
"""simple docstring"""



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 				a			(					lowercase      ):
      UpperCamelCase							:   List[str]           =     (EulerDiscreteScheduler,)
      UpperCamelCase							:   str           =     1_0



      def  __snake_case							(   self     ,	**UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					{
              'num_train_timesteps': 1_100,
              'beta_start': 0.0001,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
          }

          config.update(**UpperCamelCase_      )
          return config



      def  __snake_case							(   self      ):
          for timesteps in [10, 50, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=UpperCamelCase_      )



      def  __snake_case							(   self      ):
          for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,	[0.0002, 0.002, 0.02]      ):
              self.check_over_configs(beta_start=UpperCamelCase_     ,	beta_end=UpperCamelCase_      )



      def  __snake_case							(   self      ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=UpperCamelCase_      )



      def  __snake_case							(   self      ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					self.scheduler_classes[0]
          UpperCAmelCase__	:      Tuple     							=					self.get_scheduler_config()
          UpperCAmelCase__	:      Optional[int]     							=					scheduler_class(**UpperCamelCase_      )

          scheduler.set_timesteps(self.num_inference_steps      )

          UpperCAmelCase__	:      List[str]     							=					torch.manual_seed(0      )

          UpperCAmelCase__	:      Union[str, Any]     							=					self.dummy_model()
          UpperCAmelCase__	:      List[Any]     							=					self.dummy_sample_deter * scheduler.init_noise_sigma
          UpperCAmelCase__	:      List[str]     							=					sample.to(UpperCamelCase_      )

          for i, t in enumerate(scheduler.timesteps      ):
              UpperCAmelCase__	:      Dict     							=					scheduler.scale_model_input(UpperCamelCase_     ,	UpperCamelCase_      )

              UpperCAmelCase__	:      List[str]     							=					model(UpperCamelCase_     ,	UpperCamelCase_      )

              UpperCAmelCase__	:      str     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	generator=UpperCamelCase_      )
              UpperCAmelCase__	:      Tuple     							=					output.prev_sample

          UpperCAmelCase__	:      Any     							=					torch.sum(torch.abs(UpperCamelCase_      )      )
          UpperCAmelCase__	:      str     							=					torch.mean(torch.abs(UpperCamelCase_      )      )

          assert abs(result_sum.item() - 10.0807      ) < 1E-2
          assert abs(result_mean.item() - 0.0131      ) < 1E-3



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					self.scheduler_classes[0]
          UpperCAmelCase__	:      Any     							=					self.get_scheduler_config(prediction_type='v_prediction'      )
          UpperCAmelCase__	:      List[str]     							=					scheduler_class(**UpperCamelCase_      )

          scheduler.set_timesteps(self.num_inference_steps      )

          UpperCAmelCase__	:      str     							=					torch.manual_seed(0      )

          UpperCAmelCase__	:      Dict     							=					self.dummy_model()
          UpperCAmelCase__	:      Dict     							=					self.dummy_sample_deter * scheduler.init_noise_sigma
          UpperCAmelCase__	:      Tuple     							=					sample.to(UpperCamelCase_      )

          for i, t in enumerate(scheduler.timesteps      ):
              UpperCAmelCase__	:      Dict     							=					scheduler.scale_model_input(UpperCamelCase_     ,	UpperCamelCase_      )

              UpperCAmelCase__	:      Optional[int]     							=					model(UpperCamelCase_     ,	UpperCamelCase_      )

              UpperCAmelCase__	:      Union[str, Any]     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	generator=UpperCamelCase_      )
              UpperCAmelCase__	:      str     							=					output.prev_sample

          UpperCAmelCase__	:      List[Any]     							=					torch.sum(torch.abs(UpperCamelCase_      )      )
          UpperCAmelCase__	:      Dict     							=					torch.mean(torch.abs(UpperCamelCase_      )      )

          assert abs(result_sum.item() - 0.0002      ) < 1E-2
          assert abs(result_mean.item() - 2.2676E-06      ) < 1E-3



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.scheduler_classes[0]
          UpperCAmelCase__	:      Tuple     							=					self.get_scheduler_config()
          UpperCAmelCase__	:      Union[str, Any]     							=					scheduler_class(**UpperCamelCase_      )

          scheduler.set_timesteps(self.num_inference_steps     ,	device=UpperCamelCase_      )

          UpperCAmelCase__	:      Tuple     							=					torch.manual_seed(0      )

          UpperCAmelCase__	:      Optional[Any]     							=					self.dummy_model()
          UpperCAmelCase__	:      Optional[Any]     							=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          UpperCAmelCase__	:      int     							=					sample.to(UpperCamelCase_      )

          for t in scheduler.timesteps:
              UpperCAmelCase__	:      Optional[int]     							=					scheduler.scale_model_input(UpperCamelCase_     ,	UpperCamelCase_      )

              UpperCAmelCase__	:      str     							=					model(UpperCamelCase_     ,	UpperCamelCase_      )

              UpperCAmelCase__	:      int     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	generator=UpperCamelCase_      )
              UpperCAmelCase__	:      List[str]     							=					output.prev_sample

          UpperCAmelCase__	:      int     							=					torch.sum(torch.abs(UpperCamelCase_      )      )
          UpperCAmelCase__	:      List[Any]     							=					torch.mean(torch.abs(UpperCamelCase_      )      )

          assert abs(result_sum.item() - 10.0807      ) < 1E-2
          assert abs(result_mean.item() - 0.0131      ) < 1E-3



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					self.scheduler_classes[0]
          UpperCAmelCase__	:      List[Any]     							=					self.get_scheduler_config()
          UpperCAmelCase__	:      Optional[Any]     							=					scheduler_class(**UpperCamelCase_     ,	use_karras_sigmas=UpperCamelCase_      )

          scheduler.set_timesteps(self.num_inference_steps     ,	device=UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					torch.manual_seed(0      )

          UpperCAmelCase__	:      Optional[int]     							=					self.dummy_model()
          UpperCAmelCase__	:      Union[str, Any]     							=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          UpperCAmelCase__	:      int     							=					sample.to(UpperCamelCase_      )

          for t in scheduler.timesteps:
              UpperCAmelCase__	:      List[Any]     							=					scheduler.scale_model_input(UpperCamelCase_     ,	UpperCamelCase_      )

              UpperCAmelCase__	:      Dict     							=					model(UpperCamelCase_     ,	UpperCamelCase_      )

              UpperCAmelCase__	:      Dict     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	generator=UpperCamelCase_      )
              UpperCAmelCase__	:      List[str]     							=					output.prev_sample

          UpperCAmelCase__	:      Dict     							=					torch.sum(torch.abs(UpperCamelCase_      )      )
          UpperCAmelCase__	:      Tuple     							=					torch.mean(torch.abs(UpperCamelCase_      )      )

          assert abs(result_sum.item() - 124.52299499511719      ) < 1E-2
          assert abs(result_mean.item() - 0.16213932633399963      ) < 1E-3



"""simple docstring"""



import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase__          =	'path-to-your-trained-model'
UpperCamelCase__          =	StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')

UpperCamelCase__          =	'A photo of sks dog in a bucket'
UpperCamelCase__          =	pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('dog-bucket.png')

