diffusers/tests/pipelines/controlnet/test_controlnet_inpaint.py

# coding=utf-8
# Copyright 2025 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.

# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNet2DConditionModel,
)
from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
from diffusers.utils import load_image
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.torch_utils import randn_tensor

from ...testing_utils import (
    backend_empty_cache,
    enable_full_determinism,
    floats_tensor,
    load_numpy,
    numpy_cosine_similarity_distance,
    require_torch_accelerator,
    slow,
    torch_device,
)
from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class ControlNetInpaintPipelineFastTests(
    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
    pipeline_class = StableDiffusionControlNetInpaintPipeline
    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    image_params = frozenset({"control_image"})  # skip `image` and `mask` for now, only test for control_image
    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS

    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=9,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            cross_attention_dim=32,
        )
        torch.manual_seed(0)
        controlnet = ControlNetModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            in_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            cross_attention_dim=32,
            conditioning_embedding_out_channels=(16, 32),
        )
        torch.manual_seed(0)
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )
        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,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        components = {
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "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
        control_image = randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),
            generator=generator,
            device=torch.device(device),
        )
        init_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
        init_image = init_image.cpu().permute(0, 2, 3, 1)[0]

        image = Image.fromarray(np.uint8(init_image)).convert("RGB").resize((64, 64))
        mask_image = Image.fromarray(np.uint8(init_image + 4)).convert("RGB").resize((64, 64))

        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,
            "mask_image": mask_image,
            "control_image": control_image,
        }

        return inputs

    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)

    def test_encode_prompt_works_in_isolation(self):
        extra_required_param_value_dict = {
            "device": torch.device(torch_device).type,
            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
        }
        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)


class ControlNetSimpleInpaintPipelineFastTests(ControlNetInpaintPipelineFastTests):
    pipeline_class = StableDiffusionControlNetInpaintPipeline
    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    image_params = frozenset([])

    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"),
            cross_attention_dim=32,
        )
        torch.manual_seed(0)
        controlnet = ControlNetModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            in_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            cross_attention_dim=32,
            conditioning_embedding_out_channels=(16, 32),
        )
        torch.manual_seed(0)
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )
        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,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

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


class MultiControlNetInpaintPipelineFastTests(
    PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase
):
    pipeline_class = StableDiffusionControlNetInpaintPipeline
    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

    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=9,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            cross_attention_dim=32,
        )
        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"),
            cross_attention_dim=32,
            conditioning_embedding_out_channels=(16, 32),
        )
        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"),
            cross_attention_dim=32,
            conditioning_embedding_out_channels=(16, 32),
        )
        controlnet2.controlnet_down_blocks.apply(init_weights)

        torch.manual_seed(0)
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )
        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,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = 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,
            "safety_checker": None,
            "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

        control_image = [
            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),
            ),
        ]
        init_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
        init_image = init_image.cpu().permute(0, 2, 3, 1)[0]

        image = Image.fromarray(np.uint8(init_image)).convert("RGB").resize((64, 64))
        mask_image = Image.fromarray(np.uint8(init_image + 4)).convert("RGB").resize((64, 64))

        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,
            "mask_image": mask_image,
            "control_image": control_image,
        }

        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)

    def test_save_pretrained_raise_not_implemented_exception(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        with tempfile.TemporaryDirectory() as tmpdir:
            try:
                # save_pretrained is not implemented for Multi-ControlNet
                pipe.save_pretrained(tmpdir)
            except NotImplementedError:
                pass

    def test_encode_prompt_works_in_isolation(self):
        extra_required_param_value_dict = {
            "device": torch.device(torch_device).type,
            "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
        }
        return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)


@slow
@require_torch_accelerator
class ControlNetInpaintPipelineSlowTests(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("lllyasviel/sd-controlnet-canny")

        pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
            "botp/stable-diffusion-v1-5-inpainting", safety_checker=None, controlnet=controlnet
        )
        pipe.enable_model_cpu_offload(device=torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        image = load_image(
            "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
        ).resize((512, 512))

        mask_image = load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/input_bench_mask.png"
        ).resize((512, 512))

        prompt = "pitch black hole"

        control_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
        ).resize((512, 512))

        output = pipe(
            prompt,
            image=image,
            mask_image=mask_image,
            control_image=control_image,
            generator=generator,
            output_type="np",
            num_inference_steps=3,
        )

        image = output.images[0]

        assert image.shape == (512, 512, 3)

        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"
        )

        assert np.abs(expected_image - image).max() < 9e-2

    def test_inpaint(self):
        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint")

        pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
            "stable-diffusion-v1-5/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
        )
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        pipe.enable_model_cpu_offload(device=torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(33)

        init_image = load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
        )
        init_image = init_image.resize((512, 512))

        mask_image = load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
        )
        mask_image = mask_image.resize((512, 512))

        prompt = "a handsome man with ray-ban sunglasses"

        def make_inpaint_condition(image, image_mask):
            image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
            image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0

            assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
            image[image_mask > 0.5] = -1.0  # set as masked pixel
            image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
            image = torch.from_numpy(image)
            return image

        control_image = make_inpaint_condition(init_image, mask_image)

        output = pipe(
            prompt,
            image=init_image,
            mask_image=mask_image,
            control_image=control_image,
            guidance_scale=9.0,
            eta=1.0,
            generator=generator,
            num_inference_steps=20,
            output_type="np",
        )
        image = output.images[0]

        assert image.shape == (512, 512, 3)

        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"
        )

        assert numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten()) < 1e-2