Update app.py

app.py

@@ -1,2 +1,271 @@
 import os
-exec(os.environ.get('APP'))
+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(최대 5120 X 4096 픽셀) 이미지 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)