test_models_unet_2d.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 gc
import math
import unittest

import torch

from diffusers import UNet2DModel
from diffusers.utils import logging
from diffusers.utils.testing_utils import (
    backend_empty_cache,
    enable_full_determinism,
    floats_tensor,
    require_torch_accelerator,
    slow,
    torch_all_close,
    torch_device,
)

from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin


logger = logging.get_logger(__name__)

enable_full_determinism()


class Unet2DModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
    model_class = UNet2DModel
    main_input_name = "sample"

    @property
    def dummy_input(self):
        batch_size = 4
        num_channels = 3
        sizes = (32, 32)

        noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
        time_step = torch.tensor([10]).to(torch_device)

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

    @property
    def input_shape(self):
        return (3, 32, 32)

    @property
    def output_shape(self):
        return (3, 32, 32)

    def prepare_init_args_and_inputs_for_common(self):
        init_dict = {
            "block_out_channels": (4, 8),
            "norm_num_groups": 2,
            "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
            "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
            "attention_head_dim": 3,
            "out_channels": 3,
            "in_channels": 3,
            "layers_per_block": 2,
            "sample_size": 32,
        }
        inputs_dict = self.dummy_input
        return init_dict, inputs_dict

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

        init_dict["add_attention"] = True
        init_dict["attn_norm_num_groups"] = 4

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

        self.assertIsNotNone(
            model.mid_block.attentions[0].group_norm, "Mid block Attention group norm should exist but does not."
        )
        self.assertEqual(
            model.mid_block.attentions[0].group_norm.num_groups,
            init_dict["attn_norm_num_groups"],
            "Mid block Attention group norm does not have the expected number of groups.",
        )

        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_mid_block_none(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
        mid_none_init_dict, mid_none_inputs_dict = self.prepare_init_args_and_inputs_for_common()
        mid_none_init_dict["mid_block_type"] = None

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

        mid_none_model = self.model_class(**mid_none_init_dict)
        mid_none_model.to(torch_device)
        mid_none_model.eval()

        self.assertIsNone(mid_none_model.mid_block, "Mid block should not exist.")

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

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

        with torch.no_grad():
            mid_none_output = mid_none_model(**mid_none_inputs_dict)

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

        self.assertFalse(torch.allclose(output, mid_none_output, rtol=1e-3), "outputs should be different.")

    def test_gradient_checkpointing_is_applied(self):
        expected_set = {
            "AttnUpBlock2D",
            "AttnDownBlock2D",
            "UNetMidBlock2D",
            "UpBlock2D",
            "DownBlock2D",
        }

        # NOTE: unlike UNet2DConditionModel, UNet2DModel does not currently support tuples for `attention_head_dim`
        attention_head_dim = 8
        block_out_channels = (16, 32)

        super().test_gradient_checkpointing_is_applied(
            expected_set=expected_set, attention_head_dim=attention_head_dim, block_out_channels=block_out_channels
        )


class UNetLDMModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
    model_class = UNet2DModel
    main_input_name = "sample"

    @property
    def dummy_input(self):
        batch_size = 4
        num_channels = 4
        sizes = (32, 32)

        noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
        time_step = torch.tensor([10]).to(torch_device)

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

    @property
    def input_shape(self):
        return (4, 32, 32)

    @property
    def output_shape(self):
        return (4, 32, 32)

    def prepare_init_args_and_inputs_for_common(self):
        init_dict = {
            "sample_size": 32,
            "in_channels": 4,
            "out_channels": 4,
            "layers_per_block": 2,
            "block_out_channels": (32, 64),
            "attention_head_dim": 32,
            "down_block_types": ("DownBlock2D", "DownBlock2D"),
            "up_block_types": ("UpBlock2D", "UpBlock2D"),
        }
        inputs_dict = self.dummy_input
        return init_dict, inputs_dict

    def test_from_pretrained_hub(self):
        model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)

        self.assertIsNotNone(model)
        self.assertEqual(len(loading_info["missing_keys"]), 0)

        model.to(torch_device)
        image = model(**self.dummy_input).sample

        assert image is not None, "Make sure output is not None"

    @require_torch_accelerator
    def test_from_pretrained_accelerate(self):
        model, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
        model.to(torch_device)
        image = model(**self.dummy_input).sample

        assert image is not None, "Make sure output is not None"

    @require_torch_accelerator
    def test_from_pretrained_accelerate_wont_change_results(self):
        # by default model loading will use accelerate as `low_cpu_mem_usage=True`
        model_accelerate, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
        model_accelerate.to(torch_device)
        model_accelerate.eval()

        noise = torch.randn(
            1,
            model_accelerate.config.in_channels,
            model_accelerate.config.sample_size,
            model_accelerate.config.sample_size,
            generator=torch.manual_seed(0),
        )
        noise = noise.to(torch_device)
        time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)

        arr_accelerate = model_accelerate(noise, time_step)["sample"]

        # two models don't need to stay in the device at the same time
        del model_accelerate
        backend_empty_cache(torch_device)
        gc.collect()

        model_normal_load, _ = UNet2DModel.from_pretrained(
            "fusing/unet-ldm-dummy-update", output_loading_info=True, low_cpu_mem_usage=False
        )
        model_normal_load.to(torch_device)
        model_normal_load.eval()
        arr_normal_load = model_normal_load(noise, time_step)["sample"]

        assert torch_all_close(arr_accelerate, arr_normal_load, rtol=1e-3)

    def test_output_pretrained(self):
        model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update")
        model.eval()
        model.to(torch_device)

        noise = torch.randn(
            1,
            model.config.in_channels,
            model.config.sample_size,
            model.config.sample_size,
            generator=torch.manual_seed(0),
        )
        noise = noise.to(torch_device)
        time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)

        with torch.no_grad():
            output = model(noise, time_step).sample

        output_slice = output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
        # fmt: on

        self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-3))

    def test_gradient_checkpointing_is_applied(self):
        expected_set = {"DownBlock2D", "UNetMidBlock2D", "UpBlock2D"}

        # NOTE: unlike UNet2DConditionModel, UNet2DModel does not currently support tuples for `attention_head_dim`
        attention_head_dim = 32
        block_out_channels = (32, 64)

        super().test_gradient_checkpointing_is_applied(
            expected_set=expected_set, attention_head_dim=attention_head_dim, block_out_channels=block_out_channels
        )


class NCSNppModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
    model_class = UNet2DModel
    main_input_name = "sample"

    @property
    def dummy_input(self, sizes=(32, 32)):
        batch_size = 4
        num_channels = 3

        noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
        time_step = torch.tensor(batch_size * [10]).to(dtype=torch.int32, device=torch_device)

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

    @property
    def input_shape(self):
        return (3, 32, 32)

    @property
    def output_shape(self):
        return (3, 32, 32)

    def prepare_init_args_and_inputs_for_common(self):
        init_dict = {
            "block_out_channels": [32, 64, 64, 64],
            "in_channels": 3,
            "layers_per_block": 1,
            "out_channels": 3,
            "time_embedding_type": "fourier",
            "norm_eps": 1e-6,
            "mid_block_scale_factor": math.sqrt(2.0),
            "norm_num_groups": None,
            "down_block_types": [
                "SkipDownBlock2D",
                "AttnSkipDownBlock2D",
                "SkipDownBlock2D",
                "SkipDownBlock2D",
            ],
            "up_block_types": [
                "SkipUpBlock2D",
                "SkipUpBlock2D",
                "AttnSkipUpBlock2D",
                "SkipUpBlock2D",
            ],
        }
        inputs_dict = self.dummy_input
        return init_dict, inputs_dict

    @slow
    def test_from_pretrained_hub(self):
        model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
        self.assertIsNotNone(model)
        self.assertEqual(len(loading_info["missing_keys"]), 0)

        model.to(torch_device)
        inputs = self.dummy_input
        noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)
        inputs["sample"] = noise
        image = model(**inputs)

        assert image is not None, "Make sure output is not None"

    @slow
    def test_output_pretrained_ve_mid(self):
        model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
        model.to(torch_device)

        batch_size = 4
        num_channels = 3
        sizes = (256, 256)

        noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
        time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)

        with torch.no_grad():
            output = model(noise, time_step).sample

        output_slice = output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        expected_output_slice = torch.tensor([-4836.2178, -6487.1470, -3816.8196, -7964.9302, -10966.3037, -20043.5957, 8137.0513, 2340.3328, 544.6056])
        # fmt: on

        self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))

    def test_output_pretrained_ve_large(self):
        model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
        model.to(torch_device)

        batch_size = 4
        num_channels = 3
        sizes = (32, 32)

        noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
        time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)

        with torch.no_grad():
            output = model(noise, time_step).sample

        output_slice = output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
        # fmt: on

        self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))

    @unittest.skip("Test not supported.")
    def test_forward_with_norm_groups(self):
        # not required for this model
        pass

    def test_gradient_checkpointing_is_applied(self):
        expected_set = {
            "UNetMidBlock2D",
        }

        block_out_channels = (32, 64, 64, 64)

        super().test_gradient_checkpointing_is_applied(
            expected_set=expected_set, block_out_channels=block_out_channels
        )

    def test_effective_gradient_checkpointing(self):
        super().test_effective_gradient_checkpointing(skip={"time_proj.weight"})

    @unittest.skip(
        "To make layerwise casting work with this model, we will have to update the implementation. Due to potentially low usage, we don't support it here."
    )
    def test_layerwise_casting_inference(self):
        pass

    @unittest.skip(
        "To make layerwise casting work with this model, we will have to update the implementation. Due to potentially low usage, we don't support it here."
    )
    def test_layerwise_casting_memory(self):
        pass