So, after some more testing, i came to a conclusion:
My model (and model from dcgan pytorch tutorial i switched to) did not have enough capacity for dataset i was using. After switching to CelebA dataset, model worked as is it was suppose to. My theory is that because of high variety of images in living room dataset, different environments, camera angles, etc. and low model capacity for this task, it was forced into creating blurry approximations of them, hardly capturing even a color scheme, and falling into mode collapse, since size of latent vector and model capacity were not big enough to capture all parameters that it needed.
In this tutorial: https://machinelearningmastery.com/practical-guide-to-gan-failure-modes/ 3
Author decreases the size of latent vector from 100 to 1, which causes mode collapse.
Ideas to take with me to my other experiments:

Start with simple datasets, for example, CelebA, and test on them. If the problem is in the model’s architecture, it would show it here
If model experiences mode collapse, increase its capacity, and size of latent vectors
In my case i’ve noticed a phenomenon of “bouncing” in the beginning of training process: images start as noise, then become more realistic (Sort of. There are some color patches, in case of CelebA dataset there is a blurry kinda-face figure in the center), but after that they come back to noise, then back to sort-of realistic images, and then going back and forth a few times, before settling at final result that improves over time. Not sure if it is normal, but in my case it went through that stage, and then worked as expected, hopefully someone can explain to me whether it is normal or not

Thanks to everyone who helped me, problem is solved