FileNotFoundError: [WinError 3] 指定されたパスが見つかりません。: ''

[coronet1127]

Traceback (most recent call last): File "C:\Users\coron\Desktop\Tune-A-Video\inference.py", line 15, in save_videos_grid(video, f"{prompt}.gif") File "C:\Users\coron\Desktop\Tune-A-Video\tuneavideo\util.py", line 21, in save_videos_grid os.makedirs(os.path.dirname(path), exist_ok=True) File "C:\Users\coron\AppData\Local\Programs\Python\Python310\lib\os.py", line 225, in makedirs mkdir(name, mode) FileNotFoundError: [WinError 3] 指定されたパスが見つかりません。: ''

---

execution,inferens.py

from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline from tuneavideo.models.unet import UNet3DConditionModel from tuneavideo.util import save_videos_grid import torch

model_id = "outputs/man-surfing_lr3e-5_seed33/2023-01-30T23-03-19" unet = UNet3DConditionModel.from_pretrained(model_id, subfolder='unet', torch_dtype=torch.float16).to('cuda') unet.enable_xformers_memory_efficient_attention() pipe = TuneAVideoPipeline.from_pretrained("checkpoints/stable-diffusion-v1-4", unet=unet, torch_dtype=torch.float16).to("cuda")

torch.cuda.manual_seed(0) prompt = "a panda is surfing" video = pipe(prompt, video_length=8, height=512, width=512, num_inference_steps=50, guidance_scale=7.5).videos

save_videos_grid(video, f"{prompt}.gif")

I don't know if this is relevant. Tune-A-Video\tuneavideo\util.py

import os import imageio import numpy as np import torch import torchvision

from einops import rearrange

def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=4, fps=3): videos = rearrange(videos, "b c t h w -> t b c h w") outputs = [] for x in videos: x = torchvision.utils.make_grid(x, nrow=n_rows) x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) if rescale: x = (x + 1.0) / 2.0 # -1,1 -> 0,1 x = (x * 255).numpy().astype(np.uint8) outputs.append(x)

os.makedirs(os.path.dirname(path), exist_ok=True)
imageio.mimsave(path, outputs, fps=fps)

[coronet1127]

fix code by chatGPT This worked, but I don't know why it works.

import os import imageio import numpy as np import torch import torchvision

from einops import rearrange

def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=4, fps=3): videos = rearrange(videos, "b c t h w -> t b c h w") outputs = [] for x in videos: x = torchvision.utils.make_grid(x, nrow=n_rows) x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) if rescale: x = (x + 1.0) / 2.0 # -1,1 -> 0,1 x = (x * 255).numpy().astype(np.uint8) outputs.append(x)

dir_path = os.path.dirname(path)
if dir_path:
    os.makedirs(dir_path, exist_ok=True)
imageio.mimsave(path, outputs, fps=fps)