Create utils.py

utils.py

@@ -0,0 +1,37 @@
+import torch
+from diffusers.loaders.lora import LoraLoaderMixin
+from typing import Dict, Union
+import numpy as np
+import imageio
+
+def load_lora_weights(unet, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name = None, **kwargs):
+    # if a dict is passed, copy it instead of modifying it inplace
+    if isinstance(pretrained_model_name_or_path_or_dict, dict):
+        pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
+
+    # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
+    state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
+
+    # remove prefix if not removed when saved
+    state_dict = {name.replace('base_model.model.', ''): param for name, param in state_dict.items()}
+
+    is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
+    if not is_correct_format:
+        raise ValueError("Invalid LoRA checkpoint.")
+
+    low_cpu_mem_usage = True
+
+    LoraLoaderMixin.load_lora_into_unet(
+        state_dict,
+        network_alphas=network_alphas,
+        unet = unet,
+        low_cpu_mem_usage=low_cpu_mem_usage,
+        adapter_name=adapter_name,
+    )
+
+def save_video(frames, save_path, fps, quality=9):
+    writer = imageio.get_writer(save_path, fps=fps, quality=quality)
+    for frame in frames:
+        frame = np.array(frame)
+        writer.append_data(frame)
+    writer.close()