(Suggestion) Include more useful parameters as form inputs in colab

I’ve included these form inputs in my personal copy of your Colab notebook. In particular, reducing the image_width parameter allows for one to vastly increase the number of hidden_layers. By going to an image_width of 256 (instead of the default 512) I was able to run 32 hidden layers without problems on a T4.

from tqdm import trange
from IPython.display import Image, display

from deep_daze import Imagine

TEXT = 'blue marshmallow' #@param {type:"string"}
NUM_LAYERS = 16 #@param {type:"number"}
SAVE_EVERY =  20#@param {type:"number"}
IMAGE_WIDTH = 512 #@param {type:"number"}
SAVE_PROGRESS = False #@param {type:"boolean"}
LEARNING_RATE = 1e-5 #@param {type:"number"}
ITERATIONS = 1050 #@param {type:"number"}

model = Imagine(
    text = TEXT,
    num_layers = NUM_LAYERS,
    save_every = SAVE_EVERY,
    image_width = IMAGE_WIDTH,
    lr = LEARNING_RATE,
    iterations = ITERATIONS,
    save_progress = SAVE_PROGRESS
)

Feel free to include them in your copy if you’d like to.

[lucidrains]

@afiaka87 i barely squeak by with 10 / 11 GB usage, so you may have to tweak your layers, batch size, or gradient accumulation steps, but which are all accessible through the CLI https://github.com/lucidrains/deep-daze/blob/main/deep_daze/cli.py#L9-L11

[afiaka87]

I’m on colab pro myself. As for non-pro users, I have a friend who worked on this notebook all day. He ran it all day and kept getting a T4 (16 GiB vram) which is obviously more than enough. Your success on your 2080TI (nice card, btw) has inspired me to get it running locally on my 8GB 2070 Super.