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| # 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 gc | |
| import inspect | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer | |
| from diffusers import ( | |
| AutoencoderKL, | |
| AutoPipelineForText2Image, | |
| EulerDiscreteScheduler, | |
| StableDiffusionXLPAGPipeline, | |
| StableDiffusionXLPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.utils.testing_utils import ( | |
| enable_full_determinism, | |
| require_torch_gpu, | |
| slow, | |
| torch_device, | |
| ) | |
| from ..pipeline_params import ( | |
| TEXT_TO_IMAGE_BATCH_PARAMS, | |
| TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, | |
| TEXT_TO_IMAGE_IMAGE_PARAMS, | |
| TEXT_TO_IMAGE_PARAMS, | |
| ) | |
| from ..test_pipelines_common import ( | |
| IPAdapterTesterMixin, | |
| PipelineFromPipeTesterMixin, | |
| PipelineLatentTesterMixin, | |
| PipelineTesterMixin, | |
| SDXLOptionalComponentsTesterMixin, | |
| ) | |
| enable_full_determinism() | |
| class StableDiffusionXLPAGPipelineFastTests( | |
| PipelineTesterMixin, | |
| IPAdapterTesterMixin, | |
| PipelineLatentTesterMixin, | |
| PipelineFromPipeTesterMixin, | |
| SDXLOptionalComponentsTesterMixin, | |
| unittest.TestCase, | |
| ): | |
| pipeline_class = StableDiffusionXLPAGPipeline | |
| params = TEXT_TO_IMAGE_PARAMS.union({"pag_scale", "pag_adaptive_scale"}) | |
| batch_params = TEXT_TO_IMAGE_BATCH_PARAMS | |
| image_params = TEXT_TO_IMAGE_IMAGE_PARAMS | |
| image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS | |
| callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({"add_text_embeds", "add_time_ids"}) | |
| def get_dummy_components(self, time_cond_proj_dim=None): | |
| # Copied from tests.pipelines.stable_diffusion_xl.test_stable_diffusion_xl.StableDiffusionXLPipelineFastTests.get_dummy_components | |
| torch.manual_seed(0) | |
| unet = UNet2DConditionModel( | |
| block_out_channels=(2, 4), | |
| layers_per_block=2, | |
| time_cond_proj_dim=time_cond_proj_dim, | |
| sample_size=32, | |
| in_channels=4, | |
| out_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
| up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
| # SD2-specific config below | |
| attention_head_dim=(2, 4), | |
| use_linear_projection=True, | |
| addition_embed_type="text_time", | |
| addition_time_embed_dim=8, | |
| transformer_layers_per_block=(1, 2), | |
| projection_class_embeddings_input_dim=80, # 6 * 8 + 32 | |
| cross_attention_dim=64, | |
| norm_num_groups=1, | |
| ) | |
| scheduler = EulerDiscreteScheduler( | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| steps_offset=1, | |
| beta_schedule="scaled_linear", | |
| timestep_spacing="leading", | |
| ) | |
| torch.manual_seed(0) | |
| vae = AutoencoderKL( | |
| block_out_channels=[32, 64], | |
| in_channels=3, | |
| out_channels=3, | |
| down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
| up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
| latent_channels=4, | |
| sample_size=128, | |
| ) | |
| torch.manual_seed(0) | |
| text_encoder_config = CLIPTextConfig( | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| hidden_size=32, | |
| intermediate_size=37, | |
| layer_norm_eps=1e-05, | |
| num_attention_heads=4, | |
| num_hidden_layers=5, | |
| pad_token_id=1, | |
| vocab_size=1000, | |
| # SD2-specific config below | |
| hidden_act="gelu", | |
| projection_dim=32, | |
| ) | |
| text_encoder = CLIPTextModel(text_encoder_config) | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config) | |
| tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| components = { | |
| "unet": unet, | |
| "scheduler": scheduler, | |
| "vae": vae, | |
| "text_encoder": text_encoder, | |
| "tokenizer": tokenizer, | |
| "text_encoder_2": text_encoder_2, | |
| "tokenizer_2": tokenizer_2, | |
| "image_encoder": None, | |
| "feature_extractor": None, | |
| } | |
| 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": 5.0, | |
| "pag_scale": 0.9, | |
| "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_sd = StableDiffusionXLPipeline(**components) | |
| pipe_sd = pipe_sd.to(device) | |
| pipe_sd.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| del inputs["pag_scale"] | |
| assert ( | |
| "pag_scale" not in inspect.signature(pipe_sd.__call__).parameters | |
| ), f"`pag_scale` should not be a call parameter of the base pipeline {pipe_sd.__class__.__name__}." | |
| out = pipe_sd(**inputs).images[0, -3:, -3:, -1] | |
| # pag disabled with pag_scale=0.0 | |
| 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, pag_applied_layers=["mid", "up", "down"]) | |
| 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_save_load_optional_components(self): | |
| self._test_save_load_optional_components() | |
| 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) | |
| # pag_applied_layers = ["mid","up","down"] should apply to all self-attention layers | |
| all_self_attn_layers = [k for k in pipe.unet.attn_processors.keys() if "attn1" in k] | |
| original_attn_procs = pipe.unet.attn_processors | |
| pag_layers = ["mid", "down", "up"] | |
| 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) | |
| # pag_applied_layers = ["mid"], or ["mid.block_0"] or ["mid.block_0.attentions_0"] should apply to all self-attention layers in mid_block, i.e. | |
| # mid_block.attentions.0.transformer_blocks.0.attn1.processor | |
| # mid_block.attentions.0.transformer_blocks.1.attn1.processor | |
| all_self_attn_mid_layers = [ | |
| "mid_block.attentions.0.transformer_blocks.0.attn1.processor", | |
| "mid_block.attentions.0.transformer_blocks.1.attn1.processor", | |
| ] | |
| pipe.unet.set_attn_processor(original_attn_procs.copy()) | |
| pag_layers = ["mid"] | |
| 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_mid_layers) | |
| pipe.unet.set_attn_processor(original_attn_procs.copy()) | |
| pag_layers = ["mid_block"] | |
| 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_mid_layers) | |
| pipe.unet.set_attn_processor(original_attn_procs.copy()) | |
| pag_layers = ["mid_block.attentions.0"] | |
| 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_mid_layers) | |
| # pag_applied_layers = ["mid.block_0.attentions_1"] does not exist in the model | |
| pipe.unet.set_attn_processor(original_attn_procs.copy()) | |
| pag_layers = ["mid_block.attentions.1"] | |
| with self.assertRaises(ValueError): | |
| pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) | |
| # pag_applied_layers = "down" should apply to all self-attention layers in down_blocks | |
| # down_blocks.1.attentions.0.transformer_blocks.0.attn1.processor | |
| # down_blocks.1.attentions.0.transformer_blocks.1.attn1.processor | |
| # down_blocks.1.attentions.1.transformer_blocks.0.attn1.processor | |
| # down_blocks.1.attentions.1.transformer_blocks.1.attn1.processor | |
| pipe.unet.set_attn_processor(original_attn_procs.copy()) | |
| pag_layers = ["down"] | |
| pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) | |
| assert len(pipe.pag_attn_processors) == 4 | |
| pipe.unet.set_attn_processor(original_attn_procs.copy()) | |
| pag_layers = ["down_blocks.0"] | |
| with self.assertRaises(ValueError): | |
| pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) | |
| pipe.unet.set_attn_processor(original_attn_procs.copy()) | |
| pag_layers = ["down_blocks.1"] | |
| pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) | |
| assert len(pipe.pag_attn_processors) == 4 | |
| pipe.unet.set_attn_processor(original_attn_procs.copy()) | |
| pag_layers = ["down_blocks.1.attentions.1"] | |
| pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) | |
| assert len(pipe.pag_attn_processors) == 2 | |
| 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, pag_applied_layers=["mid", "up", "down"]) | |
| 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] | |
| assert image.shape == ( | |
| 1, | |
| 64, | |
| 64, | |
| 3, | |
| ), f"the shape of the output image should be (1, 64, 64, 3) but got {image.shape}" | |
| expected_slice = np.array([0.5382, 0.5439, 0.4704, 0.4569, 0.5234, 0.4834, 0.5289, 0.5039, 0.4764]) | |
| max_diff = np.abs(image_slice.flatten() - expected_slice).max() | |
| self.assertLessEqual(max_diff, 1e-3) | |
| class StableDiffusionXLPAGPipelineIntegrationTests(unittest.TestCase): | |
| pipeline_class = StableDiffusionXLPAGPipeline | |
| repo_id = "stabilityai/stable-diffusion-xl-base-1.0" | |
| def setUp(self): | |
| super().setUp() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def tearDown(self): | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def get_inputs(self, device, generator_device="cpu", seed=0, guidance_scale=7.0): | |
| generator = torch.Generator(device=generator_device).manual_seed(seed) | |
| inputs = { | |
| "prompt": "a polar bear sitting in a chair drinking a milkshake", | |
| "negative_prompt": "deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality", | |
| "generator": generator, | |
| "num_inference_steps": 3, | |
| "guidance_scale": guidance_scale, | |
| "pag_scale": 3.0, | |
| "output_type": "np", | |
| } | |
| return inputs | |
| def test_pag_cfg(self): | |
| pipeline = AutoPipelineForText2Image.from_pretrained(self.repo_id, enable_pag=True, torch_dtype=torch.float16) | |
| pipeline.enable_model_cpu_offload() | |
| pipeline.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs(torch_device) | |
| image = pipeline(**inputs).images | |
| image_slice = image[0, -3:, -3:, -1].flatten() | |
| assert image.shape == (1, 1024, 1024, 3) | |
| expected_slice = np.array( | |
| [0.3123679, 0.31725878, 0.32026544, 0.327533, 0.3266391, 0.3303998, 0.33544615, 0.34181812, 0.34102726] | |
| ) | |
| assert ( | |
| np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
| ), f"output is different from expected, {image_slice.flatten()}" | |
| def test_pag_uncond(self): | |
| pipeline = AutoPipelineForText2Image.from_pretrained(self.repo_id, enable_pag=True, torch_dtype=torch.float16) | |
| pipeline.enable_model_cpu_offload() | |
| pipeline.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs(torch_device, guidance_scale=0.0) | |
| image = pipeline(**inputs).images | |
| image_slice = image[0, -3:, -3:, -1].flatten() | |
| assert image.shape == (1, 1024, 1024, 3) | |
| expected_slice = np.array( | |
| [0.47400922, 0.48650584, 0.4839625, 0.4724013, 0.4890427, 0.49544555, 0.51707107, 0.54299414, 0.5224372] | |
| ) | |
| assert ( | |
| np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
| ), f"output is different from expected, {image_slice.flatten()}" | |