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import os
import cv2
import time
import random
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms
from accelerate.utils import set_seed
from src import (FontDiffuserDPMPipeline,
FontDiffuserModelDPM,
build_ddpm_scheduler,
build_unet,
build_content_encoder,
build_style_encoder)
from utils import (ttf2im,
load_ttf,
is_char_in_font,
save_args_to_yaml,
save_single_image,
save_image_with_content_style)
def arg_parse():
from configs.fontdiffuser import get_parser
parser = get_parser()
parser.add_argument("--ckpt_dir", type=str, default=None)
parser.add_argument("--demo", action="store_true")
parser.add_argument("--controlnet", type=bool, default=False,
help="If in demo mode, the controlnet can be added.")
parser.add_argument("--character_input", action="store_true")
parser.add_argument("--content_character", type=str, default=None)
parser.add_argument("--content_image_path", type=str, default=None)
parser.add_argument("--style_image_path", type=str, default=None)
parser.add_argument("--save_image", action="store_true")
parser.add_argument("--save_image_dir", type=str, default=None,
help="The saving directory.")
parser.add_argument("--device", type=str, default="cuda:0")
parser.add_argument("--ttf_path", type=str, default="ttf/KaiXinSongA.ttf")
args = parser.parse_args()
style_image_size = args.style_image_size
content_image_size = args.content_image_size
args.style_image_size = (style_image_size, style_image_size)
args.content_image_size = (content_image_size, content_image_size)
return args
def image_process(args, content_image=None, style_image=None):
if not args.demo:
# Read content image and style image
if args.character_input:
assert args.content_character is not None, "The content_character should not be None."
if not is_char_in_font(font_path=args.ttf_path, char=args.content_character):
return None, None
font = load_ttf(ttf_path=args.ttf_path)
content_image = ttf2im(font=font, char=args.content_character)
content_image_pil = content_image.copy()
else:
content_image = Image.open(args.content_image_path).convert('RGB')
content_image_pil = None
style_image = Image.open(args.style_image_path).convert('RGB')
else:
assert style_image is not None, "The style image should not be None."
if args.character_input:
assert args.content_character is not None, "The content_character should not be None."
if not is_char_in_font(font_path=args.ttf_path, char=args.content_character):
return None, None
font = load_ttf(ttf_path=args.ttf_path)
content_image = ttf2im(font=font, char=args.content_character)
else:
assert content_image is not None, "The content image should not be None."
content_image_pil = None
## Dataset transform
content_inference_transforms = transforms.Compose(
[transforms.Resize(args.content_image_size, \
interpolation=transforms.InterpolationMode.BILINEAR),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])])
style_inference_transforms = transforms.Compose(
[transforms.Resize(args.style_image_size, \
interpolation=transforms.InterpolationMode.BILINEAR),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])])
content_image = content_inference_transforms(content_image)[None, :]
style_image = style_inference_transforms(style_image)[None, :]
return content_image, style_image, content_image_pil
def load_fontdiffuer_pipeline(args):
# Load the model state_dict
unet = build_unet(args=args)
unet.load_state_dict(torch.load(f"{args.ckpt_dir}/unet.pth"))
style_encoder = build_style_encoder(args=args)
style_encoder.load_state_dict(torch.load(f"{args.ckpt_dir}/style_encoder.pth"))
content_encoder = build_content_encoder(args=args)
content_encoder.load_state_dict(torch.load(f"{args.ckpt_dir}/content_encoder.pth"))
model = FontDiffuserModelDPM(
unet=unet,
style_encoder=style_encoder,
content_encoder=content_encoder)
model.to(args.device)
print("Loaded the model state_dict successfully!")
# Load the training ddpm_scheduler.
train_scheduler = build_ddpm_scheduler(args=args)
print("Loaded training DDPM scheduler sucessfully!")
# Load the DPM_Solver to generate the sample.
pipe = FontDiffuserDPMPipeline(
model=model,
ddpm_train_scheduler=train_scheduler,
model_type=args.model_type,
guidance_type=args.guidance_type,
guidance_scale=args.guidance_scale,
)
print("Loaded dpm_solver pipeline sucessfully!")
return pipe
def sampling(args, pipe, content_image=None, style_image=None):
if not args.demo:
os.makedirs(args.save_image_dir, exist_ok=True)
# saving sampling config
save_args_to_yaml(args=args, output_file=f"{args.save_image_dir}/sampling_config.yaml")
if args.seed:
set_seed(seed=args.seed)
content_image, style_image, content_image_pil = image_process(args=args,
content_image=content_image,
style_image=style_image)
if content_image == None:
print(f"The content_character you provided is not in the ttf. \
Please change the content_character or you can change the ttf.")
return None
with torch.no_grad():
content_image = content_image.to(args.device)
style_image = style_image.to(args.device)
print(f"Sampling by DPM-Solver++ ......")
start = time.time()
images = pipe.generate(
content_images=content_image,
style_images=style_image,
batch_size=1,
order=args.order,
num_inference_step=args.num_inference_steps,
content_encoder_downsample_size=args.content_encoder_downsample_size,
t_start=args.t_start,
t_end=args.t_end,
dm_size=args.content_image_size,
algorithm_type=args.algorithm_type,
skip_type=args.skip_type,
method=args.method,
correcting_x0_fn=args.correcting_x0_fn)
end = time.time()
if args.save_image:
print(f"Saving the image ......")
save_single_image(save_dir=args.save_image_dir, image=images[0])
if args.character_input:
save_image_with_content_style(save_dir=args.save_image_dir,
image=images[0],
content_image_pil=content_image_pil,
content_image_path=None,
style_image_path=args.style_image_path,
resolution=args.resolution)
else:
save_image_with_content_style(save_dir=args.save_image_dir,
image=images[0],
content_image_pil=None,
content_image_path=args.content_image_path,
style_image_path=args.style_image_path,
resolution=args.resolution)
print(f"Finish the sampling process, costing time {end - start}s")
return images[0]
def load_controlnet_pipeline(args,
config_path="lllyasviel/sd-controlnet-canny",
ckpt_path="runwayml/stable-diffusion-v1-5"):
from diffusers import ControlNetModel, AutoencoderKL
# load controlnet model and pipeline
from diffusers import StableDiffusionControlNetPipeline, UniPCMultistepScheduler
controlnet = ControlNetModel.from_pretrained(config_path,
torch_dtype=torch.float16,
cache_dir=f"{args.ckpt_dir}/controlnet")
print(f"Loaded ControlNet Model Successfully!")
pipe = StableDiffusionControlNetPipeline.from_pretrained(ckpt_path,
controlnet=controlnet,
torch_dtype=torch.float16,
cache_dir=f"{args.ckpt_dir}/controlnet_pipeline")
# faster
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
print(f"Loaded ControlNet Pipeline Successfully!")
return pipe
def controlnet(text_prompt,
pil_image,
pipe):
image = np.array(pil_image)
# get canny image
image = cv2.Canny(image=image, threshold1=100, threshold2=200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
seed = random.randint(0, 10000)
generator = torch.manual_seed(seed)
image = pipe(text_prompt,
num_inference_steps=50,
generator=generator,
image=canny_image,
output_type='pil').images[0]
return image
def load_instructpix2pix_pipeline(args,
ckpt_path="timbrooks/instruct-pix2pix"):
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(ckpt_path,
torch_dtype=torch.float16)
pipe.to(args.device)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
return pipe
def instructpix2pix(pil_image, text_prompt, pipe):
image = pil_image.resize((512, 512))
seed = random.randint(0, 10000)
generator = torch.manual_seed(seed)
image = pipe(prompt=text_prompt, image=image, generator=generator,
num_inference_steps=20, image_guidance_scale=1.1).images[0]
return image
if __name__=="__main__":
args = arg_parse()
# load fontdiffuser pipeline
pipe = load_fontdiffuer_pipeline(args=args)
out_image = sampling(args=args, pipe=pipe)
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