import os
import yaml
import torch
import torchvision
from tqdm import tqdm
import sys
sys.path.append(os.path.abspath('./'))
from inference.utils import *
from core.utils import load_or_fail
from train import WurstCore_control_lrguide, WurstCoreB
from PIL import Image
from core.utils import load_or_fail
import math
import argparse
import time
import random
import numpy as np
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument( '--height', type=int, default=3840, help='image height')
parser.add_argument('--width', type=int, default=2160, help='image width')
parser.add_argument('--control_weight', type=float, default=0.70, help='[ 0.3, 0.8]')
parser.add_argument('--dtype', type=str, default='bf16', help=' if bf16 does not work, change it to float32 ')
parser.add_argument('--seed', type=int, default=123, help='random seed')
parser.add_argument('--config_c', type=str,
default='configs/training/cfg_control_lr.yaml' ,help='config file for stage c, latent generation')
parser.add_argument('--config_b', type=str,
default='configs/inference/stage_b_1b.yaml' ,help='config file for stage b, latent decoding')
parser.add_argument( '--prompt', type=str,
default='A peaceful lake surrounded by mountain, white cloud in the sky, high quality,', help='text prompt')
parser.add_argument( '--num_image', type=int, default=4, help='how many images generated')
parser.add_argument( '--output_dir', type=str, default='figures/controlnet_results/', help='output directory for generated image')
parser.add_argument( '--stage_a_tiled', action='store_true', help='whther or nor to use tiled decoding for stage a to save memory')
parser.add_argument( '--pretrained_path', type=str, default='models/ultrapixel_t2i.safetensors', help='pretrained path of newly added paramter of UltraPixel')
parser.add_argument( '--canny_source_url', type=str, default="figures/California_000490.jpg", help='image used to extract canny edge map')
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
width = args.width
height = args.height
torch.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if args.dtype == 'bf16' else torch.float
# SETUP STAGE C
with open(args.config_c, "r", encoding="utf-8") as file:
loaded_config = yaml.safe_load(file)
core = WurstCore_control_lrguide(config_dict=loaded_config, device=device, training=False)
# SETUP STAGE B
with open(args.config_b, "r", encoding="utf-8") as file:
config_file_b = yaml.safe_load(file)
core_b = WurstCoreB(config_dict=config_file_b, device=device, training=False)
extras = core.setup_extras_pre()
models = core.setup_models(extras)
models.generator.eval().requires_grad_(False)
print("CONTROLNET READY")
extras_b = core_b.setup_extras_pre()
models_b = core_b.setup_models(extras_b, skip_clip=True)
models_b = WurstCoreB.Models(
**{**models_b.to_dict(), 'tokenizer': models.tokenizer, 'text_model': models.text_model}
)
models_b.generator.eval().requires_grad_(False)
print("STAGE B READY")
batch_size = 1
save_dir = args.output_dir
url = args.canny_source_url
images = resize_image(Image.open(url).convert("RGB")).unsqueeze(0).expand(batch_size, -1, -1, -1)
batch = {'images': images}
cnet_multiplier = args.control_weight # 0.8 0.6 0.3 control strength
caption_list = [args.prompt] * args.num_image
height_lr, width_lr = get_target_lr_size(height / width, std_size=32)
stage_c_latent_shape_lr, stage_b_latent_shape_lr = calculate_latent_sizes(height_lr, width_lr, batch_size=batch_size)
stage_c_latent_shape, stage_b_latent_shape = calculate_latent_sizes(height, width, batch_size=batch_size)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
sdd = torch.load(args.pretrained_path, map_location='cpu')
collect_sd = {}
for k, v in sdd.items():
collect_sd[k[7:]] = v
models.train_norm.load_state_dict(collect_sd, strict=True)
models.controlnet.load_state_dict(load_or_fail(core.config.controlnet_checkpoint_path), strict=True)
# Stage C Parameters
extras.sampling_configs['cfg'] = 1
extras.sampling_configs['shift'] = 2
extras.sampling_configs['timesteps'] = 20
extras.sampling_configs['t_start'] = 1.0
# Stage B Parameters
extras_b.sampling_configs['cfg'] = 1.1
extras_b.sampling_configs['shift'] = 1
extras_b.sampling_configs['timesteps'] = 10
extras_b.sampling_configs['t_start'] = 1.0
# PREPARE CONDITIONS
for out_cnt, caption in enumerate(caption_list):
with torch.no_grad():
batch['captions'] = [caption + ' high quality'] * batch_size
conditions = core.get_conditions(batch, models, extras, is_eval=True, is_unconditional=False, eval_image_embeds=False)
unconditions = core.get_conditions(batch, models, extras, is_eval=True, is_unconditional=True, eval_image_embeds=False)
cnet, cnet_input = core.get_cnet(batch, models, extras)
cnet_uncond = cnet
conditions['cnet'] = [c.clone() * cnet_multiplier if c is not None else c for c in cnet]
unconditions['cnet'] = [c.clone() * cnet_multiplier if c is not None else c for c in cnet_uncond]
edge_images = show_images(cnet_input)
models.generator.cuda()
for idx, img in enumerate(edge_images):
img.save(os.path.join(save_dir, f"edge_{url.split('/')[-1]}"))
print('STAGE C GENERATION***************************')
with torch.cuda.amp.autocast(dtype=dtype):
sampled_c = generation_c(batch, models, extras, core, stage_c_latent_shape, stage_c_latent_shape_lr, device, conditions, unconditions)
models.generator.cpu()
torch.cuda.empty_cache()
conditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=False)
unconditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=True)
conditions_b['effnet'] = sampled_c
unconditions_b['effnet'] = torch.zeros_like(sampled_c)
print('STAGE B + A DECODING***************************')
with torch.cuda.amp.autocast(dtype=dtype):
sampled = decode_b(conditions_b, unconditions_b, models_b, stage_b_latent_shape, extras_b, device, stage_a_tiled=args.stage_a_tiled)
torch.cuda.empty_cache()
imgs = show_images(sampled)
for idx, img in enumerate(imgs):
img.save(os.path.join(save_dir, args.prompt[:20]+'_' + str(out_cnt).zfill(5) + '.jpg'))
print('finished! Results at ', save_dir )