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27 messages · Page 1 of 1 (latest)

clear surge
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Hi, trying to use a particular LoRA and Swarm doesn't do anything! It just kinda hangs and has to be restarted. This is the LoRA https://civitai.com/models/1899191?modelVersionId=2149758 and here are the logs, https://paste.denizenscript.com/View/135942

hybrid locust
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are you sure the lora's even related there?

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looks like the backend just froze up on the nunchaku model load

clear surge
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It worked before using the LoRA, and works after a restart without it.

hybrid locust
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actually did it freeze up? you submitted logs ~1 second after clicking gen

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can you wait a bit longer before submitting the log? I'd expect more messages after that point

clear surge
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However, slight update. It eventually worked, but was incredibly slow, like, 8 mins prep, then failed on the next log, https://paste.denizenscript.com/View/135943

hybrid locust
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ye okay that's more like what i expect

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it's an OOM

clear surge
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what's an OOM?

hybrid locust
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might be just... nunchaku being dumb with loras tbh

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Out Of Memory error

clear surge
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Ahh, oh yeah!!

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Any way to fix that?

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Also, any idea why it's so slow with that LoRA?

hybrid locust
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oh yeah you had a whole long list of loras

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you might have better luck with an fp8 copy of flux instead of nunchaku

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nunchaku is very unstable code

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fp8 is heavier but it's stable core code that's hard to break

clear surge
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Wasn't it the Flux model that failed, not the Nunchaku?

hybrid locust
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oh wait that was a not-nunchaku that errored i didn't even realize

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... how did you gpu oom an fp8 flux

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uhh go to Server -> Backends -> edit the comfy backend -> in ExtraArgs add --reserve-vram 2 -> save that

clear surge
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Do I need the '2 ->' or was that a typo?

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Ok, it's fixed!! I think it was the ridiculously long trigger phase, deleted that and it works fine now.

grizzled temple
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Is there a correlation at any point in the flow between longer trigger phrase length or longer prompt length and higher vram or ram usage?

hybrid locust
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yes longer prompt uses more vram, by how much is quite variable on the model in use, eg SDXL only changes when the prompt length is multiples of 77 tokens