Upload __init__.py

hyvideo/__init__.py

@@ -0,0 +1,62 @@
+from pathlib import Path
+
+import torch
+
+from .autoencoder_kl_causal_3d import AutoencoderKLCausal3D
+from ..constants import VAE_PATH, PRECISION_TO_TYPE
+
+def load_vae(vae_type: str="884-16c-hy",
+             vae_precision: str=None,
+             sample_size: tuple=None,
+             vae_path: str=None,
+             logger=None,
+             device=None
+             ):
+    """the fucntion to load the 3D VAE model
+
+    Args:
+        vae_type (str): the type of the 3D VAE model. Defaults to "884-16c-hy".
+        vae_precision (str, optional): the precision to load vae. Defaults to None.
+        sample_size (tuple, optional): the tiling size. Defaults to None.
+        vae_path (str, optional): the path to vae. Defaults to None.
+        logger (_type_, optional): logger. Defaults to None.
+        device (_type_, optional): device to load vae. Defaults to None.
+    """
+    if vae_path is None:
+        vae_path = VAE_PATH[vae_type]
+    
+    if logger is not None:
+        logger.info(f"Loading 3D VAE model ({vae_type}) from: {vae_path}")
+    config = AutoencoderKLCausal3D.load_config(vae_path)
+    if sample_size:
+        vae = AutoencoderKLCausal3D.from_config(config, sample_size=sample_size)
+    else:
+        vae = AutoencoderKLCausal3D.from_config(config)
+    
+    vae_ckpt = Path(vae_path) / "pytorch_model.pt"
+    assert vae_ckpt.exists(), f"VAE checkpoint not found: {vae_ckpt}"
+    
+    ckpt = torch.load(vae_ckpt, map_location=vae.device)
+    if "state_dict" in ckpt:
+        ckpt = ckpt["state_dict"]
+    if any(k.startswith("vae.") for k in ckpt.keys()):
+        ckpt = {k.replace("vae.", ""): v for k, v in ckpt.items() if k.startswith("vae.")}
+    vae.load_state_dict(ckpt)
+
+    spatial_compression_ratio = vae.config.spatial_compression_ratio
+    time_compression_ratio = vae.config.time_compression_ratio
+    
+    if vae_precision is not None:
+        vae = vae.to(dtype=PRECISION_TO_TYPE[vae_precision])
+
+    vae.requires_grad_(False)
+
+    if logger is not None:
+        logger.info(f"VAE to dtype: {vae.dtype}")
+
+    if device is not None:
+        vae = vae.to(device)
+
+    vae.eval()
+
+    return vae, vae_path, spatial_compression_ratio, time_compression_ratio