# coding=utf-8 # Copyright 2023 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 random import unittest import numpy as np import torch from PIL import Image from transformers import CLIPImageProcessor, CLIPVisionConfig from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNet2DConditionModel from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder from diffusers.utils import floats_tensor, load_image, slow, torch_device from diffusers.utils.testing_utils import require_torch_gpu from ...pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS from ...test_pipelines_common import PipelineTesterMixin torch.backends.cuda.matmul.allow_tf32 = False class PaintByExamplePipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = PaintByExamplePipeline params = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS batch_params = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_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, ) scheduler = PNDMScheduler(skip_prk_steps=True) 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) config = CLIPVisionConfig( hidden_size=32, projection_dim=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, image_size=32, patch_size=4, ) image_encoder = PaintByExampleImageEncoder(config, proj_size=32) feature_extractor = CLIPImageProcessor(crop_size=32, size=32) components = { "unet": unet, "scheduler": scheduler, "vae": vae, "image_encoder": image_encoder, "safety_checker": None, "feature_extractor": feature_extractor, } return components def convert_to_pt(self, image): image = np.array(image.convert("RGB")) image = image[None].transpose(0, 3, 1, 2) image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 return image def get_dummy_inputs(self, device="cpu", seed=0): # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) image = image.cpu().permute(0, 2, 3, 1)[0] init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((64, 64)) example_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32)) if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "example_image": example_image, "image": init_image, "mask_image": mask_image, "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "numpy", } return inputs def test_paint_by_example_inpaint(self): components = self.get_dummy_components() # make sure here that pndm scheduler skips prk pipe = PaintByExamplePipeline(**components) pipe = pipe.to("cpu") pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs() output = pipe(**inputs) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array([0.4701, 0.5555, 0.3994, 0.5107, 0.5691, 0.4517, 0.5125, 0.4769, 0.4539]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_paint_by_example_image_tensor(self): device = "cpu" inputs = self.get_dummy_inputs() inputs.pop("mask_image") image = self.convert_to_pt(inputs.pop("image")) mask_image = image.clamp(0, 1) / 2 # make sure here that pndm scheduler skips prk pipe = PaintByExamplePipeline(**self.get_dummy_components()) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) output = pipe(image=image, mask_image=mask_image[:, 0], **inputs) out_1 = output.images image = image.cpu().permute(0, 2, 3, 1)[0] mask_image = mask_image.cpu().permute(0, 2, 3, 1)[0] image = Image.fromarray(np.uint8(image)).convert("RGB") mask_image = Image.fromarray(np.uint8(mask_image)).convert("RGB") output = pipe(**self.get_dummy_inputs()) out_2 = output.images assert out_1.shape == (1, 64, 64, 3) assert np.abs(out_1.flatten() - out_2.flatten()).max() < 5e-2 @slow @require_torch_gpu class PaintByExamplePipelineIntegrationTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_paint_by_example(self): # make sure here that pndm scheduler skips prk init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/paint_by_example/dog_in_bucket.png" ) mask_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/paint_by_example/mask.png" ) example_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/paint_by_example/panda.jpg" ) pipe = PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example") pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) generator = torch.manual_seed(321) output = pipe( image=init_image, mask_image=mask_image, example_image=example_image, generator=generator, guidance_scale=5.0, num_inference_steps=50, output_type="np", ) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2