AttributeError: 'AutoencoderKL' object has no attribute 'quantize'

[LitaoLiu01]

I run the model correctly in the first four train epochs using my own dataset, which is 320×240, but it have the error when it runs to the first validation.

File "/home/bingxing2/home/scx6d13/litaoliu/p2p_script/instruct-pix2pix-main/./stable_diffusion/ldm/models/diffusion/ddpm_edit.py", line 1084, in p_mean_variance
x_recon, , [, _, indices] = self.first_stage_model.quantize(x_recon)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1185, in getattr
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'AutoencoderKL' object has no attribute 'quantize'

What is going on? Can he only process an image of the same length and width?

The full error is:
Traceback (most recent call last):
File "/home/bingxing2/home/scx6d13/litaoliu/p2p_script/instruct-pix2pix-main/main.py", line 817, in
trainer.fit(model, data)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 553, in fit
self._run(model)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 918, in _run
self._dispatch()
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 986, in _dispatch
self.accelerator.start_training(self)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in start_training
self.training_type_plugin.start_training(trainer)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 161, in start_training
self._results = trainer.run_stage()
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 996, in run_stage
return self._run_train()
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1045, in _run_train
self.fit_loop.run()
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py", line 200, in advance
epoch_output = self.epoch_loop.run(train_dataloader)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/loops/base.py", line 112, in run
self.on_advance_end()
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 177, in on_advance_end
self._run_validation()
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 256, in _run_validation
self.val_loop.run()
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 110, in advance
dl_outputs = self.epoch_loop.run(
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 116, in advance
self.on_evaluation_batch_end(output, batch, batch_idx, dataloader_idx)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 197, in on_evaluation_batch_end
self.trainer.call_hook(hook_name, output, batch, batch_idx, dataloader_idx)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1217, in call_hook
trainer_hook(*args, **kwargs)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/pytorch_lightning/trainer/callback_hook.py", line 199, in on_validation_batch_end
callback.on_validation_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
File "/home/bingxing2/home/scx6d13/litaoliu/p2p_script/instruct-pix2pix-main/main.py", line 473, in on_validation_batch_end
self.log_img(pl_module, batch, batch_idx, split="val")
File "/home/bingxing2/home/scx6d13/litaoliu/p2p_script/instruct-pix2pix-main/main.py", line 436, in log_img
images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/home/bingxing2/home/scx6d13/litaoliu/p2p_script/instruct-pix2pix-main/./stable_diffusion/ldm/models/diffusion/ddpm_edit.py", line 1332, in log_images
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/home/bingxing2/home/scx6d13/litaoliu/p2p_script/instruct-pix2pix-main/./stable_diffusion/ldm/models/diffusion/ddpm_edit.py", line 1259, in sample_log
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/home/bingxing2/home/scx6d13/litaoliu/p2p_script/instruct-pix2pix-main/./stable_diffusion/ldm/models/diffusion/ddpm_edit.py", line 1243, in sample
return self.p_sample_loop(cond,
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/home/bingxing2/home/scx6d13/litaoliu/p2p_script/instruct-pix2pix-main/./stable_diffusion/ldm/models/diffusion/ddpm_edit.py", line 1215, in p_sample_loop
img = self.p_sample(img, cond, ts,
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/home/bingxing2/home/scx6d13/litaoliu/p2p_script/instruct-pix2pix-main/./stable_diffusion/ldm/models/diffusion/ddpm_edit.py", line 1098, in p_sample
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
File "/home/bingxing2/home/scx6d13/litaoliu/p2p_script/instruct-pix2pix-main/./stable_diffusion/ldm/models/diffusion/ddpm_edit.py", line 1084, in p_mean_variance
x_recon, , [, _, indices] = self.first_stage_model.quantize(x_recon)
File "/home/bingxing2/home/scx6d13/.conda/envs/ip2p_cu113/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1185, in getattr
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'AutoencoderKL' object has no attribute 'quantize'

[LitaoLiu01]

It also seems there is not the Method 'quantize' in 'AutoencoderKL' Class

[LitaoLiu01]

So, should I add which 'quantize' Method to 'AutoencoderKL' Class