'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from sapiens_transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ...callbacks import MultiPipelineCallbacks, PipelineCallback
from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, ControlNetXSAdapter, UNet2DConditionModel, UNetControlNetXSModel
from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import USE_PEFT_BACKEND, deprecate, replace_example_docstring, scale_lora_layers, unscale_lora_layers
from ...utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from ..stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
EXAMPLE_DOC_STRING = '\n    Examples:\n        ```py\n        >>> # !pip install opencv-python transformers sapiens_accelerator\n        >>> from sapiens_transformers.diffusers import StableDiffusionControlNetXSPipeline, ControlNetXSAdapter\n        >>> from sapiens_transformers.diffusers.utils import load_image\n        >>> import numpy as np\n        >>> import torch\n\n        >>> import cv2\n        >>> from PIL import Image\n\n        >>> prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"\n        >>> negative_prompt = "low quality, bad quality, sketches"\n\n        >>> # download an image\n        >>> image = load_image(\n        ...     "https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"\n        ... )\n\n        >>> # initialize the models and pipeline\n        >>> controlnet_conditioning_scale = 0.5\n\n        >>> controlnet = ControlNetXSAdapter.from_pretrained(\n        ...     "UmerHA/Testing-ConrolNetXS-SD2.1-canny", torch_dtype=torch.float16\n        ... )\n        >>> pipe = StableDiffusionControlNetXSPipeline.from_pretrained(\n        ...     "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float16\n        ... )\n        >>> pipe.enable_model_cpu_offload()\n\n        >>> # get canny image\n        >>> image = np.array(image)\n        >>> image = cv2.Canny(image, 100, 200)\n        >>> image = image[:, :, None]\n        >>> image = np.concatenate([image, image, image], axis=2)\n        >>> canny_image = Image.fromarray(image)\n        >>> # generate image\n        >>> image = pipe(\n        ...     prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image\n        ... ).images[0]\n        ```\n'
class StableDiffusionControlNetXSPipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionLoraLoaderMixin, FromSingleFileMixin):
    """Args:"""
    model_cpu_offload_seq = 'text_encoder->unet->vae'
    _optional_components = ['safety_checker', 'feature_extractor']
    _exclude_from_cpu_offload = ['safety_checker']
    _callback_tensor_inputs = ['latents', 'prompt_embeds', 'negative_prompt_embeds']
    def __init__(self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: Union[UNet2DConditionModel, UNetControlNetXSModel], controlnet: ControlNetXSAdapter,
    scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool=True):
        super().__init__()
        if isinstance(unet, UNet2DConditionModel): unet = UNetControlNetXSModel.from_unet(unet, controlnet)
        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.")
        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.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
        self.control_image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False)
        self.register_to_config(requires_safety_checker=requires_safety_checker)
    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.Tensor]=None,
    negative_prompt_embeds: Optional[torch.Tensor]=None, lora_scale: Optional[float]=None, **kwargs):
        deprecation_message = '`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.'
        deprecate('_encode_prompt()', '1.0.0', deprecation_message, standard_warn=False)
        prompt_embeds_tuple = self.encode_prompt(prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=do_classifier_free_guidance,
        negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=lora_scale, **kwargs)
        prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
        return prompt_embeds
    def encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.Tensor]=None,
    negative_prompt_embeds: Optional[torch.Tensor]=None, lora_scale: Optional[float]=None, clip_skip: Optional[int]=None):
        """Args:"""
        if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
            self._lora_scale = lora_scale
            if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
            else: scale_lora_layers(self.text_encoder, lora_scale)
        if prompt is not None and isinstance(prompt, str): batch_size = 1
        elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt)
        else: batch_size = prompt_embeds.shape[0]
        if prompt_embeds is None:
            if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
            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])
            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
            if clip_skip is None:
                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
                prompt_embeds = prompt_embeds[0]
            else:
                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True)
                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
        if self.text_encoder is not None: prompt_embeds_dtype = self.text_encoder.dtype
        elif self.unet is not None: prompt_embeds_dtype = self.unet.dtype
        else: prompt_embeds_dtype = prompt_embeds.dtype
        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
        bs_embed, seq_len, _ = prompt_embeds.shape
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None: uncond_tokens = [''] * batch_size
            elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError(f'`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} != {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`: {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches the batch size of `prompt`.')
            else: uncond_tokens = negative_prompt
            if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
            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:
            seq_len = negative_prompt_embeds.shape[1]
            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_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)
        if self.text_encoder is not None:
            if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND: unscale_lora_layers(self.text_encoder, lora_scale)
        return (prompt_embeds, negative_prompt_embeds)
    def run_safety_checker(self, image, device, dtype):
        if self.safety_checker is None: has_nsfw_concept = None
        else:
            if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type='pil')
            else: feature_extractor_input = self.image_processor.numpy_to_pil(image)
            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors='pt').to(device)
            image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_checker_input.pixel_values.to(dtype))
        return (image, has_nsfw_concept)
    def decode_latents(self, latents):
        deprecation_message = 'The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead'
        deprecate('decode_latents', '1.0.0', deprecation_message, standard_warn=False)
        latents = 1 / self.vae.config.scaling_factor * latents
        image = self.vae.decode(latents, return_dict=False)[0]
        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        return image
    def prepare_extra_step_kwargs(self, generator, eta):
        accepts_eta = 'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta: extra_step_kwargs['eta'] = eta
        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, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, controlnet_conditioning_scale=1.0, control_guidance_start=0.0,
    control_guidance_end=1.0, callback_on_step_end_tensor_inputs=None):
        if callback_on_step_end_tensor_inputs is not None and (not all((k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs))): raise ValueError(f'`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}')
        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`: {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(f'`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds` {negative_prompt_embeds.shape}.')
        is_compiled = hasattr(F, 'scaled_dot_product_attention') and isinstance(self.unet, torch._dynamo.eval_frame.OptimizedModule)
        if isinstance(self.unet, UNetControlNetXSModel) or (is_compiled and isinstance(self.unet._orig_mod, UNetControlNetXSModel)):
            self.check_image(image, prompt, prompt_embeds)
            if not isinstance(controlnet_conditioning_scale, float): raise TypeError('For single controlnet: `controlnet_conditioning_scale` must be type `float`.')
        else: assert False
        start, end = (control_guidance_start, control_guidance_end)
        if start >= end: raise ValueError(f'control guidance start: {start} cannot be larger or equal to control guidance end: {end}.')
        if start < 0.0: raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
        if end > 1.0: raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
    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_np = isinstance(image, np.ndarray)
        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)
        image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
        if not image_is_pil and (not image_is_tensor) and (not image_is_np) and (not image_is_pil_list) and (not image_is_tensor_list) and (not image_is_np_list): raise TypeError(f'image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}')
        if image_is_pil: image_batch_size = 1
        else: 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_image(self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False):
        image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
        image_batch_size = image.shape[0]
        if image_batch_size == 1: repeat_by = batch_size
        else: 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_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels_latents, int(height) // self.vae_scale_factor, int(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 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)
        latents = latents * self.scheduler.init_noise_sigma
        return latents
    @property
    def guidance_scale(self): return self._guidance_scale
    @property
    def clip_skip(self): return self._clip_skip
    @property
    def do_classifier_free_guidance(self): return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
    @property
    def cross_attention_kwargs(self): return self._cross_attention_kwargs
    @property
    def num_timesteps(self): return self._num_timesteps
    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(self, prompt: Union[str, List[str]]=None, image: PipelineImageInput=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.Tensor]=None, prompt_embeds: Optional[torch.Tensor]=None, negative_prompt_embeds: Optional[torch.Tensor]=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,
    control_guidance_start: float=0.0, control_guidance_end: float=1.0, clip_skip: Optional[int]=None, callback_on_step_end: Optional[Union[Callable[[int, int, Dict], None],
    PipelineCallback, MultiPipelineCallbacks]]=None, callback_on_step_end_tensor_inputs: List[str]=['latents']):
        """Examples:"""
        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
        unet = self.unet._orig_mod if is_compiled_module(self.unet) else self.unet
        self.check_inputs(prompt, image, negative_prompt, prompt_embeds, negative_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
        self._interrupt = False
        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
        do_classifier_free_guidance = guidance_scale > 1.0
        text_encoder_lora_scale = cross_attention_kwargs.get('scale', None) if cross_attention_kwargs is not None else None
        prompt_embeds, negative_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, lora_scale=text_encoder_lora_scale, clip_skip=clip_skip)
        if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
        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=unet.dtype, do_classifier_free_guidance=do_classifier_free_guidance)
        height, width = image.shape[-2:]
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps
        num_channels_latents = self.unet.in_channels
        latents = self.prepare_latents(batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents)
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        self._num_timesteps = len(timesteps)
        is_controlnet_compiled = is_compiled_module(self.unet)
        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):
                if is_controlnet_compiled and is_torch_higher_equal_2_1: torch._inductor.cudagraph_mark_step_begin()
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                apply_control = i / len(timesteps) >= control_guidance_start and (i + 1) / len(timesteps) <= control_guidance_end
                noise_pred = self.unet(sample=latent_model_input, timestep=t, encoder_hidden_states=prompt_embeds, controlnet_cond=image, conditioning_scale=controlnet_conditioning_scale,
                cross_attention_kwargs=cross_attention_kwargs, return_dict=True, apply_control=apply_control).sample
                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)
                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)
                if i == len(timesteps) - 1 or (i + 1 > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update()
        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 not output_type == 'latent':
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]
            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
        else:
            image = latents
            has_nsfw_concept = None
        if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0]
        else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
        self.maybe_free_model_hooks()
        if not return_dict: return (image, has_nsfw_concept)
        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
