120 lines
4.1 KiB
Python
120 lines
4.1 KiB
Python
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# coding=utf-8
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# Copyright 2022 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 DDIMScheduler, LDMPipeline, UNet2DModel, VQModel
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from diffusers.utils.testing_utils import require_torch, slow, torch_device
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from transformers import CLIPTextConfig, CLIPTextModel
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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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class LDMPipelineFastTests(PipelineTesterMixin, 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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@property
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def dummy_vq_model(self):
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torch.manual_seed(0)
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model = VQModel(
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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=3,
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)
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return model
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@property
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def dummy_text_encoder(self):
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torch.manual_seed(0)
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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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return CLIPTextModel(config)
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def test_inference_uncond(self):
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unet = self.dummy_uncond_unet
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scheduler = DDIMScheduler()
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vae = self.dummy_vq_model
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ldm = LDMPipeline(unet=unet, vqvae=vae, scheduler=scheduler)
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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# Warmup pass when using mps (see #372)
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if torch_device == "mps":
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generator = torch.manual_seed(0)
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_ = ldm(generator=generator, num_inference_steps=1, output_type="numpy").images
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generator = torch.manual_seed(0)
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image = ldm(generator=generator, num_inference_steps=2, output_type="numpy").images
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generator = torch.manual_seed(0)
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image_from_tuple = ldm(generator=generator, num_inference_steps=2, output_type="numpy", 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, 64, 64, 3)
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expected_slice = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
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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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@slow
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@require_torch
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class LDMPipelineIntegrationTests(unittest.TestCase):
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def test_inference_uncond(self):
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ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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image = ldm(generator=generator, num_inference_steps=5, output_type="numpy").images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 256, 256, 3)
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expected_slice = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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