# coding=utf-8 # Copyright 2024 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import tempfile import unittest import numpy as np import torch from transformers import AutoTokenizer, T5EncoderModel import diffusers from diffusers import ( AutoencoderKL, DDIMScheduler, PixArtSigmaPAGPipeline, PixArtSigmaPipeline, PixArtTransformer2DModel, ) from diffusers.utils import logging from diffusers.utils.testing_utils import ( CaptureLogger, enable_full_determinism, print_tensor_test, torch_device, ) from ..pipeline_params import ( TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS, ) from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference, to_np enable_full_determinism() class PixArtSigmaPAGPipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = PixArtSigmaPAGPipeline params = TEXT_TO_IMAGE_PARAMS.union({"pag_scale", "pag_adaptive_scale"}) params = set(params) params.remove("cross_attention_kwargs") batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = TEXT_TO_IMAGE_IMAGE_PARAMS image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS required_optional_params = PipelineTesterMixin.required_optional_params def get_dummy_components(self): torch.manual_seed(0) transformer = PixArtTransformer2DModel( sample_size=8, num_layers=2, patch_size=2, attention_head_dim=8, num_attention_heads=3, caption_channels=32, in_channels=4, cross_attention_dim=24, out_channels=8, attention_bias=True, activation_fn="gelu-approximate", num_embeds_ada_norm=1000, norm_type="ada_norm_single", norm_elementwise_affine=False, norm_eps=1e-6, ) torch.manual_seed(0) vae = AutoencoderKL() scheduler = DDIMScheduler() text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5") tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") components = { "transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler, "text_encoder": text_encoder, "tokenizer": tokenizer, } return components def get_dummy_inputs(self, device, seed=0): if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 1.0, "pag_scale": 3.0, "use_resolution_binning": False, "output_type": "np", } return inputs def test_pag_disable_enable(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() # base pipeline (expect same output when pag is disabled) pipe = PixArtSigmaPipeline(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) del inputs["pag_scale"] assert ( "pag_scale" not in inspect.signature(pipe.__call__).parameters ), f"`pag_scale` should not be a call parameter of the base pipeline {pipe.__class__.__name__}." out = pipe(**inputs).images[0, -3:, -3:, -1] # pag disabled with pag_scale=0.0 components["pag_applied_layers"] = ["blocks.1"] pipe_pag = self.pipeline_class(**components) pipe_pag = pipe_pag.to(device) pipe_pag.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) inputs["pag_scale"] = 0.0 out_pag_disabled = pipe_pag(**inputs).images[0, -3:, -3:, -1] # pag enabled pipe_pag = self.pipeline_class(**components) pipe_pag = pipe_pag.to(device) pipe_pag.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) out_pag_enabled = pipe_pag(**inputs).images[0, -3:, -3:, -1] assert np.abs(out.flatten() - out_pag_disabled.flatten()).max() < 1e-3 assert np.abs(out.flatten() - out_pag_enabled.flatten()).max() > 1e-3 def test_pag_applied_layers(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() # base pipeline pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) # "attn1" should apply to all self-attention layers. all_self_attn_layers = [k for k in pipe.transformer.attn_processors.keys() if "attn1" in k] pag_layers = ["blocks.0", "blocks.1"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(all_self_attn_layers) def test_pag_inference(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() pipe_pag = self.pipeline_class(**components) pipe_pag = pipe_pag.to(device) pipe_pag.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = pipe_pag(**inputs).images image_slice = image[0, -3:, -3:, -1] print_tensor_test(image_slice) assert image.shape == ( 1, 8, 8, 3, ), f"the shape of the output image should be (1, 8, 8, 3) but got {image.shape}" expected_slice = np.array([0.6499, 0.3250, 0.3572, 0.6780, 0.4453, 0.4582, 0.2770, 0.5168, 0.4594]) max_diff = np.abs(image_slice.flatten() - expected_slice).max() self.assertLessEqual(max_diff, 1e-3) # Copied from tests.pipelines.pixart_sigma.test_pixart.PixArtSigmaPipelineFastTests.test_save_load_optional_components def test_save_load_optional_components(self): 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) prompt = inputs["prompt"] generator = inputs["generator"] num_inference_steps = inputs["num_inference_steps"] output_type = inputs["output_type"] ( prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask, ) = pipe.encode_prompt(prompt) # inputs with prompt converted to embeddings inputs = { "prompt_embeds": prompt_embeds, "prompt_attention_mask": prompt_attention_mask, "negative_prompt": None, "negative_prompt_embeds": negative_prompt_embeds, "negative_prompt_attention_mask": negative_prompt_attention_mask, "generator": generator, "num_inference_steps": num_inference_steps, "output_type": output_type, "use_resolution_binning": False, } # set all optional components to None for optional_component in pipe._optional_components: setattr(pipe, optional_component, None) output = pipe(**inputs)[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(tmpdir) pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, pag_applied_layers=["blocks.1"]) pipe_loaded.to(torch_device) pipe_loaded.set_progress_bar_config(disable=None) for optional_component in pipe._optional_components: self.assertTrue( getattr(pipe_loaded, optional_component) is None, f"`{optional_component}` did not stay set to None after loading.", ) inputs = self.get_dummy_inputs(torch_device) generator = inputs["generator"] num_inference_steps = inputs["num_inference_steps"] output_type = inputs["output_type"] # inputs with prompt converted to embeddings inputs = { "prompt_embeds": prompt_embeds, "prompt_attention_mask": prompt_attention_mask, "negative_prompt": None, "negative_prompt_embeds": negative_prompt_embeds, "negative_prompt_attention_mask": negative_prompt_attention_mask, "generator": generator, "num_inference_steps": num_inference_steps, "output_type": output_type, "use_resolution_binning": False, } output_loaded = pipe_loaded(**inputs)[0] max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() self.assertLess(max_diff, 1e-4) # Because the PAG PixArt Sigma has `pag_applied_layers`. # Also, we shouldn't be doing `set_default_attn_processor()` after loading # the pipeline with `pag_applied_layers`. def test_save_load_local(self, expected_max_difference=1e-4): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(torch_device) output = 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, pag_applied_layers=["blocks.1"]) 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) # We shouldn't be setting `set_default_attn_processor` here. def test_attention_slicing_forward_pass( self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 ): components = self.get_dummy_components() pipe = self.pipeline_class(**components) 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])) # Because we have `pag_applied_layers` we cannot direcly apply # `set_default_attn_processor` def test_dict_tuple_outputs_equivalent(self, expected_slice=None, expected_max_difference=1e-4): components = self.get_dummy_components() pipe = self.pipeline_class(**components) 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) # Same reason as above 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() pipe = self.pipeline_class(**components) 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 # Because we're passing `pag_applied_layers` (type of List) in the components as well. 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, list))} pipe = self.pipeline_class(**init_components) self.assertTrue(hasattr(pipe, "components")) self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))