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| import gc | |
| import unittest | |
| import torch | |
| from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer | |
| from diffusers import ( | |
| AutoencoderKL, | |
| DDIMScheduler, | |
| DDPMScheduler, | |
| PriorTransformer, | |
| StableUnCLIPPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer | |
| from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device | |
| from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS | |
| from ..test_pipelines_common import ( | |
| PipelineKarrasSchedulerTesterMixin, | |
| PipelineLatentTesterMixin, | |
| PipelineTesterMixin, | |
| assert_mean_pixel_difference, | |
| ) | |
| enable_full_determinism() | |
| class StableUnCLIPPipelineFastTests( | |
| PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase | |
| ): | |
| pipeline_class = StableUnCLIPPipeline | |
| params = TEXT_TO_IMAGE_PARAMS | |
| batch_params = TEXT_TO_IMAGE_BATCH_PARAMS | |
| image_params = TEXT_TO_IMAGE_IMAGE_PARAMS | |
| image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS | |
| # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false | |
| test_xformers_attention = False | |
| def get_dummy_components(self): | |
| embedder_hidden_size = 32 | |
| embedder_projection_dim = embedder_hidden_size | |
| # prior components | |
| torch.manual_seed(0) | |
| prior_tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| torch.manual_seed(0) | |
| prior_text_encoder = CLIPTextModelWithProjection( | |
| CLIPTextConfig( | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| hidden_size=embedder_hidden_size, | |
| projection_dim=embedder_projection_dim, | |
| 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) | |
| prior = PriorTransformer( | |
| num_attention_heads=2, | |
| attention_head_dim=12, | |
| embedding_dim=embedder_projection_dim, | |
| num_layers=1, | |
| ) | |
| torch.manual_seed(0) | |
| prior_scheduler = DDPMScheduler( | |
| variance_type="fixed_small_log", | |
| prediction_type="sample", | |
| num_train_timesteps=1000, | |
| clip_sample=True, | |
| clip_sample_range=5.0, | |
| beta_schedule="squaredcos_cap_v2", | |
| ) | |
| # 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 = { | |
| # prior components | |
| "prior_tokenizer": prior_tokenizer, | |
| "prior_text_encoder": prior_text_encoder, | |
| "prior": prior, | |
| "prior_scheduler": prior_scheduler, | |
| # 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): | |
| 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", | |
| "generator": generator, | |
| "num_inference_steps": 2, | |
| "prior_num_inference_steps": 2, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| # Overriding PipelineTesterMixin::test_attention_slicing_forward_pass | |
| # because UnCLIP GPU undeterminism requires a looser check. | |
| def test_attention_slicing_forward_pass(self): | |
| test_max_difference = torch_device == "cpu" | |
| self._test_attention_slicing_forward_pass(test_max_difference=test_max_difference) | |
| # Overriding PipelineTesterMixin::test_inference_batch_single_identical | |
| # because UnCLIP undeterminism requires a looser check. | |
| def test_inference_batch_single_identical(self): | |
| self._test_inference_batch_single_identical(expected_max_diff=1e-3) | |
| class StableUnCLIPPipelineIntegrationTests(unittest.TestCase): | |
| def tearDown(self): | |
| # clean up the VRAM after each test | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def test_stable_unclip(self): | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" | |
| ) | |
| pipe = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l", 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("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_pipeline_with_sequential_cpu_offloading(self): | |
| torch.cuda.empty_cache() | |
| torch.cuda.reset_max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| pipe = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l", 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( | |
| "anime turtle", | |
| prior_num_inference_steps=2, | |
| 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 | |