[Tests] Move stable diffusion into their own files (#936)

* [Tests] Move stable diffusion into their own files

* up

src/diffusers/pipeline_utils.py

@@ -122,7 +122,7 @@ class DiffusionPipeline(ConfigMixin):
                 library = module.__module__.split(".")[0]
 
                 # check if the module is a pipeline module
-                pipeline_dir = module.__module__.split(".")[-2]
+                pipeline_dir = module.__module__.split(".")[-2] if len(module.__module__.split(".")) > 2 else None
                 path = module.__module__.split(".")
                 is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)
 

tests/pipelines/stable_diffusion/test_stable_diffusion.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 unittest
+
+import numpy as np
+import torch
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+    UNet2DModel,
+    VQModel,
+)
+from diffusers.utils import floats_tensor, load_image, slow, torch_device
+from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+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_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_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=None,
+            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_ddim_factor_8(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=None,
+            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,
+            height=536,
+            width=536,
+            num_inference_steps=2,
+            output_type="np",
+        )
+        image = output.images
+
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 134, 134, 3)
+        expected_slice = np.array([0.7834, 0.5488, 0.5781, 0.46, 0.3609, 0.5369, 0.542, 0.4855, 0.5557])
+
+        assert np.abs(image_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(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=None,
+            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_no_safety_checker(self):
+        pipe = StableDiffusionPipeline.from_pretrained(
+            "hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None
+        )
+        assert isinstance(pipe, StableDiffusionPipeline)
+        assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
+        assert pipe.safety_checker is None
+
+        image = pipe("example prompt", num_inference_steps=2).images[0]
+        assert image is not None
+
+    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=None,
+            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=None,
+            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_stable_diffusion_negative_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        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")
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            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"
+        negative_prompt = "french fries"
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe(
+            prompt,
+            negative_prompt=negative_prompt,
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+        )
+
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 128, 128, 3)
+        expected_slice = np.array([0.4851, 0.4617, 0.4765, 0.5127, 0.4845, 0.5153, 0.5141, 0.4886, 0.4719])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+    def test_stable_diffusion_num_images_per_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        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")
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            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"
+
+        # test num_images_per_prompt=1 (default)
+        images = sd_pipe(prompt, num_inference_steps=2, output_type="np").images
+
+        assert images.shape == (1, 128, 128, 3)
+
+        # test num_images_per_prompt=1 (default) for batch of prompts
+        batch_size = 2
+        images = sd_pipe([prompt] * batch_size, num_inference_steps=2, output_type="np").images
+
+        assert images.shape == (batch_size, 128, 128, 3)
+
+        # test num_images_per_prompt for single prompt
+        num_images_per_prompt = 2
+        images = sd_pipe(
+            prompt, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
+        ).images
+
+        assert images.shape == (num_images_per_prompt, 128, 128, 3)
+
+        # test num_images_per_prompt for batch of prompts
+        batch_size = 2
+        images = sd_pipe(
+            [prompt] * batch_size, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
+        ).images
+
+        assert images.shape == (batch_size * num_images_per_prompt, 128, 128, 3)
+
+    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
+    def test_stable_diffusion_fp16(self):
+        """Test that stable diffusion works with fp16"""
+        unet = self.dummy_cond_unet
+        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")
+
+        # put models in fp16
+        unet = unet.half()
+        vae = vae.half()
+        bert = bert.half()
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            feature_extractor=self.dummy_extractor,
+        )
+        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").images
+
+        assert image.shape == (1, 128, 128, 3)
+
+
+@slow
+@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
+class PipelineIntegrationTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_stable_diffusion(self):
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1")
+        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
+
+    def test_stable_diffusion_fast_ddim(self):
+        sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1")
+        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-2
+
+    def test_lms_stable_diffusion_pipeline(self):
+        model_id = "CompVis/stable-diffusion-v1-1"
+        pipe = StableDiffusionPipeline.from_pretrained(model_id).to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler")
+        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"
+        ).images
+
+        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
+
+    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).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
+
+    def test_stable_diffusion_text2img_pipeline_fp16(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).to(
+            torch_device
+        )
+        pipe.set_progress_bar_config(disable=None)
+
+        prompt = "a photograph of an astronaut riding a horse"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output_chunked = pipe(
+            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
+        )
+        image_chunked = output_chunked.images
+
+        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 results are close enough
+        diff = np.abs(image_chunked.flatten() - image.flatten())
+        # They ARE different since ops are not run always at the same precision
+        # however, they should be extremely close.
+        assert diff.mean() < 2e-2
+
+    def test_stable_diffusion_text2img_pipeline(self):
+        expected_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/text2img/astronaut_riding_a_horse.png"
+        )
+        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+
+        model_id = "CompVis/stable-diffusion-v1-4"
+        pipe = StableDiffusionPipeline.from_pretrained(
+            model_id,
+            safety_checker=None,
+        )
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "astronaut riding a horse"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(prompt=prompt, strength=0.75, guidance_scale=7.5, 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_text2img_intermediate_state(self):
+        number_of_steps = 0
+
+        def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
+            test_callback_fn.has_been_called = True
+            nonlocal number_of_steps
+            number_of_steps += 1
+            if step == 0:
+                latents = latents.detach().cpu().numpy()
+                assert latents.shape == (1, 4, 64, 64)
+                latents_slice = latents[0, -3:, -3:, -1]
+                expected_slice = np.array(
+                    [1.8285, 1.2857, -0.1024, 1.2406, -2.3068, 1.0747, -0.0818, -0.6520, -2.9506]
+                )
+                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
+            elif step == 50:
+                latents = latents.detach().cpu().numpy()
+                assert latents.shape == (1, 4, 64, 64)
+                latents_slice = latents[0, -3:, -3:, -1]
+                expected_slice = np.array(
+                    [1.1078, 1.5803, 0.2773, -0.0589, -1.7928, -0.3665, -0.4695, -1.0727, -1.1601]
+                )
+                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2
+
+        test_callback_fn.has_been_called = False
+
+        pipe = StableDiffusionPipeline.from_pretrained(
+            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
+        )
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "Andromeda galaxy in a bottle"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        with torch.autocast(torch_device):
+            pipe(
+                prompt=prompt,
+                num_inference_steps=50,
+                guidance_scale=7.5,
+                generator=generator,
+                callback=test_callback_fn,
+                callback_steps=1,
+            )
+        assert test_callback_fn.has_been_called
+        assert number_of_steps == 51

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.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 unittest
+
+import numpy as np
+import torch
+
+from diffusers import (
+    AutoencoderKL,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionImg2ImgPipeline,
+    UNet2DConditionModel,
+    UNet2DModel,
+    VQModel,
+)
+from diffusers.utils import floats_tensor, load_image, slow, torch_device
+from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+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_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_img2img(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        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")
+
+        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=None,
+            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_negative_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        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")
+
+        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=None,
+            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"
+        negative_prompt = "french fries"
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe(
+            prompt,
+            negative_prompt=negative_prompt,
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+        )
+        image = output.images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 32, 32, 3)
+        expected_slice = np.array([0.4065, 0.3783, 0.4050, 0.5266, 0.4781, 0.4252, 0.4203, 0.4692, 0.4365])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+    def test_stable_diffusion_img2img_multiple_init_images(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        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")
+
+        init_image = self.dummy_image.to(device).repeat(2, 1, 1, 1)
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionImg2ImgPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            feature_extractor=self.dummy_extractor,
+        )
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = 2 * ["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
+
+        image_slice = image[-1, -3:, -3:, -1]
+
+        assert image.shape == (2, 32, 32, 3)
+        expected_slice = np.array([0.5144, 0.4447, 0.4735, 0.6676, 0.5526, 0.5454, 0.645, 0.5149, 0.4689])
+        assert np.abs(image_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=None,
+            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_img2img_num_images_per_prompt(self):
+        device = "cpu"
+        unet = self.dummy_cond_unet
+        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")
+
+        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=None,
+            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"
+
+        # test num_images_per_prompt=1 (default)
+        images = sd_pipe(
+            prompt,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+        ).images
+
+        assert images.shape == (1, 32, 32, 3)
+
+        # test num_images_per_prompt=1 (default) for batch of prompts
+        batch_size = 2
+        images = sd_pipe(
+            [prompt] * batch_size,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+        ).images
+
+        assert images.shape == (batch_size, 32, 32, 3)
+
+        # test num_images_per_prompt for single prompt
+        num_images_per_prompt = 2
+        images = sd_pipe(
+            prompt,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+            num_images_per_prompt=num_images_per_prompt,
+        ).images
+
+        assert images.shape == (num_images_per_prompt, 32, 32, 3)
+
+        # test num_images_per_prompt for batch of prompts
+        batch_size = 2
+        images = sd_pipe(
+            [prompt] * batch_size,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+            num_images_per_prompt=num_images_per_prompt,
+        ).images
+
+        assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
+
+    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
+    def test_stable_diffusion_img2img_fp16(self):
+        """Test that stable diffusion img2img works with fp16"""
+        unet = self.dummy_cond_unet
+        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")
+
+        init_image = self.dummy_image.to(torch_device)
+
+        # put models in fp16
+        unet = unet.half()
+        vae = vae.half()
+        bert = bert.half()
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionImg2ImgPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            feature_extractor=self.dummy_extractor,
+        )
+        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",
+            init_image=init_image,
+        ).images
+
+        assert image.shape == (1, 32, 32, 3)
+
+
+@slow
+@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
+class PipelineIntegrationTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_stable_diffusion_img2img_pipeline(self):
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/img2img/sketch-mountains-input.jpg"
+        )
+        expected_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/img2img/fantasy_landscape.png"
+        )
+        init_image = init_image.resize((768, 512))
+        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+
+        model_id = "CompVis/stable-diffusion-v1-4"
+        pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+            model_id,
+            safety_checker=None,
+        )
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "A fantasy landscape, trending on artstation"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(
+            prompt=prompt,
+            init_image=init_image,
+            strength=0.75,
+            guidance_scale=7.5,
+            generator=generator,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        assert image.shape == (512, 768, 3)
+        # img2img is flaky across GPUs even in fp32, so using MAE here
+        assert np.abs(expected_image - image).mean() < 1e-2
+
+    def test_stable_diffusion_img2img_pipeline_k_lms(self):
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/img2img/sketch-mountains-input.jpg"
+        )
+        expected_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/img2img/fantasy_landscape_k_lms.png"
+        )
+        init_image = init_image.resize((768, 512))
+        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+
+        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,
+            safety_checker=None,
+        )
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "A fantasy landscape, trending on artstation"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(
+            prompt=prompt,
+            init_image=init_image,
+            strength=0.75,
+            guidance_scale=7.5,
+            generator=generator,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        assert image.shape == (512, 768, 3)
+        # img2img is flaky across GPUs even in fp32, so using MAE here
+        assert np.abs(expected_image - image).mean() < 1e-2
+
+    def test_stable_diffusion_img2img_intermediate_state(self):
+        number_of_steps = 0
+
+        def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
+            test_callback_fn.has_been_called = True
+            nonlocal number_of_steps
+            number_of_steps += 1
+            if step == 0:
+                latents = latents.detach().cpu().numpy()
+                assert latents.shape == (1, 4, 64, 96)
+                latents_slice = latents[0, -3:, -3:, -1]
+                expected_slice = np.array([0.9052, -0.0184, 0.4810, 0.2898, 0.5851, 1.4920, 0.5362, 1.9838, 0.0530])
+                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
+            elif step == 37:
+                latents = latents.detach().cpu().numpy()
+                assert latents.shape == (1, 4, 64, 96)
+                latents_slice = latents[0, -3:, -3:, -1]
+                expected_slice = np.array([0.7071, 0.7831, 0.8300, 1.8140, 1.7840, 1.9402, 1.3651, 1.6590, 1.2828])
+                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2
+
+        test_callback_fn.has_been_called = False
+
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/img2img/sketch-mountains-input.jpg"
+        )
+        init_image = init_image.resize((768, 512))
+
+        pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
+        )
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "A fantasy landscape, trending on artstation"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        with torch.autocast(torch_device):
+            pipe(
+                prompt=prompt,
+                init_image=init_image,
+                strength=0.75,
+                num_inference_steps=50,
+                guidance_scale=7.5,
+                generator=generator,
+                callback=test_callback_fn,
+                callback_steps=1,
+            )
+        assert test_callback_fn.has_been_called
+        assert number_of_steps == 38

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.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 unittest
+
+import numpy as np
+import torch
+
+from diffusers import (
+    AutoencoderKL,
+    PNDMScheduler,
+    StableDiffusionInpaintPipeline,
+    UNet2DConditionModel,
+    UNet2DModel,
+    VQModel,
+)
+from diffusers.utils import floats_tensor, load_image, slow, torch_device
+from PIL import Image
+from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+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_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
+
+    @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
+@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
+class PipelineIntegrationTests(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_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/yellow_cat_sitting_on_a_park_bench.png"
+        )
+        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+
+        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-2
+
+    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_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/yellow_cat_sitting_on_a_park_bench_fp16.png"
+        )
+        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+
+        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() < 1e-2
+
+    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_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/yellow_cat_sitting_on_a_park_bench_pndm.png"
+        )
+        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+
+        pndm = PNDMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True)
+        model_id = "runwayml/stable-diffusion-inpainting"
+        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

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint_legacy.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 unittest
+
+import numpy as np
+import torch
+
+from diffusers import (
+    AutoencoderKL,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionInpaintPipeline,
+    StableDiffusionInpaintPipelineLegacy,
+    UNet2DConditionModel,
+    UNet2DModel,
+    VQModel,
+)
+from diffusers.utils import floats_tensor, load_image, slow, torch_device
+from PIL import Image
+from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+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_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_legacy(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        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")
+        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionInpaintPipelineLegacy(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            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
+
+    def test_stable_diffusion_inpaint_legacy_negative_prompt(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        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")
+        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionInpaintPipelineLegacy(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            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"
+        negative_prompt = "french fries"
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe(
+            prompt,
+            negative_prompt=negative_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
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 32, 32, 3)
+        expected_slice = np.array([0.4765, 0.5339, 0.4541, 0.6240, 0.5439, 0.4055, 0.5503, 0.5891, 0.5150])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
+    def test_stable_diffusion_inpaint_legacy_num_images_per_prompt(self):
+        device = "cpu"
+        unet = self.dummy_cond_unet
+        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")
+        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionInpaintPipelineLegacy(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            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"
+
+        # test num_images_per_prompt=1 (default)
+        images = sd_pipe(
+            prompt,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+            mask_image=mask_image,
+        ).images
+
+        assert images.shape == (1, 32, 32, 3)
+
+        # test num_images_per_prompt=1 (default) for batch of prompts
+        batch_size = 2
+        images = sd_pipe(
+            [prompt] * batch_size,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+            mask_image=mask_image,
+        ).images
+
+        assert images.shape == (batch_size, 32, 32, 3)
+
+        # test num_images_per_prompt for single prompt
+        num_images_per_prompt = 2
+        images = sd_pipe(
+            prompt,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+            mask_image=mask_image,
+            num_images_per_prompt=num_images_per_prompt,
+        ).images
+
+        assert images.shape == (num_images_per_prompt, 32, 32, 3)
+
+        # test num_images_per_prompt for batch of prompts
+        batch_size = 2
+        images = sd_pipe(
+            [prompt] * batch_size,
+            num_inference_steps=2,
+            output_type="np",
+            init_image=init_image,
+            mask_image=mask_image,
+            num_images_per_prompt=num_images_per_prompt,
+        ).images
+
+        assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
+
+
+@slow
+@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
+class PipelineIntegrationTests(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_legacy_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_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/red_cat_sitting_on_a_park_bench.png"
+        )
+        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+
+        model_id = "CompVis/stable-diffusion-v1-4"
+        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 = "A red cat sitting on a park bench"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(
+            prompt=prompt,
+            init_image=init_image,
+            mask_image=mask_image,
+            strength=0.75,
+            guidance_scale=7.5,
+            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_legacy_pipeline_k_lms(self):
+        # TODO(Anton, Patrick) - I think we can remove this test soon
+        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_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/red_cat_sitting_on_a_park_bench_k_lms.png"
+        )
+        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+
+        lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+
+        model_id = "CompVis/stable-diffusion-v1-4"
+        pipe = StableDiffusionInpaintPipeline.from_pretrained(
+            model_id,
+            scheduler=lms,
+            safety_checker=None,
+        )
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "A red cat sitting on a park bench"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(
+            prompt=prompt,
+            init_image=init_image,
+            mask_image=mask_image,
+            strength=0.75,
+            guidance_scale=7.5,
+            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_legacy_intermediate_state(self):
+        number_of_steps = 0
+
+        def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
+            test_callback_fn.has_been_called = True
+            nonlocal number_of_steps
+            number_of_steps += 1
+            if step == 0:
+                latents = latents.detach().cpu().numpy()
+                assert latents.shape == (1, 4, 64, 64)
+                latents_slice = latents[0, -3:, -3:, -1]
+                expected_slice = np.array(
+                    [-0.5472, 1.1218, -0.5505, -0.9390, -1.0794, 0.4063, 0.5158, 0.6429, -1.5246]
+                )
+                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
+            elif step == 37:
+                latents = latents.detach().cpu().numpy()
+                assert latents.shape == (1, 4, 64, 64)
+                latents_slice = latents[0, -3:, -3:, -1]
+                expected_slice = np.array([0.4781, 1.1572, 0.6258, 0.2291, 0.2554, -0.1443, 0.7085, -0.1598, -0.5659])
+                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
+
+        test_callback_fn.has_been_called = False
+
+        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"
+        )
+
+        pipe = StableDiffusionInpaintPipeline.from_pretrained(
+            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
+        )
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "A red cat sitting on a park bench"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        with torch.autocast(torch_device):
+            pipe(
+                prompt=prompt,
+                init_image=init_image,
+                mask_image=mask_image,
+                strength=0.75,
+                num_inference_steps=50,
+                guidance_scale=7.5,
+                generator=generator,
+                callback=test_callback_fn,
+                callback_steps=1,
+            )
+        assert test_callback_fn.has_been_called
+        assert number_of_steps == 38

tests/test_pipelines.py

@@ -36,7 +36,6 @@ from diffusers import (
     KarrasVeScheduler,
     LDMPipeline,
     LDMTextToImagePipeline,
-    LMSDiscreteScheduler,
     OnnxStableDiffusionImg2ImgPipeline,
     OnnxStableDiffusionInpaintPipeline,
     OnnxStableDiffusionPipeline,
@@ -45,7 +44,6 @@ from diffusers import (
     ScoreSdeVePipeline,
     ScoreSdeVeScheduler,
     StableDiffusionImg2ImgPipeline,
-    StableDiffusionInpaintPipeline,
     StableDiffusionInpaintPipelineLegacy,
     StableDiffusionPipeline,
     UNet2DConditionModel,
@@ -248,13 +246,6 @@ class PipelineFastTests(unittest.TestCase):
         )
         return CLIPTextModel(config)
 
-    @property
-    def dummy_safety_checker(self):
-        def check(images, *args, **kwargs):
-            return images, [False] * len(images)
-
-        return check
-
     @property
     def dummy_extractor(self):
         def extract(*args, **kwargs):
@@ -364,287 +355,6 @@ class PipelineFastTests(unittest.TestCase):
         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_ddim_factor_8(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,
-            height=536,
-            width=536,
-            num_inference_steps=2,
-            output_type="np",
-        )
-        image = output.images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 134, 134, 3)
-        expected_slice = np.array([0.7834, 0.5488, 0.5781, 0.46, 0.3609, 0.5369, 0.542, 0.4855, 0.5557])
-
-        assert np.abs(image_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(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_from_pretrained_error_message_uninstalled_packages(self):
-        # TODO(Patrick, Pedro) - need better test here for the future
-        pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-lms-pipe")
-        assert isinstance(pipe, StableDiffusionPipeline)
-        assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
-
-    def test_stable_diffusion_no_safety_checker(self):
-        pipe = StableDiffusionPipeline.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None
-        )
-        assert isinstance(pipe, StableDiffusionPipeline)
-        assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
-        assert pipe.safety_checker is None
-
-        image = pipe("example prompt", num_inference_steps=2).images[0]
-        assert image is not None
-
-    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_stable_diffusion_negative_prompt(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        unet = self.dummy_cond_unet
-        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")
-
-        # 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"
-        negative_prompt = "french fries"
-        generator = torch.Generator(device=device).manual_seed(0)
-        output = sd_pipe(
-            prompt,
-            negative_prompt=negative_prompt,
-            generator=generator,
-            guidance_scale=6.0,
-            num_inference_steps=2,
-            output_type="np",
-        )
-
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 128, 128, 3)
-        expected_slice = np.array([0.4851, 0.4617, 0.4765, 0.5127, 0.4845, 0.5153, 0.5141, 0.4886, 0.4719])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
     def test_score_sde_ve_pipeline(self):
         unet = self.dummy_uncond_unet
         scheduler = ScoreSdeVeScheduler()
@@ -719,731 +429,58 @@ class PipelineFastTests(unittest.TestCase):
         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
+    def test_components(self):
+        """Test that components property works correctly"""
         unet = self.dummy_cond_unet
         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")
 
-        init_image = self.dummy_image.to(device)
+        image = self.dummy_image.cpu().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 = StableDiffusionImg2ImgPipeline(
+        inpaint = StableDiffusionInpaintPipelineLegacy(
             unet=unet,
             scheduler=scheduler,
             vae=vae,
             text_encoder=bert,
             tokenizer=tokenizer,
-            safety_checker=self.dummy_safety_checker,
+            safety_checker=None,
             feature_extractor=self.dummy_extractor,
-        )
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
+        ).to(torch_device)
+        img2img = StableDiffusionImg2ImgPipeline(**inpaint.components).to(torch_device)
+        text2img = StableDiffusionPipeline(**inpaint.components).to(torch_device)
 
         prompt = "A painting of a squirrel eating a burger"
-        generator = torch.Generator(device=device).manual_seed(0)
-        output = sd_pipe(
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        image_inpaint = inpaint(
             [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(
+            mask_image=mask_image,
+        ).images
+        image_img2img = img2img(
             [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_negative_prompt(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        unet = self.dummy_cond_unet
-        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")
-
-        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"
-        negative_prompt = "french fries"
-        generator = torch.Generator(device=device).manual_seed(0)
-        output = sd_pipe(
-            prompt,
-            negative_prompt=negative_prompt,
+        ).images
+        image_text2img = text2img(
+            [prompt],
             generator=generator,
-            guidance_scale=6.0,
             num_inference_steps=2,
             output_type="np",
-            init_image=init_image,
-        )
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.4065, 0.3783, 0.4050, 0.5266, 0.4781, 0.4252, 0.4203, 0.4692, 0.4365])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+        ).images
 
-    def test_stable_diffusion_img2img_multiple_init_images(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        unet = self.dummy_cond_unet
-        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")
-
-        init_image = self.dummy_image.to(device).repeat(2, 1, 1, 1)
-
-        # 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 = 2 * ["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
-
-        image_slice = image[-1, -3:, -3:, -1]
-
-        assert image.shape == (2, 32, 32, 3)
-        expected_slice = np.array([0.5144, 0.4447, 0.4735, 0.6676, 0.5526, 0.5454, 0.645, 0.5149, 0.4689])
-        assert np.abs(image_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_legacy(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        unet = self.dummy_cond_unet
-        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")
-        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
-
-        # make sure here that pndm scheduler skips prk
-        sd_pipe = StableDiffusionInpaintPipelineLegacy(
-            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
-
-    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_legacy_negative_prompt(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        unet = self.dummy_cond_unet
-        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")
-        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
-
-        # make sure here that pndm scheduler skips prk
-        sd_pipe = StableDiffusionInpaintPipelineLegacy(
-            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"
-        negative_prompt = "french fries"
-        generator = torch.Generator(device=device).manual_seed(0)
-        output = sd_pipe(
-            prompt,
-            negative_prompt=negative_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
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.4765, 0.5339, 0.4541, 0.6240, 0.5439, 0.4055, 0.5503, 0.5891, 0.5150])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_stable_diffusion_num_images_per_prompt(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        unet = self.dummy_cond_unet
-        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")
-
-        # 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"
-
-        # test num_images_per_prompt=1 (default)
-        images = sd_pipe(prompt, num_inference_steps=2, output_type="np").images
-
-        assert images.shape == (1, 128, 128, 3)
-
-        # test num_images_per_prompt=1 (default) for batch of prompts
-        batch_size = 2
-        images = sd_pipe([prompt] * batch_size, num_inference_steps=2, output_type="np").images
-
-        assert images.shape == (batch_size, 128, 128, 3)
-
-        # test num_images_per_prompt for single prompt
-        num_images_per_prompt = 2
-        images = sd_pipe(
-            prompt, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
-        ).images
-
-        assert images.shape == (num_images_per_prompt, 128, 128, 3)
-
-        # test num_images_per_prompt for batch of prompts
-        batch_size = 2
-        images = sd_pipe(
-            [prompt] * batch_size, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
-        ).images
-
-        assert images.shape == (batch_size * num_images_per_prompt, 128, 128, 3)
-
-    def test_stable_diffusion_img2img_num_images_per_prompt(self):
-        device = "cpu"
-        unet = self.dummy_cond_unet
-        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")
-
-        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"
-
-        # test num_images_per_prompt=1 (default)
-        images = sd_pipe(
-            prompt,
-            num_inference_steps=2,
-            output_type="np",
-            init_image=init_image,
-        ).images
-
-        assert images.shape == (1, 32, 32, 3)
-
-        # test num_images_per_prompt=1 (default) for batch of prompts
-        batch_size = 2
-        images = sd_pipe(
-            [prompt] * batch_size,
-            num_inference_steps=2,
-            output_type="np",
-            init_image=init_image,
-        ).images
-
-        assert images.shape == (batch_size, 32, 32, 3)
-
-        # test num_images_per_prompt for single prompt
-        num_images_per_prompt = 2
-        images = sd_pipe(
-            prompt,
-            num_inference_steps=2,
-            output_type="np",
-            init_image=init_image,
-            num_images_per_prompt=num_images_per_prompt,
-        ).images
-
-        assert images.shape == (num_images_per_prompt, 32, 32, 3)
-
-        # test num_images_per_prompt for batch of prompts
-        batch_size = 2
-        images = sd_pipe(
-            [prompt] * batch_size,
-            num_inference_steps=2,
-            output_type="np",
-            init_image=init_image,
-            num_images_per_prompt=num_images_per_prompt,
-        ).images
-
-        assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
-
-    def test_stable_diffusion_inpaint_legacy_num_images_per_prompt(self):
-        device = "cpu"
-        unet = self.dummy_cond_unet
-        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")
-        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
-
-        # make sure here that pndm scheduler skips prk
-        sd_pipe = StableDiffusionInpaintPipelineLegacy(
-            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"
-
-        # test num_images_per_prompt=1 (default)
-        images = sd_pipe(
-            prompt,
-            num_inference_steps=2,
-            output_type="np",
-            init_image=init_image,
-            mask_image=mask_image,
-        ).images
-
-        assert images.shape == (1, 32, 32, 3)
-
-        # test num_images_per_prompt=1 (default) for batch of prompts
-        batch_size = 2
-        images = sd_pipe(
-            [prompt] * batch_size,
-            num_inference_steps=2,
-            output_type="np",
-            init_image=init_image,
-            mask_image=mask_image,
-        ).images
-
-        assert images.shape == (batch_size, 32, 32, 3)
-
-        # test num_images_per_prompt for single prompt
-        num_images_per_prompt = 2
-        images = sd_pipe(
-            prompt,
-            num_inference_steps=2,
-            output_type="np",
-            init_image=init_image,
-            mask_image=mask_image,
-            num_images_per_prompt=num_images_per_prompt,
-        ).images
-
-        assert images.shape == (num_images_per_prompt, 32, 32, 3)
-
-        # test num_images_per_prompt for batch of prompts
-        batch_size = 2
-        images = sd_pipe(
-            [prompt] * batch_size,
-            num_inference_steps=2,
-            output_type="np",
-            init_image=init_image,
-            mask_image=mask_image,
-            num_images_per_prompt=num_images_per_prompt,
-        ).images
-
-        assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
-
-    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
-    def test_stable_diffusion_fp16(self):
-        """Test that stable diffusion works with fp16"""
-        unet = self.dummy_cond_unet
-        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")
-
-        # put models in fp16
-        unet = unet.half()
-        vae = vae.half()
-        bert = bert.half()
-
-        # 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(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").images
-
-        assert image.shape == (1, 128, 128, 3)
-
-    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
-    def test_stable_diffusion_img2img_fp16(self):
-        """Test that stable diffusion img2img works with fp16"""
-        unet = self.dummy_cond_unet
-        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")
-
-        init_image = self.dummy_image.to(torch_device)
-
-        # put models in fp16
-        unet = unet.half()
-        vae = vae.half()
-        bert = bert.half()
-
-        # 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(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",
-            init_image=init_image,
-        ).images
-
-        assert image.shape == (1, 32, 32, 3)
-
-    @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)
-
-    def test_components(self):
-        """Test that components property works correctly"""
-        unet = self.dummy_cond_unet
-        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")
-        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))
-
-        # make sure here that pndm scheduler skips prk
-        inpaint = StableDiffusionInpaintPipelineLegacy(
-            unet=unet,
-            scheduler=scheduler,
-            vae=vae,
-            text_encoder=bert,
-            tokenizer=tokenizer,
-            safety_checker=self.dummy_safety_checker,
-            feature_extractor=self.dummy_extractor,
-        ).to(torch_device)
-        img2img = StableDiffusionImg2ImgPipeline(**inpaint.components).to(torch_device)
-        text2img = StableDiffusionPipeline(**inpaint.components).to(torch_device)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        image_inpaint = inpaint(
-            [prompt],
-            generator=generator,
-            num_inference_steps=2,
-            output_type="np",
-            init_image=init_image,
-            mask_image=mask_image,
-        ).images
-        image_img2img = img2img(
-            [prompt],
-            generator=generator,
-            num_inference_steps=2,
-            output_type="np",
-            init_image=init_image,
-        ).images
-        image_text2img = text2img(
-            [prompt],
-            generator=generator,
-            num_inference_steps=2,
-            output_type="np",
-        ).images
-
-        assert image_inpaint.shape == (1, 32, 32, 3)
-        assert image_img2img.shape == (1, 32, 32, 3)
-        assert image_text2img.shape == (1, 128, 128, 3)
+        assert image_inpaint.shape == (1, 32, 32, 3)
+        assert image_img2img.shape == (1, 32, 32, 3)
+        assert image_text2img.shape == (1, 128, 128, 3)
 
 
 class PipelineTesterMixin(unittest.TestCase):
@@ -1483,13 +520,6 @@ class PipelineTesterMixin(unittest.TestCase):
 
         assert cap_logger.out == "Keyword arguments {'not_used': True} not recognized.\n"
 
-    @property
-    def dummy_safety_checker(self):
-        def check(images, *args, **kwargs):
-            return images, [False] * len(images)
-
-        return check
-
     def test_from_pretrained_save_pretrained(self):
         # 1. Load models
         model = UNet2DModel(
@@ -1597,682 +627,214 @@ class PipelineTesterMixin(unittest.TestCase):
 
         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()
-
-        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()
-
-        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"
-        ).images
-
-        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")
-        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")
-        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-2
-
-    @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)
-
-        generator = torch.manual_seed(0)
-        image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 256, 256, 3)
-
-        expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])
-        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)
+        ddpm.set_progress_bar_config(disable=None)
 
         generator = torch.manual_seed(0)
-        image = ldm(generator=generator, num_inference_steps=5, output_type="numpy").images
+        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([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
+        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_ddpm_ddim_equality(self):
-        model_id = "google/ddpm-cifar10-32"
+    def test_ddim_lsun(self):
+        model_id = "google/ddpm-ema-bedroom-256"
 
         unet = UNet2DModel.from_pretrained(model_id)
-        ddpm_scheduler = DDPMScheduler()
-        ddim_scheduler = DDIMScheduler()
+        scheduler = DDIMScheduler.from_config(model_id)
 
-        ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
+        ddpm = DDIMPipeline(unet=unet, scheduler=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
+        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
+        image_slice = image[0, -3:, -3:, -1]
 
-        # the values aren't exactly equal, but the images look the same visually
-        assert np.abs(ddpm_image - ddim_image).max() < 1e-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
 
-    @unittest.skip("(Anton) The test is failing for large batch sizes, needs investigation")
-    def test_ddpm_ddim_equality_batched(self):
+    @slow
+    def test_ddim_cifar10(self):
         model_id = "google/ddpm-cifar10-32"
 
         unet = UNet2DModel.from_pretrained(model_id)
-        ddpm_scheduler = DDPMScheduler()
-        ddim_scheduler = DDIMScheduler()
-
-        ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
-        ddpm.to(torch_device)
-        ddpm.set_progress_bar_config(disable=None)
+        scheduler = DDIMScheduler()
 
-        ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
+        ddim = DDIMPipeline(unet=unet, scheduler=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
+        image = ddim(generator=generator, eta=0.0, 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"
-        ).images
+        image_slice = image[0, -3:, -3:, -1]
 
-        # the values aren't exactly equal, but the images look the same visually
-        assert np.abs(ddpm_images - ddim_images).max() < 1e-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_karras_ve_pipeline(self):
-        model_id = "google/ncsnpp-celebahq-256"
-        model = UNet2DModel.from_pretrained(model_id)
-        scheduler = KarrasVeScheduler()
+    def test_pndm_cifar10(self):
+        model_id = "google/ddpm-cifar10-32"
 
-        pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
+        unet = UNet2DModel.from_pretrained(model_id)
+        scheduler = PNDMScheduler()
 
+        pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
+        pndm.to(torch_device)
+        pndm.set_progress_bar_config(disable=None)
         generator = torch.manual_seed(0)
-        image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images
+        image = pndm(generator=generator, output_type="numpy").images
 
         image_slice = image[0, -3:, -3:, -1]
-        assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])
+
+        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
-    @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).to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler")
-        pipe.scheduler = scheduler
+    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 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"
+        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"
         ).images
 
         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).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_text2img_pipeline_fp16(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).to(
-            torch_device
-        )
-        pipe.set_progress_bar_config(disable=None)
-
-        prompt = "a photograph of an astronaut riding a horse"
-
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        output_chunked = pipe(
-            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
-        )
-        image_chunked = output_chunked.images
-
-        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 results are close enough
-        diff = np.abs(image_chunked.flatten() - image.flatten())
-        # They ARE different since ops are not run always at the same precision
-        # however, they should be extremely close.
-        assert diff.mean() < 2e-2
-
-    @slow
-    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-    def test_stable_diffusion_text2img_pipeline(self):
-        expected_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/text2img/astronaut_riding_a_horse.png"
-        )
-        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
-
-        model_id = "CompVis/stable-diffusion-v1-4"
-        pipe = StableDiffusionPipeline.from_pretrained(
-            model_id,
-            safety_checker=self.dummy_safety_checker,
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-
-        prompt = "astronaut riding a horse"
 
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        output = pipe(prompt=prompt, strength=0.75, guidance_scale=7.5, generator=generator, output_type="np")
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-        assert np.abs(expected_image - image).max() < 1e-2
+        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
-    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-    def test_stable_diffusion_img2img_pipeline(self):
-        init_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/img2img/sketch-mountains-input.jpg"
-        )
-        expected_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/img2img/fantasy_landscape.png"
-        )
-        init_image = init_image.resize((768, 512))
-        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
-
-        model_id = "CompVis/stable-diffusion-v1-4"
-        pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-            model_id,
-            safety_checker=self.dummy_safety_checker,
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
+    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 fantasy landscape, trending on artstation"
+        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
 
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        output = pipe(
-            prompt=prompt,
-            init_image=init_image,
-            strength=0.75,
-            guidance_scale=7.5,
-            generator=generator,
-            output_type="np",
-        )
-        image = output.images[0]
+        image_slice = image[0, -3:, -3:, -1]
 
-        assert image.shape == (512, 768, 3)
-        # img2img is flaky across GPUs even in fp32, so using MAE here
-        assert np.abs(expected_image - image).mean() < 1e-2
+        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_img2img_pipeline_k_lms(self):
-        init_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/img2img/sketch-mountains-input.jpg"
-        )
-        expected_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/img2img/fantasy_landscape_k_lms.png"
-        )
-        init_image = init_image.resize((768, 512))
-        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
-
-        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,
-            safety_checker=self.dummy_safety_checker,
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-
-        prompt = "A fantasy landscape, trending on artstation"
-
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        output = pipe(
-            prompt=prompt,
-            init_image=init_image,
-            strength=0.75,
-            guidance_scale=7.5,
-            generator=generator,
-            output_type="np",
-        )
-        image = output.images[0]
+    def test_score_sde_ve_pipeline(self):
+        model_id = "google/ncsnpp-church-256"
+        model = UNet2DModel.from_pretrained(model_id)
 
-        assert image.shape == (512, 768, 3)
-        # img2img is flaky across GPUs even in fp32, so using MAE here
-        assert np.abs(expected_image - image).mean() < 1e-2
+        scheduler = ScoreSdeVeScheduler.from_config(model_id)
 
-    @slow
-    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-    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_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/in_paint/yellow_cat_sitting_on_a_park_bench.png"
-        )
-        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+        sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
+        sde_ve.to(torch_device)
+        sde_ve.set_progress_bar_config(disable=None)
 
-        model_id = "runwayml/stable-diffusion-inpainting"
-        pipe = StableDiffusionInpaintPipeline.from_pretrained(
-            model_id,
-            safety_checker=self.dummy_safety_checker,
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
+        generator = torch.manual_seed(0)
+        image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images
 
-        prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+        image_slice = image[0, -3:, -3:, -1]
 
-        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 == (1, 256, 256, 3)
 
-        assert image.shape == (512, 512, 3)
-        assert np.abs(expected_image - image).max() < 1e-2
+        expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])
+        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_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_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/in_paint/yellow_cat_sitting_on_a_park_bench_fp16.png"
-        )
-        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
-
-        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()
+    def test_ldm_uncond(self):
+        ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
+        ldm.to(torch_device)
+        ldm.set_progress_bar_config(disable=None)
 
-        prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+        generator = torch.manual_seed(0)
+        image = ldm(generator=generator, num_inference_steps=5, output_type="numpy").images
 
-        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]
+        image_slice = image[0, -3:, -3:, -1]
 
-        assert image.shape == (512, 512, 3)
-        assert np.abs(expected_image - image).max() < 1e-2
+        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
-    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-    def test_stable_diffusion_inpaint_legacy_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_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/in_paint/red_cat_sitting_on_a_park_bench.png"
-        )
-        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+    def test_ddpm_ddim_equality(self):
+        model_id = "google/ddpm-cifar10-32"
 
-        model_id = "CompVis/stable-diffusion-v1-4"
-        pipe = StableDiffusionInpaintPipeline.from_pretrained(
-            model_id,
-            safety_checker=self.dummy_safety_checker,
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
+        unet = UNet2DModel.from_pretrained(model_id)
+        ddpm_scheduler = DDPMScheduler()
+        ddim_scheduler = DDIMScheduler()
 
-        prompt = "A red cat sitting on a park bench"
+        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.Generator(device=torch_device).manual_seed(0)
-        output = pipe(
-            prompt=prompt,
-            init_image=init_image,
-            mask_image=mask_image,
-            strength=0.75,
-            guidance_scale=7.5,
-            generator=generator,
-            output_type="np",
-        )
-        image = output.images[0]
+        generator = torch.manual_seed(0)
+        ddpm_image = ddpm(generator=generator, output_type="numpy").images
 
-        assert image.shape == (512, 512, 3)
-        assert np.abs(expected_image - image).max() < 1e-2
+        generator = torch.manual_seed(0)
+        ddim_image = ddim(generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy").images
 
-    @slow
-    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-    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_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/in_paint/yellow_cat_sitting_on_a_park_bench_pndm.png"
-        )
-        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+        # the values aren't exactly equal, but the images look the same visually
+        assert np.abs(ddpm_image - ddim_image).max() < 1e-1
 
-        pndm = PNDMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True)
-        model_id = "runwayml/stable-diffusion-inpainting"
-        pipe = StableDiffusionInpaintPipeline.from_pretrained(
-            model_id, safety_checker=self.dummy_safety_checker, scheduler=pndm
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
+    @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"
 
-        prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+        unet = UNet2DModel.from_pretrained(model_id)
+        ddpm_scheduler = DDPMScheduler()
+        ddim_scheduler = DDIMScheduler()
 
-        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]
+        ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
+        ddpm.to(torch_device)
+        ddpm.set_progress_bar_config(disable=None)
 
-        assert image.shape == (512, 512, 3)
-        assert np.abs(expected_image - image).max() < 1e-2
+        ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
+        ddim.to(torch_device)
+        ddim.set_progress_bar_config(disable=None)
 
-    @slow
-    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-    def test_stable_diffusion_inpaint_legacy_pipeline_k_lms(self):
-        # TODO(Anton, Patrick) - I think we can remove this test soon
-        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_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/in_paint/red_cat_sitting_on_a_park_bench_k_lms.png"
-        )
-        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+        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"
+        ).images
 
-        lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        # the values aren't exactly equal, but the images look the same visually
+        assert np.abs(ddpm_images - ddim_images).max() < 1e-1
 
-        model_id = "CompVis/stable-diffusion-v1-4"
-        pipe = StableDiffusionInpaintPipeline.from_pretrained(
-            model_id,
-            scheduler=lms,
-            safety_checker=self.dummy_safety_checker,
-        )
+    @slow
+    def test_karras_ve_pipeline(self):
+        model_id = "google/ncsnpp-celebahq-256"
+        model = UNet2DModel.from_pretrained(model_id)
+        scheduler = KarrasVeScheduler()
+
+        pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-
-        prompt = "A red cat sitting on a park bench"
 
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        output = pipe(
-            prompt=prompt,
-            init_image=init_image,
-            mask_image=mask_image,
-            strength=0.75,
-            guidance_scale=7.5,
-            generator=generator,
-            output_type="np",
-        )
-        image = output.images[0]
+        generator = torch.manual_seed(0)
+        image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images
 
-        assert image.shape == (512, 512, 3)
-        assert np.abs(expected_image - image).max() < 1e-2
+        image_slice = image[0, -3:, -3:, -1]
+        assert image.shape == (1, 256, 256, 3)
+        expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
     @slow
     def test_stable_diffusion_onnx(self):
@@ -2356,170 +918,6 @@ class PipelineTesterMixin(unittest.TestCase):
         expected_slice = np.array([0.2951, 0.2955, 0.2922, 0.2036, 0.1977, 0.2279, 0.1716, 0.1641, 0.1799])
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
-    @slow
-    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-    def test_stable_diffusion_text2img_intermediate_state(self):
-        number_of_steps = 0
-
-        def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
-            test_callback_fn.has_been_called = True
-            nonlocal number_of_steps
-            number_of_steps += 1
-            if step == 0:
-                latents = latents.detach().cpu().numpy()
-                assert latents.shape == (1, 4, 64, 64)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array(
-                    [1.8285, 1.2857, -0.1024, 1.2406, -2.3068, 1.0747, -0.0818, -0.6520, -2.9506]
-                )
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
-            elif step == 50:
-                latents = latents.detach().cpu().numpy()
-                assert latents.shape == (1, 4, 64, 64)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array(
-                    [1.1078, 1.5803, 0.2773, -0.0589, -1.7928, -0.3665, -0.4695, -1.0727, -1.1601]
-                )
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2
-
-        test_callback_fn.has_been_called = False
-
-        pipe = StableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
-        )
-        pipe = pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-
-        prompt = "Andromeda galaxy in a bottle"
-
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        with torch.autocast(torch_device):
-            pipe(
-                prompt=prompt,
-                num_inference_steps=50,
-                guidance_scale=7.5,
-                generator=generator,
-                callback=test_callback_fn,
-                callback_steps=1,
-            )
-        assert test_callback_fn.has_been_called
-        assert number_of_steps == 51
-
-    @slow
-    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-    def test_stable_diffusion_img2img_intermediate_state(self):
-        number_of_steps = 0
-
-        def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
-            test_callback_fn.has_been_called = True
-            nonlocal number_of_steps
-            number_of_steps += 1
-            if step == 0:
-                latents = latents.detach().cpu().numpy()
-                assert latents.shape == (1, 4, 64, 96)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array([0.9052, -0.0184, 0.4810, 0.2898, 0.5851, 1.4920, 0.5362, 1.9838, 0.0530])
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
-            elif step == 37:
-                latents = latents.detach().cpu().numpy()
-                assert latents.shape == (1, 4, 64, 96)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array([0.7071, 0.7831, 0.8300, 1.8140, 1.7840, 1.9402, 1.3651, 1.6590, 1.2828])
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2
-
-        test_callback_fn.has_been_called = False
-
-        init_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/img2img/sketch-mountains-input.jpg"
-        )
-        init_image = init_image.resize((768, 512))
-
-        pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-
-        prompt = "A fantasy landscape, trending on artstation"
-
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        with torch.autocast(torch_device):
-            pipe(
-                prompt=prompt,
-                init_image=init_image,
-                strength=0.75,
-                num_inference_steps=50,
-                guidance_scale=7.5,
-                generator=generator,
-                callback=test_callback_fn,
-                callback_steps=1,
-            )
-        assert test_callback_fn.has_been_called
-        assert number_of_steps == 38
-
-    @slow
-    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-    def test_stable_diffusion_inpaint_legacy_intermediate_state(self):
-        number_of_steps = 0
-
-        def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
-            test_callback_fn.has_been_called = True
-            nonlocal number_of_steps
-            number_of_steps += 1
-            if step == 0:
-                latents = latents.detach().cpu().numpy()
-                assert latents.shape == (1, 4, 64, 64)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array(
-                    [-0.5472, 1.1218, -0.5505, -0.9390, -1.0794, 0.4063, 0.5158, 0.6429, -1.5246]
-                )
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
-            elif step == 37:
-                latents = latents.detach().cpu().numpy()
-                assert latents.shape == (1, 4, 64, 64)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array([0.4781, 1.1572, 0.6258, 0.2291, 0.2554, -0.1443, 0.7085, -0.1598, -0.5659])
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
-
-        test_callback_fn.has_been_called = False
-
-        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"
-        )
-
-        pipe = StableDiffusionInpaintPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-
-        prompt = "A red cat sitting on a park bench"
-
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        with torch.autocast(torch_device):
-            pipe(
-                prompt=prompt,
-                init_image=init_image,
-                mask_image=mask_image,
-                strength=0.75,
-                num_inference_steps=50,
-                guidance_scale=7.5,
-                generator=generator,
-                callback=test_callback_fn,
-                callback_steps=1,
-            )
-        assert test_callback_fn.has_been_called
-        assert number_of_steps == 38
-
     @slow
     def test_stable_diffusion_onnx_intermediate_state(self):
         number_of_steps = 0