




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Union[str, Any]										=(CMStochasticIterativeScheduler,)
				__a							: str										=1_0



				def       __snake_case		( self       , **UpperCAmelCase_						):
											lowerCAmelCase   =		{
											    '''num_train_timesteps''': 2_01,
											    '''sigma_min''': 0.002,
											    '''sigma_max''': 80.0,
											}

											config.update(**UpperCAmelCase_						)
											return config



				def       __snake_case		( self						):
											lowerCAmelCase   =		10

											lowerCAmelCase   =		self.get_scheduler_config()
											lowerCAmelCase   =		self.scheduler_classes[0](**UpperCAmelCase_						)

											scheduler.set_timesteps(UpperCAmelCase_						)

											lowerCAmelCase   =		scheduler.timesteps[0]
											lowerCAmelCase   =		scheduler.timesteps[1]

											lowerCAmelCase   =		self.dummy_sample
											lowerCAmelCase   =		0.1 * sample

											lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						).prev_sample
											lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						).prev_sample

											self.assertEqual(output_a.shape       , sample.shape						)
											self.assertEqual(output_a.shape       , output_a.shape						)



				def       __snake_case		( self						):
											for timesteps in [10, 50, 1_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=UpperCAmelCase_						)



				def       __snake_case		( self						):
											for clip_denoised in [True, False]:
																		self.check_over_configs(clip_denoised=UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config()
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

											lowerCAmelCase   =		1
											scheduler.set_timesteps(UpperCAmelCase_						)
											lowerCAmelCase   =		scheduler.timesteps

											lowerCAmelCase   =		torch.manual_seed(0						)

											lowerCAmelCase   =		self.dummy_model()
											lowerCAmelCase   =		self.dummy_sample_deter * scheduler.init_noise_sigma

											for i, t in enumerate(UpperCAmelCase_						):
																		# 1. scale model input
																		lowerCAmelCase   =		scheduler.scale_model_input(UpperCAmelCase_       , UpperCAmelCase_						)

																		# 2. predict noise residual
																		lowerCAmelCase   =		model(UpperCAmelCase_       , UpperCAmelCase_						)

																		# 3. predict previous sample x_t-1
																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_						).prev_sample

																		lowerCAmelCase   =		pred_prev_sample

											lowerCAmelCase   =		torch.sum(torch.abs(UpperCAmelCase_						)						)
											lowerCAmelCase   =		torch.mean(torch.abs(UpperCAmelCase_						)						)

											assert abs(result_sum.item() - 192.7614						) < 1E-2
											assert abs(result_mean.item() - 0.2510						) < 1E-3



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config()
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

											lowerCAmelCase   =		[1_06, 0]
											scheduler.set_timesteps(timesteps=UpperCAmelCase_						)
											lowerCAmelCase   =		scheduler.timesteps

											lowerCAmelCase   =		torch.manual_seed(0						)

											lowerCAmelCase   =		self.dummy_model()
											lowerCAmelCase   =		self.dummy_sample_deter * scheduler.init_noise_sigma

											for t in timesteps:
																		# 1. scale model input
																		lowerCAmelCase   =		scheduler.scale_model_input(UpperCAmelCase_       , UpperCAmelCase_						)

																		# 2. predict noise residual
																		lowerCAmelCase   =		model(UpperCAmelCase_       , UpperCAmelCase_						)

																		# 3. predict previous sample x_t-1
																		lowerCAmelCase   =		scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_						).prev_sample

																		lowerCAmelCase   =		pred_prev_sample

											lowerCAmelCase   =		torch.sum(torch.abs(UpperCAmelCase_						)						)
											lowerCAmelCase   =		torch.mean(torch.abs(UpperCAmelCase_						)						)

											assert abs(result_sum.item() - 347.6357						) < 1E-2
											assert abs(result_mean.item() - 0.4527						) < 1E-3



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config()
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

											lowerCAmelCase   =		[39, 30, 12, 15, 0]

											with self.assertRaises(UpperCAmelCase_       , msg='''`timesteps` must be in descending order.'''						):
																		scheduler.set_timesteps(timesteps=UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config()
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

											lowerCAmelCase   =		[39, 30, 12, 1, 0]
											lowerCAmelCase   =		len(UpperCAmelCase_						)

											with self.assertRaises(UpperCAmelCase_       , msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''						):
																		scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_       , timesteps=UpperCAmelCase_						)






				def       __snake_case		( self						):
											lowerCAmelCase   =		self.scheduler_classes[0]
											lowerCAmelCase   =		self.get_scheduler_config()
											lowerCAmelCase   =		scheduler_class(**UpperCAmelCase_						)

											lowerCAmelCase   =		[scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    UpperCAmelCase_       , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       , ):
																		scheduler.set_timesteps(timesteps=UpperCAmelCase_						)









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""")



