test_animatediff.py

import gc
import unittest

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

import diffusers
from diffusers import (
    AnimateDiffPipeline,
    AutoencoderKL,
    DDIMScheduler,
    MotionAdapter,
    UNet2DConditionModel,
    UNetMotionModel,
)
from diffusers.utils import is_xformers_available, logging
from diffusers.utils.testing_utils import numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    IPAdapterTesterMixin,
    PipelineFromPipeTesterMixin,
    PipelineTesterMixin,
    SDFunctionTesterMixin,
)


def to_np(tensor):
    if isinstance(tensor, torch.Tensor):
        tensor = tensor.detach().cpu().numpy()

    return tensor


class AnimateDiffPipelineFastTests(
    IPAdapterTesterMixin, SDFunctionTesterMixin, PipelineTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase
):
    pipeline_class = AnimateDiffPipeline
    params = TEXT_TO_IMAGE_PARAMS
    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
    required_optional_params = frozenset(
        [
            "num_inference_steps",
            "generator",
            "latents",
            "return_dict",
            "callback_on_step_end",
            "callback_on_step_end_tensor_inputs",
        ]
    )

    def get_dummy_components(self):
        torch.manual_seed(0)
        unet = UNet2DConditionModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            sample_size=32,
            in_channels=4,
            out_channels=4,
            down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            cross_attention_dim=32,
            norm_num_groups=2,
        )
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="linear",
            clip_sample=False,
        )
        torch.manual_seed(0)
        vae = AutoencoderKL(
            block_out_channels=[32, 64],
            in_channels=3,
            out_channels=3,
            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
            latent_channels=4,
        )
        torch.manual_seed(0)
        text_encoder_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,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        motion_adapter = MotionAdapter(
            block_out_channels=(32, 64),
            motion_layers_per_block=2,
            motion_norm_num_groups=2,
            motion_num_attention_heads=4,
        )

        components = {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "motion_adapter": motion_adapter,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "feature_extractor": None,
            "image_encoder": None,
        }
        return components

    def get_dummy_inputs(self, device, seed=0):
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)

        inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 7.5,
            "output_type": "pt",
        }
        return inputs

    def test_motion_unet_loading(self):
        components = self.get_dummy_components()
        pipe = AnimateDiffPipeline(**components)

        assert isinstance(pipe.unet, UNetMotionModel)

    @unittest.skip("Attention slicing is not enabled in this pipeline")
    def test_attention_slicing_forward_pass(self):
        pass

    def test_ip_adapter_single(self):
        expected_pipe_slice = None
        if torch_device == "cpu":
            expected_pipe_slice = np.array(
                [
                    0.5541,
                    0.5802,
                    0.5074,
                    0.4583,
                    0.4729,
                    0.5374,
                    0.4051,
                    0.4495,
                    0.4480,
                    0.5292,
                    0.6322,
                    0.6265,
                    0.5455,
                    0.4771,
                    0.5795,
                    0.5845,
                    0.4172,
                    0.6066,
                    0.6535,
                    0.4113,
                    0.6833,
                    0.5736,
                    0.3589,
                    0.5730,
                    0.4205,
                    0.3786,
                    0.5323,
                ]
            )
        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)

    def test_inference_batch_single_identical(
        self,
        batch_size=2,
        expected_max_diff=1e-4,
        additional_params_copy_to_batched_inputs=["num_inference_steps"],
    ):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        for components in pipe.components.values():
            if hasattr(components, "set_default_attn_processor"):
                components.set_default_attn_processor()

        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        inputs = self.get_dummy_inputs(torch_device)
        # Reset generator in case it is has been used in self.get_dummy_inputs
        inputs["generator"] = self.get_generator(0)

        logger = logging.get_logger(pipe.__module__)
        logger.setLevel(level=diffusers.logging.FATAL)

        # batchify inputs
        batched_inputs = {}
        batched_inputs.update(inputs)

        for name in self.batch_params:
            if name not in inputs:
                continue

            value = inputs[name]
            if name == "prompt":
                len_prompt = len(value)
                batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
                batched_inputs[name][-1] = 100 * "very long"

            else:
                batched_inputs[name] = batch_size * [value]

        if "generator" in inputs:
            batched_inputs["generator"] = [self.get_generator(i) for i in range(batch_size)]

        if "batch_size" in inputs:
            batched_inputs["batch_size"] = batch_size

        for arg in additional_params_copy_to_batched_inputs:
            batched_inputs[arg] = inputs[arg]

        output = pipe(**inputs)
        output_batch = pipe(**batched_inputs)

        assert output_batch[0].shape[0] == batch_size

        max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()
        assert max_diff < expected_max_diff

    @unittest.skipIf(torch_device != "cuda", reason="CUDA and CPU are required to switch devices")
    def test_to_device(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.set_progress_bar_config(disable=None)

        pipe.to("cpu")
        # pipeline creates a new motion UNet under the hood. So we need to check the device from pipe.components
        model_devices = [
            component.device.type for component in pipe.components.values() if hasattr(component, "device")
        ]
        self.assertTrue(all(device == "cpu" for device in model_devices))

        output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0]
        self.assertTrue(np.isnan(output_cpu).sum() == 0)

        pipe.to("cuda")
        model_devices = [
            component.device.type for component in pipe.components.values() if hasattr(component, "device")
        ]
        self.assertTrue(all(device == "cuda" for device in model_devices))

        output_cuda = pipe(**self.get_dummy_inputs("cuda"))[0]
        self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)

    def test_to_dtype(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.set_progress_bar_config(disable=None)

        # pipeline creates a new motion UNet under the hood. So we need to check the dtype from pipe.components
        model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, "dtype")]
        self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))

        pipe.to(dtype=torch.float16)
        model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, "dtype")]
        self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))

    def test_prompt_embeds(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.set_progress_bar_config(disable=None)
        pipe.to(torch_device)

        inputs = self.get_dummy_inputs(torch_device)
        inputs.pop("prompt")
        inputs["prompt_embeds"] = torch.randn((1, 4, 32), device=torch_device)
        pipe(**inputs)

    def test_free_init(self):
        components = self.get_dummy_components()
        pipe: AnimateDiffPipeline = self.pipeline_class(**components)
        pipe.set_progress_bar_config(disable=None)
        pipe.to(torch_device)

        inputs_normal = self.get_dummy_inputs(torch_device)
        frames_normal = pipe(**inputs_normal).frames[0]

        pipe.enable_free_init(
            num_iters=2,
            use_fast_sampling=True,
            method="butterworth",
            order=4,
            spatial_stop_frequency=0.25,
            temporal_stop_frequency=0.25,
        )
        inputs_enable_free_init = self.get_dummy_inputs(torch_device)
        frames_enable_free_init = pipe(**inputs_enable_free_init).frames[0]

        pipe.disable_free_init()
        inputs_disable_free_init = self.get_dummy_inputs(torch_device)
        frames_disable_free_init = pipe(**inputs_disable_free_init).frames[0]

        sum_enabled = np.abs(to_np(frames_normal) - to_np(frames_enable_free_init)).sum()
        max_diff_disabled = np.abs(to_np(frames_normal) - to_np(frames_disable_free_init)).max()
        self.assertGreater(
            sum_enabled, 1e1, "Enabling of FreeInit should lead to results different from the default pipeline results"
        )
        self.assertLess(
            max_diff_disabled,
            1e-4,
            "Disabling of FreeInit should lead to results similar to the default pipeline results",
        )

    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available(),
        reason="XFormers attention is only available with CUDA and `xformers` installed",
    )
    def test_xformers_attention_forwardGenerator_pass(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        for component in pipe.components.values():
            if hasattr(component, "set_default_attn_processor"):
                component.set_default_attn_processor()
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(torch_device)
        output_without_offload = pipe(**inputs).frames[0]
        output_without_offload = (
            output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload
        )

        pipe.enable_xformers_memory_efficient_attention()
        inputs = self.get_dummy_inputs(torch_device)
        output_with_offload = pipe(**inputs).frames[0]
        output_with_offload = (
            output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload
        )

        max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
        self.assertLess(max_diff, 1e-4, "XFormers attention should not affect the inference results")

    def test_vae_slicing(self):
        return super().test_vae_slicing(image_count=2)


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

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

    def test_animatediff(self):
        adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
        pipe = AnimateDiffPipeline.from_pretrained("frankjoshua/toonyou_beta6", motion_adapter=adapter)
        pipe = pipe.to(torch_device)
        pipe.scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="linear",
            steps_offset=1,
            clip_sample=False,
        )
        pipe.enable_vae_slicing()
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=None)

        prompt = "night, b&w photo of old house, post apocalypse, forest, storm weather, wind, rocks, 8k uhd, dslr, soft lighting, high quality, film grain"
        negative_prompt = "bad quality, worse quality"

        generator = torch.Generator("cpu").manual_seed(0)
        output = pipe(
            prompt,
            negative_prompt=negative_prompt,
            num_frames=16,
            generator=generator,
            guidance_scale=7.5,
            num_inference_steps=3,
            output_type="np",
        )

        image = output.frames[0]
        assert image.shape == (16, 512, 512, 3)

        image_slice = image[0, -3:, -3:, -1]
        expected_slice = np.array(
            [
                0.11357737,
                0.11285847,
                0.11180121,
                0.11084166,
                0.11414117,
                0.09785956,
                0.10742754,
                0.10510018,
                0.08045256,
            ]
        )
        assert numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice.flatten()) < 1e-3