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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):
# Ensure the file is saved with a video format
if not save_path.endswith(('.mp4', '.avi', '.gif')):
save_path += '.mp4' # Default to mp4 if no valid extension is provided
writer = imageio.get_writer(save_path, fps=fps, quality=quality)
for frame in frames:
frame = np.array(frame)
writer.append_data(frame)
writer.close() |