# 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 CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DPMSolverMultistepScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel, ) from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device from diffusers.utils.testing_utils import require_torch_gpu from ...pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS from ...test_pipelines_common import PipelineTesterMixin torch.backends.cuda.matmul.allow_tf32 = False class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = StableDiffusionInpaintPipeline params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS batch_params = TEXT_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) 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, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, } return components def get_dummy_inputs(self, device, 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)) if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "prompt": "A painting of a squirrel eating a burger", "image": init_image, "mask_image": mask_image, "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "numpy", } return inputs def test_stable_diffusion_inpaint(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionInpaintPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = sd_pipe(**inputs).images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array([0.4723, 0.5731, 0.3939, 0.5441, 0.5922, 0.4392, 0.5059, 0.4651, 0.4474]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_inpaint_image_tensor(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionInpaintPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) output = sd_pipe(**inputs) out_pil = output.images inputs = self.get_dummy_inputs(device) inputs["image"] = torch.tensor(np.array(inputs["image"]) / 127.5 - 1).permute(2, 0, 1).unsqueeze(0) inputs["mask_image"] = torch.tensor(np.array(inputs["mask_image"]) / 255).permute(2, 0, 1)[:1].unsqueeze(0) output = sd_pipe(**inputs) out_tensor = output.images assert out_pil.shape == (1, 64, 64, 3) assert np.abs(out_pil.flatten() - out_tensor.flatten()).max() < 5e-2 @slow @require_torch_gpu class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase): def setUp(self): super().setUp() def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): generator = torch.Generator(device=generator_device).manual_seed(seed) init_image = load_image( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" "/stable_diffusion_inpaint/input_bench_image.png" ) mask_image = load_image( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" "/stable_diffusion_inpaint/input_bench_mask.png" ) inputs = { "prompt": "Face of a yellow cat, high resolution, sitting on a park bench", "image": init_image, "mask_image": mask_image, "generator": generator, "num_inference_steps": 3, "guidance_scale": 7.5, "output_type": "numpy", } return inputs def test_stable_diffusion_inpaint_ddim(self): pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", safety_checker=None ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() inputs = self.get_inputs(torch_device) image = pipe(**inputs).images image_slice = image[0, 253:256, 253:256, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.0427, 0.0460, 0.0483, 0.0460, 0.0584, 0.0521, 0.1549, 0.1695, 0.1794]) assert np.abs(expected_slice - image_slice).max() < 1e-4 def test_stable_diffusion_inpaint_fp16(self): pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() inputs = self.get_inputs(torch_device, dtype=torch.float16) image = pipe(**inputs).images image_slice = image[0, 253:256, 253:256, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.1350, 0.1123, 0.1350, 0.1641, 0.1328, 0.1230, 0.1289, 0.1531, 0.1687]) assert np.abs(expected_slice - image_slice).max() < 5e-2 def test_stable_diffusion_inpaint_pndm(self): pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", safety_checker=None ) pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() inputs = self.get_inputs(torch_device) image = pipe(**inputs).images image_slice = image[0, 253:256, 253:256, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.0425, 0.0273, 0.0344, 0.1694, 0.1727, 0.1812, 0.3256, 0.3311, 0.3272]) assert np.abs(expected_slice - image_slice).max() < 1e-4 def test_stable_diffusion_inpaint_k_lms(self): pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", safety_checker=None ) pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() inputs = self.get_inputs(torch_device) image = pipe(**inputs).images image_slice = image[0, 253:256, 253:256, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.9314, 0.7575, 0.9432, 0.8885, 0.9028, 0.7298, 0.9811, 0.9667, 0.7633]) assert np.abs(expected_slice - image_slice).max() < 1e-4 def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self): torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() pipe = StableDiffusionInpaintPipeline.from_pretrained( "runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16 ) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing(1) pipe.enable_sequential_cpu_offload() inputs = self.get_inputs(torch_device, dtype=torch.float16) _ = pipe(**inputs) mem_bytes = torch.cuda.max_memory_allocated() # make sure that less than 2.2 GB is allocated assert mem_bytes < 2.2 * 10**9 @nightly @require_torch_gpu class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase): def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): generator = torch.Generator(device=generator_device).manual_seed(seed) init_image = load_image( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" "/stable_diffusion_inpaint/input_bench_image.png" ) mask_image = load_image( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" "/stable_diffusion_inpaint/input_bench_mask.png" ) inputs = { "prompt": "Face of a yellow cat, high resolution, sitting on a park bench", "image": init_image, "mask_image": mask_image, "generator": generator, "num_inference_steps": 50, "guidance_scale": 7.5, "output_type": "numpy", } return inputs def test_inpaint_ddim(self): sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_inputs(torch_device) image = sd_pipe(**inputs).images[0] expected_image = load_numpy( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" "/stable_diffusion_inpaint/stable_diffusion_inpaint_ddim.npy" ) max_diff = np.abs(expected_image - image).max() assert max_diff < 1e-3 def test_inpaint_pndm(self): sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") sd_pipe.scheduler = PNDMScheduler.from_config(sd_pipe.scheduler.config) sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_inputs(torch_device) image = sd_pipe(**inputs).images[0] expected_image = load_numpy( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" "/stable_diffusion_inpaint/stable_diffusion_inpaint_pndm.npy" ) max_diff = np.abs(expected_image - image).max() assert max_diff < 1e-3 def test_inpaint_lms(self): sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config) sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_inputs(torch_device) image = sd_pipe(**inputs).images[0] expected_image = load_numpy( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" "/stable_diffusion_inpaint/stable_diffusion_inpaint_lms.npy" ) max_diff = np.abs(expected_image - image).max() assert max_diff < 1e-3 def test_inpaint_dpm(self): sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config) sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_inputs(torch_device) inputs["num_inference_steps"] = 30 image = sd_pipe(**inputs).images[0] expected_image = load_numpy( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" "/stable_diffusion_inpaint/stable_diffusion_inpaint_dpm_multi.npy" ) max_diff = np.abs(expected_image - image).max() assert max_diff < 1e-3 class StableDiffusionInpaintingPrepareMaskAndMaskedImageTests(unittest.TestCase): def test_pil_inputs(self): im = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8) im = Image.fromarray(im) mask = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5 mask = Image.fromarray((mask * 255).astype(np.uint8)) t_mask, t_masked = prepare_mask_and_masked_image(im, mask) self.assertTrue(isinstance(t_mask, torch.Tensor)) self.assertTrue(isinstance(t_masked, torch.Tensor)) self.assertEqual(t_mask.ndim, 4) self.assertEqual(t_masked.ndim, 4) self.assertEqual(t_mask.shape, (1, 1, 32, 32)) self.assertEqual(t_masked.shape, (1, 3, 32, 32)) self.assertTrue(t_mask.dtype == torch.float32) self.assertTrue(t_masked.dtype == torch.float32) self.assertTrue(t_mask.min() >= 0.0) self.assertTrue(t_mask.max() <= 1.0) self.assertTrue(t_masked.min() >= -1.0) self.assertTrue(t_masked.min() <= 1.0) self.assertTrue(t_mask.sum() > 0.0) def test_np_inputs(self): im_np = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8) im_pil = Image.fromarray(im_np) mask_np = np.random.randint(0, 255, (32, 32), dtype=np.uint8) > 127.5 mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8)) t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) t_mask_pil, t_masked_pil = prepare_mask_and_masked_image(im_pil, mask_pil) self.assertTrue((t_mask_np == t_mask_pil).all()) self.assertTrue((t_masked_np == t_masked_pil).all()) def test_torch_3D_2D_inputs(self): im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8) mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5 im_np = im_tensor.numpy().transpose(1, 2, 0) mask_np = mask_tensor.numpy() t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) self.assertTrue((t_mask_tensor == t_mask_np).all()) self.assertTrue((t_masked_tensor == t_masked_np).all()) def test_torch_3D_3D_inputs(self): im_tensor = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8) mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5 im_np = im_tensor.numpy().transpose(1, 2, 0) mask_np = mask_tensor.numpy()[0] t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) self.assertTrue((t_mask_tensor == t_mask_np).all()) self.assertTrue((t_masked_tensor == t_masked_np).all()) def test_torch_4D_2D_inputs(self): im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8) mask_tensor = torch.randint(0, 255, (32, 32), dtype=torch.uint8) > 127.5 im_np = im_tensor.numpy()[0].transpose(1, 2, 0) mask_np = mask_tensor.numpy() t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) self.assertTrue((t_mask_tensor == t_mask_np).all()) self.assertTrue((t_masked_tensor == t_masked_np).all()) def test_torch_4D_3D_inputs(self): im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8) mask_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8) > 127.5 im_np = im_tensor.numpy()[0].transpose(1, 2, 0) mask_np = mask_tensor.numpy()[0] t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) self.assertTrue((t_mask_tensor == t_mask_np).all()) self.assertTrue((t_masked_tensor == t_masked_np).all()) def test_torch_4D_4D_inputs(self): im_tensor = torch.randint(0, 255, (1, 3, 32, 32), dtype=torch.uint8) mask_tensor = torch.randint(0, 255, (1, 1, 32, 32), dtype=torch.uint8) > 127.5 im_np = im_tensor.numpy()[0].transpose(1, 2, 0) mask_np = mask_tensor.numpy()[0][0] t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) t_mask_np, t_masked_np = prepare_mask_and_masked_image(im_np, mask_np) self.assertTrue((t_mask_tensor == t_mask_np).all()) self.assertTrue((t_masked_tensor == t_masked_np).all()) def test_torch_batch_4D_3D(self): im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8) mask_tensor = torch.randint(0, 255, (2, 32, 32), dtype=torch.uint8) > 127.5 im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor] mask_nps = [mask.numpy() for mask in mask_tensor] t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)] t_mask_np = torch.cat([n[0] for n in nps]) t_masked_np = torch.cat([n[1] for n in nps]) self.assertTrue((t_mask_tensor == t_mask_np).all()) self.assertTrue((t_masked_tensor == t_masked_np).all()) def test_torch_batch_4D_4D(self): im_tensor = torch.randint(0, 255, (2, 3, 32, 32), dtype=torch.uint8) mask_tensor = torch.randint(0, 255, (2, 1, 32, 32), dtype=torch.uint8) > 127.5 im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor] mask_nps = [mask.numpy()[0] for mask in mask_tensor] t_mask_tensor, t_masked_tensor = prepare_mask_and_masked_image(im_tensor / 127.5 - 1, mask_tensor) nps = [prepare_mask_and_masked_image(i, m) for i, m in zip(im_nps, mask_nps)] t_mask_np = torch.cat([n[0] for n in nps]) t_masked_np = torch.cat([n[1] for n in nps]) self.assertTrue((t_mask_tensor == t_mask_np).all()) self.assertTrue((t_masked_tensor == t_masked_np).all()) def test_shape_mismatch(self): # test height and width with self.assertRaises(AssertionError): prepare_mask_and_masked_image(torch.randn(3, 32, 32), torch.randn(64, 64)) # test batch dim with self.assertRaises(AssertionError): prepare_mask_and_masked_image(torch.randn(2, 3, 32, 32), torch.randn(4, 64, 64)) # test batch dim with self.assertRaises(AssertionError): prepare_mask_and_masked_image(torch.randn(2, 3, 32, 32), torch.randn(4, 1, 64, 64)) def test_type_mismatch(self): # test tensors-only with self.assertRaises(TypeError): prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.rand(3, 32, 32).numpy()) # test tensors-only with self.assertRaises(TypeError): prepare_mask_and_masked_image(torch.rand(3, 32, 32).numpy(), torch.rand(3, 32, 32)) def test_channels_first(self): # test channels first for 3D tensors with self.assertRaises(AssertionError): prepare_mask_and_masked_image(torch.rand(32, 32, 3), torch.rand(3, 32, 32)) def test_tensor_range(self): # test im <= 1 with self.assertRaises(ValueError): prepare_mask_and_masked_image(torch.ones(3, 32, 32) * 2, torch.rand(32, 32)) # test im >= -1 with self.assertRaises(ValueError): prepare_mask_and_masked_image(torch.ones(3, 32, 32) * (-2), torch.rand(32, 32)) # test mask <= 1 with self.assertRaises(ValueError): prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.ones(32, 32) * 2) # test mask >= 0 with self.assertRaises(ValueError): prepare_mask_and_masked_image(torch.rand(3, 32, 32), torch.ones(32, 32) * -1)