M. Tolga Cangöz
5d848ec07c
* Add properties and `IPAdapterTesterMixin` tests for `StableDiffusionPanoramaPipeline`
* Fix variable name typo and update comments
* Update deprecated `output_type="numpy"` to "np" in test files
* Discard changes to src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py
* Update test_stable_diffusion_panorama.py
* Update numbers in README.md
* Update get_guidance_scale_embedding method to use timesteps instead of w
* Update number of checkpoints in README.md
* Add type hints and fix var name
* Fix PyTorch's convention for inplace functions
* Fix a typo
* Revert "Fix PyTorch's convention for inplace functions"
This reverts commit 74350cf65b.
* Fix typos
* Indent
* Refactor get_guidance_scale_embedding method in LEditsPPPipelineStableDiffusionXL class
112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
# coding=utf-8
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# Copyright 2024 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 unittest
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import numpy as np
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import torch
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from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
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from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
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enable_full_determinism()
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class DDPMPipelineFastTests(unittest.TestCase):
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@property
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def dummy_uncond_unet(self):
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torch.manual_seed(0)
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model = UNet2DModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownBlock2D", "AttnDownBlock2D"),
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up_block_types=("AttnUpBlock2D", "UpBlock2D"),
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)
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return model
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def test_fast_inference(self):
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device = "cpu"
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unet = self.dummy_uncond_unet
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scheduler = DDPMScheduler()
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ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
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ddpm.to(device)
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ddpm.set_progress_bar_config(disable=None)
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generator = torch.Generator(device=device).manual_seed(0)
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image = ddpm(generator=generator, num_inference_steps=2, output_type="np").images
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generator = torch.Generator(device=device).manual_seed(0)
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image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="np", return_dict=False)[0]
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image_slice = image[0, -3:, -3:, -1]
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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array(
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[9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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def test_inference_predict_sample(self):
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unet = self.dummy_uncond_unet
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scheduler = DDPMScheduler(prediction_type="sample")
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ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
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ddpm.to(torch_device)
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ddpm.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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image = ddpm(generator=generator, num_inference_steps=2, output_type="np").images
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generator = torch.manual_seed(0)
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image_eps = ddpm(generator=generator, num_inference_steps=2, output_type="np")[0]
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image_slice = image[0, -3:, -3:, -1]
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image_eps_slice = image_eps[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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tolerance = 1e-2 if torch_device != "mps" else 3e-2
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assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance
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@slow
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@require_torch_gpu
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class DDPMPipelineIntegrationTests(unittest.TestCase):
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def test_inference_cifar10(self):
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model_id = "google/ddpm-cifar10-32"
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unet = UNet2DModel.from_pretrained(model_id)
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scheduler = DDPMScheduler.from_pretrained(model_id)
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ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
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ddpm.to(torch_device)
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ddpm.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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image = ddpm(generator=generator, output_type="np").images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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