Optimizing VMEM

[LordMilutin]

When generating images, I am getting the following error (I have 8GB GPU):

 *** Arguments: ('task(ftv6541tpgfrjd8)', 'Greek god Zeus, portrait, unreal engine 5', '', [], 20, 0, False, False, 1, 1, 7, -1.0, -1.0, 0, 0, 0, False, 256, 256, False, 0.7, 2, 'Latent', 0, 0, 0, 0, '', '', [], <gradio.routes.Request object at 0x7f0638293460>, 0, False, False, 'positive', 'comma', 0, False, False, '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, 0) {}
     Traceback (most recent call last):
       File "/stable-diffusion-webui/modules/call_queue.py", line 58, in f
         res = list(func(*args, **kwargs))
       File "/stable-diffusion-webui/modules/call_queue.py", line 37, in f
         res = func(*args, **kwargs)
       File "/stable-diffusion-webui/modules/txt2img.py", line 62, in txt2img
         processed = processing.process_images(p)
       File "/stable-diffusion-webui/modules/processing.py", line 677, in process_images
         res = process_images_inner(p)
       File "/stable-diffusion-webui/modules/processing.py", line 796, in process_images_inner
         x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
       File "/stable-diffusion-webui/modules/processing.py", line 545, in decode_latent_batch
         sample = decode_first_stage(model, batch[i:i + 1])[0]
       File "/stable-diffusion-webui/modules/processing.py", line 576, in decode_first_stage
         x = model.decode_first_stage(x.to(devices.dtype_vae))
       File "/venv/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
         return func(*args, **kwargs)
       File "/stable-diffusion-webui/repositories/generative-models/sgm/models/diffusion.py", line 121, in decode_first_stage
         out = self.first_stage_model.decode(z)
       File "/stable-diffusion-webui/repositories/generative-models/sgm/models/autoencoder.py", line 315, in decode
         dec = self.decoder(z, **decoder_kwargs)
       File "/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
         return forward_call(*args, **kwargs)
       File "/stable-diffusion-webui/repositories/generative-models/sgm/modules/diffusionmodules/model.py", line 732, in forward
         h = self.up[i_level].upsample(h)
       File "/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
         return forward_call(*args, **kwargs)
       File "/stable-diffusion-webui/repositories/generative-models/sgm/modules/diffusionmodules/model.py", line 67, in forward
         x = self.conv(x)
       File "/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
         return forward_call(*args, **kwargs)
       File "/stable-diffusion-webui/extensions-builtin/Lora/networks.py", line 376, in network_Conv2d_forward
         return torch.nn.Conv2d_forward_before_network(self, input)
       File "/venv/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 463, in forward
         return self._conv_forward(input, self.weight, self.bias)
       File "/venv/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 459, in _conv_forward
         return F.conv2d(input, weight, bias, self.stride,
     torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 7.78 GiB total capacity; 6.97 GiB already allocated; 36.44 MiB free; 7.25 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

Is there any optimization I can do so that it doesn't crash all the time?

I have tried setting resolution to 256x256 as well, but it still crashes.