
import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=[]       ):
				lowerCamelCase_         =     size[0] - overlap_pixels * 2
				lowerCamelCase_         =     size[1] - overlap_pixels * 2
				for letter in ["l", "r"]:
								if letter in remove_borders:
												size_x += overlap_pixels
				for letter in ["t", "b"]:
								if letter in remove_borders:
												size_y += overlap_pixels
				lowerCamelCase_         =     np.ones((size_y, size_x)      ,					dtype=np.uinta       ) * 2_5_5
				lowerCamelCase_         =     np.pad(lowerCamelCase__      ,					mode="linear_ramp"      ,					pad_width=lowerCamelCase__      ,					end_values=0       )

				if "l" in remove_borders:
								lowerCamelCase_         =     mask[:, overlap_pixels : mask.shape[1]]
				if "r" in remove_borders:
								lowerCamelCase_         =     mask[:, 0 : mask.shape[1] - overlap_pixels]
				if "t" in remove_borders:
								lowerCamelCase_         =     mask[overlap_pixels : mask.shape[0], :]
				if "b" in remove_borders:
								lowerCamelCase_         =     mask[0 : mask.shape[0] - overlap_pixels, :]
				return mask





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				return max(lowerCamelCase__      ,					min(lowerCamelCase__      ,					lowerCamelCase__       )       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				return (
				    clamp(rect[0]      ,					min[0]      ,					max[0]       ),
				    clamp(rect[1]      ,					min[1]      ,					max[1]       ),
				    clamp(rect[2]      ,					min[0]      ,					max[0]       ),
				    clamp(rect[3]      ,					min[1]      ,					max[1]       ),
				)





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     list(lowerCamelCase__       )
				rect[0] -= overlap
				rect[1] -= overlap
				rect[2] += overlap
				rect[3] += overlap
				lowerCamelCase_         =     clamp_rect(lowerCamelCase__      ,					[0, 0]      ,					[image_size[0], image_size[1]]       )
				return rect





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     Image.new("RGB"      ,					(tile.size[0] + original_slice, tile.size[1])       )
				result.paste(
				    original_image.resize((tile.size[0], tile.size[1])      ,					Image.BICUBIC       ).crop(
				        (slice_x, 0, slice_x + original_slice, tile.size[1])       )      ,					(0, 0)      ,					)
				result.paste(lowerCamelCase__      ,					(original_slice, 0)       )
				return result





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     (original_image_slice * 4, 0, tile.size[0], tile.size[1])
				lowerCamelCase_         =     tile.crop(lowerCamelCase__       )
				return tile





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     n % d
				return n - divisor




class        _SCREAMING_SNAKE_CASE	( snake_case_		):



	def __init__(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase = 350		,    )							->			Optional[Any]:
					super().__init__(
					    vae=lowercase		,    text_encoder=lowercase		,    tokenizer=lowercase		,    unet=lowercase		,    low_res_scheduler=lowercase		,    scheduler=lowercase		,    max_noise_level=lowercase		,    )



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    **lowercase					)							->			Tuple:
					torch.manual_seed(0					)
					lowerCamelCase_         =     (
					    min(image.size[0] - (tile_size + original_image_slice)		,    x * tile_size					),
					    min(image.size[1] - (tile_size + original_image_slice)		,    y * tile_size					),
					    min(image.size[0]		,    (x + 1) * tile_size					),
					    min(image.size[1]		,    (y + 1) * tile_size					),
					)
					lowerCamelCase_         =     add_overlap_rect(lowercase		,    lowercase		,    image.size					)
					lowerCamelCase_         =     image.crop(lowercase					)
					lowerCamelCase_         =     ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
					lowerCamelCase_         =     translated_slice_x - (original_image_slice / 2)
					lowerCamelCase_         =     max(0		,    lowercase					)
					lowerCamelCase_         =     squeeze_tile(lowercase		,    lowercase		,    lowercase		,    lowercase					)
					lowerCamelCase_         =     to_input.size
					lowerCamelCase_         =     to_input.resize((tile_size, tile_size)		,    Image.BICUBIC					)
					lowerCamelCase_         =     super(lowercase		,    self					).__call__(image=lowercase		,    **lowercase					).images[0]
					lowerCamelCase_         =     upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)		,    Image.BICUBIC					)
					lowerCamelCase_         =     unsqueeze_tile(lowercase		,    lowercase					)
					lowerCamelCase_         =     upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)		,    Image.BICUBIC					)
					lowerCamelCase_         =     []
					if x == 0:
									remove_borders.append("l"					)
					elif crop_rect[2] == image.size[0]:
									remove_borders.append("r"					)
					if y == 0:
									remove_borders.append("t"					)
					elif crop_rect[3] == image.size[1]:
									remove_borders.append("b"					)
					lowerCamelCase_         =     Image.fromarray(
					    make_transparency_mask(
					        (upscaled_tile.size[0], upscaled_tile.size[1])		,    tile_border * 4		,    remove_borders=lowercase					)		,    mode="L"		,    )
					final_image.paste(
					    lowercase		,    (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)		,    lowercase					)



	@torch.no_grad()
	def __call__(       self		,    lowercase		,    lowercase		,    lowercase = 75		,    lowercase = 9.0		,    lowercase = 50		,    lowercase = None		,    lowercase = 1		,    lowercase = 0.0		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = 1		,    lowercase = 128		,    lowercase = 32		,    lowercase = 32		,    )							->			Dict:
					lowerCamelCase_         =     Image.new("RGB"		,    (image.size[0] * 4, image.size[1] * 4)					)
					lowerCamelCase_         =     math.ceil(image.size[0] / tile_size					)
					lowerCamelCase_         =     math.ceil(image.size[1] / tile_size					)
					lowerCamelCase_         =     tcx * tcy
					lowerCamelCase_         =     0
					for y in range(lowercase					):
									for x in range(lowercase					):
													self._process_tile(
													    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    prompt=lowercase		,    num_inference_steps=lowercase		,    guidance_scale=lowercase		,    noise_level=lowercase		,    negative_prompt=lowercase		,    num_images_per_prompt=lowercase		,    eta=lowercase		,    generator=lowercase		,    latents=lowercase		,    )
													current_count += 1
													if callback is not None:
																	callback({"progress": current_count / total_tile_count, "image": final_image}					)
					return final_image





def      lowerCamelCase_					(							):
				# Run a demo
				lowerCamelCase_         =     "stabilityai/stable-diffusion-x4-upscaler"
				lowerCamelCase_         =     StableDiffusionTiledUpscalePipeline.from_pretrained(lowerCamelCase__      ,					revision="fp16"      ,					torch_dtype=torch.floataa       )
				lowerCamelCase_         =     pipe.to("cuda"       )
				lowerCamelCase_         =     Image.open("../../docs/source/imgs/diffusers_library.jpg"       )

				def callback(lowerCamelCase__       ):
								print(F'progress: {obj["progress"]:.4f}'       )
								obj["image"].save("diffusers_library_progress.jpg"       )

				lowerCamelCase_         =     pipe(image=lowerCamelCase__      ,					prompt="Black font, white background, vector"      ,					noise_level=4_0      ,					callback=lowerCamelCase__       )
				final_image.save("diffusers_library.jpg"       )


if __name__ == "__main__":
			main()




# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




class        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_     ,			snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					StableDiffusionControlNetImgaImgPipeline
	lowerCAmelCase__   =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
	lowerCAmelCase__   =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowerCAmelCase__   =					IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}		)
	lowerCAmelCase__   =					IMAGE_TO_IMAGE_IMAGE_PARAMS



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					torch.manual_seed(0					)
					lowerCamelCase_         =     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		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     ControlNetModel(
					    block_out_channels=(32, 64)		,    layers_per_block=2		,    in_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,    cross_attention_dim=32		,    conditioning_embedding_out_channels=(16, 32)		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     DDIMScheduler(
					    beta_start=0.0_0_0_8_5		,    beta_end=0.0_1_2		,    beta_schedule="scaled_linear"		,    clip_sample=lowercase		,    set_alpha_to_one=lowercase		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     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		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     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=1000		,    )
					lowerCamelCase_         =     CLIPTextModel(lowercase					)
					lowerCamelCase_         =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

					lowerCamelCase_         =     {
					    "unet": unet,
					    "controlnet": controlnet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=0					)							->			int:
					if str(lowercase					).startswith("mps"					):
									lowerCamelCase_         =     torch.manual_seed(lowercase					)
					else:
									lowerCamelCase_         =     torch.Generator(device=lowercase					).manual_seed(lowercase					)

					lowerCamelCase_         =     2
					lowerCamelCase_         =     randn_tensor(
					    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,    generator=lowercase		,    device=torch.device(lowercase					)		,    )
					lowerCamelCase_         =     floats_tensor(control_image.shape		,    rng=random.Random(lowercase					)					).to(lowercase					)
					lowerCamelCase_         =     image.cpu().permute(0		,    2		,    3		,    1					)[0]
					lowerCamelCase_         =     Image.fromarray(np.uinta(lowercase					)					).convert("RGB"					).resize((64, 64)					)
					lowerCamelCase_         =     {
					    "prompt": "A painting of a squirrel eating a burger",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 6.0,
					    "output_type": "numpy",
					    "image": image,
					    "control_image": control_image,
					}

					return inputs



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3					)



	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()		,    reason="XFormers attention is only available with CUDA and `xformers` installed"		,    )
	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					self._test_inference_batch_single_identical(expected_max_diff=2e-3					)




class        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					StableDiffusionControlNetImgaImgPipeline
	lowerCAmelCase__   =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
	lowerCAmelCase__   =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowerCAmelCase__   =					frozenset([]		)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					torch.manual_seed(0					)
					lowerCamelCase_         =     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		,    )
					torch.manual_seed(0					)

					def init_weights(lowercase					):
									if isinstance(lowercase		,    torch.nn.Convad					):
													torch.nn.init.normal(m.weight					)
													m.bias.data.fill_(1.0					)

					lowerCamelCase_         =     ControlNetModel(
					    block_out_channels=(32, 64)		,    layers_per_block=2		,    in_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,    cross_attention_dim=32		,    conditioning_embedding_out_channels=(16, 32)		,    )
					controlneta.controlnet_down_blocks.apply(lowercase					)

					torch.manual_seed(0					)
					lowerCamelCase_         =     ControlNetModel(
					    block_out_channels=(32, 64)		,    layers_per_block=2		,    in_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,    cross_attention_dim=32		,    conditioning_embedding_out_channels=(16, 32)		,    )
					controlneta.controlnet_down_blocks.apply(lowercase					)

					torch.manual_seed(0					)
					lowerCamelCase_         =     DDIMScheduler(
					    beta_start=0.0_0_0_8_5		,    beta_end=0.0_1_2		,    beta_schedule="scaled_linear"		,    clip_sample=lowercase		,    set_alpha_to_one=lowercase		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     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		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     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=1000		,    )
					lowerCamelCase_         =     CLIPTextModel(lowercase					)
					lowerCamelCase_         =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

					lowerCamelCase_         =     MultiControlNetModel([controlneta, controlneta]					)

					lowerCamelCase_         =     {
					    "unet": unet,
					    "controlnet": controlnet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=0					)							->			List[Any]:
					if str(lowercase					).startswith("mps"					):
									lowerCamelCase_         =     torch.manual_seed(lowercase					)
					else:
									lowerCamelCase_         =     torch.Generator(device=lowercase					).manual_seed(lowercase					)

					lowerCamelCase_         =     2

					lowerCamelCase_         =     [
					    randn_tensor(
					        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,    generator=lowercase		,    device=torch.device(lowercase					)		,    ),
					    randn_tensor(
					        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,    generator=lowercase		,    device=torch.device(lowercase					)		,    ),
					]

					lowerCamelCase_         =     floats_tensor(control_image[0].shape		,    rng=random.Random(lowercase					)					).to(lowercase					)
					lowerCamelCase_         =     image.cpu().permute(0		,    2		,    3		,    1					)[0]
					lowerCamelCase_         =     Image.fromarray(np.uinta(lowercase					)					).convert("RGB"					).resize((64, 64)					)
					lowerCamelCase_         =     {
					    "prompt": "A painting of a squirrel eating a burger",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 6.0,
					    "output_type": "numpy",
					    "image": image,
					    "control_image": control_image,
					}

					return inputs



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					lowerCamelCase_         =     self.get_dummy_components()
					lowerCamelCase_         =     self.pipeline_class(**lowercase					)
					pipe.to(lowercase					)

					lowerCamelCase_         =     1_0.0
					lowerCamelCase_         =     4

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase					)[0]

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase		,    control_guidance_start=0.1		,    control_guidance_end=0.2					)[0]

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase		,    control_guidance_start=[0.1, 0.3]		,    control_guidance_end=[0.2, 0.7]					)[0]

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase		,    control_guidance_start=0.4		,    control_guidance_end=[0.5, 0.8]					)[0]

					# make sure that all outputs are different
					assert np.sum(np.abs(output_a - output_a					)					) > 1e-3
					assert np.sum(np.abs(output_a - output_a					)					) > 1e-3
					assert np.sum(np.abs(output_a - output_a					)					) > 1e-3



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3					)



	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()		,    reason="XFormers attention is only available with CUDA and `xformers` installed"		,    )
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					self._test_inference_batch_single_identical(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					lowerCamelCase_         =     self.get_dummy_components()
					lowerCamelCase_         =     self.pipeline_class(**lowercase					)
					pipe.to(lowercase					)
					pipe.set_progress_bar_config(disable=lowercase					)
					with tempfile.TemporaryDirectory() as tmpdir:
									try:
													# save_pretrained is not implemented for Multi-ControlNet
													pipe.save_pretrained(lowercase					)
									except NotImplementedError:
													pass




@slow
@require_torch_gpu
class        _SCREAMING_SNAKE_CASE	( unittest.TestCase		):



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					lowerCamelCase_         =     ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"					)

					lowerCamelCase_         =     StableDiffusionControlNetImgaImgPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"		,    safety_checker=lowercase		,    controlnet=lowercase					)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=lowercase					)

					lowerCamelCase_         =     torch.Generator(device="cpu"					).manual_seed(0					)
					lowerCamelCase_         =     "evil space-punk bird"
					lowerCamelCase_         =     load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"					).resize((512, 512)					)
					lowerCamelCase_         =     load_image(
					    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"					).resize((512, 512)					)

					lowerCamelCase_         =     pipe(
					    lowercase		,    lowercase		,    control_image=lowercase		,    generator=lowercase		,    output_type="np"		,    num_inference_steps=50		,    strength=0.6		,    )

					lowerCamelCase_         =     output.images[0]

					assert image.shape == (512, 512, 3)

					lowerCamelCase_         =     load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"					)

					assert np.abs(expected_image - image					).max() < 9e-2


