killabee777 / tests /pipelines /stable_diffusion /test_stable_diffusion_inpaint.py

Add stable diffusion weights

af7c068 almost 4 years ago

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# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    AutoencoderKL,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInpaintPipeline,
    UNet2DConditionModel,
    UNet2DModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from ...test_pipelines_common import PipelineTesterMixin


torch.backends.cuda.matmul.allow_tf32 = False


class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    def tearDown(self):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    @property
    def dummy_image(self):
        batch_size = 1
        num_channels = 3
        sizes = (32, 32)

        image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
        return image

    @property
    def dummy_uncond_unet(self):
        torch.manual_seed(0)
        model = UNet2DModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            sample_size=32,
            in_channels=3,
            out_channels=3,
            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
        )
        return model

    @property
    def dummy_cond_unet(self):
        torch.manual_seed(0)
        model = UNet2DConditionModel(
            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,
        )
        return model

    @property
    def dummy_cond_unet_inpaint(self):
        torch.manual_seed(0)
        model = UNet2DConditionModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            sample_size=32,
            in_channels=9,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            cross_attention_dim=32,
        )
        return model

    @property
    def dummy_vq_model(self):
        torch.manual_seed(0)
        model = VQModel(
            block_out_channels=[32, 64],
            in_channels=3,
            out_channels=3,
            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
            latent_channels=3,
        )
        return model

    @property
    def dummy_vae(self):
        torch.manual_seed(0)
        model = 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,
        )
        return model

    @property
    def dummy_text_encoder(self):
        torch.manual_seed(0)
        config = 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,
        )
        return CLIPTextModel(config)

    @property
    def dummy_extractor(self):
        def extract(*args, **kwargs):
            class Out:
                def __init__(self):
                    self.pixel_values = torch.ones([0])

                def to(self, device):
                    self.pixel_values.to(device)
                    return self

            return Out()

        return extract

    def test_stable_diffusion_inpaint(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet_inpaint
        scheduler = PNDMScheduler(skip_prk_steps=True)
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
        init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionInpaintPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=None,
            feature_extractor=None,
        )
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.Generator(device=device).manual_seed(0)
        output = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=6.0,
            num_inference_steps=2,
            output_type="np",
            image=init_image,
            mask_image=mask_image,
        )

        image = output.images

        generator = torch.Generator(device=device).manual_seed(0)
        image_from_tuple = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=6.0,
            num_inference_steps=2,
            output_type="np",
            image=init_image,
            mask_image=mask_image,
            return_dict=False,
        )[0]

        image_slice = image[0, -3:, -3:, -1]
        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        expected_slice = np.array([0.5075, 0.4485, 0.4558, 0.5369, 0.5369, 0.5236, 0.5127, 0.4983, 0.4776])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2

    def test_stable_diffusion_inpaint_with_num_images_per_prompt(self):
        device = "cpu"
        unet = self.dummy_cond_unet_inpaint
        scheduler = PNDMScheduler(skip_prk_steps=True)
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
        init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionInpaintPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=None,
            feature_extractor=None,
        )
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.Generator(device=device).manual_seed(0)
        images = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=6.0,
            num_inference_steps=2,
            output_type="np",
            image=init_image,
            mask_image=mask_image,
            num_images_per_prompt=2,
        ).images

        # check if the output is a list of 2 images
        assert len(images) == 2

    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
    def test_stable_diffusion_inpaint_fp16(self):
        """Test that stable diffusion inpaint_legacy works with fp16"""
        unet = self.dummy_cond_unet_inpaint
        scheduler = PNDMScheduler(skip_prk_steps=True)
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
        init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))

        # put models in fp16
        unet = unet.half()
        vae = vae.half()
        bert = bert.half()

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionInpaintPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=None,
            feature_extractor=None,
        )
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.Generator(device=torch_device).manual_seed(0)
        image = sd_pipe(
            [prompt],
            generator=generator,
            num_inference_steps=2,
            output_type="np",
            image=init_image,
            mask_image=mask_image,
        ).images

        assert image.shape == (1, 128, 128, 3)


@slow
@require_torch_gpu
class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
    def tearDown(self):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    def test_stable_diffusion_inpaint_pipeline(self):
        init_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo.png"
        )
        mask_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
        )
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
            "/yellow_cat_sitting_on_a_park_bench.npy"
        )

        model_id = "runwayml/stable-diffusion-inpainting"
        pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

        generator = torch.Generator(device=torch_device).manual_seed(0)
        output = pipe(
            prompt=prompt,
            image=init_image,
            mask_image=mask_image,
            generator=generator,
            output_type="np",
        )
        image = output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image).max() < 1e-3

    def test_stable_diffusion_inpaint_pipeline_fp16(self):
        init_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo.png"
        )
        mask_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
        )
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
            "/yellow_cat_sitting_on_a_park_bench_fp16.npy"
        )

        model_id = "runwayml/stable-diffusion-inpainting"
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            model_id,
            revision="fp16",
            torch_dtype=torch.float16,
            safety_checker=None,
        )
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

        generator = torch.Generator(device=torch_device).manual_seed(0)
        output = pipe(
            prompt=prompt,
            image=init_image,
            mask_image=mask_image,
            generator=generator,
            output_type="np",
        )
        image = output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image).max() < 5e-1

    def test_stable_diffusion_inpaint_pipeline_pndm(self):
        init_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo.png"
        )
        mask_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
        )
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
            "/yellow_cat_sitting_on_a_park_bench_pndm.npy"
        )

        model_id = "runwayml/stable-diffusion-inpainting"
        pndm = PNDMScheduler.from_pretrained(model_id, subfolder="scheduler")
        pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None, scheduler=pndm)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

        generator = torch.Generator(device=torch_device).manual_seed(0)
        output = pipe(
            prompt=prompt,
            image=init_image,
            mask_image=mask_image,
            generator=generator,
            output_type="np",
        )
        image = output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image).max() < 1e-2

    def test_stable_diffusion_inpaint_pipeline_k_lms(self):
        init_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo.png"
        )
        mask_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
        )
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
            "/yellow_cat_sitting_on_a_park_bench_k_lms.npy"
        )

        model_id = "runwayml/stable-diffusion-inpainting"
        pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
        pipe.to(torch_device)

        # switch to LMS
        pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)

        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

        generator = torch.Generator(device=torch_device).manual_seed(0)
        output = pipe(
            prompt=prompt,
            image=init_image,
            mask_image=mask_image,
            generator=generator,
            output_type="np",
        )
        image = output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image).max() < 1e-2

    @unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU")
    def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        init_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo.png"
        )
        mask_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
        )

        model_id = "runwayml/stable-diffusion-inpainting"
        pndm = PNDMScheduler.from_pretrained(model_id, subfolder="scheduler")
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            model_id,
            safety_checker=None,
            scheduler=pndm,
            device_map="auto",
            revision="fp16",
            torch_dtype=torch.float16,
        )
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing(1)
        pipe.enable_sequential_cpu_offload()

        prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

        generator = torch.Generator(device=torch_device).manual_seed(0)
        _ = pipe(
            prompt=prompt,
            image=init_image,
            mask_image=mask_image,
            generator=generator,
            num_inference_steps=5,
            output_type="np",
        )

        mem_bytes = torch.cuda.max_memory_allocated()
        # make sure that less than 2.2 GB is allocated
        assert mem_bytes < 2.2 * 10**9

	# coding=utf-8
	# Copyright 2022 HuggingFace Inc.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import gc
	import random
	import unittest

	import numpy as np
	import torch

	from diffusers import (
	AutoencoderKL,
	LMSDiscreteScheduler,
	PNDMScheduler,
	StableDiffusionInpaintPipeline,
	UNet2DConditionModel,
	UNet2DModel,
	VQModel,
	)
	from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
	from diffusers.utils.testing_utils import require_torch_gpu
	from PIL import Image
	from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

	from ...test_pipelines_common import PipelineTesterMixin


	torch.backends.cuda.matmul.allow_tf32 = False


	class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
	def tearDown(self):
	# clean up the VRAM after each test
	super().tearDown()
	gc.collect()
	torch.cuda.empty_cache()

	@property
	def dummy_image(self):
	batch_size = 1
	num_channels = 3
	sizes = (32, 32)

	image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
	return image

	@property
	def dummy_uncond_unet(self):
	torch.manual_seed(0)
	model = UNet2DModel(
	block_out_channels=(32, 64),
	layers_per_block=2,
	sample_size=32,
	in_channels=3,
	out_channels=3,
	down_block_types=("DownBlock2D", "AttnDownBlock2D"),
	up_block_types=("AttnUpBlock2D", "UpBlock2D"),
	)
	return model

	@property
	def dummy_cond_unet(self):
	torch.manual_seed(0)
	model = UNet2DConditionModel(
	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,
	)
	return model

	@property
	def dummy_cond_unet_inpaint(self):
	torch.manual_seed(0)
	model = UNet2DConditionModel(
	block_out_channels=(32, 64),
	layers_per_block=2,
	sample_size=32,
	in_channels=9,
	out_channels=4,
	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
	cross_attention_dim=32,
	)
	return model

	@property
	def dummy_vq_model(self):
	torch.manual_seed(0)
	model = VQModel(
	block_out_channels=[32, 64],
	in_channels=3,
	out_channels=3,
	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
	latent_channels=3,
	)
	return model

	@property
	def dummy_vae(self):
	torch.manual_seed(0)
	model = 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,
	)
	return model

	@property
	def dummy_text_encoder(self):
	torch.manual_seed(0)
	config = 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,
	)
	return CLIPTextModel(config)

	@property
	def dummy_extractor(self):
	def extract(args, *kwargs):
	class Out:
	def __init__(self):
	self.pixel_values = torch.ones([0])

	def to(self, device):
	self.pixel_values.to(device)
	return self

	return Out()

	return extract

	def test_stable_diffusion_inpaint(self):
	device = "cpu" # ensure determinism for the device-dependent torch.Generator
	unet = self.dummy_cond_unet_inpaint
	scheduler = PNDMScheduler(skip_prk_steps=True)
	vae = self.dummy_vae
	bert = self.dummy_text_encoder
	tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

	image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
	init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
	mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))

	# make sure here that pndm scheduler skips prk
	sd_pipe = StableDiffusionInpaintPipeline(
	unet=unet,
	scheduler=scheduler,
	vae=vae,
	text_encoder=bert,
	tokenizer=tokenizer,
	safety_checker=None,
	feature_extractor=None,
	)
	sd_pipe = sd_pipe.to(device)
	sd_pipe.set_progress_bar_config(disable=None)

	prompt = "A painting of a squirrel eating a burger"
	generator = torch.Generator(device=device).manual_seed(0)
	output = sd_pipe(
	[prompt],
	generator=generator,
	guidance_scale=6.0,
	num_inference_steps=2,
	output_type="np",
	image=init_image,
	mask_image=mask_image,
	)

	image = output.images

	generator = torch.Generator(device=device).manual_seed(0)
	image_from_tuple = sd_pipe(
	[prompt],
	generator=generator,
	guidance_scale=6.0,
	num_inference_steps=2,
	output_type="np",
	image=init_image,
	mask_image=mask_image,
	return_dict=False,
	)[0]

	image_slice = image[0, -3:, -3:, -1]
	image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]

	assert image.shape == (1, 128, 128, 3)
	expected_slice = np.array([0.5075, 0.4485, 0.4558, 0.5369, 0.5369, 0.5236, 0.5127, 0.4983, 0.4776])
	assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
	assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2

	def test_stable_diffusion_inpaint_with_num_images_per_prompt(self):
	device = "cpu"
	unet = self.dummy_cond_unet_inpaint
	scheduler = PNDMScheduler(skip_prk_steps=True)
	vae = self.dummy_vae
	bert = self.dummy_text_encoder
	tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

	image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
	init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
	mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))

	# make sure here that pndm scheduler skips prk
	sd_pipe = StableDiffusionInpaintPipeline(
	unet=unet,
	scheduler=scheduler,
	vae=vae,
	text_encoder=bert,
	tokenizer=tokenizer,
	safety_checker=None,
	feature_extractor=None,
	)
	sd_pipe = sd_pipe.to(device)
	sd_pipe.set_progress_bar_config(disable=None)

	prompt = "A painting of a squirrel eating a burger"
	generator = torch.Generator(device=device).manual_seed(0)
	images = sd_pipe(
	[prompt],
	generator=generator,
	guidance_scale=6.0,
	num_inference_steps=2,
	output_type="np",
	image=init_image,
	mask_image=mask_image,
	num_images_per_prompt=2,
	).images

	# check if the output is a list of 2 images
	assert len(images) == 2

	@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
	def test_stable_diffusion_inpaint_fp16(self):
	"""Test that stable diffusion inpaint_legacy works with fp16"""
	unet = self.dummy_cond_unet_inpaint
	scheduler = PNDMScheduler(skip_prk_steps=True)
	vae = self.dummy_vae
	bert = self.dummy_text_encoder
	tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

	image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
	init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128))
	mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))

	# put models in fp16
	unet = unet.half()
	vae = vae.half()
	bert = bert.half()

	# make sure here that pndm scheduler skips prk
	sd_pipe = StableDiffusionInpaintPipeline(
	unet=unet,
	scheduler=scheduler,
	vae=vae,
	text_encoder=bert,
	tokenizer=tokenizer,
	safety_checker=None,
	feature_extractor=None,
	)
	sd_pipe = sd_pipe.to(torch_device)
	sd_pipe.set_progress_bar_config(disable=None)

	prompt = "A painting of a squirrel eating a burger"
	generator = torch.Generator(device=torch_device).manual_seed(0)
	image = sd_pipe(
	[prompt],
	generator=generator,
	num_inference_steps=2,
	output_type="np",
	image=init_image,
	mask_image=mask_image,
	).images

	assert image.shape == (1, 128, 128, 3)


	@slow
	@require_torch_gpu
	class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
	def tearDown(self):
	# clean up the VRAM after each test
	super().tearDown()
	gc.collect()
	torch.cuda.empty_cache()

	def test_stable_diffusion_inpaint_pipeline(self):
	init_image = load_image(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/in_paint/overture-creations-5sI6fQgYIuo.png"
	)
	mask_image = load_image(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
	)
	expected_image = load_numpy(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
	"/yellow_cat_sitting_on_a_park_bench.npy"
	)

	model_id = "runwayml/stable-diffusion-inpainting"
	pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
	pipe.to(torch_device)
	pipe.set_progress_bar_config(disable=None)
	pipe.enable_attention_slicing()

	prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

	generator = torch.Generator(device=torch_device).manual_seed(0)
	output = pipe(
	prompt=prompt,
	image=init_image,
	mask_image=mask_image,
	generator=generator,
	output_type="np",
	)
	image = output.images[0]

	assert image.shape == (512, 512, 3)
	assert np.abs(expected_image - image).max() < 1e-3

	def test_stable_diffusion_inpaint_pipeline_fp16(self):
	init_image = load_image(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/in_paint/overture-creations-5sI6fQgYIuo.png"
	)
	mask_image = load_image(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
	)
	expected_image = load_numpy(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
	"/yellow_cat_sitting_on_a_park_bench_fp16.npy"
	)

	model_id = "runwayml/stable-diffusion-inpainting"
	pipe = StableDiffusionInpaintPipeline.from_pretrained(
	model_id,
	revision="fp16",
	torch_dtype=torch.float16,
	safety_checker=None,
	)
	pipe.to(torch_device)
	pipe.set_progress_bar_config(disable=None)
	pipe.enable_attention_slicing()

	prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

	generator = torch.Generator(device=torch_device).manual_seed(0)
	output = pipe(
	prompt=prompt,
	image=init_image,
	mask_image=mask_image,
	generator=generator,
	output_type="np",
	)
	image = output.images[0]

	assert image.shape == (512, 512, 3)
	assert np.abs(expected_image - image).max() < 5e-1

	def test_stable_diffusion_inpaint_pipeline_pndm(self):
	init_image = load_image(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/in_paint/overture-creations-5sI6fQgYIuo.png"
	)
	mask_image = load_image(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
	)
	expected_image = load_numpy(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
	"/yellow_cat_sitting_on_a_park_bench_pndm.npy"
	)

	model_id = "runwayml/stable-diffusion-inpainting"
	pndm = PNDMScheduler.from_pretrained(model_id, subfolder="scheduler")
	pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None, scheduler=pndm)
	pipe.to(torch_device)
	pipe.set_progress_bar_config(disable=None)
	pipe.enable_attention_slicing()

	prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

	generator = torch.Generator(device=torch_device).manual_seed(0)
	output = pipe(
	prompt=prompt,
	image=init_image,
	mask_image=mask_image,
	generator=generator,
	output_type="np",
	)
	image = output.images[0]

	assert image.shape == (512, 512, 3)
	assert np.abs(expected_image - image).max() < 1e-2

	def test_stable_diffusion_inpaint_pipeline_k_lms(self):
	init_image = load_image(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/in_paint/overture-creations-5sI6fQgYIuo.png"
	)
	mask_image = load_image(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
	)
	expected_image = load_numpy(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint"
	"/yellow_cat_sitting_on_a_park_bench_k_lms.npy"
	)

	model_id = "runwayml/stable-diffusion-inpainting"
	pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
	pipe.to(torch_device)

	# switch to LMS
	pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)

	pipe.set_progress_bar_config(disable=None)
	pipe.enable_attention_slicing()

	prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

	generator = torch.Generator(device=torch_device).manual_seed(0)
	output = pipe(
	prompt=prompt,
	image=init_image,
	mask_image=mask_image,
	generator=generator,
	output_type="np",
	)
	image = output.images[0]

	assert image.shape == (512, 512, 3)
	assert np.abs(expected_image - image).max() < 1e-2

	@unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU")
	def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
	torch.cuda.empty_cache()
	torch.cuda.reset_max_memory_allocated()
	torch.cuda.reset_peak_memory_stats()

	init_image = load_image(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/in_paint/overture-creations-5sI6fQgYIuo.png"
	)
	mask_image = load_image(
	"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
	"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
	)

	model_id = "runwayml/stable-diffusion-inpainting"
	pndm = PNDMScheduler.from_pretrained(model_id, subfolder="scheduler")
	pipe = StableDiffusionInpaintPipeline.from_pretrained(
	model_id,
	safety_checker=None,
	scheduler=pndm,
	device_map="auto",
	revision="fp16",
	torch_dtype=torch.float16,
	)
	pipe.to(torch_device)
	pipe.set_progress_bar_config(disable=None)
	pipe.enable_attention_slicing(1)
	pipe.enable_sequential_cpu_offload()

	prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

	generator = torch.Generator(device=torch_device).manual_seed(0)
	_ = pipe(
	prompt=prompt,
	image=init_image,
	mask_image=mask_image,
	generator=generator,
	num_inference_steps=5,
	output_type="np",
	)

	mem_bytes = torch.cuda.max_memory_allocated()
	# make sure that less than 2.2 GB is allocated
	assert mem_bytes < 2.2 * 10**9