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| # 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, | |
| LMSDiscreteScheduler, | |
| PNDMScheduler, | |
| StableDiffusionPanoramaPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.utils import slow, torch_device | |
| from diffusers.utils.testing_utils import require_torch_gpu, skip_mps | |
| from ...pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS | |
| from ...test_pipelines_common import PipelineTesterMixin | |
| torch.backends.cuda.matmul.allow_tf32 = False | |
| class StableDiffusionPanoramaPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = StableDiffusionPanoramaPipeline | |
| params = TEXT_TO_IMAGE_PARAMS | |
| batch_params = TEXT_TO_IMAGE_BATCH_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 photo of the dolomites", | |
| "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_panorama_default_case(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| sd_pipe = StableDiffusionPanoramaPipeline(**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([0.5101, 0.5006, 0.4962, 0.3995, 0.3501, 0.4632, 0.5339, 0.525, 0.4878]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_panorama_negative_prompt(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| sd_pipe = StableDiffusionPanoramaPipeline(**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([0.5326, 0.5009, 0.5074, 0.4133, 0.371, 0.464, 0.5432, 0.5429, 0.4896]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_panorama_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 = StableDiffusionPanoramaPipeline(**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( | |
| [0.48235387, 0.5423796, 0.46016198, 0.5377287, 0.5803722, 0.4876525, 0.5515428, 0.5045897, 0.50709957] | |
| ) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_panorama_pndm(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| components["scheduler"] = PNDMScheduler() | |
| sd_pipe = StableDiffusionPanoramaPipeline(**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 | |
| class StableDiffusionPanoramaSlowTests(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 photo of the dolomites", | |
| "generator": generator, | |
| "num_inference_steps": 3, | |
| "guidance_scale": 7.5, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_stable_diffusion_panorama_default(self): | |
| model_ckpt = "stabilityai/stable-diffusion-2-base" | |
| scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler") | |
| pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, 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, 2048, 3) | |
| expected_slice = np.array( | |
| [ | |
| 0.36968392, | |
| 0.27025372, | |
| 0.32446766, | |
| 0.28379387, | |
| 0.36363274, | |
| 0.30733347, | |
| 0.27100027, | |
| 0.27054125, | |
| 0.25536096, | |
| ] | |
| ) | |
| assert np.abs(expected_slice - image_slice).max() < 1e-2 | |
| def test_stable_diffusion_panorama_k_lms(self): | |
| pipe = StableDiffusionPanoramaPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2-base", safety_checker=None | |
| ) | |
| pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) | |
| 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, 2048, 3) | |
| expected_slice = np.array( | |
| [ | |
| [ | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| ] | |
| ] | |
| ) | |
| assert np.abs(expected_slice - image_slice).max() < 1e-3 | |
| def test_stable_diffusion_panorama_intermediate_state(self): | |
| number_of_steps = 0 | |
| def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None: | |
| callback_fn.has_been_called = True | |
| nonlocal number_of_steps | |
| number_of_steps += 1 | |
| if step == 1: | |
| latents = latents.detach().cpu().numpy() | |
| assert latents.shape == (1, 4, 64, 256) | |
| latents_slice = latents[0, -3:, -3:, -1] | |
| expected_slice = np.array( | |
| [ | |
| 0.18681869, | |
| 0.33907816, | |
| 0.5361276, | |
| 0.14432865, | |
| -0.02856611, | |
| -0.73941123, | |
| 0.23397987, | |
| 0.47322682, | |
| -0.37823164, | |
| ] | |
| ) | |
| assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
| elif step == 2: | |
| latents = latents.detach().cpu().numpy() | |
| assert latents.shape == (1, 4, 64, 256) | |
| latents_slice = latents[0, -3:, -3:, -1] | |
| expected_slice = np.array( | |
| [ | |
| 0.18539645, | |
| 0.33987248, | |
| 0.5378559, | |
| 0.14437142, | |
| -0.02455261, | |
| -0.7338317, | |
| 0.23990755, | |
| 0.47356272, | |
| -0.3786505, | |
| ] | |
| ) | |
| assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
| callback_fn.has_been_called = False | |
| model_ckpt = "stabilityai/stable-diffusion-2-base" | |
| scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler") | |
| pipe = StableDiffusionPanoramaPipeline.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() | |
| inputs = self.get_inputs() | |
| pipe(**inputs, callback=callback_fn, callback_steps=1) | |
| assert callback_fn.has_been_called | |
| assert number_of_steps == 3 | |
| def test_stable_diffusion_panorama_pipeline_with_sequential_cpu_offloading(self): | |
| torch.cuda.empty_cache() | |
| torch.cuda.reset_max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| model_ckpt = "stabilityai/stable-diffusion-2-base" | |
| scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler") | |
| pipe = StableDiffusionPanoramaPipeline.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 5.2 GB is allocated | |
| assert mem_bytes < 5.5 * 10**9 | |