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Recommendations: - Linux w/ Display drivers on CPU (and just...ditch miniconda please) - Use lower FP precision mode if available to use the tensor cores (also

Recommendations: - Linux w/ Display drivers on CPU (and just...ditch miniconda please) - Use lower FP precision mode if available to use the tensor cores (also to double "effective" memory) - Batch things! - I don't know what the max resolution of the diffusion network is, you may have to just simply tile it past a certain point (with overlap please! ;P Hope that helps somewhat. A 3090 should be more than enough for what you're doing, I'm stuck with P100s at best for me! (Cost :'( ) Best of luck! :D :)

yoru-sulfur 4y ago
> and just...ditch miniconda please Why the recommendation to stop using miniconda?
- r2_pilot 4y ago
  Well, for starters(not that proficient with PyCharm as I only use it for less-professional use), I have tried for a few days to get PyCharm to use the miniconda environment on Windows, to no avail. It at least works on the command line so I get to play around with it. I'm going to spend more time tomorrow trying alternatives.
cbozeman 4y ago
I ordered an NVIDIA Tesla K80 off eBay (and the power adapter... and the blower fan shroud, etc.) and intend to install it when it arrives around Thursday or Friday. I'm hoping that after I install the NVIDIA Linux datacenter drivers I'll be able to use the card with SD. My only worry is that because the K80 is two GPUs on one board, that it might only utilize one of them, with only 12 GB of VRAM instead of both chips and all 24 GB. 5992 CUDA cores and 24 GB VRAM would be a pretty decent SD accelerator for only $150.
- monkmartinez 4y ago
  I really want to put one of these in my Dell Precision workstation, and I share similar concerns. I have an older Quadro in it now and could use a proper upgrade, but I really want to wait for the 4000 series cards due in a few months. Craft Computing on Youtube has the best information from what I have seen so far. I don't like watching Youtube videos for information like this, but I understand why creators have moved to this medium in general. Linux should be much easier to configure for using the K80 to capacity.
- glenneroo 4y ago
  You'll be able to render but it won't be fast, those CUDA cores are ancient and VRAM speed is slow. Check the Stable Diffusion discord for more info, but I found these comments: > one minute per 512x512 @ 50 steps > 1m20s to run 50 ddims on 512x512 vs 2080 ti in 12 seconds You'll have to run the optimized model as well, since you can't connect the 2x 12GB together.
  - cbozeman 4y ago
    > since you can't connect the 2x 12GB together. Any idea why this is?
    - simcop2387 4y ago
      The architecture of the k80 just doesn't allow memory sharing or pooling. The newer systems (Pascal and above I think?) can allow it on the right hardware but it's all datacenter and workstation cards for that.
      - cbozeman 4y ago
        Man, feel like I shoulda picked up the M40 instead then, since it was 24 GB VRAM in total. :( Oh well, I've spent $90 on dinners and had far less fun than I will have with this video card when it arrives, so I can't say it was wasted money... and I can always just buy an M40 off eBay. If RTX 3090 prices keep dropping through the floor, I may just bite the bullet and pick one up. I saw a ZOTAC on sale for $999 recently, which is $500 less than the launch MSRP of $1499 (which honestly is where it should have launched anyway... so far as I'm concerned, these cards only just now hit reasonable pricing).
monkmartinez 4y ago
I totally disagree with ditching miniconda. The Colab notebooks that make use of it have been super easy to run and modify. There is documentation everywhere and its very easy to find on SO, and Google. Its a joy to use and really like it for all of my Python workloads. I think of it like a Python VM that just works where ever I place it... so far, haven't been let down.
- traverseda 4y ago
  Miniconda is a pain as it introduces it's own package build format that (IMHO) just isn't very good. It might have been an improvement on python's binary packages when it was released, but now days the conda package format creates more problems than it solves. >I think of it like a Python VM that just works where ever I place it. That's called a virtualenv, which is a feature built into python. Miniconda is a thin wrapper around virtualenv (actual python packages) and the conda package format. If you're using an IDE it probably has virtualenv support baked in. Personally I prefer to use python-poetry for managing virtual envs, but honestly just using the virtualenv command directly is not hard if you're already using conda from the CLI.

yoru-sulfur 4y ago

> and just...ditch miniconda please Why the recommendation to stop using miniconda?

r2_pilot 4y ago
Well, for starters(not that proficient with PyCharm as I only use it for less-professional use), I have tried for a few days to get PyCharm to use the miniconda environment on Windows, to no avail. It at least works on the command line so I get to play around with it. I'm going to spend more time tomorrow trying alternatives.

cbozeman 4y ago

I ordered an NVIDIA Tesla K80 off eBay (and the power adapter... and the blower fan shroud, etc.) and intend to install it when it arrives around Thursday or Friday. I'm hoping that after I install the NVIDIA Linux datacenter drivers I'll be able to use the card with SD. My only worry is that because the K80 is two GPUs on one board, that it might only utilize one of them, with only 12 GB of VRAM instead of both chips and all 24 GB. 5992 CUDA cores and 24 GB VRAM would be a pretty decent SD accelerator for only $150.

monkmartinez 4y ago
I really want to put one of these in my Dell Precision workstation, and I share similar concerns. I have an older Quadro in it now and could use a proper upgrade, but I really want to wait for the 4000 series cards due in a few months. Craft Computing on Youtube has the best information from what I have seen so far. I don't like watching Youtube videos for information like this, but I understand why creators have moved to this medium in general. Linux should be much easier to configure for using the K80 to capacity.
glenneroo 4y ago
You'll be able to render but it won't be fast, those CUDA cores are ancient and VRAM speed is slow. Check the Stable Diffusion discord for more info, but I found these comments: > one minute per 512x512 @ 50 steps > 1m20s to run 50 ddims on 512x512 vs 2080 ti in 12 seconds You'll have to run the optimized model as well, since you can't connect the 2x 12GB together.
- cbozeman 4y ago
  > since you can't connect the 2x 12GB together. Any idea why this is?
  - simcop2387 4y ago
    The architecture of the k80 just doesn't allow memory sharing or pooling. The newer systems (Pascal and above I think?) can allow it on the right hardware but it's all datacenter and workstation cards for that.
    - cbozeman 4y ago
      Man, feel like I shoulda picked up the M40 instead then, since it was 24 GB VRAM in total. :( Oh well, I've spent $90 on dinners and had far less fun than I will have with this video card when it arrives, so I can't say it was wasted money... and I can always just buy an M40 off eBay. If RTX 3090 prices keep dropping through the floor, I may just bite the bullet and pick one up. I saw a ZOTAC on sale for $999 recently, which is $500 less than the launch MSRP of $1499 (which honestly is where it should have launched anyway... so far as I'm concerned, these cards only just now hit reasonable pricing).

monkmartinez 4y ago

I totally disagree with ditching miniconda. The Colab notebooks that make use of it have been super easy to run and modify. There is documentation everywhere and its very easy to find on SO, and Google. Its a joy to use and really like it for all of my Python workloads. I think of it like a Python VM that just works where ever I place it... so far, haven't been let down.

traverseda 4y ago
Miniconda is a pain as it introduces it's own package build format that (IMHO) just isn't very good. It might have been an improvement on python's binary packages when it was released, but now days the conda package format creates more problems than it solves. >I think of it like a Python VM that just works where ever I place it. That's called a virtualenv, which is a feature built into python. Miniconda is a thin wrapper around virtualenv (actual python packages) and the conda package format. If you're using an IDE it probably has virtualenv support baked in. Personally I prefer to use python-poetry for managing virtual envs, but honestly just using the virtualenv command directly is not hard if you're already using conda from the CLI.