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| import gc | |
| import random | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from transformers import ( | |
| CLIPImageProcessor, | |
| CLIPTextConfig, | |
| CLIPTextModel, | |
| CLIPTokenizer, | |
| CLIPVisionConfig, | |
| CLIPVisionModelWithProjection, | |
| ) | |
| from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImg2ImgPipeline, UNet2DConditionModel | |
| from diffusers.pipelines.pipeline_utils import DiffusionPipeline | |
| from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer | |
| from diffusers.utils.import_utils import is_xformers_available | |
| from diffusers.utils.testing_utils import floats_tensor, load_image, load_numpy, require_torch_gpu, slow, torch_device | |
| from ...pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS | |
| from ...test_pipelines_common import ( | |
| PipelineTesterMixin, | |
| assert_mean_pixel_difference, | |
| ) | |
| class StableUnCLIPImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = StableUnCLIPImg2ImgPipeline | |
| params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS | |
| batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS | |
| def get_dummy_components(self): | |
| embedder_hidden_size = 32 | |
| embedder_projection_dim = embedder_hidden_size | |
| # image encoding components | |
| feature_extractor = CLIPImageProcessor(crop_size=32, size=32) | |
| image_encoder = CLIPVisionModelWithProjection( | |
| CLIPVisionConfig( | |
| hidden_size=embedder_hidden_size, | |
| projection_dim=embedder_projection_dim, | |
| num_hidden_layers=5, | |
| num_attention_heads=4, | |
| image_size=32, | |
| intermediate_size=37, | |
| patch_size=1, | |
| ) | |
| ) | |
| # regular denoising components | |
| torch.manual_seed(0) | |
| image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedder_hidden_size) | |
| image_noising_scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2") | |
| torch.manual_seed(0) | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| torch.manual_seed(0) | |
| text_encoder = CLIPTextModel( | |
| CLIPTextConfig( | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| hidden_size=embedder_hidden_size, | |
| projection_dim=32, | |
| intermediate_size=37, | |
| layer_norm_eps=1e-05, | |
| num_attention_heads=4, | |
| num_hidden_layers=5, | |
| pad_token_id=1, | |
| vocab_size=1000, | |
| ) | |
| ) | |
| torch.manual_seed(0) | |
| unet = UNet2DConditionModel( | |
| sample_size=32, | |
| in_channels=4, | |
| out_channels=4, | |
| down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"), | |
| up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"), | |
| block_out_channels=(32, 64), | |
| attention_head_dim=(2, 4), | |
| class_embed_type="projection", | |
| # The class embeddings are the noise augmented image embeddings. | |
| # I.e. the image embeddings concated with the noised embeddings of the same dimension | |
| projection_class_embeddings_input_dim=embedder_projection_dim * 2, | |
| cross_attention_dim=embedder_hidden_size, | |
| layers_per_block=1, | |
| upcast_attention=True, | |
| use_linear_projection=True, | |
| ) | |
| torch.manual_seed(0) | |
| scheduler = DDIMScheduler( | |
| beta_schedule="scaled_linear", | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| prediction_type="v_prediction", | |
| set_alpha_to_one=False, | |
| steps_offset=1, | |
| ) | |
| torch.manual_seed(0) | |
| vae = AutoencoderKL() | |
| components = { | |
| # image encoding components | |
| "feature_extractor": feature_extractor, | |
| "image_encoder": image_encoder, | |
| # image noising components | |
| "image_normalizer": image_normalizer, | |
| "image_noising_scheduler": image_noising_scheduler, | |
| # regular denoising components | |
| "tokenizer": tokenizer, | |
| "text_encoder": text_encoder, | |
| "unet": unet, | |
| "scheduler": scheduler, | |
| "vae": vae, | |
| } | |
| return components | |
| def get_dummy_inputs(self, device, seed=0, pil_image=True): | |
| if str(device).startswith("mps"): | |
| generator = torch.manual_seed(seed) | |
| else: | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| input_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) | |
| if pil_image: | |
| input_image = input_image * 0.5 + 0.5 | |
| input_image = input_image.clamp(0, 1) | |
| input_image = input_image.cpu().permute(0, 2, 3, 1).float().numpy() | |
| input_image = DiffusionPipeline.numpy_to_pil(input_image)[0] | |
| return { | |
| "prompt": "An anime racoon running a marathon", | |
| "image": input_image, | |
| "generator": generator, | |
| "num_inference_steps": 2, | |
| "output_type": "np", | |
| } | |
| def test_image_embeds_none(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| sd_pipe = StableUnCLIPImg2ImgPipeline(**components) | |
| sd_pipe = sd_pipe.to(device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| inputs.update({"image_embeds": None}) | |
| image = sd_pipe(**inputs).images | |
| image_slice = image[0, -3:, -3:, -1] | |
| assert image.shape == (1, 32, 32, 3) | |
| expected_slice = np.array( | |
| [0.34588397, 0.7747054, 0.5453714, 0.5227859, 0.57656777, 0.6532228, 0.5177634, 0.49932978, 0.56626225] | |
| ) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
| # Overriding PipelineTesterMixin::test_attention_slicing_forward_pass | |
| # because GPU undeterminism requires a looser check. | |
| def test_attention_slicing_forward_pass(self): | |
| test_max_difference = torch_device in ["cpu", "mps"] | |
| self._test_attention_slicing_forward_pass(test_max_difference=test_max_difference) | |
| # Overriding PipelineTesterMixin::test_inference_batch_single_identical | |
| # because undeterminism requires a looser check. | |
| def test_inference_batch_single_identical(self): | |
| test_max_difference = torch_device in ["cpu", "mps"] | |
| self._test_inference_batch_single_identical(test_max_difference=test_max_difference) | |
| def test_xformers_attention_forwardGenerator_pass(self): | |
| self._test_xformers_attention_forwardGenerator_pass(test_max_difference=False) | |
| class StableUnCLIPImg2ImgPipelineIntegrationTests(unittest.TestCase): | |
| def tearDown(self): | |
| # clean up the VRAM after each test | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def test_stable_unclip_l_img2img(self): | |
| input_image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" | |
| ) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" | |
| ) | |
| pipe = StableUnCLIPImg2ImgPipeline.from_pretrained( | |
| "fusing/stable-unclip-2-1-l-img2img", torch_dtype=torch.float16 | |
| ) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| # stable unclip will oom when integration tests are run on a V100, | |
| # so turn on memory savings | |
| pipe.enable_attention_slicing() | |
| pipe.enable_sequential_cpu_offload() | |
| generator = torch.Generator(device="cpu").manual_seed(0) | |
| output = pipe(input_image, "anime turle", generator=generator, output_type="np") | |
| image = output.images[0] | |
| assert image.shape == (768, 768, 3) | |
| assert_mean_pixel_difference(image, expected_image) | |
| def test_stable_unclip_h_img2img(self): | |
| input_image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" | |
| ) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" | |
| ) | |
| pipe = StableUnCLIPImg2ImgPipeline.from_pretrained( | |
| "fusing/stable-unclip-2-1-h-img2img", torch_dtype=torch.float16 | |
| ) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| # stable unclip will oom when integration tests are run on a V100, | |
| # so turn on memory savings | |
| pipe.enable_attention_slicing() | |
| pipe.enable_sequential_cpu_offload() | |
| generator = torch.Generator(device="cpu").manual_seed(0) | |
| output = pipe(input_image, "anime turle", generator=generator, output_type="np") | |
| image = output.images[0] | |
| assert image.shape == (768, 768, 3) | |
| assert_mean_pixel_difference(image, expected_image) | |
| def test_stable_unclip_img2img_pipeline_with_sequential_cpu_offloading(self): | |
| input_image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" | |
| ) | |
| torch.cuda.empty_cache() | |
| torch.cuda.reset_max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| pipe = StableUnCLIPImg2ImgPipeline.from_pretrained( | |
| "fusing/stable-unclip-2-1-h-img2img", torch_dtype=torch.float16 | |
| ) | |
| pipe = pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| pipe.enable_sequential_cpu_offload() | |
| _ = pipe( | |
| input_image, | |
| "anime turtle", | |
| num_inference_steps=2, | |
| output_type="np", | |
| ) | |
| mem_bytes = torch.cuda.max_memory_allocated() | |
| # make sure that less than 7 GB is allocated | |
| assert mem_bytes < 7 * 10**9 | |