# coding=utf-8 # Copyright 2023 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, EulerAncestralDiscreteScheduler, PNDMScheduler, StableDiffusionModelEditingPipeline, UNet2DConditionModel, ) from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, skip_mps, 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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() @skip_mps class StableDiffusionModelEditingPipelineFastTests( PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase ): pipeline_class = StableDiffusionModelEditingPipeline 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=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, ) scheduler = DDIMScheduler() 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, ) 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, ) 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, } return components def get_dummy_inputs(self, device, seed=0): generator = torch.manual_seed(seed) inputs = { "prompt": "A field of roses", "generator": generator, # Setting height and width to None to prevent OOMs on CPU. "height": None, "width": None, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "numpy", } return inputs def test_stable_diffusion_model_editing_default_case(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionModelEditingPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = sd_pipe(**inputs).images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_model_editing_negative_prompt(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionModelEditingPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) negative_prompt = "french fries" output = sd_pipe(**inputs, negative_prompt=negative_prompt) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_model_editing_euler(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() components["scheduler"] = EulerAncestralDiscreteScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" ) sd_pipe = StableDiffusionModelEditingPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = sd_pipe(**inputs).images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_model_editing_pndm(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() components["scheduler"] = PNDMScheduler() sd_pipe = StableDiffusionModelEditingPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) # the pipeline does not expect pndm so test if it raises error. with self.assertRaises(ValueError): _ = sd_pipe(**inputs).images def test_inference_batch_single_identical(self): super().test_inference_batch_single_identical(expected_max_diff=5e-3) def test_attention_slicing_forward_pass(self): super().test_attention_slicing_forward_pass(expected_max_diff=5e-3) @nightly @require_torch_gpu class StableDiffusionModelEditingSlowTests(unittest.TestCase): def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def get_inputs(self, seed=0): generator = torch.manual_seed(seed) inputs = { "prompt": "A field of roses", "generator": generator, "num_inference_steps": 3, "guidance_scale": 7.5, "output_type": "numpy", } return inputs def test_stable_diffusion_model_editing_default(self): model_ckpt = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt, safety_checker=None) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() inputs = self.get_inputs() image = pipe(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array( [0.6749496, 0.6386453, 0.51443267, 0.66094905, 0.61921215, 0.5491332, 0.5744417, 0.58075106, 0.5174658] ) assert np.abs(expected_slice - image_slice).max() < 1e-2 # make sure image changes after editing pipe.edit_model("A pack of roses", "A pack of blue roses") image = pipe(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 512, 3) assert np.abs(expected_slice - image_slice).max() > 1e-1 def test_stable_diffusion_model_editing_pipeline_with_sequential_cpu_offloading(self): torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() model_ckpt = "CompVis/stable-diffusion-v1-4" scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler") pipe = StableDiffusionModelEditingPipeline.from_pretrained( model_ckpt, scheduler=scheduler, safety_checker=None ) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing(1) pipe.enable_sequential_cpu_offload() inputs = self.get_inputs() _ = pipe(**inputs) mem_bytes = torch.cuda.max_memory_allocated() # make sure that less than 4.4 GB is allocated assert mem_bytes < 4.4 * 10**9