stable-diffusion/scripts/sample_diffusion.py at main · ConAlgorithm/stable-diffusion · GitHub

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import argparse, os, sys, glob, datetime, yaml

import torch

import time

import numpy as np

from tqdm import trange

from omegaconf import OmegaConf

from PIL import Image

from ldm.models.diffusion.ddim import DDIMSampler

from ldm.util import instantiate_from_config

rescale = lambda x: (x + 1.) / 2.

def custom_to_pil(x):

x = x.detach().cpu()

x = torch.clamp(x, -1., 1.)

x = (x + 1.) / 2.

x = x.permute(1, 2, 0).numpy()

x = (255 * x).astype(np.uint8)

x = Image.fromarray(x)

if not x.mode == "RGB":

x = x.convert("RGB")

return x

def custom_to_np(x):

# saves the batch in adm style as in https://github.com/openai/guided-diffusion/blob/main/scripts/image_sample.py

sample = x.detach().cpu()

sample = ((sample + 1) * 127.5).clamp(0, 255).to(torch.uint8)

sample = sample.permute(0, 2, 3, 1)

sample = sample.contiguous()

return sample

def logs2pil(logs, keys=["sample"]):

imgs = dict()

for k in logs:

try:

if len(logs[k].shape) == 4:

img = custom_to_pil(logs[k][0, ...])

elif len(logs[k].shape) == 3:

img = custom_to_pil(logs[k])

else:

print(f"Unknown format for key {k}. ")

img = None

except:

img = None

imgs[k] = img

return imgs

@torch.no_grad()

def convsample(model, shape, return_intermediates=True,

verbose=True,

make_prog_row=False):

if not make_prog_row:

return model.p_sample_loop(None, shape,

return_intermediates=return_intermediates, verbose=verbose)

else:

return model.progressive_denoising(

None, shape, verbose=True

)

@torch.no_grad()

def convsample_ddim(model, steps, shape, eta=1.0

ddim = DDIMSampler(model)

bs = shape[0]

shape = shape[1:]

samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, eta=eta, verbose=False,)

return samples, intermediates

@torch.no_grad()

def make_convolutional_sample(model, batch_size, vanilla=False, custom_steps=None, eta=1.0,):

log = dict()

shape = [batch_size,

model.model.diffusion_model.in_channels,

model.model.diffusion_model.image_size,

model.model.diffusion_model.image_size]

with model.ema_scope("Plotting"):

t0 = time.time()

if vanilla:

sample, progrow = convsample(model, shape,

make_prog_row=True)

else:

sample, intermediates = convsample_ddim(model, steps=custom_steps, shape=shape,

eta=eta)

t1 = time.time()

x_sample = model.decode_first_stage(sample)

log["sample"] = x_sample

log["time"] = t1 - t0

log['throughput'] = sample.shape[0] / (t1 - t0)

print(f'Throughput for this batch: {log["throughput"]}')

return log

def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, eta=None, n_samples=50000, nplog=None):

if vanilla:

print(f'Using Vanilla DDPM sampling with {model.num_timesteps} sampling steps.')

else:

print(f'Using DDIM sampling with {custom_steps} sampling steps and eta={eta}')

tstart = time.time()

n_saved = len(glob.glob(os.path.join(logdir,'*.png')))-1

# path = logdir

if model.cond_stage_model is None:

all_images = []

print(f"Running unconditional sampling for {n_samples} samples")

for _ in trange(n_samples // batch_size, desc="Sampling Batches (unconditional)"):

logs = make_convolutional_sample(model, batch_size=batch_size,

vanilla=vanilla, custom_steps=custom_steps,

eta=eta)

n_saved = save_logs(logs, logdir, n_saved=n_saved, key="sample")

all_images.extend([custom_to_np(logs["sample"])])

if n_saved >= n_samples:

print(f'Finish after generating {n_saved} samples')

break

all_img = np.concatenate(all_images, axis=0)

all_img = all_img[:n_samples]

shape_str = "x".join([str(x) for x in all_img.shape])

nppath = os.path.join(nplog, f"{shape_str}-samples.npz")

np.savez(nppath, all_img)

else:

raise NotImplementedError('Currently only sampling for unconditional models supported.')

print(f"sampling of {n_saved} images finished in {(time.time() - tstart) / 60.:.2f} minutes.")

def save_logs(logs, path, n_saved=0, key="sample", np_path=None):

for k in logs:

if k == key:

batch = logs[key]

if np_path is None:

for x in batch:

img = custom_to_pil(x)

imgpath = os.path.join(path, f"{key}_{n_saved:06}.png")

img.save(imgpath)

n_saved += 1

else:

npbatch = custom_to_np(batch)

shape_str = "x".join([str(x) for x in npbatch.shape])

nppath = os.path.join(np_path, f"{n_saved}-{shape_str}-samples.npz")

np.savez(nppath, npbatch)

n_saved += npbatch.shape[0]

return n_saved

def get_parser():

parser = argparse.ArgumentParser()

parser.add_argument(

"-r",

"--resume",

type=str,

nargs="?",

help="load from logdir or checkpoint in logdir",

)

parser.add_argument(

"-n",

"--n_samples",

type=int,

nargs="?",

help="number of samples to draw",

default=50000

)

parser.add_argument(

"-e",

"--eta",

type=float,

nargs="?",

help="eta for ddim sampling (0.0 yields deterministic sampling)",

default=1.0

)

parser.add_argument(

"-v",

"--vanilla_sample",

default=False,

action='store_true',

help="vanilla sampling (default option is DDIM sampling)?",

)

parser.add_argument(

"-l",

"--logdir",

type=str,

nargs="?",

help="extra logdir",

default="none"

)

parser.add_argument(

"-c",

"--custom_steps",

type=int,

nargs="?",

help="number of steps for ddim and fastdpm sampling",

default=50

)

parser.add_argument(

"--batch_size",

type=int,

nargs="?",

help="the bs",

default=10

)

return parser

def load_model_from_config(config, sd):

model = instantiate_from_config(config)

model.load_state_dict(sd,strict=False)

model.cuda()

model.eval()

return model

def load_model(config, ckpt, gpu, eval_mode):

if ckpt:

print(f"Loading model from {ckpt}")

pl_sd = torch.load(ckpt, map_location="cpu")

global_step = pl_sd["global_step"]

else:

pl_sd = {"state_dict": None}

global_step = None

model = load_model_from_config(config.model,

pl_sd["state_dict"])

return model, global_step

if __name__ == "__main__":

now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")

sys.path.append(os.getcwd())

command = " ".join(sys.argv)

parser = get_parser()

opt, unknown = parser.parse_known_args()

ckpt = None

if not os.path.exists(opt.resume):

raise ValueError("Cannot find {}".format(opt.resume))

if os.path.isfile(opt.resume):

# paths = opt.resume.split("/")

try:

logdir = '/'.join(opt.resume.split('/')[:-1])

# idx = len(paths)-paths[::-1].index("logs")+1

print(f'Logdir is {logdir}')

except ValueError:

paths = opt.resume.split("/")

idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt

logdir = "/".join(paths[:idx])

ckpt = opt.resume

else:

assert os.path.isdir(opt.resume), f"{opt.resume} is not a directory"

logdir = opt.resume.rstrip("/")

ckpt = os.path.join(logdir, "model.ckpt")

base_configs = sorted(glob.glob(os.path.join(logdir, "config.yaml")))

opt.base = base_configs

configs = [OmegaConf.load(cfg) for cfg in opt.base]

cli = OmegaConf.from_dotlist(unknown)

config = OmegaConf.merge(*configs, cli)

gpu = True

eval_mode = True

if opt.logdir != "none":

locallog = logdir.split(os.sep)[-1]

if locallog == "": locallog = logdir.split(os.sep)[-2]

print(f"Switching logdir from '{logdir}' to '{os.path.join(opt.logdir, locallog)}'")

logdir = os.path.join(opt.logdir, locallog)

print(config)

model, global_step = load_model(config, ckpt, gpu, eval_mode)

print(f"global step: {global_step}")

print(75 * "=")

print("logging to:")

logdir = os.path.join(logdir, "samples", f"{global_step:08}", now)

imglogdir = os.path.join(logdir, "img")

numpylogdir = os.path.join(logdir, "numpy")

os.makedirs(imglogdir)

os.makedirs(numpylogdir)

print(logdir)

print(75 * "=")

# write config out

sampling_file = os.path.join(logdir, "sampling_config.yaml")

sampling_conf = vars(opt)

with open(sampling_file, 'w') as f:

yaml.dump(sampling_conf, f, default_flow_style=False)

print(sampling_conf)

run(model, imglogdir, eta=opt.eta,

vanilla=opt.vanilla_sample, n_samples=opt.n_samples, custom_steps=opt.custom_steps,

batch_size=opt.batch_size, nplog=numpylogdir)

print("done.")

stable-diffusion/scripts/sample_diffusion.py at main · ConAlgorithm/stable-diffusion · GitHub

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