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Running
on
Zero
File size: 8,582 Bytes
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import os
import yaml
import torch
import sys
sys.path.append(os.path.abspath('./'))
from inference.utils import *
from train import WurstCoreB
from gdf import DDPMSampler
from train import WurstCore_t2i as WurstCoreC
from core.utils import load_or_fail
import numpy as np
import random
import argparse
import gradio as gr
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=1, 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
def clear_image():
return None
def load_message(height, width, seed, prompt, args, stage_a_tiled):
args.height = height
args.width = width
args.seed = seed
args.prompt = prompt + ' rich detail, 4k, high quality'
args.stage_a_tiled = stage_a_tiled
return args
def get_image(height, width, seed, prompt, cfg, timesteps, stage_a_tiled):
global args
args = load_message(height, width, seed, prompt, args, stage_a_tiled)
torch.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
dtype = torch.bfloat16 if args.dtype == 'bf16' else torch.float
captions = [args.prompt] * args.num_image
height, width = args.height, args.width
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 _, 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'))
return imgs[0]
#print('finished! Results ')
with gr.Blocks() as demo:
with gr.Column():
with gr.Row():
with gr.Column():
height = gr.Slider(value=2304, step=32, minimum=1536, maximum=4096, label='Height')
width = gr.Slider(value=4096, step=32, minimum=1536, maximum=5120, label='Width')
seed = gr.Number(value=123, step=1, label='Random Seed')
prompt = gr.Textbox(value='', max_lines=4, label='Text Prompt')
cfg = gr.Slider(value=4, step=0.1, minimum=3, maximum=10, label='CFG')
timesteps = gr.Slider(value=20, step=1, minimum=10, maximum=50, label='Timesteps')
stage_a_tiled = gr.Checkbox(value=False, label='Stage_a_tiled')
with gr.Row():
clear_button = gr.Button("Clear!")
polish_button = gr.Button("Submit!")
with gr.Column():
output_img = gr.Image(label='Output Image', sources=None)
with gr.Column():
prompt2 = gr.Textbox(
value='''
1. a happy cat
2. a happy girl
''', label='Text prompt examples'
)
polish_button.click(get_image, inputs=[height, width, seed, prompt, cfg, timesteps, stage_a_tiled], outputs=output_img)
polish_button.click(clear_image, inputs=[], outputs=output_img)
if __name__ == "__main__":
args = parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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")
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()
demo.launch(
debug=True, share=True,
#server_name='10.160.211.26', server_port=7867
) |