test_stable_diffusion_inpaint.py 50.3 KB
Newer Older
1
# coding=utf-8
Patrick von Platen's avatar
Patrick von Platen committed
2
# Copyright 2023 HuggingFace Inc.
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
#
# 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 random
18
import traceback
19
20
21
22
import unittest

import numpy as np
import torch
23
from huggingface_hub import hf_hub_download
24
25
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
26
27

from diffusers import (
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
28
    AsymmetricAutoencoderKL,
29
    AutoencoderKL,
30
    DDIMScheduler,
31
    DPMSolverMultistepScheduler,
32
    LMSDiscreteScheduler,
33
34
35
36
    PNDMScheduler,
    StableDiffusionInpaintPipeline,
    UNet2DConditionModel,
)
37
from diffusers.models.attention_processor import AttnProcessor
38
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image
39
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
40
41
42
43
44
45
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_2,
    require_torch_gpu,
    run_test_in_subprocess,
)
46

47
from ...models.test_models_unet_2d_condition import create_lora_layers
48
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
49
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
50

51

52
enable_full_determinism()
53
54


55
56
57
58
59
60
61
62
63
64
65
66
# Will be run via run_test_in_subprocess
def _test_inpaint_compile(in_queue, out_queue, timeout):
    error = None
    try:
        inputs = in_queue.get(timeout=timeout)
        torch_device = inputs.pop("torch_device")
        seed = inputs.pop("seed")
        inputs["generator"] = torch.Generator(device=torch_device).manual_seed(seed)

        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", safety_checker=None
        )
67
        pipe.unet.set_default_attn_processor()
68
69
70
71
72
73
74
75
76
77
78
        pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        pipe.unet.to(memory_format=torch.channels_last)
        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

        image = pipe(**inputs).images
        image_slice = image[0, 253:256, 253:256, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
79
        expected_slice = np.array([0.0689, 0.0699, 0.0790, 0.0536, 0.0470, 0.0488, 0.041, 0.0508, 0.04179])
80
81
82
83
84
85
86
87
88
        assert np.abs(expected_slice - image_slice).max() < 3e-3
    except Exception:
        error = f"{traceback.format_exc()}"

    results = {"error": error}
    out_queue.put(results, timeout=timeout)
    out_queue.join()


89
90
91
class StableDiffusionInpaintPipelineFastTests(
    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
):
92
    pipeline_class = StableDiffusionInpaintPipeline
93
94
    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
95
96
    image_params = frozenset([])
    # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
97
    image_latents_params = frozenset([])
98

99
    def get_dummy_components(self):
100
        torch.manual_seed(0)
101
        unet = UNet2DConditionModel(
102
103
104
105
106
107
108
109
110
            block_out_channels=(32, 64),
            layers_per_block=2,
            sample_size=32,
            in_channels=9,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            cross_attention_dim=32,
        )
111
        scheduler = PNDMScheduler(skip_prk_steps=True)
112
        torch.manual_seed(0)
113
        vae = AutoencoderKL(
114
115
116
117
118
119
120
121
            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)
122
        text_encoder_config = CLIPTextConfig(
123
124
125
126
127
128
129
130
131
132
            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,
        )
133
        text_encoder = CLIPTextModel(text_encoder_config)
134
135
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

136
137
138
139
140
141
142
        components = {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
143
            "feature_extractor": None,
144
145
146
147
148
149
150
        }
        return components

    def get_dummy_inputs(self, device, seed=0):
        # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
        image = image.cpu().permute(0, 2, 3, 1)[0]
Patrick von Platen's avatar
Patrick von Platen committed
151
152
        init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))
        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((64, 64))
153
154
155
156
157
158
159
160
161
162
163
164
165
166
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)
        inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "image": init_image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs
167

168
169
170
171
    def test_stable_diffusion_inpaint(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        sd_pipe = StableDiffusionInpaintPipeline(**components)
172
173
174
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

175
176
        inputs = self.get_dummy_inputs(device)
        image = sd_pipe(**inputs).images
177
178
        image_slice = image[0, -3:, -3:, -1]

179
        assert image.shape == (1, 64, 64, 3)
Patrick von Platen's avatar
Patrick von Platen committed
180
        expected_slice = np.array([0.4723, 0.5731, 0.3939, 0.5441, 0.5922, 0.4392, 0.5059, 0.4651, 0.4474])
181

182
183
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

184
185
    def test_stable_diffusion_inpaint_image_tensor(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
186
187
        components = self.get_dummy_components()
        sd_pipe = StableDiffusionInpaintPipeline(**components)
188
189
190
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

191
192
193
        inputs = self.get_dummy_inputs(device)
        output = sd_pipe(**inputs)
        out_pil = output.images
194

195
196
197
198
199
        inputs = self.get_dummy_inputs(device)
        inputs["image"] = torch.tensor(np.array(inputs["image"]) / 127.5 - 1).permute(2, 0, 1).unsqueeze(0)
        inputs["mask_image"] = torch.tensor(np.array(inputs["mask_image"]) / 255).permute(2, 0, 1)[:1].unsqueeze(0)
        output = sd_pipe(**inputs)
        out_tensor = output.images
200

201
202
        assert out_pil.shape == (1, 64, 64, 3)
        assert np.abs(out_pil.flatten() - out_tensor.flatten()).max() < 5e-2
203

204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
    def test_stable_diffusion_inpaint_lora(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator

        components = self.get_dummy_components()
        sd_pipe = StableDiffusionInpaintPipeline(**components)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        # forward 1
        inputs = self.get_dummy_inputs(device)
        output = sd_pipe(**inputs)
        image = output.images
        image_slice = image[0, -3:, -3:, -1]

        # set lora layers
        lora_attn_procs = create_lora_layers(sd_pipe.unet)
        sd_pipe.unet.set_attn_processor(lora_attn_procs)
        sd_pipe = sd_pipe.to(torch_device)

        # forward 2
        inputs = self.get_dummy_inputs(device)
        output = sd_pipe(**inputs, cross_attention_kwargs={"scale": 0.0})
        image = output.images
        image_slice_1 = image[0, -3:, -3:, -1]

        # forward 3
        inputs = self.get_dummy_inputs(device)
        output = sd_pipe(**inputs, cross_attention_kwargs={"scale": 0.5})
        image = output.images
        image_slice_2 = image[0, -3:, -3:, -1]

        assert np.abs(image_slice - image_slice_1).max() < 1e-2
        assert np.abs(image_slice - image_slice_2).max() > 1e-2

238
239
240
    def test_inference_batch_single_identical(self):
        super().test_inference_batch_single_identical(expected_max_diff=3e-3)

241
242
243
244
245
246
247
248
249
250
251
252
253
254
    def test_stable_diffusion_inpaint_strength_zero_test(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        sd_pipe = StableDiffusionInpaintPipeline(**components)
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)

        # check that the pipeline raises value error when num_inference_steps is < 1
        inputs["strength"] = 0.01
        with self.assertRaises(ValueError):
            sd_pipe(**inputs).images

255

256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
class StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipelineFastTests):
    pipeline_class = StableDiffusionInpaintPipeline
    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    image_params = frozenset([])
    # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess

    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 = PNDMScheduler(skip_prk_steps=True)
        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 test_stable_diffusion_inpaint(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        sd_pipe = StableDiffusionInpaintPipeline(**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([0.4925, 0.4967, 0.4100, 0.5234, 0.5322, 0.4532, 0.5805, 0.5877, 0.4151])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @unittest.skip("skipped here because area stays unchanged due to mask")
    def test_stable_diffusion_inpaint_lora(self):
        ...


332
@slow
333
@require_torch_gpu
334
335
336
337
class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
    def setUp(self):
        super().setUp()

338
339
340
341
342
    def tearDown(self):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

343
344
    def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
        generator = torch.Generator(device=generator_device).manual_seed(seed)
345
        init_image = load_image(
346
347
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/input_bench_image.png"
348
349
        )
        mask_image = load_image(
350
351
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/input_bench_mask.png"
352
        )
353
354
355
356
357
358
359
360
361
362
        inputs = {
            "prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
            "image": init_image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs
363

364
365
366
367
    def test_stable_diffusion_inpaint_ddim(self):
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", safety_checker=None
        )
368
369
370
371
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

372
373
374
        inputs = self.get_inputs(torch_device)
        image = pipe(**inputs).images
        image_slice = image[0, 253:256, 253:256, -1].flatten()
375

376
        assert image.shape == (1, 512, 512, 3)
377
378
        expected_slice = np.array([0.0427, 0.0460, 0.0483, 0.0460, 0.0584, 0.0521, 0.1549, 0.1695, 0.1794])

379
        assert np.abs(expected_slice - image_slice).max() < 6e-4
380
381
382

    def test_stable_diffusion_inpaint_fp16(self):
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
383
            "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None
384
        )
385
        pipe.unet.set_default_attn_processor()
386
387
388
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()
389

390
391
392
        inputs = self.get_inputs(torch_device, dtype=torch.float16)
        image = pipe(**inputs).images
        image_slice = image[0, 253:256, 253:256, -1].flatten()
393

394
        assert image.shape == (1, 512, 512, 3)
395
        expected_slice = np.array([0.1509, 0.1245, 0.1672, 0.1655, 0.1519, 0.1226, 0.1462, 0.1567, 0.2451])
396
        assert np.abs(expected_slice - image_slice).max() < 5e-2
397

398
    def test_stable_diffusion_inpaint_pndm(self):
399
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
400
            "runwayml/stable-diffusion-inpainting", safety_checker=None
401
        )
402
        pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
403
404
405
406
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

407
408
409
        inputs = self.get_inputs(torch_device)
        image = pipe(**inputs).images
        image_slice = image[0, 253:256, 253:256, -1].flatten()
410

411
        assert image.shape == (1, 512, 512, 3)
412
413
        expected_slice = np.array([0.0425, 0.0273, 0.0344, 0.1694, 0.1727, 0.1812, 0.3256, 0.3311, 0.3272])

414
        assert np.abs(expected_slice - image_slice).max() < 5e-3
415

416
417
418
    def test_stable_diffusion_inpaint_k_lms(self):
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", safety_checker=None
419
        )
420
        pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
421
422
423
424
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

425
426
427
428
429
        inputs = self.get_inputs(torch_device)
        image = pipe(**inputs).images
        image_slice = image[0, 253:256, 253:256, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
430
431
        expected_slice = np.array([0.9314, 0.7575, 0.9432, 0.8885, 0.9028, 0.7298, 0.9811, 0.9667, 0.7633])

432
        assert np.abs(expected_slice - image_slice).max() < 6e-3
433

434
435
436
437
438
439
    def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        pipe = StableDiffusionInpaintPipeline.from_pretrained(
440
            "runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16
441
        )
442
443
444
445
446
447
448
449
450
451
452
453
        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(torch_device, dtype=torch.float16)
        _ = pipe(**inputs)

        mem_bytes = torch.cuda.max_memory_allocated()
        # make sure that less than 2.2 GB is allocated
        assert mem_bytes < 2.2 * 10**9

454
    @require_torch_2
455
    def test_inpaint_compile(self):
456
457
458
459
460
461
462
        seed = 0
        inputs = self.get_inputs(torch_device, seed=seed)
        # Can't pickle a Generator object
        del inputs["generator"]
        inputs["torch_device"] = torch_device
        inputs["seed"] = seed
        run_test_in_subprocess(test_case=self, target_func=_test_inpaint_compile, inputs=inputs)
463

464
    def test_stable_diffusion_inpaint_pil_input_resolution_test(self):
Patrick von Platen's avatar
Patrick von Platen committed
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", 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(torch_device)
        # change input image to a random size (one that would cause a tensor mismatch error)
        inputs["image"] = inputs["image"].resize((127, 127))
        inputs["mask_image"] = inputs["mask_image"].resize((127, 127))
        inputs["height"] = 128
        inputs["width"] = 128
        image = pipe(**inputs).images
        # verify that the returned image has the same height and width as the input height and width
        assert image.shape == (1, inputs["height"], inputs["width"], 3)
482

483
484
485
486
487
    def test_stable_diffusion_inpaint_strength_test(self):
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", safety_checker=None
        )
        pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
488
        pipe.unet.set_default_attn_processor()
489
490
491
492
493
494
495
496
497
498
499
500
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        inputs = self.get_inputs(torch_device)
        # change input strength
        inputs["strength"] = 0.75
        image = pipe(**inputs).images
        # verify that the returned image has the same height and width as the input height and width
        assert image.shape == (1, 512, 512, 3)

        image_slice = image[0, 253:256, 253:256, -1].flatten()
501
502
        expected_slice = np.array([0.2728, 0.2803, 0.2665, 0.2511, 0.2774, 0.2586, 0.2391, 0.2392, 0.2582])
        assert np.abs(expected_slice - image_slice).max() < 1e-3
503

504
505
    def test_stable_diffusion_simple_inpaint_ddim(self):
        pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
506
        pipe.unet.set_default_attn_processor()
507
508
509
510
511
512
513
514
515
516
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        inputs = self.get_inputs(torch_device)
        image = pipe(**inputs).images

        image_slice = image[0, 253:256, 253:256, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
517
518
        expected_slice = np.array([0.3757, 0.3875, 0.4445, 0.4353, 0.3780, 0.4513, 0.3965, 0.3984, 0.4362])
        assert np.abs(expected_slice - image_slice).max() < 1e-3
519

520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
    def test_download_local(self):
        filename = hf_hub_download("runwayml/stable-diffusion-inpainting", filename="sd-v1-5-inpainting.ckpt")

        pipe = StableDiffusionInpaintPipeline.from_single_file(filename, torch_dtype=torch.float16)
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        pipe.to("cuda")

        inputs = self.get_inputs(torch_device)
        inputs["num_inference_steps"] = 1
        image_out = pipe(**inputs).images[0]

        assert image_out.shape == (512, 512, 3)

    def test_download_ckpt_diff_format_is_same(self):
        ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-inpainting/blob/main/sd-v1-5-inpainting.ckpt"

        pipe = StableDiffusionInpaintPipeline.from_single_file(ckpt_path)
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        pipe.unet.set_attn_processor(AttnProcessor())
        pipe.to("cuda")

        inputs = self.get_inputs(torch_device)
        inputs["num_inference_steps"] = 5
        image_ckpt = pipe(**inputs).images[0]

        pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        pipe.unet.set_attn_processor(AttnProcessor())
        pipe.to("cuda")

        inputs = self.get_inputs(torch_device)
        inputs["num_inference_steps"] = 5
        image = pipe(**inputs).images[0]

        assert np.max(np.abs(image - image_ckpt)) < 1e-4

556

Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
@slow
@require_torch_gpu
class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.TestCase):
    def setUp(self):
        super().setUp()

    def tearDown(self):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
        generator = torch.Generator(device=generator_device).manual_seed(seed)
        init_image = load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/input_bench_image.png"
        )
        mask_image = load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/input_bench_mask.png"
        )
        inputs = {
            "prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
            "image": init_image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs

    def test_stable_diffusion_inpaint_ddim(self):
        vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", safety_checker=None
        )
        pipe.vae = vae
595
        pipe.unet.set_default_attn_processor()
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
596
597
598
599
600
601
602
603
604
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        inputs = self.get_inputs(torch_device)
        image = pipe(**inputs).images
        image_slice = image[0, 253:256, 253:256, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
605
        expected_slice = np.array([0.0522, 0.0604, 0.0596, 0.0449, 0.0493, 0.0427, 0.1186, 0.1289, 0.1442])
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
606

607
        assert np.abs(expected_slice - image_slice).max() < 1e-3
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
608
609
610
611
612
613
614
615

    def test_stable_diffusion_inpaint_fp16(self):
        vae = AsymmetricAutoencoderKL.from_pretrained(
            "cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16
        )
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None
        )
616
        pipe.unet.set_default_attn_processor()
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
        pipe.vae = vae
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        inputs = self.get_inputs(torch_device, dtype=torch.float16)
        image = pipe(**inputs).images
        image_slice = image[0, 253:256, 253:256, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        expected_slice = np.array([0.1343, 0.1406, 0.1440, 0.1504, 0.1729, 0.0989, 0.1807, 0.2822, 0.1179])

        assert np.abs(expected_slice - image_slice).max() < 5e-2

    def test_stable_diffusion_inpaint_pndm(self):
        vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", safety_checker=None
        )
636
        pipe.unet.set_default_attn_processor()
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
637
638
639
640
641
642
643
644
645
646
647
        pipe.vae = vae
        pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        inputs = self.get_inputs(torch_device)
        image = pipe(**inputs).images
        image_slice = image[0, 253:256, 253:256, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
648
        expected_slice = np.array([0.0966, 0.1083, 0.1148, 0.1422, 0.1318, 0.1197, 0.3702, 0.3537, 0.3288])
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
649
650
651
652
653
654
655
656

        assert np.abs(expected_slice - image_slice).max() < 5e-3

    def test_stable_diffusion_inpaint_k_lms(self):
        vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", safety_checker=None
        )
657
        pipe.unet.set_default_attn_processor()
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
658
659
660
661
662
663
664
665
666
667
        pipe.vae = vae
        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(torch_device)
        image = pipe(**inputs).images
        image_slice = image[0, 253:256, 253:256, -1].flatten()
        assert image.shape == (1, 512, 512, 3)
668
        expected_slice = np.array([0.8931, 0.8683, 0.8965, 0.8501, 0.8592, 0.9118, 0.8734, 0.7463, 0.8990])
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
        assert np.abs(expected_slice - image_slice).max() < 6e-3

    def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        vae = AsymmetricAutoencoderKL.from_pretrained(
            "cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16
        )
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16
        )
        pipe.vae = vae
        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(torch_device, dtype=torch.float16)
        _ = pipe(**inputs)

        mem_bytes = torch.cuda.max_memory_allocated()
        # make sure that less than 2.45 GB is allocated
        assert mem_bytes < 2.45 * 10**9

    @require_torch_2
    def test_inpaint_compile(self):
        pass

    def test_stable_diffusion_inpaint_pil_input_resolution_test(self):
        vae = AsymmetricAutoencoderKL.from_pretrained(
            "cross-attention/asymmetric-autoencoder-kl-x-1-5",
        )
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", safety_checker=None
        )
        pipe.vae = vae
        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(torch_device)
        # change input image to a random size (one that would cause a tensor mismatch error)
        inputs["image"] = inputs["image"].resize((127, 127))
        inputs["mask_image"] = inputs["mask_image"].resize((127, 127))
        inputs["height"] = 128
        inputs["width"] = 128
        image = pipe(**inputs).images
        # verify that the returned image has the same height and width as the input height and width
        assert image.shape == (1, inputs["height"], inputs["width"], 3)

    def test_stable_diffusion_inpaint_strength_test(self):
        vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
        pipe = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", safety_checker=None
        )
727
        pipe.unet.set_default_attn_processor()
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
        pipe.vae = vae
        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(torch_device)
        # change input strength
        inputs["strength"] = 0.75
        image = pipe(**inputs).images
        # verify that the returned image has the same height and width as the input height and width
        assert image.shape == (1, 512, 512, 3)

        image_slice = image[0, 253:256, 253:256, -1].flatten()
        expected_slice = np.array([0.2458, 0.2576, 0.3124, 0.2679, 0.2669, 0.2796, 0.2872, 0.2975, 0.2661])
        assert np.abs(expected_slice - image_slice).max() < 3e-3

    def test_stable_diffusion_simple_inpaint_ddim(self):
        vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
        pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
        pipe.vae = vae
749
        pipe.unet.set_default_attn_processor()
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
750
751
752
753
754
755
756
757
758
759
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        inputs = self.get_inputs(torch_device)
        image = pipe(**inputs).images

        image_slice = image[0, 253:256, 253:256, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
760
761
        expected_slice = np.array([0.3296, 0.4041, 0.4097, 0.4145, 0.4342, 0.4152, 0.4927, 0.4931, 0.4430])
        assert np.abs(expected_slice - image_slice).max() < 1e-3
Ruslan Vorovchenko's avatar
Ruslan Vorovchenko committed
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783

    def test_download_local(self):
        vae = AsymmetricAutoencoderKL.from_pretrained(
            "cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16
        )
        filename = hf_hub_download("runwayml/stable-diffusion-inpainting", filename="sd-v1-5-inpainting.ckpt")

        pipe = StableDiffusionInpaintPipeline.from_single_file(filename, torch_dtype=torch.float16)
        pipe.vae = vae
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        pipe.to("cuda")

        inputs = self.get_inputs(torch_device)
        inputs["num_inference_steps"] = 1
        image_out = pipe(**inputs).images[0]

        assert image_out.shape == (512, 512, 3)

    def test_download_ckpt_diff_format_is_same(self):
        pass


784
785
786
787
788
789
790
@nightly
@require_torch_gpu
class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):
    def tearDown(self):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
791

792
793
    def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
        generator = torch.Generator(device=generator_device).manual_seed(seed)
794
        init_image = load_image(
795
796
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/input_bench_image.png"
797
798
        )
        mask_image = load_image(
799
800
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/input_bench_mask.png"
801
        )
802
803
804
805
806
807
808
809
810
811
        inputs = {
            "prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
            "image": init_image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 50,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs
812

813
814
815
816
    def test_inpaint_ddim(self):
        sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
        sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)
817

818
819
        inputs = self.get_inputs(torch_device)
        image = sd_pipe(**inputs).images[0]
820

821
822
823
824
825
826
        expected_image = load_numpy(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/stable_diffusion_inpaint_ddim.npy"
        )
        max_diff = np.abs(expected_image - image).max()
        assert max_diff < 1e-3
827

828
829
830
831
832
833
834
835
    def test_inpaint_pndm(self):
        sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
        sd_pipe.scheduler = PNDMScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_inputs(torch_device)
        image = sd_pipe(**inputs).images[0]
836

837
838
839
        expected_image = load_numpy(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/stable_diffusion_inpaint_pndm.npy"
840
        )
841
842
        max_diff = np.abs(expected_image - image).max()
        assert max_diff < 1e-3
843

844
845
846
847
848
    def test_inpaint_lms(self):
        sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
        sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)
849

850
851
        inputs = self.get_inputs(torch_device)
        image = sd_pipe(**inputs).images[0]
852

853
854
855
        expected_image = load_numpy(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/stable_diffusion_inpaint_lms.npy"
856
        )
857
858
        max_diff = np.abs(expected_image - image).max()
        assert max_diff < 1e-3
859

860
861
862
863
864
    def test_inpaint_dpm(self):
        sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
        sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)
865

866
867
868
        inputs = self.get_inputs(torch_device)
        inputs["num_inference_steps"] = 30
        image = sd_pipe(**inputs).images[0]
869

870
871
872
        expected_image = load_numpy(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/stable_diffusion_inpaint_dpm_multi.npy"
873
        )
874
875
        max_diff = np.abs(expected_image - image).max()
        assert max_diff < 1e-3
876

Patrick von Platen's avatar
Patrick von Platen committed
877

878
879
class StableDiffusionInpaintingPrepareMaskAndMaskedImageTests(unittest.TestCase):
    def test_pil_inputs(self):
880
881
        height, width = 32, 32
        im = np.random.randint(0, 255, (height, width, 3), dtype=np.uint8)
882
        im = Image.fromarray(im)
883
        mask = np.random.randint(0, 255, (height, width), dtype=np.uint8) > 127.5
884
885
        mask = Image.fromarray((mask * 255).astype(np.uint8))

886
        t_mask, t_masked, t_image = prepare_mask_and_masked_image(im, mask, height, width, return_image=True)
887
888
889

        self.assertTrue(isinstance(t_mask, torch.Tensor))
        self.assertTrue(isinstance(t_masked, torch.Tensor))
890
        self.assertTrue(isinstance(t_image, torch.Tensor))
891
892
893

        self.assertEqual(t_mask.ndim, 4)
        self.assertEqual(t_masked.ndim, 4)
894
        self.assertEqual(t_image.ndim, 4)
895

896
897
        self.assertEqual(t_mask.shape, (1, 1, height, width))
        self.assertEqual(t_masked.shape, (1, 3, height, width))
898
        self.assertEqual(t_image.shape, (1, 3, height, width))
899
900
901

        self.assertTrue(t_mask.dtype == torch.float32)
        self.assertTrue(t_masked.dtype == torch.float32)
902
        self.assertTrue(t_image.dtype == torch.float32)
903
904
905
906
907

        self.assertTrue(t_mask.min() >= 0.0)
        self.assertTrue(t_mask.max() <= 1.0)
        self.assertTrue(t_masked.min() >= -1.0)
        self.assertTrue(t_masked.min() <= 1.0)
908
909
        self.assertTrue(t_image.min() >= -1.0)
        self.assertTrue(t_image.min() >= -1.0)
910
911
912
913

        self.assertTrue(t_mask.sum() > 0.0)

    def test_np_inputs(self):
914
915
916
        height, width = 32, 32

        im_np = np.random.randint(0, 255, (height, width, 3), dtype=np.uint8)
917
        im_pil = Image.fromarray(im_np)
Patrick von Platen's avatar
Patrick von Platen committed
918
919
920
921
922
923
924
925
926
927
928
929
        mask_np = (
            np.random.randint(
                0,
                255,
                (
                    height,
                    width,
                ),
                dtype=np.uint8,
            )
            > 127.5
        )
930
931
        mask_pil = Image.fromarray((mask_np * 255).astype(np.uint8))

932
933
934
935
936
937
        t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
            im_np, mask_np, height, width, return_image=True
        )
        t_mask_pil, t_masked_pil, t_image_pil = prepare_mask_and_masked_image(
            im_pil, mask_pil, height, width, return_image=True
        )
938
939
940

        self.assertTrue((t_mask_np == t_mask_pil).all())
        self.assertTrue((t_masked_np == t_masked_pil).all())
941
        self.assertTrue((t_image_np == t_image_pil).all())
942
943

    def test_torch_3D_2D_inputs(self):
944
945
        height, width = 32, 32

Patrick von Platen's avatar
Patrick von Platen committed
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
        im_tensor = torch.randint(
            0,
            255,
            (
                3,
                height,
                width,
            ),
            dtype=torch.uint8,
        )
        mask_tensor = (
            torch.randint(
                0,
                255,
                (
                    height,
                    width,
                ),
                dtype=torch.uint8,
            )
            > 127.5
        )
968
969
970
        im_np = im_tensor.numpy().transpose(1, 2, 0)
        mask_np = mask_tensor.numpy()

971
972
973
974
975
        t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
            im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
        )
        t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
            im_np, mask_np, height, width, return_image=True
Patrick von Platen's avatar
Patrick von Platen committed
976
        )
977
978
979

        self.assertTrue((t_mask_tensor == t_mask_np).all())
        self.assertTrue((t_masked_tensor == t_masked_np).all())
980
        self.assertTrue((t_image_tensor == t_image_np).all())
981
982

    def test_torch_3D_3D_inputs(self):
983
984
        height, width = 32, 32

Patrick von Platen's avatar
Patrick von Platen committed
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
        im_tensor = torch.randint(
            0,
            255,
            (
                3,
                height,
                width,
            ),
            dtype=torch.uint8,
        )
        mask_tensor = (
            torch.randint(
                0,
                255,
                (
                    1,
                    height,
                    width,
                ),
                dtype=torch.uint8,
            )
            > 127.5
        )
1008
1009
1010
        im_np = im_tensor.numpy().transpose(1, 2, 0)
        mask_np = mask_tensor.numpy()[0]

1011
1012
1013
1014
1015
        t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
            im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
        )
        t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
            im_np, mask_np, height, width, return_image=True
Patrick von Platen's avatar
Patrick von Platen committed
1016
        )
1017
1018
1019

        self.assertTrue((t_mask_tensor == t_mask_np).all())
        self.assertTrue((t_masked_tensor == t_masked_np).all())
1020
        self.assertTrue((t_image_tensor == t_image_np).all())
1021
1022

    def test_torch_4D_2D_inputs(self):
1023
1024
        height, width = 32, 32

Patrick von Platen's avatar
Patrick von Platen committed
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
        im_tensor = torch.randint(
            0,
            255,
            (
                1,
                3,
                height,
                width,
            ),
            dtype=torch.uint8,
        )
        mask_tensor = (
            torch.randint(
                0,
                255,
                (
                    height,
                    width,
                ),
                dtype=torch.uint8,
            )
            > 127.5
        )
1048
1049
1050
        im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
        mask_np = mask_tensor.numpy()

1051
1052
1053
1054
1055
        t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
            im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
        )
        t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
            im_np, mask_np, height, width, return_image=True
Patrick von Platen's avatar
Patrick von Platen committed
1056
        )
1057
1058
1059

        self.assertTrue((t_mask_tensor == t_mask_np).all())
        self.assertTrue((t_masked_tensor == t_masked_np).all())
1060
        self.assertTrue((t_image_tensor == t_image_np).all())
1061
1062

    def test_torch_4D_3D_inputs(self):
1063
1064
        height, width = 32, 32

Patrick von Platen's avatar
Patrick von Platen committed
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
        im_tensor = torch.randint(
            0,
            255,
            (
                1,
                3,
                height,
                width,
            ),
            dtype=torch.uint8,
        )
        mask_tensor = (
            torch.randint(
                0,
                255,
                (
                    1,
                    height,
                    width,
                ),
                dtype=torch.uint8,
            )
            > 127.5
        )
1089
1090
1091
        im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
        mask_np = mask_tensor.numpy()[0]

1092
1093
1094
1095
1096
        t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
            im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
        )
        t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
            im_np, mask_np, height, width, return_image=True
Patrick von Platen's avatar
Patrick von Platen committed
1097
        )
1098
1099
1100

        self.assertTrue((t_mask_tensor == t_mask_np).all())
        self.assertTrue((t_masked_tensor == t_masked_np).all())
1101
        self.assertTrue((t_image_tensor == t_image_np).all())
1102
1103

    def test_torch_4D_4D_inputs(self):
1104
1105
        height, width = 32, 32

Patrick von Platen's avatar
Patrick von Platen committed
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
        im_tensor = torch.randint(
            0,
            255,
            (
                1,
                3,
                height,
                width,
            ),
            dtype=torch.uint8,
        )
        mask_tensor = (
            torch.randint(
                0,
                255,
                (
                    1,
                    1,
                    height,
                    width,
                ),
                dtype=torch.uint8,
            )
            > 127.5
        )
1131
1132
1133
        im_np = im_tensor.numpy()[0].transpose(1, 2, 0)
        mask_np = mask_tensor.numpy()[0][0]

1134
1135
1136
1137
1138
        t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
            im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
        )
        t_mask_np, t_masked_np, t_image_np = prepare_mask_and_masked_image(
            im_np, mask_np, height, width, return_image=True
Patrick von Platen's avatar
Patrick von Platen committed
1139
        )
1140
1141
1142

        self.assertTrue((t_mask_tensor == t_mask_np).all())
        self.assertTrue((t_masked_tensor == t_masked_np).all())
1143
        self.assertTrue((t_image_tensor == t_image_np).all())
1144
1145

    def test_torch_batch_4D_3D(self):
1146
1147
        height, width = 32, 32

Patrick von Platen's avatar
Patrick von Platen committed
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
        im_tensor = torch.randint(
            0,
            255,
            (
                2,
                3,
                height,
                width,
            ),
            dtype=torch.uint8,
        )
        mask_tensor = (
            torch.randint(
                0,
                255,
                (
                    2,
                    height,
                    width,
                ),
                dtype=torch.uint8,
            )
            > 127.5
        )
1172
1173
1174
1175

        im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
        mask_nps = [mask.numpy() for mask in mask_tensor]

1176
1177
        t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
            im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
Patrick von Platen's avatar
Patrick von Platen committed
1178
        )
1179
        nps = [prepare_mask_and_masked_image(i, m, height, width, return_image=True) for i, m in zip(im_nps, mask_nps)]
1180
1181
        t_mask_np = torch.cat([n[0] for n in nps])
        t_masked_np = torch.cat([n[1] for n in nps])
1182
        t_image_np = torch.cat([n[2] for n in nps])
1183
1184
1185

        self.assertTrue((t_mask_tensor == t_mask_np).all())
        self.assertTrue((t_masked_tensor == t_masked_np).all())
1186
        self.assertTrue((t_image_tensor == t_image_np).all())
1187
1188

    def test_torch_batch_4D_4D(self):
1189
1190
        height, width = 32, 32

Patrick von Platen's avatar
Patrick von Platen committed
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
        im_tensor = torch.randint(
            0,
            255,
            (
                2,
                3,
                height,
                width,
            ),
            dtype=torch.uint8,
        )
        mask_tensor = (
            torch.randint(
                0,
                255,
                (
                    2,
                    1,
                    height,
                    width,
                ),
                dtype=torch.uint8,
            )
            > 127.5
        )
1216
1217
1218
1219

        im_nps = [im.numpy().transpose(1, 2, 0) for im in im_tensor]
        mask_nps = [mask.numpy()[0] for mask in mask_tensor]

1220
1221
        t_mask_tensor, t_masked_tensor, t_image_tensor = prepare_mask_and_masked_image(
            im_tensor / 127.5 - 1, mask_tensor, height, width, return_image=True
Patrick von Platen's avatar
Patrick von Platen committed
1222
        )
1223
        nps = [prepare_mask_and_masked_image(i, m, height, width, return_image=True) for i, m in zip(im_nps, mask_nps)]
1224
1225
        t_mask_np = torch.cat([n[0] for n in nps])
        t_masked_np = torch.cat([n[1] for n in nps])
1226
        t_image_np = torch.cat([n[2] for n in nps])
1227
1228
1229

        self.assertTrue((t_mask_tensor == t_mask_np).all())
        self.assertTrue((t_masked_tensor == t_masked_np).all())
1230
        self.assertTrue((t_image_tensor == t_image_np).all())
1231
1232

    def test_shape_mismatch(self):
1233
1234
        height, width = 32, 32

1235
1236
        # test height and width
        with self.assertRaises(AssertionError):
Patrick von Platen's avatar
Patrick von Platen committed
1237
1238
1239
1240
1241
1242
1243
1244
1245
            prepare_mask_and_masked_image(
                torch.randn(
                    3,
                    height,
                    width,
                ),
                torch.randn(64, 64),
                height,
                width,
1246
                return_image=True,
Patrick von Platen's avatar
Patrick von Platen committed
1247
            )
1248
1249
        # test batch dim
        with self.assertRaises(AssertionError):
Patrick von Platen's avatar
Patrick von Platen committed
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
            prepare_mask_and_masked_image(
                torch.randn(
                    2,
                    3,
                    height,
                    width,
                ),
                torch.randn(4, 64, 64),
                height,
                width,
1260
                return_image=True,
Patrick von Platen's avatar
Patrick von Platen committed
1261
            )
1262
1263
        # test batch dim
        with self.assertRaises(AssertionError):
Patrick von Platen's avatar
Patrick von Platen committed
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
            prepare_mask_and_masked_image(
                torch.randn(
                    2,
                    3,
                    height,
                    width,
                ),
                torch.randn(4, 1, 64, 64),
                height,
                width,
1274
                return_image=True,
Patrick von Platen's avatar
Patrick von Platen committed
1275
            )
1276
1277

    def test_type_mismatch(self):
1278
1279
        height, width = 32, 32

1280
1281
        # test tensors-only
        with self.assertRaises(TypeError):
Patrick von Platen's avatar
Patrick von Platen committed
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
            prepare_mask_and_masked_image(
                torch.rand(
                    3,
                    height,
                    width,
                ),
                torch.rand(
                    3,
                    height,
                    width,
                ).numpy(),
                height,
                width,
1295
                return_image=True,
Patrick von Platen's avatar
Patrick von Platen committed
1296
            )
1297
1298
        # test tensors-only
        with self.assertRaises(TypeError):
Patrick von Platen's avatar
Patrick von Platen committed
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
            prepare_mask_and_masked_image(
                torch.rand(
                    3,
                    height,
                    width,
                ).numpy(),
                torch.rand(
                    3,
                    height,
                    width,
                ),
                height,
                width,
1312
                return_image=True,
Patrick von Platen's avatar
Patrick von Platen committed
1313
            )
1314
1315

    def test_channels_first(self):
1316
1317
        height, width = 32, 32

1318
1319
        # test channels first for 3D tensors
        with self.assertRaises(AssertionError):
Patrick von Platen's avatar
Patrick von Platen committed
1320
1321
1322
1323
1324
1325
1326
1327
1328
            prepare_mask_and_masked_image(
                torch.rand(height, width, 3),
                torch.rand(
                    3,
                    height,
                    width,
                ),
                height,
                width,
1329
                return_image=True,
Patrick von Platen's avatar
Patrick von Platen committed
1330
            )
1331
1332

    def test_tensor_range(self):
1333
1334
        height, width = 32, 32

1335
1336
        # test im <= 1
        with self.assertRaises(ValueError):
Patrick von Platen's avatar
Patrick von Platen committed
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
            prepare_mask_and_masked_image(
                torch.ones(
                    3,
                    height,
                    width,
                )
                * 2,
                torch.rand(
                    height,
                    width,
                ),
                height,
                width,
1350
                return_image=True,
Patrick von Platen's avatar
Patrick von Platen committed
1351
            )
1352
1353
        # test im >= -1
        with self.assertRaises(ValueError):
Patrick von Platen's avatar
Patrick von Platen committed
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
            prepare_mask_and_masked_image(
                torch.ones(
                    3,
                    height,
                    width,
                )
                * (-2),
                torch.rand(
                    height,
                    width,
                ),
                height,
                width,
1367
                return_image=True,
Patrick von Platen's avatar
Patrick von Platen committed
1368
            )
1369
1370
        # test mask <= 1
        with self.assertRaises(ValueError):
Patrick von Platen's avatar
Patrick von Platen committed
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
            prepare_mask_and_masked_image(
                torch.rand(
                    3,
                    height,
                    width,
                ),
                torch.ones(
                    height,
                    width,
                )
                * 2,
                height,
                width,
1384
                return_image=True,
Patrick von Platen's avatar
Patrick von Platen committed
1385
            )
1386
1387
        # test mask >= 0
        with self.assertRaises(ValueError):
Patrick von Platen's avatar
Patrick von Platen committed
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
            prepare_mask_and_masked_image(
                torch.rand(
                    3,
                    height,
                    width,
                ),
                torch.ones(
                    height,
                    width,
                )
                * -1,
                height,
                width,
1401
                return_image=True,
Patrick von Platen's avatar
Patrick von Platen committed
1402
            )