RuntimeError: CUDA out of memory: Allocated memory try setting max_split_size_mb to avoid fragmentation

Question

I have a problem, I get the following error RuntimeError: CUDA out of memory.. I have already visited the following solutions: RuntimeError: CUDA out of memory. How setting max_split_size_mb?, Pytorch RuntimeError: CUDA out of memory with a huge amount of free memory, How to solve RuntimeError: CUDA out of memory?. However, this does not help. How can I solve this error?

I also tried

!export 'PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:4000'

Is there an option to somehow split this implementation?

with autocast(config.DEVICE):
      for i, t in tqdm(enumerate(scheduler.timesteps)):
        
        latent_model_input = torch.cat([latents] * 2)
        sigma = scheduler.sigmas[i]
        latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)

        with torch.no_grad():
              noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
        noise_pred = noise_pred_uncond + config.GUIDANCE_SCALE * (noise_pred_text - noise_pred_uncond)

        latents = scheduler.step(noise_pred, i, latents).prev_sample

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-50-c064e16dfce6> in <module>
      7 
      8         with torch.no_grad():
----> 9               noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
     10 
     11         noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)

11 frames
/usr/local/lib/python3.7/dist-packages/torch/functional.py in einsum(*args)
    358         return einsum(equation, *_operands)
    359 
--> 360     return _VF.einsum(equation, operands)  # type: ignore[attr-defined]
    361 
    362 

RuntimeError: CUDA out of memory. Tried to allocate 35.60 GiB (GPU 0; 39.59 GiB total capacity; 7.00 GiB already allocated; 30.62 GiB free; 7.24 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