import contextlib import gc import inspect import io import re import tempfile import unittest from typing import Callable, Union import numpy as np import torch import diffusers from diffusers import DiffusionPipeline from diffusers.utils import logging from diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version, is_xformers_available from diffusers.utils.testing_utils import require_torch, torch_device torch.backends.cuda.matmul.allow_tf32 = False @require_torch class PipelineTesterMixin: """ This mixin is designed to be used with unittest.TestCase classes. It provides a set of common tests for each PyTorch pipeline, e.g. saving and loading the pipeline, equivalence of dict and tuple outputs, etc. """ # Canonical parameters that are passed to `__call__` regardless # of the type of pipeline. They are always optional and have common # sense default values. required_optional_params = frozenset( [ "num_inference_steps", "num_images_per_prompt", "generator", "latents", "output_type", "return_dict", "callback", "callback_steps", ] ) # set these parameters to False in the child class if the pipeline does not support the corresponding functionality test_attention_slicing = True test_cpu_offload = True test_xformers_attention = True def get_generator(self, seed): device = torch_device if torch_device != "mps" else "cpu" generator = torch.Generator(device).manual_seed(seed) return generator @property def pipeline_class(self) -> Union[Callable, DiffusionPipeline]: raise NotImplementedError( "You need to set the attribute `pipeline_class = ClassNameOfPipeline` in the child test class. " "See existing pipeline tests for reference." ) def get_dummy_components(self): raise NotImplementedError( "You need to implement `get_dummy_components(self)` in the child test class. " "See existing pipeline tests for reference." ) def get_dummy_inputs(self, device, seed=0): raise NotImplementedError( "You need to implement `get_dummy_inputs(self, device, seed)` in the child test class. " "See existing pipeline tests for reference." ) @property def params(self) -> frozenset: raise NotImplementedError( "You need to set the attribute `params` in the child test class. " "`params` are checked for if all values are present in `__call__`'s signature." " You can set `params` using one of the common set of parameters defined in`pipeline_params.py`" " e.g., `TEXT_TO_IMAGE_PARAMS` defines the common parameters used in text to " "image pipelines, including prompts and prompt embedding overrides." "If your pipeline's set of arguments has minor changes from one of the common sets of arguments, " "do not make modifications to the existing common sets of arguments. I.e. a text to image pipeline " "with non-configurable height and width arguments should set the attribute as " "`params = TEXT_TO_IMAGE_PARAMS - {'height', 'width'}`. " "See existing pipeline tests for reference." ) @property def batch_params(self) -> frozenset: raise NotImplementedError( "You need to set the attribute `batch_params` in the child test class. " "`batch_params` are the parameters required to be batched when passed to the pipeline's " "`__call__` method. `pipeline_params.py` provides some common sets of parameters such as " "`TEXT_TO_IMAGE_BATCH_PARAMS`, `IMAGE_VARIATION_BATCH_PARAMS`, etc... If your pipeline's " "set of batch arguments has minor changes from one of the common sets of batch arguments, " "do not make modifications to the existing common sets of batch arguments. I.e. a text to " "image pipeline `negative_prompt` is not batched should set the attribute as " "`batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {'negative_prompt'}`. " "See existing pipeline tests for reference." ) def tearDown(self): # clean up the VRAM after each test in case of CUDA runtime errors super().tearDown() gc.collect() torch.cuda.empty_cache() def test_save_load_local(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) # Warmup pass when using mps (see #372) if torch_device == "mps": _ = pipe(**self.get_dummy_inputs(torch_device)) inputs = self.get_dummy_inputs(torch_device) output = pipe(**inputs)[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(tmpdir) pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) pipe_loaded.to(torch_device) pipe_loaded.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) output_loaded = pipe_loaded(**inputs)[0] max_diff = np.abs(output - output_loaded).max() self.assertLess(max_diff, 1e-4) def test_pipeline_call_signature(self): self.assertTrue( hasattr(self.pipeline_class, "__call__"), f"{self.pipeline_class} should have a `__call__` method" ) parameters = inspect.signature(self.pipeline_class.__call__).parameters optional_parameters = set() for k, v in parameters.items(): if v.default != inspect._empty: optional_parameters.add(k) parameters = set(parameters.keys()) parameters.remove("self") parameters.discard("kwargs") # kwargs can be added if arguments of pipeline call function are deprecated remaining_required_parameters = set() for param in self.params: if param not in parameters: remaining_required_parameters.add(param) self.assertTrue( len(remaining_required_parameters) == 0, f"Required parameters not present: {remaining_required_parameters}", ) remaining_required_optional_parameters = set() for param in self.required_optional_params: if param not in optional_parameters: remaining_required_optional_parameters.add(param) self.assertTrue( len(remaining_required_optional_parameters) == 0, f"Required optional parameters not present: {remaining_required_optional_parameters}", ) def test_inference_batch_consistent(self): self._test_inference_batch_consistent() def _test_inference_batch_consistent( self, batch_sizes=[2, 4, 13], additional_params_copy_to_batched_inputs=["num_inference_steps"] ): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) logger = logging.get_logger(pipe.__module__) logger.setLevel(level=diffusers.logging.FATAL) # batchify inputs for batch_size in batch_sizes: batched_inputs = {} for name, value in inputs.items(): if name in self.batch_params: # prompt is string if name == "prompt": len_prompt = len(value) # make unequal batch sizes batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)] # make last batch super long batched_inputs[name][-1] = 2000 * "very long" # or else we have images else: batched_inputs[name] = batch_size * [value] elif name == "batch_size": batched_inputs[name] = batch_size else: batched_inputs[name] = value for arg in additional_params_copy_to_batched_inputs: batched_inputs[arg] = inputs[arg] batched_inputs["output_type"] = None if self.pipeline_class.__name__ == "DanceDiffusionPipeline": batched_inputs.pop("output_type") output = pipe(**batched_inputs) assert len(output[0]) == batch_size batched_inputs["output_type"] = "np" if self.pipeline_class.__name__ == "DanceDiffusionPipeline": batched_inputs.pop("output_type") output = pipe(**batched_inputs)[0] assert output.shape[0] == batch_size logger.setLevel(level=diffusers.logging.WARNING) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical() def _test_inference_batch_single_identical( self, test_max_difference=None, test_mean_pixel_difference=None, relax_max_difference=False, expected_max_diff=1e-4, additional_params_copy_to_batched_inputs=["num_inference_steps"], ): if test_max_difference is None: # TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems # make sure that batched and non-batched is identical test_max_difference = torch_device != "mps" if test_mean_pixel_difference is None: # TODO same as above test_mean_pixel_difference = torch_device != "mps" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) logger = logging.get_logger(pipe.__module__) logger.setLevel(level=diffusers.logging.FATAL) # batchify inputs batched_inputs = {} batch_size = 3 for name, value in inputs.items(): if name in self.batch_params: # prompt is string if name == "prompt": len_prompt = len(value) # make unequal batch sizes batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)] # make last batch super long batched_inputs[name][-1] = 2000 * "very long" # or else we have images else: batched_inputs[name] = batch_size * [value] elif name == "batch_size": batched_inputs[name] = batch_size elif name == "generator": batched_inputs[name] = [self.get_generator(i) for i in range(batch_size)] else: batched_inputs[name] = value for arg in additional_params_copy_to_batched_inputs: batched_inputs[arg] = inputs[arg] if self.pipeline_class.__name__ != "DanceDiffusionPipeline": batched_inputs["output_type"] = "np" output_batch = pipe(**batched_inputs) assert output_batch[0].shape[0] == batch_size inputs["generator"] = self.get_generator(0) output = pipe(**inputs) logger.setLevel(level=diffusers.logging.WARNING) if test_max_difference: if relax_max_difference: # Taking the median of the largest differences # is resilient to outliers diff = np.abs(output_batch[0][0] - output[0][0]) diff = diff.flatten() diff.sort() max_diff = np.median(diff[-5:]) else: max_diff = np.abs(output_batch[0][0] - output[0][0]).max() assert max_diff < expected_max_diff if test_mean_pixel_difference: assert_mean_pixel_difference(output_batch[0][0], output[0][0]) def test_dict_tuple_outputs_equivalent(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) # Warmup pass when using mps (see #372) if torch_device == "mps": _ = pipe(**self.get_dummy_inputs(torch_device)) output = pipe(**self.get_dummy_inputs(torch_device))[0] output_tuple = pipe(**self.get_dummy_inputs(torch_device), return_dict=False)[0] max_diff = np.abs(output - output_tuple).max() self.assertLess(max_diff, 1e-4) def test_components_function(self): init_components = self.get_dummy_components() pipe = self.pipeline_class(**init_components) self.assertTrue(hasattr(pipe, "components")) self.assertTrue(set(pipe.components.keys()) == set(init_components.keys())) @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA") def test_float16_inference(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) for name, module in components.items(): if hasattr(module, "half"): components[name] = module.half() pipe_fp16 = self.pipeline_class(**components) pipe_fp16.to(torch_device) pipe_fp16.set_progress_bar_config(disable=None) output = pipe(**self.get_dummy_inputs(torch_device))[0] output_fp16 = pipe_fp16(**self.get_dummy_inputs(torch_device))[0] max_diff = np.abs(output - output_fp16).max() self.assertLess(max_diff, 1e-2, "The outputs of the fp16 and fp32 pipelines are too different.") @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA") def test_save_load_float16(self): components = self.get_dummy_components() for name, module in components.items(): if hasattr(module, "half"): components[name] = module.to(torch_device).half() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) output = pipe(**inputs)[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(tmpdir) pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16) pipe_loaded.to(torch_device) pipe_loaded.set_progress_bar_config(disable=None) for name, component in pipe_loaded.components.items(): if hasattr(component, "dtype"): self.assertTrue( component.dtype == torch.float16, f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.", ) inputs = self.get_dummy_inputs(torch_device) output_loaded = pipe_loaded(**inputs)[0] max_diff = np.abs(output - output_loaded).max() self.assertLess(max_diff, 3e-3, "The output of the fp16 pipeline changed after saving and loading.") def test_save_load_optional_components(self): if not hasattr(self.pipeline_class, "_optional_components"): return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) # Warmup pass when using mps (see #372) if torch_device == "mps": _ = pipe(**self.get_dummy_inputs(torch_device)) # set all optional components to None for optional_component in pipe._optional_components: setattr(pipe, optional_component, None) inputs = self.get_dummy_inputs(torch_device) output = pipe(**inputs)[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(tmpdir) pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) pipe_loaded.to(torch_device) pipe_loaded.set_progress_bar_config(disable=None) for optional_component in pipe._optional_components: self.assertTrue( getattr(pipe_loaded, optional_component) is None, f"`{optional_component}` did not stay set to None after loading.", ) inputs = self.get_dummy_inputs(torch_device) output_loaded = pipe_loaded(**inputs)[0] max_diff = np.abs(output - output_loaded).max() self.assertLess(max_diff, 1e-4) @unittest.skipIf(torch_device != "cuda", reason="CUDA and CPU are required to switch devices") def test_to_device(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.set_progress_bar_config(disable=None) pipe.to("cpu") model_devices = [component.device.type for component in components.values() if hasattr(component, "device")] self.assertTrue(all(device == "cpu" for device in model_devices)) output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0] self.assertTrue(np.isnan(output_cpu).sum() == 0) pipe.to("cuda") model_devices = [component.device.type for component in components.values() if hasattr(component, "device")] self.assertTrue(all(device == "cuda" for device in model_devices)) output_cuda = pipe(**self.get_dummy_inputs("cuda"))[0] self.assertTrue(np.isnan(output_cuda).sum() == 0) def test_attention_slicing_forward_pass(self): self._test_attention_slicing_forward_pass() def _test_attention_slicing_forward_pass( self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 ): if not self.test_attention_slicing: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) # Warmup pass when using mps (see #372) if torch_device == "mps": _ = pipe(**self.get_dummy_inputs(torch_device)) inputs = self.get_dummy_inputs(torch_device) output_without_slicing = pipe(**inputs)[0] pipe.enable_attention_slicing(slice_size=1) inputs = self.get_dummy_inputs(torch_device) output_with_slicing = pipe(**inputs)[0] if test_max_difference: max_diff = np.abs(output_with_slicing - output_without_slicing).max() self.assertLess(max_diff, expected_max_diff, "Attention slicing should not affect the inference results") if test_mean_pixel_difference: assert_mean_pixel_difference(output_with_slicing[0], output_without_slicing[0]) @unittest.skipIf( torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.14.0"), reason="CPU offload is only available with CUDA and `accelerate v0.14.0` or higher", ) def test_cpu_offload_forward_pass(self): if not self.test_cpu_offload: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) output_without_offload = pipe(**inputs)[0] pipe.enable_sequential_cpu_offload() inputs = self.get_dummy_inputs(torch_device) output_with_offload = pipe(**inputs)[0] max_diff = np.abs(output_with_offload - output_without_offload).max() self.assertLess(max_diff, 1e-4, "CPU offloading should not affect the inference results") @unittest.skipIf( torch_device != "cuda" or not is_xformers_available(), reason="XFormers attention is only available with CUDA and `xformers` installed", ) def test_xformers_attention_forwardGenerator_pass(self): self._test_xformers_attention_forwardGenerator_pass() def _test_xformers_attention_forwardGenerator_pass(self, test_max_difference=True, expected_max_diff=1e-4): if not self.test_xformers_attention: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) output_without_offload = pipe(**inputs)[0] pipe.enable_xformers_memory_efficient_attention() inputs = self.get_dummy_inputs(torch_device) output_with_offload = pipe(**inputs)[0] if test_max_difference: max_diff = np.abs(output_with_offload - output_without_offload).max() self.assertLess(max_diff, expected_max_diff, "XFormers attention should not affect the inference results") assert_mean_pixel_difference(output_with_offload[0], output_without_offload[0]) def test_progress_bar(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) inputs = self.get_dummy_inputs(torch_device) with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): _ = pipe(**inputs) stderr = stderr.getvalue() # we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img, # so we just match "5" in "#####| 1/5 [00:01<00:00]" max_steps = re.search("/(.*?) ", stderr).group(1) self.assertTrue(max_steps is not None and len(max_steps) > 0) self.assertTrue( f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step" ) pipe.set_progress_bar_config(disable=True) with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): _ = pipe(**inputs) self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled") def test_num_images_per_prompt(self): sig = inspect.signature(self.pipeline_class.__call__) if "num_images_per_prompt" not in sig.parameters: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) batch_sizes = [1, 2] num_images_per_prompts = [1, 2] for batch_size in batch_sizes: for num_images_per_prompt in num_images_per_prompts: inputs = self.get_dummy_inputs(torch_device) for key in inputs.keys(): if key in self.batch_params: inputs[key] = batch_size * [inputs[key]] images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt).images assert images.shape[0] == batch_size * num_images_per_prompt # Some models (e.g. unCLIP) are extremely likely to significantly deviate depending on which hardware is used. # This helper function is used to check that the image doesn't deviate on average more than 10 pixels from a # reference image. def assert_mean_pixel_difference(image, expected_image): image = np.asarray(DiffusionPipeline.numpy_to_pil(image)[0], dtype=np.float32) expected_image = np.asarray(DiffusionPipeline.numpy_to_pil(expected_image)[0], dtype=np.float32) avg_diff = np.abs(image - expected_image).mean() assert avg_diff < 10, f"Error image deviates {avg_diff} pixels on average"