test_controlnet_sd3.py

# coding=utf-8
# Copyright 2024 HuggingFace Inc and The InstantX 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
from typing import Optional

import numpy as np
import pytest
import torch
from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel

from diffusers import (
    AutoencoderKL,
    FlowMatchEulerDiscreteScheduler,
    SD3Transformer2DModel,
    StableDiffusion3ControlNetPipeline,
)
from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    numpy_cosine_similarity_distance,
    require_big_gpu_with_torch_cuda,
    slow,
    torch_device,
)
from diffusers.utils.torch_utils import randn_tensor

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class StableDiffusion3ControlNetPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
    pipeline_class = StableDiffusion3ControlNetPipeline
    params = frozenset(
        [
            "prompt",
            "height",
            "width",
            "guidance_scale",
            "negative_prompt",
            "prompt_embeds",
            "negative_prompt_embeds",
        ]
    )
    batch_params = frozenset(["prompt", "negative_prompt"])

    def get_dummy_components(self, num_controlnet_layers: int = 3, qk_norm: Optional[str] = "rms_norm"):
        torch.manual_seed(0)
        transformer = SD3Transformer2DModel(
            sample_size=32,
            patch_size=1,
            in_channels=8,
            num_layers=4,
            attention_head_dim=8,
            num_attention_heads=4,
            joint_attention_dim=32,
            caption_projection_dim=32,
            pooled_projection_dim=64,
            out_channels=8,
            qk_norm=qk_norm,
        )

        torch.manual_seed(0)
        controlnet = SD3ControlNetModel(
            sample_size=32,
            patch_size=1,
            in_channels=8,
            num_layers=num_controlnet_layers,
            attention_head_dim=8,
            num_attention_heads=4,
            joint_attention_dim=32,
            caption_projection_dim=32,
            pooled_projection_dim=64,
            out_channels=8,
        )
        clip_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,
            hidden_act="gelu",
            projection_dim=32,
        )

        torch.manual_seed(0)
        text_encoder = CLIPTextModelWithProjection(clip_text_encoder_config)

        torch.manual_seed(0)
        text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)

        torch.manual_seed(0)
        text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")

        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        tokenizer_3 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

        torch.manual_seed(0)
        vae = AutoencoderKL(
            sample_size=32,
            in_channels=3,
            out_channels=3,
            block_out_channels=(4,),
            layers_per_block=1,
            latent_channels=8,
            norm_num_groups=1,
            use_quant_conv=False,
            use_post_quant_conv=False,
            shift_factor=0.0609,
            scaling_factor=1.5035,
        )

        scheduler = FlowMatchEulerDiscreteScheduler()

        return {
            "scheduler": scheduler,
            "text_encoder": text_encoder,
            "text_encoder_2": text_encoder_2,
            "text_encoder_3": text_encoder_3,
            "tokenizer": tokenizer,
            "tokenizer_2": tokenizer_2,
            "tokenizer_3": tokenizer_3,
            "transformer": transformer,
            "vae": vae,
            "controlnet": controlnet,
        }

    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, 32, 32),
            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_sd3(self):
        components = self.get_dummy_components()
        sd_pipe = StableDiffusion3ControlNetPipeline(**components)
        sd_pipe = sd_pipe.to(torch_device, dtype=torch.float16)
        sd_pipe.set_progress_bar_config(disable=None)

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

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

        assert image.shape == (1, 32, 32, 3)

        expected_slice = np.array([0.5767, 0.7100, 0.5981, 0.5674, 0.5952, 0.4102, 0.5093, 0.5044, 0.6030])

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

    @unittest.skip("xFormersAttnProcessor does not work with SD3 Joint Attention")
    def test_xformers_attention_forwardGenerator_pass(self):
        pass


@slow
@require_big_gpu_with_torch_cuda
@pytest.mark.big_gpu_with_torch_cuda
class StableDiffusion3ControlNetPipelineSlowTests(unittest.TestCase):
    pipeline_class = StableDiffusion3ControlNetPipeline

    def setUp(self):
        super().setUp()
        gc.collect()
        torch.cuda.empty_cache()

    def tearDown(self):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    def test_canny(self):
        controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny", torch_dtype=torch.float16)
        pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
            "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        prompt = "Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text 'InstantX' on image"
        n_prompt = "NSFW, nude, naked, porn, ugly"
        control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg")

        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([0.7314, 0.7075, 0.6611, 0.7539, 0.7563, 0.6650, 0.6123, 0.7275, 0.7222])

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

    def test_pose(self):
        controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Pose", torch_dtype=torch.float16)
        pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
            "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        prompt = 'Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text "InstantX" on image'
        n_prompt = "NSFW, nude, naked, porn, ugly"
        control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Pose/resolve/main/pose.jpg")

        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([0.9048, 0.8740, 0.8936, 0.8516, 0.8799, 0.9360, 0.8379, 0.8408, 0.8652])

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

    def test_tile(self):
        controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Tile", torch_dtype=torch.float16)
        pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
            "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        prompt = 'Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text "InstantX" on image'
        n_prompt = "NSFW, nude, naked, porn, ugly"
        control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Tile/resolve/main/tile.jpg")

        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([0.6699, 0.6836, 0.6226, 0.6572, 0.7310, 0.6646, 0.6650, 0.6694, 0.6011])

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

    def test_multi_controlnet(self):
        controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny", torch_dtype=torch.float16)
        controlnet = SD3MultiControlNetModel([controlnet, controlnet])

        pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
            "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device="cpu").manual_seed(0)
        prompt = "Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text 'InstantX' on image"
        n_prompt = "NSFW, nude, naked, porn, ugly"
        control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg")

        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([0.7207, 0.7041, 0.6543, 0.7500, 0.7490, 0.6592, 0.6001, 0.7168, 0.7231])

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