Closed jacobkreider closed 2 years ago
you need to config the corresponding keras distribution strategy.
e.g. see this example for TPUs https://colab.research.google.com/drive/17zQOm_2Iu6pcP8otT-v6rx0D-pKgfaLm?usp=sharing
Thanks, got that to work. One more quick related before I close out:
If I run a batch_size of, say 20, I run out of resources. I assume because it holds all arrays in memory until the batches all finished and I save them out or display them and the script closes.
I can get around this by running a batch_size = 1 20x, of course.
Is there any functional difference in output between the two? (I'm guessing not?)
it's almost the same, but as the code it, not exacly (because of the way the latent is seeded). see https://github.com/divamgupta/stable-diffusion-tensorflow/pull/32 for a way to decouple the latent seeding from the batch size
Yep, after some experimentation I see that. Thanks for the link. Closing out.
Hi, sorry to reopen this. I'm trying to run the stable diffusion on 2xA6000.
Below is the code:
import tensorflow as tf
from stable_diffusion_tf.stable_diffusion import Text2Image
from PIL import Image
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
generator = Text2Image(
img_height=512,
img_width=512,
jit_compile=False, # You can try True as well (different performance profile)
)
img = generator.generate(
"kermit the frog with wings goes to heaven. fantasy, intricate, elegant, highly detailed, digital painting, artstation, concept art, matte, sharp focus, illustration.",
num_steps=50,
unconditional_guidance_scale=7,
temperature=1,
batch_size=15,
)
It does appear that only one GPU is running (print below). Additionally, the time that it takes to run on a single GPU is the same that it takes to run on two. Is it supposed to or am I doing something wrong?
Btw here's Kermit with wings in heaven, according to stable diffusion :D
To run on several GPUs, do I set a setting within stable_diffusion_tf, or do I set that in tensorflow/keras and then run the stable_d code?