app.py

import tempfile
import time
from collections.abc import Sequence
from typing import Any, cast
import os
import gc
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

#############################################################
# 메모리 정리 함수
def clear_memory():
    gc.collect()
    try:
        if torch.cuda.is_available():
            with torch.cuda.device(0):  # 명시적으로 device 0 사용
                torch.cuda.empty_cache()
    except Exception as e:
        pass

#############################################################
# GPU 설정 (Zero GPU 환경)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    try:
        with torch.cuda.device(0):
            torch.cuda.empty_cache()
            torch.backends.cudnn.benchmark = True
            torch.backends.cuda.matmul.allow_tf32 = True
    except Exception as e:
        print("Warning: Could not configure CUDA settings")

#############################################################
# 번역 모델 초기화 (CPU에서 동작)
model_name = "Helsinki-NLP/opus-mt-ko-en"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# 번역 모델은 CPU에 올림
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:
    raise ValueError("Please set the HF_TOKEN environment variable")

try:
    login(token=HF_TOKEN)
except Exception as e:
    raise ValueError(f"Failed to login to Hugging Face: {str(e)}")

#############################################################
# 객체 분할 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 (Zero GPU용)
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.float16,
    use_auth_token=HF_TOKEN
)
pipe.enable_attention_slicing(slice_size="auto")
pipe.load_lora_weights(
    hf_hub_download(
        "ByteDance/Hyper-SD",
        "Hyper-FLUX.1-dev-8steps-lora.safetensors",
        use_auth_token=HF_TOKEN
    )
)
pipe.fuse_lora(lora_scale=0.125)
try:
    if torch.cuda.is_available():
        pipe = pipe.to("cuda:0")  # 명시적으로 cuda:0로 이동
except Exception as e:
    print(f"Warning: Could not move pipeline to CUDA: {str(e)}")

#############################################################
# 타이머 클래스
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")

#############################################################
# 유틸리티 함수들
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),
    )

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]:
    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]:
    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

#############################################################
# 배경 생성 함수 (Zero GPU에 맞게 수정)
@spaces.GPU(duration=20)
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]:
    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:
    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:
    result = background.convert('RGBA')
    scaled_foreground = resize_object(foreground, scale_percent)
    x, y = calculate_object_position(position, result.size, scaled_foreground.size)
    result.paste(scaled_foreground, (x, y), scaled_foreground)
    return result

#############################################################
# GPU 처리 함수 (Zero GPU에 맞게 수정)
@spaces.GPU(duration=30)
def _gpu_process(img: Image.Image, prompt: str | BoundingBox | None) -> tuple[Image.Image, BoundingBox | None, list[str]]:
    time_log: list[str] = []
    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
    except Exception as e:
        print(f"GPU process error: {str(e)}")
        raise

#############################################################
# 전체 처리 함수
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)
        
        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)}")

def on_change_bbox(prompts: dict[str, Any] | None):
    return gr.update(interactive=prompts is not None)

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 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]:
    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}")
        
        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)}")
        
        results, _ = _process(img, prompt, bg_prompt, aspect_ratio)
        
        if bg_prompt:
            try:
                combined = combine_with_background(
                    foreground=results[2],
                    background=results[1],
                    position=position,
                    scale_percent=scale_percent
                )
                print(f"Combined image created with position: {position}")
                return combined, results[2]
            except Exception as e:
                print(f"Combination error: {str(e)}")
                return results[1], results[2]
        
        return results[1], results[2]
    except Exception as e:
        print(f"Error in process_prompt: {str(e)}")
        raise gr.Error(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]")
        
        results, _ = _process(img, bbox)
        return results[1], results[2]
    except Exception as e:
        raise gr.Error(str(e))

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 정의
css = """
footer {display: none}
.main-title {
    text-align: center;
    margin: 2em 0;
    padding: 1em;
    background: #f7f7f7;
    border-radius: 10px;
}
.main-title h1 {
    color: #2196F3;
    font-size: 2.5em;
    margin-bottom: 0.5em;
}
.main-title p {
    color: #666;
    font-size: 1.2em;
}
.container {
    max-width: 1200px;
    margin: auto;
    padding: 20px;
}
.tabs {
    margin-top: 1em;
}
.input-group {
    background: white;
    padding: 1em;
    border-radius: 8px;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.output-group {
    background: white;
    padding: 1em;
    border-radius: 8px;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
button.primary {
    background: #2196F3;
    border: none;
    color: white;
    padding: 0.5em 1em;
    border-radius: 4px;
    cursor: pointer;
    transition: background 0.3s ease;
}
button.primary:hover {
    background: #1976D2;
}
.position-btn {
    transition: all 0.3s ease;
}
.position-btn:hover {
    background-color: #e3f2fd;
}
.position-btn.selected {
    background-color: #2196F3;
    color: white;
}
"""

#############################################################
# UI 구성
with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
    gr.HTML("""
        <div class="main-title">
            <h1>🎨GiniGen Canvas</h1>
            <p>AI Integrated Image Creator: Extract objects, generate backgrounds, and adjust ratios and positions to create complete images with AI.</p>
        </div>
    """)
    with gr.Row():
        with gr.Column(scale=1):
            input_image = gr.Image(
                type="pil",
                label="Upload Image",
                interactive=True
            )
            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.Row(visible=False) as object_controls:
                with gr.Column(scale=1):
                    with gr.Row():
                        position = gr.State(value="bottom-center")
                        btn_top_left = gr.Button("↖")
                        btn_top_center = gr.Button("↑")
                        btn_top_right = gr.Button("↗")
                    with gr.Row():
                        btn_middle_left = gr.Button("←")
                        btn_middle_center = gr.Button("•")
                        btn_middle_right = gr.Button("→")
                    with gr.Row():
                        btn_bottom_left = gr.Button("↙")
                        btn_bottom_center = gr.Button("↓")
                        btn_bottom_right = gr.Button("↘")
                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
            )
            # 각 버튼에 대한 클릭 이벤트 처리
            def update_position(new_position):
                return new_position
            btn_top_left.click(fn=lambda: update_position("top-left"), outputs=position)
            btn_top_center.click(fn=lambda: update_position("top-center"), outputs=position)
            btn_top_right.click(fn=lambda: update_position("top-right"), outputs=position)
            btn_middle_left.click(fn=lambda: update_position("middle-left"), outputs=position)
            btn_middle_center.click(fn=lambda: update_position("middle-center"), outputs=position)
            btn_middle_right.click(fn=lambda: update_position("middle-right"), outputs=position)
            btn_bottom_left.click(fn=lambda: update_position("bottom-left"), outputs=position)
            btn_bottom_center.click(fn=lambda: update_position("bottom-center"), outputs=position)
            btn_bottom_right.click(fn=lambda: update_position("bottom-right"), outputs=position)
        with gr.Column(scale=1):
            with gr.Row():
                combined_image = gr.Image(
                    label="Combined Result",
                    show_download_button=True,
                    type="pil",
                    height=512
                )
            with gr.Row():
                extracted_image = gr.Image(
                    label="Extracted Object",
                    show_download_button=True,
                    type="pil",
                    height=256
                )
    # Event bindings
    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
    )
    def update_controls(bg_prompt):
        is_visible = bool(bg_prompt)
        return [
            gr.update(visible=is_visible),
            gr.update(visible=is_visible),
        ]
    bg_prompt.change(
        fn=update_controls,
        inputs=bg_prompt,
        outputs=[aspect_ratio, object_controls],
        queue=False
    )
    process_btn.click(
        fn=process_prompt,
        inputs=[
            input_image,
            text_prompt,
            bg_prompt,
            aspect_ratio,
            position,
            scale_slider
        ],
        outputs=[combined_image, extracted_image],
        queue=True
    )
    # 예제 섹션 추가
    with gr.Accordion("Show Example", open=True):
        gr.Markdown("### Example")
        with gr.Row():
            with gr.Column():
                gr.Markdown("**Upload Image(aa1.png)**")
                gr.Image(value="aa1.png", label="Upload")
            with gr.Column():
                gr.Markdown("**Cut Object (aa2.png)**<br>(Prompt: 'text')", elem_classes="center")
                gr.Image(value="aa2.png", label="Object")
            with gr.Column():
                gr.Markdown("**Generated Image (aa3.png)**<br>(Background Prompt: 'alps mountain')", elem_classes="center")
                gr.Image(value="aa3.png", label="Output")
demo.queue(max_size=5)
demo.launch(
    server_name="0.0.0.0",
    server_port=7860,
    share=False,
    max_threads=2
)