




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""")









import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort


class 							__UpperCamelCase			( __UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Any										="""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



				def       __snake_case		( self       , UpperCAmelCase_=0						):
											lowerCAmelCase   =		floats_tensor((1, 3, 1_28, 1_28)       , rng=random.Random(UpperCAmelCase_						)						)
											lowerCAmelCase   =		np.random.RandomState(UpperCAmelCase_						)
											lowerCAmelCase   =		{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''image''': image,
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''strength''': 0.75,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]						)
											assert np.abs(image_slice - expected_slice						).max() < 1E-1



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		PNDMScheduler.from_config(pipe.scheduler.config       , skip_prk_steps=UpperCAmelCase_						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											# warmup pass to apply optimizations
											lowerCAmelCase   =		pipe(**self.get_dummy_inputs()						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1






				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1




@nightly
@require_onnxruntime
@require_torch_gpu
class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				@property
				def       __snake_case		( self						):
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)



				@property
				def       __snake_case		( self						):
											lowerCAmelCase   =		ort.SessionOptions()
											lowerCAmelCase   =		False
											return options



				def       __snake_case		( self						):
											lowerCAmelCase   =		load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/img2img/sketch-mountains-input.jpg'''						)
											lowerCAmelCase   =		init_image.resize((7_68, 5_12)						)
											# using the PNDM scheduler by default
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(
											    '''CompVis/stable-diffusion-v1-4'''       , revision='''onnx'''       , safety_checker=UpperCAmelCase_       , feature_extractor=UpperCAmelCase_       , provider=self.gpu_provider       , sess_options=self.gpu_options       , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		'''A fantasy landscape, trending on artstation'''

											lowerCAmelCase   =		np.random.RandomState(0						)
											lowerCAmelCase   =		pipe(
											    prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , strength=0.75       , guidance_scale=7.5       , num_inference_steps=10       , generator=UpperCAmelCase_       , output_type='''np'''       , )
											lowerCAmelCase   =		output.images
											lowerCAmelCase   =		images[0, 2_55:2_58, 3_83:3_86, -1]

											assert images.shape == (1, 5_12, 7_68, 3)
											lowerCAmelCase   =		np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]						)
											# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 2E-2






				def       __snake_case		( self						):
											lowerCAmelCase   =		load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/img2img/sketch-mountains-input.jpg'''						)
											lowerCAmelCase   =		init_image.resize((7_68, 5_12)						)
											lowerCAmelCase   =		LMSDiscreteScheduler.from_pretrained(
											    '''runwayml/stable-diffusion-v1-5'''       , subfolder='''scheduler'''       , revision='''onnx'''						)
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(
											    '''runwayml/stable-diffusion-v1-5'''       , revision='''onnx'''       , scheduler=UpperCAmelCase_       , safety_checker=UpperCAmelCase_       , feature_extractor=UpperCAmelCase_       , provider=self.gpu_provider       , sess_options=self.gpu_options       , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		'''A fantasy landscape, trending on artstation'''

											lowerCAmelCase   =		np.random.RandomState(0						)
											lowerCAmelCase   =		pipe(
											    prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , strength=0.75       , guidance_scale=7.5       , num_inference_steps=20       , generator=UpperCAmelCase_       , output_type='''np'''       , )
											lowerCAmelCase   =		output.images
											lowerCAmelCase   =		images[0, 2_55:2_58, 3_83:3_86, -1]

											assert images.shape == (1, 5_12, 7_68, 3)
											lowerCAmelCase   =		np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]						)
											# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 2E-2



