# coding=utf-8 # Copyright 2022 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 diffusers import ( AutoencoderKL, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel, UNet2DModel, VQModel, ) 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, slow, torch_device from diffusers.utils.testing_utils import require_torch_gpu from PIL import Image from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from ...test_pipelines_common import PipelineTesterMixin torch.backends.cuda.matmul.allow_tf32 = False class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() @property def dummy_image(self): batch_size = 1 num_channels = 3 sizes = (32, 32) image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device) return image @property def dummy_uncond_unet(self): torch.manual_seed(0) model = UNet2DModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=("DownBlock2D", "AttnDownBlock2D"), up_block_types=("AttnUpBlock2D", "UpBlock2D"), ) return model @property def dummy_cond_unet(self): torch.manual_seed(0) model = 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, ) return model @property def dummy_cond_unet_inpaint(self): torch.manual_seed(0) model = 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, ) return model @property def dummy_vq_model(self): torch.manual_seed(0) model = VQModel( block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=3, ) return model @property def dummy_vae(self): torch.manual_seed(0) model = 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, ) return model @property def dummy_text_encoder(self): torch.manual_seed(0) 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, ) return CLIPTextModel(config) @property def dummy_extractor(self): def extract(*args, **kwargs): class Out: def __init__(self): self.pixel_values = torch.ones([0]) def to(self, device): self.pixel_values.to(device) return self return Out() return extract def test_stable_diffusion_inpaint(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet_inpaint scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128)) mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128)) # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionInpaintPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=None, ) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" generator = torch.Generator(device=device).manual_seed(0) output = sd_pipe( [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np", image=init_image, mask_image=mask_image, ) image = output.images generator = torch.Generator(device=device).manual_seed(0) image_from_tuple = sd_pipe( [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np", image=init_image, mask_image=mask_image, return_dict=False, )[0] image_slice = image[0, -3:, -3:, -1] image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 128, 128, 3) expected_slice = np.array([0.5075, 0.4485, 0.4558, 0.5369, 0.5369, 0.5236, 0.5127, 0.4983, 0.4776]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_inpaint_with_num_images_per_prompt(self): device = "cpu" unet = self.dummy_cond_unet_inpaint scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128)) mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128)) # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionInpaintPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=None, ) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" generator = torch.Generator(device=device).manual_seed(0) images = sd_pipe( [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np", image=init_image, mask_image=mask_image, num_images_per_prompt=2, ).images # check if the output is a list of 2 images assert len(images) == 2 @unittest.skipIf(torch_device != "cuda", "This test requires a GPU") def test_stable_diffusion_inpaint_fp16(self): """Test that stable diffusion inpaint_legacy works with fp16""" unet = self.dummy_cond_unet_inpaint scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((128, 128)) mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128)) # put models in fp16 unet = unet.half() vae = vae.half() bert = bert.half() # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionInpaintPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=None, ) sd_pipe = sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" generator = torch.Generator(device=torch_device).manual_seed(0) image = sd_pipe( [prompt], generator=generator, num_inference_steps=2, output_type="np", image=init_image, mask_image=mask_image, ).images assert image.shape == (1, 128, 128, 3) @slow @require_torch_gpu class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_stable_diffusion_inpaint_pipeline(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo.png" ) mask_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" ) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint" "/yellow_cat_sitting_on_a_park_bench.npy" ) model_id = "runwayml/stable-diffusion-inpainting" pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "Face of a yellow cat, high resolution, sitting on a park bench" generator = torch.Generator(device=torch_device).manual_seed(0) output = pipe( prompt=prompt, image=init_image, mask_image=mask_image, generator=generator, output_type="np", ) image = output.images[0] assert image.shape == (512, 512, 3) assert np.abs(expected_image - image).max() < 1e-3 def test_stable_diffusion_inpaint_pipeline_fp16(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo.png" ) mask_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" ) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint" "/yellow_cat_sitting_on_a_park_bench_fp16.npy" ) model_id = "runwayml/stable-diffusion-inpainting" pipe = StableDiffusionInpaintPipeline.from_pretrained( model_id, revision="fp16", torch_dtype=torch.float16, safety_checker=None, ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "Face of a yellow cat, high resolution, sitting on a park bench" generator = torch.Generator(device=torch_device).manual_seed(0) output = pipe( prompt=prompt, image=init_image, mask_image=mask_image, generator=generator, output_type="np", ) image = output.images[0] assert image.shape == (512, 512, 3) assert np.abs(expected_image - image).max() < 5e-1 def test_stable_diffusion_inpaint_pipeline_pndm(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo.png" ) mask_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" ) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint" "/yellow_cat_sitting_on_a_park_bench_pndm.npy" ) model_id = "runwayml/stable-diffusion-inpainting" pndm = PNDMScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None, scheduler=pndm) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "Face of a yellow cat, high resolution, sitting on a park bench" generator = torch.Generator(device=torch_device).manual_seed(0) output = pipe( prompt=prompt, image=init_image, mask_image=mask_image, generator=generator, output_type="np", ) image = output.images[0] assert image.shape == (512, 512, 3) assert np.abs(expected_image - image).max() < 1e-2 def test_stable_diffusion_inpaint_pipeline_k_lms(self): init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo.png" ) mask_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" ) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint" "/yellow_cat_sitting_on_a_park_bench_k_lms.npy" ) model_id = "runwayml/stable-diffusion-inpainting" pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None) pipe.to(torch_device) # switch to LMS pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "Face of a yellow cat, high resolution, sitting on a park bench" generator = torch.Generator(device=torch_device).manual_seed(0) output = pipe( prompt=prompt, image=init_image, mask_image=mask_image, generator=generator, output_type="np", ) image = output.images[0] assert image.shape == (512, 512, 3) assert np.abs(expected_image - image).max() < 1e-2 @unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU") def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self): torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo.png" ) mask_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" ) model_id = "runwayml/stable-diffusion-inpainting" pndm = PNDMScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionInpaintPipeline.from_pretrained( model_id, safety_checker=None, scheduler=pndm, device_map="auto", revision="fp16", torch_dtype=torch.float16, ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing(1) pipe.enable_sequential_cpu_offload() prompt = "Face of a yellow cat, high resolution, sitting on a park bench" generator = torch.Generator(device=torch_device).manual_seed(0) _ = pipe( prompt=prompt, image=init_image, mask_image=mask_image, generator=generator, num_inference_steps=5, output_type="np", ) mem_bytes = torch.cuda.max_memory_allocated() # make sure that less than 2.2 GB is allocated assert mem_bytes < 2.2 * 10**9 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)