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added command-line completion

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This commit is contained in:
Lincoln Stein 2022-08-18 12:43:59 -04:00
parent bf76c4f283
commit 750408f793
2 changed files with 95 additions and 25 deletions
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40
ldm/simplet2i.py
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@ -8,7 +8,7 @@ t2i = T2I(outdir = <path> // outputs/txt2img-samples
model = <path> // models/ldm/stable-diffusion-v1/model.ckpt model = <path> // models/ldm/stable-diffusion-v1/model.ckpt
config = <path> // default="configs/stable-diffusion/v1-inference.yaml config = <path> // default="configs/stable-diffusion/v1-inference.yaml
iterations = <integer> // how many times to run the sampling (1) iterations = <integer> // how many times to run the sampling (1)
batch = <integer> // how many images to generate per sampling (1) batch_size = <integer> // how many images to generate per sampling (1)
steps = <integer> // 50 steps = <integer> // 50
seed = <integer> // current system time seed = <integer> // current system time
sampler = ['ddim','plms'] // ddim sampler = ['ddim','plms'] // ddim
@ -73,7 +73,7 @@ class T2I:
model model
config config
iterations iterations
batch batch_size
steps steps
seed seed
sampler sampler
@ -90,7 +90,7 @@ class T2I:
""" """
def __init__(self, def __init__(self,
outdir="outputs/txt2img-samples", outdir="outputs/txt2img-samples",
batch=1, batch_size=1,
iterations = 1, iterations = 1,
width=512, width=512,
height=512, height=512,
@ -110,7 +110,7 @@ class T2I:
strength=0.75 # default in scripts/img2img.py strength=0.75 # default in scripts/img2img.py
): ):
self.outdir = outdir self.outdir = outdir
self.batch = batch self.batch_size = batch_size
self.iterations = iterations self.iterations = iterations
self.width = width self.width = width
self.height = height self.height = height
@ -133,7 +133,7 @@ class T2I:
else: else:
self.seed = seed self.seed = seed
def txt2img(self,prompt,outdir=None,batch=None,iterations=None, def txt2img(self,prompt,outdir=None,batch_size=None,iterations=None,
steps=None,seed=None,grid=None,individual=None,width=None,height=None, steps=None,seed=None,grid=None,individual=None,width=None,height=None,
cfg_scale=None,ddim_eta=None,strength=None,init_img=None): cfg_scale=None,ddim_eta=None,strength=None,init_img=None):
""" """
@ -147,7 +147,7 @@ class T2I:
height = height or self.height height = height or self.height
cfg_scale = cfg_scale or self.cfg_scale cfg_scale = cfg_scale or self.cfg_scale
ddim_eta = ddim_eta or self.ddim_eta ddim_eta = ddim_eta or self.ddim_eta
batch = batch or self.batch batch_size = batch_size or self.batch_size
iterations = iterations or self.iterations iterations = iterations or self.iterations
strength = strength or self.strength # not actually used here, but preserved for code refactoring strength = strength or self.strength # not actually used here, but preserved for code refactoring
@ -160,7 +160,7 @@ class T2I:
if individual: if individual:
grid = False grid = False
data = [batch * [prompt]] data = [batch_size * [prompt]]
# make directories and establish names for the output files # make directories and establish names for the output files
os.makedirs(outdir, exist_ok=True) os.makedirs(outdir, exist_ok=True)
@ -168,7 +168,7 @@ class T2I:
start_code = None start_code = None
if self.fixed_code: if self.fixed_code:
start_code = torch.randn([batch, start_code = torch.randn([batch_size,
self.latent_channels, self.latent_channels,
height // self.downsampling_factor, height // self.downsampling_factor,
width // self.downsampling_factor], width // self.downsampling_factor],
@ -190,14 +190,14 @@ class T2I:
for prompts in tqdm(data, desc="data", dynamic_ncols=True): for prompts in tqdm(data, desc="data", dynamic_ncols=True):
uc = None uc = None
if cfg_scale != 1.0: if cfg_scale != 1.0:
uc = model.get_learned_conditioning(batch * [""]) uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple): if isinstance(prompts, tuple):
prompts = list(prompts) prompts = list(prompts)
c = model.get_learned_conditioning(prompts) c = model.get_learned_conditioning(prompts)
shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor] shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
samples_ddim, _ = sampler.sample(S=steps, samples_ddim, _ = sampler.sample(S=steps,
conditioning=c, conditioning=c,
batch_size=batch, batch_size_size=batch_size,
shape=shape, shape=shape,
verbose=False, verbose=False,
unconditional_guidance_scale=cfg_scale, unconditional_guidance_scale=cfg_scale,
@ -224,17 +224,17 @@ class T2I:
if grid: if grid:
images = self._make_grid(samples=all_samples, images = self._make_grid(samples=all_samples,
seeds=seeds, seeds=seeds,
batch_size=batch, batch_size=batch_size,
iterations=iterations, iterations=iterations,
outdir=outdir) outdir=outdir)
toc = time.time() toc = time.time()
print(f'{batch * iterations} images generated in',"%4.2fs"% (toc-tic)) print(f'{batch_size * iterations} images generated in',"%4.2fs"% (toc-tic))
return images return images
# There is lots of shared code between this and txt2img and should be refactored. # There is lots of shared code between this and txt2img and should be refactored.
def img2img(self,prompt,outdir=None,init_img=None,batch=None,iterations=None, def img2img(self,prompt,outdir=None,init_img=None,batch_size=None,iterations=None,
steps=None,seed=None,grid=None,individual=None,width=None,height=None, steps=None,seed=None,grid=None,individual=None,width=None,height=None,
cfg_scale=None,ddim_eta=None,strength=None): cfg_scale=None,ddim_eta=None,strength=None):
""" """
@ -246,7 +246,7 @@ class T2I:
seed = seed or self.seed seed = seed or self.seed
cfg_scale = cfg_scale or self.cfg_scale cfg_scale = cfg_scale or self.cfg_scale
ddim_eta = ddim_eta or self.ddim_eta ddim_eta = ddim_eta or self.ddim_eta
batch = batch or self.batch batch_size = batch_size or self.batch_size
iterations = iterations or self.iterations iterations = iterations or self.iterations
strength = strength or self.strength strength = strength or self.strength
@ -263,7 +263,7 @@ class T2I:
if individual: if individual:
grid = False grid = False
data = [batch * [prompt]] data = [batch_size * [prompt]]
# PLMS sampler not supported yet, so ignore previous sampler # PLMS sampler not supported yet, so ignore previous sampler
if self.sampler_name!='ddim': if self.sampler_name!='ddim':
@ -278,7 +278,7 @@ class T2I:
assert os.path.isfile(init_img) assert os.path.isfile(init_img)
init_image = self._load_img(init_img).to(self.device) init_image = self._load_img(init_img).to(self.device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch) init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False) sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
@ -307,13 +307,13 @@ class T2I:
for prompts in tqdm(data, desc="data", dynamic_ncols=True): for prompts in tqdm(data, desc="data", dynamic_ncols=True):
uc = None uc = None
if cfg_scale != 1.0: if cfg_scale != 1.0:
uc = model.get_learned_conditioning(batch * [""]) uc = model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple): if isinstance(prompts, tuple):
prompts = list(prompts) prompts = list(prompts)
c = model.get_learned_conditioning(prompts) c = model.get_learned_conditioning(prompts)
# encode (scaled latent) # encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch).to(self.device)) z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
# decode it # decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale, samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,) unconditional_conditioning=uc,)
@ -337,12 +337,12 @@ class T2I:
if grid: if grid:
images = self._make_grid(samples=all_samples, images = self._make_grid(samples=all_samples,
seeds=seeds, seeds=seeds,
batch_size=batch, batch_size=batch_size,
iterations=iterations, iterations=iterations,
outdir=outdir) outdir=outdir)
toc = time.time() toc = time.time()
print(f'{batch * iterations} images generated in',"%4.2fs"% (toc-tic)) print(f'{batch_size * iterations} images generated in',"%4.2fs"% (toc-tic))
return images return images

78
scripts/dream.py
View File

@ -6,6 +6,8 @@ import shlex
import atexit import atexit
import os import os
debugging = False
def main(): def main():
''' Initialize command-line parsers and the diffusion model ''' ''' Initialize command-line parsers and the diffusion model '''
arg_parser = create_argv_parser() arg_parser = create_argv_parser()
@ -24,7 +26,7 @@ def main():
weights = "models/ldm/stable-diffusion-v1/model.ckpt" weights = "models/ldm/stable-diffusion-v1/model.ckpt"
# command line history will be stored in a file called "~/.dream_history" # command line history will be stored in a file called "~/.dream_history"
load_history() setup_readline()
print("* Initializing, be patient...\n") print("* Initializing, be patient...\n")
from pytorch_lightning import logging from pytorch_lightning import logging
@ -36,7 +38,7 @@ def main():
# the user input loop # the user input loop
t2i = T2I(width=width, t2i = T2I(width=width,
height=height, height=height,
batch=opt.batch, batch_size=opt.batch_size,
outdir=opt.outdir, outdir=opt.outdir,
sampler=opt.sampler, sampler=opt.sampler,
weights=weights, weights=weights,
@ -50,6 +52,7 @@ def main():
logging.getLogger("pytorch_lightning").setLevel(logging.ERROR) logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)
# preload the model # preload the model
if not debugging:
t2i.load_model() t2i.load_model()
print("\n* Initialization done! Awaiting your command (-h for help)...") print("\n* Initialization done! Awaiting your command (-h for help)...")
@ -139,7 +142,7 @@ def create_argv_parser():
type=int, type=int,
default=1, default=1,
help="number of images to generate") help="number of images to generate")
parser.add_argument('-b','--batch', parser.add_argument('-b','--batch_size',
type=int, type=int,
default=1, default=1,
help="number of images to produce per iteration (currently not working properly - producing too many images)") help="number of images to produce per iteration (currently not working properly - producing too many images)")
@ -161,7 +164,7 @@ def create_cmd_parser():
parser.add_argument('-s','--steps',type=int,help="number of steps") parser.add_argument('-s','--steps',type=int,help="number of steps")
parser.add_argument('-S','--seed',type=int,help="image seed") parser.add_argument('-S','--seed',type=int,help="image seed")
parser.add_argument('-n','--iterations',type=int,default=1,help="number of samplings to perform") parser.add_argument('-n','--iterations',type=int,default=1,help="number of samplings to perform")
parser.add_argument('-b','--batch',type=int,default=1,help="number of images to produce per sampling (currently broken)") parser.add_argument('-b','--batch_size',type=int,default=1,help="number of images to produce per sampling (currently broken)")
parser.add_argument('-W','--width',type=int,help="image width, multiple of 64") parser.add_argument('-W','--width',type=int,help="image width, multiple of 64")
parser.add_argument('-H','--height',type=int,help="image height, multiple of 64") parser.add_argument('-H','--height',type=int,help="image height, multiple of 64")
parser.add_argument('-C','--cfg_scale',default=7.5,type=float,help="prompt configuration scale") parser.add_argument('-C','--cfg_scale',default=7.5,type=float,help="prompt configuration scale")
@ -171,6 +174,14 @@ def create_cmd_parser():
parser.add_argument('-f','--strength',default=0.75,type=float,help="strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely") parser.add_argument('-f','--strength',default=0.75,type=float,help="strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely")
return parser return parser
def setup_readline():
readline.set_completer(Completer(['--steps','-s','--seed','-S','--iterations','-n','--batch_size','-b',
'--width','-W','--height','-H','--cfg_scale','-C','--grid','-g',
'--individual','-i','--init_img','-I','--strength','-f']).complete)
readline.set_completer_delims(" ")
readline.parse_and_bind('tab: complete')
load_history()
def load_history(): def load_history():
histfile = os.path.join(os.path.expanduser('~'),".dream_history") histfile = os.path.join(os.path.expanduser('~'),".dream_history")
try: try:
@ -180,5 +191,64 @@ def load_history():
pass pass
atexit.register(readline.write_history_file,histfile) atexit.register(readline.write_history_file,histfile)
class Completer():
def __init__(self,options):
self.options = sorted(options)
return
def complete(self,text,state):
if text.startswith('-I') or text.startswith('--init_img'):
return self._image_completions(text,state)
response = None
if state == 0:
# This is the first time for this text, so build a match list.
if text:
self.matches = [s
for s in self.options
if s and s.startswith(text)]
else:
self.matches = self.options[:]
# Return the state'th item from the match list,
# if we have that many.
try:
response = self.matches[state]
except IndexError:
response = None
return response
def _image_completions(self,text,state):
# get the path so far
if text.startswith('-I'):
path = text.replace('-I','',1).lstrip()
elif text.startswith('--init_img='):
path = text.replace('--init_img=','',1).lstrip()
matches = list()
path = os.path.expanduser(path)
if len(path)==0:
matches.append(text+'./')
else:
dir = os.path.dirname(path)
dir_list = os.listdir(dir)
for n in dir_list:
if n.startswith('.') and len(n)>1:
continue
full_path = os.path.join(dir,n)
if full_path.startswith(path):
if os.path.isdir(full_path):
matches.append(os.path.join(os.path.dirname(text),n)+'/')
elif n.endswith('.png'):
matches.append(os.path.join(os.path.dirname(text),n))
try:
response = matches[state]
except IndexError:
response = None
return response
if __name__ == "__main__": if __name__ == "__main__":
main() main()