import os import yaml import torch 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 WurstCoreB from gdf import VPScaler, CosineTNoiseCond, DDPMSampler, P2LossWeight, AdaptiveLossWeight from train import WurstCore_t2i as WurstCoreC import torch.nn.functional as F from core.utils import load_or_fail import numpy as np import random import math import argparse from einops import rearrange import math #inrfft_3b_strc_WurstCore def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( '--height', type=int, default=2560, help='image height') parser.add_argument('--width', type=int, default=5120, help='image width') parser.add_argument('--seed', type=int, default=123, help='random seed') parser.add_argument('--dtype', type=str, default='bf16', help=' if bf16 does not work, change it to float32 ') parser.add_argument('--config_c', type=str, default='configs/training/t2i.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 photo-realistic image of a west highland white terrier in the garden, high quality, detail rich, 8K', help='text prompt') parser.add_argument( '--num_image', type=int, default=10, help='how many images generated') parser.add_argument( '--output_dir', type=str, default='figures/output_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') args = parser.parse_args() return args if __name__ == "__main__": args = parse_args() print(args) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(device) torch.manual_seed(args.seed) random.seed(args.seed) np.random.seed(args.seed) dtype = torch.bfloat16 if args.dtype == 'bf16' else torch.float #gdf = gdf_refine( # schedule=CosineSchedule(clamp_range=[0.0001, 0.9999]), # input_scaler=VPScaler(), target=EpsilonTarget(), # noise_cond=CosineTNoiseCond(), # loss_weight=AdaptiveLossWeight() if self.config.adaptive_loss_weight is True else P2LossWeight(), # ) # SETUP STAGE C config_file = args.config_c with open(config_file, "r", encoding="utf-8") as file: loaded_config = yaml.safe_load(file) core = WurstCoreC(config_dict=loaded_config, device=device, training=False) # SETUP STAGE B config_file_b = args.config_b with open(config_file_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("STAGE C 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.bfloat16().eval().requires_grad_(False) print("STAGE B READY") captions = [args.prompt] * args.num_image height, width = args.height, args.width save_dir = args.output_dir if not os.path.exists(save_dir): os.makedirs(save_dir) pretrained_path = args.pretrained_path sdd = torch.load(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) models.generator.eval() models.train_norm.eval() batch_size=1 height_lr, width_lr = get_target_lr_size(height / width, std_size=32) stage_c_latent_shape, stage_b_latent_shape = calculate_latent_sizes(height, width, batch_size=batch_size) stage_c_latent_shape_lr, stage_b_latent_shape_lr = calculate_latent_sizes(height_lr, width_lr, batch_size=batch_size) # Stage C Parameters extras.sampling_configs['cfg'] = 4 extras.sampling_configs['shift'] = 1 extras.sampling_configs['timesteps'] = 20 extras.sampling_configs['t_start'] = 1.0 extras.sampling_configs['sampler'] = DDPMSampler(extras.gdf) # 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 for cnt, caption in enumerate(captions): batch = {'captions': [caption] * 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) 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) with torch.no_grad(): models.generator.cuda() 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) 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): print(os.path.join(save_dir, args.prompt[:20]+'_' + str(cnt).zfill(5) + '.jpg'), idx) img.save(os.path.join(save_dir, args.prompt[:20]+'_' + str(cnt).zfill(5) + '.jpg')) print('finished! Results at ', save_dir )