dynamicrafter

prompts/256/test_prompts.txt

+man fishing in a boat at sunset
+a brown bear is walking in a zoo enclosure, some rocks around
+boy walking on the street
+two people dancing
+a campfire on the beach and the ocean waves in the background
+girl with fires and smoke on his head
+girl talking and blinking
+bear playing guitar happily, snowing
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prompts/512/test_prompts.txt

+time-lapse of a blooming flower with leaves and a stem
+a bonfire is lit in the middle of a field
+a woman looking out in the rain
+rotating view, small house
+pouring honey onto some slices of bread
+a sailboat sailing in rough seas with a dramatic sunset 
+a boat traveling on the ocean
+a group of penguins walking on a beach
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requirements.txt

+decord==0.6.0
+einops==0.3.0
+imageio==2.9.0
+numpy==1.24.2
+omegaconf==2.1.1
+opencv_python
+pandas==2.0.0
+Pillow==9.5.0
+pytorch_lightning==1.8.3
+PyYAML==6.0
+setuptools==65.6.3
+# torch>=2.0.0
+# torchvision
+tqdm==4.65.0
+transformers==4.25.1
+moviepy
+av
+# xformers
+gradio
+timm
+scikit-learn 
+open_clip_torch==2.22.0
+kornia
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scripts/evaluation/ddp_wrapper.py

+import datetime
+import argparse, importlib
+from pytorch_lightning import seed_everything
+
+import torch
+import torch.distributed as dist
+
+def setup_dist(local_rank):
+    if dist.is_initialized():
+        return
+    torch.cuda.set_device(local_rank)
+    torch.distributed.init_process_group('nccl', init_method='env://')
+
+
+def get_dist_info():
+    if dist.is_available():
+        initialized = dist.is_initialized()
+    else:
+        initialized = False
+    if initialized:
+        rank = dist.get_rank()
+        world_size = dist.get_world_size()
+    else:
+        rank = 0
+        world_size = 1
+    return rank, world_size
+
+
+if __name__ == '__main__':
+    now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--module", type=str, help="module name", default="inference")
+    parser.add_argument("--local_rank", type=int, nargs="?", help="for ddp", default=0)
+    args, unknown = parser.parse_known_args()
+    inference_api = importlib.import_module(args.module, package=None)
+
+    inference_parser = inference_api.get_parser()
+    inference_args, unknown = inference_parser.parse_known_args()
+
+    seed_everything(inference_args.seed)
+    setup_dist(args.local_rank)
+    torch.backends.cudnn.benchmark = True
+    rank, gpu_num = get_dist_info()
+
+    # inference_args.savedir = inference_args.savedir+str('_seed')+str(inference_args.seed)
+    print("@DynamiCrafter Inference [rank%d]: %s"%(rank, now))
+    inference_api.run_inference(inference_args, gpu_num, rank)
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