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Zongsheng
commited on
Commit
β’
692826c
1
Parent(s):
ea3f9a7
add alinged case
Browse files
app.py
CHANGED
@@ -62,79 +62,92 @@ def predict(im_path, background_enhance, face_upsample, upscale, started_timeste
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upscale = 2 # avoid momory exceeded due to too large img resolution
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configs.detection.upscale = int(upscale)
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restored_image_dir = Path('restored_output')
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if not restored_image_dir.exists():
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restored_image_dir.mkdir()
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# save the whole image
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save_path = restored_image_dir / Path(im_path).name
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image_restored = util_image.bgr2rgb(image_restored)
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util_image.imwrite(image_restored, save_path, chn='rgb', dtype_in='uint8')
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return image_restored, str(save_path)
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title = "DifFace: Blind Face Restoration with Diffused Error Contraction"
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description = r"""
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<b>Official Gradio demo</b> for <a href='https://github.com/zsyOAOA/DifFace' target='_blank'><b>DifFace: Blind Face Restoration with Diffused Error Contraction</b></a>.<br>
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π₯ DifFace is a robust face restoration algorithm for old or corrupted photos.<br>
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"""
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article = r"""
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If DifFace is helpful for your work, please help to β the <a href='https://github.com/zsyOAOA/DifFace' target='_blank'>Github Repo</a>. Thanks!
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[![GitHub Stars](https://img.shields.io/github/stars/zsyOAOA/DifFace?affiliations=OWNER&color=green&style=social)](https://github.com/zsyOAOA/DifFace)
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---
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π **Citation**
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If our work is useful for your research, please consider citing:
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```bibtex
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@article{yue2022difface,
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title={DifFace: Blind Face Restoration with Diffused Error Contraction},
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author={Yue, Zongsheng and Loy, Chen Change},
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journal={arXiv preprint arXiv:2212.06512},
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year={2022}
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}
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```
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π **License**
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This project is licensed under <a rel="license" href="https://github.com/zsyOAOA/DifFace/blob/master/LICENSE">S-Lab License 1.0</a>.
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Redistribution and use for non-commercial purposes should follow this license.
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π§ **Contact**
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If you have any questions, please feel free to contact me via <b>zsyzam@gmail.com</b>.
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![visitors](https://visitor-badge.laobi.icu/badge?page_id=zsyOAOA/DifFace)
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"""
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demo = gr.Interface(
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predict,
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inputs=[
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gr.Image(type="filepath", label="Input"),
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gr.Checkbox(value=True, label="Background_Enhance"),
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gr.Checkbox(value=True, label="Face_Upsample"),
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gr.Number(value=2, label="Rescaling_Factor (up to 4)"),
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gr.Slider(1, 160, value=80, step=10, label='Realism-Fidelity Trade-off
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],
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outputs=[
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gr.Image(type="numpy", label="Output"),
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gr.outputs.File(label="Download the output")
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],
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title=title,
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description=description,
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article=article,
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examples=[
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['./testdata/whole_imgs/00.jpg', True, True, 2, 80],
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['./testdata/whole_imgs/01.jpg', True, True, 2, 80],
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['./testdata/whole_imgs/04.jpg', True, True, 2, 80],
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['./testdata/whole_imgs/05.jpg', True, True, 2, 80],
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]
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)
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demo.queue(concurrency_count=4)
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demo.launch()
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upscale = 2 # avoid momory exceeded due to too large img resolution
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configs.detection.upscale = int(upscale)
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if background_enhance:
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image_restored, face_restored, face_cropped = sampler_dist.sample_func_bfr_unaligned(
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y0=im_lq,
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start_timesteps=started_timesteps,
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need_restoration=True,
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draw_box=False,
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) # h x w x c, numpy array, [0, 255], uint8, BGR
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image_restored = util_image.bgr2rgb(image_restored)
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else:
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image_restored = sampler_dist.sample_func_ir_aligned(
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y0=im_lq,
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start_timesteps=started_timesteps,
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need_restoration=True,
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)[0] # b x c x h x w, [0, 1], torch tensor, RGB
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image_restored = util_image.tensor2img(
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image_restored.cpu(),
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rgb2bgr=False,
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out_type=np.uint8,
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min_max=(0, 1),
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) # h x w x c, [0, 255], uint8, RGB, numpy array
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restored_image_dir = Path('restored_output')
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if not restored_image_dir.exists():
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restored_image_dir.mkdir()
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# save the whole image
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save_path = restored_image_dir / Path(im_path).name
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util_image.imwrite(image_restored, save_path, chn='rgb', dtype_in='uint8')
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return image_restored, str(save_path)
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# title = "DifFace: Blind Face Restoration with Diffused Error Contraction"
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# description = r"""
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# <b>Official Gradio demo</b> for <a href='https://github.com/zsyOAOA/DifFace' target='_blank'><b>DifFace: Blind Face Restoration with Diffused Error Contraction</b></a>.<br>
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# π₯ DifFace is a robust face restoration algorithm for old or corrupted photos.<br>
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# """
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# article = r"""
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# If DifFace is helpful for your work, please help to β the <a href='https://github.com/zsyOAOA/DifFace' target='_blank'>Github Repo</a>. Thanks!
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# [![GitHub Stars](https://img.shields.io/github/stars/zsyOAOA/DifFace?affiliations=OWNER&color=green&style=social)](https://github.com/zsyOAOA/DifFace)
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# ---
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# π **Citation**
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# If our work is useful for your research, please consider citing:
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# ```bibtex
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# @article{yue2022difface,
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# title={DifFace: Blind Face Restoration with Diffused Error Contraction},
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# author={Yue, Zongsheng and Loy, Chen Change},
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# journal={arXiv preprint arXiv:2212.06512},
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# year={2022}
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# }
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# ```
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# π **License**
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# This project is licensed under <a rel="license" href="https://github.com/zsyOAOA/DifFace/blob/master/LICENSE">S-Lab License 1.0</a>.
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# Redistribution and use for non-commercial purposes should follow this license.
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# π§ **Contact**
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# If you have any questions, please feel free to contact me via <b>zsyzam@gmail.com</b>.
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# ![visitors](https://visitor-badge.laobi.icu/badge?page_id=zsyOAOA/DifFace)
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# """
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# demo = gr.Interface(
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# predict,
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# inputs=[
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# gr.Image(type="filepath", label="Input"),
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# gr.Checkbox(value=True, label="Background_Enhance"),
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# gr.Checkbox(value=True, label="Face_Upsample"),
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# gr.Number(value=2, label="Rescaling_Factor (up to 4)"),
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# gr.Slider(1, 160, value=80, step=10, label='Realism-Fidelity Trade-off')
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# ],
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# outputs=[
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# gr.Image(type="numpy", label="Output"),
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# gr.outputs.File(label="Download the output")
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# ],
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# title=title,
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# description=description,
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# article=article,
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# examples=[
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# ['./testdata/whole_imgs/00.jpg', True, True, 2, 80],
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# ['./testdata/whole_imgs/01.jpg', True, True, 2, 80],
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# ['./testdata/whole_imgs/04.jpg', True, True, 2, 80],
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# ['./testdata/whole_imgs/05.jpg', True, True, 2, 80],
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# ]
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# )
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# demo.queue(concurrency_count=4)
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# demo.launch()
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