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import torch.cuda
import argparse
from SUPIR.util import create_SUPIR_model, PIL2Tensor, Tensor2PIL, convert_dtype
from PIL import Image
from llava.llava_agent import LLavaAgent
from CKPT_PTH import LLAVA_MODEL_PATH
import os
from torch.nn.functional import interpolate
if torch.cuda.device_count() >= 2:
SUPIR_device = 'cuda:0'
LLaVA_device = 'cuda:1'
elif torch.cuda.device_count() == 1:
SUPIR_device = 'cuda:0'
LLaVA_device = 'cuda:0'
else:
raise ValueError('Currently support CUDA only.')
# hyparams here
parser = argparse.ArgumentParser()
parser.add_argument("--img_dir", type=str)
parser.add_argument("--save_dir", type=str)
parser.add_argument("--upscale", type=int, default=1)
parser.add_argument("--SUPIR_sign", type=str, default='Q', choices=['F', 'Q'])
parser.add_argument("--seed", type=int, default=1234)
parser.add_argument("--min_size", type=int, default=1024)
parser.add_argument("--edm_steps", type=int, default=50)
parser.add_argument("--s_stage1", type=int, default=-1)
parser.add_argument("--s_churn", type=int, default=5)
parser.add_argument("--s_noise", type=float, default=1.003)
parser.add_argument("--s_cfg", type=float, default=7.5)
parser.add_argument("--s_stage2", type=float, default=1.)
parser.add_argument("--num_samples", type=int, default=1)
parser.add_argument("--a_prompt", type=str,
default='Cinematic, High Contrast, highly detailed, taken using a Canon EOS R '
'camera, hyper detailed photo - realistic maximum detail, 32k, Color '
'Grading, ultra HD, extreme meticulous detailing, skin pore detailing, '
'hyper sharpness, perfect without deformations.')
parser.add_argument("--n_prompt", type=str,
default='painting, oil painting, illustration, drawing, art, sketch, oil painting, '
'cartoon, CG Style, 3D render, unreal engine, blurring, dirty, messy, '
'worst quality, low quality, frames, watermark, signature, jpeg artifacts, '
'deformed, lowres, over-smooth')
parser.add_argument("--color_fix_type", type=str, default='Wavelet', choices=["None", "AdaIn", "Wavelet"])
parser.add_argument("--linear_CFG", action='store_true', default=True)
parser.add_argument("--linear_s_stage2", action='store_true', default=False)
parser.add_argument("--spt_linear_CFG", type=float, default=4.0)
parser.add_argument("--spt_linear_s_stage2", type=float, default=0.)
parser.add_argument("--ae_dtype", type=str, default="bf16", choices=['fp32', 'bf16'])
parser.add_argument("--diff_dtype", type=str, default="fp16", choices=['fp32', 'fp16', 'bf16'])
parser.add_argument("--no_llava", action='store_true', default=False)
parser.add_argument("--loading_half_params", action='store_true', default=False)
parser.add_argument("--use_tile_vae", action='store_true', default=False)
parser.add_argument("--encoder_tile_size", type=int, default=512)
parser.add_argument("--decoder_tile_size", type=int, default=64)
parser.add_argument("--load_8bit_llava", action='store_true', default=False)
args = parser.parse_args()
print(args)
use_llava = not args.no_llava
# load SUPIR
model = create_SUPIR_model('options/SUPIR_v0.yaml', SUPIR_sign=args.SUPIR_sign)
if args.loading_half_params:
model = model.half()
if args.use_tile_vae:
model.init_tile_vae(encoder_tile_size=args.encoder_tile_size, decoder_tile_size=args.decoder_tile_size)
model.ae_dtype = convert_dtype(args.ae_dtype)
model.model.dtype = convert_dtype(args.diff_dtype)
model = model.to(SUPIR_device)
# load LLaVA
if use_llava:
llava_agent = LLavaAgent(LLAVA_MODEL_PATH, device=LLaVA_device, load_8bit=args.load_8bit_llava, load_4bit=False)
else:
llava_agent = None
os.makedirs(args.save_dir, exist_ok=True)
for img_pth in os.listdir(args.img_dir):
img_name = os.path.splitext(img_pth)[0]
LQ_ips = Image.open(os.path.join(args.img_dir, img_pth))
LQ_img, h0, w0 = PIL2Tensor(LQ_ips, upsacle=args.upscale, min_size=args.min_size)
LQ_img = LQ_img.unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
# step 1: Pre-denoise for LLaVA, resize to 512
LQ_img_512, h1, w1 = PIL2Tensor(LQ_ips, upsacle=args.upscale, min_size=args.min_size, fix_resize=512)
LQ_img_512 = LQ_img_512.unsqueeze(0).to(SUPIR_device)[:, :3, :, :]
clean_imgs = model.batchify_denoise(LQ_img_512)
clean_PIL_img = Tensor2PIL(clean_imgs[0], h1, w1)
# step 2: LLaVA
if use_llava:
captions = llava_agent.gen_image_caption([clean_PIL_img])
else:
captions = ['']
print(captions)
# # step 3: Diffusion Process
samples = model.batchify_sample(LQ_img, captions, num_steps=args.edm_steps, restoration_scale=args.s_stage1, s_churn=args.s_churn,
s_noise=args.s_noise, cfg_scale=args.s_cfg, control_scale=args.s_stage2, seed=args.seed,
num_samples=args.num_samples, p_p=args.a_prompt, n_p=args.n_prompt, color_fix_type=args.color_fix_type,
use_linear_CFG=args.linear_CFG, use_linear_control_scale=args.linear_s_stage2,
cfg_scale_start=args.spt_linear_CFG, control_scale_start=args.spt_linear_s_stage2)
# save
for _i, sample in enumerate(samples):
Tensor2PIL(sample, h0, w0).save(f'{args.save_dir}/{img_name}_{_i}.png')
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