custom_diffusers/tests/models/unets/test_models_unet_motion.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 copy
import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers import MotionAdapter, UNet2DConditionModel, UNetMotionModel
from diffusers.utils import logging
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    torch_device,
)

from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin


logger = logging.get_logger(__name__)

enable_full_determinism()


class UNetMotionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
    model_class = UNetMotionModel
    main_input_name = "sample"

    @property
    def dummy_input(self):
        batch_size = 4
        num_channels = 4
        num_frames = 4
        sizes = (16, 16)

        noise = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)
        time_step = torch.tensor([10]).to(torch_device)
        encoder_hidden_states = floats_tensor((batch_size * num_frames, 4, 16)).to(torch_device)

        return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}

    @property
    def input_shape(self):
        return (4, 4, 16, 16)

    @property
    def output_shape(self):
        return (4, 4, 16, 16)

    def prepare_init_args_and_inputs_for_common(self):
        init_dict = {
            "block_out_channels": (16, 32),
            "norm_num_groups": 16,
            "down_block_types": ("CrossAttnDownBlockMotion", "DownBlockMotion"),
            "up_block_types": ("UpBlockMotion", "CrossAttnUpBlockMotion"),
            "cross_attention_dim": 16,
            "num_attention_heads": 2,
            "out_channels": 4,
            "in_channels": 4,
            "layers_per_block": 1,
            "sample_size": 16,
        }
        inputs_dict = self.dummy_input
        return init_dict, inputs_dict

    def test_from_unet2d(self):
        torch.manual_seed(0)
        unet2d = UNet2DConditionModel()

        torch.manual_seed(1)
        model = self.model_class.from_unet2d(unet2d)
        model_state_dict = model.state_dict()

        for param_name, param_value in unet2d.named_parameters():
            self.assertTrue(torch.equal(model_state_dict[param_name], param_value))

    def test_freeze_unet2d(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
        model = self.model_class(**init_dict)
        model.freeze_unet2d_params()

        for param_name, param_value in model.named_parameters():
            if "motion_modules" not in param_name:
                self.assertFalse(param_value.requires_grad)

            else:
                self.assertTrue(param_value.requires_grad)

    def test_loading_motion_adapter(self):
        model = self.model_class()
        adapter = MotionAdapter()
        model.load_motion_modules(adapter)

        for idx, down_block in enumerate(model.down_blocks):
            adapter_state_dict = adapter.down_blocks[idx].motion_modules.state_dict()
            for param_name, param_value in down_block.motion_modules.named_parameters():
                self.assertTrue(torch.equal(adapter_state_dict[param_name], param_value))

        for idx, up_block in enumerate(model.up_blocks):
            adapter_state_dict = adapter.up_blocks[idx].motion_modules.state_dict()
            for param_name, param_value in up_block.motion_modules.named_parameters():
                self.assertTrue(torch.equal(adapter_state_dict[param_name], param_value))

        mid_block_adapter_state_dict = adapter.mid_block.motion_modules.state_dict()
        for param_name, param_value in model.mid_block.motion_modules.named_parameters():
            self.assertTrue(torch.equal(mid_block_adapter_state_dict[param_name], param_value))

    def test_saving_motion_modules(self):
        torch.manual_seed(0)
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
        model = self.model_class(**init_dict)
        model.to(torch_device)

        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_motion_modules(tmpdirname)
            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "diffusion_pytorch_model.safetensors")))

            adapter_loaded = MotionAdapter.from_pretrained(tmpdirname)

            torch.manual_seed(0)
            model_loaded = self.model_class(**init_dict)
            model_loaded.load_motion_modules(adapter_loaded)
            model_loaded.to(torch_device)

        with torch.no_grad():
            output = model(**inputs_dict)[0]
            output_loaded = model_loaded(**inputs_dict)[0]

        max_diff = (output - output_loaded).abs().max().item()
        self.assertLessEqual(max_diff, 1e-4, "Models give different forward passes")

    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available(),
        reason="XFormers attention is only available with CUDA and `xformers` installed",
    )
    def test_xformers_enable_works(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
        model = self.model_class(**init_dict)

        model.enable_xformers_memory_efficient_attention()

        assert (
            model.mid_block.attentions[0].transformer_blocks[0].attn1.processor.__class__.__name__
            == "XFormersAttnProcessor"
        ), "xformers is not enabled"

    def test_gradient_checkpointing_is_applied(self):
        expected_set = {
            "CrossAttnUpBlockMotion",
            "CrossAttnDownBlockMotion",
            "UNetMidBlockCrossAttnMotion",
            "UpBlockMotion",
            "Transformer2DModel",
            "DownBlockMotion",
        }
        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)

    def test_feed_forward_chunking(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
        init_dict["block_out_channels"] = (32, 64)
        init_dict["norm_num_groups"] = 32

        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            output = model(**inputs_dict)[0]

        model.enable_forward_chunking()
        with torch.no_grad():
            output_2 = model(**inputs_dict)[0]

        self.assertEqual(output.shape, output_2.shape, "Shape doesn't match")
        assert np.abs(output.cpu() - output_2.cpu()).max() < 1e-2

    def test_pickle(self):
        # enable deterministic behavior for gradient checkpointing
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
        model = self.model_class(**init_dict)
        model.to(torch_device)

        with torch.no_grad():
            sample = model(**inputs_dict).sample

        sample_copy = copy.copy(sample)

        assert (sample - sample_copy).abs().max() < 1e-4

    def test_from_save_pretrained(self, expected_max_diff=5e-5):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

        torch.manual_seed(0)
        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(tmpdirname, safe_serialization=False)
            torch.manual_seed(0)
            new_model = self.model_class.from_pretrained(tmpdirname)
            new_model.to(torch_device)

        with torch.no_grad():
            image = model(**inputs_dict)
            if isinstance(image, dict):
                image = image.to_tuple()[0]

            new_image = new_model(**inputs_dict)

            if isinstance(new_image, dict):
                new_image = new_image.to_tuple()[0]

        max_diff = (image - new_image).abs().max().item()
        self.assertLessEqual(max_diff, expected_max_diff, "Models give different forward passes")

    def test_from_save_pretrained_variant(self, expected_max_diff=5e-5):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

        torch.manual_seed(0)
        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(tmpdirname, variant="fp16", safe_serialization=False)

            torch.manual_seed(0)
            new_model = self.model_class.from_pretrained(tmpdirname, variant="fp16")
            # non-variant cannot be loaded
            with self.assertRaises(OSError) as error_context:
                self.model_class.from_pretrained(tmpdirname)

            # make sure that error message states what keys are missing
            assert "Error no file named diffusion_pytorch_model.bin found in directory" in str(error_context.exception)

            new_model.to(torch_device)

        with torch.no_grad():
            image = model(**inputs_dict)
            if isinstance(image, dict):
                image = image.to_tuple()[0]

            new_image = new_model(**inputs_dict)

            if isinstance(new_image, dict):
                new_image = new_image.to_tuple()[0]

        max_diff = (image - new_image).abs().max().item()
        self.assertLessEqual(max_diff, expected_max_diff, "Models give different forward passes")

    def test_forward_with_norm_groups(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

        init_dict["norm_num_groups"] = 16
        init_dict["block_out_channels"] = (16, 32)

        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            output = model(**inputs_dict)

            if isinstance(output, dict):
                output = output.to_tuple()[0]

        self.assertIsNotNone(output)
        expected_shape = inputs_dict["sample"].shape
        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")

    def test_asymmetric_motion_model(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

        init_dict["layers_per_block"] = (2, 3)
        init_dict["transformer_layers_per_block"] = ((1, 2), (3, 4, 5))
        init_dict["reverse_transformer_layers_per_block"] = ((7, 6, 7, 4), (4, 2, 2))

        init_dict["temporal_transformer_layers_per_block"] = ((2, 5), (2, 3, 5))
        init_dict["reverse_temporal_transformer_layers_per_block"] = ((5, 4, 3, 4), (3, 2, 2))

        init_dict["num_attention_heads"] = (2, 4)
        init_dict["motion_num_attention_heads"] = (4, 4)
        init_dict["reverse_motion_num_attention_heads"] = (2, 2)

        init_dict["use_motion_mid_block"] = True
        init_dict["mid_block_layers"] = 2
        init_dict["transformer_layers_per_mid_block"] = (1, 5)
        init_dict["temporal_transformer_layers_per_mid_block"] = (2, 4)

        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            output = model(**inputs_dict)

            if isinstance(output, dict):
                output = output.to_tuple()[0]

        self.assertIsNotNone(output)
        expected_shape = inputs_dict["sample"].shape
        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")