diffusers/tests/pipelines/controlnet_hunyuandit/test_controlnet_hunyuandit.py

# coding=utf-8
# Copyright 2025 HuggingFace Inc and Tencent Hunyuan 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 gc
import unittest

import numpy as np
import torch
from transformers import AutoTokenizer, BertModel, T5EncoderModel

from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    HunyuanDiT2DModel,
    HunyuanDiTControlNetPipeline,
)
from diffusers.models import HunyuanDiT2DControlNetModel, HunyuanDiT2DMultiControlNetModel
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
    backend_empty_cache,
    enable_full_determinism,
    require_torch_accelerator,
    slow,
    torch_device,
)
from diffusers.utils.torch_utils import randn_tensor

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class HunyuanDiTControlNetPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
    pipeline_class = HunyuanDiTControlNetPipeline
    params = frozenset(
        [
            "prompt",
            "height",
            "width",
            "guidance_scale",
            "negative_prompt",
            "prompt_embeds",
            "negative_prompt_embeds",
        ]
    )
    batch_params = frozenset(["prompt", "negative_prompt"])
    test_layerwise_casting = True

    def get_dummy_components(self):
        torch.manual_seed(0)
        transformer = HunyuanDiT2DModel(
            sample_size=16,
            num_layers=4,
            patch_size=2,
            attention_head_dim=8,
            num_attention_heads=3,
            in_channels=4,
            cross_attention_dim=32,
            cross_attention_dim_t5=32,
            pooled_projection_dim=16,
            hidden_size=24,
            activation_fn="gelu-approximate",
        )

        torch.manual_seed(0)
        controlnet = HunyuanDiT2DControlNetModel(
            sample_size=16,
            transformer_num_layers=4,
            patch_size=2,
            attention_head_dim=8,
            num_attention_heads=3,
            in_channels=4,
            cross_attention_dim=32,
            cross_attention_dim_t5=32,
            pooled_projection_dim=16,
            hidden_size=24,
            activation_fn="gelu-approximate",
        )

        torch.manual_seed(0)
        vae = AutoencoderKL()

        scheduler = DDPMScheduler()
        text_encoder = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel")
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel")
        text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
        tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

        components = {
            "transformer": transformer.eval(),
            "vae": vae.eval(),
            "scheduler": scheduler,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_2,
            "tokenizer_2": tokenizer_2,
            "safety_checker": None,
            "feature_extractor": None,
            "controlnet": controlnet,
        }
        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="cpu").manual_seed(seed)

        control_image = randn_tensor(
            (1, 3, 16, 16),
            generator=generator,
            device=torch.device(device),
            dtype=torch.float16,
        )

        controlnet_conditioning_scale = 0.5

        inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 5.0,
            "output_type": "np",
            "control_image": control_image,
            "controlnet_conditioning_scale": controlnet_conditioning_scale,
        }

        return inputs

    def test_controlnet_hunyuandit(self):
        components = self.get_dummy_components()
        pipe = HunyuanDiTControlNetPipeline(**components)
        pipe = pipe.to(torch_device, dtype=torch.float16)
        pipe.set_progress_bar_config(disable=None)

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

        image_slice = image[0, -3:, -3:, -1]
        assert image.shape == (1, 16, 16, 3)

        if torch_device == "xpu":
            expected_slice = np.array(
                [0.6376953, 0.84375, 0.58691406, 0.48046875, 0.43652344, 0.5517578, 0.54248047, 0.5644531, 0.48217773]
            )
        else:
            expected_slice = np.array(
                [0.6953125, 0.89208984, 0.59375, 0.5078125, 0.5786133, 0.6035156, 0.5839844, 0.53564453, 0.52246094]
            )

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
            f"Expected: {expected_slice}, got: {image_slice.flatten()}"
        )

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

    def test_sequential_cpu_offload_forward_pass(self):
        # TODO(YiYi) need to fix later
        pass

    def test_sequential_offload_forward_pass_twice(self):
        # TODO(YiYi) need to fix later
        pass

    def test_save_load_optional_components(self):
        # TODO(YiYi) need to fix later
        pass

    @unittest.skip(
        "Test not supported as `encode_prompt` is called two times separately which deivates from about 99% of the pipelines we have."
    )
    def test_encode_prompt_works_in_isolation(self):
        pass


@slow
@require_torch_accelerator
class HunyuanDiTControlNetPipelineSlowTests(unittest.TestCase):
    pipeline_class = HunyuanDiTControlNetPipeline

    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 = HunyuanDiT2DControlNetModel.from_pretrained(
            "Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Canny", torch_dtype=torch.float16
        )
        pipe = HunyuanDiTControlNetPipeline.from_pretrained(
            "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers", controlnet=controlnet, torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload(device=torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        prompt = "At night, an ancient Chinese-style lion statue stands in front of the hotel, its eyes gleaming as if guarding the building. The background is the hotel entrance at night, with a close-up, eye-level, and centered composition. This photo presents a realistic photographic style, embodies Chinese sculpture culture, and reveals a mysterious atmosphere."
        n_prompt = ""
        control_image = load_image(
            "https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Canny/resolve/main/canny.jpg?download=true"
        )

        output = pipe(
            prompt,
            negative_prompt=n_prompt,
            control_image=control_image,
            controlnet_conditioning_scale=0.5,
            guidance_scale=5.0,
            num_inference_steps=2,
            output_type="np",
            generator=generator,
        )
        image = output.images[0]

        assert image.shape == (1024, 1024, 3)

        original_image = image[-3:, -3:, -1].flatten()

        expected_image = np.array(
            [0.43652344, 0.4399414, 0.44921875, 0.45043945, 0.45703125, 0.44873047, 0.43579102, 0.44018555, 0.42578125]
        )

        assert np.abs(original_image.flatten() - expected_image).max() < 1e-2

    def test_pose(self):
        controlnet = HunyuanDiT2DControlNetModel.from_pretrained(
            "Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Pose", torch_dtype=torch.float16
        )
        pipe = HunyuanDiTControlNetPipeline.from_pretrained(
            "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers", controlnet=controlnet, torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload(device=torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        prompt = "An Asian woman, dressed in a green top, wearing a purple headscarf and a purple scarf, stands in front of a blackboard. The background is the blackboard. The photo is presented in a close-up, eye-level, and centered composition, adopting a realistic photographic style"
        n_prompt = ""
        control_image = load_image(
            "https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Pose/resolve/main/pose.jpg?download=true"
        )

        output = pipe(
            prompt,
            negative_prompt=n_prompt,
            control_image=control_image,
            controlnet_conditioning_scale=0.5,
            guidance_scale=5.0,
            num_inference_steps=2,
            output_type="np",
            generator=generator,
        )
        image = output.images[0]

        assert image.shape == (1024, 1024, 3)

        original_image = image[-3:, -3:, -1].flatten()

        expected_image = np.array(
            [0.4091797, 0.4177246, 0.39526367, 0.4194336, 0.40356445, 0.3857422, 0.39208984, 0.40429688, 0.37451172]
        )

        assert np.abs(original_image.flatten() - expected_image).max() < 1e-2

    def test_depth(self):
        controlnet = HunyuanDiT2DControlNetModel.from_pretrained(
            "Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Depth", torch_dtype=torch.float16
        )
        pipe = HunyuanDiTControlNetPipeline.from_pretrained(
            "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers", controlnet=controlnet, torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload(device=torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        prompt = "In the dense forest, a black and white panda sits quietly in green trees and red flowers, surrounded by mountains, rivers, and the ocean. The background is the forest in a bright environment."
        n_prompt = ""
        control_image = load_image(
            "https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Depth/resolve/main/depth.jpg?download=true"
        )

        output = pipe(
            prompt,
            negative_prompt=n_prompt,
            control_image=control_image,
            controlnet_conditioning_scale=0.5,
            guidance_scale=5.0,
            num_inference_steps=2,
            output_type="np",
            generator=generator,
        )
        image = output.images[0]

        assert image.shape == (1024, 1024, 3)

        original_image = image[-3:, -3:, -1].flatten()

        expected_image = np.array(
            [0.31982422, 0.32177734, 0.30126953, 0.3190918, 0.3100586, 0.31396484, 0.3232422, 0.33544922, 0.30810547]
        )

        assert np.abs(original_image.flatten() - expected_image).max() < 1e-2

    def test_multi_controlnet(self):
        controlnet = HunyuanDiT2DControlNetModel.from_pretrained(
            "Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Canny", torch_dtype=torch.float16
        )
        controlnet = HunyuanDiT2DMultiControlNetModel([controlnet, controlnet])

        pipe = HunyuanDiTControlNetPipeline.from_pretrained(
            "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers", controlnet=controlnet, torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload(device=torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        prompt = "At night, an ancient Chinese-style lion statue stands in front of the hotel, its eyes gleaming as if guarding the building. The background is the hotel entrance at night, with a close-up, eye-level, and centered composition. This photo presents a realistic photographic style, embodies Chinese sculpture culture, and reveals a mysterious atmosphere."
        n_prompt = ""
        control_image = load_image(
            "https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.1-ControlNet-Diffusers-Canny/resolve/main/canny.jpg?download=true"
        )

        output = pipe(
            prompt,
            negative_prompt=n_prompt,
            control_image=[control_image, control_image],
            controlnet_conditioning_scale=[0.25, 0.25],
            guidance_scale=5.0,
            num_inference_steps=2,
            output_type="np",
            generator=generator,
        )
        image = output.images[0]

        assert image.shape == (1024, 1024, 3)

        original_image = image[-3:, -3:, -1].flatten()

        expected_image = np.array(
            [0.43652344, 0.44018555, 0.4494629, 0.44995117, 0.45654297, 0.44848633, 0.43603516, 0.4404297, 0.42626953]
        )

        assert np.abs(original_image.flatten() - expected_image).max() < 1e-2