Delete test_minimax_remover.py

test_minimax_remover.py

@@ -1,56 +0,0 @@
-import torch
-from diffusers.utils import export_to_video
-from decord import VideoReader
-from diffusers.models import AutoencoderKLWan
-from transformer_minimax_remover import Transformer3DModel
-from diffusers.schedulers import UniPCMultistepScheduler
-from pipeline_minimax_remover import Minimax_Remover_Pipeline
-
-random_seed = 42
-video_length = 81
-device = torch.device("cuda:0")
-
-vae = AutoencoderKLWan.from_pretrained("./vae", torch_dtype=torch.float16)
-transformer = Transformer3DModel.from_pretrained("./transformer", torch_dtype=torch.float16)
-scheduler = UniPCMultistepScheduler.from_pretrained("./scheduler")
-
-pipe = Minimax_Remover_Pipeline(transformer=transformer, vae=vae, scheduler=scheduler)
-pipe.to(device)
-
-# the iterations is the hyperparameter for mask dilation
-def inference(pixel_values, masks, iterations=6):
-    video = pipe(
-        images=pixel_values,
-        masks=masks,
-        num_frames=video_length,
-        height=480,
-        width=832,
-        num_inference_steps=12,
-        generator=torch.Generator(device=device).manual_seed(random_seed),
-        iterations=iterations
-    ).frames[0]
-    export_to_video(video, "./output.mp4")
-
-def load_video(video_path):
-    vr = VideoReader(video_path)
-    images = vr.get_batch(list(range(video_length))).asnumpy()
-    images = torch.from_numpy(images)/127.5 - 1.0
-    return images
-
-def load_mask(mask_path):
-    vr = VideoReader(mask_path)
-    masks = vr.get_batch(list(range(video_length))).asnumpy()
-    masks = torch.from_numpy(masks)
-    masks = masks[:, :, :, :1]
-    masks[masks > 20] = 255
-    masks[masks < 255] = 0
-    masks = masks / 255.0
-    return masks
-
-video_path = "./video.mp4"
-mask_path = "./mask.mp4"
-
-images = load_video(video_path)
-masks = load_mask(mask_path)
-
-inference(images, masks)