ComfyUI-InstantID/pipeline_stable_diffusion_xl_instantid.py

# Copyright 2024 The InstantX 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.


from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import cv2
import math

import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F

from diffusers.image_processor import PipelineImageInput

from diffusers.models import ControlNetModel

from diffusers.utils import (
    deprecate,
    logging,
    replace_example_docstring,
)
from diffusers.utils.torch_utils import is_compiled_module, is_torch_version
from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput

from diffusers import StableDiffusionXLControlNetPipeline
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.utils.import_utils import is_xformers_available

from .ip_adapter.resampler import Resampler
from .ip_adapter.utils import is_torch2_available

from .ip_adapter.attention_processor import AttnProcessor, IPAttnProcessor

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


EXAMPLE_DOC_STRING = """

    Examples:

        ```py

        >>> # !pip install opencv-python transformers accelerate insightface

        >>> import diffusers

        >>> from diffusers.utils import load_image

        >>> from diffusers.models import ControlNetModel



        >>> import cv2

        >>> import torch

        >>> import numpy as np

        >>> from PIL import Image

        

        >>> from insightface.app import FaceAnalysis

        >>> from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps



        >>> # download 'antelopev2' under ./models

        >>> app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])

        >>> app.prepare(ctx_id=0, det_size=(640, 640))

        

        >>> # download models under ./checkpoints

        >>> face_adapter = f'./checkpoints/ip-adapter.bin'

        >>> controlnet_path = f'./checkpoints/ControlNetModel'

        

        >>> # load IdentityNet

        >>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)

        

        >>> pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(

        ...     "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16

        ... )

        >>> pipe.cuda()

        

        >>> # load adapter

        >>> pipe.load_ip_adapter_instantid(face_adapter)



        >>> prompt = "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality"

        >>> negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured"



        >>> # load an image

        >>> image = load_image("your-example.jpg")

        

        >>> face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))[-1]

        >>> face_emb = face_info['embedding']

        >>> face_kps = draw_kps(face_image, face_info['kps'])

        

        >>> pipe.set_ip_adapter_scale(0.8)



        >>> # generate image

        >>> image = pipe(

        ...     prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8

        ... ).images[0]

        ```

"""

def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
    
    stickwidth = 4
    limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
    kps = np.array(kps)

    w, h = image_pil.size
    out_img = np.zeros([h, w, 3])

    for i in range(len(limbSeq)):
        index = limbSeq[i]
        color = color_list[index[0]]

        x = kps[index][:, 0]
        y = kps[index][:, 1]
        length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
        angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
        polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
        out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
    out_img = (out_img * 0.6).astype(np.uint8)

    for idx_kp, kp in enumerate(kps):
        color = color_list[idx_kp]
        x, y = kp
        out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)

    out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))
    return out_img_pil
    
class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
    
    def cuda(self, dtype=torch.float16, use_xformers=False):
        self.to('cuda', dtype)
        
        if hasattr(self, 'image_proj_model'):
            self.image_proj_model.to(self.unet.device).to(self.unet.dtype)
        
        if use_xformers:
            if is_xformers_available():
                import xformers
                from packaging import version

                xformers_version = version.parse(xformers.__version__)
                if xformers_version == version.parse("0.0.16"):
                    logger.warn(
                        "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
                    )
                self.enable_xformers_memory_efficient_attention()
            else:
                raise ValueError("xformers is not available. Make sure it is installed correctly")
    
    def load_ip_adapter_instantid(self, model_ckpt, image_emb_dim=512, num_tokens=16, scale=0.5):     
        self.set_image_proj_model(model_ckpt, image_emb_dim, num_tokens)
        self.set_ip_adapter(model_ckpt, num_tokens, scale)
        
    def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16):
        
        image_proj_model = Resampler(
            dim=1280,
            depth=4,
            dim_head=64,
            heads=20,
            num_queries=num_tokens,
            embedding_dim=image_emb_dim,
            output_dim=self.unet.config.cross_attention_dim,
            ff_mult=4,
        )

        image_proj_model.eval()
        
        self.image_proj_model = image_proj_model.to(self.device, dtype=self.dtype)
        state_dict = torch.load(model_ckpt, map_location="cpu")
        if 'image_proj' in state_dict:
            state_dict = state_dict["image_proj"]
        self.image_proj_model.load_state_dict(state_dict)
        
        self.image_proj_model_in_features = image_emb_dim
    
    def set_ip_adapter(self, model_ckpt, num_tokens, scale):
        
        unet = self.unet
        attn_procs = {}
        for name in unet.attn_processors.keys():
            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
            if name.startswith("mid_block"):
                hidden_size = unet.config.block_out_channels[-1]
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = unet.config.block_out_channels[block_id]
            if cross_attention_dim is None:
                attn_procs[name] = AttnProcessor().to(unet.device, dtype=unet.dtype)
            else:
                attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, 
                                                   cross_attention_dim=cross_attention_dim, 
                                                   scale=scale,
                                                   num_tokens=num_tokens).to(unet.device, dtype=unet.dtype)
        unet.set_attn_processor(attn_procs)
        
        state_dict = torch.load(model_ckpt, map_location="cpu")
        ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values())
        if 'ip_adapter' in state_dict:
            state_dict = state_dict['ip_adapter']
        ip_layers.load_state_dict(state_dict)
    
    def set_ip_adapter_scale(self, scale):
        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        for attn_processor in unet.attn_processors.values():
            if isinstance(attn_processor, IPAttnProcessor):
                attn_processor.scale = scale

    def _encode_prompt_image_emb(self, prompt_image_emb, device, dtype, do_classifier_free_guidance):
        
        if isinstance(prompt_image_emb, torch.Tensor):
            prompt_image_emb = prompt_image_emb.clone().detach()
        else:
            prompt_image_emb = torch.tensor(prompt_image_emb)
            
        prompt_image_emb = prompt_image_emb.to(device=device, dtype=dtype)
        prompt_image_emb = prompt_image_emb.reshape([1, -1, self.image_proj_model_in_features])
        
        if do_classifier_free_guidance:
            prompt_image_emb = torch.cat([torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0)
        else:
            prompt_image_emb = torch.cat([prompt_image_emb], dim=0)
        
        prompt_image_emb = self.image_proj_model(prompt_image_emb)
        return prompt_image_emb

    @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,

        image: PipelineImageInput = None,

        height: Optional[int] = None,

        width: Optional[int] = None,

        num_inference_steps: int = 50,

        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,

        image_embeds: Optional[torch.FloatTensor] = None,

        output_type: Optional[str] = "pil",

        return_dict: bool = True,

        cross_attention_kwargs: Optional[Dict[str, Any]] = None,

        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,

        guess_mode: bool = False,

        control_guidance_start: Union[float, List[float]] = 0.0,

        control_guidance_end: Union[float, List[float]] = 1.0,

        original_size: Tuple[int, int] = None,

        crops_coords_top_left: Tuple[int, int] = (0, 0),

        target_size: 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,

        clip_skip: Optional[int] = None,

        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,

        callback_on_step_end_tensor_inputs: List[str] = ["latents"],

        **kwargs,

    ):
        r"""

        The call function to the pipeline for generation.



        Args:

            prompt (`str` or `List[str]`, *optional*):

                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.

            prompt_2 (`str` or `List[str]`, *optional*):

                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is

                used in both text-encoders.

            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:

                    `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):

                The ControlNet input condition to provide guidance to the `unet` for generation. If the type is

                specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be

                accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height

                and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in

                `init`, images must be passed as a list such that each element of the list can be correctly batched for

                input to a single ControlNet.

            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):

                The height in pixels of the generated image. 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. 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.

            guidance_scale (`float`, *optional*, defaults to 5.0):

                A higher guidance scale value encourages the model to generate images closely linked to the text

                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.

            negative_prompt (`str` or `List[str]`, *optional*):

                The prompt or prompts to guide what to not include in image generation. If not defined, you need to

                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).

            negative_prompt_2 (`str` or `List[str]`, *optional*):

                The prompt or prompts to guide what to not include in image generation. This is 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies

                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.

            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):

                A [`torch.Generator`](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 is 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 (prompt weighting). If not

                provided, text embeddings are generated from the `prompt` input argument.

            negative_prompt_embeds (`torch.FloatTensor`, *optional*):

                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If

                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.

            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):

                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If

                not provided, pooled text embeddings are 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 (prompt

                weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input

                argument.

            image_embeds (`torch.FloatTensor`, *optional*):

                Pre-generated image embeddings.

            output_type (`str`, *optional*, defaults to `"pil"`):

                The output format of the generated image. Choose between `PIL.Image` or `np.array`.

            return_dict (`bool`, *optional*, defaults to `True`):

                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a

                plain tuple.

            cross_attention_kwargs (`dict`, *optional*):

                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in

                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).

            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):

                The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added

                to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set

                the corresponding scale as a list.

            guess_mode (`bool`, *optional*, defaults to `False`):

                The ControlNet encoder tries to recognize the content of the input image even if you remove all

                prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.

            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):

                The percentage of total steps at which the ControlNet starts applying.

            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):

                The percentage of total steps at which the ControlNet stops applying.

            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 `(height, width)` 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 `(height, width)`. 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.

            clip_skip (`int`, *optional*):

                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that

                the output of the pre-final layer will be used for computing the prompt embeddings.

            callback_on_step_end (`Callable`, *optional*):

                A function that calls at the end of each denoising steps during the inference. The function is called

                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,

                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by

                `callback_on_step_end_tensor_inputs`.

            callback_on_step_end_tensor_inputs (`List`, *optional*):

                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list

                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the

                `._callback_tensor_inputs` attribute of your pipeine class.



        Examples:



        Returns:

            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:

                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,

                otherwise a `tuple` is returned containing the output images.

        """

        callback = kwargs.pop("callback", None)
        callback_steps = kwargs.pop("callback_steps", None)

        if callback is not None:
            deprecate(
                "callback",
                "1.0.0",
                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
            )
        if callback_steps is not None:
            deprecate(
                "callback_steps",
                "1.0.0",
                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
            )

        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet

        # align format for control guidance
        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
            control_guidance_start, control_guidance_end = (
                mult * [control_guidance_start],
                mult * [control_guidance_end],
            )

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            prompt_2,
            image,
            callback_steps,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
            controlnet_conditioning_scale,
            control_guidance_start,
            control_guidance_end,
            callback_on_step_end_tensor_inputs,
        )

        self._guidance_scale = guidance_scale
        self._clip_skip = clip_skip
        self._cross_attention_kwargs = cross_attention_kwargs

        # 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

        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)

        global_pool_conditions = (
            controlnet.config.global_pool_conditions
            if isinstance(controlnet, ControlNetModel)
            else controlnet.nets[0].config.global_pool_conditions
        )
        guess_mode = guess_mode or global_pool_conditions

        # 3.1 Encode input prompt
        text_encoder_lora_scale = (
            self.cross_attention_kwargs.get("scale", None) if self.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_2,
            device,
            num_images_per_prompt,
            self.do_classifier_free_guidance,
            negative_prompt,
            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,
            clip_skip=self.clip_skip,
        )
        
        # 3.2 Encode image prompt
        prompt_image_emb = self._encode_prompt_image_emb(image_embeds, 
                                                         device,
                                                         self.unet.dtype,
                                                         self.do_classifier_free_guidance)
        
        # 4. Prepare image
        if isinstance(controlnet, ControlNetModel):
            image = self.prepare_image(
                image=image,
                width=width,
                height=height,
                batch_size=batch_size * num_images_per_prompt,
                num_images_per_prompt=num_images_per_prompt,
                device=device,
                dtype=controlnet.dtype,
                do_classifier_free_guidance=self.do_classifier_free_guidance,
                guess_mode=guess_mode,
            )
            height, width = image.shape[-2:]
        elif isinstance(controlnet, MultiControlNetModel):
            images = []

            for image_ in image:
                image_ = self.prepare_image(
                    image=image_,
                    width=width,
                    height=height,
                    batch_size=batch_size * num_images_per_prompt,
                    num_images_per_prompt=num_images_per_prompt,
                    device=device,
                    dtype=controlnet.dtype,
                    do_classifier_free_guidance=self.do_classifier_free_guidance,
                    guess_mode=guess_mode,
                )

                images.append(image_)

            image = images
            height, width = image[0].shape[-2:]
        else:
            assert False

        # 5. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps
        self._num_timesteps = len(timesteps)

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

        # 6.5 Optionally get Guidance Scale Embedding
        timestep_cond = None
        if self.unet.config.time_cond_proj_dim is not None:
            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
            timestep_cond = self.get_guidance_scale_embedding(
                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
            ).to(device=device, dtype=latents.dtype)

        # 7. 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)

        # 7.1 Create tensor stating which controlnets to keep
        controlnet_keep = []
        for i in range(len(timesteps)):
            keeps = [
                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
                for s, e in zip(control_guidance_start, control_guidance_end)
            ]
            controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)

        # 7.2 Prepare added time ids & embeddings
        if isinstance(image, list):
            original_size = original_size or image[0].shape[-2:]
        else:
            original_size = original_size or image.shape[-2:]
        target_size = target_size or (height, width)

        add_text_embeds = pooled_prompt_embeds
        if self.text_encoder_2 is None:
            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
        else:
            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim

        add_time_ids = self._get_add_time_ids(
            original_size,
            crops_coords_top_left,
            target_size,
            dtype=prompt_embeds.dtype,
            text_encoder_projection_dim=text_encoder_projection_dim,
        )

        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,
                text_encoder_projection_dim=text_encoder_projection_dim,
            )
        else:
            negative_add_time_ids = add_time_ids

        if self.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)

        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)
        encoder_hidden_states = torch.cat([prompt_embeds, prompt_image_emb], dim=1)

        # 8. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        is_unet_compiled = is_compiled_module(self.unet)
        is_controlnet_compiled = is_compiled_module(self.controlnet)
        is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
                
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # Relevant thread:
                # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
                if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:
                    torch._inductor.cudagraph_mark_step_begin()
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}

                # controlnet(s) inference
                if guess_mode and self.do_classifier_free_guidance:
                    # Infer ControlNet only for the conditional batch.
                    control_model_input = latents
                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
                    controlnet_added_cond_kwargs = {
                        "text_embeds": add_text_embeds.chunk(2)[1],
                        "time_ids": add_time_ids.chunk(2)[1],
                    }
                else:
                    control_model_input = latent_model_input
                    controlnet_prompt_embeds = prompt_embeds
                    controlnet_added_cond_kwargs = added_cond_kwargs
                
                if isinstance(controlnet_keep[i], list):
                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
                else:
                    controlnet_cond_scale = controlnet_conditioning_scale
                    if isinstance(controlnet_cond_scale, list):
                        controlnet_cond_scale = controlnet_cond_scale[0]
                    cond_scale = controlnet_cond_scale * controlnet_keep[i]

                down_block_res_samples, mid_block_res_sample = self.controlnet(
                    control_model_input,
                    t,
                    encoder_hidden_states=prompt_image_emb,
                    controlnet_cond=image,
                    conditioning_scale=cond_scale,
                    guess_mode=guess_mode,
                    added_cond_kwargs=controlnet_added_cond_kwargs,
                    return_dict=False,
                )

                if guess_mode and self.do_classifier_free_guidance:
                    # Infered ControlNet only for the conditional batch.
                    # To apply the output of ControlNet to both the unconditional and conditional batches,
                    # add 0 to the unconditional batch to keep it unchanged.
                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=encoder_hidden_states,
                    timestep_cond=timestep_cond,
                    cross_attention_kwargs=self.cross_attention_kwargs,
                    down_block_additional_residuals=down_block_res_samples,
                    mid_block_additional_residual=mid_block_res_sample,
                    added_cond_kwargs=added_cond_kwargs,
                    return_dict=False,
                )[0]

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

                # 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]

                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)
                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)

                # 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)
        
        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 needs_upcasting:
                self.upcast_vae()
                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
            
            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":
            # apply watermark if available
            if self.watermark is not None:
                image = self.watermark.apply_watermark(image)

            image = self.image_processor.postprocess(image, output_type=output_type)

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image,)

        return StableDiffusionXLPipelineOutput(images=image)