tests/pipelines/text_to_video_synthesis/test_video_to_video.py

# coding=utf-8
# Copyright 2024 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 random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNet3DConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    is_flaky,
    nightly,
    numpy_cosine_similarity_distance,
    skip_mps,
    torch_device,
)

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


@skip_mps
class VideoToVideoSDPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    pipeline_class = VideoToVideoSDPipeline
    params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}) - {"image", "width", "height"}
    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}) - {"image"}
    required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
    test_attention_slicing = False

    # No `output_type`.
    required_optional_params = frozenset(
        [
            "num_inference_steps",
            "generator",
            "latents",
            "return_dict",
            "callback",
            "callback_steps",
        ]
    )

    def get_dummy_components(self):
        torch.manual_seed(0)
        unet = UNet3DConditionModel(
            block_out_channels=(4, 8),
            layers_per_block=1,
            sample_size=32,
            in_channels=4,
            out_channels=4,
            down_block_types=("CrossAttnDownBlock3D", "DownBlock3D"),
            up_block_types=("UpBlock3D", "CrossAttnUpBlock3D"),
            cross_attention_dim=32,
            attention_head_dim=4,
            norm_num_groups=2,
        )
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=True,
            set_alpha_to_one=False,
        )
        torch.manual_seed(0)
        vae = AutoencoderKL(
            block_out_channels=[
                8,
            ],
            in_channels=3,
            out_channels=3,
            down_block_types=[
                "DownEncoderBlock2D",
            ],
            up_block_types=["UpDecoderBlock2D"],
            latent_channels=4,
            sample_size=32,
            norm_num_groups=2,
        )
        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,
            hidden_act="gelu",
            projection_dim=512,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        components = {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
        }
        return components

    def get_dummy_inputs(self, device, seed=0):
        # 3 frames
        video = floats_tensor((1, 3, 3, 32, 32), rng=random.Random(seed)).to(device)

        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",
            "video": video,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "pt",
        }
        return inputs

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

        inputs = self.get_dummy_inputs(device)
        inputs["output_type"] = "np"
        frames = sd_pipe(**inputs).frames
        image_slice = frames[0][0][-3:, -3:, -1]

        assert frames[0][0].shape == (32, 32, 3)
        expected_slice = np.array([0.6391, 0.5350, 0.5202, 0.5521, 0.5453, 0.5393, 0.6652, 0.5270, 0.5185])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @is_flaky()
    def test_save_load_optional_components(self):
        super().test_save_load_optional_components(expected_max_difference=0.001)

    @is_flaky()
    def test_dict_tuple_outputs_equivalent(self):
        super().test_dict_tuple_outputs_equivalent()

    @is_flaky()
    def test_save_load_local(self):
        super().test_save_load_local()

    @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):
        self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False, expected_max_diff=5e-3)

    # (todo): sayakpaul
    @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline.")
    def test_inference_batch_consistent(self):
        pass

    # (todo): sayakpaul
    @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline.")
    def test_inference_batch_single_identical(self):
        pass

    @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline.")
    def test_num_images_per_prompt(self):
        pass

    def test_progress_bar(self):
        return super().test_progress_bar()


@nightly
@skip_mps
class VideoToVideoSDPipelineSlowTests(unittest.TestCase):
    def test_two_step_model(self):
        pipe = VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
        pipe.enable_model_cpu_offload()

        # 10 frames
        generator = torch.Generator(device="cpu").manual_seed(0)
        video = torch.randn((1, 10, 3, 320, 576), generator=generator)

        prompt = "Spiderman is surfing"

        generator = torch.Generator(device="cpu").manual_seed(0)
        video_frames = pipe(prompt, video=video, generator=generator, num_inference_steps=3, output_type="np").frames

        expected_array = np.array(
            [0.17114258, 0.13720703, 0.08886719, 0.14819336, 0.1730957, 0.24584961, 0.22021484, 0.35180664, 0.2607422]
        )
        output_array = video_frames[0, 0, :3, :3, 0].flatten()
        assert numpy_cosine_similarity_distance(expected_array, output_array) < 1e-3