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image_sample_grid.py
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image_sample_grid.py
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"""
Image (generation) sampling script used for generating a batch of image samples
from a model and save them as image grid.
"""
import os
import argparse
import numpy as np
from model.utils import distribute_util
import torch as th
import torch.distributed as dist
import torchvision
from model import logger
from model.utils.script_util import (
NUM_CLASSES,
model_and_diffusion_defaults,
create_model_and_diffusion,
add_dict_to_argparser,
args_to_dict,
)
from model.utils.random_util import get_generator
from model.karras_diffusion import karras_sample
def create_argparser():
defaults = dict(
training_mode="edm",
generator="determ",
clip_denoised=True,
num_samples=10000,
batch_size=16,
sampler="heun",
s_churn=0.0,
s_tmin=0.0,
s_tmax=float("inf"),
s_noise=1.0,
steps=40,
model_path="",
seed=42,
ts="",
)
defaults.update(model_and_diffusion_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
return parser
def main():
args = create_argparser().parse_args()
if args.batch_size > args.num_samples:
logger.log("batch_size > num_samples; reducing batch_size.")
args.batch_size = args.num_samples
distribute_util.setup_dist()
logger.configure()
if "consistency" in args.training_mode:
distillation = True
else:
distillation = False
logger.log("creating model and diffusion...")
model, diffusion = create_model_and_diffusion(
**args_to_dict(args, model_and_diffusion_defaults().keys()),
distillation=distillation,
)
model.load_state_dict(
distribute_util.load_state_dict(args.model_path, map_location="cpu")
)
model.to(distribute_util.dev())
if args.use_fp16:
model.convert_to_fp16()
model.eval()
logger.log("sampling...")
if args.sampler == "multistep":
assert len(args.ts) > 0
ts = tuple(int(x) for x in args.ts.split(","))
else:
ts = None
all_images = []
all_labels = []
generator = get_generator(args.generator, args.num_samples, args.seed)
# Code for performing incremental image sampling during training.
# TODO: Make this function more general and accept model paramters during sampling
# rather than the hard coded values used currently.
# This sould be modified if training on a dataset of different resolution.
logger.log("generating samples...")
while len(all_images)*args.batch_size < args.num_samples:
model_kwargs = {}
if args.class_cond:
classes = th.arange(start=0, end=9, dtype=int, device=distribute_util.dev())
i = 0
while len(classes) < args.batch_size and i < args.num_samples:
classes = classes.append[classes[i]]
i += 1
classes = classes.reshape(args.batch_size,)
model_kwargs["y"] = classes
sample = karras_sample(
diffusion,
model,
(args.batch_size, 3, args.image_size, args.image_size),
steps=args.steps,
model_kwargs=model_kwargs,
device=distribute_util.dev(),
clip_denoised=args.clip_denoised,
sampler=args.sampler,
sigma_min=args.sigma_min,
sigma_max=args.sigma_max,
s_churn=args.s_churn,
s_tmin=args.s_tmin,
s_tmax=args.s_tmax,
s_noise=args.s_noise,
generator=generator,
ts=ts,
)
gathered_samples = [th.zeros_like(sample) for _ in range(dist.get_world_size())]
dist.all_gather(gathered_samples, sample) # gather not supported with NCCL
all_images.extend([sample.cpu().numpy() for sample in gathered_samples])
if args.class_cond:
gathered_labels = [
th.zeros_like(classes) for _ in range(dist.get_world_size())
]
dist.all_gather(gathered_labels, classes)
all_labels.extend([labels.cpu().numpy() for labels in gathered_labels])
logger.log(f"created {len(all_images) * args.batch_size} samples")
# Save the generated sample images
logger.log("sampled tensor shape: "+str(sample.shape))
grid_img = torchvision.utils.make_grid(sample, nrow = 10, normalize = True)
torchvision.utils.save_image(grid_img, f"tmp_imgs/generated_sample.pdf")
logger.log("sampling complete")
if __name__ == "__main__":
main()