Comments (2)
A very impressive job. There are several issues when using SkyTimelapse data for image and video pre training
- From utils import (clip_gradnorm, create'logger, update_ema,
Requires_grad, cleanup, create_tensorboard,
Write_tensorboard, setup_distributed, fetch files by numbers,
Fetch files by numbers and separation content motion were not found in utils in get_experiment_dir, separation content motion,)
"ImportError: Unable to import the name 'fetch files by num bers' from' utils' (Last/utils. py). After commenting out the corresponding file, it is sufficient. Can you ask what this mainly does? Does it directly use the original video?" Commenting out directly is not a problem, right? "”
- If args. dataset=='webvideo2mlaion ':
Traceback (most recent call last):
File "/data/zqzx/latte/latte_main/latte/train_with_img. py", line 361, in
Main (OmegaConf. load (args. config))
File "/data/zqzx/latte/latte_main/latte/train_with_img. py", line 221, in main
Logger. info (f "Dataset contains {len (dataset):,} videos ({args. webvideo_data_path})")
File "/data/miniconde3/envs/yxl/lib/python3.9/site packages/omegaconf/docconfiguration. py", line 355, in getattr_
Self_ Formad_and_raise()
This can be directly solved by adding the corresponding solution to the sky_img_train.yaml corresponding to the actual video, which is. mp4? Or can we think of our own video dataset through this path?
- If args. test_run: After commenting it out directly, it can be run now
Thank you.
- These functions are auxiliary functions for my previous experiments, you do not need them. I've removed the redundant functions.
- The training code for T2V is not currently supported in the Latte repo. However, we provide a complete training code for four datasets such as UCF101, SkyTimelapse, etc. (including video-image joint training). You can refer to them to modify it for your data set. Please use the UCF101 dataset as the base if your dataset has classes.
- args. test_run is also an auxiliary function. I will clean it.
from latte.
Thank you very much
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Related Issues (20)
- T2V training and evaluation HOT 7
- Results & ckpts of different sized Latte on UCF-101 HOT 3
- 视频帧率 HOT 2
- Question: model code and design choices HOT 2
- how to place and preprocess these datasets HOT 7
- the code of variant 4 HOT 1
- Question: evaluate the FVD HOT 6
- Error once speed up training HOT 2
- How to get preprocessed_ffs HOT 4
- Any plan to implement Latte in HuggingFace's diffusers library? HOT 3
- 模型在ucf101上无法收敛 HOT 5
- Can Latte train for I2V tasks? HOT 2
- Batch Size Ablations HOT 1
- what the param <input_sq_size> stands for? HOT 2
- Can we use batch_size>1 in sample_t2x.py HOT 7
- Evaluate the `FVD` on FFS HOT 4
- How can I utilize the weights of pre-trained PixArt-α to initialize the parameters of the spatial Transformer block in the Latte T2V model? HOT 2
- FVD on UCF-101 HOT 15
- Is there tutorial on transfering t2i to t2v model? HOT 5
- inference memory with torch.set_grad_enabled(True) HOT 2
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