimum=2, maximum=5, value=2, step=1, label="x Scale")
+            seed = gr.Slider(
+                minimum=0,
+                maximum=2**64 - 1,
+                value=1415926535897932,
+                step=1,
+                label="Seed",
+                randomize=True,
+            )
+            btn = gr.Button()
+        with gr.Column(scale=2):
+            image_slider = ImageSlider()
+            files = gr.Files()
+    inputs = [image_input, prompt, negative_prompt, seed, scale]
+    outputs = [image_slider, files]
+    btn.click(predict, inputs=inputs, outputs=outputs, concurrency_limit=1)
+    gr.Examples(
+        fn=predict,
+        examples=[
+            [
+                "./examples/lara.jpeg",
+                "photography of lara croft 8k high definition award winning",
+                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
+                1415535897932,
+                2,
+            ],
+            [
+                "./examples/cybetruck.jpeg",
+                "photo of tesla cybertruck futuristic car 8k high definition on a sand dune in mars, future",
+                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
+                1415535897932,
+                2,
+            ],
+            [
+                "./examples/jesus.png",
+                "a photorealistic painting of Jesus Christ, 4k high definition",
+                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
+                1415535897932,
+                2,
+            ],
+        ],
+        inputs=inputs,
+        outputs=outputs,
+        cache_examples=True,
+    )
+
+
+demo.queue(api_open=False)
+demo.launch(show_api=False)

gradio_cached_examples/14/log.csv

@@ -0,0 +1,4 @@
+component 0,component 1,flag,username,timestamp
+"[{""path"":""gradio_cached_examples/14/component 0/b5498d7a96d85e965a9b/image.png"",""url"":null,""size"":null,""orig_name"":null,""mime_type"":null},{""path"":""gradio_cached_examples/14/component 0/b34adaae151a4459167b/image.png"",""url"":null,""size"":null,""orig_name"":null,""mime_type"":null}]","[{""path"":""gradio_cached_examples/14/component 1/8df9754c0e60183ba8f8/img_bf8b5bfd-5769-445e-8b04-398fa66b36b1_1024.jpg"",""url"":null,""size"":65560,""orig_name"":""img_bf8b5bfd-5769-445e-8b04-398fa66b36b1_1024.jpg"",""mime_type"":null},{""path"":""gradio_cached_examples/14/component 1/3c86c69e7b46e996c594/img_bf8b5bfd-5769-445e-8b04-398fa66b36b1_1024.jpg"",""url"":null,""size"":65560,""orig_name"":""img_bf8b5bfd-5769-445e-8b04-398fa66b36b1_1024.jpg"",""mime_type"":null},{""path"":""gradio_cached_examples/14/component 1/e80581741e20de234eb8/img_bf8b5bfd-5769-445e-8b04-398fa66b36b1_2048.jpg"",""url"":null,""size"":165512,""orig_name"":""img_bf8b5bfd-5769-445e-8b04-398fa66b36b1_2048.jpg"",""mime_type"":null}]",,,2023-12-06 19:52:37.669846
+"[{""path"":""gradio_cached_examples/14/component 0/351a0bfafaaecc7814de/image.png"",""url"":null,""size"":null,""orig_name"":null,""mime_type"":null},{""path"":""gradio_cached_examples/14/component 0/e06cb354ee474e562e51/image.png"",""url"":null,""size"":null,""orig_name"":null,""mime_type"":null}]","[{""path"":""gradio_cached_examples/14/component 1/ee84949074ae1250919e/img_87222732-8f7b-4e26-bfa9-bd5842e2af61_1024.jpg"",""url"":null,""size"":65506,""orig_name"":""img_87222732-8f7b-4e26-bfa9-bd5842e2af61_1024.jpg"",""mime_type"":null},{""path"":""gradio_cached_examples/14/component 1/d20358db86db5687c4d3/img_87222732-8f7b-4e26-bfa9-bd5842e2af61_1024.jpg"",""url"":null,""size"":65506,""orig_name"":""img_87222732-8f7b-4e26-bfa9-bd5842e2af61_1024.jpg"",""mime_type"":null},{""path"":""gradio_cached_examples/14/component 1/cdf61b32958e252f00b9/img_87222732-8f7b-4e26-bfa9-bd5842e2af61_2048.jpg"",""url"":null,""size"":244888,""orig_name"":""img_87222732-8f7b-4e26-bfa9-bd5842e2af61_2048.jpg"",""mime_type"":null}]",,,2023-12-06 19:53:55.398688
+"[{""path"":""gradio_cached_examples/14/component 0/bdf0acf00cd9b1c85287/image.png"",""url"":null,""size"":null,""orig_name"":null,""mime_type"":null},{""path"":""gradio_cached_examples/14/component 0/b3e9f831c3822912c747/image.png"",""url"":null,""size"":null,""orig_name"":null,""mime_type"":null}]","[{""path"":""gradio_cached_examples/14/component 1/e96cc92c329d8c5f7b30/img_2b3a159b-b19d-4810-8678-8dd6762db0c0_1024.jpg"",""url"":null,""size"":47077,""orig_name"":""img_2b3a159b-b19d-4810-8678-8dd6762db0c0_1024.jpg"",""mime_type"":null},{""path"":""gradio_cached_examples/14/component 1/ec2cd95acdcb6c98d37e/img_2b3a159b-b19d-4810-8678-8dd6762db0c0_1024.jpg"",""url"":null,""size"":47077,""orig_name"":""img_2b3a159b-b19d-4810-8678-8dd6762db0c0_1024.jpg"",""mime_type"":null},{""path"":""gradio_cached_examples/14/component 1/d51a78500d54f78aef8f/img_2b3a159b-b19d-4810-8678-8dd6762db0c0_2048.jpg"",""url"":null,""size"":143073,""orig_name"":""img_2b3a159b-b19d-4810-8678-8dd6762db0c0_2048.jpg"",""mime_type"":null}]",,,2023-12-06 19:55:13.167461

pipeline_demofusion_sdxl.py

@@ -0,0 +1,1788 @@
+# 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 inspect
+import os
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import matplotlib.pyplot as plt
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+import random
+import warnings
+from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
+
+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
+
+
+if is_invisible_watermark_available():
+    from diffusers.pipelines.stable_diffusion_xl.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 DemoFusionSDXLPipeline(
+    DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+):
+    r"""
+    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`."
+            )
+
+        # DemoFusion specific checks
+        if max(height, width) % 512 != 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 DemoFusion 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)
+                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((h_start, h_end, w_start, w_end))
+        return views
+
+    def tiled_decode(self, latents, current_height, current_width):
+        sample_size = self.unet.config.sample_size
+        core_size = self.unet.config.sample_size // 4
+        core_stride = core_size
+        pad_size = self.unet.config.sample_size // 4 * 3
+        decoder_view_batch_size = 1
+
+        if self.lowvram:
+            core_stride = core_size // 2
+            pad_size = core_size
+
+        views = self.get_views(
+            current_height, current_width, stride=core_stride, window_size=core_size
+        )
+        views_batch = [
+            views[i : i + decoder_view_batch_size]
+            for i in range(0, len(views), decoder_view_batch_size)
+        ]
+        latents_ = F.pad(
+            latents, (pad_size, pad_size, pad_size, pad_size), "constant", 0
+        )
+        image = torch.zeros(latents.size(0), 3, current_height, current_width).to(
+            latents.device
+        )
+        count = torch.zeros_like(image).to(latents.device)
+        # get the latents corresponding to the current view coordinates
+        with self.progress_bar(total=len(views_batch)) as progress_bar:
+            for j, batch_view in enumerate(views_batch):
+                vb_size = len(batch_view)
+                latents_for_view = torch.cat(
+                    [
+                        latents_[
+                            :,
+                            :,
+                            h_start : h_end + pad_size * 2,
+                            w_start : w_end + pad_size * 2,
+                        ]
+                        for h_start, h_end, w_start, w_end in batch_view
+                    ]
+                ).to(self.vae.device)
+                image_patch = self.vae.decode(
+                    latents_for_view / self.vae.config.scaling_factor, return_dict=False
+                )[0]
+                h_start, h_end, w_start, w_end = views[j]
+                h_start, h_end, w_start, w_end = (
+                    h_start * self.vae_scale_factor,
+                    h_end * self.vae_scale_factor,
+                    w_start * self.vae_scale_factor,
+                    w_end * self.vae_scale_factor,
+                )
+                p_h_start, p_h_end, p_w_start, p_w_end = (
+                    pad_size * self.vae_scale_factor,
+                    image_patch.size(2) - pad_size * self.vae_scale_factor,
+                    pad_size * self.vae_scale_factor,
+                    image_patch.size(3) - pad_size * self.vae_scale_factor,
+                )
+                image[:, :, h_start:h_end, w_start:w_end] += image_patch[
+                    :, :, p_h_start:p_h_end, p_w_start:p_w_end
+                ].to(latents.device)
+                count[:, :, h_start:h_end, w_start:w_end] += 1
+                progress_bar.update()
+        image = image / count
+
+        return image
+
+    # 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)
+
+    @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,
+        ################### DemoFusion specific parameters ####################
+        view_batch_size: int = 16,
+        multi_decoder: bool = True,
+        stride: Optional[int] = 64,
+        cosine_scale_1: Optional[float] = 3.0,
+        cosine_scale_2: Optional[float] = 1.0,
+        cosine_scale_3: Optional[float] = 1.0,
+        sigma: Optional[float] = 1.0,
+        show_image: bool = False,
+        lowvram: bool = False,
+        image_lr: Optional[torch.FloatTensor] = None,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            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.
+            ################### DemoFusion specific parameters ####################
+            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.
+            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.
+            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.
+            sigma (`float`, defaults to 1):
+                The standerd 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.
+        """
+
+        # 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)
+
+        # 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)
+
+        # 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)
+            add_text_embeds = torch.cat(
+                [negative_pooled_prompt_embeds, add_text_embeds], dim=0
+            )
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+        del negative_prompt_embeds, negative_pooled_prompt_embeds, negative_add_time_ids
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(
+            batch_size * num_images_per_prompt, 1
+        )
+
+        # 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 1 #################################################################
+
+        if self.lowvram:
+            self.text_encoder.cpu()
+            self.text_encoder_2.cpu()
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            if image_lr == None:
+                print("### Phase 1 Denoising ###")
+                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]
+
+                    # 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,
+                )
+            else:
+                print("### Phase Encoding ###")
+                self.vae.to(device)
+                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)
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+            if show_image:
+                plt.figure(figsize=(10, 10))
+                plt.imshow(image[0])
+                plt.axis("off")  # Turn off axis numbers and ticks
+                plt.show()
+            output_images.append(image[0])
+
+        ####################################################### Phase 2+ #####################################################
+        for current_scale_num in range(1, scale_num + 1):
+            if self.lowvram:
+                latents = latents.to(device)
+                self.unet.to(device)
+                torch.cuda.empty_cache()
+            print("### Phase {} Denoising ###".format(current_scale_num))
+            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)
+
+            latents = F.interpolate(
+                latents.to(device),
+                size=(
+                    int(current_height / self.vae_scale_factor),
+                    int(current_width / self.vae_scale_factor),
+                ),
+                mode="bicubic",
+            )
+
+            noise_latents = []
+            noise = torch.randn_like(latents)
+            for timestep in timesteps:
+                noise_latent = self.scheduler.add_noise(
+                    latents, noise, timestep.unsqueeze(0)
+                )
+                noise_latents.append(noise_latent)
+            latents = noise_latents[0]
+
+            with self.progress_bar(total=num_inference_steps) as progress_bar:
+                for i, t in enumerate(timesteps):
+                    count = torch.zeros_like(latents)
+                    value = torch.zeros_like(latents)
+                    cosine_factor = (
+                        0.5
+                        * (
+                            1
+                            + torch.cos(
+                                torch.pi
+                                * (self.scheduler.config.num_train_timesteps - t)
+                                / self.scheduler.config.num_train_timesteps
+                            )
+                        ).cpu()
+                    )
+
+                    c1 = cosine_factor**cosine_scale_1
+                    latents = latents * (1 - c1) + noise_latents[i] * c1
+
+                    ############################################# MultiDiffusion #############################################
+
+                    views = self.get_views(
+                        current_height,
+                        current_width,
+                        stride=stride,
+                        window_size=self.unet.config.sample_size,
+                        random_jitter=True,
+                    )
+                    views_batch = [
+                        views[i : i + view_batch_size]
+                        for i in range(0, len(views), view_batch_size)
+                    ]
+
+                    jitter_range = (self.unet.config.sample_size - stride) // 4
+                    latents_ = F.pad(
+                        latents,
+                        (jitter_range, jitter_range, jitter_range, jitter_range),
+                        "constant",
+                        0,
+                    )
+
+                    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
+                        )
+
+                        prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)
+                        add_text_embeds_input = torch.cat([add_text_embeds] * vb_size)
+                        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)
+
+                        # 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,
+                            )
+
+                        # 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
+
+                    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,
+                    ]
+
+                    c2 = cosine_factor**cosine_scale_2
+
+                    value += value_local / count_local * (1 - c2)
+                    count += torch.ones_like(value_local) * (1 - c2)
+
+                    ############################################# 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_)
+
+                    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_
+
+                    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
+                            ]
+                        )
+
+                        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,
+                            )
+
+                        # 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
+
+                    c2 = cosine_factor**cosine_scale_2
+
+                    value_global = value_global[:, :, h_pad:, w_pad:]
+
+                    value += value_global * c2
+                    count += torch.ones_like(value_global) * c2
+
+                    ###########################################################
+
+                    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 multi_decoder:
+                        image = self.tiled_decode(
+                            latents, current_height, current_width
+                        )
+                    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)
+                else:
+                    image = latents
+
+                if not output_type == "latent":
+                    image = self.image_processor.postprocess(
+                        image, output_type=output_type
+                    )
+                    if show_image:
+                        plt.figure(figsize=(10, 10))
+                        plt.imshow(image[0])
+                        plt.axis("off")  # Turn off axis numbers and ticks
+                        plt.show()
+                    output_images.append(image[0])
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        return output_images
+
+    # 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)

requirements.txt

@@ -0,0 +1,13 @@
+gradio==4.8.0
+git+https://github.com/huggingface/diffusers@4684ea2fe8d568f44c491068c3eb94aac27045f3
+accelerate
+transformers
+--extra-index-url https://download.pytorch.org/whl/cu118
+torch
+torchvision
+xformers
+accelerate
+invisible-watermark
+huggingface-hub
+hf-transfer
+gradio-imageslider