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322b5cb2af
* fix: norm group test for UNet3D. * chore: speed up the panorama tests (fast). * set default value of _test_inference_batch_single_identical. * fix: batch_sizes default value.
349 lines
12 KiB
Python
349 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2023 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import unittest
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import numpy as np
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import torch
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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EulerAncestralDiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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StableDiffusionPanoramaPipeline,
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UNet2DConditionModel,
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)
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from diffusers.utils import slow, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu, skip_mps
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from ...pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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@skip_mps
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class StableDiffusionPanoramaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = StableDiffusionPanoramaPipeline
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params = TEXT_TO_IMAGE_PARAMS
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batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
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def get_dummy_components(self):
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torch.manual_seed(0)
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unet = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=1,
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sample_size=32,
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in_channels=4,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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scheduler = DDIMScheduler()
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torch.manual_seed(0)
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vae = AutoencoderKL(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=4,
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)
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torch.manual_seed(0)
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text_encoder_config = CLIPTextConfig(
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bos_token_id=0,
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eos_token_id=2,
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hidden_size=32,
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intermediate_size=37,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=5,
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pad_token_id=1,
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vocab_size=1000,
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)
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text_encoder = CLIPTextModel(text_encoder_config)
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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components = {
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"unet": unet,
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"scheduler": scheduler,
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"vae": vae,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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"safety_checker": None,
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"feature_extractor": None,
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}
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return components
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def get_dummy_inputs(self, device, seed=0):
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generator = torch.manual_seed(seed)
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inputs = {
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"prompt": "a photo of the dolomites",
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"generator": generator,
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# Setting height and width to None to prevent OOMs on CPU.
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"height": None,
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"width": None,
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"num_inference_steps": 1,
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"guidance_scale": 6.0,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_panorama_default_case(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionPanoramaPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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image = sd_pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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# override to speed the overall test timing up.
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def test_inference_batch_consistent(self):
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super().test_inference_batch_consistent(batch_sizes=[1, 2])
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# override to speed the overall test timing up.
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def test_inference_batch_single_identical(self):
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super().test_inference_batch_single_identical(batch_size=2)
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def test_stable_diffusion_panorama_negative_prompt(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionPanoramaPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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negative_prompt = "french fries"
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output = sd_pipe(**inputs, negative_prompt=negative_prompt)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_panorama_euler(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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components["scheduler"] = EulerAncestralDiscreteScheduler(
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beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
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)
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sd_pipe = StableDiffusionPanoramaPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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image = sd_pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.4886, 0.5586, 0.4476, 0.5053, 0.6013, 0.4737, 0.5538, 0.5100, 0.4927])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_panorama_pndm(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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components["scheduler"] = PNDMScheduler()
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sd_pipe = StableDiffusionPanoramaPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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# the pipeline does not expect pndm so test if it raises error.
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with self.assertRaises(ValueError):
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_ = sd_pipe(**inputs).images
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@slow
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@require_torch_gpu
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class StableDiffusionPanoramaSlowTests(unittest.TestCase):
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def tearDown(self):
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def get_inputs(self, seed=0):
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generator = torch.manual_seed(seed)
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inputs = {
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"prompt": "a photo of the dolomites",
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"generator": generator,
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"num_inference_steps": 3,
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"guidance_scale": 7.5,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_panorama_default(self):
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model_ckpt = "stabilityai/stable-diffusion-2-base"
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scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
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pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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inputs = self.get_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1].flatten()
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assert image.shape == (1, 512, 2048, 3)
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expected_slice = np.array(
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[
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0.36968392,
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0.27025372,
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0.32446766,
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0.28379387,
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0.36363274,
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0.30733347,
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0.27100027,
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0.27054125,
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0.25536096,
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]
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)
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assert np.abs(expected_slice - image_slice).max() < 1e-2
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def test_stable_diffusion_panorama_k_lms(self):
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pipe = StableDiffusionPanoramaPipeline.from_pretrained(
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"stabilityai/stable-diffusion-2-base", safety_checker=None
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)
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pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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inputs = self.get_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1].flatten()
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assert image.shape == (1, 512, 2048, 3)
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expected_slice = np.array(
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[
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[
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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]
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]
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)
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assert np.abs(expected_slice - image_slice).max() < 1e-3
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def test_stable_diffusion_panorama_intermediate_state(self):
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number_of_steps = 0
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def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
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callback_fn.has_been_called = True
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nonlocal number_of_steps
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number_of_steps += 1
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if step == 1:
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latents = latents.detach().cpu().numpy()
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assert latents.shape == (1, 4, 64, 256)
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latents_slice = latents[0, -3:, -3:, -1]
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expected_slice = np.array(
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[
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0.18681869,
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0.33907816,
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0.5361276,
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0.14432865,
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-0.02856611,
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-0.73941123,
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0.23397987,
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0.47322682,
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-0.37823164,
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]
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)
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assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
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elif step == 2:
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latents = latents.detach().cpu().numpy()
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assert latents.shape == (1, 4, 64, 256)
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latents_slice = latents[0, -3:, -3:, -1]
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expected_slice = np.array(
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[
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0.18539645,
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0.33987248,
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0.5378559,
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0.14437142,
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-0.02455261,
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-0.7338317,
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0.23990755,
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0.47356272,
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-0.3786505,
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]
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)
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assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
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callback_fn.has_been_called = False
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model_ckpt = "stabilityai/stable-diffusion-2-base"
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scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
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pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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inputs = self.get_inputs()
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pipe(**inputs, callback=callback_fn, callback_steps=1)
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assert callback_fn.has_been_called
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assert number_of_steps == 3
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def test_stable_diffusion_panorama_pipeline_with_sequential_cpu_offloading(self):
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torch.cuda.empty_cache()
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torch.cuda.reset_max_memory_allocated()
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torch.cuda.reset_peak_memory_stats()
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model_ckpt = "stabilityai/stable-diffusion-2-base"
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scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
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pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing(1)
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pipe.enable_sequential_cpu_offload()
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inputs = self.get_inputs()
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_ = pipe(**inputs)
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mem_bytes = torch.cuda.max_memory_allocated()
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# make sure that less than 5.2 GB is allocated
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assert mem_bytes < 5.5 * 10**9
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