Multi controlnet inpainting pipeline

[ghpkishore]

Model/Pipeline/Scheduler description

@patrickvonplaten @sayakpaul Given that controlnet v1.1 has released an inpainting model, is it possible to use. multi controlnet with the inpainting model? The @haofanwang directory, which is the current goto repo for inpainting pipeline, doesn't work. And neither does the community pipeline. Any ideas on how to proceed here? It doesn't work due to the latest additions which have come up since the repo got released. The entire pipeline inputs changed, since Feb 26, the day it was last made.

Open source status

• The model implementation is available
• The model weights are available (Only relevant if addition is not a scheduler).

Provide useful links for the implementation

No response

[sayakpaul]

For discussion-related questions, please stick to the forum or the Discord server (which you have already done).

Thanks.

[ghpkishore]

Hi @sayakpaul @patrickvonplaten I have created a pipeline which can be used to do multi controlnet inpainting. It is inspired from the community pipeline which enables controlnet inpainting. I added multi controlnet capabilities from the Official controlnet pipeline.

I would like someone to check it once to ensure that there are no errors. Once it is fine, I would want to add it as a community pipeline to huggingface diffusers example . Please let me know if anyone if someone can do it. I have pasted the code below. I have added this as a pipeline in stable diffusion folder in my diffusers based on @haofanwang's controlnet for diffusers library.

# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/

import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from torch import nn

from ...loaders import TextualInversionLoaderMixin
from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
from ...models.controlnet import ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
    PIL_INTERPOLATION,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker

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

class MultiControlNetModel(ModelMixin):
    r"""
    Multiple `ControlNetModel` wrapper class for Multi-ControlNet

    This module is a wrapper for multiple instances of the `ControlNetModel`. The `forward()` API is designed to be
    compatible with `ControlNetModel`.

    Args:
        controlnets (`List[ControlNetModel]`):
            Provides additional conditioning to the unet during the denoising process. You must set multiple
            `ControlNetModel` as a list.
    """

    def __init__(self, controlnets: Union[List[ControlNetModel], Tuple[ControlNetModel]]):
        super().__init__()
        self.nets = nn.ModuleList(controlnets)

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        controlnet_cond: List[torch.tensor],
        conditioning_scale: List[float],
        class_labels: Optional[torch.Tensor] = None,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        return_dict: bool = True,
    ) -> Union[ControlNetOutput, Tuple]:
        for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):
            down_samples, mid_sample = controlnet(
                sample,
                timestep,
                encoder_hidden_states,
                image,
                scale,
                class_labels,
                timestep_cond,
                attention_mask,
                cross_attention_kwargs,
                return_dict,
            )

            # merge samples
            if i == 0:
                down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
            else:
                down_block_res_samples = [
                    samples_prev + samples_curr
                    for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
                ]
                mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample


def prepare_image(image):
    if isinstance(image, torch.Tensor):
        # Batch single image
        if image.ndim == 3:
            image = image.unsqueeze(0)

        image = image.to(dtype=torch.float32)
    else:
        # preprocess image
        if isinstance(image, (PIL.Image.Image, np.ndarray)):
            image = [image]

        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
            image = [np.array(i.convert("RGB"))[None, :] for i in image]
            image = np.concatenate(image, axis=0)
        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
            image = np.concatenate([i[None, :] for i in image], axis=0)

        image = image.transpose(0, 3, 1, 2)
        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0

    return image


def prepare_mask_image(mask_image):
    if isinstance(mask_image, torch.Tensor):
        if mask_image.ndim == 2:
            # Batch and add channel dim for single mask
            mask_image = mask_image.unsqueeze(0).unsqueeze(0)
        elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
            # Single mask, the 0'th dimension is considered to be
            # the existing batch size of 1
            mask_image = mask_image.unsqueeze(0)
        elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
            # Batch of mask, the 0'th dimension is considered to be
            # the batching dimension
            mask_image = mask_image.unsqueeze(1)

        # Binarize mask
        mask_image[mask_image < 0.5] = 0
        mask_image[mask_image >= 0.5] = 1
    else:
        # preprocess mask
        if isinstance(mask_image, (PIL.Image.Image, np.ndarray)):
            mask_image = [mask_image]

        if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image):
            mask_image = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask_image], axis=0)
            mask_image = mask_image.astype(np.float32) / 255.0
        elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray):
            mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)

        mask_image[mask_image < 0.5] = 0
        mask_image[mask_image >= 0.5] = 1
        mask_image = torch.from_numpy(mask_image)

    return mask_image

def prepare_controlnet_conditioning_image(
    image, width, height, batch_size, num_images_per_prompt, device, dtype,do_classifier_free_guidance
):
    if not isinstance(image, torch.Tensor):
        if isinstance(image, PIL.Image.Image):
            image = [image]

        if isinstance(image[0], PIL.Image.Image):
            images = []

            for image_ in image:
                image_ = image_.convert("RGB")
                image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
                image_ = np.array(image_)
                image_ = image_[None, :]
                images.append(image_)

            image = images

            image = np.concatenate(image, axis=0)
            image = np.array(image).astype(np.float32) / 255.0
            image = image.transpose(0, 3, 1, 2)
            image = torch.from_numpy(image)
        elif isinstance(image[0], torch.Tensor):
            image = torch.cat(image, dim=0)

    image_batch_size = image.shape[0]

    if image_batch_size == 1:
        repeat_by = batch_size
    else:
        # image batch size is the same as prompt batch size
        repeat_by = num_images_per_prompt

    image = image.repeat_interleave(repeat_by, dim=0)

    image = image.to(device=device, dtype=dtype)

    if do_classifier_free_guidance:
        image = torch.cat([image] * 2)

    return image


def prepare_controlnet_conditioning_image_actual(
    controlnet_conditioning_image, width, height, batch_size, num_images_per_prompt, device, dtype
):
    if not isinstance(controlnet_conditioning_image, torch.Tensor):
        if isinstance(controlnet_conditioning_image, PIL.Image.Image):
            controlnet_conditioning_image = [controlnet_conditioning_image]

        if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):
            controlnet_conditioning_image = [
                np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
                for i in controlnet_conditioning_image
            ]
            controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)
            controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0
            controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)
            controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)
        elif isinstance(controlnet_conditioning_image[0], torch.Tensor):
            controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)

    image_batch_size = controlnet_conditioning_image.shape[0]

    if image_batch_size == 1:
        repeat_by = batch_size
    else:
        # image batch size is the same as prompt batch size
        repeat_by = num_images_per_prompt

    controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)

    controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype)

    return controlnet_conditioning_image


class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline):
    """
    Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
    """

    _optional_components = ["safety_checker", "feature_extractor"]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
        scheduler: KarrasDiffusionSchedulers,
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPImageProcessor,
        requires_safety_checker: bool = True,
    ):
        super().__init__()

        if safety_checker is None and requires_safety_checker:
            logger.warning(
                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
            )

        if safety_checker is not None and feature_extractor is None:
            raise ValueError(
                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )
        
        if isinstance(controlnet, (list, tuple)):
            controlnet = MultiControlNetModel(controlnet)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            controlnet=controlnet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    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()

    def disable_vae_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_slicing()

    def enable_sequential_cpu_offload(self, gpu_id=0):
        r"""
        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
        text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a
        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
        Note that offloading happens on a submodule basis. Memory savings are higher than with
        `enable_model_cpu_offload`, but performance is lower.
        """
        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError("Please install accelerate via `pip install accelerate`")

        device = torch.device(f"cuda:{gpu_id}")

        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.controlnet]:
            cpu_offload(cpu_offloaded_model, device)

        if self.safety_checker is not None:
            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)

    def enable_model_cpu_offload(self, gpu_id=0):
        r"""
        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
        """
        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
            from accelerate import cpu_offload_with_hook
        else:
            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")

        device = torch.device(f"cuda:{gpu_id}")

        hook = None
        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)

        if self.safety_checker is not None:
            # the safety checker can offload the vae again
            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)

        # control net hook has be manually offloaded as it alternates with unet
        cpu_offload_with_hook(self.controlnet, device)

        # We'll offload the last model manually.
        self.final_offload_hook = hook

    @property
    def _execution_device(self):
        r"""
        Returns the device on which the pipeline's models will be executed. After calling
        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
        hooks.
        """
        if not hasattr(self.unet, "_hf_hook"):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(module, "_hf_hook")
                and hasattr(module._hf_hook, "execution_device")
                and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device)
        return self.device

    def _encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
             prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            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`).
            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.
        """
        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]

        if prompt_embeds is None:
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.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 = self.tokenizer.batch_decode(
                    untruncated_ids[:, self.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" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = text_inputs.attention_mask.to(device)
            else:
                attention_mask = None

            prompt_embeds = self.text_encoder(
                text_input_ids.to(device),
                attention_mask=attention_mask,
            )
            prompt_embeds = prompt_embeds[0]

        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.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)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif 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]
            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

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = uncond_input.attention_mask.to(device)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        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.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)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        return prompt_embeds

    def run_safety_checker(self, image, device, dtype):
        if self.safety_checker is not None:
            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
            image, has_nsfw_concept = self.safety_checker(
                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
            )
        else:
            has_nsfw_concept = None
        return image, has_nsfw_concept

    def decode_latents(self, latents):
        latents = 1 / self.vae.config.scaling_factor * latents
        image = self.vae.decode(latents).sample
        image = (image / 2 + 0.5).clamp(0, 1)
        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        return image

    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,
        image,
        mask_image,
        controlnet_conditioning_image,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=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 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)}")

        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."
            )

        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}."
                )
        # `prompt` needs more sophisticated handling when there are multiple
        # conditionings.
        if isinstance(self.controlnet, MultiControlNetModel):
            if isinstance(prompt, list):
                logger.warning(
                    f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
                    " prompts. The conditionings will be fixed across the prompts."
                )


        # Check control Image
        if isinstance(self.controlnet, ControlNetModel):
            self.check_image(controlnet_conditioning_image, prompt, prompt_embeds)
        elif isinstance(self.controlnet, MultiControlNetModel):
            if not isinstance(controlnet_conditioning_image, list):
                raise TypeError("For multiple controlnets: `image` must be type `list`")

            # When `image` is a nested list:
            # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
            elif any(isinstance(i, list) for i in controlnet_conditioning_image):
                raise ValueError("A single batch of multiple conditionings are supported at the moment.")
            elif len(controlnet_conditioning_image) != len(self.controlnet.nets):
                raise ValueError(
                    "For multiple controlnets: `image` must have the same length as the number of controlnets."
                )

            for image_ in controlnet_conditioning_image:
                self.check_image(image_, prompt, prompt_embeds)
        else:
            assert False


        if isinstance(image, torch.Tensor) and not isinstance(mask_image, torch.Tensor):
            raise TypeError("if `image` is a tensor, `mask_image` must also be a tensor")

        if isinstance(image, PIL.Image.Image) and not isinstance(mask_image, PIL.Image.Image):
            raise TypeError("if `image` is a PIL image, `mask_image` must also be a PIL image")

        if isinstance(image, torch.Tensor):
            if image.ndim != 3 and image.ndim != 4:
                raise ValueError("`image` must have 3 or 4 dimensions")

            if mask_image.ndim != 2 and mask_image.ndim != 3 and mask_image.ndim != 4:
                raise ValueError("`mask_image` must have 2, 3, or 4 dimensions")

            if image.ndim == 3:
                image_batch_size = 1
                image_channels, image_height, image_width = image.shape
            elif image.ndim == 4:
                image_batch_size, image_channels, image_height, image_width = image.shape

            if mask_image.ndim == 2:
                mask_image_batch_size = 1
                mask_image_channels = 1
                mask_image_height, mask_image_width = mask_image.shape
            elif mask_image.ndim == 3:
                mask_image_channels = 1
                mask_image_batch_size, mask_image_height, mask_image_width = mask_image.shape
            elif mask_image.ndim == 4:
                mask_image_batch_size, mask_image_channels, mask_image_height, mask_image_width = mask_image.shape

            if image_channels != 3:
                raise ValueError("`image` must have 3 channels")

            if mask_image_channels != 1:
                raise ValueError("`mask_image` must have 1 channel")

            if image_batch_size != mask_image_batch_size:
                raise ValueError("`image` and `mask_image` mush have the same batch sizes")

            if image_height != mask_image_height or image_width != mask_image_width:
                raise ValueError("`image` and `mask_image` must have the same height and width dimensions")

            if image.min() < -1 or image.max() > 1:
                raise ValueError("`image` should be in range [-1, 1]")

            if mask_image.min() < 0 or mask_image.max() > 1:
                raise ValueError("`mask_image` should be in range [0, 1]")
        else:
            mask_image_channels = 1
            image_channels = 3

        single_image_latent_channels = self.vae.config.latent_channels

        total_latent_channels = single_image_latent_channels * 2 + mask_image_channels

        if total_latent_channels != self.unet.config.in_channels:
            raise ValueError(
                f"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received"
                f" non inpainting latent channels: {single_image_latent_channels},"
                f" mask channels: {mask_image_channels}, and masked image channels: {single_image_latent_channels}."
                f" Please verify the config of `pipeline.unet` and the `mask_image` and `image` inputs."
            )


    def check_image(self, image, prompt, prompt_embeds):
        image_is_pil = isinstance(image, PIL.Image.Image)
        image_is_tensor = isinstance(image, torch.Tensor)
        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)

        if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:
            raise TypeError(
                "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
            )

        if image_is_pil:
            image_batch_size = 1
        elif image_is_tensor:
            image_batch_size = image.shape[0]
        elif image_is_pil_list:
            image_batch_size = len(image)
        elif image_is_tensor_list:
            image_batch_size = len(image)

        if prompt is not None and isinstance(prompt, str):
            prompt_batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            prompt_batch_size = len(prompt)
        elif prompt_embeds is not None:
            prompt_batch_size = prompt_embeds.shape[0]

        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
            raise ValueError(
                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
            )


    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 prepare_mask_latents(self, mask_image, batch_size, height, width, dtype, device, do_classifier_free_guidance):
        # resize the mask to latents shape as we concatenate the mask to the latents
        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
        # and half precision
        mask_image = F.interpolate(mask_image, size=(height // self.vae_scale_factor, width // self.vae_scale_factor))
        mask_image = mask_image.to(device=device, dtype=dtype)

        # duplicate mask for each generation per prompt, using mps friendly method
        if mask_image.shape[0] < batch_size:
            if not batch_size % mask_image.shape[0] == 0:
                raise ValueError(
                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
                    f" a total batch size of {batch_size}, but {mask_image.shape[0]} masks were passed. Make sure the number"
                    " of masks that you pass is divisible by the total requested batch size."
                )
            mask_image = mask_image.repeat(batch_size // mask_image.shape[0], 1, 1, 1)

        mask_image = torch.cat([mask_image] * 2) if do_classifier_free_guidance else mask_image

        mask_image_latents = mask_image

        return mask_image_latents

    def prepare_masked_image_latents(
        self, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
    ):
        masked_image = masked_image.to(device=device, dtype=dtype)

        # encode the mask image into latents space so we can concatenate it to the latents
        if isinstance(generator, list):
            masked_image_latents = [
                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
                for i in range(batch_size)
            ]
            masked_image_latents = torch.cat(masked_image_latents, dim=0)
        else:
            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents

        # duplicate masked_image_latents for each generation per prompt, using mps friendly method
        if masked_image_latents.shape[0] < batch_size:
            if not batch_size % masked_image_latents.shape[0] == 0:
                raise ValueError(
                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
                    " Make sure the number of images that you pass is divisible by the total requested batch size."
                )
            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)

        masked_image_latents = (
            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
        )

        # aligning device to prevent device errors when concating it with the latent model input
        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
        return masked_image_latents

    def _default_height_width(self, height, width, image):
        if isinstance(image, list):
            image = image[0]

        if height is None:
            if isinstance(image, PIL.Image.Image):
                height = image.height
            elif isinstance(image, torch.Tensor):
                height = image.shape[3]

            height = (height // 8) * 8  # round down to nearest multiple of 8

        if width is None:
            if isinstance(image, PIL.Image.Image):
                width = image.width
            elif isinstance(image, torch.Tensor):
                width = image.shape[2]

            width = (width // 8) * 8  # round down to nearest multiple of 8

        return height, width

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        image: Union[torch.Tensor, PIL.Image.Image] = None,
        mask_image: Union[torch.Tensor, PIL.Image.Image] = None,
        controlnet_conditioning_image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: 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,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,

    ):
        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.
            image (`torch.Tensor` or `PIL.Image.Image`):
                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
                be masked out with `mask_image` and repainted according to `prompt`.
            mask_image (`torch.Tensor` or `PIL.Image.Image`):
                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
                instead of 3, so the expected shape would be `(B, H, W, 1)`.
            controlnet_conditioning_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `List[PIL.Image.Image]`):
                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. 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 control image is automatically resized to fit the output image.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image.
            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 7.5):
                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`).
            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.
            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.StableDiffusionPipelineOutput`] 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.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
            controlnet_conditioning_scale (`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.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """
        # 0. Default height and width to unet
        height, width = self._default_height_width(height, width, controlnet_conditioning_image)

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            image,
            mask_image,
            controlnet_conditioning_image,
            height,
            width,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )

        # 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
        # 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

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

        # 3. Encode input prompt
        prompt_embeds = self._encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )

        # 4. Prepare mask, image, and controlnet_conditioning_image
        image = prepare_image(image)

        mask_image = prepare_mask_image(mask_image)

        masked_image = image * (mask_image < 0.5)


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


        if isinstance(self.controlnet, ControlNetModel):
            controlnet_conditioning_image = prepare_controlnet_conditioning_image(
                controlnet_conditioning_image,
                width,
                height,
                batch_size * num_images_per_prompt,
                num_images_per_prompt,
                device,
                self.controlnet.dtype,
                do_classifier_free_guidance
            )
        elif isinstance(self.controlnet, MultiControlNetModel):
            images = []

            for image_ in controlnet_conditioning_image:
                print("We are iterating over the controlnet conditioning images")
                image_ = prepare_controlnet_conditioning_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=self.controlnet.dtype,
                    do_classifier_free_guidance=do_classifier_free_guidance
                )

                images.append(image_)

            controlnet_conditioning_image = images
        else:
            assert False

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

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

        mask_image_latents = self.prepare_mask_latents(
            mask_image,
            batch_size * num_images_per_prompt,
            height,
            width,
            prompt_embeds.dtype,
            device,
            do_classifier_free_guidance,
        )

        masked_image_latents = self.prepare_masked_image_latents(
            masked_image,
            batch_size * num_images_per_prompt,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            do_classifier_free_guidance,
        )

        # if do_classifier_free_guidance:
        #     controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)

        # 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)

        # 8. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                non_inpainting_latent_model_input = (
                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                )

                non_inpainting_latent_model_input = self.scheduler.scale_model_input(
                    non_inpainting_latent_model_input, t
                )

                inpainting_latent_model_input = torch.cat(
                    [non_inpainting_latent_model_input, mask_image_latents, masked_image_latents], dim=1
                )

                down_block_res_samples, mid_block_res_sample = self.controlnet(
                    non_inpainting_latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    controlnet_cond=controlnet_conditioning_image,
                    conditioning_scale=controlnet_conditioning_scale,                    
                    return_dict=False,
                )

                # down_block_res_samples = [
                #     down_block_res_sample * controlnet_conditioning_scale
                #     for down_block_res_sample in down_block_res_samples
                # ]
                # mid_block_res_sample *= controlnet_conditioning_scale

                # predict the noise residual
                noise_pred = self.unet(
                    inpainting_latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                    down_block_additional_residuals=down_block_res_samples,
                    mid_block_additional_residual=mid_block_res_sample,
                ).sample

                # perform guidance
                if 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).prev_sample

                # 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:
                        callback(i, t, latents)

        # If we do sequential model offloading, let's offload unet and controlnet
        # manually for max memory savings
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.unet.to("cpu")
            self.controlnet.to("cpu")
            torch.cuda.empty_cache()

        if output_type == "latent":
            image = latents
            has_nsfw_concept = None
        elif output_type == "pil":
            # 8. Post-processing
            image = self.decode_latents(latents)

            # 9. Run safety checker
            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)

            # 10. Convert to PIL
            image = self.numpy_to_pil(image)
        else:
            # 8. Post-processing
            image = self.decode_latents(latents)

            # 9. Run safety checker
            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)

        # Offload last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

[sayakpaul]

Thank you for being willing to contribute this!

Directly iterating on the PR is better :) Don't forget to share some results in the PR description. I think @haofanwang could maybe guide here as well.