Update app.py

app.py

@@ -1,48 +1,32 @@
+import os
+import gc
+import uuid
+import random
 import tempfile
 import time
-from collections.abc import Sequence
-from typing import Any, cast
-import os
+from datetime import datetime
+from typing import Any
 from huggingface_hub import login, hf_hub_download
 
 import gradio as gr
 import numpy as np
-import pillow_heif
-import spaces
 import torch
-from gradio_image_annotation import image_annotator
-from gradio_imageslider import ImageSlider
-from PIL import Image
-from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
-from refiners.fluxion.utils import no_grad
-from refiners.solutions import BoxSegmenter
-from transformers import GroundingDinoForObjectDetection, GroundingDinoProcessor
-from diffusers import FluxPipeline
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
-import gc
-
 from PIL import Image, ImageDraw, ImageFont
-from PIL import Image
-from gradio_client import Client, handle_file
-import uuid
-
-import random
-from datetime import datetime
+from diffusers import FluxPipeline
 
+# 메모리 정리 함수
 def clear_memory():
-    """메모리 정리 함수"""
     gc.collect()
     try:
         if torch.cuda.is_available():
-            with torch.cuda.device(0):  # 명시적으로 device 0 사용
+            with torch.cuda.device(0):
                 torch.cuda.empty_cache()
     except:
         pass
 
 # GPU 설정
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")  # 명시적으로 cuda:0 지정
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-# GPU 설정을 try-except로 감싸기
 if torch.cuda.is_available():
     try:
         with torch.cuda.device(0):
@@ -52,29 +36,6 @@ if torch.cuda.is_available():
     except:
         print("Warning: Could not configure CUDA settings")
 
-# 번역 모델 초기화
-model_name = "Helsinki-NLP/opus-mt-ko-en"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to('cpu')
-translator = pipeline("translation", model=model, tokenizer=tokenizer, device=-1)
-
-def translate_to_english(text: str) -> str:
-    """한글 텍스트를 영어로 번역"""
-    try:
-        if any(ord('가') <= ord(char) <= ord('힣') for char in text):
-            translated = translator(text, max_length=128)[0]['translation_text']
-            print(f"Translated '{text}' to '{translated}'")
-            return translated
-        return text
-    except Exception as e:
-        print(f"Translation error: {str(e)}")
-        return text
-
-BoundingBox = tuple[int, int, int, int]
-
-pillow_heif.register_heif_opener()
-pillow_heif.register_avif_opener()
-
 # HF 토큰 설정
 HF_TOKEN = os.getenv("HF_TOKEN")
 if HF_TOKEN is None:
@@ -85,17 +46,6 @@ try:
 except Exception as e:
     raise ValueError(f"Failed to login to Hugging Face: {str(e)}")
 
-# 모델 초기화
-segmenter = BoxSegmenter(device="cpu")
-segmenter.device = device
-segmenter.model = segmenter.model.to(device=segmenter.device)
-
-gd_model_path = "IDEA-Research/grounding-dino-base"
-gd_processor = GroundingDinoProcessor.from_pretrained(gd_model_path)
-gd_model = GroundingDinoForObjectDetection.from_pretrained(gd_model_path, torch_dtype=torch.float32)
-gd_model = gd_model.to(device=device)
-assert isinstance(gd_model, GroundingDinoForObjectDetection)
-
 # FLUX 파이프라인 초기화
 pipe = FluxPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-dev",
@@ -104,468 +54,84 @@ pipe = FluxPipeline.from_pretrained(
 )
 pipe.enable_attention_slicing(slice_size="auto")
 
-# LoRA 가중치 로드
-pipe.load_lora_weights(
-    hf_hub_download(
-        "ByteDance/Hyper-SD",
-        "Hyper-FLUX.1-dev-8steps-lora.safetensors",
+# Eric cat LoRA 가중치 로드
+try:
+    lora_path = hf_hub_download(
+        "ginipick/flux-lora-eric-cat",
+        "flux-lora-eric-cat.safetensors",
         use_auth_token=HF_TOKEN
     )
-)
-pipe.fuse_lora(lora_scale=0.125)
-
-# GPU 설정을 try-except로 감싸기
-try:
-    if torch.cuda.is_available():
-        pipe = pipe.to("cuda:0")  # 명시적으로 cuda:0 지정
+    pipe.load_lora_weights(lora_path)
+    pipe.fuse_lora(lora_scale=0.125)
 except Exception as e:
-    print(f"Warning: Could not move pipeline to CUDA: {str(e)}")
-
-client = Client("NabeelShar/BiRefNet_for_text_writing")
-
-class timer:
-    def __init__(self, method_name="timed process"):
-        self.method = method_name
-    def __enter__(self):
-        self.start = time.time()
-        print(f"{self.method} starts")
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        end = time.time()
-        print(f"{self.method} took {str(round(end - self.start, 2))}s")
-
-def bbox_union(bboxes: Sequence[list[int]]) -> BoundingBox | None:
-    if not bboxes:
-        return None
-    for bbox in bboxes:
-        assert len(bbox) == 4
-        assert all(isinstance(x, int) for x in bbox)
-    return (
-        min(bbox[0] for bbox in bboxes),
-        min(bbox[1] for bbox in bboxes),
-        max(bbox[2] for bbox in bboxes),
-        max(bbox[3] for bbox in bboxes),
-    )
+    print(f"Error loading LoRA weights: {str(e)}")
+    raise ValueError("Failed to load LoRA weights. Please check your HF_TOKEN and model access.")
 
-def corners_to_pixels_format(bboxes: torch.Tensor, width: int, height: int) -> torch.Tensor:
-    x1, y1, x2, y2 = bboxes.round().to(torch.int32).unbind(-1)
-    return torch.stack((x1.clamp_(0, width), y1.clamp_(0, height), x2.clamp_(0, width), y2.clamp_(0, height)), dim=-1)
-
-def gd_detect(img: Image.Image, prompt: str) -> BoundingBox | None:
-    inputs = gd_processor(images=img, text=f"{prompt}.", return_tensors="pt").to(device=device)
-    with no_grad():
-        outputs = gd_model(**inputs)
-    width, height = img.size
-    results: dict[str, Any] = gd_processor.post_process_grounded_object_detection(
-        outputs,
-        inputs["input_ids"],
-        target_sizes=[(height, width)],
-    )[0]
-    assert "boxes" in results and isinstance(results["boxes"], torch.Tensor)
-    bboxes = corners_to_pixels_format(results["boxes"].cpu(), width, height)
-    return bbox_union(bboxes.numpy().tolist())
-
-def apply_mask(img: Image.Image, mask_img: Image.Image, defringe: bool = True) -> Image.Image:
-    assert img.size == mask_img.size
-    img = img.convert("RGB")
-    mask_img = mask_img.convert("L")
-    if defringe:
-        rgb, alpha = np.asarray(img) / 255.0, np.asarray(mask_img) / 255.0
-        foreground = cast(np.ndarray[Any, np.dtype[np.uint8]], estimate_foreground_ml(rgb, alpha))
-        img = Image.fromarray((foreground * 255).astype("uint8"))
-    result = Image.new("RGBA", img.size)
-    result.paste(img, (0, 0), mask_img)
-    return result
-
-
-def adjust_size_to_multiple_of_8(width: int, height: int) -> tuple[int, int]:
-    """이미지 크기를 8의 배수로 조정하는 함수"""
-    new_width = ((width + 7) // 8) * 8
-    new_height = ((height + 7) // 8) * 8
-    return new_width, new_height
-
-def calculate_dimensions(aspect_ratio: str, base_size: int = 512) -> tuple[int, int]:
-    """선택된 비율에 따라 이미지 크기 계산"""
-    if aspect_ratio == "1:1":
-        return base_size, base_size
-    elif aspect_ratio == "16:9":
-        return base_size * 16 // 9, base_size
-    elif aspect_ratio == "9:16":
-        return base_size, base_size * 16 // 9
-    elif aspect_ratio == "4:3":
-        return base_size * 4 // 3, base_size
-    return base_size, base_size
-
-@spaces.GPU(duration=20)  # 40초에서 20초로 감소
-def generate_background(prompt: str, aspect_ratio: str) -> Image.Image:
-    try:
-        width, height = calculate_dimensions(aspect_ratio)
-        width, height = adjust_size_to_multiple_of_8(width, height)
-        
-        max_size = 768
-        if width > max_size or height > max_size:
-            ratio = max_size / max(width, height)
-            width = int(width * ratio)
-            height = int(height * ratio)
-            width, height = adjust_size_to_multiple_of_8(width, height)
-
-        with timer("Background generation"):
-            try:
-                with torch.inference_mode():
-                    image = pipe(
-                        prompt=prompt,
-                        width=width,
-                        height=height,
-                        num_inference_steps=8,
-                        guidance_scale=4.0
-                    ).images[0]
-            except Exception as e:
-                print(f"Pipeline error: {str(e)}")
-                return Image.new('RGB', (width, height), 'white')
-                
-        return image
-    except Exception as e:
-        print(f"Background generation error: {str(e)}")
-        return Image.new('RGB', (512, 512), 'white')
-
-def create_position_grid():
-    return """
-    <div class="position-grid" style="display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px; width: 150px; margin: auto;">
-        <button class="position-btn" data-pos="top-left">↖</button>
-        <button class="position-btn" data-pos="top-center">↑</button>
-        <button class="position-btn" data-pos="top-right">↗</button>
-        <button class="position-btn" data-pos="middle-left">←</button>
-        <button class="position-btn" data-pos="middle-center">•</button>
-        <button class="position-btn" data-pos="middle-right">→</button>
-        <button class="position-btn" data-pos="bottom-left">↙</button>
-        <button class="position-btn" data-pos="bottom-center" data-default="true">↓</button>
-        <button class="position-btn" data-pos="bottom-right">↘</button>
-    </div>
-    """
-
-def calculate_object_position(position: str, bg_size: tuple[int, int], obj_size: tuple[int, int]) -> tuple[int, int]:
-    """오브젝트의 위치 계산"""
-    bg_width, bg_height = bg_size
-    obj_width, obj_height = obj_size
-    
-    positions = {
-        "top-left": (0, 0),
-        "top-center": ((bg_width - obj_width) // 2, 0),
-        "top-right": (bg_width - obj_width, 0),
-        "middle-left": (0, (bg_height - obj_height) // 2),
-        "middle-center": ((bg_width - obj_width) // 2, (bg_height - obj_height) // 2),
-        "middle-right": (bg_width - obj_width, (bg_height - obj_height) // 2),
-        "bottom-left": (0, bg_height - obj_height),
-        "bottom-center": ((bg_width - obj_width) // 2, bg_height - obj_height),
-        "bottom-right": (bg_width - obj_width, bg_height - obj_height)
-    }
-    
-    return positions.get(position, positions["bottom-center"])
-
-def resize_object(image: Image.Image, scale_percent: float) -> Image.Image:
-    """오브젝트 크기 조정"""
-    width = int(image.width * scale_percent / 100)
-    height = int(image.height * scale_percent / 100)
-    return image.resize((width, height), Image.Resampling.LANCZOS)
-
-def combine_with_background(foreground: Image.Image, background: Image.Image, 
-                          position: str = "bottom-center", scale_percent: float = 100) -> Image.Image:
-    """전경과 배경 합성 함수"""
-    print(f"Combining with position: {position}, scale: {scale_percent}")
-    
-    result = background.convert('RGBA')
-    scaled_foreground = resize_object(foreground, scale_percent)
-    
-    x, y = calculate_object_position(position, result.size, scaled_foreground.size)
-    print(f"Calculated position coordinates: ({x}, {y})")
-    
-    result.paste(scaled_foreground, (x, y), scaled_foreground)
-    return result
-
-@spaces.GPU(duration=30)  # 120초에서 30초로 감소
-def _gpu_process(img: Image.Image, prompt: str | BoundingBox | None) -> tuple[Image.Image, BoundingBox | None, list[str]]:
-    time_log: list[str] = []
+# GPU로 이동
+if torch.cuda.is_available():
     try:
-        if isinstance(prompt, str):
-            t0 = time.time()
-            bbox = gd_detect(img, prompt)
-            time_log.append(f"detect: {time.time() - t0}")
-            if not bbox:
-                print(time_log[0])
-                raise gr.Error("No object detected")
-        else:
-            bbox = prompt
-        t0 = time.time()
-        mask = segmenter(img, bbox)
-        time_log.append(f"segment: {time.time() - t0}")
-        return mask, bbox, time_log
+        pipe = pipe.to("cuda:0")
     except Exception as e:
-        print(f"GPU process error: {str(e)}")
-        raise
-
-def _process(img: Image.Image, prompt: str | BoundingBox | None, bg_prompt: str | None = None, aspect_ratio: str = "1:1") -> tuple[tuple[Image.Image, Image.Image, Image.Image], gr.DownloadButton]:
-    try:
-        # 입력 이미지 크기 제한
-        max_size = 1024
-        if img.width > max_size or img.height > max_size:
-            ratio = max_size / max(img.width, img.height)
-            new_size = (int(img.width * ratio), int(img.height * ratio))
-            img = img.resize(new_size, Image.LANCZOS)
-
-        # CUDA 메모리 관리 수정
-        try:
-            if torch.cuda.is_available():
-                current_device = torch.cuda.current_device()
-                with torch.cuda.device(current_device):
-                    torch.cuda.empty_cache()
-        except Exception as e:
-            print(f"CUDA memory management failed: {e}")
-
-        with torch.cuda.amp.autocast(enabled=torch.cuda.is_available()):
-            mask, bbox, time_log = _gpu_process(img, prompt)
-            masked_alpha = apply_mask(img, mask, defringe=True)
-
-        if bg_prompt:
-            background = generate_background(bg_prompt, aspect_ratio)
-            combined = background
-        else:
-            combined = Image.alpha_composite(Image.new("RGBA", masked_alpha.size, "white"), masked_alpha)
-
-        clear_memory()
-
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp:
-            combined.save(temp.name)
-            return (img, combined, masked_alpha), gr.DownloadButton(value=temp.name, interactive=True)
-    except Exception as e:
-        clear_memory()
-        print(f"Processing error: {str(e)}")
-        raise gr.Error(f"Processing failed: {str(e)}")
+        print(f"Warning: Could not move pipeline to CUDA: {str(e)}")
 
-def on_change_bbox(prompts: dict[str, Any] | None):
-    return gr.update(interactive=prompts is not None)
+# 저장 디렉토리 설정
+SAVE_DIR = "saved_images"
+if not os.path.exists(SAVE_DIR):
+    os.makedirs(SAVE_DIR, exist_ok=True)
 
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
 
-def on_change_prompt(img: Image.Image | None, prompt: str | None, bg_prompt: str | None = None):
-    return gr.update(interactive=bool(img and prompt))
+def save_generated_image(image, prompt):
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    unique_id = str(uuid.uuid4())[:8]
+    filename = f"{timestamp}_{unique_id}.png"
+    filepath = os.path.join(SAVE_DIR, filename)
+    image.save(filepath)
+    return filepath
 
 
-def process_prompt(img: Image.Image, prompt: str, bg_prompt: str | None = None, 
-                  aspect_ratio: str = "1:1", position: str = "bottom-center", 
-                  scale_percent: float = 100) -> tuple[Image.Image, Image.Image]:
+@gr.GPU(duration=60)
+def generate_image(
+    prompt: str,
+    seed: int,
+    randomize_seed: bool,
+    width: int,
+    height: int,
+    guidance_scale: float,
+    num_inference_steps: int,
+    progress: gr.Progress = gr.Progress()
+):
     try:
-        if img is None or prompt.strip() == "":
-            raise gr.Error("Please provide both image and prompt")
-        
-        print(f"Processing with position: {position}, scale: {scale_percent}")  # 디버깅용
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
         
-        try:
-            prompt = translate_to_english(prompt)
-            if bg_prompt:
-                bg_prompt = translate_to_english(bg_prompt)
-        except Exception as e:
-            print(f"Translation error (continuing with original text): {str(e)}")
+        generator = torch.Generator(device=device).manual_seed(seed)
         
-        results, _ = _process(img, prompt, bg_prompt, aspect_ratio)
+        with torch.inference_mode():
+            image = pipe(
+                prompt=prompt,
+                width=width,
+                height=height,
+                num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale,
+                generator=generator,
+            ).images[0]
         
-        if bg_prompt:
-            try:
-                print(f"Using position: {position}")  # 디버깅용
-                # 위치 값 검증
-                valid_positions = ["top-left", "top-center", "top-right",
-                                 "middle-left", "middle-center", "middle-right",
-                                 "bottom-left", "bottom-center", "bottom-right"]
-                if position not in valid_positions:
-                    position = "bottom-center"
-                    print(f"Invalid position, using default: {position}")
-                
-                combined = combine_with_background(
-                    foreground=results[2],
-                    background=results[1],
-                    position=position,
-                    scale_percent=scale_percent
-                )
-                return combined, results[2]
-            except Exception as e:
-                print(f"Combination error: {str(e)}")
-                return results[1], results[2]
+        filepath = save_generated_image(image, prompt)
+        return image, seed
         
-        return results[1], results[2]  # 기본 반환 추가
     except Exception as e:
-        print(f"Error in process_prompt: {str(e)}")
-        raise gr.Error(str(e))
+        raise gr.Error(f"Image generation failed: {str(e)}")
     finally:
         clear_memory()
 
-    
-def process_bbox(img: Image.Image, box_input: str) -> tuple[Image.Image, Image.Image]:
-    try:
-        if img is None or box_input.strip() == "":
-            raise gr.Error("Please provide both image and bounding box coordinates")
-        
-        try:
-            coords = eval(box_input)
-            if not isinstance(coords, list) or len(coords) != 4:
-                raise ValueError("Invalid box format")
-            bbox = tuple(int(x) for x in coords)
-        except:
-            raise gr.Error("Invalid box format. Please provide [xmin, ymin, xmax, ymax]")
-        
-        # Process the image
-        results, _ = _process(img, bbox)
-        
-        # 합성된 이미지와 추출된 이미지만 반환
-        return results[1], results[2]
-    except Exception as e:
-        raise gr.Error(str(e))
-
-# Event handler functions 수정
-def update_process_button(img, prompt):
-    return gr.update(
-        interactive=bool(img and prompt),
-        variant="primary" if bool(img and prompt) else "secondary"
-    )
-
-def update_box_button(img, box_input):
-    try:
-        if img and box_input:
-            coords = eval(box_input)
-            if isinstance(coords, list) and len(coords) == 4:
-                return gr.update(interactive=True, variant="primary")
-        return gr.update(interactive=False, variant="secondary")
-    except:
-        return gr.update(interactive=False, variant="secondary")
-
-
-css = """
-footer {display: none}
-.main-title {
-    text-align: center;
-    margin: 1em 0;
-    padding: 1.5em;
-    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
-    border-radius: 15px;
-    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
-}
-.main-title h1 {
-    color: #2196F3;
-    font-size: 2.8em;
-    margin-bottom: 0.3em;
-    font-weight: 700;
-}
-.main-title p {
-    color: #555;
-    font-size: 1.3em;
-    line-height: 1.4;
-}
-.container {
-    max-width: 1200px;
-    margin: auto;
-    padding: 20px;
-}
-.input-panel, .output-panel {
-    background: white;
-    padding: 1.5em;
-    border-radius: 12px;
-    box-shadow: 0 2px 8px rgba(0,0,0,0.08);
-    margin-bottom: 1em;
-}
-.controls-panel {
-    background: #f8f9fa;
-    padding: 1em;
-    border-radius: 8px;
-    margin: 1em 0;
-}
-.image-display {
-    min-height: 512px;
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    background: #fafafa;
-    border-radius: 8px;
-    margin: 1em 0;
-}
-.example-section {
-    text-align: center;
-    padding: 2em;
-    background: #f5f5f5;
-    border-radius: 12px;
-    margin-top: 2em;
-}
-.example-section img {
-    max-width: 100%;
-    border-radius: 8px;
-    box-shadow: 0 4px 8px rgba(0,0,0,0.1);
-}
-.accordion {
-    border: 1px solid #e0e0e0;
-    border-radius: 8px;
-    margin: 1em 0;
-}
-.accordion-header {
-    padding: 1em;
-    background: #f5f5f5;
-    cursor: pointer;
-}
-.accordion-content {
-    padding: 1em;
-    display: none;
-}
-.accordion.open .accordion-content {
-    display: block;
-}
-.position-grid {
-    display: grid;
-    grid-template-columns: repeat(3, 1fr);
-    gap: 8px;
-    margin: 1em 0;
-}
-.position-btn {
-    padding: 10px;
-    border: 1px solid #ddd;
-    border-radius: 4px;
-    background: white;
-    cursor: pointer;
-    transition: all 0.3s ease;
-    width: 40px;
-    height: 40px;
-    display: flex;
-    align-items: center;
-    justify-content: center;
-}
-.position-btn:hover {
-    background: #e3f2fd;
-}
-.position-btn.selected {
-    background-color: #2196F3;
-    color: white;
-    border-color: #1976D2;
-}
-"""
-
-
 def add_text_with_stroke(draw, text, x, y, font, text_color, stroke_width):
-    """Helper function to draw text with stroke"""
-    # Draw the stroke/outline
+    """텍스트에 외곽선을 추가하는 함수"""
     for adj_x in range(-stroke_width, stroke_width + 1):
         for adj_y in range(-stroke_width, stroke_width + 1):
             draw.text((x + adj_x, y + adj_y), text, font=font, fill=text_color)
 
-def remove_background(image):
-    # Save the image to a specific location
-    filename = f"image_{uuid.uuid4()}.png"  # Generates a universally unique identifier (UUID) for the filename
-    image.save(filename)
-    # Call gradio client for background removal
-    result = client.predict(images=handle_file(filename), api_name="/image")
-    return Image.open(result[0])
-
-def superimpose(image_with_text, overlay_image):
-    # Open image as RGBA to handle transparency
-    overlay_image = overlay_image.convert("RGBA")
-    # Paste overlay on the background
-    image_with_text.paste(overlay_image, (0, 0), overlay_image)
-    # Save the final image
-    # image_with_text.save("output_image.png")
-    return image_with_text
-
 def add_text_to_image(
     input_image,
     text,
@@ -582,7 +148,6 @@ def add_text_to_image(
         if input_image is None or text.strip() == "":
             return input_image
 
-        # PIL Image 객체로 변환
         if not isinstance(input_image, Image.Image):
             if isinstance(input_image, np.ndarray):
                 image = Image.fromarray(input_image)
@@ -591,11 +156,9 @@ def add_text_to_image(
         else:
             image = input_image.copy()
 
-        # 이미지를 RGBA 모드로 변환
         if image.mode != 'RGBA':
             image = image.convert('RGBA')
 
-        # 폰트 설정
         font_files = {
             "Default": "DejaVuSans.ttf",
             "Korean Regular": "ko-Regular.ttf"
@@ -608,7 +171,6 @@ def add_text_to_image(
             print(f"Font loading error ({font_choice}): {str(e)}")
             font = ImageFont.load_default()
         
-        # 색상 설정
         color_map = {
             'White': (255, 255, 255),
             'Black': (0, 0, 0),
@@ -620,419 +182,187 @@ def add_text_to_image(
         }
         rgb_color = color_map.get(color, (255, 255, 255))
         
-        # 임시 Draw 객체 생성하여 텍스트 크기 계산
         temp_draw = ImageDraw.Draw(image)
         text_bbox = temp_draw.textbbox((0, 0), text, font=font)
         text_width = text_bbox[2] - text_bbox[0]
         text_height = text_bbox[3] - text_bbox[1]
 
-        # 위치 계산
         actual_x = int((image.width - text_width) * (x_position / 100))
         actual_y = int((image.height - text_height) * (y_position / 100))
 
-        # 텍스트 색상 설정
         text_color = (*rgb_color, int(opacity))
         
-        if text_position_type == "Text Behind Image":
-            try:
-                # 원본 이미지에서 전경 객체만 추출
-                foreground = remove_background(image)
-                
-                # 배경 이미지 생성 (원본 이미지 복사)
-                background = image.copy()
-                
-                # 텍스트를 그릴 임시 레이어 생성
-                text_layer = Image.new('RGBA', image.size, (255, 255, 255, 0))
-                draw_text = ImageDraw.Draw(text_layer)
-                
-                # 텍스트 그리기
-                add_text_with_stroke(
-                    draw_text,
-                    text,
-                    actual_x,
-                    actual_y,
-                    font,
-                    text_color,
-                    int(thickness)
-                )
-                
-                # 배경에 텍스트 합성
-                background = Image.alpha_composite(background, text_layer)
-                
-                # 텍스트가 있는 배경 위에 전경 객체 합성
-                output_image = Image.alpha_composite(background, foreground)
-            except Exception as e:
-                print(f"Error in Text Behind Image processing: {str(e)}")
-                return input_image
-        else:
-            # 텍스트 오버레이 생성
-            txt_overlay = Image.new('RGBA', image.size, (255, 255, 255, 0))
-            draw = ImageDraw.Draw(txt_overlay)
-            
-            # 텍스트를 이미지 위에 그리기
-            add_text_with_stroke(
-                draw,
-                text,
-                actual_x,
-                actual_y,
-                font,
-                text_color,
-                int(thickness)
-            )
-            output_image = Image.alpha_composite(image, txt_overlay)
+        txt_overlay = Image.new('RGBA', image.size, (255, 255, 255, 0))
+        draw = ImageDraw.Draw(txt_overlay)
+        
+        add_text_with_stroke(
+            draw,
+            text,
+            actual_x,
+            actual_y,
+            font,
+            text_color,
+            int(thickness)
+        )
+        output_image = Image.alpha_composite(image, txt_overlay)
         
-        # RGB로 변환
         output_image = output_image.convert('RGB')
         
         return output_image
 
     except Exception as e:
         print(f"Error in add_text_to_image: {str(e)}")
-        return input_image
-
-    
-def update_position(new_position):
-    """위치 업데이트 함수"""
-    print(f"Position updated to: {new_position}")
-    return new_position
-
-def update_controls(bg_prompt):
-    """배경 프롬프트 입력 여부에 따라 컨트롤 표시 업데이트"""
-    is_visible = bool(bg_prompt)
-    return [
-        gr.update(visible=is_visible),  # aspect_ratio
-        gr.update(visible=is_visible),  # object_controls
-    ]
-
-
-# 저장 디렉토리 설정
-SAVE_DIR = "saved_images"
-if not os.path.exists(SAVE_DIR):
-    os.makedirs(SAVE_DIR, exist_ok=True)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+        return input_image    
 
-def save_generated_image(image, prompt):
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    unique_id = str(uuid.uuid4())[:8]
-    filename = f"{timestamp}_{unique_id}.png"
-    filepath = os.path.join(SAVE_DIR, filename)
-    
-    image.save(filepath)
-    return filepath
 
-gen_pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    torch_dtype=torch.float16,
-    use_auth_token=HF_TOKEN
-)
-gen_pipe.enable_attention_slicing(slice_size="auto")
-
-# 이미지 생성용 LoRA 가중치 로드
-try:
-    lora_path = hf_hub_download(
-        "ginipick/flux-lora-eric-cat",
-        "flux-lora-eric-cat.safetensors",  # 실제 파일명 확인 필요
-        use_auth_token=HF_TOKEN
-    )
-    gen_pipe.load_lora_weights(lora_path)
-    gen_pipe.fuse_lora(lora_scale=0.125)
-except Exception as e:
-    print(f"Error loading generation LoRA weights: {str(e)}")
-    raise ValueError("Failed to load generation LoRA weights. Please check your HF_TOKEN and model access.")
-
-# GPU로 이동
-if torch.cuda.is_available():
-    try:
-        gen_pipe = gen_pipe.to("cuda:0")
-    except Exception as e:
-        print(f"Warning: Could not move generation pipeline to CUDA: {str(e)}")
-
-# generate_image 함수 수정
-@spaces.GPU(duration=60)
-def generate_image(
-    prompt: str,
-    seed: int,
-    randomize_seed: bool,
-    width: int,
-    height: int,
-    guidance_scale: float,
-    num_inference_steps: int,
-    progress: gr.Progress = gr.Progress()
-):
-    try:
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-        
-        generator = torch.Generator(device=device).manual_seed(seed)
-        
-        with torch.inference_mode():
-            # gen_pipe 사용
-            image = gen_pipe(
-                prompt=prompt,
-                width=width,
-                height=height,
-                num_inference_steps=num_inference_steps,
-                guidance_scale=guidance_scale,
-                generator=generator,
-            ).images[0]
-        
-        filepath = save_generated_image(image, prompt)
-        return image, seed
-        
-    except Exception as e:
-        raise gr.Error(f"Image generation failed: {str(e)}")
-    finally:
-        clear_memory()
+css = """
+footer {display: none}
+.main-title {
+    text-align: center;
+    margin: 1em 0;
+    padding: 1.5em;
+    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
+    border-radius: 15px;
+    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+}
+.main-title h1 {
+    color: #2196F3;
+    font-size: 2.8em;
+    margin-bottom: 0.3em;
+    font-weight: 700;
+}
+.main-title p {
+    color: #555;
+    font-size: 1.3em;
+    line-height: 1.4;
+}
+.container {
+    max-width: 1200px;
+    margin: auto;
+    padding: 20px;
+}
+.input-panel, .output-panel {
+    background: white;
+    padding: 1.5em;
+    border-radius: 12px;
+    box-shadow: 0 2px 8px rgba(0,0,0,0.08);
+    margin-bottom: 1em;
+}
+"""
 
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
-    position = gr.State(value="bottom-center")
-
     gr.HTML("""
         <div class="main-title">
             <h1>🎨 Webtoon Canvas</h1>
-            <p>Webtoon generated, Remove background of specified objects, generate new backgrounds, and insert text over or behind images with prompts.</p>
+            <p>Generate webtoon-style images and add text with various styles and positions.</p>
         </div>
     """)
 
-    with gr.Tabs():
-        with gr.Tab("Edit & Combine"):
-            with gr.Row(equal_height=True):
-                # 왼쪽 패널 (입력)
-                with gr.Column(scale=1):
-                    with gr.Group(elem_classes="input-panel"):
-                        input_image = gr.Image(
-                            type="pil",
-                            label="Upload Image",
-                            interactive=True,
-                            height=400
-                        )
-                        text_prompt = gr.Textbox(
-                            label="Object to Extract",
-                            placeholder="Enter what you want to extract...",
-                            interactive=True
-                        )
-                        with gr.Row():
-                            bg_prompt = gr.Textbox(
-                                label="Background Prompt (optional)",
-                                placeholder="Describe the background...",
-                                interactive=True,
-                                scale=3
-                            )
-                            aspect_ratio = gr.Dropdown(
-                                choices=["1:1", "16:9", "9:16", "4:3"],
-                                value="1:1",
-                                label="Aspect Ratio",
-                                interactive=True,
-                                visible=True,
-                                scale=1
-                            )
-
-                        with gr.Group(elem_classes="controls-panel", visible=False) as object_controls:
-                            with gr.Column(scale=1):
-                                position = gr.State(value="bottom-center")
-                                with gr.Row():
-                                    btn_top_left = gr.Button("↖", elem_classes="position-btn")
-                                    btn_top_center = gr.Button("↑", elem_classes="position-btn")
-                                    btn_top_right = gr.Button("↗", elem_classes="position-btn")
-                                with gr.Row():
-                                    btn_middle_left = gr.Button("←", elem_classes="position-btn")
-                                    btn_middle_center = gr.Button("•", elem_classes="position-btn")
-                                    btn_middle_right = gr.Button("→", elem_classes="position-btn")
-                                with gr.Row():
-                                    btn_bottom_left = gr.Button("↙", elem_classes="position-btn")
-                                    btn_bottom_center = gr.Button("↓", elem_classes="position-btn", value="selected")
-                                    btn_bottom_right = gr.Button("↘", elem_classes="position-btn")
-                            with gr.Column(scale=1):
-                                scale_slider = gr.Slider(
-                                    minimum=10,
-                                    maximum=200,
-                                    value=50,
-                                    step=5,
-                                    label="Object Size (%)"
-                                )
-
-                        process_btn = gr.Button(
-                            "Process",
-                            variant="primary",
-                            interactive=False,
-                            size="lg"
-                        )
-
-                # 오른쪽 패널 (출력)
-                with gr.Column(scale=1):
-                    with gr.Group(elem_classes="output-panel"):
-                        combined_image = gr.Image(
-                            label="Combined Result",
-                            show_download_button=True,
-                            type="pil",
-                            height=400
-                        )
-                        
-                        with gr.Accordion("Text Insertion Options", open=False):
-                            with gr.Group():
-                                with gr.Row():
-                                    text_input = gr.Textbox(
-                                        label="Text Content",
-                                        placeholder="Enter text to add..."
-                                    )
-                                    text_position_type = gr.Radio(
-                                        choices=["Text Over Image", "Text Behind Image"],
-                                        value="Text Over Image",
-                                        label="Text Position"
-                                    )
-                                
-                                with gr.Row():
-                                    with gr.Column(scale=1):
-                                        font_choice = gr.Dropdown(
-                                            choices=["Default", "Korean Regular"],
-                                            value="Default",
-                                            label="Font Selection",
-                                            interactive=True
-                                        )
-                                        font_size = gr.Slider(
-                                            minimum=10,
-                                            maximum=200,
-                                            value=40,
-                                            step=5,
-                                            label="Font Size"
-                                        )
-                                        color_dropdown = gr.Dropdown(
-                                            choices=["White", "Black", "Red", "Green", "Blue", "Yellow", "Purple"],
-                                            value="White",
-                                            label="Text Color"
-                                        )
-                                        thickness = gr.Slider(
-                                            minimum=0,
-                                            maximum=10,
-                                            value=1,
-                                            step=1,
-                                            label="Text Thickness"
-                                        )
-                                    with gr.Column(scale=1):
-                                        opacity_slider = gr.Slider(
-                                            minimum=0,
-                                            maximum=255,
-                                            value=255,
-                                            step=1,
-                                            label="Opacity"
-                                        )
-                                        x_position = gr.Slider(
-                                            minimum=0,
-                                            maximum=100,
-                                            value=50,
-                                            step=1,
-                                            label="Left(0%)~Right(100%)"
-                                        )
-                                        y_position = gr.Slider(
-                                            minimum=0,
-                                            maximum=100,
-                                            value=50,
-                                            step=1,
-                                            label="High(0%)~Low(100%)"
-                                        )
-                                add_text_btn = gr.Button("Apply Text", variant="primary")
-
-                        extracted_image = gr.Image(
-                            label="Extracted Object",
-                            show_download_button=True,
-                            type="pil",
-                            height=200
-                        )
+    with gr.Row():
+        with gr.Column(scale=1):
+            # 이미지 생성 섹션
+            gen_prompt = gr.Textbox(
+                label="Generation Prompt",
+                placeholder="Enter your image generation prompt..."
+            )
+            with gr.Row():
+                gen_width = gr.Slider(512, 1024, 768, step=64, label="Width")
+                gen_height = gr.Slider(512, 1024, 768, step=64, label="Height")
+            
+            with gr.Row():
+                guidance_scale = gr.Slider(1, 20, 7.5, step=0.5, label="Guidance Scale")
+                num_steps = gr.Slider(1, 50, 30, step=1, label="Number of Steps")
+            
+            with gr.Row():
+                seed = gr.Number(label="Seed", value=-1)
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+            
+            generate_btn = gr.Button("Generate Image", variant="primary")
+            
+            output_image = gr.Image(
+                label="Generated Image",
+                type="pil",
+                show_download_button=True
+            )
+            output_seed = gr.Number(label="Used Seed", interactive=False)
 
-        with gr.Tab("Generate Image"):
-            with gr.Column():
-                gen_prompt = gr.Textbox(
-                    label="Generation Prompt",
-                    placeholder="Enter your image generation prompt..."
+            # 텍스트 추가 섹션
+            with gr.Accordion("Text Options", open=False):
+                text_input = gr.Textbox(
+                    label="Text Content",
+                    placeholder="Enter text to add..."
                 )
                 with gr.Row():
-                    gen_width = gr.Slider(512, 1024, 768, step=64, label="Width")
-                    gen_height = gr.Slider(512, 1024, 768, step=64, label="Height")
-                
+                    font_choice = gr.Dropdown(
+                        choices=["Default", "Korean Regular"],
+                        value="Default",
+                        label="Font Selection",
+                        interactive=True
+                    )
+                    font_size = gr.Slider(
+                        minimum=10,
+                        maximum=200,
+                        value=40,
+                        step=5,
+                        label="Font Size"
+                    )
                 with gr.Row():
-                    guidance_scale = gr.Slider(1, 20, 7.5, step=0.5, label="Guidance Scale")
-                    num_steps = gr.Slider(1, 50, 30, step=1, label="Number of Steps")
-                
+                    color_dropdown = gr.Dropdown(
+                        choices=["White", "Black", "Red", "Green", "Blue", "Yellow", "Purple"],
+                        value="White",
+                        label="Text Color"
+                    )
+                    thickness = gr.Slider(
+                        minimum=0,
+                        maximum=10,
+                        value=1,
+                        step=1,
+                        label="Text Thickness"
+                    )
                 with gr.Row():
-                    seed = gr.Number(label="Seed", value=-1)
-                    randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-                
-                generate_btn = gr.Button("Generate Image", variant="primary")
-                
-                output_image = gr.Image(label="Generated Image", type="pil")
-                output_seed = gr.Number(label="Used Seed", interactive=False)
-
-    # CSS 클래스를 위한 스타일 추가
-    gr.HTML("""
-        <style>
-        .position-btn.selected {
-            background-color: #2196F3 !important;
-            color: white !important;
-        }
-        </style>
-    """)
-
-    # 버튼 클릭 이벤트 바인딩
-    position_mapping = {
-        btn_top_left: "top-left",
-        btn_top_center: "top-center",
-        btn_top_right: "top-right",
-        btn_middle_left: "middle-left",
-        btn_middle_center: "middle-center",
-        btn_middle_right: "middle-right",
-        btn_bottom_left: "bottom-left",
-        btn_bottom_center: "bottom-center",
-        btn_bottom_right: "bottom-right"
-    }
-
-    for btn, pos in position_mapping.items():
-        btn.click(
-            fn=lambda pos=pos: update_position(pos),
-            outputs=position
-        )
+                    opacity_slider = gr.Slider(
+                        minimum=0,
+                        maximum=255,
+                        value=255,
+                        step=1,
+                        label="Opacity"
+                    )
+                with gr.Row():
+                    x_position = gr.Slider(
+                        minimum=0,
+                        maximum=100,
+                        value=50,
+                        step=1,
+                        label="Left(0%)~Right(100%)"
+                    )
+                    y_position = gr.Slider(
+                        minimum=0,
+                        maximum=100,
+                        value=50,
+                        step=1,
+                        label="High(0%)~Low(100%)"
+                    )
+                add_text_btn = gr.Button("Apply Text", variant="primary")
 
     # 이벤트 바인딩
-    bg_prompt.change(
-        fn=update_controls,
-        inputs=bg_prompt,
-        outputs=[aspect_ratio, object_controls],
-        queue=False
-    )
-    
-    input_image.change(
-        fn=update_process_button,
-        inputs=[input_image, text_prompt],
-        outputs=process_btn,
-        queue=False
-    )
-    
-    text_prompt.change(
-        fn=update_process_button,
-        inputs=[input_image, text_prompt],
-        outputs=process_btn,
-        queue=False
-    )
-
-    process_btn.click(
-        fn=process_prompt,
+    generate_btn.click(
+        fn=generate_image,
         inputs=[
-            input_image,
-            text_prompt,
-            bg_prompt,
-            aspect_ratio,
-            position,
-            scale_slider
+            gen_prompt,
+            seed,
+            randomize_seed,
+            gen_width,
+            gen_height,
+            guidance_scale,
+            num_steps,
         ],
-        outputs=[combined_image, extracted_image],
-        queue=True
+        outputs=[output_image, output_seed]
     )
 
     add_text_btn.click(
         fn=add_text_to_image,
         inputs=[
-            combined_image,
+            output_image,
             text_input,
             font_size,
             color_dropdown,
@@ -1040,25 +370,10 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
             x_position,
             y_position,
             thickness,
-            text_position_type,
+            "Text Over Image",  # text_position_type 고정
             font_choice
         ],
-        outputs=combined_image,
-        api_name="add_text"
-    )
-
-    generate_btn.click(
-        fn=generate_image,
-        inputs=[
-            gen_prompt,
-            seed,
-            randomize_seed,
-            gen_width,
-            gen_height,
-            guidance_scale,
-            num_steps,
-        ],
-        outputs=[output_image, output_seed]
+        outputs=output_image
     )
 
 demo.queue(max_size=5)
@@ -1067,4 +382,7 @@ demo.launch(
     server_port=7860,
     share=False,
     max_threads=2
-)
+)
+
+
+