import gc import inspect import json import os import tempfile import unittest from typing import Any, Callable, Dict import numpy as np import PIL.Image import pytest import torch import torch.nn as nn import diffusers from diffusers import ( AsymmetricAutoencoderKL, AutoencoderKL, AutoencoderTiny, ConsistencyDecoderVAE, DiffusionPipeline, FasterCacheConfig, KolorsPipeline, PyramidAttentionBroadcastConfig, StableDiffusionPipeline, StableDiffusionXLPipeline, UNet2DConditionModel, apply_faster_cache, ) from diffusers.hooks import apply_group_offloading from diffusers.hooks.faster_cache import FasterCacheBlockHook, FasterCacheDenoiserHook from diffusers.hooks.first_block_cache import FirstBlockCacheConfig from diffusers.hooks.mag_cache import MagCacheConfig from diffusers.hooks.pyramid_attention_broadcast import PyramidAttentionBroadcastHook from diffusers.hooks.taylorseer_cache import TaylorSeerCacheConfig from diffusers.image_processor import VaeImageProcessor from diffusers.loaders import FluxIPAdapterMixin, IPAdapterMixin from diffusers.models.attention import AttentionModuleMixin from diffusers.models.attention_processor import AttnProcessor from diffusers.models.controlnets.controlnet_xs import UNetControlNetXSModel from diffusers.models.unets.unet_3d_condition import UNet3DConditionModel from diffusers.models.unets.unet_i2vgen_xl import I2VGenXLUNet from diffusers.models.unets.unet_motion_model import UNetMotionModel from diffusers.pipelines.pipeline_utils import StableDiffusionMixin from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import logging from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.source_code_parsing_utils import ReturnNameVisitor from ..models.autoencoders.vae import ( get_asym_autoencoder_kl_config, get_autoencoder_kl_config, get_autoencoder_tiny_config, get_consistency_vae_config, ) from ..models.transformers.test_models_transformer_flux import create_flux_ip_adapter_state_dict from ..models.unets.test_models_unet_2d_condition import ( create_ip_adapter_faceid_state_dict, create_ip_adapter_state_dict, ) from ..testing_utils import ( CaptureLogger, backend_empty_cache, numpy_cosine_similarity_distance, require_accelerate_version_greater, require_accelerator, require_torch, require_torch_accelerator, skip_mps, torch_device, ) def to_np(tensor): if isinstance(tensor, torch.Tensor): tensor = tensor.detach().cpu().numpy() return tensor def check_same_shape(tensor_list): shapes = [tensor.shape for tensor in tensor_list] return all(shape == shapes[0] for shape in shapes[1:]) def check_qkv_fusion_matches_attn_procs_length(model, original_attn_processors): current_attn_processors = model.attn_processors return len(current_attn_processors) == len(original_attn_processors) def check_qkv_fusion_processors_exist(model): current_attn_processors = model.attn_processors proc_names = [v.__class__.__name__ for _, v in current_attn_processors.items()] return all(p.startswith("Fused") for p in proc_names) def check_qkv_fused_layers_exist(model, layer_names): is_fused_submodules = [] for submodule in model.modules(): if not isinstance(submodule, AttentionModuleMixin) or not submodule._supports_qkv_fusion: continue is_fused_attribute_set = submodule.fused_projections is_fused_layer = True for layer in layer_names: is_fused_layer = is_fused_layer and getattr(submodule, layer, None) is not None is_fused = is_fused_attribute_set and is_fused_layer is_fused_submodules.append(is_fused) return all(is_fused_submodules) class SDFunctionTesterMixin: """ This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes. It provides a set of common tests for PyTorch pipeline that inherit from StableDiffusionMixin, e.g. vae_slicing, vae_tiling, freeu, etc. """ def test_vae_slicing(self, image_count=4): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() # components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config) pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) inputs["prompt"] = [inputs["prompt"]] * image_count if "image" in inputs: # fix batch size mismatch in I2V_Gen pipeline inputs["image"] = [inputs["image"]] * image_count output_1 = pipe(**inputs) # make sure sliced vae decode yields the same result pipe.vae.enable_slicing() inputs = self.get_dummy_inputs(device) inputs["prompt"] = [inputs["prompt"]] * image_count if "image" in inputs: inputs["image"] = [inputs["image"]] * image_count inputs["return_dict"] = False output_2 = pipe(**inputs) assert np.abs(output_2[0].flatten() - output_1[0].flatten()).max() < 1e-2 def test_vae_tiling(self): components = self.get_dummy_components() # make sure here that pndm scheduler skips prk if "safety_checker" in components: components["safety_checker"] = None pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) inputs["return_dict"] = False # Test that tiled decode at 512x512 yields the same result as the non-tiled decode output_1 = pipe(**inputs)[0] # make sure tiled vae decode yields the same result pipe.vae.enable_tiling() inputs = self.get_dummy_inputs(torch_device) inputs["return_dict"] = False output_2 = pipe(**inputs)[0] assert np.abs(to_np(output_2) - to_np(output_1)).max() < 5e-1 # test that tiled decode works with various shapes shapes = [(1, 4, 73, 97), (1, 4, 65, 49)] with torch.no_grad(): for shape in shapes: zeros = torch.zeros(shape).to(torch_device) pipe.vae.decode(zeros) # MPS currently doesn't support ComplexFloats, which are required for FreeU - see https://github.com/huggingface/diffusers/issues/7569. @skip_mps def test_freeu(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) # Normal inference inputs = self.get_dummy_inputs(torch_device) inputs["return_dict"] = False inputs["output_type"] = "np" output = pipe(**inputs)[0] # FreeU-enabled inference pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4) inputs = self.get_dummy_inputs(torch_device) inputs["return_dict"] = False inputs["output_type"] = "np" output_freeu = pipe(**inputs)[0] # FreeU-disabled inference pipe.disable_freeu() freeu_keys = {"s1", "s2", "b1", "b2"} for upsample_block in pipe.unet.up_blocks: for key in freeu_keys: assert getattr(upsample_block, key) is None, f"Disabling of FreeU should have set {key} to None." inputs = self.get_dummy_inputs(torch_device) inputs["return_dict"] = False inputs["output_type"] = "np" output_no_freeu = pipe(**inputs)[0] assert not np.allclose(output[0, -3:, -3:, -1], output_freeu[0, -3:, -3:, -1]), ( "Enabling of FreeU should lead to different results." ) assert np.allclose(output, output_no_freeu, atol=1e-2), ( f"Disabling of FreeU should lead to results similar to the default pipeline results but Max Abs Error={np.abs(output_no_freeu - output).max()}." ) def test_fused_qkv_projections(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) inputs["return_dict"] = False image = pipe(**inputs)[0] original_image_slice = image[0, -3:, -3:, -1] pipe.fuse_qkv_projections() for _, component in pipe.components.items(): if ( isinstance(component, nn.Module) and hasattr(component, "original_attn_processors") and component.original_attn_processors is not None ): assert check_qkv_fusion_processors_exist(component), ( "Something wrong with the fused attention processors. Expected all the attention processors to be fused." ) assert check_qkv_fusion_matches_attn_procs_length(component, component.original_attn_processors), ( "Something wrong with the attention processors concerning the fused QKV projections." ) inputs = self.get_dummy_inputs(device) inputs["return_dict"] = False image_fused = pipe(**inputs)[0] image_slice_fused = image_fused[0, -3:, -3:, -1] pipe.unfuse_qkv_projections() inputs = self.get_dummy_inputs(device) inputs["return_dict"] = False image_disabled = pipe(**inputs)[0] image_slice_disabled = image_disabled[0, -3:, -3:, -1] assert np.allclose(original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2), ( "Fusion of QKV projections shouldn't affect the outputs." ) assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2), ( "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled." ) assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), ( "Original outputs should match when fused QKV projections are disabled." ) class IPAdapterTesterMixin: """ This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes. It provides a set of common tests for pipelines that support IP Adapters. """ def test_pipeline_signature(self): parameters = inspect.signature(self.pipeline_class.__call__).parameters assert issubclass(self.pipeline_class, IPAdapterMixin) self.assertIn( "ip_adapter_image", parameters, "`ip_adapter_image` argument must be supported by the `__call__` method", ) self.assertIn( "ip_adapter_image_embeds", parameters, "`ip_adapter_image_embeds` argument must be supported by the `__call__` method", ) def _get_dummy_image_embeds(self, cross_attention_dim: int = 32): return torch.randn((2, 1, cross_attention_dim), device=torch_device) def _get_dummy_faceid_image_embeds(self, cross_attention_dim: int = 32): return torch.randn((2, 1, 1, cross_attention_dim), device=torch_device) def _get_dummy_masks(self, input_size: int = 64): _masks = torch.zeros((1, 1, input_size, input_size), device=torch_device) _masks[0, :, :, : int(input_size / 2)] = 1 return _masks def _modify_inputs_for_ip_adapter_test(self, inputs: Dict[str, Any]): parameters = inspect.signature(self.pipeline_class.__call__).parameters if "image" in parameters.keys() and "strength" in parameters.keys(): inputs["num_inference_steps"] = 4 inputs["output_type"] = "np" inputs["return_dict"] = False return inputs def test_ip_adapter(self, expected_max_diff: float = 1e-4, expected_pipe_slice=None): r"""Tests for IP-Adapter. The following scenarios are tested: - Single IP-Adapter with scale=0 should produce same output as no IP-Adapter. - Multi IP-Adapter with scale=0 should produce same output as no IP-Adapter. - Single IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter. - Multi IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter. """ # Raising the tolerance for this test when it's run on a CPU because we # compare against static slices and that can be shaky (with a VVVV low probability). expected_max_diff = 9e-4 if torch_device == "cpu" else expected_max_diff components = self.get_dummy_components() pipe = self.pipeline_class(**components).to(torch_device) pipe.set_progress_bar_config(disable=None) cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32) # forward pass without ip adapter inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) if expected_pipe_slice is None: output_without_adapter = pipe(**inputs)[0] else: output_without_adapter = expected_pipe_slice # 1. Single IP-Adapter test cases adapter_state_dict = create_ip_adapter_state_dict(pipe.unet) pipe.unet._load_ip_adapter_weights(adapter_state_dict) # forward pass with single ip adapter, but scale=0 which should have no effect inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] pipe.set_ip_adapter_scale(0.0) output_without_adapter_scale = pipe(**inputs)[0] if expected_pipe_slice is not None: output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten() # forward pass with single ip adapter, but with scale of adapter weights inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] pipe.set_ip_adapter_scale(42.0) output_with_adapter_scale = pipe(**inputs)[0] if expected_pipe_slice is not None: output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten() max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max() max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max() self.assertLess( max_diff_without_adapter_scale, expected_max_diff, "Output without ip-adapter must be same as normal inference", ) self.assertGreater( max_diff_with_adapter_scale, 1e-2, "Output with ip-adapter must be different from normal inference" ) # 2. Multi IP-Adapter test cases adapter_state_dict_1 = create_ip_adapter_state_dict(pipe.unet) adapter_state_dict_2 = create_ip_adapter_state_dict(pipe.unet) pipe.unet._load_ip_adapter_weights([adapter_state_dict_1, adapter_state_dict_2]) # forward pass with multi ip adapter, but scale=0 which should have no effect inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] * 2 pipe.set_ip_adapter_scale([0.0, 0.0]) output_without_multi_adapter_scale = pipe(**inputs)[0] if expected_pipe_slice is not None: output_without_multi_adapter_scale = output_without_multi_adapter_scale[0, -3:, -3:, -1].flatten() # forward pass with multi ip adapter, but with scale of adapter weights inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] * 2 pipe.set_ip_adapter_scale([42.0, 42.0]) output_with_multi_adapter_scale = pipe(**inputs)[0] if expected_pipe_slice is not None: output_with_multi_adapter_scale = output_with_multi_adapter_scale[0, -3:, -3:, -1].flatten() max_diff_without_multi_adapter_scale = np.abs( output_without_multi_adapter_scale - output_without_adapter ).max() max_diff_with_multi_adapter_scale = np.abs(output_with_multi_adapter_scale - output_without_adapter).max() self.assertLess( max_diff_without_multi_adapter_scale, expected_max_diff, "Output without multi-ip-adapter must be same as normal inference", ) self.assertGreater( max_diff_with_multi_adapter_scale, 1e-2, "Output with multi-ip-adapter scale must be different from normal inference", ) def test_ip_adapter_cfg(self, expected_max_diff: float = 1e-4): parameters = inspect.signature(self.pipeline_class.__call__).parameters if "guidance_scale" not in parameters: return components = self.get_dummy_components() pipe = self.pipeline_class(**components).to(torch_device) pipe.set_progress_bar_config(disable=None) cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32) adapter_state_dict = create_ip_adapter_state_dict(pipe.unet) pipe.unet._load_ip_adapter_weights(adapter_state_dict) pipe.set_ip_adapter_scale(1.0) # forward pass with CFG not applied inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)[0].unsqueeze(0)] inputs["guidance_scale"] = 1.0 out_no_cfg = pipe(**inputs)[0] # forward pass with CFG applied inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] inputs["guidance_scale"] = 7.5 out_cfg = pipe(**inputs)[0] assert out_cfg.shape == out_no_cfg.shape def test_ip_adapter_masks(self, expected_max_diff: float = 1e-4): components = self.get_dummy_components() pipe = self.pipeline_class(**components).to(torch_device) pipe.set_progress_bar_config(disable=None) cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32) sample_size = pipe.unet.config.get("sample_size", 32) block_out_channels = pipe.vae.config.get("block_out_channels", [128, 256, 512, 512]) input_size = sample_size * (2 ** (len(block_out_channels) - 1)) # forward pass without ip adapter inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) output_without_adapter = pipe(**inputs)[0] output_without_adapter = output_without_adapter[0, -3:, -3:, -1].flatten() adapter_state_dict = create_ip_adapter_state_dict(pipe.unet) pipe.unet._load_ip_adapter_weights(adapter_state_dict) # forward pass with single ip adapter and masks, but scale=0 which should have no effect inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] inputs["cross_attention_kwargs"] = {"ip_adapter_masks": [self._get_dummy_masks(input_size)]} pipe.set_ip_adapter_scale(0.0) output_without_adapter_scale = pipe(**inputs)[0] output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten() # forward pass with single ip adapter and masks, but with scale of adapter weights inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] inputs["cross_attention_kwargs"] = {"ip_adapter_masks": [self._get_dummy_masks(input_size)]} pipe.set_ip_adapter_scale(42.0) output_with_adapter_scale = pipe(**inputs)[0] output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten() max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max() max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max() self.assertLess( max_diff_without_adapter_scale, expected_max_diff, "Output without ip-adapter must be same as normal inference", ) self.assertGreater( max_diff_with_adapter_scale, 1e-3, "Output with ip-adapter must be different from normal inference" ) def test_ip_adapter_faceid(self, expected_max_diff: float = 1e-4): components = self.get_dummy_components() pipe = self.pipeline_class(**components).to(torch_device) pipe.set_progress_bar_config(disable=None) cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32) # forward pass without ip adapter inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) output_without_adapter = pipe(**inputs)[0] output_without_adapter = output_without_adapter[0, -3:, -3:, -1].flatten() adapter_state_dict = create_ip_adapter_faceid_state_dict(pipe.unet) pipe.unet._load_ip_adapter_weights(adapter_state_dict) # forward pass with single ip adapter, but scale=0 which should have no effect inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_faceid_image_embeds(cross_attention_dim)] pipe.set_ip_adapter_scale(0.0) output_without_adapter_scale = pipe(**inputs)[0] output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten() # forward pass with single ip adapter, but with scale of adapter weights inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_faceid_image_embeds(cross_attention_dim)] pipe.set_ip_adapter_scale(42.0) output_with_adapter_scale = pipe(**inputs)[0] output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten() max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max() max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max() self.assertLess( max_diff_without_adapter_scale, expected_max_diff, "Output without ip-adapter must be same as normal inference", ) self.assertGreater( max_diff_with_adapter_scale, 1e-3, "Output with ip-adapter must be different from normal inference" ) class FluxIPAdapterTesterMixin: """ This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes. It provides a set of common tests for pipelines that support IP Adapters. """ def test_pipeline_signature(self): parameters = inspect.signature(self.pipeline_class.__call__).parameters assert issubclass(self.pipeline_class, FluxIPAdapterMixin) self.assertIn( "ip_adapter_image", parameters, "`ip_adapter_image` argument must be supported by the `__call__` method", ) self.assertIn( "ip_adapter_image_embeds", parameters, "`ip_adapter_image_embeds` argument must be supported by the `__call__` method", ) def _get_dummy_image_embeds(self, image_embed_dim: int = 768): return torch.randn((1, 1, image_embed_dim), device=torch_device) def _modify_inputs_for_ip_adapter_test(self, inputs: Dict[str, Any]): inputs["negative_prompt"] = "" if "true_cfg_scale" in inspect.signature(self.pipeline_class.__call__).parameters: inputs["true_cfg_scale"] = 4.0 inputs["output_type"] = "np" inputs["return_dict"] = False return inputs def test_ip_adapter(self, expected_max_diff: float = 1e-4, expected_pipe_slice=None): r"""Tests for IP-Adapter. The following scenarios are tested: - Single IP-Adapter with scale=0 should produce same output as no IP-Adapter. - Multi IP-Adapter with scale=0 should produce same output as no IP-Adapter. - Single IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter. - Multi IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter. """ # Raising the tolerance for this test when it's run on a CPU because we # compare against static slices and that can be shaky (with a VVVV low probability). expected_max_diff = 9e-4 if torch_device == "cpu" else expected_max_diff components = self.get_dummy_components() pipe = self.pipeline_class(**components).to(torch_device) pipe.set_progress_bar_config(disable=None) image_embed_dim = ( pipe.transformer.config.pooled_projection_dim if hasattr(pipe.transformer.config, "pooled_projection_dim") else 768 ) # forward pass without ip adapter inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) if expected_pipe_slice is None: output_without_adapter = pipe(**inputs)[0] else: output_without_adapter = expected_pipe_slice # 1. Single IP-Adapter test cases adapter_state_dict = create_flux_ip_adapter_state_dict(pipe.transformer) pipe.transformer._load_ip_adapter_weights(adapter_state_dict) # forward pass with single ip adapter, but scale=0 which should have no effect inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] inputs["negative_ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] pipe.set_ip_adapter_scale(0.0) output_without_adapter_scale = pipe(**inputs)[0] if expected_pipe_slice is not None: output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten() # forward pass with single ip adapter, but with scale of adapter weights inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] inputs["negative_ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] pipe.set_ip_adapter_scale(42.0) output_with_adapter_scale = pipe(**inputs)[0] if expected_pipe_slice is not None: output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten() max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max() max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max() self.assertLess( max_diff_without_adapter_scale, expected_max_diff, "Output without ip-adapter must be same as normal inference", ) self.assertGreater( max_diff_with_adapter_scale, 1e-2, "Output with ip-adapter must be different from normal inference" ) # 2. Multi IP-Adapter test cases adapter_state_dict_1 = create_flux_ip_adapter_state_dict(pipe.transformer) adapter_state_dict_2 = create_flux_ip_adapter_state_dict(pipe.transformer) pipe.transformer._load_ip_adapter_weights([adapter_state_dict_1, adapter_state_dict_2]) # forward pass with multi ip adapter, but scale=0 which should have no effect inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] * 2 inputs["negative_ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] * 2 pipe.set_ip_adapter_scale([0.0, 0.0]) output_without_multi_adapter_scale = pipe(**inputs)[0] if expected_pipe_slice is not None: output_without_multi_adapter_scale = output_without_multi_adapter_scale[0, -3:, -3:, -1].flatten() # forward pass with multi ip adapter, but with scale of adapter weights inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] * 2 inputs["negative_ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] * 2 pipe.set_ip_adapter_scale([42.0, 42.0]) output_with_multi_adapter_scale = pipe(**inputs)[0] if expected_pipe_slice is not None: output_with_multi_adapter_scale = output_with_multi_adapter_scale[0, -3:, -3:, -1].flatten() max_diff_without_multi_adapter_scale = np.abs( output_without_multi_adapter_scale - output_without_adapter ).max() max_diff_with_multi_adapter_scale = np.abs(output_with_multi_adapter_scale - output_without_adapter).max() self.assertLess( max_diff_without_multi_adapter_scale, expected_max_diff, "Output without multi-ip-adapter must be same as normal inference", ) self.assertGreater( max_diff_with_multi_adapter_scale, 1e-2, "Output with multi-ip-adapter scale must be different from normal inference", ) class PipelineLatentTesterMixin: """ This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes. It provides a set of common tests for PyTorch pipeline that has vae, e.g. equivalence of different input and output types, etc. """ @property def image_params(self) -> frozenset: raise NotImplementedError( "You need to set the attribute `image_params` in the child test class. " "`image_params` are tested for if all accepted input image types (i.e. `pt`,`pil`,`np`) are producing same results" ) @property def image_latents_params(self) -> frozenset: raise NotImplementedError( "You need to set the attribute `image_latents_params` in the child test class. " "`image_latents_params` are tested for if passing latents directly are producing same results" ) def get_dummy_inputs_by_type(self, device, seed=0, input_image_type="pt", output_type="np"): inputs = self.get_dummy_inputs(device, seed) def convert_to_pt(image): if isinstance(image, torch.Tensor): input_image = image elif isinstance(image, np.ndarray): input_image = VaeImageProcessor.numpy_to_pt(image) elif isinstance(image, PIL.Image.Image): input_image = VaeImageProcessor.pil_to_numpy(image) input_image = VaeImageProcessor.numpy_to_pt(input_image) else: raise ValueError(f"unsupported input_image_type {type(image)}") return input_image def convert_pt_to_type(image, input_image_type): if input_image_type == "pt": input_image = image elif input_image_type == "np": input_image = VaeImageProcessor.pt_to_numpy(image) elif input_image_type == "pil": input_image = VaeImageProcessor.pt_to_numpy(image) input_image = VaeImageProcessor.numpy_to_pil(input_image) else: raise ValueError(f"unsupported input_image_type {input_image_type}.") return input_image for image_param in self.image_params: if image_param in inputs.keys(): inputs[image_param] = convert_pt_to_type( convert_to_pt(inputs[image_param]).to(device), input_image_type ) inputs["output_type"] = output_type return inputs def test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4): self._test_pt_np_pil_outputs_equivalent(expected_max_diff=expected_max_diff) def _test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4, input_image_type="pt"): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) output_pt = pipe( **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="pt") )[0] output_np = pipe( **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="np") )[0] output_pil = pipe( **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="pil") )[0] max_diff = np.abs(output_pt.cpu().numpy().transpose(0, 2, 3, 1) - output_np).max() self.assertLess( max_diff, expected_max_diff, "`output_type=='pt'` generate different results from `output_type=='np'`" ) max_diff = np.abs(np.array(output_pil[0]) - (output_np * 255).round()).max() self.assertLess(max_diff, 2.0, "`output_type=='pil'` generate different results from `output_type=='np'`") def test_pt_np_pil_inputs_equivalent(self): if len(self.image_params) == 0: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) out_input_pt = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0] out_input_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0] out_input_pil = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pil"))[0] max_diff = np.abs(out_input_pt - out_input_np).max() self.assertLess(max_diff, 1e-4, "`input_type=='pt'` generate different result from `input_type=='np'`") max_diff = np.abs(out_input_pil - out_input_np).max() self.assertLess(max_diff, 1e-2, "`input_type=='pt'` generate different result from `input_type=='np'`") def test_latents_input(self): if len(self.image_latents_params) == 0: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0] vae = components["vae"] inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type="pt") generator = inputs["generator"] for image_param in self.image_latents_params: if image_param in inputs.keys(): inputs[image_param] = ( vae.encode(inputs[image_param]).latent_dist.sample(generator) * vae.config.scaling_factor ) out_latents_inputs = pipe(**inputs)[0] max_diff = np.abs(out - out_latents_inputs).max() self.assertLess(max_diff, 1e-4, "passing latents as image input generate different result from passing image") def test_multi_vae(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) block_out_channels = pipe.vae.config.block_out_channels norm_num_groups = pipe.vae.config.norm_num_groups vae_classes = [AutoencoderKL, AsymmetricAutoencoderKL, ConsistencyDecoderVAE, AutoencoderTiny] configs = [ get_autoencoder_kl_config(block_out_channels, norm_num_groups), get_asym_autoencoder_kl_config(block_out_channels, norm_num_groups), get_consistency_vae_config(block_out_channels, norm_num_groups), get_autoencoder_tiny_config(block_out_channels), ] out_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0] for vae_cls, config in zip(vae_classes, configs): vae = vae_cls(**config) vae = vae.to(torch_device) components["vae"] = vae vae_pipe = self.pipeline_class(**components) out_vae_np = vae_pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0] assert out_vae_np.shape == out_np.shape @require_torch class PipelineFromPipeTesterMixin: @property def original_pipeline_class(self): if "xl" in self.pipeline_class.__name__.lower(): original_pipeline_class = StableDiffusionXLPipeline elif "kolors" in self.pipeline_class.__name__.lower(): original_pipeline_class = KolorsPipeline else: original_pipeline_class = StableDiffusionPipeline return original_pipeline_class def get_dummy_inputs_pipe(self, device, seed=0): inputs = self.get_dummy_inputs(device, seed=seed) inputs["output_type"] = "np" inputs["return_dict"] = False return inputs def get_dummy_inputs_for_pipe_original(self, device, seed=0): inputs = {} for k, v in self.get_dummy_inputs_pipe(device, seed=seed).items(): if k in set(inspect.signature(self.original_pipeline_class.__call__).parameters.keys()): inputs[k] = v return inputs def test_from_pipe_consistent_config(self): if self.original_pipeline_class == StableDiffusionPipeline: original_repo = "hf-internal-testing/tiny-stable-diffusion-torch" original_kwargs = {"requires_safety_checker": False} elif self.original_pipeline_class == StableDiffusionXLPipeline: original_repo = "hf-internal-testing/tiny-stable-diffusion-xl-pipe" original_kwargs = {"requires_aesthetics_score": True, "force_zeros_for_empty_prompt": False} elif self.original_pipeline_class == KolorsPipeline: original_repo = "hf-internal-testing/tiny-kolors-pipe" original_kwargs = {"force_zeros_for_empty_prompt": False} else: raise ValueError( "original_pipeline_class must be either StableDiffusionPipeline or StableDiffusionXLPipeline" ) # create original_pipeline_class(sd/sdxl) pipe_original = self.original_pipeline_class.from_pretrained(original_repo, **original_kwargs) # original_pipeline_class(sd/sdxl) -> pipeline_class pipe_components = self.get_dummy_components() pipe_additional_components = {} for name, component in pipe_components.items(): if name not in pipe_original.components: pipe_additional_components[name] = component pipe = self.pipeline_class.from_pipe(pipe_original, **pipe_additional_components) # pipeline_class -> original_pipeline_class(sd/sdxl) original_pipe_additional_components = {} for name, component in pipe_original.components.items(): if name not in pipe.components or not isinstance(component, pipe.components[name].__class__): original_pipe_additional_components[name] = component pipe_original_2 = self.original_pipeline_class.from_pipe(pipe, **original_pipe_additional_components) # compare the config original_config = {k: v for k, v in pipe_original.config.items() if not k.startswith("_")} original_config_2 = {k: v for k, v in pipe_original_2.config.items() if not k.startswith("_")} assert original_config_2 == original_config def test_from_pipe_consistent_forward_pass(self, expected_max_diff=1e-3): components = self.get_dummy_components() original_expected_modules, _ = self.original_pipeline_class._get_signature_keys(self.original_pipeline_class) # pipeline components that are also expected to be in the original pipeline original_pipe_components = {} # additional components that are not in the pipeline, but expected in the original pipeline original_pipe_additional_components = {} # additional components that are in the pipeline, but not expected in the original pipeline current_pipe_additional_components = {} for name, component in components.items(): if name in original_expected_modules: original_pipe_components[name] = component else: current_pipe_additional_components[name] = component for name in original_expected_modules: if name not in original_pipe_components: if name in self.original_pipeline_class._optional_components: original_pipe_additional_components[name] = None else: raise ValueError(f"missing required module for {self.original_pipeline_class.__class__}: {name}") pipe_original = self.original_pipeline_class(**original_pipe_components, **original_pipe_additional_components) for component in pipe_original.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe_original.to(torch_device) pipe_original.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs_for_pipe_original(torch_device) output_original = pipe_original(**inputs)[0] pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs_pipe(torch_device) output = pipe(**inputs)[0] pipe_from_original = self.pipeline_class.from_pipe(pipe_original, **current_pipe_additional_components) pipe_from_original.to(torch_device) pipe_from_original.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs_pipe(torch_device) output_from_original = pipe_from_original(**inputs)[0] max_diff = np.abs(to_np(output) - to_np(output_from_original)).max() self.assertLess( max_diff, expected_max_diff, "The outputs of the pipelines created with `from_pipe` and `__init__` are different.", ) inputs = self.get_dummy_inputs_for_pipe_original(torch_device) output_original_2 = pipe_original(**inputs)[0] max_diff = np.abs(to_np(output_original) - to_np(output_original_2)).max() self.assertLess(max_diff, expected_max_diff, "`from_pipe` should not change the output of original pipeline.") for component in pipe_original.components.values(): if hasattr(component, "attn_processors"): assert all(type(proc) == AttnProcessor for proc in component.attn_processors.values()), ( "`from_pipe` changed the attention processor in original pipeline." ) @require_accelerator @require_accelerate_version_greater("0.14.0") def test_from_pipe_consistent_forward_pass_cpu_offload(self, expected_max_diff=1e-3): components = self.get_dummy_components() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.enable_model_cpu_offload(device=torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs_pipe(torch_device) output = pipe(**inputs)[0] original_expected_modules, _ = self.original_pipeline_class._get_signature_keys(self.original_pipeline_class) # pipeline components that are also expected to be in the original pipeline original_pipe_components = {} # additional components that are not in the pipeline, but expected in the original pipeline original_pipe_additional_components = {} # additional components that are in the pipeline, but not expected in the original pipeline current_pipe_additional_components = {} for name, component in components.items(): if name in original_expected_modules: original_pipe_components[name] = component else: current_pipe_additional_components[name] = component for name in original_expected_modules: if name not in original_pipe_components: if name in self.original_pipeline_class._optional_components: original_pipe_additional_components[name] = None else: raise ValueError(f"missing required module for {self.original_pipeline_class.__class__}: {name}") pipe_original = self.original_pipeline_class(**original_pipe_components, **original_pipe_additional_components) for component in pipe_original.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe_original.set_progress_bar_config(disable=None) pipe_from_original = self.pipeline_class.from_pipe(pipe_original, **current_pipe_additional_components) for component in pipe_from_original.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe_from_original.enable_model_cpu_offload(device=torch_device) pipe_from_original.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs_pipe(torch_device) output_from_original = pipe_from_original(**inputs)[0] max_diff = np.abs(to_np(output) - to_np(output_from_original)).max() self.assertLess( max_diff, expected_max_diff, "The outputs of the pipelines created with `from_pipe` and `__init__` are different.", ) @require_torch class PipelineKarrasSchedulerTesterMixin: """ This mixin is designed to be used with unittest.TestCase classes. It provides a set of common tests for each PyTorch pipeline that makes use of KarrasDiffusionSchedulers equivalence of dict and tuple outputs, etc. """ def test_karras_schedulers_shape( self, num_inference_steps_for_strength=4, num_inference_steps_for_strength_for_iterations=5 ): components = self.get_dummy_components() pipe = self.pipeline_class(**components) # make sure that PNDM does not need warm-up pipe.scheduler.register_to_config(skip_prk_steps=True) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = 2 if "strength" in inputs: inputs["num_inference_steps"] = num_inference_steps_for_strength inputs["strength"] = 0.5 outputs = [] for scheduler_enum in KarrasDiffusionSchedulers: if "KDPM2" in scheduler_enum.name: inputs["num_inference_steps"] = num_inference_steps_for_strength_for_iterations scheduler_cls = getattr(diffusers, scheduler_enum.name) pipe.scheduler = scheduler_cls.from_config(pipe.scheduler.config) output = pipe(**inputs)[0] outputs.append(output) if "KDPM2" in scheduler_enum.name: inputs["num_inference_steps"] = 2 assert check_same_shape(outputs) @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", ] ) # set these parameters to False in the child class if the pipeline does not support the corresponding functionality test_attention_slicing = True test_xformers_attention = True test_layerwise_casting = False test_group_offloading = False 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) -> 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." ) @property def callback_cfg_params(self) -> frozenset: raise NotImplementedError( "You need to set the attribute `callback_cfg_params` in the child test class that requires to run test_callback_cfg. " "`callback_cfg_params` are the parameters that needs to be passed to the pipeline's callback " "function when dynamically adjusting `guidance_scale`. They are variables that require special" "treatment when `do_classifier_free_guidance` is `True`. `pipeline_params.py` provides some common" " sets of parameters such as `TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS`. If your pipeline's " "set of cfg arguments has minor changes from one of the common sets of cfg arguments, " "do not make modifications to the existing common sets of cfg arguments. I.e. for inpaint pipeline, you " " need to adjust batch size of `mask` and `masked_image_latents` so should set the attribute as" "`callback_cfg_params = TEXT_TO_IMAGE_CFG_PARAMS.union({'mask', 'masked_image_latents'})`" ) def setUp(self): # clean up the VRAM before each test super().setUp() torch.compiler.reset() gc.collect() backend_empty_cache(torch_device) # Skip tests for pipelines that inherit from DeprecatedPipelineMixin from diffusers.pipelines.pipeline_utils import DeprecatedPipelineMixin if hasattr(self, "pipeline_class") and issubclass(self.pipeline_class, DeprecatedPipelineMixin): import pytest pytest.skip(reason=f"Deprecated Pipeline: {self.pipeline_class.__name__}") def tearDown(self): # clean up the VRAM after each test in case of CUDA runtime errors super().tearDown() torch.compiler.reset() gc.collect() backend_empty_cache(torch_device) def test_save_load_local(self, expected_max_difference=5e-4): components = self.get_dummy_components() for key in components: if "text_encoder" in key and hasattr(components[key], "eval"): components[key].eval() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) output = pipe(**inputs)[0] logger = logging.get_logger("diffusers.pipelines.pipeline_utils") logger.setLevel(diffusers.logging.INFO) with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(tmpdir, safe_serialization=False) with CaptureLogger(logger) as cap_logger: pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) for component in pipe_loaded.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() for name in pipe_loaded.components.keys(): if name not in pipe_loaded._optional_components: assert name in str(cap_logger) 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(to_np(output) - to_np(output_loaded)).max() self.assertLess(max_diff, expected_max_difference) 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, batch_sizes=[2]): self._test_inference_batch_consistent(batch_sizes=batch_sizes) def _test_inference_batch_consistent( self, batch_sizes=[2], additional_params_copy_to_batched_inputs=["num_inference_steps"], batch_generator=True ): 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) inputs["generator"] = self.get_generator(0) logger = logging.get_logger(pipe.__module__) logger.setLevel(level=diffusers.logging.FATAL) # prepare batched inputs batched_inputs = [] for batch_size in batch_sizes: batched_input = {} batched_input.update(inputs) for name in self.batch_params: if name not in inputs: continue value = inputs[name] if name == "prompt": len_prompt = len(value) # make unequal batch sizes batched_input[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)] # make last batch super long batched_input[name][-1] = 100 * "very long" else: batched_input[name] = batch_size * [value] if batch_generator and "generator" in inputs: batched_input["generator"] = [self.get_generator(i) for i in range(batch_size)] if "batch_size" in inputs: batched_input["batch_size"] = batch_size batched_inputs.append(batched_input) logger.setLevel(level=diffusers.logging.WARNING) for batch_size, batched_input in zip(batch_sizes, batched_inputs): output = pipe(**batched_input) assert len(output[0]) == batch_size def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=1e-4): self._test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff) def _test_inference_batch_single_identical( self, batch_size=2, expected_max_diff=1e-4, additional_params_copy_to_batched_inputs=["num_inference_steps"], ): components = self.get_dummy_components() for key in components: if "text_encoder" in key and hasattr(components[key], "eval"): components[key].eval() pipe = self.pipeline_class(**components) for components in pipe.components.values(): if hasattr(components, "set_default_attn_processor"): components.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) # Reset generator in case it is has been used in self.get_dummy_inputs inputs["generator"] = self.get_generator(0) logger = logging.get_logger(pipe.__module__) logger.setLevel(level=diffusers.logging.FATAL) # batchify inputs batched_inputs = {} batched_inputs.update(inputs) for name in self.batch_params: if name not in inputs: continue value = inputs[name] if name == "prompt": len_prompt = len(value) batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)] batched_inputs[name][-1] = 100 * "very long" else: batched_inputs[name] = batch_size * [value] if "generator" in inputs: batched_inputs["generator"] = [self.get_generator(i) for i in range(batch_size)] if "batch_size" in inputs: batched_inputs["batch_size"] = batch_size for arg in additional_params_copy_to_batched_inputs: batched_inputs[arg] = inputs[arg] output = pipe(**inputs) output_batch = pipe(**batched_inputs) assert output_batch[0].shape[0] == batch_size max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max() assert max_diff < expected_max_diff def test_dict_tuple_outputs_equivalent(self, expected_slice=None, expected_max_difference=1e-4): components = self.get_dummy_components() for key in components: if "text_encoder" in key and hasattr(components[key], "eval"): components[key].eval() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) generator_device = "cpu" if expected_slice is None: output = pipe(**self.get_dummy_inputs(generator_device))[0] else: output = expected_slice output_tuple = pipe(**self.get_dummy_inputs(generator_device), return_dict=False)[0] if expected_slice is None: max_diff = np.abs(to_np(output) - to_np(output_tuple)).max() else: if output_tuple.ndim != 5: max_diff = np.abs(to_np(output) - to_np(output_tuple)[0, -3:, -3:, -1].flatten()).max() else: max_diff = np.abs(to_np(output) - to_np(output_tuple)[0, -3:, -3:, -1, -1].flatten()).max() self.assertLess(max_diff, expected_max_difference) def test_components_function(self): init_components = self.get_dummy_components() init_components = {k: v for k, v in init_components.items() if not isinstance(v, (str, int, float))} 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 not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU") @require_accelerator def test_float16_inference(self, expected_max_diff=5e-2): components = self.get_dummy_components() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) components = self.get_dummy_components() pipe_fp16 = self.pipeline_class(**components) for component in pipe_fp16.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe_fp16.to(torch_device, torch.float16) pipe_fp16.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) # Reset generator in case it is used inside dummy inputs if "generator" in inputs: inputs["generator"] = self.get_generator(0) output = pipe(**inputs)[0] fp16_inputs = self.get_dummy_inputs(torch_device) # Reset generator in case it is used inside dummy inputs if "generator" in fp16_inputs: fp16_inputs["generator"] = self.get_generator(0) output_fp16 = pipe_fp16(**fp16_inputs)[0] if isinstance(output, torch.Tensor): output = output.cpu() output_fp16 = output_fp16.cpu() max_diff = numpy_cosine_similarity_distance(output.flatten(), output_fp16.flatten()) assert max_diff < expected_max_diff @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU") @require_accelerator def test_save_load_float16(self, expected_max_diff=1e-2): components = self.get_dummy_components() for name, module in components.items(): # Account for components with _keep_in_fp32_modules if hasattr(module, "_keep_in_fp32_modules") and module._keep_in_fp32_modules is not None: for name, param in module.named_parameters(): if any( module_to_keep_in_fp32 in name.split(".") for module_to_keep_in_fp32 in module._keep_in_fp32_modules ): param.data = param.data.to(torch_device).to(torch.float32) else: param.data = param.data.to(torch_device).to(torch.float16) for name, buf in module.named_buffers(): if not buf.is_floating_point(): buf.data = buf.data.to(torch_device) elif any( module_to_keep_in_fp32 in name.split(".") for module_to_keep_in_fp32 in module._keep_in_fp32_modules ): buf.data = buf.data.to(torch_device).to(torch.float32) else: buf.data = buf.data.to(torch_device).to(torch.float16) elif hasattr(module, "half"): components[name] = module.to(torch_device).half() for key, component in components.items(): if hasattr(component, "eval"): component.eval() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() 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, dtype=torch.float16) for component in pipe_loaded.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() 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(to_np(output) - to_np(output_loaded)).max() self.assertLess( max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading." ) def test_save_load_optional_components(self, expected_max_difference=1e-4): if not hasattr(self.pipeline_class, "_optional_components"): return if not self.pipeline_class._optional_components: return components = self.get_dummy_components() for key in components: if "text_encoder" in key and hasattr(components[key], "eval"): components[key].eval() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) # set all optional components to None for optional_component in pipe._optional_components: setattr(pipe, optional_component, None) generator_device = "cpu" inputs = self.get_dummy_inputs(generator_device) torch.manual_seed(0) output = pipe(**inputs)[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(tmpdir, safe_serialization=False) pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) for component in pipe_loaded.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() 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(generator_device) torch.manual_seed(0) output_loaded = pipe_loaded(**inputs)[0] max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() self.assertLess(max_diff, expected_max_difference) @require_accelerator 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 getattr(component, "device", None) ] 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(torch_device) model_devices = [ component.device.type for component in components.values() if getattr(component, "device", None) ] self.assertTrue(all(device == torch_device for device in model_devices)) output_device = pipe(**self.get_dummy_inputs(torch_device))[0] self.assertTrue(np.isnan(to_np(output_device)).sum() == 0) def test_to_dtype(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.set_progress_bar_config(disable=None) model_dtypes = [component.dtype for component in components.values() if getattr(component, "dtype", None)] self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes)) pipe.to(dtype=torch.float16) model_dtypes = [component.dtype for component in components.values() if getattr(component, "dtype", None)] self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes)) def test_attention_slicing_forward_pass(self, expected_max_diff=1e-3): self._test_attention_slicing_forward_pass(expected_max_diff=expected_max_diff) 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() for key in components: if "text_encoder" in key and hasattr(components[key], "eval"): components[key].eval() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) generator_device = "cpu" inputs = self.get_dummy_inputs(generator_device) output_without_slicing = pipe(**inputs)[0] pipe.enable_attention_slicing(slice_size=1) inputs = self.get_dummy_inputs(generator_device) output_with_slicing1 = pipe(**inputs)[0] pipe.enable_attention_slicing(slice_size=2) inputs = self.get_dummy_inputs(generator_device) output_with_slicing2 = pipe(**inputs)[0] if test_max_difference: max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max() max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max() self.assertLess( max(max_diff1, max_diff2), expected_max_diff, "Attention slicing should not affect the inference results", ) if test_mean_pixel_difference: assert_mean_pixel_difference(to_np(output_with_slicing1[0]), to_np(output_without_slicing[0])) assert_mean_pixel_difference(to_np(output_with_slicing2[0]), to_np(output_without_slicing[0])) @require_accelerator @require_accelerate_version_greater("0.14.0") def test_sequential_cpu_offload_forward_pass(self, expected_max_diff=1e-4): import accelerate components = self.get_dummy_components() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) generator_device = "cpu" inputs = self.get_dummy_inputs(generator_device) torch.manual_seed(0) output_without_offload = pipe(**inputs)[0] pipe.enable_sequential_cpu_offload(device=torch_device) assert pipe._execution_device.type == torch_device inputs = self.get_dummy_inputs(generator_device) torch.manual_seed(0) output_with_offload = pipe(**inputs)[0] max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results") # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly offloaded_modules = { k: v for k, v in pipe.components.items() if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload } # 1. all offloaded modules should be saved to cpu and moved to meta device self.assertTrue( all(v.device.type == "meta" for v in offloaded_modules.values()), f"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'meta']}", ) # 2. all offloaded modules should have hook installed self.assertTrue( all(hasattr(v, "_hf_hook") for k, v in offloaded_modules.items()), f"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}", ) # 3. all offloaded modules should have correct hooks installed, should be either one of these two # - `AlignDevicesHook` # - a SequentialHook` that contains `AlignDevicesHook` offloaded_modules_with_incorrect_hooks = {} for k, v in offloaded_modules.items(): if hasattr(v, "_hf_hook"): if isinstance(v._hf_hook, accelerate.hooks.SequentialHook): # if it is a `SequentialHook`, we loop through its `hooks` attribute to check if it only contains `AlignDevicesHook` for hook in v._hf_hook.hooks: if not isinstance(hook, accelerate.hooks.AlignDevicesHook): offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook.hooks[0]) elif not isinstance(v._hf_hook, accelerate.hooks.AlignDevicesHook): offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook) self.assertTrue( len(offloaded_modules_with_incorrect_hooks) == 0, f"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}", ) @require_accelerator @require_accelerate_version_greater("0.17.0") def test_model_cpu_offload_forward_pass(self, expected_max_diff=2e-4): import accelerate generator_device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(generator_device) torch.manual_seed(0) output_without_offload = pipe(**inputs)[0] pipe.enable_model_cpu_offload(device=torch_device) assert pipe._execution_device.type == torch_device inputs = self.get_dummy_inputs(generator_device) torch.manual_seed(0) output_with_offload = pipe(**inputs)[0] max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results") # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly offloaded_modules = { k: v for k, v in pipe.components.items() if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload } # 1. check if all offloaded modules are saved to cpu self.assertTrue( all(v.device.type == "cpu" for v in offloaded_modules.values()), f"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'cpu']}", ) # 2. check if all offloaded modules have hooks installed self.assertTrue( all(hasattr(v, "_hf_hook") for k, v in offloaded_modules.items()), f"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}", ) # 3. check if all offloaded modules have correct type of hooks installed, should be `CpuOffload` offloaded_modules_with_incorrect_hooks = {} for k, v in offloaded_modules.items(): if hasattr(v, "_hf_hook") and not isinstance(v._hf_hook, accelerate.hooks.CpuOffload): offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook) self.assertTrue( len(offloaded_modules_with_incorrect_hooks) == 0, f"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}", ) @require_accelerator @require_accelerate_version_greater("0.17.0") def test_cpu_offload_forward_pass_twice(self, expected_max_diff=2e-4): import accelerate generator_device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.set_progress_bar_config(disable=None) pipe.enable_model_cpu_offload() inputs = self.get_dummy_inputs(generator_device) output_with_offload = pipe(**inputs)[0] pipe.enable_model_cpu_offload() inputs = self.get_dummy_inputs(generator_device) output_with_offload_twice = pipe(**inputs)[0] max_diff = np.abs(to_np(output_with_offload) - to_np(output_with_offload_twice)).max() self.assertLess( max_diff, expected_max_diff, "running CPU offloading 2nd time should not affect the inference results" ) # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly offloaded_modules = { k: v for k, v in pipe.components.items() if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload } # 1. check if all offloaded modules are saved to cpu self.assertTrue( all(v.device.type == "cpu" for v in offloaded_modules.values()), f"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'cpu']}", ) # 2. check if all offloaded modules have hooks installed self.assertTrue( all(hasattr(v, "_hf_hook") for k, v in offloaded_modules.items()), f"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}", ) # 3. check if all offloaded modules have correct type of hooks installed, should be `CpuOffload` offloaded_modules_with_incorrect_hooks = {} for k, v in offloaded_modules.items(): if hasattr(v, "_hf_hook") and not isinstance(v._hf_hook, accelerate.hooks.CpuOffload): offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook) self.assertTrue( len(offloaded_modules_with_incorrect_hooks) == 0, f"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}", ) @require_accelerator @require_accelerate_version_greater("0.14.0") def test_sequential_offload_forward_pass_twice(self, expected_max_diff=2e-4): import accelerate generator_device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.set_progress_bar_config(disable=None) pipe.enable_sequential_cpu_offload(device=torch_device) inputs = self.get_dummy_inputs(generator_device) output_with_offload = pipe(**inputs)[0] pipe.enable_sequential_cpu_offload(device=torch_device) inputs = self.get_dummy_inputs(generator_device) output_with_offload_twice = pipe(**inputs)[0] max_diff = np.abs(to_np(output_with_offload) - to_np(output_with_offload_twice)).max() self.assertLess( max_diff, expected_max_diff, "running sequential offloading second time should have the inference results" ) # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly offloaded_modules = { k: v for k, v in pipe.components.items() if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload } # 1. check if all offloaded modules are moved to meta device self.assertTrue( all(v.device.type == "meta" for v in offloaded_modules.values()), f"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'meta']}", ) # 2. check if all offloaded modules have hook installed self.assertTrue( all(hasattr(v, "_hf_hook") for k, v in offloaded_modules.items()), f"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}", ) # 3. check if all offloaded modules have correct hooks installed, should be either one of these two # - `AlignDevicesHook` # - a SequentialHook` that contains `AlignDevicesHook` offloaded_modules_with_incorrect_hooks = {} for k, v in offloaded_modules.items(): if hasattr(v, "_hf_hook"): if isinstance(v._hf_hook, accelerate.hooks.SequentialHook): # if it is a `SequentialHook`, we loop through its `hooks` attribute to check if it only contains `AlignDevicesHook` for hook in v._hf_hook.hooks: if not isinstance(hook, accelerate.hooks.AlignDevicesHook): offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook.hooks[0]) elif not isinstance(v._hf_hook, accelerate.hooks.AlignDevicesHook): offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook) self.assertTrue( len(offloaded_modules_with_incorrect_hooks) == 0, f"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}", ) @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, test_mean_pixel_difference=True, expected_max_diff=1e-4 ): if not self.test_xformers_attention: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) for component in pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) output_without_offload = pipe(**inputs)[0] output_without_offload = ( output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload ) pipe.enable_xformers_memory_efficient_attention() inputs = self.get_dummy_inputs(torch_device) output_with_offload = pipe(**inputs)[0] output_with_offload = ( output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload ) if test_max_difference: max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() self.assertLess(max_diff, expected_max_diff, "XFormers attention should not affect the inference results") if test_mean_pixel_difference: assert_mean_pixel_difference(output_with_offload[0], output_without_offload[0]) 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)[0] assert images.shape[0] == batch_size * num_images_per_prompt def test_cfg(self): sig = inspect.signature(self.pipeline_class.__call__) if "guidance_scale" 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) inputs = self.get_dummy_inputs(torch_device) inputs["guidance_scale"] = 1.0 out_no_cfg = pipe(**inputs)[0] inputs["guidance_scale"] = 7.5 out_cfg = pipe(**inputs)[0] assert out_cfg.shape == out_no_cfg.shape def test_callback_inputs(self): sig = inspect.signature(self.pipeline_class.__call__) has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters has_callback_step_end = "callback_on_step_end" in sig.parameters if not (has_callback_tensor_inputs and has_callback_step_end): return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) self.assertTrue( hasattr(pipe, "_callback_tensor_inputs"), f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs", ) def callback_inputs_subset(pipe, i, t, callback_kwargs): # iterate over callback args for tensor_name, tensor_value in callback_kwargs.items(): # check that we're only passing in allowed tensor inputs assert tensor_name in pipe._callback_tensor_inputs return callback_kwargs def callback_inputs_all(pipe, i, t, callback_kwargs): for tensor_name in pipe._callback_tensor_inputs: assert tensor_name in callback_kwargs # iterate over callback args for tensor_name, tensor_value in callback_kwargs.items(): # check that we're only passing in allowed tensor inputs assert tensor_name in pipe._callback_tensor_inputs return callback_kwargs inputs = self.get_dummy_inputs(torch_device) # Test passing in a subset inputs["callback_on_step_end"] = callback_inputs_subset inputs["callback_on_step_end_tensor_inputs"] = ["latents"] inputs["output_type"] = "latent" output = pipe(**inputs)[0] # Test passing in a everything inputs["callback_on_step_end"] = callback_inputs_all inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs inputs["output_type"] = "latent" output = pipe(**inputs)[0] def callback_inputs_change_tensor(pipe, i, t, callback_kwargs): is_last = i == (pipe.num_timesteps - 1) if is_last: callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"]) return callback_kwargs inputs["callback_on_step_end"] = callback_inputs_change_tensor inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs inputs["output_type"] = "latent" output = pipe(**inputs)[0] assert output.abs().sum() == 0 def test_callback_cfg(self): sig = inspect.signature(self.pipeline_class.__call__) has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters has_callback_step_end = "callback_on_step_end" in sig.parameters if not (has_callback_tensor_inputs and has_callback_step_end): return if "guidance_scale" not in sig.parameters: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) self.assertTrue( hasattr(pipe, "_callback_tensor_inputs"), f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs", ) def callback_increase_guidance(pipe, i, t, callback_kwargs): pipe._guidance_scale += 1.0 return callback_kwargs inputs = self.get_dummy_inputs(torch_device) # use cfg guidance because some pipelines modify the shape of the latents # outside of the denoising loop inputs["guidance_scale"] = 2.0 inputs["callback_on_step_end"] = callback_increase_guidance inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs _ = pipe(**inputs)[0] # we increase the guidance scale by 1.0 at every step # check that the guidance scale is increased by the number of scheduler timesteps # accounts for models that modify the number of inference steps based on strength assert pipe.guidance_scale == (inputs["guidance_scale"] + pipe.num_timesteps) def test_serialization_with_variants(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) model_components = [ component_name for component_name, component in pipe.components.items() if isinstance(component, nn.Module) ] variant = "fp16" with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(tmpdir, variant=variant, safe_serialization=False) with open(f"{tmpdir}/model_index.json", "r") as f: config = json.load(f) for subfolder in os.listdir(tmpdir): if not os.path.isfile(subfolder) and subfolder in model_components: folder_path = os.path.join(tmpdir, subfolder) is_folder = os.path.isdir(folder_path) and subfolder in config assert is_folder and any(p.split(".")[1].startswith(variant) for p in os.listdir(folder_path)) def test_loading_with_variants(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) variant = "fp16" def is_nan(tensor): if tensor.ndimension() == 0: has_nan = torch.isnan(tensor).item() else: has_nan = torch.isnan(tensor).any() return has_nan with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(tmpdir, variant=variant, safe_serialization=False) pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, variant=variant) model_components_pipe = { component_name: component for component_name, component in pipe.components.items() if isinstance(component, nn.Module) } model_components_pipe_loaded = { component_name: component for component_name, component in pipe_loaded.components.items() if isinstance(component, nn.Module) } for component_name in model_components_pipe: pipe_component = model_components_pipe[component_name] pipe_loaded_component = model_components_pipe_loaded[component_name] model_loaded_params = dict(pipe_loaded_component.named_parameters()) model_original_params = dict(pipe_component.named_parameters()) for name, p1 in model_original_params.items(): # Skip tied weights that aren't saved with variants (transformers v5 behavior) if name not in model_loaded_params: continue p2 = model_loaded_params[name] # nan check for luminanext (mps). if not (is_nan(p1) and is_nan(p2)): self.assertTrue(torch.equal(p1, p2)) def test_loading_with_incorrect_variants_raises_error(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) variant = "fp16" with tempfile.TemporaryDirectory() as tmpdir: # Don't save with variants. pipe.save_pretrained(tmpdir, safe_serialization=False) with self.assertRaises(ValueError) as error: _ = self.pipeline_class.from_pretrained(tmpdir, variant=variant) assert f"You are trying to load the model files of the `variant={variant}`" in str(error.exception) def test_encode_prompt_works_in_isolation(self, extra_required_param_value_dict=None, atol=1e-4, rtol=1e-4): if not hasattr(self.pipeline_class, "encode_prompt"): return components = self.get_dummy_components() for key in components: if "text_encoder" in key and hasattr(components[key], "eval"): components[key].eval() # We initialize the pipeline with only text encoders and tokenizers, # mimicking a real-world scenario. components_with_text_encoders = {} for k in components: if "text" in k or "tokenizer" in k: components_with_text_encoders[k] = components[k] else: components_with_text_encoders[k] = None pipe_with_just_text_encoder = self.pipeline_class(**components_with_text_encoders) pipe_with_just_text_encoder = pipe_with_just_text_encoder.to(torch_device) # Get inputs and also the args of `encode_prompts`. inputs = self.get_dummy_inputs(torch_device) encode_prompt_signature = inspect.signature(pipe_with_just_text_encoder.encode_prompt) encode_prompt_parameters = list(encode_prompt_signature.parameters.values()) # Required args in encode_prompt with those with no default. required_params = [] for param in encode_prompt_parameters: if param.name == "self" or param.name == "kwargs": continue if param.default is inspect.Parameter.empty: required_params.append(param.name) # Craft inputs for the `encode_prompt()` method to run in isolation. encode_prompt_param_names = [p.name for p in encode_prompt_parameters if p.name != "self"] input_keys = list(inputs.keys()) encode_prompt_inputs = {k: inputs.pop(k) for k in input_keys if k in encode_prompt_param_names} pipe_call_signature = inspect.signature(pipe_with_just_text_encoder.__call__) pipe_call_parameters = pipe_call_signature.parameters # For each required arg in encode_prompt, check if it's missing # in encode_prompt_inputs. If so, see if __call__ has a default # for that arg and use it if available. for required_param_name in required_params: if required_param_name not in encode_prompt_inputs: pipe_call_param = pipe_call_parameters.get(required_param_name, None) if pipe_call_param is not None and pipe_call_param.default is not inspect.Parameter.empty: # Use the default from pipe.__call__ encode_prompt_inputs[required_param_name] = pipe_call_param.default elif extra_required_param_value_dict is not None and isinstance(extra_required_param_value_dict, dict): encode_prompt_inputs[required_param_name] = extra_required_param_value_dict[required_param_name] else: raise ValueError( f"Required parameter '{required_param_name}' in " f"encode_prompt has no default in either encode_prompt or __call__." ) # Compute `encode_prompt()`. with torch.no_grad(): encoded_prompt_outputs = pipe_with_just_text_encoder.encode_prompt(**encode_prompt_inputs) # Programmatically determine the return names of `encode_prompt.` ast_visitor = ReturnNameVisitor() encode_prompt_tree = ast_visitor.get_ast_tree(cls=self.pipeline_class) ast_visitor.visit(encode_prompt_tree) prompt_embed_kwargs = ast_visitor.return_names prompt_embeds_kwargs = dict(zip(prompt_embed_kwargs, encoded_prompt_outputs)) # Pack the outputs of `encode_prompt`. adapted_prompt_embeds_kwargs = { k: prompt_embeds_kwargs.pop(k) for k in list(prompt_embeds_kwargs.keys()) if k in pipe_call_parameters } # now initialize a pipeline without text encoders and compute outputs with the # `encode_prompt()` outputs and other relevant inputs. components_with_text_encoders = {} for k in components: if "text" in k or "tokenizer" in k: components_with_text_encoders[k] = None else: components_with_text_encoders[k] = components[k] pipe_without_text_encoders = self.pipeline_class(**components_with_text_encoders).to(torch_device) # Set `negative_prompt` to None as we have already calculated its embeds # if it was present in `inputs`. This is because otherwise we will interfere wrongly # for non-None `negative_prompt` values as defaults (PixArt for example). pipe_without_tes_inputs = {**inputs, **adapted_prompt_embeds_kwargs} if ( pipe_call_parameters.get("negative_prompt", None) is not None and pipe_call_parameters.get("negative_prompt").default is not None ): pipe_without_tes_inputs.update({"negative_prompt": None}) # Pipelines like attend and excite have `prompt` as a required argument. if ( pipe_call_parameters.get("prompt", None) is not None and pipe_call_parameters.get("prompt").default is inspect.Parameter.empty and pipe_call_parameters.get("prompt_embeds", None) is not None and pipe_call_parameters.get("prompt_embeds").default is None ): pipe_without_tes_inputs.update({"prompt": None}) pipe_out = pipe_without_text_encoders(**pipe_without_tes_inputs)[0] # Compare against regular pipeline outputs. full_pipe = self.pipeline_class(**components).to(torch_device) inputs = self.get_dummy_inputs(torch_device) pipe_out_2 = full_pipe(**inputs)[0] if isinstance(pipe_out, np.ndarray) and isinstance(pipe_out_2, np.ndarray): self.assertTrue(np.allclose(pipe_out, pipe_out_2, atol=atol, rtol=rtol)) elif isinstance(pipe_out, torch.Tensor) and isinstance(pipe_out_2, torch.Tensor): self.assertTrue(torch.allclose(pipe_out, pipe_out_2, atol=atol, rtol=rtol)) def test_StableDiffusionMixin_component(self): """Any pipeline that have LDMFuncMixin should have vae and unet components.""" if not issubclass(self.pipeline_class, StableDiffusionMixin): return components = self.get_dummy_components() pipe = self.pipeline_class(**components) self.assertTrue(hasattr(pipe, "vae") and isinstance(pipe.vae, (AutoencoderKL, AutoencoderTiny))) self.assertTrue( hasattr(pipe, "unet") and isinstance( pipe.unet, (UNet2DConditionModel, UNet3DConditionModel, I2VGenXLUNet, UNetMotionModel, UNetControlNetXSModel), ) ) @pytest.mark.xfail( condition=torch_device == "mps", reason="MPS does not support float8 casting.", strict=True, ) def test_layerwise_casting_inference(self): if not self.test_layerwise_casting: return components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device, dtype=torch.bfloat16) pipe.set_progress_bar_config(disable=None) denoiser = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet denoiser.enable_layerwise_casting(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16) inputs = self.get_dummy_inputs(torch_device) _ = pipe(**inputs)[0] @require_torch_accelerator def test_group_offloading_inference(self): if not self.test_group_offloading: return def create_pipe(): torch.manual_seed(0) components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.set_progress_bar_config(disable=None) return pipe def enable_group_offload_on_component(pipe, group_offloading_kwargs): # We intentionally don't test VAE's here. This is because some tests enable tiling on the VAE. If # tiling is enabled and a forward pass is run, when accelerator streams are used, the execution order of # the layers is not traced correctly. This causes errors. For apply group offloading to VAE, a # warmup forward pass (even with dummy small inputs) is recommended. for component_name in [ "text_encoder", "text_encoder_2", "text_encoder_3", "transformer", "unet", "controlnet", ]: if not hasattr(pipe, component_name): continue component = getattr(pipe, component_name) if not getattr(component, "_supports_group_offloading", True): continue if hasattr(component, "enable_group_offload"): # For diffusers ModelMixin implementations component.enable_group_offload(torch.device(torch_device), **group_offloading_kwargs) else: # For other models not part of diffusers apply_group_offloading( component, onload_device=torch.device(torch_device), **group_offloading_kwargs ) self.assertTrue( all( module._diffusers_hook.get_hook("group_offloading") is not None for module in component.modules() if hasattr(module, "_diffusers_hook") ) ) for component_name in ["vae", "vqvae", "image_encoder"]: component = getattr(pipe, component_name, None) if isinstance(component, torch.nn.Module): component.to(torch_device) def run_forward(pipe): torch.manual_seed(0) inputs = self.get_dummy_inputs(torch_device) return pipe(**inputs)[0] pipe = create_pipe().to(torch_device) output_without_group_offloading = run_forward(pipe) pipe = create_pipe() enable_group_offload_on_component(pipe, {"offload_type": "block_level", "num_blocks_per_group": 1}) output_with_group_offloading1 = run_forward(pipe) pipe = create_pipe() enable_group_offload_on_component(pipe, {"offload_type": "leaf_level"}) output_with_group_offloading2 = run_forward(pipe) if torch.is_tensor(output_without_group_offloading): output_without_group_offloading = output_without_group_offloading.detach().cpu().numpy() output_with_group_offloading1 = output_with_group_offloading1.detach().cpu().numpy() output_with_group_offloading2 = output_with_group_offloading2.detach().cpu().numpy() self.assertTrue(np.allclose(output_without_group_offloading, output_with_group_offloading1, atol=1e-4)) self.assertTrue(np.allclose(output_without_group_offloading, output_with_group_offloading2, atol=1e-4)) def test_dtype_dict(self): components = self.get_dummy_components() if not components: self.skipTest("No dummy components defined.") pipe = self.pipeline_class(**components) specified_key = next(iter(components.keys())) with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as tmpdirname: pipe.save_pretrained(tmpdirname, safe_serialization=False) dtype_dict = {specified_key: torch.bfloat16, "default": torch.float16} loaded_pipe = self.pipeline_class.from_pretrained(tmpdirname, dtype=dtype_dict) for name, component in loaded_pipe.components.items(): if isinstance(component, torch.nn.Module) and hasattr(component, "dtype"): expected_dtype = dtype_dict.get(name, dtype_dict.get("default", torch.float32)) self.assertEqual( component.dtype, expected_dtype, f"Component '{name}' has dtype {component.dtype} but expected {expected_dtype}", ) def test_dtype_alias(self): # `torch_dtype` is deprecated in favor of `dtype` in `from_pretrained`. components = self.get_dummy_components() pipe = self.pipeline_class(**components) with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as tmpdirname: pipe.save_pretrained(tmpdirname, safe_serialization=False) loaded_pipe = self.pipeline_class.from_pretrained(tmpdirname, dtype=torch.float16) for name, component in loaded_pipe.components.items(): if isinstance(component, torch.nn.Module) and hasattr(component, "dtype"): self.assertEqual( component.dtype, torch.float16, f"Component '{name}' has dtype {component.dtype} but expected {torch.float16}", ) def test_pipeline_with_accelerator_device_map(self, expected_max_difference=1e-4): components = self.get_dummy_components() # Set text encoders to eval mode to match from_pretrained behavior # This ensures deterministic outputs when models are loaded with device_map for key in components: if "text_encoder" in key and hasattr(components[key], "eval"): components[key].eval() pipe = self.pipeline_class(**components) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) torch.manual_seed(0) inputs = self.get_dummy_inputs(torch_device) inputs["generator"] = torch.manual_seed(0) out = pipe(**inputs)[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(tmpdir) loaded_pipe = self.pipeline_class.from_pretrained(tmpdir, device_map=torch_device) for component in loaded_pipe.components.values(): if hasattr(component, "set_default_attn_processor"): component.set_default_attn_processor() inputs["generator"] = torch.manual_seed(0) loaded_out = loaded_pipe(**inputs)[0] max_diff = np.abs(to_np(out) - to_np(loaded_out)).max() self.assertLess(max_diff, expected_max_difference) @require_torch_accelerator def test_pipeline_level_group_offloading_sanity_checks(self): components = self.get_dummy_components() pipe: DiffusionPipeline = self.pipeline_class(**components) for name, component in pipe.components.items(): if hasattr(component, "_supports_group_offloading"): if not component._supports_group_offloading: pytest.skip(f"{self.pipeline_class.__name__} is not suitable for this test.") module_names = sorted( [name for name, component in pipe.components.items() if isinstance(component, torch.nn.Module)] ) exclude_module_name = module_names[0] offload_device = "cpu" pipe.enable_group_offload( onload_device=torch_device, offload_device=offload_device, offload_type="leaf_level", exclude_modules=exclude_module_name, ) excluded_module = getattr(pipe, exclude_module_name) self.assertTrue(torch.device(excluded_module.device).type == torch.device(torch_device).type) for name, component in pipe.components.items(): if name not in [exclude_module_name] and isinstance(component, torch.nn.Module): # `component.device` prints the `onload_device` type. We should probably override the # `device` property in `ModelMixin`. # Skip modules with no parameters (e.g., dummy safety checkers with only buffers) params = list(component.parameters()) if not params: continue component_device = params[0].device self.assertTrue(torch.device(component_device).type == torch.device(offload_device).type) @require_torch_accelerator def test_pipeline_level_group_offloading_inference(self, expected_max_difference=1e-4): components = self.get_dummy_components() pipe: DiffusionPipeline = self.pipeline_class(**components) for name, component in pipe.components.items(): if hasattr(component, "_supports_group_offloading"): if not component._supports_group_offloading: pytest.skip(f"{self.pipeline_class.__name__} is not suitable for this test.") # Regular inference. pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) torch.manual_seed(0) inputs = self.get_dummy_inputs(torch_device) inputs["generator"] = torch.manual_seed(0) out = pipe(**inputs)[0] pipe.to("cpu") del pipe # Inference with offloading pipe: DiffusionPipeline = self.pipeline_class(**components) offload_device = "cpu" pipe.enable_group_offload( onload_device=torch_device, offload_device=offload_device, offload_type="leaf_level", ) pipe.set_progress_bar_config(disable=None) inputs["generator"] = torch.manual_seed(0) out_offload = pipe(**inputs)[0] max_diff = np.abs(to_np(out) - to_np(out_offload)).max() self.assertLess(max_diff, expected_max_difference) class PyramidAttentionBroadcastTesterMixin: pab_config = PyramidAttentionBroadcastConfig( spatial_attention_block_skip_range=2, spatial_attention_timestep_skip_range=(100, 800), spatial_attention_block_identifiers=["transformer_blocks"], ) def test_pyramid_attention_broadcast_layers(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator num_layers = 0 num_single_layers = 0 dummy_component_kwargs = {} dummy_component_parameters = inspect.signature(self.get_dummy_components).parameters if "num_layers" in dummy_component_parameters: num_layers = 2 dummy_component_kwargs["num_layers"] = num_layers if "num_single_layers" in dummy_component_parameters: num_single_layers = 2 dummy_component_kwargs["num_single_layers"] = num_single_layers components = self.get_dummy_components(**dummy_component_kwargs) pipe = self.pipeline_class(**components) pipe.set_progress_bar_config(disable=None) self.pab_config.current_timestep_callback = lambda: pipe.current_timestep denoiser = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet denoiser.enable_cache(self.pab_config) expected_hooks = 0 if self.pab_config.spatial_attention_block_skip_range is not None: expected_hooks += num_layers + num_single_layers if self.pab_config.temporal_attention_block_skip_range is not None: expected_hooks += num_layers + num_single_layers if self.pab_config.cross_attention_block_skip_range is not None: expected_hooks += num_layers + num_single_layers denoiser = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet count = 0 for module in denoiser.modules(): if hasattr(module, "_diffusers_hook"): hook = module._diffusers_hook.get_hook("pyramid_attention_broadcast") if hook is None: continue count += 1 self.assertTrue( isinstance(hook, PyramidAttentionBroadcastHook), "Hook should be of type PyramidAttentionBroadcastHook.", ) self.assertTrue(hook.state.cache is None, "Cache should be None at initialization.") self.assertEqual(count, expected_hooks, "Number of hooks should match the expected number.") # Perform dummy inference step to ensure state is updated def pab_state_check_callback(pipe, i, t, kwargs): for module in denoiser.modules(): if hasattr(module, "_diffusers_hook"): hook = module._diffusers_hook.get_hook("pyramid_attention_broadcast") if hook is None: continue self.assertTrue( hook.state.cache is not None, "Cache should have updated during inference.", ) self.assertTrue( hook.state.iteration == i + 1, "Hook iteration state should have updated during inference.", ) return {} inputs = self.get_dummy_inputs(device) inputs["num_inference_steps"] = 2 inputs["callback_on_step_end"] = pab_state_check_callback pipe(**inputs)[0] # After inference, reset_stateful_hooks is called within the pipeline, which should have reset the states for module in denoiser.modules(): if hasattr(module, "_diffusers_hook"): hook = module._diffusers_hook.get_hook("pyramid_attention_broadcast") if hook is None: continue self.assertTrue( hook.state.cache is None, "Cache should be reset to None after inference.", ) self.assertTrue( hook.state.iteration == 0, "Iteration should be reset to 0 after inference.", ) def test_pyramid_attention_broadcast_inference(self, expected_atol: float = 0.2): # We need to use higher tolerance because we are using a random model. With a converged/trained # model, the tolerance can be lower. device = "cpu" # ensure determinism for the device-dependent torch.Generator num_layers = 2 components = self.get_dummy_components(num_layers=num_layers) for key in components: if "text_encoder" in key and hasattr(components[key], "eval"): components[key].eval() pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) # Run inference without PAB inputs = self.get_dummy_inputs(device) inputs["num_inference_steps"] = 4 output = pipe(**inputs)[0] original_image_slice = output.flatten() original_image_slice = np.concatenate((original_image_slice[:8], original_image_slice[-8:])) # Run inference with PAB enabled self.pab_config.current_timestep_callback = lambda: pipe.current_timestep denoiser = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet denoiser.enable_cache(self.pab_config) inputs = self.get_dummy_inputs(device) inputs["num_inference_steps"] = 4 output = pipe(**inputs)[0] image_slice_pab_enabled = output.flatten() image_slice_pab_enabled = np.concatenate((image_slice_pab_enabled[:8], image_slice_pab_enabled[-8:])) # Run inference with PAB disabled denoiser.disable_cache() inputs = self.get_dummy_inputs(device) inputs["num_inference_steps"] = 4 output = pipe(**inputs)[0] image_slice_pab_disabled = output.flatten() image_slice_pab_disabled = np.concatenate((image_slice_pab_disabled[:8], image_slice_pab_disabled[-8:])) assert np.allclose(original_image_slice, image_slice_pab_enabled, atol=expected_atol), ( "PAB outputs should not differ much in specified timestep range." ) assert np.allclose(original_image_slice, image_slice_pab_disabled, atol=1e-4), ( "Outputs from normal inference and after disabling cache should not differ." ) class FasterCacheTesterMixin: faster_cache_config = FasterCacheConfig( spatial_attention_block_skip_range=2, spatial_attention_timestep_skip_range=(-1, 901), unconditional_batch_skip_range=2, attention_weight_callback=lambda _: 0.5, ) def test_faster_cache_basic_warning_or_errors_raised(self): components = self.get_dummy_components() logger = logging.get_logger("diffusers.hooks.faster_cache") logger.setLevel(logging.INFO) # Check if warning is raise when no attention_weight_callback is provided pipe = self.pipeline_class(**components) with CaptureLogger(logger) as cap_logger: config = FasterCacheConfig(spatial_attention_block_skip_range=2, attention_weight_callback=None) apply_faster_cache(pipe.transformer, config) self.assertTrue("No `attention_weight_callback` provided when enabling FasterCache" in cap_logger.out) # Check if error raised when unsupported tensor format used pipe = self.pipeline_class(**components) with self.assertRaises(ValueError): config = FasterCacheConfig(spatial_attention_block_skip_range=2, tensor_format="BFHWC") apply_faster_cache(pipe.transformer, config) def test_faster_cache_inference(self, expected_atol: float = 0.1): device = "cpu" # ensure determinism for the device-dependent torch.Generator def create_pipe(): torch.manual_seed(0) num_layers = 2 components = self.get_dummy_components(num_layers=num_layers) pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) return pipe def run_forward(pipe): torch.manual_seed(0) inputs = self.get_dummy_inputs(device) inputs["num_inference_steps"] = 4 return pipe(**inputs)[0] # Run inference without FasterCache pipe = create_pipe() output = run_forward(pipe).flatten() original_image_slice = np.concatenate((output[:8], output[-8:])) # Run inference with FasterCache enabled self.faster_cache_config.current_timestep_callback = lambda: pipe.current_timestep pipe = create_pipe() pipe.transformer.enable_cache(self.faster_cache_config) output = run_forward(pipe).flatten() image_slice_faster_cache_enabled = np.concatenate((output[:8], output[-8:])) # Run inference with FasterCache disabled pipe.transformer.disable_cache() output = run_forward(pipe).flatten() image_slice_faster_cache_disabled = np.concatenate((output[:8], output[-8:])) assert np.allclose(original_image_slice, image_slice_faster_cache_enabled, atol=expected_atol), ( "FasterCache outputs should not differ much in specified timestep range." ) assert np.allclose(original_image_slice, image_slice_faster_cache_disabled, atol=1e-4), ( "Outputs from normal inference and after disabling cache should not differ." ) def test_faster_cache_state(self): from diffusers.hooks.faster_cache import _FASTER_CACHE_BLOCK_HOOK, _FASTER_CACHE_DENOISER_HOOK device = "cpu" # ensure determinism for the device-dependent torch.Generator num_layers = 0 num_single_layers = 0 dummy_component_kwargs = {} dummy_component_parameters = inspect.signature(self.get_dummy_components).parameters if "num_layers" in dummy_component_parameters: num_layers = 2 dummy_component_kwargs["num_layers"] = num_layers if "num_single_layers" in dummy_component_parameters: num_single_layers = 2 dummy_component_kwargs["num_single_layers"] = num_single_layers components = self.get_dummy_components(**dummy_component_kwargs) pipe = self.pipeline_class(**components) pipe.set_progress_bar_config(disable=None) self.faster_cache_config.current_timestep_callback = lambda: pipe.current_timestep pipe.transformer.enable_cache(self.faster_cache_config) expected_hooks = 0 if self.faster_cache_config.spatial_attention_block_skip_range is not None: expected_hooks += num_layers + num_single_layers if self.faster_cache_config.temporal_attention_block_skip_range is not None: expected_hooks += num_layers + num_single_layers # Check if faster_cache denoiser hook is attached denoiser = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet self.assertTrue( hasattr(denoiser, "_diffusers_hook") and isinstance(denoiser._diffusers_hook.get_hook(_FASTER_CACHE_DENOISER_HOOK), FasterCacheDenoiserHook), "Hook should be of type FasterCacheDenoiserHook.", ) # Check if all blocks have faster_cache block hook attached count = 0 for name, module in denoiser.named_modules(): if hasattr(module, "_diffusers_hook"): if name == "": # Skip the root denoiser module continue count += 1 self.assertTrue( isinstance(module._diffusers_hook.get_hook(_FASTER_CACHE_BLOCK_HOOK), FasterCacheBlockHook), "Hook should be of type FasterCacheBlockHook.", ) self.assertEqual(count, expected_hooks, "Number of hooks should match expected number.") # Perform inference to ensure that states are updated correctly def faster_cache_state_check_callback(pipe, i, t, kwargs): for name, module in denoiser.named_modules(): if not hasattr(module, "_diffusers_hook"): continue if name == "": # Root denoiser module state = module._diffusers_hook.get_hook(_FASTER_CACHE_DENOISER_HOOK).state if not self.faster_cache_config.is_guidance_distilled: self.assertTrue(state.low_frequency_delta is not None, "Low frequency delta should be set.") self.assertTrue(state.high_frequency_delta is not None, "High frequency delta should be set.") else: # Internal blocks state = module._diffusers_hook.get_hook(_FASTER_CACHE_BLOCK_HOOK).state self.assertTrue(state.cache is not None and len(state.cache) == 2, "Cache should be set.") self.assertTrue(state.iteration == i + 1, "Hook iteration state should have updated during inference.") return {} inputs = self.get_dummy_inputs(device) inputs["num_inference_steps"] = 4 inputs["callback_on_step_end"] = faster_cache_state_check_callback _ = pipe(**inputs)[0] # After inference, reset_stateful_hooks is called within the pipeline, which should have reset the states for name, module in denoiser.named_modules(): if not hasattr(module, "_diffusers_hook"): continue if name == "": # Root denoiser module state = module._diffusers_hook.get_hook(_FASTER_CACHE_DENOISER_HOOK).state self.assertTrue(state.iteration == 0, "Iteration should be reset to 0.") self.assertTrue(state.low_frequency_delta is None, "Low frequency delta should be reset to None.") self.assertTrue(state.high_frequency_delta is None, "High frequency delta should be reset to None.") else: # Internal blocks state = module._diffusers_hook.get_hook(_FASTER_CACHE_BLOCK_HOOK).state self.assertTrue(state.iteration == 0, "Iteration should be reset to 0.") self.assertTrue(state.batch_size is None, "Batch size should be reset to None.") self.assertTrue(state.cache is None, "Cache should be reset to None.") # TODO(aryan, dhruv): the cache tester mixins should probably be rewritten so that more models can be tested out # of the box once there is better cache support/implementation class FirstBlockCacheTesterMixin: # threshold is intentionally set higher than usual values since we're testing with random unconverged models # that will not satisfy the expected properties of the denoiser for caching to be effective first_block_cache_config = FirstBlockCacheConfig(threshold=0.8) def test_first_block_cache_inference(self, expected_atol: float = 0.1): device = "cpu" # ensure determinism for the device-dependent torch.Generator def create_pipe(): torch.manual_seed(0) num_layers = 2 components = self.get_dummy_components(num_layers=num_layers) pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) return pipe def run_forward(pipe): torch.manual_seed(0) inputs = self.get_dummy_inputs(device) inputs["num_inference_steps"] = 4 return pipe(**inputs)[0] # Run inference without FirstBlockCache pipe = create_pipe() output = run_forward(pipe).flatten() original_image_slice = np.concatenate((output[:8], output[-8:])) # Run inference with FirstBlockCache enabled pipe = create_pipe() pipe.transformer.enable_cache(self.first_block_cache_config) output = run_forward(pipe).flatten() image_slice_fbc_enabled = np.concatenate((output[:8], output[-8:])) # Run inference with FirstBlockCache disabled pipe.transformer.disable_cache() output = run_forward(pipe).flatten() image_slice_fbc_disabled = np.concatenate((output[:8], output[-8:])) assert np.allclose(original_image_slice, image_slice_fbc_enabled, atol=expected_atol), ( "FirstBlockCache outputs should not differ much." ) assert np.allclose(original_image_slice, image_slice_fbc_disabled, atol=1e-4), ( "Outputs from normal inference and after disabling cache should not differ." ) class TaylorSeerCacheTesterMixin: taylorseer_cache_config = TaylorSeerCacheConfig( cache_interval=5, disable_cache_before_step=10, max_order=1, taylor_factors_dtype=torch.bfloat16, use_lite_mode=True, ) def test_taylorseer_cache_inference(self, expected_atol: float = 0.1): device = "cpu" # ensure determinism for the device-dependent torch.Generator def create_pipe(): torch.manual_seed(0) num_layers = 2 components = self.get_dummy_components(num_layers=num_layers) pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) return pipe def run_forward(pipe): torch.manual_seed(0) inputs = self.get_dummy_inputs(device) inputs["num_inference_steps"] = 50 return pipe(**inputs)[0] # Run inference without TaylorSeerCache pipe = create_pipe() output = run_forward(pipe).flatten() original_image_slice = np.concatenate((output[:8], output[-8:])) # Run inference with TaylorSeerCache enabled pipe = create_pipe() pipe.transformer.enable_cache(self.taylorseer_cache_config) output = run_forward(pipe).flatten() image_slice_fbc_enabled = np.concatenate((output[:8], output[-8:])) # Run inference with TaylorSeerCache disabled pipe.transformer.disable_cache() output = run_forward(pipe).flatten() image_slice_fbc_disabled = np.concatenate((output[:8], output[-8:])) assert np.allclose(original_image_slice, image_slice_fbc_enabled, atol=expected_atol), ( "TaylorSeerCache outputs should not differ much." ) assert np.allclose(original_image_slice, image_slice_fbc_disabled, atol=1e-4), ( "Outputs from normal inference and after disabling cache should not differ." ) class MagCacheTesterMixin: mag_cache_config = MagCacheConfig( threshold=0.06, max_skip_steps=3, retention_ratio=0.2, num_inference_steps=50, mag_ratios=torch.ones(50), ) def test_mag_cache_inference(self, expected_atol: float = 0.1): device = "cpu" def create_pipe(): torch.manual_seed(0) num_layers = 2 components = self.get_dummy_components(num_layers=num_layers) pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) return pipe def run_forward(pipe): torch.manual_seed(0) inputs = self.get_dummy_inputs(device) # Match the config steps inputs["num_inference_steps"] = 50 return pipe(**inputs)[0] # 1. Run inference without MagCache (Baseline) pipe = create_pipe() output = run_forward(pipe).flatten() original_image_slice = np.concatenate((output[:8], output[-8:])) # 2. Run inference with MagCache ENABLED pipe = create_pipe() pipe.transformer.enable_cache(self.mag_cache_config) output = run_forward(pipe).flatten() image_slice_enabled = np.concatenate((output[:8], output[-8:])) # 3. Run inference with MagCache DISABLED pipe.transformer.disable_cache() output = run_forward(pipe).flatten() image_slice_disabled = np.concatenate((output[:8], output[-8:])) assert np.allclose(original_image_slice, image_slice_enabled, atol=expected_atol), ( "MagCache outputs should not differ too much from baseline." ) assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-4), ( "Outputs after disabling cache should match original inference exactly." ) # 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, expected_max_diff=10): 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 < expected_max_diff, f"Error image deviates {avg_diff} pixels on average"