# 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 unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DEISMultistepScheduler, DPMSolverMultistepScheduler, EulerDiscreteScheduler, StableDiffusionSAGPipeline, UNet2DConditionModel, ) from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, 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 ( IPAdapterTesterMixin, PipelineFromPipeTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, ) enable_full_determinism() class StableDiffusionSAGPipelineFastTests( IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase, ): pipeline_class = StableDiffusionSAGPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = TEXT_TO_IMAGE_IMAGE_PARAMS image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS def get_dummy_components(self): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(4, 8), layers_per_block=2, sample_size=8, norm_num_groups=1, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=8, ) scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) torch.manual_seed(0) vae = AutoencoderKL( block_out_channels=[4, 8], norm_num_groups=1, in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=4, ) torch.manual_seed(0) text_encoder_config = CLIPTextConfig( bos_token_id=0, eos_token_id=2, hidden_size=8, num_hidden_layers=2, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, pad_token_id=1, vocab_size=1000, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") components = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, "image_encoder": 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": ".", "generator": generator, "num_inference_steps": 2, "guidance_scale": 1.0, "sag_scale": 1.0, "output_type": "np", } return inputs def test_inference_batch_single_identical(self): super().test_inference_batch_single_identical(expected_max_diff=3e-3) @unittest.skip("Not necessary to test here.") def test_xformers_attention_forwardGenerator_pass(self): pass def test_pipeline_different_schedulers(self): pipeline = self.pipeline_class(**self.get_dummy_components()) inputs = self.get_dummy_inputs("cpu") expected_image_size = (16, 16, 3) for scheduler_cls in [DDIMScheduler, DEISMultistepScheduler, DPMSolverMultistepScheduler]: pipeline.scheduler = scheduler_cls.from_config(pipeline.scheduler.config) image = pipeline(**inputs).images[0] shape = image.shape assert shape == expected_image_size pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config) with self.assertRaises(ValueError): # Karras schedulers are not supported image = pipeline(**inputs).images[0] @nightly @require_torch_gpu class StableDiffusionPipelineIntegrationTests(unittest.TestCase): def setUp(self): # clean up the VRAM before each test super().setUp() gc.collect() torch.cuda.empty_cache() def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_stable_diffusion_1(self): sag_pipe = StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") sag_pipe = sag_pipe.to(torch_device) sag_pipe.set_progress_bar_config(disable=None) prompt = "." generator = torch.manual_seed(0) output = sag_pipe( [prompt], generator=generator, guidance_scale=7.5, sag_scale=1.0, num_inference_steps=20, output_type="np" ) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]) assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2 def test_stable_diffusion_2(self): sag_pipe = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base") sag_pipe = sag_pipe.to(torch_device) sag_pipe.set_progress_bar_config(disable=None) prompt = "." generator = torch.manual_seed(0) output = sag_pipe( [prompt], generator=generator, guidance_scale=7.5, sag_scale=1.0, num_inference_steps=20, output_type="np" ) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]) assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2 def test_stable_diffusion_2_non_square(self): sag_pipe = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base") sag_pipe = sag_pipe.to(torch_device) sag_pipe.set_progress_bar_config(disable=None) prompt = "." generator = torch.manual_seed(0) output = sag_pipe( [prompt], width=768, height=512, generator=generator, guidance_scale=7.5, sag_scale=1.0, num_inference_steps=20, output_type="np", ) image = output.images assert image.shape == (1, 512, 768, 3)