Open oconnoob opened 2 years ago
I got it somewhat working on a rtx 3060ti (8GB), also got OOM errors , reduced to 256x256 and it continued, but the output looks more like something dalle-mini would produce, not what I got from the web interface dreamstudio and in the beta discord.
How much RAM do you have on your motherboard? Did you try to create a swapfile on disk?
@leszekhanusz There are 15 GB RAM on the motherboard for that VM. I haven't tried creating a swapfile yet - cutting the size all the way down to 32 x 32 and still getting OOM is unexpected to me
@smoke2007 Interesting - thanks for the comparison. I'm trying on a K80 with 30 GB motherboard RAM. Seems to be working - will update when/if I get results.
You could try the float16 version available in diffusers v0.2.4
It works for me with 512x512 images with a RTX3080 10GB VRAM and 32GB RAM on CPU
You could try the float16 version available in diffusers v0.2.4
It works for me with 512x512 images with a RTX3080 10GB VRAM and 32GB RAM on CPU
I'm not really using anything from hugginface aside from the checkpoint file. I'm using the code from this github with conda
I just redeployed everything using this fork https://github.com/lstein/stable-diffusion
and now it works fine, with 512x512 and the output is a lot more what I would expect it to be.
Thanks @smoke2007 I was stuck in OOM realm too but this fork solved it. (using the v1.4 checkpoints from HuggingFace for CompVis, not diffusers).
@leszekhanusz I'm not using diffusers either - is there any way to use the float16 version with just the source and a checkpoint?
@smoke2007 is there any difference between dream.py
in that fork and txt2imgpy
in the original?
@smoke2007 is there any difference between
dream.py
in that fork andtxt2imgpy
in the original?
yes the code is quite different, the safety filter seems off , some verbose things are surpressed and it optimizes some things for memory.
It also gives you an interactive prompt that understands the parameters as the discord bot did
@smoke2007 there's an orig_scripts
directory that preserves the originals it seems, but it looks like there are extra imports required. Would love to get a fix in the original repo when possible, but I'll try out this fork in the meantime!
@oconnoob,
this gets you down to 5-6GB of VRAM:
# !pip install diffusers
from torch import autocast
from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True, revision="fp16", torch_device=torch.float16)
# remove VAE encoder as it's not needed
del pipe.vae.encoder
# now move to GPU which should not consume more than 5GB
pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt)["sample"][0]
Hi there,
I'm running into OOM error using a Tesla T4 on GCP Ubuntu 18.04 using the v1.4 checkpoint. I see
Global seed set to 42
andLoading model from sd-v1-4.ckpt
followed bykilled
, even when I tried making the images 128x128 and then 32x32 and finally 2x2.Any tips for figuring out what the issue is would be greatly appreciated!