test_pipelines.py

# 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 tempfile
import unittest

import numpy as np
import torch

import PIL
from datasets import load_dataset
from diffusers import (
    AutoencoderKL,
    DDIMPipeline,
    DDIMScheduler,
    DDPMPipeline,
    DDPMScheduler,
    KarrasVePipeline,
    KarrasVeScheduler,
    LDMPipeline,
    LDMTextToImagePipeline,
    LMSDiscreteScheduler,
    PNDMPipeline,
    PNDMScheduler,
    ScoreSdeVePipeline,
    ScoreSdeVeScheduler,
    StableDiffusionImg2ImgPipeline,
    StableDiffusionInpaintPipeline,
    StableDiffusionPipeline,
    UNet2DConditionModel,
    UNet2DModel,
    VQModel,
)
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.testing_utils import floats_tensor, slow, torch_device
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer


torch.backends.cuda.matmul.allow_tf32 = False


def test_progress_bar(capsys):
    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"),
    )
    scheduler = DDPMScheduler(num_train_timesteps=10)

    ddpm = DDPMPipeline(model, scheduler).to(torch_device)
    ddpm(output_type="numpy").images
    captured = capsys.readouterr()
    assert "10/10" in captured.err, "Progress bar has to be displayed"

    ddpm.set_progress_bar_config(disable=True)
    ddpm(output_type="numpy").images
    captured = capsys.readouterr()
    assert captured.err == "", "Progress bar should be disabled"


class PipelineFastTests(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_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_safety_checker(self):
        def check(images, *args, **kwargs):
            return images, False

        return check

    @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_ddim(self):
        unet = self.dummy_uncond_unet
        scheduler = DDIMScheduler(tensor_format="pt")

        ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
        ddpm.to(torch_device)
        ddpm.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images

        generator = torch.manual_seed(0)
        image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", 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, 32, 32, 3)
        expected_slice = np.array(
            [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]
        )
        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_pndm_cifar10(self):
        unet = self.dummy_uncond_unet
        scheduler = PNDMScheduler(tensor_format="pt")

        pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
        pndm.to(torch_device)
        pndm.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        image = pndm(generator=generator, num_inference_steps=20, output_type="numpy").images

        generator = torch.manual_seed(0)
        image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="numpy", 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, 32, 32, 3)
        expected_slice = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])
        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_ldm_text2img(self):
        unet = self.dummy_cond_unet
        scheduler = DDIMScheduler(tensor_format="pt")
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        ldm = LDMTextToImagePipeline(vqvae=vae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
        ldm.to(torch_device)
        ldm.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.manual_seed(0)
        image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy")[
            "sample"
        ]

        generator = torch.manual_seed(0)
        image_from_tuple = ldm(
            [prompt],
            generator=generator,
            guidance_scale=6.0,
            num_inference_steps=2,
            output_type="numpy",
            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, 64, 64, 3)
        expected_slice = np.array([0.5074, 0.5026, 0.4998, 0.4056, 0.3523, 0.4649, 0.5289, 0.5299, 0.4897])
        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_ddim(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )

        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=self.dummy_safety_checker,
            feature_extractor=self.dummy_extractor,
        )
        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 = 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",
            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([0.5112, 0.4692, 0.4715, 0.5206, 0.4894, 0.5114, 0.5096, 0.4932, 0.4755])
        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_pndm(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet
        scheduler = PNDMScheduler(tensor_format="pt", skip_prk_steps=True)
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=self.dummy_safety_checker,
            feature_extractor=self.dummy_extractor,
        )
        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 = 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",
            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([0.4937, 0.4649, 0.4716, 0.5145, 0.4889, 0.513, 0.513, 0.4905, 0.4738])
        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_k_lms(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet
        scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=self.dummy_safety_checker,
            feature_extractor=self.dummy_extractor,
        )
        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 = 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",
            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([0.5067, 0.4689, 0.4614, 0.5233, 0.4903, 0.5112, 0.524, 0.5069, 0.4785])
        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_attention_chunk(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet
        scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=self.dummy_safety_checker,
            feature_extractor=self.dummy_extractor,
        )
        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_1 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")

        # make sure chunking the attention yields the same result
        sd_pipe.enable_attention_slicing(slice_size=1)
        generator = torch.Generator(device=device).manual_seed(0)
        output_2 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")

        assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 1e-4

    def test_score_sde_ve_pipeline(self):
        unet = self.dummy_uncond_unet
        scheduler = ScoreSdeVeScheduler(tensor_format="pt")

        sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler)
        sde_ve.to(torch_device)
        sde_ve.set_progress_bar_config(disable=None)

        torch.manual_seed(0)
        image = sde_ve(num_inference_steps=2, output_type="numpy").images

        torch.manual_seed(0)
        image_from_tuple = sde_ve(num_inference_steps=2, output_type="numpy", 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, 32, 32, 3)

        expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
        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_ldm_uncond(self):
        unet = self.dummy_uncond_unet
        scheduler = DDIMScheduler(tensor_format="pt")
        vae = self.dummy_vq_model

        ldm = LDMPipeline(unet=unet, vqvae=vae, scheduler=scheduler)
        ldm.to(torch_device)
        ldm.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        image = ldm(generator=generator, num_inference_steps=2, output_type="numpy").images

        generator = torch.manual_seed(0)
        image_from_tuple = ldm(generator=generator, num_inference_steps=2, output_type="numpy", 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, 64, 64, 3)
        expected_slice = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
        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_karras_ve_pipeline(self):
        unet = self.dummy_uncond_unet
        scheduler = KarrasVeScheduler(tensor_format="pt")

        pipe = KarrasVePipeline(unet=unet, scheduler=scheduler)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        image = pipe(num_inference_steps=2, generator=generator, output_type="numpy").images

        generator = torch.manual_seed(0)
        image_from_tuple = pipe(num_inference_steps=2, generator=generator, output_type="numpy", 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, 32, 32, 3)
        expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
        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_img2img(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet
        scheduler = PNDMScheduler(tensor_format="pt", skip_prk_steps=True)
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        init_image = self.dummy_image.to(device)

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionImg2ImgPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=self.dummy_safety_checker,
            feature_extractor=self.dummy_extractor,
        )
        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",
            init_image=init_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",
            init_image=init_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, 32, 32, 3)
        expected_slice = np.array([0.4492, 0.3865, 0.4222, 0.5854, 0.5139, 0.4379, 0.4193, 0.48, 0.4218])
        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_img2img_k_lms(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet
        scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")

        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        init_image = self.dummy_image.to(device)

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionImg2ImgPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=self.dummy_safety_checker,
            feature_extractor=self.dummy_extractor,
        )
        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",
            init_image=init_image,
        )
        image = output.images

        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",
            init_image=init_image,
            return_dict=False,
        )
        image_from_tuple = output[0]

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

        assert image.shape == (1, 32, 32, 3)
        expected_slice = np.array([0.4367, 0.4986, 0.4372, 0.6706, 0.5665, 0.444, 0.5864, 0.6019, 0.5203])
        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(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet
        scheduler = PNDMScheduler(tensor_format="pt", 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.to(device).permute(0, 2, 3, 1)[0]
        init_image = Image.fromarray(np.uint8(image)).convert("RGB")
        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=self.dummy_safety_checker,
            feature_extractor=self.dummy_extractor,
        )
        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",
            init_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",
            init_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, 32, 32, 3)
        expected_slice = np.array([0.4731, 0.5346, 0.4531, 0.6251, 0.5446, 0.4057, 0.5527, 0.5896, 0.5153])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2


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

    def test_from_pretrained_save_pretrained(self):
        # 1. Load models
        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"),
        )
        schedular = DDPMScheduler(num_train_timesteps=10)

        ddpm = DDPMPipeline(model, schedular)
        ddpm.to(torch_device)
        ddpm.set_progress_bar_config(disable=None)

        with tempfile.TemporaryDirectory() as tmpdirname:
            ddpm.save_pretrained(tmpdirname)
            new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
            new_ddpm.to(torch_device)

        generator = torch.manual_seed(0)

        image = ddpm(generator=generator, output_type="numpy").images
        generator = generator.manual_seed(0)
        new_image = new_ddpm(generator=generator, output_type="numpy").images

        assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"

    @slow
    def test_from_pretrained_hub(self):
        model_path = "google/ddpm-cifar10-32"

        scheduler = DDPMScheduler(num_train_timesteps=10)

        ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler)
        ddpm.to(torch_device)
        ddpm.set_progress_bar_config(disable=None)
        ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
        ddpm_from_hub.to(torch_device)
        ddpm_from_hub.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)

        image = ddpm(generator=generator, output_type="numpy").images
        generator = generator.manual_seed(0)
        new_image = ddpm_from_hub(generator=generator, output_type="numpy").images

        assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"

    @slow
    def test_from_pretrained_hub_pass_model(self):
        model_path = "google/ddpm-cifar10-32"

        scheduler = DDPMScheduler(num_train_timesteps=10)

        # pass unet into DiffusionPipeline
        unet = UNet2DModel.from_pretrained(model_path)
        ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler)
        ddpm_from_hub_custom_model.to(torch_device)
        ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)

        ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
        ddpm_from_hub.to(torch_device)
        ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)

        image = ddpm_from_hub_custom_model(generator=generator, output_type="numpy").images
        generator = generator.manual_seed(0)
        new_image = ddpm_from_hub(generator=generator, output_type="numpy").images

        assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"

    @slow
    def test_output_format(self):
        model_path = "google/ddpm-cifar10-32"

        pipe = DDIMPipeline.from_pretrained(model_path)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        images = pipe(generator=generator, output_type="numpy").images
        assert images.shape == (1, 32, 32, 3)
        assert isinstance(images, np.ndarray)

        images = pipe(generator=generator, output_type="pil").images
        assert isinstance(images, list)
        assert len(images) == 1
        assert isinstance(images[0], PIL.Image.Image)

        # use PIL by default
        images = pipe(generator=generator).images
        assert isinstance(images, list)
        assert isinstance(images[0], PIL.Image.Image)

    @slow
    def test_ddpm_cifar10(self):
        model_id = "google/ddpm-cifar10-32"

        unet = UNet2DModel.from_pretrained(model_id)
        scheduler = DDPMScheduler.from_config(model_id)
        scheduler = scheduler.set_format("pt")

        ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
        ddpm.to(torch_device)
        ddpm.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        image = ddpm(generator=generator, output_type="numpy").images

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

        assert image.shape == (1, 32, 32, 3)
        expected_slice = np.array([0.41995, 0.35885, 0.19385, 0.38475, 0.3382, 0.2647, 0.41545, 0.3582, 0.33845])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    def test_ddim_lsun(self):
        model_id = "google/ddpm-ema-bedroom-256"

        unet = UNet2DModel.from_pretrained(model_id)
        scheduler = DDIMScheduler.from_config(model_id)

        ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
        ddpm.to(torch_device)
        ddpm.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        image = ddpm(generator=generator, output_type="numpy").images

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

        assert image.shape == (1, 256, 256, 3)
        expected_slice = np.array([0.00605, 0.0201, 0.0344, 0.00235, 0.00185, 0.00025, 0.00215, 0.0, 0.00685])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    def test_ddim_cifar10(self):
        model_id = "google/ddpm-cifar10-32"

        unet = UNet2DModel.from_pretrained(model_id)
        scheduler = DDIMScheduler(tensor_format="pt")

        ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
        ddim.to(torch_device)
        ddim.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        image = ddim(generator=generator, eta=0.0, output_type="numpy").images

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

        assert image.shape == (1, 32, 32, 3)
        expected_slice = np.array([0.17235, 0.16175, 0.16005, 0.16255, 0.1497, 0.1513, 0.15045, 0.1442, 0.1453])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    def test_pndm_cifar10(self):
        model_id = "google/ddpm-cifar10-32"

        unet = UNet2DModel.from_pretrained(model_id)
        scheduler = PNDMScheduler(tensor_format="pt")

        pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
        pndm.to(torch_device)
        pndm.set_progress_bar_config(disable=None)
        generator = torch.manual_seed(0)
        image = pndm(generator=generator, output_type="numpy").images

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

        assert image.shape == (1, 32, 32, 3)
        expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    def test_ldm_text2img(self):
        ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
        ldm.to(torch_device)
        ldm.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.manual_seed(0)
        image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy")[
            "sample"
        ]

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

        assert image.shape == (1, 256, 256, 3)
        expected_slice = np.array([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    def test_ldm_text2img_fast(self):
        ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
        ldm.to(torch_device)
        ldm.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.manual_seed(0)
        image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy").images

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

        assert image.shape == (1, 256, 256, 3)
        expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
    def test_stable_diffusion(self):
        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1", use_auth_token=True)
        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)
        with torch.autocast("cuda"):
            output = sd_pipe(
                [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np"
            )

        image = output.images

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

        assert image.shape == (1, 512, 512, 3)
        expected_slice = np.array([0.8887, 0.915, 0.91, 0.894, 0.909, 0.912, 0.919, 0.925, 0.883])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
    def test_stable_diffusion_fast_ddim(self):
        sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1", use_auth_token=True)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )
        sd_pipe.scheduler = scheduler

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.Generator(device=torch_device).manual_seed(0)

        with torch.autocast("cuda"):
            output = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy")
        image = output.images

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

        assert image.shape == (1, 512, 512, 3)
        expected_slice = np.array([0.9326, 0.923, 0.951, 0.9365, 0.9214, 0.951, 0.9365, 0.9414, 0.918])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

    @slow
    def test_score_sde_ve_pipeline(self):
        model_id = "google/ncsnpp-church-256"
        model = UNet2DModel.from_pretrained(model_id)

        scheduler = ScoreSdeVeScheduler.from_config(model_id)

        sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
        sde_ve.to(torch_device)
        sde_ve.set_progress_bar_config(disable=None)

        torch.manual_seed(0)
        image = sde_ve(num_inference_steps=300, output_type="numpy").images

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

        assert image.shape == (1, 256, 256, 3)

        expected_slice = np.array([0.64363, 0.5868, 0.3031, 0.2284, 0.7409, 0.3216, 0.25643, 0.6557, 0.2633])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    def test_ldm_uncond(self):
        ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
        ldm.to(torch_device)
        ldm.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        image = ldm(generator=generator, num_inference_steps=5, output_type="numpy").images

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

        assert image.shape == (1, 256, 256, 3)
        expected_slice = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    def test_ddpm_ddim_equality(self):
        model_id = "google/ddpm-cifar10-32"

        unet = UNet2DModel.from_pretrained(model_id)
        ddpm_scheduler = DDPMScheduler(tensor_format="pt")
        ddim_scheduler = DDIMScheduler(tensor_format="pt")

        ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
        ddpm.to(torch_device)
        ddpm.set_progress_bar_config(disable=None)
        ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
        ddim.to(torch_device)
        ddim.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        ddpm_image = ddpm(generator=generator, output_type="numpy").images

        generator = torch.manual_seed(0)
        ddim_image = ddim(generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy").images

        # the values aren't exactly equal, but the images look the same visually
        assert np.abs(ddpm_image - ddim_image).max() < 1e-1

    @unittest.skip("(Anton) The test is failing for large batch sizes, needs investigation")
    def test_ddpm_ddim_equality_batched(self):
        model_id = "google/ddpm-cifar10-32"

        unet = UNet2DModel.from_pretrained(model_id)
        ddpm_scheduler = DDPMScheduler(tensor_format="pt")
        ddim_scheduler = DDIMScheduler(tensor_format="pt")

        ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
        ddpm.to(torch_device)
        ddpm.set_progress_bar_config(disable=None)

        ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
        ddim.to(torch_device)
        ddim.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        ddpm_images = ddpm(batch_size=4, generator=generator, output_type="numpy").images

        generator = torch.manual_seed(0)
        ddim_images = ddim(batch_size=4, generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy")[
            "sample"
        ]

        # the values aren't exactly equal, but the images look the same visually
        assert np.abs(ddpm_images - ddim_images).max() < 1e-1

    @slow
    def test_karras_ve_pipeline(self):
        model_id = "google/ncsnpp-celebahq-256"
        model = UNet2DModel.from_pretrained(model_id)
        scheduler = KarrasVeScheduler(tensor_format="pt")

        pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.manual_seed(0)
        image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images

        image_slice = image[0, -3:, -3:, -1]
        assert image.shape == (1, 256, 256, 3)
        expected_slice = np.array([0.26815, 0.1581, 0.2658, 0.23248, 0.1550, 0.2539, 0.1131, 0.1024, 0.0837])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
    def test_lms_stable_diffusion_pipeline(self):
        model_id = "CompVis/stable-diffusion-v1-1"
        pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True).to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler", use_auth_token=True)
        pipe.scheduler = scheduler

        prompt = "a photograph of an astronaut riding a horse"
        generator = torch.Generator(device=torch_device).manual_seed(0)
        image = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy")[
            "sample"
        ]

        image_slice = image[0, -3:, -3:, -1]
        assert image.shape == (1, 512, 512, 3)
        expected_slice = np.array([0.9077, 0.9254, 0.9181, 0.9227, 0.9213, 0.9367, 0.9399, 0.9406, 0.9024])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @slow
    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
    def test_stable_diffusion_memory_chunking(self):
        torch.cuda.reset_peak_memory_stats()
        model_id = "CompVis/stable-diffusion-v1-4"
        pipe = StableDiffusionPipeline.from_pretrained(
            model_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True
        ).to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "a photograph of an astronaut riding a horse"

        # make attention efficient
        pipe.enable_attention_slicing()
        generator = torch.Generator(device=torch_device).manual_seed(0)
        with torch.autocast(torch_device):
            output_chunked = pipe(
                [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
            )
            image_chunked = output_chunked.images

        mem_bytes = torch.cuda.max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()
        # make sure that less than 3.75 GB is allocated
        assert mem_bytes < 3.75 * 10**9

        # disable chunking
        pipe.disable_attention_slicing()
        generator = torch.Generator(device=torch_device).manual_seed(0)
        with torch.autocast(torch_device):
            output = pipe(
                [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
            )
            image = output.images

        # make sure that more than 3.75 GB is allocated
        mem_bytes = torch.cuda.max_memory_allocated()
        assert mem_bytes > 3.75 * 10**9
        assert np.abs(image_chunked.flatten() - image.flatten()).max() < 1e-3

    @slow
    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
    def test_stable_diffusion_img2img_pipeline(self):
        ds = load_dataset("hf-internal-testing/diffusers-images", split="train")

        init_image = ds[2]["image"].resize((768, 512))
        output_image = ds[0]["image"].resize((768, 512))

        model_id = "CompVis/stable-diffusion-v1-4"
        pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
            model_id,
            revision="fp16",  # fp16 to infer 768x512 images with 16GB of VRAM
            torch_dtype=torch.float16,
            use_auth_token=True,
        )
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "A fantasy landscape, trending on artstation"

        generator = torch.Generator(device=torch_device).manual_seed(0)
        with torch.autocast("cuda"):
            output = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5, generator=generator)
        image = output.images[0]

        expected_array = np.array(output_image) / 255.0
        sampled_array = np.array(image) / 255.0

        assert sampled_array.shape == (512, 768, 3)
        assert np.max(np.abs(sampled_array - expected_array)) < 1e-4

    @slow
    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
    def test_stable_diffusion_img2img_pipeline_k_lms(self):
        ds = load_dataset("hf-internal-testing/diffusers-images", split="train")

        init_image = ds[2]["image"].resize((768, 512))
        output_image = ds[1]["image"].resize((768, 512))

        lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")

        model_id = "CompVis/stable-diffusion-v1-4"
        pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
            model_id,
            scheduler=lms,
            revision="fp16",  # fp16 to infer 768x512 images with 16GB of VRAM
            torch_dtype=torch.float16,
            use_auth_token=True,
        )
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "A fantasy landscape, trending on artstation"

        generator = torch.Generator(device=torch_device).manual_seed(0)
        with torch.autocast("cuda"):
            output = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5, generator=generator)
        image = output.images[0]

        expected_array = np.array(output_image) / 255.0
        sampled_array = np.array(image) / 255.0

        assert sampled_array.shape == (512, 768, 3)
        assert np.max(np.abs(sampled_array - expected_array)) < 1e-4

    @slow
    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
    def test_stable_diffusion_inpaint_pipeline(self):
        ds = load_dataset("hf-internal-testing/diffusers-images", split="train")

        init_image = ds[3]["image"].resize((768, 512))
        mask_image = ds[4]["image"].resize((768, 512))
        output_image = ds[5]["image"].resize((768, 512))

        model_id = "CompVis/stable-diffusion-v1-4"
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            model_id,
            revision="fp16",  # fp16 to infer 768x512 images in 16GB of VRAM
            torch_dtype=torch.float16,
            use_auth_token=True,
        )
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "A red cat sitting on a parking bench"

        generator = torch.Generator(device=torch_device).manual_seed(0)
        with torch.autocast("cuda"):
            output = pipe(
                prompt=prompt,
                init_image=init_image,
                mask_image=mask_image,
                strength=0.75,
                guidance_scale=7.5,
                generator=generator,
            )
        image = output.images[0]

        expected_array = np.array(output_image) / 255.0
        sampled_array = np.array(image) / 255.0

        assert sampled_array.shape == (512, 768, 3)
        assert np.max(np.abs(sampled_array - expected_array)) < 1e-3