test_unidiffuser.py

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionModelWithProjection,
    GPT2Tokenizer,
)

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    UniDiffuserModel,
    UniDiffuserPipeline,
    UniDiffuserTextDecoder,
)
from diffusers.utils.testing_utils import (
    backend_empty_cache,
    enable_full_determinism,
    floats_tensor,
    load_image,
    nightly,
    require_torch_accelerator,
    torch_device,
)
from diffusers.utils.torch_utils import randn_tensor

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


enable_full_determinism()


class UniDiffuserPipelineFastTests(
    PipelineTesterMixin, PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
):
    pipeline_class = UniDiffuserPipeline
    params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
    # vae_latents, not latents, is the argument that corresponds to VAE latent inputs
    image_latents_params = frozenset(["vae_latents"])

    supports_dduf = False

    def get_dummy_components(self):
        unet = UniDiffuserModel.from_pretrained(
            "hf-internal-testing/unidiffuser-diffusers-test",
            subfolder="unet",
        )

        scheduler = DPMSolverMultistepScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            solver_order=3,
        )

        vae = AutoencoderKL.from_pretrained(
            "hf-internal-testing/unidiffuser-diffusers-test",
            subfolder="vae",
        )

        text_encoder = CLIPTextModel.from_pretrained(
            "hf-internal-testing/unidiffuser-diffusers-test",
            subfolder="text_encoder",
        )
        clip_tokenizer = CLIPTokenizer.from_pretrained(
            "hf-internal-testing/unidiffuser-diffusers-test",
            subfolder="clip_tokenizer",
        )

        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
            "hf-internal-testing/unidiffuser-diffusers-test",
            subfolder="image_encoder",
        )
        # From the Stable Diffusion Image Variation pipeline tests
        clip_image_processor = CLIPImageProcessor(crop_size=32, size=32)
        # image_processor = CLIPImageProcessor.from_pretrained("hf-internal-testing/tiny-random-clip")

        text_tokenizer = GPT2Tokenizer.from_pretrained(
            "hf-internal-testing/unidiffuser-diffusers-test",
            subfolder="text_tokenizer",
        )
        text_decoder = UniDiffuserTextDecoder.from_pretrained(
            "hf-internal-testing/unidiffuser-diffusers-test",
            subfolder="text_decoder",
        )

        components = {
            "vae": vae,
            "text_encoder": text_encoder,
            "image_encoder": image_encoder,
            "clip_image_processor": clip_image_processor,
            "clip_tokenizer": clip_tokenizer,
            "text_decoder": text_decoder,
            "text_tokenizer": text_tokenizer,
            "unet": unet,
            "scheduler": scheduler,
        }

        return components

    def get_dummy_inputs(self, device, seed=0):
        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
        image = image.cpu().permute(0, 2, 3, 1)[0]
        image = Image.fromarray(np.uint8(image)).convert("RGB")
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)
        inputs = {
            "prompt": "an elephant under the sea",
            "image": image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "np",
        }
        return inputs

    def get_fixed_latents(self, device, seed=0):
        if isinstance(device, str):
            device = torch.device(device)
        generator = torch.Generator(device=device).manual_seed(seed)
        # Hardcode the shapes for now.
        prompt_latents = randn_tensor((1, 77, 32), generator=generator, device=device, dtype=torch.float32)
        vae_latents = randn_tensor((1, 4, 16, 16), generator=generator, device=device, dtype=torch.float32)
        clip_latents = randn_tensor((1, 1, 32), generator=generator, device=device, dtype=torch.float32)

        latents = {
            "prompt_latents": prompt_latents,
            "vae_latents": vae_latents,
            "clip_latents": clip_latents,
        }
        return latents

    def get_dummy_inputs_with_latents(self, device, seed=0):
        # image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
        # image = image.cpu().permute(0, 2, 3, 1)[0]
        # image = Image.fromarray(np.uint8(image)).convert("RGB")
        image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg",
        )
        image = image.resize((32, 32))
        latents = self.get_fixed_latents(device, seed=seed)

        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)

        inputs = {
            "prompt": "an elephant under the sea",
            "image": image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "np",
            "prompt_latents": latents.get("prompt_latents"),
            "vae_latents": latents.get("vae_latents"),
            "clip_latents": latents.get("clip_latents"),
        }
        return inputs

    def test_dict_tuple_outputs_equivalent(self):
        expected_slice = None
        if torch_device == "cpu":
            expected_slice = np.array([0.7489, 0.3722, 0.4475, 0.5630, 0.5923, 0.4992, 0.3936, 0.5844, 0.4975])
        super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)

    def test_unidiffuser_default_joint_v0(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        unidiffuser_pipe = UniDiffuserPipeline(**components)
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'joint'
        unidiffuser_pipe.set_joint_mode()
        assert unidiffuser_pipe.mode == "joint"

        # inputs = self.get_dummy_inputs(device)
        inputs = self.get_dummy_inputs_with_latents(device)
        # Delete prompt and image for joint inference.
        del inputs["prompt"]
        del inputs["image"]
        sample = unidiffuser_pipe(**inputs)
        image = sample.images
        text = sample.text
        assert image.shape == (1, 32, 32, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_img_slice = np.array([0.5760, 0.6270, 0.6571, 0.4965, 0.4638, 0.5663, 0.5254, 0.5068, 0.5716])
        assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-3

        expected_text_prefix = " no no no "
        assert text[0][:10] == expected_text_prefix

    def test_unidiffuser_default_joint_no_cfg_v0(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        unidiffuser_pipe = UniDiffuserPipeline(**components)
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'joint'
        unidiffuser_pipe.set_joint_mode()
        assert unidiffuser_pipe.mode == "joint"

        # inputs = self.get_dummy_inputs(device)
        inputs = self.get_dummy_inputs_with_latents(device)
        # Delete prompt and image for joint inference.
        del inputs["prompt"]
        del inputs["image"]
        # Set guidance scale to 1.0 to turn off CFG
        inputs["guidance_scale"] = 1.0
        sample = unidiffuser_pipe(**inputs)
        image = sample.images
        text = sample.text
        assert image.shape == (1, 32, 32, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_img_slice = np.array([0.5760, 0.6270, 0.6571, 0.4965, 0.4638, 0.5663, 0.5254, 0.5068, 0.5716])
        assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-3

        expected_text_prefix = " no no no "
        assert text[0][:10] == expected_text_prefix

    def test_unidiffuser_default_text2img_v0(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        unidiffuser_pipe = UniDiffuserPipeline(**components)
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'text2img'
        unidiffuser_pipe.set_text_to_image_mode()
        assert unidiffuser_pipe.mode == "text2img"

        inputs = self.get_dummy_inputs_with_latents(device)
        # Delete image for text-conditioned image generation
        del inputs["image"]
        image = unidiffuser_pipe(**inputs).images
        assert image.shape == (1, 32, 32, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_slice = np.array([0.5758, 0.6269, 0.6570, 0.4967, 0.4639, 0.5664, 0.5257, 0.5067, 0.5715])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

    def test_unidiffuser_default_image_0(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        unidiffuser_pipe = UniDiffuserPipeline(**components)
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'img'
        unidiffuser_pipe.set_image_mode()
        assert unidiffuser_pipe.mode == "img"

        inputs = self.get_dummy_inputs(device)
        # Delete prompt and image for unconditional ("marginal") text generation.
        del inputs["prompt"]
        del inputs["image"]
        image = unidiffuser_pipe(**inputs).images
        assert image.shape == (1, 32, 32, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_slice = np.array([0.5760, 0.6270, 0.6571, 0.4966, 0.4638, 0.5663, 0.5254, 0.5068, 0.5715])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

    def test_unidiffuser_default_text_v0(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        unidiffuser_pipe = UniDiffuserPipeline(**components)
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'img'
        unidiffuser_pipe.set_text_mode()
        assert unidiffuser_pipe.mode == "text"

        inputs = self.get_dummy_inputs(device)
        # Delete prompt and image for unconditional ("marginal") text generation.
        del inputs["prompt"]
        del inputs["image"]
        text = unidiffuser_pipe(**inputs).text

        expected_text_prefix = " no no no "
        assert text[0][:10] == expected_text_prefix

    def test_unidiffuser_default_img2text_v0(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        unidiffuser_pipe = UniDiffuserPipeline(**components)
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'img2text'
        unidiffuser_pipe.set_image_to_text_mode()
        assert unidiffuser_pipe.mode == "img2text"

        inputs = self.get_dummy_inputs_with_latents(device)
        # Delete text for image-conditioned text generation
        del inputs["prompt"]
        text = unidiffuser_pipe(**inputs).text

        expected_text_prefix = " no no no "
        assert text[0][:10] == expected_text_prefix

    def test_unidiffuser_default_joint_v1(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unidiffuser_pipe = UniDiffuserPipeline.from_pretrained("hf-internal-testing/unidiffuser-test-v1")
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'joint'
        unidiffuser_pipe.set_joint_mode()
        assert unidiffuser_pipe.mode == "joint"

        # inputs = self.get_dummy_inputs(device)
        inputs = self.get_dummy_inputs_with_latents(device)
        # Delete prompt and image for joint inference.
        del inputs["prompt"]
        del inputs["image"]
        inputs["data_type"] = 1
        sample = unidiffuser_pipe(**inputs)
        image = sample.images
        text = sample.text
        assert image.shape == (1, 32, 32, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_img_slice = np.array([0.5760, 0.6270, 0.6571, 0.4965, 0.4638, 0.5663, 0.5254, 0.5068, 0.5716])
        assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-3

        expected_text_prefix = " no no no "
        assert text[0][:10] == expected_text_prefix

    def test_unidiffuser_default_text2img_v1(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unidiffuser_pipe = UniDiffuserPipeline.from_pretrained("hf-internal-testing/unidiffuser-test-v1")
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'text2img'
        unidiffuser_pipe.set_text_to_image_mode()
        assert unidiffuser_pipe.mode == "text2img"

        inputs = self.get_dummy_inputs_with_latents(device)
        # Delete image for text-conditioned image generation
        del inputs["image"]
        image = unidiffuser_pipe(**inputs).images
        assert image.shape == (1, 32, 32, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_slice = np.array([0.5758, 0.6269, 0.6570, 0.4967, 0.4639, 0.5664, 0.5257, 0.5067, 0.5715])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

    def test_unidiffuser_default_img2text_v1(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unidiffuser_pipe = UniDiffuserPipeline.from_pretrained("hf-internal-testing/unidiffuser-test-v1")
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'img2text'
        unidiffuser_pipe.set_image_to_text_mode()
        assert unidiffuser_pipe.mode == "img2text"

        inputs = self.get_dummy_inputs_with_latents(device)
        # Delete text for image-conditioned text generation
        del inputs["prompt"]
        text = unidiffuser_pipe(**inputs).text

        expected_text_prefix = " no no no "
        assert text[0][:10] == expected_text_prefix

    def test_unidiffuser_text2img_multiple_images(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        unidiffuser_pipe = UniDiffuserPipeline(**components)
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'text2img'
        unidiffuser_pipe.set_text_to_image_mode()
        assert unidiffuser_pipe.mode == "text2img"

        inputs = self.get_dummy_inputs(device)
        # Delete image for text-conditioned image generation
        del inputs["image"]
        inputs["num_images_per_prompt"] = 2
        inputs["num_prompts_per_image"] = 3
        image = unidiffuser_pipe(**inputs).images
        assert image.shape == (2, 32, 32, 3)

    def test_unidiffuser_img2text_multiple_prompts(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        unidiffuser_pipe = UniDiffuserPipeline(**components)
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'img2text'
        unidiffuser_pipe.set_image_to_text_mode()
        assert unidiffuser_pipe.mode == "img2text"

        inputs = self.get_dummy_inputs(device)
        # Delete text for image-conditioned text generation
        del inputs["prompt"]
        inputs["num_images_per_prompt"] = 2
        inputs["num_prompts_per_image"] = 3
        text = unidiffuser_pipe(**inputs).text

        assert len(text) == 3

    def test_unidiffuser_text2img_multiple_images_with_latents(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        unidiffuser_pipe = UniDiffuserPipeline(**components)
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'text2img'
        unidiffuser_pipe.set_text_to_image_mode()
        assert unidiffuser_pipe.mode == "text2img"

        inputs = self.get_dummy_inputs_with_latents(device)
        # Delete image for text-conditioned image generation
        del inputs["image"]
        inputs["num_images_per_prompt"] = 2
        inputs["num_prompts_per_image"] = 3
        image = unidiffuser_pipe(**inputs).images
        assert image.shape == (2, 32, 32, 3)

    def test_unidiffuser_img2text_multiple_prompts_with_latents(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        unidiffuser_pipe = UniDiffuserPipeline(**components)
        unidiffuser_pipe = unidiffuser_pipe.to(device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'img2text'
        unidiffuser_pipe.set_image_to_text_mode()
        assert unidiffuser_pipe.mode == "img2text"

        inputs = self.get_dummy_inputs_with_latents(device)
        # Delete text for image-conditioned text generation
        del inputs["prompt"]
        inputs["num_images_per_prompt"] = 2
        inputs["num_prompts_per_image"] = 3
        text = unidiffuser_pipe(**inputs).text

        assert len(text) == 3

    def test_inference_batch_single_identical(self):
        super().test_inference_batch_single_identical(expected_max_diff=2e-4)

    @require_torch_accelerator
    def test_unidiffuser_default_joint_v1_fp16(self):
        unidiffuser_pipe = UniDiffuserPipeline.from_pretrained(
            "hf-internal-testing/unidiffuser-test-v1", torch_dtype=torch.float16
        )
        unidiffuser_pipe = unidiffuser_pipe.to(torch_device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'joint'
        unidiffuser_pipe.set_joint_mode()
        assert unidiffuser_pipe.mode == "joint"

        inputs = self.get_dummy_inputs_with_latents(torch_device)
        # Delete prompt and image for joint inference.
        del inputs["prompt"]
        del inputs["image"]
        inputs["data_type"] = 1
        sample = unidiffuser_pipe(**inputs)
        image = sample.images
        text = sample.text
        assert image.shape == (1, 32, 32, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_img_slice = np.array([0.5049, 0.5498, 0.5854, 0.3052, 0.4460, 0.6489, 0.5122, 0.4810, 0.6138])
        assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-3

        expected_text_prefix = '" This This'
        assert text[0][: len(expected_text_prefix)] == expected_text_prefix

    @require_torch_accelerator
    def test_unidiffuser_default_text2img_v1_fp16(self):
        unidiffuser_pipe = UniDiffuserPipeline.from_pretrained(
            "hf-internal-testing/unidiffuser-test-v1", torch_dtype=torch.float16
        )
        unidiffuser_pipe = unidiffuser_pipe.to(torch_device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'text2img'
        unidiffuser_pipe.set_text_to_image_mode()
        assert unidiffuser_pipe.mode == "text2img"

        inputs = self.get_dummy_inputs_with_latents(torch_device)
        # Delete prompt and image for joint inference.
        del inputs["image"]
        inputs["data_type"] = 1
        sample = unidiffuser_pipe(**inputs)
        image = sample.images
        assert image.shape == (1, 32, 32, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_img_slice = np.array([0.5054, 0.5498, 0.5854, 0.3052, 0.4458, 0.6489, 0.5122, 0.4810, 0.6138])
        assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-3

    @require_torch_accelerator
    def test_unidiffuser_default_img2text_v1_fp16(self):
        unidiffuser_pipe = UniDiffuserPipeline.from_pretrained(
            "hf-internal-testing/unidiffuser-test-v1", torch_dtype=torch.float16
        )
        unidiffuser_pipe = unidiffuser_pipe.to(torch_device)
        unidiffuser_pipe.set_progress_bar_config(disable=None)

        # Set mode to 'img2text'
        unidiffuser_pipe.set_image_to_text_mode()
        assert unidiffuser_pipe.mode == "img2text"

        inputs = self.get_dummy_inputs_with_latents(torch_device)
        # Delete prompt and image for joint inference.
        del inputs["prompt"]
        inputs["data_type"] = 1
        text = unidiffuser_pipe(**inputs).text

        expected_text_prefix = '" This This'
        assert text[0][: len(expected_text_prefix)] == expected_text_prefix

    @unittest.skip(
        "Test not supported because it has a bunch of direct configs at init and also, this pipeline isn't used that much now."
    )
    def test_encode_prompt_works_in_isolation():
        pass


@nightly
@require_torch_accelerator
class UniDiffuserPipelineSlowTests(unittest.TestCase):
    def setUp(self):
        super().setUp()
        gc.collect()
        backend_empty_cache(torch_device)

    def tearDown(self):
        super().tearDown()
        gc.collect()
        backend_empty_cache(torch_device)

    def get_inputs(self, device, seed=0, generate_latents=False):
        generator = torch.manual_seed(seed)
        image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg"
        )
        inputs = {
            "prompt": "an elephant under the sea",
            "image": image,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 8.0,
            "output_type": "np",
        }
        if generate_latents:
            latents = self.get_fixed_latents(device, seed=seed)
            for latent_name, latent_tensor in latents.items():
                inputs[latent_name] = latent_tensor
        return inputs

    def get_fixed_latents(self, device, seed=0):
        if isinstance(device, str):
            device = torch.device(device)
        latent_device = torch.device("cpu")
        generator = torch.Generator(device=latent_device).manual_seed(seed)
        # Hardcode the shapes for now.
        prompt_latents = randn_tensor((1, 77, 768), generator=generator, device=device, dtype=torch.float32)
        vae_latents = randn_tensor((1, 4, 64, 64), generator=generator, device=device, dtype=torch.float32)
        clip_latents = randn_tensor((1, 1, 512), generator=generator, device=device, dtype=torch.float32)

        # Move latents onto desired device.
        prompt_latents = prompt_latents.to(device)
        vae_latents = vae_latents.to(device)
        clip_latents = clip_latents.to(device)

        latents = {
            "prompt_latents": prompt_latents,
            "vae_latents": vae_latents,
            "clip_latents": clip_latents,
        }
        return latents

    def test_unidiffuser_default_joint_v1(self):
        pipe = UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1")
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        # inputs = self.get_dummy_inputs(device)
        inputs = self.get_inputs(device=torch_device, generate_latents=True)
        # Delete prompt and image for joint inference.
        del inputs["prompt"]
        del inputs["image"]
        sample = pipe(**inputs)
        image = sample.images
        text = sample.text
        assert image.shape == (1, 512, 512, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_img_slice = np.array([0.2402, 0.2375, 0.2285, 0.2378, 0.2407, 0.2263, 0.2354, 0.2307, 0.2520])
        assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-1

        expected_text_prefix = "a living room"
        assert text[0][: len(expected_text_prefix)] == expected_text_prefix

    def test_unidiffuser_default_text2img_v1(self):
        pipe = UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1")
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        inputs = self.get_inputs(device=torch_device, generate_latents=True)
        del inputs["image"]
        sample = pipe(**inputs)
        image = sample.images
        assert image.shape == (1, 512, 512, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_slice = np.array([0.0242, 0.0103, 0.0022, 0.0129, 0.0000, 0.0090, 0.0376, 0.0508, 0.0005])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1

    def test_unidiffuser_default_img2text_v1(self):
        pipe = UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1")
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        inputs = self.get_inputs(device=torch_device, generate_latents=True)
        del inputs["prompt"]
        sample = pipe(**inputs)
        text = sample.text

        expected_text_prefix = "An astronaut"
        assert text[0][: len(expected_text_prefix)] == expected_text_prefix


@nightly
@require_torch_accelerator
class UniDiffuserPipelineNightlyTests(unittest.TestCase):
    def setUp(self):
        super().setUp()
        gc.collect()
        backend_empty_cache(torch_device)

    def tearDown(self):
        super().tearDown()
        gc.collect()
        backend_empty_cache(torch_device)

    def get_inputs(self, device, seed=0, generate_latents=False):
        generator = torch.manual_seed(seed)
        image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg"
        )
        inputs = {
            "prompt": "an elephant under the sea",
            "image": image,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 8.0,
            "output_type": "np",
        }
        if generate_latents:
            latents = self.get_fixed_latents(device, seed=seed)
            for latent_name, latent_tensor in latents.items():
                inputs[latent_name] = latent_tensor
        return inputs

    def get_fixed_latents(self, device, seed=0):
        if isinstance(device, str):
            device = torch.device(device)
        latent_device = torch.device("cpu")
        generator = torch.Generator(device=latent_device).manual_seed(seed)
        # Hardcode the shapes for now.
        prompt_latents = randn_tensor((1, 77, 768), generator=generator, device=device, dtype=torch.float32)
        vae_latents = randn_tensor((1, 4, 64, 64), generator=generator, device=device, dtype=torch.float32)
        clip_latents = randn_tensor((1, 1, 512), generator=generator, device=device, dtype=torch.float32)

        # Move latents onto desired device.
        prompt_latents = prompt_latents.to(device)
        vae_latents = vae_latents.to(device)
        clip_latents = clip_latents.to(device)

        latents = {
            "prompt_latents": prompt_latents,
            "vae_latents": vae_latents,
            "clip_latents": clip_latents,
        }
        return latents

    def test_unidiffuser_default_joint_v1_fp16(self):
        pipe = UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1", torch_dtype=torch.float16)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        # inputs = self.get_dummy_inputs(device)
        inputs = self.get_inputs(device=torch_device, generate_latents=True)
        # Delete prompt and image for joint inference.
        del inputs["prompt"]
        del inputs["image"]
        sample = pipe(**inputs)
        image = sample.images
        text = sample.text
        assert image.shape == (1, 512, 512, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_img_slice = np.array([0.2402, 0.2375, 0.2285, 0.2378, 0.2407, 0.2263, 0.2354, 0.2307, 0.2520])
        assert np.abs(image_slice.flatten() - expected_img_slice).max() < 2e-1

        expected_text_prefix = "a living room"
        assert text[0][: len(expected_text_prefix)] == expected_text_prefix

    def test_unidiffuser_default_text2img_v1_fp16(self):
        pipe = UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1", torch_dtype=torch.float16)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        inputs = self.get_inputs(device=torch_device, generate_latents=True)
        del inputs["image"]
        sample = pipe(**inputs)
        image = sample.images
        assert image.shape == (1, 512, 512, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_slice = np.array([0.0242, 0.0103, 0.0022, 0.0129, 0.0000, 0.0090, 0.0376, 0.0508, 0.0005])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1

    def test_unidiffuser_default_img2text_v1_fp16(self):
        pipe = UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1", torch_dtype=torch.float16)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        inputs = self.get_inputs(device=torch_device, generate_latents=True)
        del inputs["prompt"]
        sample = pipe(**inputs)
        text = sample.text

        expected_text_prefix = "An astronaut"
        assert text[0][: len(expected_text_prefix)] == expected_text_prefix