Delete uhdimage.cod

uhdimage.cod

@@ -1,271 +0,0 @@
-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
-import numpy as np
-import random
-import argparse
-import gradio as gr
-import spaces
-from huggingface_hub import hf_hub_url
-import subprocess
-from huggingface_hub import hf_hub_download
-from transformers import pipeline
-
-# Initialize the translation pipeline
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
-
-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='whether or not 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 parameter 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 is_korean(text):
-    return any('\uac00' <= char <= '\ud7a3' for char in text)
-
-def translate_if_korean(text):
-    if is_korean(text):
-        translated = translator(text, max_length=512)[0]['translation_text']
-        print(f"Translated from Korean: {text} -> {translated}")
-        return translated
-    return text
-
-@spaces.GPU(duration=120)
-def get_image(height, width, seed, prompt, cfg, timesteps, stage_a_tiled):
-    global args
-    
-    # Translate the prompt if it's in Korean
-    prompt = translate_if_korean(prompt)
-    
-    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_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)
-                    
-    return imgs[0]           
-
-css = """
-footer {
-    visibility: hidden;
-}
-"""
-
-with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("<h1><center>초고해상도 UHD 이미지(최대 5120 X 4096 픽셀) 생성</center></h1>")
-        
-        with gr.Row():
-            prompt = gr.Textbox(
-                label="Text Prompt (한글 또는 영어로 입력하세요)",
-                show_label=False,
-                max_lines=1,
-                placeholder="프롬프트를 입력하세요 (Enter your prompt in Korean or English)",
-                container=False
-            )
-            polish_button = gr.Button("제출! (Submit!)", scale=0)
-        
-        output_img = gr.Image(label="Output Image", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            seed = gr.Number(
-                label="Random Seed",
-                value=123,
-                step=1,
-                minimum=0,
-            )
-            
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=1536,
-                    maximum=5120,
-                    step=32,
-                    value=4096
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=1536,
-                    maximum=4096,
-                    step=32,
-                    value=2304
-                )
-            
-            with gr.Row():
-                cfg = gr.Slider(
-                    label="CFG",
-                    minimum=3,
-                    maximum=10,
-                    step=0.1,
-                    value=4
-                )
-                
-                timesteps = gr.Slider(
-                    label="Timesteps",
-                    minimum=10,
-                    maximum=50,
-                    step=1,
-                    value=20
-                )
-            
-            stage_a_tiled = gr.Checkbox(label="Stage_a_tiled", value=False)
-        
-        clear_button = gr.Button("Clear!")
-        
-        gr.Examples(
-            examples=[
-                "A detailed view of a blooming magnolia tree, with large, white flowers and dark green leaves, set against a clear blue sky.",
-                "눈 덮인 산맥의 장엄한 전경, 푸른 하늘을 배경으로 한 고요한 호수가 있는 모습",
-                "The image features a snow-covered mountain range with a large, snow-covered mountain in the background. The mountain is surrounded by a forest of trees, and the sky is filled with clouds. The scene is set during the winter season, with snow covering the ground and the trees.",
-                "스웨터를 입은 악어",
-                "A vibrant anime scene of a young girl with long, flowing pink hair, big sparkling blue eyes, and a school uniform, standing under a cherry blossom tree with petals falling around her. The background shows a traditional Japanese school with cherry blossoms in full bloom.",
-                "골든 리트리버 강아지가 푸른 잔디밭에서 빨간 공을 쫓는 귀여운 모습",
-                "A cozy, rustic log cabin nestled in a snow-covered forest, with smoke rising from the stone chimney, warm lights glowing from the windows, and a path of footprints leading to the front door.",
-                "캐나다 밴프 국립공원의 아름다운 풍경, 청록색 호수와 눈 덮인 산들, 울창한 소나무 숲이 어우러진 모습",
-                "귀여운 시츄가 욕조에서 목욕하는 모습, 거품에 둘러싸인 채 살짝 젖은 모습으로 카메라를 바라보고 있음",
-            ],
-            inputs=[prompt],
-            outputs=[output_img],
-            examples_per_page=5
-        )
-        
-        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)
-
-def download_with_wget(url, save_path):
-    try:
-        subprocess.run(['wget', url, '-O', save_path], check=True)
-        print(f"Downloaded to {save_path}")
-    except subprocess.CalledProcessError as e:
-        print(f"Error downloading file: {e}")
-
-def download_model():
-    urls = [
-        'https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_a.safetensors',
-        'https://huggingface.co/stabilityai/StableWurst/resolve/main/previewer.safetensors',
-        'https://huggingface.co/stabilityai/StableWurst/resolve/main/effnet_encoder.safetensors',
-        'https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_b_lite_bf16.safetensors', 
-        'https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_c_bf16.safetensors', 
-    ]
-    for file_url in urls:
-        hf_hub_download(repo_id="stabilityai/stable-cascade", filename=file_url.split('/')[-1], local_dir='models')
-    hf_hub_download(repo_id="roubaofeipi/UltraPixel", filename='ultrapixel_t2i.safetensors', local_dir='models')
-    
-if __name__ == "__main__":
-    args = parse_args()
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    download_model()
-    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, auth=("gini","pick"))