diffusers/tests/pipelines/cogvideo/test_cogvideox_video2video.py

# Copyright 2024 The HuggingFace Team.
#
# 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 inspect
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import AutoTokenizer, T5EncoderModel

from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXVideoToVideoPipeline, DDIMScheduler
from diffusers.utils.testing_utils import enable_full_determinism, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineTesterMixin,
    check_qkv_fusion_matches_attn_procs_length,
    check_qkv_fusion_processors_exist,
    to_np,
)


enable_full_determinism()


class CogVideoXVideoToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    pipeline_class = CogVideoXVideoToVideoPipeline
    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS.union({"video"})
    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
    required_optional_params = frozenset(
        [
            "num_inference_steps",
            "generator",
            "latents",
            "return_dict",
            "callback_on_step_end",
            "callback_on_step_end_tensor_inputs",
        ]
    )
    test_xformers_attention = False

    def get_dummy_components(self):
        torch.manual_seed(0)
        transformer = CogVideoXTransformer3DModel(
            # Product of num_attention_heads * attention_head_dim must be divisible by 16 for 3D positional embeddings
            # But, since we are using tiny-random-t5 here, we need the internal dim of CogVideoXTransformer3DModel
            # to be 32. The internal dim is product of num_attention_heads and attention_head_dim
            num_attention_heads=4,
            attention_head_dim=8,
            in_channels=4,
            out_channels=4,
            time_embed_dim=2,
            text_embed_dim=32,  # Must match with tiny-random-t5
            num_layers=1,
            sample_width=2,  # latent width: 2 -> final width: 16
            sample_height=2,  # latent height: 2 -> final height: 16
            sample_frames=9,  # latent frames: (9 - 1) / 4 + 1 = 3 -> final frames: 9
            patch_size=2,
            temporal_compression_ratio=4,
            max_text_seq_length=16,
        )

        torch.manual_seed(0)
        vae = AutoencoderKLCogVideoX(
            in_channels=3,
            out_channels=3,
            down_block_types=(
                "CogVideoXDownBlock3D",
                "CogVideoXDownBlock3D",
                "CogVideoXDownBlock3D",
                "CogVideoXDownBlock3D",
            ),
            up_block_types=(
                "CogVideoXUpBlock3D",
                "CogVideoXUpBlock3D",
                "CogVideoXUpBlock3D",
                "CogVideoXUpBlock3D",
            ),
            block_out_channels=(8, 8, 8, 8),
            latent_channels=4,
            layers_per_block=1,
            norm_num_groups=2,
            temporal_compression_ratio=4,
        )

        torch.manual_seed(0)
        scheduler = DDIMScheduler()
        text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

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

    def get_dummy_inputs(self, device, seed: int = 0, num_frames: int = 8):
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)

        video_height = 16
        video_width = 16
        video = [Image.new("RGB", (video_width, video_height))] * num_frames

        inputs = {
            "video": video,
            "prompt": "dance monkey",
            "negative_prompt": "",
            "generator": generator,
            "num_inference_steps": 2,
            "strength": 0.5,
            "guidance_scale": 6.0,
            # Cannot reduce because convolution kernel becomes bigger than sample
            "height": video_height,
            "width": video_width,
            "max_sequence_length": 16,
            "output_type": "pt",
        }
        return inputs

    def test_inference(self):
        device = "cpu"

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(device)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        video = pipe(**inputs).frames
        generated_video = video[0]

        self.assertEqual(generated_video.shape, (8, 3, 16, 16))
        expected_video = torch.randn(8, 3, 16, 16)
        max_diff = np.abs(generated_video - expected_video).max()
        self.assertLessEqual(max_diff, 1e10)

    def test_callback_inputs(self):
        sig = inspect.signature(self.pipeline_class.__call__)
        has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
        has_callback_step_end = "callback_on_step_end" in sig.parameters

        if not (has_callback_tensor_inputs and has_callback_step_end):
            return

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe = pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        self.assertTrue(
            hasattr(pipe, "_callback_tensor_inputs"),
            f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
        )

        def callback_inputs_subset(pipe, i, t, callback_kwargs):
            # iterate over callback args
            for tensor_name, tensor_value in callback_kwargs.items():
                # check that we're only passing in allowed tensor inputs
                assert tensor_name in pipe._callback_tensor_inputs

            return callback_kwargs

        def callback_inputs_all(pipe, i, t, callback_kwargs):
            for tensor_name in pipe._callback_tensor_inputs:
                assert tensor_name in callback_kwargs

            # iterate over callback args
            for tensor_name, tensor_value in callback_kwargs.items():
                # check that we're only passing in allowed tensor inputs
                assert tensor_name in pipe._callback_tensor_inputs

            return callback_kwargs

        inputs = self.get_dummy_inputs(torch_device)

        # Test passing in a subset
        inputs["callback_on_step_end"] = callback_inputs_subset
        inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
        output = pipe(**inputs)[0]

        # Test passing in a everything
        inputs["callback_on_step_end"] = callback_inputs_all
        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
        output = pipe(**inputs)[0]

        def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
            is_last = i == (pipe.num_timesteps - 1)
            if is_last:
                callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
            return callback_kwargs

        inputs["callback_on_step_end"] = callback_inputs_change_tensor
        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
        output = pipe(**inputs)[0]
        assert output.abs().sum() < 1e10

    def test_inference_batch_single_identical(self):
        self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3)

    def test_attention_slicing_forward_pass(
        self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
    ):
        if not self.test_attention_slicing:
            return

        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)

        generator_device = "cpu"
        inputs = self.get_dummy_inputs(generator_device)
        output_without_slicing = pipe(**inputs)[0]

        pipe.enable_attention_slicing(slice_size=1)
        inputs = self.get_dummy_inputs(generator_device)
        output_with_slicing1 = pipe(**inputs)[0]

        pipe.enable_attention_slicing(slice_size=2)
        inputs = self.get_dummy_inputs(generator_device)
        output_with_slicing2 = pipe(**inputs)[0]

        if test_max_difference:
            max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max()
            max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max()
            self.assertLess(
                max(max_diff1, max_diff2),
                expected_max_diff,
                "Attention slicing should not affect the inference results",
            )

    def test_vae_tiling(self, expected_diff_max: float = 0.2):
        # Since VideoToVideo uses both encoder and decoder tiling, there seems to be much more numerical
        # difference. We seem to need a higher tolerance here...
        # TODO(aryan): Look into this more deeply
        expected_diff_max = 0.4

        generator_device = "cpu"
        components = self.get_dummy_components()

        pipe = self.pipeline_class(**components)
        pipe.to("cpu")
        pipe.set_progress_bar_config(disable=None)

        # Without tiling
        inputs = self.get_dummy_inputs(generator_device)
        inputs["height"] = inputs["width"] = 128
        output_without_tiling = pipe(**inputs)[0]

        # With tiling
        pipe.vae.enable_tiling(
            tile_sample_min_height=96,
            tile_sample_min_width=96,
            tile_overlap_factor_height=1 / 12,
            tile_overlap_factor_width=1 / 12,
        )
        inputs = self.get_dummy_inputs(generator_device)
        inputs["height"] = inputs["width"] = 128
        output_with_tiling = pipe(**inputs)[0]

        self.assertLess(
            (to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
            expected_diff_max,
            "VAE tiling should not affect the inference results",
        )

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

        inputs = self.get_dummy_inputs(device)
        frames = pipe(**inputs).frames  # [B, F, C, H, W]
        original_image_slice = frames[0, -2:, -1, -3:, -3:]

        pipe.fuse_qkv_projections()
        assert check_qkv_fusion_processors_exist(
            pipe.transformer
        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
        assert check_qkv_fusion_matches_attn_procs_length(
            pipe.transformer, pipe.transformer.original_attn_processors
        ), "Something wrong with the attention processors concerning the fused QKV projections."

        inputs = self.get_dummy_inputs(device)
        frames = pipe(**inputs).frames
        image_slice_fused = frames[0, -2:, -1, -3:, -3:]

        pipe.transformer.unfuse_qkv_projections()
        inputs = self.get_dummy_inputs(device)
        frames = pipe(**inputs).frames
        image_slice_disabled = frames[0, -2:, -1, -3:, -3:]

        assert np.allclose(
            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
        ), "Fusion of QKV projections shouldn't affect the outputs."
        assert np.allclose(
            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
        assert np.allclose(
            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
        ), "Original outputs should match when fused QKV projections are disabled."