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d38c804320
* feat: add repaint * fix: fix quality check with `make fix-copies` * fix: remove old unnecessary arg * chore: change default to DDPM (looks better in experiments) * ".to(device)" changed to "device=" Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * make generator device-specific Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * make generator device-specific and change shape Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * fix: add preprocessing for image and mask Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * fix: update test Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * Update src/diffusers/pipelines/repaint/pipeline_repaint.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Add docs and examples * Fix toctree Co-authored-by: fja <fja@zurich.ibm.com> Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Anton Lozhkov <anton@huggingface.co>
66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
# 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 RePaintPipeline, RePaintScheduler, UNet2DModel
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from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
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torch.backends.cuda.matmul.allow_tf32 = False
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@slow
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@require_torch_gpu
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class RepaintPipelineIntegrationTests(unittest.TestCase):
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def test_celebahq(self):
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original_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
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"repaint/celeba_hq_256.png"
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)
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mask_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
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)
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expected_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
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"repaint/celeba_hq_256_result.png"
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)
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expected_image = np.array(expected_image, dtype=np.float32) / 255.0
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model_id = "google/ddpm-ema-celebahq-256"
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unet = UNet2DModel.from_pretrained(model_id)
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scheduler = RePaintScheduler.from_config(model_id)
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repaint = RePaintPipeline(unet=unet, scheduler=scheduler).to(torch_device)
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = repaint(
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original_image,
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mask_image,
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num_inference_steps=250,
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eta=0.0,
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jump_length=10,
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jump_n_sample=10,
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generator=generator,
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output_type="np",
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)
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image = output.images[0]
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assert image.shape == (256, 256, 3)
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assert np.abs(expected_image - image).mean() < 1e-2
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