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catch and handle malformed user inputs; documentation fixes

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This commit is contained in:
Lincoln Stein 2022-08-17 12:35:49 -04:00
parent a7532b386a
commit c477525036
3 changed files with 47 additions and 23 deletions
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20
README.md
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@ -12,10 +12,11 @@ lets you create images from a prompt in just three lines of code:
~~~~
from ldm.simplet2i import T2I
model = T2I()
model.text2image("a unicorn in manhattan")
model = T2I()
outputs = model.text2image("a unicorn in manhattan")
~~~~
Outputs is a list of lists in the format [[filename1,seed1],[filename2,seed2]...]
Please see ldm/simplet2i.py for more information.
## Interactive command-line interface similar to the Discord bot
@ -27,6 +28,9 @@ server. The advantage of this is that the lengthy model
initialization only happens once. After that image generation is
fast.
The script uses the readline library to allow for in-line editing,
command history (up and down arrows) and more.
Note that this has only been tested in the Linux environment!
~~~~
@ -48,14 +52,18 @@ Outputs:
outputs/txt2img-samples/00010.png: "ashley judd riding a camel" -n2 -S 1362479620
dream> "your prompt here" -n6 -g
...
outputs/txt2img-samples/00041.png: "your prompt here" -n6 -g -S 2685670268
seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
~~~~
Command-line arguments (`./scripts/dream.py -h`) allow you to change
Command-line arguments passed to the script allow you to change
various defaults, and select between the mature stable-diffusion
weights (512x512) and the older (256x256) latent diffusion weights
(laion400m). Within the script, the switches are (mostly) identical to
those used in the Discord bot, except you don't need to type "!dream".
(laion400m). From the dream> prompt, the arguments are (mostly)
identical to those used in the Discord bot, except you don't need to
type "!dream". Pass "-h" (or "--help") to list the arguments.
For command-line help, type -h (or --help) at the dream> prompt.
## Workaround for machines with limited internet connectivity

38
ldm/simplet2i.py
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@ -7,7 +7,8 @@ from ldm.simplet2i import T2I
t2i = T2I(outdir = <path> // outputs/txt2img-samples
model = <path> // models/ldm/stable-diffusion-v1/model.ckpt
config = <path> // default="configs/stable-diffusion/v1-inference.yaml
batch = <integer> // 1
iterations = <integer> // how many times to run the sampling (1)
batch = <integer> // how many images to generate per sampling (1)
steps = <integer> // 50
seed = <integer> // current system time
sampler = ['ddim','plms'] // ddim
@ -17,27 +18,33 @@ t2i = T2I(outdir = <path> // outputs/txt2img-samples
cfg_scale = <float> // unconditional guidance scale (7.5)
fixed_code = <boolean> // False
)
# do the slow model initialization
t2i.load_model()
# Do the fast inference & image generation. Any options passed here
# override the default values assigned during class initialization
# Will call load_model() if the model was not previously loaded.
t2i.txt2img(prompt = <string> // required
// the remaining option arguments override constructur value when present
outdir = <path>
iterations = <integer>
batch = <integer>
steps = <integer>
seed = <integer>
sampler = ['ddim','plms']
grid = <boolean>
width = <integer>
height = <integer>
cfg_scale = <float>
# The method returns a list of images. Each row of the list is a sub-list of [filename,seed]
results = t2i.txt2img(prompt = <string> // required
outdir = <path> // the remaining option arguments override constructur value when present
iterations = <integer>
batch = <integer>
steps = <integer>
seed = <integer>
sampler = ['ddim','plms']
grid = <boolean>
width = <integer>
height = <integer>
cfg_scale = <float>
) -> boolean
for row in results:
print(f'filename={row[0]}')
print(f'seed ={row[1]}')
"""
import torch
import numpy as np
import random
@ -126,7 +133,10 @@ class T2I:
def txt2img(self,prompt,outdir=None,batch=None,iterations=None,
steps=None,seed=None,grid=None,individual=None,width=None,height=None,
cfg_scale=None,ddim_eta=None):
""" generate an image from the prompt, writing iteration images into the outdir """
"""
Generate an image from the prompt, writing iteration images into the outdir
The output is a list of lists in the format: [[filename1,seed1], [filename2,seed2],...]
"""
outdir = outdir or self.outdir
steps = steps or self.steps
seed = seed or self.seed

12
scripts/dream.py
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@ -51,7 +51,7 @@ def main():
# preload the model
t2i.load_model()
print("\n* Initialization done! Awaiting your command...")
print("\n* Initialization done! Awaiting your command (-h for help)...")
log_path = os.path.join(opt.outdir,"dream_log.txt")
with open(log_path,'a') as log:
@ -68,6 +68,7 @@ def main_loop(t2i,parser,log):
print("goodbye!")
break
# rearrange the arguments to mimic how it works in the Dream bot.
elements = shlex.split(command)
switches = ['']
switches_started = False
@ -81,11 +82,16 @@ def main_loop(t2i,parser,log):
switches[0] += el
switches[0] += ' '
switches[0] = switches[0][:len(switches[0])-1]
try:
opt = parser.parse_args(switches)
except SystemExit:
parser.print_help()
pass
continue
if len(opt.prompt)==0:
print("Try again with a prompt!")
continue
results = t2i.txt2img(**vars(opt))
print("Outputs:")
write_log_message(opt,switches,results,log)
@ -147,7 +153,7 @@ def create_argv_parser():
def create_cmd_parser():
parser = argparse.ArgumentParser(description="Parse terminal input in a discord 'dreambot' fashion")
parser = argparse.ArgumentParser(description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12')
parser.add_argument('prompt')
parser.add_argument('-s','--steps',type=int,help="number of steps")
parser.add_argument('-S','--seed',type=int,help="image seed")