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import os |
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import sys |
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sys.path.insert(0, os.path.abspath(os.path.dirname(__file__))) |
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import shutil |
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import torch |
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import torch.nn.functional as F |
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import PIL |
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import torchvision.transforms.functional as transform |
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from vfi_utils import load_file_from_github_release |
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from vfi_models import gmfss_fortuna, ifrnet, ifunet, m2m, rife, sepconv, amt, xvfi, cain, flavr |
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import numpy as np |
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frame_0 = torch.from_numpy(np.array(PIL.Image.open("demo_frames/anime0.png").convert("RGB")).astype(np.float32) / 255.0).unsqueeze(0) |
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frame_1 = torch.from_numpy(np.array(PIL.Image.open("demo_frames/anime1.png").convert("RGB")).astype(np.float32) / 255.0).unsqueeze(0) |
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if os.path.exists("test_result"): |
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shutil.rmtree("test_result") |
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vfi_node_class = gmfss_fortuna.GMFSS_Fortuna_VFI() |
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for i, ckpt_name in enumerate(vfi_node_class.INPUT_TYPES()["required"]["ckpt_name"][0][:2]): |
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result = vfi_node_class.vfi(ckpt_name, torch.cat([ |
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frame_0, |
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frame_1, |
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frame_0, |
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frame_1 |
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], dim=0).cuda(), multipler=4, batch_size=2)[0] |
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print(result.shape) |
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print(f"Generated {result.size(0)} frames") |
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frames = [PIL.Image.fromarray(np.clip((frame * 255).numpy(), 0, 255).astype(np.uint8)) for frame in result] |
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print(result[0].shape) |
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os.makedirs(f"test_result/video{i}", exist_ok=True) |
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for j, frame in enumerate(frames): |
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frame.save(f"test_result/video{i}/{j}.jpg") |
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frames[0].save(f"test_result/video{i}.gif", save_all=True, append_images=frames[1:], optimize=True, duration=1/3, loop=0) |
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os.startfile(f"test_result{os.path.sep}video{i}.gif") |
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