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