How to test my model

[Revanthraja]

Hello sir after training the model then how to test my model giving text as input please help me in this issue

[bruefire]

Hi @Revanthraja.
Please use the inference.py script.

python inference.py --model '/path/to/checkpoint/' --prompt 'apple' -n "orange"

The usage are written in the readme file.

[Revanthraja]

Hello sir in which folder I will get this path '/path/to/checkpoint/' please help me sir

[bruefire]

You should have executed a command like the following:

 python train.py --config configs/my_train_config.yaml

The "output_dir" in config.yaml used by your training, is where checkpoints are saved.
Please check that directory.

[Revanthraja]

like I need use like this
!python inference.py
--model /content/Text-To-Video-Finetuning/models/model_scope_diffusers
--prompt "The two persons are playing the cricket"
--num-frames 30
--window-size 12
--width 1024
--height 576
--sdp

[Revanthraja]

Hello sir then how to pass the video and text directory please help in this

[Revanthraja]

%cd /content/Text-To-Video-Finetuning

while True:

model = "/content/outputs/train_2023-07-13T11-37-44" #@param {type:"string"}
prompt = "Cricket playing" #@param {type:"string"}
negative = "text, watermark, copyright, blurry, low resolution, blur, low quality" #@param {type:"string"}
prompt = f""{prompt}""
negative = f""{negative}""
num_steps = 25 #@param {type:"raw"}
guidance_scale = 23 #@param {type:"raw"}
fps = 24 #@param {type:"raw"}
num_frames = 10 #@param {type:"raw"}
!python inference.py -m {model} -p {prompt} -n {negative} -W 1024 -H 576 -o /content/video_outputs -d cuda -x -s {num_steps} -g {guidance_scale} -f {fps} -T {num_frames} when I excecuting this code
I am getting OSError: Error no file named scheduler_config.json found in directory
/content/outputs/train_2023-07-13T11-37-44. this error please any one help me in this

[bruefire]

Is there a 'scheduler' directory in the 'train_2023-07-13T11-37-44' ?
That directory must contain 'scheduler_config.json', but it probably disappeared for any reason.

[Revanthraja]

Hello sir can you tell me for which reason it is disappeared for trainig I used low_vram_config file

[Revanthraja]

Steps: 0% 0/100 [00:00<?, ?it/s]2200 params have been unfrozen for training.
/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:134: FutureWarning: Accessing config attribute num_train_timesteps directly via 'DDPMScheduler' object attribute is deprecated. Please access 'num_train_timesteps' over 'DDPMScheduler's config object instead, e.g. 'scheduler.config.num_train_timesteps'.
deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:134: FutureWarning: Accessing config attribute prediction_type directly via 'DDPMScheduler' object attribute is deprecated. Please access 'prediction_type' over 'DDPMScheduler's config object instead, e.g. 'scheduler.config.prediction_type'.
deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
Steps: 100% 100/100 [01:12<00:00, 1.84it/s, lr=1e-6, step_loss=0.00567]Traceback (most recent call last):
File "/content/Text-To-Video-Finetuning/train.py", line 999, in
main(**OmegaConf.load(args.config))
File "/content/Text-To-Video-Finetuning/train.py", line 978, in main
save_pipe(
File "/content/Text-To-Video-Finetuning/train.py", line 488, in save_pipe
unet_out = copy.deepcopy(accelerator.unwrap_model(unet, keep_fp32_wrapper=False))
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/usr/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/usr/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 297, in _reconstruct
value = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/usr/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/usr/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 297, in _reconstruct
value = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/usr/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/usr/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 297, in _reconstruct
value = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/usr/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/usr/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 297, in _reconstruct
value = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/usr/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/usr/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 297, in _reconstruct
value = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/usr/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/usr/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 297, in _reconstruct
value = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/usr/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/usr/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 297, in _reconstruct
value = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 271, in _reconstruct
state = deepcopy(state, memo)
File "/usr/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/usr/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 172, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/usr/lib/python3.10/copy.py", line 297, in _reconstruct
value = deepcopy(value, memo)
File "/usr/lib/python3.10/copy.py", line 153, in deepcopy
y = copier(memo)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/parameter.py", line 55, in deepcopy
result = type(self)(self.data.clone(memory_format=torch.preserve_format), self.requires_grad)
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 14.75 GiB total capacity; 13.22 GiB already allocated; 10.81 M iB free; 13.37 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing.
wandb:
wandb: Run history:
wandb: train_loss ▂█▁▁▅▁▅▃▁▆▂▃▃▅█▄▄▂▁▇▁▂▃▂▅▁▃▄▂▅▁▇▄▄▂▁▁▁▃▁
wandb:
wandb: Run summary:
wandb: train_loss 0.00567
wandb:
wandb: 🚀 View run legendary-wood-38 at: https://wandb.ai/genrative/text2video-fine-tune/runs/uwo0ozwf
wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20230715_161213-uwo0ozwf/logs

I am getting this error in google colab plz anyone help me