Choose 16x512x512 model instead of 64x512x512 model

.dockerignore

@@ -17,4 +17,3 @@ __pycache__
 /venv
 pretrained_models/
 output*.mp4
-.cog

predict.py

@@ -22,7 +22,7 @@
 
 from opensora.datasets import save_sample
 from opensora.registry import MODELS, SCHEDULERS, build_module
-from opensora.utils.config_utils import parse_configs
+from opensora.utils.config_utils import merge_args
 from opensora.utils.misc import to_torch_dtype
 from opensora.acceleration.parallel_states import set_sequence_parallel_group
 from colossalai.cluster import DistCoordinator
@@ -95,12 +95,30 @@ def setup(self) -> None:
         # extra config:
         ckpt_path = "pretrained_models/Open-Sora/OpenSora-v1-HQ-16x512x512.pth"
         config_file = "configs/opensora/inference/64x512x512.py"
+        config_file = "configs/opensora/inference/16x512x512.py"
 
         # load config file
-        self.cfg = cog_config()
-        self.cfg.model["from_pretrained"] = ckpt_path
-        if "multi_resolution" not in self.cfg:
-            self.cfg["multi_resolution"] = False
+        # option 1: manually
+        #self.cfg = cog_config()
+        #self.cfg.model["from_pretrained"] = ckpt_path
+        #if "multi_resolution" not in self.cfg:
+        #    self.cfg["multi_resolution"] = False
+
+        # command line arguments from config_utils
+        extra_args = Config({
+            'seed': 42,
+            'ckpt_path': ckpt_path,
+            'batch-size': None,
+            'prompt-path': None,
+            'save-dir': None,
+            'num-sampling-steps': None,
+            'cfg_scale': None,
+        })
+
+        # option 2: use config_utils
+        self.cfg = Config.fromfile(config_file)
+        self.cfg = merge_args(self.cfg, args=extra_args, training=False)
+
 
         # from scripts/inference
 
@@ -134,8 +152,12 @@ def setup(self) -> None:
         # ======================================================
         # 3.1. build model
         input_size = (self.cfg.num_frames, *self.cfg.image_size)
+        print(f"number of frames: {self.cfg.num_frames}, image_size: {self.cfg.image_size}")
+        print(f"resulting input size: {input_size}")
         self.vae = build_module(self.cfg.vae, MODELS)
+        print("vae", self.vae)
         self.latent_size = self.vae.get_latent_size(input_size)
+        print("latent size:", self.latent_size)
         self.text_encoder = build_module(self.cfg.text_encoder, MODELS, device=self.device)  # T5 must be fp32
         self.model = build_module(
             self.cfg.model,