Create app-backup.py

app-backup.py

@@ -0,0 +1,469 @@
+import spaces
+import argparse
+import os
+import time
+from os import path
+import shutil
+from datetime import datetime
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
+import gradio as gr
+import torch
+from diffusers import FluxPipeline
+from diffusers.pipelines.stable_diffusion import safety_checker
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+import subprocess
+
+# Flash Attention 설치
+subprocess.run('pip install flash-attn --no-build-isolation', 
+              env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, 
+              shell=True)
+
+# Setup and initialization code
+cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
+PERSISTENT_DIR = os.environ.get("PERSISTENT_DIR", ".")
+gallery_path = path.join(PERSISTENT_DIR, "gallery")
+
+os.environ["TRANSFORMERS_CACHE"] = cache_path
+os.environ["HF_HUB_CACHE"] = cache_path
+os.environ["HF_HOME"] = cache_path
+
+torch.backends.cuda.matmul.allow_tf32 = True
+
+# Create gallery directory
+if not path.exists(gallery_path):
+    os.makedirs(gallery_path, exist_ok=True)
+
+# Florence 모델 초기화
+florence_models = {
+    'gokaygokay/Florence-2-Flux-Large': AutoModelForCausalLM.from_pretrained(
+        'gokaygokay/Florence-2-Flux-Large', 
+        trust_remote_code=True
+    ).eval(),
+    'gokaygokay/Florence-2-Flux': AutoModelForCausalLM.from_pretrained(
+        'gokaygokay/Florence-2-Flux', 
+        trust_remote_code=True
+    ).eval(),
+}
+
+florence_processors = {
+    'gokaygokay/Florence-2-Flux-Large': AutoProcessor.from_pretrained(
+        'gokaygokay/Florence-2-Flux-Large', 
+        trust_remote_code=True
+    ),
+    'gokaygokay/Florence-2-Flux': AutoProcessor.from_pretrained(
+        'gokaygokay/Florence-2-Flux', 
+        trust_remote_code=True
+    ),
+}
+
+def filter_prompt(prompt):
+    inappropriate_keywords = [
+    "sex"    
+    ]
+    
+#        "nude", "naked", "nsfw", "porn", "sex", "explicit", "adult", "xxx",
+#        "erotic", "sensual", "seductive", "provocative", "intimate",
+#        "violence", "gore", "blood", "death", "kill", "murder", "torture",
+#        "drug", "suicide", "abuse", "hate", "discrimination"
+#    ]
+    
+    prompt_lower = prompt.lower()
+    
+    for keyword in inappropriate_keywords:
+        if keyword in prompt_lower:
+            return False, "부적절한 내용이 포함된 프롬프트입니다."
+            
+    return True, prompt
+
+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")
+
+# Model initialization
+if not path.exists(cache_path):
+    os.makedirs(cache_path, exist_ok=True)
+
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev", 
+    torch_dtype=torch.bfloat16
+)
+pipe.load_lora_weights(
+    hf_hub_download(
+        "ByteDance/Hyper-SD", 
+        "Hyper-FLUX.1-dev-8steps-lora.safetensors"
+    )
+)
+pipe.fuse_lora(lora_scale=0.125)
+pipe.to(device="cuda", dtype=torch.bfloat16)
+pipe.safety_checker = safety_checker.StableDiffusionSafetyChecker.from_pretrained(
+    "CompVis/stable-diffusion-safety-checker"
+)
+
+
+
+def save_image(image):
+    """Save the generated image and return the path"""
+    try:
+        if not os.path.exists(gallery_path):
+            try:
+                os.makedirs(gallery_path, exist_ok=True)
+            except Exception as e:
+                print(f"Failed to create gallery directory: {str(e)}")
+                return None
+        
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        random_suffix = os.urandom(4).hex()
+        filename = f"generated_{timestamp}_{random_suffix}.png"
+        filepath = os.path.join(gallery_path, filename)
+        
+        try:
+            if isinstance(image, Image.Image):
+                image.save(filepath, "PNG", quality=100)
+            else:
+                image = Image.fromarray(image)
+                image.save(filepath, "PNG", quality=100)
+            
+            if not os.path.exists(filepath):
+                print(f"Warning: Failed to verify saved image at {filepath}")
+                return None
+                
+            return filepath
+        except Exception as e:
+            print(f"Failed to save image: {str(e)}")
+            return None
+            
+    except Exception as e:
+        print(f"Error in save_image: {str(e)}")
+        return None
+
+def load_gallery():
+    try:
+        os.makedirs(gallery_path, exist_ok=True)
+        
+        image_files = []
+        for f in os.listdir(gallery_path):
+            if f.lower().endswith(('.png', '.jpg', '.jpeg')):
+                full_path = os.path.join(gallery_path, f)
+                image_files.append((full_path, os.path.getmtime(full_path)))
+        
+        image_files.sort(key=lambda x: x[1], reverse=True)
+        return [f[0] for f in image_files]
+    except Exception as e:
+        print(f"Error loading gallery: {str(e)}")
+        return []
+
+@spaces.GPU
+def generate_caption(image, model_name='gokaygokay/Florence-2-Flux-Large'):
+    image = Image.fromarray(image)
+    task_prompt = "<DESCRIPTION>"
+    prompt = task_prompt + "Describe this image in great detail."
+
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    model = florence_models[model_name]
+    processor = florence_processors[model_name]
+
+    inputs = processor(text=prompt, images=image, return_tensors="pt")
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        num_beams=3,
+        repetition_penalty=1.10,
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
+    return parsed_answer["<DESCRIPTION>"]
+
+@spaces.GPU
+def process_and_save_image(height, width, steps, scales, prompt, seed):
+    is_safe, filtered_prompt = filter_prompt(prompt)
+    if not is_safe:
+        gr.Warning("The prompt contains inappropriate content.")
+        return None, load_gallery()
+            
+    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
+        try:
+            generated_image = pipe(
+                prompt=[filtered_prompt],
+                generator=torch.Generator().manual_seed(int(seed)),
+                num_inference_steps=int(steps),
+                guidance_scale=float(scales),
+                height=int(height),
+                width=int(width),
+                max_sequence_length=256
+            ).images[0]
+            
+            saved_path = save_image(generated_image)
+            if saved_path is None:
+                print("Warning: Failed to save generated image")
+            
+            return generated_image, load_gallery()
+        except Exception as e:
+            print(f"Error in image generation: {str(e)}")
+            return None, load_gallery()
+
+def get_random_seed():
+    return torch.randint(0, 1000000, (1,)).item()
+
+def update_seed():
+    return get_random_seed()
+
+# CSS 스타일
+css = """
+footer {display: none !important}
+.gradio-container {
+    max-width: 1200px;
+    margin: auto;
+}
+.contain {
+    background: rgba(255, 255, 255, 0.05);
+    border-radius: 12px;
+    padding: 20px;
+}
+.generate-btn {
+    background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
+    border: none !important;
+    color: white !important;
+}
+.generate-btn:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+}
+.title {
+    text-align: center;
+    font-size: 2.5em;
+    font-weight: bold;
+    margin-bottom: 1em;
+    background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+}
+.tabs {
+    margin-top: 20px;
+    border-radius: 10px;
+    overflow: hidden;
+}
+.tab-nav {
+    background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%);
+    padding: 10px;
+}
+.tab-nav button {
+    color: white;
+    border: none;
+    padding: 10px 20px;
+    margin: 0 5px;
+    border-radius: 5px;
+    transition: all 0.3s ease;
+}
+.tab-nav button.selected {
+    background: rgba(255, 255, 255, 0.2);
+}
+.image-upload-container {
+    border: 2px dashed #4B79A1;
+    border-radius: 10px;
+    padding: 20px;
+    text-align: center;
+    transition: all 0.3s ease;
+}
+.image-upload-container:hover {
+    border-color: #283E51;
+    background: rgba(75, 121, 161, 0.1);
+}
+.primary-btn {
+    background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
+    font-size: 1.2em !important;
+    padding: 12px 20px !important;
+    margin-top: 20px !important;
+}
+hr {
+    border: none;
+    border-top: 1px solid rgba(75, 121, 161, 0.2);
+    margin: 20px 0;
+}
+.input-section {
+    background: rgba(255, 255, 255, 0.03);
+    border-radius: 12px;
+    padding: 20px;
+    margin-bottom: 20px;
+}
+.output-section {
+    background: rgba(255, 255, 255, 0.03);
+    border-radius: 12px;
+    padding: 20px;
+}
+.example-images {
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+    gap: 10px;
+    margin-bottom: 20px;
+}
+.example-images img {
+    width: 100%;
+    height: 150px;
+    object-fit: cover;
+    border-radius: 8px;
+    cursor: pointer;
+    transition: transform 0.2s;
+}
+.example-images img:hover {
+    transform: scale(1.05);
+}
+"""
+
+with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+    gr.HTML('<div class="title">FLUX VisionReply</div>')
+    gr.HTML('<div style="text-align: center; margin-bottom: 2em;">Upload an image(Image2Text2Image)</div>')
+    
+    with gr.Row():
+        # 왼쪽 컬럼: 입력 섹션
+        with gr.Column(scale=3):
+            # 이미지 업로드 섹션
+            input_image = gr.Image(
+                label="Upload Image (Optional)",
+                type="numpy",
+                elem_classes=["image-upload-container"]
+            )
+            
+            # 예시 이미지 갤러리 추가
+            example_images = [
+                "5.jpg",
+                "6.jpg",
+                "2.jpg",   
+                "3.jpg",                
+                "1.jpg",
+                "4.jpg",
+              
+            ]
+            gr.Examples(
+                examples=example_images,
+                inputs=input_image,
+                label="Example Images",
+                examples_per_page=4
+            )
+            
+            # Florence 모델 선택 - 숨김 처리
+            florence_model = gr.Dropdown(
+                choices=list(florence_models.keys()),
+                label="Caption Model",
+                value='gokaygokay/Florence-2-Flux-Large',
+                visible=False
+            )
+            
+            caption_button = gr.Button(
+                "🔍 Generate Caption from Image",
+                elem_classes=["generate-btn"]
+            )
+            
+            # 구분선
+            gr.HTML('<hr style="margin: 20px 0;">')
+            
+            # 텍스트 프롬프트 섹션
+            prompt = gr.Textbox(
+                label="Image Description",
+                placeholder="Enter text description or use generated caption above...",
+                lines=3
+            )
+            
+            with gr.Accordion("Advanced Settings", open=False):
+                with gr.Row():
+                    height = gr.Slider(
+                        label="Height",
+                        minimum=256,
+                        maximum=1152,
+                        step=64,
+                        value=1024
+                    )
+                    width = gr.Slider(
+                        label="Width",
+                        minimum=256,
+                        maximum=1152,
+                        step=64,
+                        value=1024
+                    )
+                
+                with gr.Row():
+                    steps = gr.Slider(
+                        label="Inference Steps",
+                        minimum=6,
+                        maximum=25,
+                        step=1,
+                        value=8
+                    )
+                    scales = gr.Slider(
+                        label="Guidance Scale",
+                        minimum=0.0,
+                        maximum=5.0,
+                        step=0.1,
+                        value=3.5
+                    )
+                
+                seed = gr.Number(
+                    label="Seed",
+                    value=get_random_seed(),
+                    precision=0
+                )
+                
+                randomize_seed = gr.Button(
+                    "🎲 Randomize Seed", 
+                    elem_classes=["generate-btn"]
+                )
+            
+            generate_btn = gr.Button(
+                "✨ Generate Image",
+                elem_classes=["generate-btn", "primary-btn"]
+            )
+
+        # 오른쪽 컬럼: 출력 섹션
+        with gr.Column(scale=4):
+            output = gr.Image(
+                label="Generated Image",
+                elem_classes=["output-image"]
+            )
+            
+            gallery = gr.Gallery(
+                label="Generated Images Gallery",
+                show_label=True,
+                columns=[4],
+                rows=[2],
+                height="auto",
+                object_fit="cover",
+                elem_classes=["gallery-container"]
+            )
+            
+            gallery.value = load_gallery()
+
+    # Event handlers
+    caption_button.click(
+        generate_caption,
+        inputs=[input_image, florence_model],
+        outputs=[prompt]
+    )
+    
+    generate_btn.click(
+        process_and_save_image,
+        inputs=[height, width, steps, scales, prompt, seed],
+        outputs=[output, gallery]
+    )
+    
+    randomize_seed.click(
+        update_seed,
+        outputs=[seed]
+    )
+    
+    generate_btn.click(
+        update_seed,
+        outputs=[seed]
+    )
+
+if __name__ == "__main__":
+    demo.launch(allowed_paths=[PERSISTENT_DIR])