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import argparse
import datetime
import logging
import sys
import shlex
import cmd
from pathlib import Path
from torch import autocast
import torch
import json
from diffusers import StableDiffusionPipeline, AutoencoderKL, UNet2DConditionModel, PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler
from transformers import CLIPTextModel, CLIPTokenizer, CLIPFeatureExtractor
from slugify import slugify
from pipelines.stable_diffusion.clip_guided_stable_diffusion import CLIPGuidedStableDiffusion
from schedulers.scheduling_euler_a import EulerAScheduler
def create_args_parser():
parser = argparse.ArgumentParser(
description="Simple example of a training script."
)
parser.add_argument(
"--model",
type=str,
default=None,
)
parser.add_argument(
"--scheduler",
type=str,
choices=["plms", "ddim", "klms", "euler_a"],
default="euler_a",
)
parser.add_argument(
"--output_dir",
type=str,
default="output/inference",
)
parser.add_argument(
"--config",
type=str,
default=None,
)
return parser
def create_cmd_parser():
parser = argparse.ArgumentParser(
description="Simple example of a training script."
)
parser.add_argument(
"--prompt",
type=str,
default=None,
)
parser.add_argument(
"--negative_prompt",
type=str,
default=None,
)
parser.add_argument(
"--width",
type=int,
default=512,
)
parser.add_argument(
"--height",
type=int,
default=512,
)
parser.add_argument(
"--batch_size",
type=int,
default=1,
)
parser.add_argument(
"--batch_num",
type=int,
default=1,
)
parser.add_argument(
"--steps",
type=int,
default=70,
)
parser.add_argument(
"--guidance_scale",
type=int,
default=7,
)
parser.add_argument(
"--seed",
type=int,
default=torch.random.seed(),
)
parser.add_argument(
"--config",
type=str,
default=None,
)
return parser
def run_parser(parser, input=None):
args = parser.parse_known_args(input)[0]
if args.config is not None:
with open(args.config, 'rt') as f:
args = parser.parse_known_args(
namespace=argparse.Namespace(**json.load(f)["args"]))[0]
return args
def save_args(basepath, args, extra={}):
info = {"args": vars(args)}
info["args"].update(extra)
with open(f"{basepath}/args.json", "w") as f:
json.dump(info, f, indent=4)
def create_pipeline(model, scheduler, dtype=torch.bfloat16):
print("Loading Stable Diffusion pipeline...")
tokenizer = CLIPTokenizer.from_pretrained(model + '/tokenizer', torch_dtype=dtype)
text_encoder = CLIPTextModel.from_pretrained(model + '/text_encoder', torch_dtype=dtype)
vae = AutoencoderKL.from_pretrained(model + '/vae', torch_dtype=dtype)
unet = UNet2DConditionModel.from_pretrained(model + '/unet', torch_dtype=dtype)
feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32", torch_dtype=dtype)
if scheduler == "plms":
scheduler = PNDMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
)
elif scheduler == "klms":
scheduler = LMSDiscreteScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
elif scheduler == "ddim":
scheduler = DDIMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False
)
else:
scheduler = EulerAScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False
)
pipeline = CLIPGuidedStableDiffusion(
text_encoder=text_encoder,
vae=vae,
unet=unet,
tokenizer=tokenizer,
scheduler=scheduler,
feature_extractor=feature_extractor
)
pipeline.enable_attention_slicing()
pipeline.to("cuda")
print("Pipeline loaded.")
return pipeline
def generate(output_dir, pipeline, args):
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
output_dir = output_dir.joinpath(f"{now}_{slugify(args.prompt)[:100]}")
output_dir.mkdir(parents=True, exist_ok=True)
save_args(output_dir, args)
with autocast("cuda"):
for i in range(args.batch_num):
generator = torch.Generator(device="cuda").manual_seed(args.seed + i)
images = pipeline(
prompt=[args.prompt] * args.batch_size,
height=args.height,
width=args.width,
negative_prompt=args.negative_prompt,
num_inference_steps=args.steps,
guidance_scale=args.guidance_scale,
generator=generator,
).images
for j, image in enumerate(images):
image.save(output_dir.joinpath(f"{args.seed + i}_{j}.jpg"))
class CmdParse(cmd.Cmd):
prompt = 'dream> '
commands = []
def __init__(self, output_dir, pipeline, parser):
super().__init__()
self.output_dir = output_dir
self.pipeline = pipeline
self.parser = parser
def default(self, line):
line = line.replace("'", "\\'")
try:
elements = shlex.split(line)
except ValueError as e:
print(str(e))
if elements[0] == 'q':
return True
try:
args = run_parser(self.parser, elements)
except SystemExit:
self.parser.print_help()
if len(args.prompt) == 0:
print('Try again with a prompt!')
try:
generate(self.output_dir, self.pipeline, args)
except KeyboardInterrupt:
print('Generation cancelled.')
def do_exit(self, line):
return True
def main():
logging.basicConfig(stream=sys.stdout, level=logging.WARN)
args_parser = create_args_parser()
args = run_parser(args_parser)
output_dir = Path(args.output_dir)
pipeline = create_pipeline(args.model, args.scheduler)
cmd_parser = create_cmd_parser()
cmd_prompt = CmdParse(output_dir, pipeline, cmd_parser)
cmd_prompt.cmdloop()
if __name__ == "__main__":
main()
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