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| # coding=utf-8 | |
| # Copyright 2024 HuggingFace Inc. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer | |
| from diffusers import AmusedInpaintPipeline, AmusedScheduler, UVit2DModel, VQModel | |
| from diffusers.utils import load_image | |
| from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device | |
| from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS | |
| from ..test_pipelines_common import PipelineTesterMixin | |
| enable_full_determinism() | |
| class AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = AmusedInpaintPipeline | |
| params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"} | |
| batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS | |
| required_optional_params = PipelineTesterMixin.required_optional_params - { | |
| "latents", | |
| } | |
| def get_dummy_components(self): | |
| torch.manual_seed(0) | |
| transformer = UVit2DModel( | |
| hidden_size=8, | |
| use_bias=False, | |
| hidden_dropout=0.0, | |
| cond_embed_dim=8, | |
| micro_cond_encode_dim=2, | |
| micro_cond_embed_dim=10, | |
| encoder_hidden_size=8, | |
| vocab_size=32, | |
| codebook_size=32, # codebook size needs to be consistent with num_vq_embeddings for inpaint tests | |
| in_channels=8, | |
| block_out_channels=8, | |
| num_res_blocks=1, | |
| downsample=True, | |
| upsample=True, | |
| block_num_heads=1, | |
| num_hidden_layers=1, | |
| num_attention_heads=1, | |
| attention_dropout=0.0, | |
| intermediate_size=8, | |
| layer_norm_eps=1e-06, | |
| ln_elementwise_affine=True, | |
| ) | |
| scheduler = AmusedScheduler(mask_token_id=31) | |
| torch.manual_seed(0) | |
| vqvae = VQModel( | |
| act_fn="silu", | |
| block_out_channels=[8], | |
| down_block_types=[ | |
| "DownEncoderBlock2D", | |
| ], | |
| in_channels=3, | |
| latent_channels=8, | |
| layers_per_block=1, | |
| norm_num_groups=8, | |
| num_vq_embeddings=32, # reducing this to 16 or 8 -> RuntimeError: "cdist_cuda" not implemented for 'Half' | |
| out_channels=3, | |
| sample_size=8, | |
| up_block_types=[ | |
| "UpDecoderBlock2D", | |
| ], | |
| mid_block_add_attention=False, | |
| lookup_from_codebook=True, | |
| ) | |
| torch.manual_seed(0) | |
| text_encoder_config = CLIPTextConfig( | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| hidden_size=8, | |
| intermediate_size=8, | |
| layer_norm_eps=1e-05, | |
| num_attention_heads=1, | |
| num_hidden_layers=1, | |
| pad_token_id=1, | |
| vocab_size=1000, | |
| projection_dim=8, | |
| ) | |
| text_encoder = CLIPTextModelWithProjection(text_encoder_config) | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| components = { | |
| "transformer": transformer, | |
| "scheduler": scheduler, | |
| "vqvae": vqvae, | |
| "text_encoder": text_encoder, | |
| "tokenizer": tokenizer, | |
| } | |
| return components | |
| def get_dummy_inputs(self, device, seed=0): | |
| if str(device).startswith("mps"): | |
| generator = torch.manual_seed(seed) | |
| else: | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| image = torch.full((1, 3, 4, 4), 1.0, dtype=torch.float32, device=device) | |
| mask_image = torch.full((1, 1, 4, 4), 1.0, dtype=torch.float32, device=device) | |
| mask_image[0, 0, 0, 0] = 0 | |
| mask_image[0, 0, 0, 1] = 0 | |
| inputs = { | |
| "prompt": "A painting of a squirrel eating a burger", | |
| "generator": generator, | |
| "num_inference_steps": 2, | |
| "output_type": "np", | |
| "image": image, | |
| "mask_image": mask_image, | |
| } | |
| return inputs | |
| def test_inference_batch_consistent(self, batch_sizes=[2]): | |
| self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False) | |
| def test_inference_batch_single_identical(self): | |
| ... | |
| class AmusedInpaintPipelineSlowTests(unittest.TestCase): | |
| def test_amused_256(self): | |
| pipe = AmusedInpaintPipeline.from_pretrained("amused/amused-256") | |
| pipe.to(torch_device) | |
| image = ( | |
| load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg") | |
| .resize((256, 256)) | |
| .convert("RGB") | |
| ) | |
| mask_image = ( | |
| load_image( | |
| "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png" | |
| ) | |
| .resize((256, 256)) | |
| .convert("L") | |
| ) | |
| image = pipe( | |
| "winter mountains", | |
| image, | |
| mask_image, | |
| generator=torch.Generator().manual_seed(0), | |
| num_inference_steps=2, | |
| output_type="np", | |
| ).images | |
| image_slice = image[0, -3:, -3:, -1].flatten() | |
| assert image.shape == (1, 256, 256, 3) | |
| expected_slice = np.array([0.0699, 0.0716, 0.0608, 0.0715, 0.0797, 0.0638, 0.0802, 0.0924, 0.0634]) | |
| assert np.abs(image_slice - expected_slice).max() < 0.1 | |
| def test_amused_256_fp16(self): | |
| pipe = AmusedInpaintPipeline.from_pretrained("amused/amused-256", variant="fp16", torch_dtype=torch.float16) | |
| pipe.to(torch_device) | |
| image = ( | |
| load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg") | |
| .resize((256, 256)) | |
| .convert("RGB") | |
| ) | |
| mask_image = ( | |
| load_image( | |
| "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png" | |
| ) | |
| .resize((256, 256)) | |
| .convert("L") | |
| ) | |
| image = pipe( | |
| "winter mountains", | |
| image, | |
| mask_image, | |
| generator=torch.Generator().manual_seed(0), | |
| num_inference_steps=2, | |
| output_type="np", | |
| ).images | |
| image_slice = image[0, -3:, -3:, -1].flatten() | |
| assert image.shape == (1, 256, 256, 3) | |
| expected_slice = np.array([0.0735, 0.0749, 0.0650, 0.0739, 0.0805, 0.0667, 0.0802, 0.0923, 0.0622]) | |
| assert np.abs(image_slice - expected_slice).max() < 0.1 | |
| def test_amused_512(self): | |
| pipe = AmusedInpaintPipeline.from_pretrained("amused/amused-512") | |
| pipe.to(torch_device) | |
| image = ( | |
| load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg") | |
| .resize((512, 512)) | |
| .convert("RGB") | |
| ) | |
| mask_image = ( | |
| load_image( | |
| "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png" | |
| ) | |
| .resize((512, 512)) | |
| .convert("L") | |
| ) | |
| image = pipe( | |
| "winter mountains", | |
| image, | |
| mask_image, | |
| generator=torch.Generator().manual_seed(0), | |
| num_inference_steps=2, | |
| output_type="np", | |
| ).images | |
| image_slice = image[0, -3:, -3:, -1].flatten() | |
| assert image.shape == (1, 512, 512, 3) | |
| expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0005, 0.0]) | |
| assert np.abs(image_slice - expected_slice).max() < 0.05 | |
| def test_amused_512_fp16(self): | |
| pipe = AmusedInpaintPipeline.from_pretrained("amused/amused-512", variant="fp16", torch_dtype=torch.float16) | |
| pipe.to(torch_device) | |
| image = ( | |
| load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg") | |
| .resize((512, 512)) | |
| .convert("RGB") | |
| ) | |
| mask_image = ( | |
| load_image( | |
| "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png" | |
| ) | |
| .resize((512, 512)) | |
| .convert("L") | |
| ) | |
| image = pipe( | |
| "winter mountains", | |
| image, | |
| mask_image, | |
| generator=torch.Generator().manual_seed(0), | |
| num_inference_steps=2, | |
| output_type="np", | |
| ).images | |
| image_slice = image[0, -3:, -3:, -1].flatten() | |
| assert image.shape == (1, 512, 512, 3) | |
| expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0025, 0.0]) | |
| assert np.abs(image_slice - expected_slice).max() < 3e-3 | |