| import os |
| import argparse |
| import random |
| import numpy as np |
| import torch |
|
|
| from diffusers import DDIMScheduler |
| from pipeline_lsrna_demofusion_sdxl import DemoFusionLSRNASDXLPipeline |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument('--prompt', type=str, required=True) |
| parser.add_argument('--negative_prompt', type=str) |
| parser.add_argument('--height', type=int, default=2048, help='target height') |
| parser.add_argument('--width', type=int, default=2048, help='target width') |
| parser.add_argument('--seed', type=int) |
| parser.add_argument('--lsr_path', type=str, default='lsr/checkpoints/swinir-liif-latent-sdxl.pth') |
| parser.add_argument('--rna_min_std', type=float, default=0.0) |
| parser.add_argument('--rna_max_std', type=float, default=1.2) |
| parser.add_argument('--inversion_depth', type=int, default=30) |
| parser.add_argument('--save_dir', type=str, default='results') |
| parser.add_argument('--low_vram', action='store_true') |
| args = parser.parse_args() |
|
|
| |
| model_ckpt = 'stabilityai/stable-diffusion-xl-base-1.0' |
| scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder='scheduler') |
| pipe = DemoFusionLSRNASDXLPipeline.from_pretrained(model_ckpt, scheduler=scheduler, torch_dtype=torch.float16).to('cuda') |
| pipe.vae.enable_tiling() |
|
|
| |
| if args.seed is not None: |
| seed = args.seed |
| random.seed(seed) |
| np.random.seed(seed) |
| torch.manual_seed(seed) |
| torch.cuda.manual_seed_all(seed) |
| torch.backends.cudnn.deterministic = True |
| torch.backends.cudnn.benchmark = False |
|
|
| |
| images = pipe( |
| args.prompt, |
| negative_prompt=args.negative_prompt, |
| height=args.height, width=args.width, |
| view_batch_size=8, |
| stride_ratio=0.5, |
| lsr_path=args.lsr_path, |
| cosine_scale_1=3, |
| cosine_scale_2=1, |
| cosine_scale_3=1, |
| sigma=0.8, |
| rna_min_std=args.rna_min_std, |
| rna_max_std=args.rna_max_std, |
| inversion_depth=args.inversion_depth, |
| low_vram=args.low_vram |
| ) |
| os.makedirs(args.save_dir, exist_ok=True) |
| images[0].save(os.path.join(args.save_dir, 'ref.png')) |
| images[1].save(os.path.join(args.save_dir, 'trg.png')) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
