code: 
  
  
  """simple docstring"""
  
  
  
  
  import gc
  import os
  import tempfile
  import unittest
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
  from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
  from diffusers.models.attention_processor import (
      Attention,
      AttnProcessor,
      AttnProcessor2_0,
      LoRAAttnProcessor,
      LoRAAttnProcessor2_0,
      LoRAXFormersAttnProcessor,
      XFormersAttnProcessor,
  )
  from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				{}
         for name in unet.attn_processors.keys():
                _a          =				None if name.endswith("attn1.processor"					) else unet.config.cross_attention_dim
                if name.startswith("mid_block"					):
                       _a          =				unet.config.block_out_channels[-1]
                elif name.startswith("up_blocks"					):
                       _a          =				int(name[len("up_blocks."					)]					)
                       _a          =				list(reversed(unet.config.block_out_channels					)					)[block_id]
                elif name.startswith("down_blocks"					):
                       _a          =				int(name[len("down_blocks."					)]					)
                       _a          =				unet.config.block_out_channels[block_id]
                _a          =				(
                    LoRAAttnProcessor2_0 if hasattr(a,		"scaled_dot_product_attention"					) else LoRAAttnProcessor
                )
                _a          =				lora_attn_processor_class(
                    hidden_size=a,		cross_attention_dim=a					)
         _a          =				AttnProcsLayers(a					)
         return lora_attn_procs, unet_lora_layers
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				{}
         _a          =				(
             LoRAAttnProcessor2_0 if hasattr(a,		"scaled_dot_product_attention"					) else LoRAAttnProcessor
         )
         for name, module in text_encoder.named_modules():
                if name.endswith(a					):
                       _a          =				lora_attn_processor_class(
                           hidden_size=module.out_proj.out_features,		cross_attention_dim=a					)
         return text_lora_attn_procs
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				create_text_encoder_lora_attn_procs(a					)
         _a          =				AttnProcsLayers(a					)
         return text_encoder_lora_layers
  
  
  
  def 						__a      (  a,		a=False					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         for _, attn_proc in text_lora_attn_procs.items():
                # set up.weights
                for layer_name, layer_module in attn_proc.named_modules():
                       if layer_name.endswith("_lora"					):
                              _a          =				(
                                  torch.randn_like(layer_module.up.weight					)
                                  if randn_weight
                                  else torch.zeros_like(layer_module.up.weight					)
                              )
                              _a          =				torch.nn.Parameter(a					)
  
  
  
  
  
  class __snake_case		(					unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]       ):
            torch.manual_seed(0       )
            _a          =				UNetaDConditionModel(
                block_out_channels=(32, 64)				,  layers_per_block=2				,  sample_size=32				,  in_channels=4				,  out_channels=4				,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				,  cross_attention_dim=32				,  )
            _a          =				DDIMScheduler(
                beta_start=0.00085				,  beta_end=0.012				,  beta_schedule="scaled_linear"				,  clip_sample=UpperCamelCase__				,  set_alpha_to_one=UpperCamelCase__				,  steps_offset=1				,  )
            torch.manual_seed(0       )
            _a          =				AutoencoderKL(
                block_out_channels=[32, 64]				,  in_channels=3				,  out_channels=3				,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,  latent_channels=4				,  )
            _a          =				CLIPTextConfig(
                bos_token_id=0				,  eos_token_id=2				,  hidden_size=32				,  intermediate_size=37				,  layer_norm_eps=1E-05				,  num_attention_heads=4				,  num_hidden_layers=5				,  pad_token_id=1				,  vocab_size=1_000				,  )
            _a          =				CLIPTextModel(UpperCamelCase__       )
            _a          =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
  
            _a   , _a          =				create_unet_lora_layers(UpperCamelCase__       )
            _a          =				create_text_encoder_lora_layers(UpperCamelCase__       )
  
            _a          =				{
                "unet": unet,
                "scheduler": scheduler,
                "vae": vae,
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "safety_checker": None,
                "feature_extractor": None,
            }
            _a          =				{
                "unet_lora_layers": unet_lora_layers,
                "text_encoder_lora_layers": text_encoder_lora_layers,
                "unet_lora_attn_procs": unet_lora_attn_procs,
            }
            return pipeline_components, lora_components
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
            _a          =				1
            _a          =				10
            _a          =				4
            _a          =				(32, 32)
  
            _a          =				torch.manual_seed(0       )
            _a          =				floats_tensor((batch_size, num_channels) + sizes       )
            _a          =				torch.randint(1				,  UpperCamelCase__				,  size=(batch_size, sequence_length)				,  generator=UpperCamelCase__       )
  
            _a          =				{
                "prompt": "A painting of a squirrel eating a burger",
                "generator": generator,
                "num_inference_steps": 2,
                "guidance_scale": 6.0,
                "output_type": "numpy",
            }
  
            return noise, input_ids, pipeline_inputs
  
            # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            _a          =				77
  
            _a          =				torch.randint(2				,  56				,  size=(1, max_seq_length)				,  generator=torch.manual_seed(0       )       )
  
            _a          =				{}
            _a          =				inputs
            return prepared_inputs
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any				,  UpperCamelCase__							:List[Any]       ):
            _a   , _a          =				self.get_dummy_components()
            LoraLoaderMixin.save_lora_weights(
                save_directory=UpperCamelCase__				,  unet_lora_layers=lora_components["unet_lora_layers"]				,  text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]				,  )
            self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,  "pytorch_lora_weights.bin"       )       )       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str       ):
            _a   , _a          =				self.get_dummy_components()
            _a          =				StableDiffusionPipeline(**UpperCamelCase__       )
            _a          =				sd_pipe.to(UpperCamelCase__       )
            sd_pipe.set_progress_bar_config(disable=UpperCamelCase__       )
  
            _a   , _a   , _a          =				self.get_dummy_inputs()
  
            _a          =				sd_pipe(**UpperCamelCase__       ).images
            _a          =				original_images[0, -3:, -3:, -1]
  
            with tempfile.TemporaryDirectory() as tmpdirname:
                   LoraLoaderMixin.save_lora_weights(
                       save_directory=UpperCamelCase__				,  unet_lora_layers=lora_components["unet_lora_layers"]				,  text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]				,  )
                   self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,  "pytorch_lora_weights.bin"       )       )       )
                   sd_pipe.load_lora_weights(UpperCamelCase__       )
  
            _a          =				sd_pipe(**UpperCamelCase__       ).images
            _a          =				lora_images[0, -3:, -3:, -1]
  
            # Outputs shouldn't match.
            self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__       )				,  torch.from_numpy(UpperCamelCase__       )       )       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            _a   , _a          =				self.get_dummy_components()
            _a          =				StableDiffusionPipeline(**UpperCamelCase__       )
            _a          =				sd_pipe.to(UpperCamelCase__       )
            sd_pipe.set_progress_bar_config(disable=UpperCamelCase__       )
  
            _a   , _a   , _a          =				self.get_dummy_inputs()
  
            _a          =				sd_pipe(**UpperCamelCase__       ).images
            _a          =				original_images[0, -3:, -3:, -1]
  
            with tempfile.TemporaryDirectory() as tmpdirname:
                   LoraLoaderMixin.save_lora_weights(
                       save_directory=UpperCamelCase__				,  unet_lora_layers=lora_components["unet_lora_layers"]				,  text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]				,  safe_serialization=UpperCamelCase__				,  )
                   self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,  "pytorch_lora_weights.safetensors"       )       )       )
                   sd_pipe.load_lora_weights(UpperCamelCase__       )
  
            _a          =				sd_pipe(**UpperCamelCase__       ).images
            _a          =				lora_images[0, -3:, -3:, -1]
  
            # Outputs shouldn't match.
            self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__       )				,  torch.from_numpy(UpperCamelCase__       )       )       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a   , _a          =				self.get_dummy_components()
            _a          =				lora_components["unet_lora_attn_procs"]
            _a          =				StableDiffusionPipeline(**UpperCamelCase__       )
            _a          =				sd_pipe.to(UpperCamelCase__       )
            sd_pipe.set_progress_bar_config(disable=UpperCamelCase__       )
  
            _a   , _a   , _a          =				self.get_dummy_inputs()
  
            _a          =				sd_pipe(**UpperCamelCase__       ).images
            _a          =				original_images[0, -3:, -3:, -1]
  
            with tempfile.TemporaryDirectory() as tmpdirname:
                   _a          =				sd_pipe.unet
                   unet.set_attn_processor(UpperCamelCase__       )
                   unet.save_attn_procs(UpperCamelCase__       )
                   self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,  "pytorch_lora_weights.bin"       )       )       )
                   sd_pipe.load_lora_weights(UpperCamelCase__       )
  
            _a          =				sd_pipe(**UpperCamelCase__       ).images
            _a          =				lora_images[0, -3:, -3:, -1]
  
            # Outputs shouldn't match.
            self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__       )				,  torch.from_numpy(UpperCamelCase__       )       )       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a   , _a          =				self.get_dummy_components()
            _a          =				StableDiffusionPipeline(**UpperCamelCase__       )
  
            _a          =				self.get_dummy_tokens()
  
            # inference without lora
            _a          =				pipe.text_encoder(**UpperCamelCase__       )[0]
            assert outputs_without_lora.shape == (1, 77, 32)
  
            # create lora_attn_procs with zeroed out up.weights
            _a          =				create_text_encoder_lora_attn_procs(pipe.text_encoder       )
            set_lora_up_weights(UpperCamelCase__				,  randn_weight=UpperCamelCase__       )
  
            # monkey patch
            pipe._modify_text_encoder(UpperCamelCase__       )
  
            # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
            del text_attn_procs
            gc.collect()
  
            # inference with lora
            _a          =				pipe.text_encoder(**UpperCamelCase__       )[0]
            assert outputs_with_lora.shape == (1, 77, 32)
  
            assert torch.allclose(
                UpperCamelCase__				,  UpperCamelCase__       ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"
  
            # create lora_attn_procs with randn up.weights
            _a          =				create_text_encoder_lora_attn_procs(pipe.text_encoder       )
            set_lora_up_weights(UpperCamelCase__				,  randn_weight=UpperCamelCase__       )
  
            # monkey patch
            pipe._modify_text_encoder(UpperCamelCase__       )
  
            # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
            del text_attn_procs
            gc.collect()
  
            # inference with lora
            _a          =				pipe.text_encoder(**UpperCamelCase__       )[0]
            assert outputs_with_lora.shape == (1, 77, 32)
  
            assert not torch.allclose(
                UpperCamelCase__				,  UpperCamelCase__       ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            _a   , _a          =				self.get_dummy_components()
            _a          =				StableDiffusionPipeline(**UpperCamelCase__       )
  
            _a          =				self.get_dummy_tokens()
  
            # inference without lora
            _a          =				pipe.text_encoder(**UpperCamelCase__       )[0]
            assert outputs_without_lora.shape == (1, 77, 32)
  
            # create lora_attn_procs with randn up.weights
            _a          =				create_text_encoder_lora_attn_procs(pipe.text_encoder       )
            set_lora_up_weights(UpperCamelCase__				,  randn_weight=UpperCamelCase__       )
  
            # monkey patch
            pipe._modify_text_encoder(UpperCamelCase__       )
  
            # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
            del text_attn_procs
            gc.collect()
  
            # inference with lora
            _a          =				pipe.text_encoder(**UpperCamelCase__       )[0]
            assert outputs_with_lora.shape == (1, 77, 32)
  
            assert not torch.allclose(
                UpperCamelCase__				,  UpperCamelCase__       ), "lora outputs should be different to without lora outputs"
  
            # remove monkey patch
            pipe._remove_text_encoder_monkey_patch()
  
            # inference with removed lora
            _a          =				pipe.text_encoder(**UpperCamelCase__       )[0]
            assert outputs_without_lora_removed.shape == (1, 77, 32)
  
            assert torch.allclose(
                UpperCamelCase__				,  UpperCamelCase__       ), "remove lora monkey patch should restore the original outputs"
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            _a   , _a          =				self.get_dummy_components()
            _a          =				StableDiffusionPipeline(**UpperCamelCase__       )
            _a          =				sd_pipe.to(UpperCamelCase__       )
            sd_pipe.set_progress_bar_config(disable=UpperCamelCase__       )
  
            _a   , _a   , _a          =				self.get_dummy_inputs()
  
            with tempfile.TemporaryDirectory() as tmpdirname:
                   LoraLoaderMixin.save_lora_weights(
                       save_directory=UpperCamelCase__				,  unet_lora_layers=lora_components["unet_lora_layers"]				,  text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]				,  )
                   self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,  "pytorch_lora_weights.bin"       )       )       )
                   sd_pipe.load_lora_weights(UpperCamelCase__       )
  
            _a          =				sd_pipe(**UpperCamelCase__       ).images
            _a          =				lora_images[0, -3:, -3:, -1]
  
            _a          =				sd_pipe(**UpperCamelCase__				,  cross_attention_kwargs={"scale": 0.5}       ).images
            _a          =				lora_images_with_scale[0, -3:, -3:, -1]
  
            # Outputs shouldn't match.
            self.assertFalse(
                torch.allclose(torch.from_numpy(UpperCamelCase__       )				,  torch.from_numpy(UpperCamelCase__       )       )       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[Any]       ):
            with tempfile.TemporaryDirectory() as tmpdirname:
                   self.create_lora_weight_file(UpperCamelCase__       )
  
                   _a   , _a          =				self.get_dummy_components()
                   _a          =				StableDiffusionPipeline(**UpperCamelCase__       )
                   _a          =				sd_pipe.to(UpperCamelCase__       )
                   sd_pipe.set_progress_bar_config(disable=UpperCamelCase__       )
  
                   # check if vanilla attention processors are used
                   for _, module in sd_pipe.unet.named_modules():
                          if isinstance(UpperCamelCase__				,  UpperCamelCase__       ):
                                 self.assertIsInstance(module.processor				,  (AttnProcessor, AttnProcessor2_0)       )
  
              # load LoRA weight file
                   sd_pipe.load_lora_weights(UpperCamelCase__       )
  
                   # check if lora attention processors are used
                   for _, module in sd_pipe.unet.named_modules():
                          if isinstance(UpperCamelCase__				,  UpperCamelCase__       ):
                                 _a          =				(
                                     LoRAAttnProcessor2_0 if hasattr(UpperCamelCase__				,  "scaled_dot_product_attention"       ) else LoRAAttnProcessor
                                 )
                                 self.assertIsInstance(module.processor				,  UpperCamelCase__       )
  
  
  
  
     @unittest.skipIf(torch_device != "cuda"				,  "This test is supposed to run on GPU"       )
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            with tempfile.TemporaryDirectory() as tmpdirname:
                   self.create_lora_weight_file(UpperCamelCase__       )
  
                   _a   , _a          =				self.get_dummy_components()
                   _a          =				StableDiffusionPipeline(**UpperCamelCase__       )
                   _a          =				sd_pipe.to(UpperCamelCase__       )
                   sd_pipe.set_progress_bar_config(disable=UpperCamelCase__       )
  
                   # enable XFormers
                   sd_pipe.enable_xformers_memory_efficient_attention()
  
                   # check if xFormers attention processors are used
                   for _, module in sd_pipe.unet.named_modules():
                          if isinstance(UpperCamelCase__				,  UpperCamelCase__       ):
                                 self.assertIsInstance(module.processor				,  UpperCamelCase__       )
  
              # load LoRA weight file
                   sd_pipe.load_lora_weights(UpperCamelCase__       )
  
                   # check if lora attention processors are used
                   for _, module in sd_pipe.unet.named_modules():
                          if isinstance(UpperCamelCase__				,  UpperCamelCase__       ):
                                 self.assertIsInstance(module.processor				,  UpperCamelCase__       )
  
  
  
  
     @unittest.skipIf(torch_device != "cuda"				,  "This test is supposed to run on GPU"       )
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
            _a   , _a          =				self.get_dummy_components()
            _a          =				StableDiffusionPipeline(**UpperCamelCase__       )
            _a          =				sd_pipe.to(UpperCamelCase__       )
            sd_pipe.set_progress_bar_config(disable=UpperCamelCase__       )
  
            _a   , _a   , _a          =				self.get_dummy_inputs()
  
            # enable XFormers
            sd_pipe.enable_xformers_memory_efficient_attention()
  
            _a          =				sd_pipe(**UpperCamelCase__       ).images
            _a          =				original_images[0, -3:, -3:, -1]
  
            with tempfile.TemporaryDirectory() as tmpdirname:
                   LoraLoaderMixin.save_lora_weights(
                       save_directory=UpperCamelCase__				,  unet_lora_layers=lora_components["unet_lora_layers"]				,  text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]				,  )
                   self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,  "pytorch_lora_weights.bin"       )       )       )
                   sd_pipe.load_lora_weights(UpperCamelCase__       )
  
            _a          =				sd_pipe(**UpperCamelCase__       ).images
            _a          =				lora_images[0, -3:, -3:, -1]
  
            # Outputs shouldn't match.
            self.assertFalse(torch.allclose(torch.from_numpy(UpperCamelCase__       )				,  torch.from_numpy(UpperCamelCase__       )       )       )
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import Union
  
  import torch
  from PIL import Image
  from torchvision import transforms as tfms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      UNetaDConditionModel,
  )
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]				,  ):
            super().__init__()
  
            self.register_modules(vae=UpperCamelCase__				,  text_encoder=UpperCamelCase__				,  tokenizer=UpperCamelCase__				,  unet=UpperCamelCase__				,  scheduler=UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
            with torch.no_grad():
                   _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                   _a          =				0.18215 * latent.latent_dist.sample()
            return latent
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Any       ):
            _a          =				(1 / 0.18215) * latent
            with torch.no_grad():
                   _a          =				self.vae.decode(UpperCamelCase__       ).sample
            _a          =				(img / 2 + 0.5).clamp(0				,  1       )
            _a          =				img.detach().cpu().permute(0				,  2				,  3				,  1       ).numpy()
            _a          =				(img * 255).round().astype("uint8"       )
            return Image.fromarray(img[0]       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:List[Any]       ):
            _a          =				self.tokenizer(
                UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )
  
            _a          =				self.text_encoder(text_input.input_ids.to(self.device       )       )[0]
  
            _a          =				self.tokenizer(
                ""				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )
  
            _a          =				self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
  
            return torch.cat([uncond_embedding, text_embedding]       )
  
  
  
  
     def __call__(		self							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
            _a          =				steps - int(kmin * steps       )
            _a          =				steps - int(kmax * steps       )
  
            _a          =				self.prep_text(UpperCamelCase__       )
  
            self.scheduler.set_timesteps(UpperCamelCase__       )
  
            _a   , _a          =				img.size
            _a          =				self.encode(UpperCamelCase__       )
  
            torch.manual_seed(UpperCamelCase__       )
            _a          =				torch.randn(
                (1, self.unet.config.in_channels, height // 8, width // 8)				,  ).to(self.device       )
  
            _a          =				self.scheduler.add_noise(
                UpperCamelCase__				,  UpperCamelCase__				,  timesteps=self.scheduler.timesteps[tmax]				,  )
  
            _a          =				torch.cat([latents] * 2       )
  
            _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  self.scheduler.timesteps[tmax]       )
  
            with torch.no_grad():
                   _a          =				self.unet(
                       UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  encoder_hidden_states=UpperCamelCase__				,  ).sample
  
            _a   , _a          =				pred.chunk(2       )
            _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
            _a          =				self.scheduler.step(UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  UpperCamelCase__       ).prev_sample
  
            for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
                   if i > tmax:
                          if i < tmin:  # layout generation phase
                                 _a          =				self.scheduler.add_noise(
                                     UpperCamelCase__				,  UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )
  
                                 _a          =				(mix_factor * latents) + (
                                     1 - mix_factor
                                 ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                                 _a          =				torch.cat([input] * 2       )
  
                          else:  # content generation phase
                                 _a          =				torch.cat([latents] * 2       )
  
                          _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  UpperCamelCase__       )
  
                          with torch.no_grad():
                                 _a          =				self.unet(
                                     UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__				,  ).sample
  
                          _a   , _a          =				pred.chunk(2       )
                          _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
                          _a          =				self.scheduler.step(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       ).prev_sample
  
            return self.decode(UpperCamelCase__       )
  