# 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 gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    EulerDiscreteScheduler,
    HeunDiscreteScheduler,
    LCMScheduler,
    StableDiffusionXLControlNetPipeline,
    StableDiffusionXLImg2ImgPipeline,
    UNet2DConditionModel,
)
from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D
from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    backend_empty_cache,
    enable_full_determinism,
    load_image,
    require_torch_accelerator,
    slow,
    torch_device,
)
from diffusers.utils.torch_utils import randn_tensor

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_TO_IMAGE_BATCH_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
    TEXT_TO_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    IPAdapterTesterMixin,
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()


class StableDiffusionXLControlNetPipelineFastTests(
    IPAdapterTesterMixin,
    PipelineLatentTesterMixin,
    PipelineKarrasSchedulerTesterMixin,
    PipelineTesterMixin,
    unittest.TestCase,
):
    pipeline_class = StableDiffusionXLControlNetPipeline
    params = TEXT_TO_IMAGE_PARAMS
    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
    image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
    test_layerwise_casting = True
    test_group_offloading = True

    def get_dummy_components(self, time_cond_proj_dim=None):
        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=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
            time_cond_proj_dim=time_cond_proj_dim,
        )
        torch.manual_seed(0)
        controlnet = ControlNetModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            in_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            conditioning_embedding_out_channels=(16, 32),
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
        )
        torch.manual_seed(0)
        scheduler = EulerDiscreteScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            steps_offset=1,
            beta_schedule="scaled_linear",
            timestep_spacing="leading",
        )
        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,
            # SD2-specific config below
            hidden_act="gelu",
            projection_dim=32,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        components = {
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_2,
            "tokenizer_2": tokenizer_2,
            "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)

        controlnet_embedder_scale_factor = 2
        image = randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
            generator=generator,
            device=torch.device(device),
        )

        inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "np",
            "image": image,
        }

        return inputs

    def test_attention_slicing_forward_pass(self):
        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)

    def test_ip_adapter(self, from_ssd1b=False, expected_pipe_slice=None):
        if not from_ssd1b:
            expected_pipe_slice = None
            if torch_device == "cpu":
                expected_pipe_slice = np.array(
                    [0.7335, 0.5866, 0.5623, 0.6242, 0.5751, 0.5999, 0.4091, 0.4590, 0.5054]
                )
        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)

    @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(expected_max_diff=2e-3)

    def test_inference_batch_single_identical(self):
        self._test_inference_batch_single_identical(expected_max_diff=2e-3)

    @unittest.skip("We test this functionality elsewhere already.")
    def test_save_load_optional_components(self):
        pass

    @require_torch_accelerator
    def test_stable_diffusion_xl_offloads(self):
        pipes = []
        components = self.get_dummy_components()
        sd_pipe = self.pipeline_class(**components).to(torch_device)
        pipes.append(sd_pipe)

        components = self.get_dummy_components()
        sd_pipe = self.pipeline_class(**components)
        sd_pipe.enable_model_cpu_offload(device=torch_device)
        pipes.append(sd_pipe)

        components = self.get_dummy_components()
        sd_pipe = self.pipeline_class(**components)
        sd_pipe.enable_sequential_cpu_offload(device=torch_device)
        pipes.append(sd_pipe)

        image_slices = []
        for pipe in pipes:
            pipe.unet.set_default_attn_processor()

            inputs = self.get_dummy_inputs(torch_device)
            image = pipe(**inputs).images

            image_slices.append(image[0, -3:, -3:, -1].flatten())

        assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
        assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3

    def test_stable_diffusion_xl_multi_prompts(self):
        components = self.get_dummy_components()
        sd_pipe = self.pipeline_class(**components).to(torch_device)

        # forward with single prompt
        inputs = self.get_dummy_inputs(torch_device)
        output = sd_pipe(**inputs)
        image_slice_1 = output.images[0, -3:, -3:, -1]

        # forward with same prompt duplicated
        inputs = self.get_dummy_inputs(torch_device)
        inputs["prompt_2"] = inputs["prompt"]
        output = sd_pipe(**inputs)
        image_slice_2 = output.images[0, -3:, -3:, -1]

        # ensure the results are equal
        assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4

        # forward with different prompt
        inputs = self.get_dummy_inputs(torch_device)
        inputs["prompt_2"] = "different prompt"
        output = sd_pipe(**inputs)
        image_slice_3 = output.images[0, -3:, -3:, -1]

        # ensure the results are not equal
        assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4

        # manually set a negative_prompt
        inputs = self.get_dummy_inputs(torch_device)
        inputs["negative_prompt"] = "negative prompt"
        output = sd_pipe(**inputs)
        image_slice_1 = output.images[0, -3:, -3:, -1]

        # forward with same negative_prompt duplicated
        inputs = self.get_dummy_inputs(torch_device)
        inputs["negative_prompt"] = "negative prompt"
        inputs["negative_prompt_2"] = inputs["negative_prompt"]
        output = sd_pipe(**inputs)
        image_slice_2 = output.images[0, -3:, -3:, -1]

        # ensure the results are equal
        assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4

        # forward with different negative_prompt
        inputs = self.get_dummy_inputs(torch_device)
        inputs["negative_prompt"] = "negative prompt"
        inputs["negative_prompt_2"] = "different negative prompt"
        output = sd_pipe(**inputs)
        image_slice_3 = output.images[0, -3:, -3:, -1]

        # ensure the results are not equal
        assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4

    def test_controlnet_sdxl_guess(self):
        device = "cpu"

        components = self.get_dummy_components()

        sd_pipe = self.pipeline_class(**components)
        sd_pipe = sd_pipe.to(device)

        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        inputs["guess_mode"] = True

        output = sd_pipe(**inputs)
        image_slice = output.images[0, -3:, -3:, -1]

        expected_slice = np.array([0.7335, 0.5866, 0.5623, 0.6242, 0.5751, 0.5999, 0.4091, 0.4590, 0.5054])

        # make sure that it's equal
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4

    def test_controlnet_sdxl_lcm(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator

        components = self.get_dummy_components(time_cond_proj_dim=256)
        sd_pipe = StableDiffusionXLControlNetPipeline(**components)
        sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        output = sd_pipe(**inputs)
        image = output.images

        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.7820, 0.6195, 0.6193, 0.7045, 0.6706, 0.5837, 0.4147, 0.5232, 0.4868])

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

    # Copied from test_stable_diffusion_xl.py:test_stable_diffusion_two_xl_mixture_of_denoiser_fast
    # with `StableDiffusionXLControlNetPipeline` instead of `StableDiffusionXLPipeline`
    def test_controlnet_sdxl_two_mixture_of_denoiser_fast(self):
        components = self.get_dummy_components()
        pipe_1 = StableDiffusionXLControlNetPipeline(**components).to(torch_device)
        pipe_1.unet.set_default_attn_processor()

        components_without_controlnet = {k: v for k, v in components.items() if k != "controlnet"}
        pipe_2 = StableDiffusionXLImg2ImgPipeline(**components_without_controlnet).to(torch_device)
        pipe_2.unet.set_default_attn_processor()

        def assert_run_mixture(
            num_steps,
            split,
            scheduler_cls_orig,
            expected_tss,
            num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,
        ):
            inputs = self.get_dummy_inputs(torch_device)
            inputs["num_inference_steps"] = num_steps

            class scheduler_cls(scheduler_cls_orig):
                pass

            pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)
            pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)

            # Let's retrieve the number of timesteps we want to use
            pipe_1.scheduler.set_timesteps(num_steps)
            expected_steps = pipe_1.scheduler.timesteps.tolist()

            if pipe_1.scheduler.order == 2:
                expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
                expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss))
                expected_steps = expected_steps_1 + expected_steps_2
            else:
                expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
                expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss))

            # now we monkey patch step `done_steps`
            # list into the step function for testing
            done_steps = []
            old_step = copy.copy(scheduler_cls.step)

            def new_step(self, *args, **kwargs):
                done_steps.append(args[1].cpu().item())  # args[1] is always the passed `t`
                return old_step(self, *args, **kwargs)

            scheduler_cls.step = new_step

            inputs_1 = {
                **inputs,
                **{
                    "denoising_end": 1.0 - (split / num_train_timesteps),
                    "output_type": "latent",
                },
            }
            latents = pipe_1(**inputs_1).images[0]

            assert expected_steps_1 == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"

            inputs_2 = {
                **inputs,
                **{
                    "denoising_start": 1.0 - (split / num_train_timesteps),
                    "image": latents,
                },
            }
            pipe_2(**inputs_2).images[0]

            assert expected_steps_2 == done_steps[len(expected_steps_1) :]
            assert expected_steps == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"

        steps = 10
        for split in [300, 700]:
            for scheduler_cls_timesteps in [
                (EulerDiscreteScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),
                (
                    HeunDiscreteScheduler,
                    [
                        901.0,
                        801.0,
                        801.0,
                        701.0,
                        701.0,
                        601.0,
                        601.0,
                        501.0,
                        501.0,
                        401.0,
                        401.0,
                        301.0,
                        301.0,
                        201.0,
                        201.0,
                        101.0,
                        101.0,
                        1.0,
                        1.0,
                    ],
                ),
            ]:
                assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])


class StableDiffusionXLMultiControlNetPipelineFastTests(
    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
):
    pipeline_class = StableDiffusionXLControlNetPipeline
    params = TEXT_TO_IMAGE_PARAMS
    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
    image_params = frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess

    supports_dduf = False

    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=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
        )
        torch.manual_seed(0)

        def init_weights(m):
            if isinstance(m, torch.nn.Conv2d):
                torch.nn.init.normal_(m.weight)
                m.bias.data.fill_(1.0)

        controlnet1 = ControlNetModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            in_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            conditioning_embedding_out_channels=(16, 32),
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
        )
        controlnet1.controlnet_down_blocks.apply(init_weights)

        torch.manual_seed(0)
        controlnet2 = ControlNetModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            in_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            conditioning_embedding_out_channels=(16, 32),
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
        )
        controlnet2.controlnet_down_blocks.apply(init_weights)

        torch.manual_seed(0)
        scheduler = EulerDiscreteScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            steps_offset=1,
            beta_schedule="scaled_linear",
            timestep_spacing="leading",
        )
        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,
            # SD2-specific config below
            hidden_act="gelu",
            projection_dim=32,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        controlnet = MultiControlNetModel([controlnet1, controlnet2])

        components = {
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_2,
            "tokenizer_2": tokenizer_2,
            "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)

        controlnet_embedder_scale_factor = 2

        images = [
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
                generator=generator,
                device=torch.device(device),
            ),
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
                generator=generator,
                device=torch.device(device),
            ),
        ]

        inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "np",
            "image": images,
        }

        return inputs

    def test_control_guidance_switch(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(torch_device)

        scale = 10.0
        steps = 4

        inputs = self.get_dummy_inputs(torch_device)
        inputs["num_inference_steps"] = steps
        inputs["controlnet_conditioning_scale"] = scale
        output_1 = pipe(**inputs)[0]

        inputs = self.get_dummy_inputs(torch_device)
        inputs["num_inference_steps"] = steps
        inputs["controlnet_conditioning_scale"] = scale
        output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]

        inputs = self.get_dummy_inputs(torch_device)
        inputs["num_inference_steps"] = steps
        inputs["controlnet_conditioning_scale"] = scale
        output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]

        inputs = self.get_dummy_inputs(torch_device)
        inputs["num_inference_steps"] = steps
        inputs["controlnet_conditioning_scale"] = scale
        output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]

        # make sure that all outputs are different
        assert np.sum(np.abs(output_1 - output_2)) > 1e-3
        assert np.sum(np.abs(output_1 - output_3)) > 1e-3
        assert np.sum(np.abs(output_1 - output_4)) > 1e-3

    def test_attention_slicing_forward_pass(self):
        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)

    @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(expected_max_diff=2e-3)

    def test_inference_batch_single_identical(self):
        self._test_inference_batch_single_identical(expected_max_diff=2e-3)

    @unittest.skip("We test this functionality elsewhere already.")
    def test_save_load_optional_components(self):
        pass


class StableDiffusionXLMultiControlNetOneModelPipelineFastTests(
    PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
    pipeline_class = StableDiffusionXLControlNetPipeline
    params = TEXT_TO_IMAGE_PARAMS
    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
    image_params = frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess

    supports_dduf = False

    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=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
        )
        torch.manual_seed(0)

        def init_weights(m):
            if isinstance(m, torch.nn.Conv2d):
                torch.nn.init.normal_(m.weight)
                m.bias.data.fill_(1.0)

        controlnet = ControlNetModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            in_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            conditioning_embedding_out_channels=(16, 32),
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
        )
        controlnet.controlnet_down_blocks.apply(init_weights)

        torch.manual_seed(0)
        scheduler = EulerDiscreteScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            steps_offset=1,
            beta_schedule="scaled_linear",
            timestep_spacing="leading",
        )
        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,
            # SD2-specific config below
            hidden_act="gelu",
            projection_dim=32,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        controlnet = MultiControlNetModel([controlnet])

        components = {
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_2,
            "tokenizer_2": tokenizer_2,
            "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)

        controlnet_embedder_scale_factor = 2
        images = [
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
                generator=generator,
                device=torch.device(device),
            ),
        ]

        inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "np",
            "image": images,
        }

        return inputs

    def test_control_guidance_switch(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(torch_device)

        scale = 10.0
        steps = 4

        inputs = self.get_dummy_inputs(torch_device)
        inputs["num_inference_steps"] = steps
        inputs["controlnet_conditioning_scale"] = scale
        output_1 = pipe(**inputs)[0]

        inputs = self.get_dummy_inputs(torch_device)
        inputs["num_inference_steps"] = steps
        inputs["controlnet_conditioning_scale"] = scale
        output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]

        inputs = self.get_dummy_inputs(torch_device)
        inputs["num_inference_steps"] = steps
        inputs["controlnet_conditioning_scale"] = scale
        output_3 = pipe(
            **inputs,
            control_guidance_start=[0.1],
            control_guidance_end=[0.2],
        )[0]

        inputs = self.get_dummy_inputs(torch_device)
        inputs["num_inference_steps"] = steps
        inputs["controlnet_conditioning_scale"] = scale
        output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5])[0]

        # make sure that all outputs are different
        assert np.sum(np.abs(output_1 - output_2)) > 1e-3
        assert np.sum(np.abs(output_1 - output_3)) > 1e-3
        assert np.sum(np.abs(output_1 - output_4)) > 1e-3

    def test_attention_slicing_forward_pass(self):
        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)

    @unittest.skip("We test this functionality elsewhere already.")
    def test_save_load_optional_components(self):
        pass

    @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(expected_max_diff=2e-3)

    def test_inference_batch_single_identical(self):
        self._test_inference_batch_single_identical(expected_max_diff=2e-3)

    def test_negative_conditions(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(torch_device)

        inputs = self.get_dummy_inputs(torch_device)
        image = pipe(**inputs).images
        image_slice_without_neg_cond = image[0, -3:, -3:, -1]

        image = pipe(
            **inputs,
            negative_original_size=(512, 512),
            negative_crops_coords_top_left=(0, 0),
            negative_target_size=(1024, 1024),
        ).images
        image_slice_with_neg_cond = image[0, -3:, -3:, -1]

        self.assertTrue(np.abs(image_slice_without_neg_cond - image_slice_with_neg_cond).max() > 1e-2)


@slow
@require_torch_accelerator
class ControlNetSDXLPipelineSlowTests(unittest.TestCase):
    def setUp(self):
        super().setUp()
        gc.collect()
        backend_empty_cache(torch_device)

    def tearDown(self):
        super().tearDown()
        gc.collect()
        backend_empty_cache(torch_device)

    def test_canny(self):
        controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-canny-sdxl-1.0")

        pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet
        )
        pipe.enable_sequential_cpu_offload(device=torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        prompt = "bird"
        image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
        )

        images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images

        assert images[0].shape == (768, 512, 3)

        original_image = images[0, -3:, -3:, -1].flatten()
        expected_image = np.array([0.4185, 0.4127, 0.4089, 0.4046, 0.4115, 0.4096, 0.4081, 0.4112, 0.3913])
        assert np.allclose(original_image, expected_image, atol=1e-04)

    def test_depth(self):
        controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0")

        pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet
        )
        pipe.enable_sequential_cpu_offload(device=torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        prompt = "Stormtrooper's lecture"
        image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png"
        )

        images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images

        assert images[0].shape == (512, 512, 3)

        original_image = images[0, -3:, -3:, -1].flatten()
        expected_image = np.array([0.4399, 0.5112, 0.5478, 0.4314, 0.472, 0.4823, 0.4647, 0.4957, 0.4853])
        assert np.allclose(original_image, expected_image, atol=1e-04)


class StableDiffusionSSD1BControlNetPipelineFastTests(StableDiffusionXLControlNetPipelineFastTests):
    def test_controlnet_sdxl_guess(self):
        device = "cpu"

        components = self.get_dummy_components()

        sd_pipe = self.pipeline_class(**components)
        sd_pipe = sd_pipe.to(device)

        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        inputs["guess_mode"] = True

        output = sd_pipe(**inputs)
        image_slice = output.images[0, -3:, -3:, -1]

        expected_slice = np.array([0.7212, 0.5890, 0.5491, 0.6425, 0.5970, 0.6091, 0.4418, 0.4556, 0.5032])

        # make sure that it's equal
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4

    def test_ip_adapter(self):
        expected_pipe_slice = None
        if torch_device == "cpu":
            expected_pipe_slice = np.array([0.7212, 0.5890, 0.5491, 0.6425, 0.5970, 0.6091, 0.4418, 0.4556, 0.5032])

        return super().test_ip_adapter(from_ssd1b=True, expected_pipe_slice=expected_pipe_slice)

    def test_controlnet_sdxl_lcm(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator

        components = self.get_dummy_components(time_cond_proj_dim=256)
        sd_pipe = StableDiffusionXLControlNetPipeline(**components)
        sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        output = sd_pipe(**inputs)
        image = output.images

        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.6787, 0.5117, 0.5558, 0.6963, 0.6571, 0.5928, 0.4121, 0.5468, 0.5057])

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

    def test_conditioning_channels(self):
        unet = UNet2DConditionModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            sample_size=32,
            in_channels=4,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            mid_block_type="UNetMidBlock2D",
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
            time_cond_proj_dim=None,
        )

        controlnet = ControlNetModel.from_unet(unet, conditioning_channels=4)
        assert type(controlnet.mid_block) is UNetMidBlock2D
        assert controlnet.conditioning_channels == 4

    def get_dummy_components(self, time_cond_proj_dim=None):
        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=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            mid_block_type="UNetMidBlock2D",
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
            time_cond_proj_dim=time_cond_proj_dim,
        )
        torch.manual_seed(0)
        controlnet = ControlNetModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            in_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            conditioning_embedding_out_channels=(16, 32),
            mid_block_type="UNetMidBlock2D",
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
        )
        torch.manual_seed(0)
        scheduler = EulerDiscreteScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            steps_offset=1,
            beta_schedule="scaled_linear",
            timestep_spacing="leading",
        )
        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,
            # SD2-specific config below
            hidden_act="gelu",
            projection_dim=32,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        components = {
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_2,
            "tokenizer_2": tokenizer_2,
            "feature_extractor": None,
            "image_encoder": None,
        }
        return components
