Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,193 +1,19 @@
-import spaces
-import gradio as gr
-import torch
-from PIL import Image
 import os
-import sys
-import subprocess
-import tempfile
-from pathlib import Path
-import glob
-
-# Default negative prompts
-NEGATIVE_PROMPT_CN = "泛黄，发绿，模糊，低分辨率，低质量图像，扭曲的肢体，诡异的外观，丑陋，AI感，噪点，网格感，JPEG压缩条纹，异常的肢体，水印，乱码，意义不明的字符"
-NEGATIVE_PROMPT_EN = "Yellowed, green-tinted, blurry, low-resolution, low-quality image, distorted limbs, eerie appearance, ugly, AI-looking, noise, grid-like artifacts, JPEG compression artifacts, abnormal limbs, watermark, garbled text, meaningless characters"
-
-# Model paths - can be overridden via environment variables
-MODELS_DIR = Path(os.environ.get("ZIMAGE_MODELS_DIR", "./models"))
-
-
-# =============================================================================
-# Model Download Functions
-# =============================================================================
-
-def download_hf_models(output_dir: Path) -> dict:
-    """
-    Download required models from Hugging Face using huggingface_hub.
-    
-    Downloads:
-    - DiffSynth-Studio/Z-Image-i2L
-    - Tongyi-MAI/Z-Image
-    - DiffSynth-Studio/General-Image-Encoders
-    - Tongyi-MAI/Z-Image-Turbo  
-    
-    Returns dict with paths to downloaded models.
-    """
-    from huggingface_hub import snapshot_download
-    
-    output_dir.mkdir(parents=True, exist_ok=True)
-    
-    models = [
-        {
-            "repo_id": "DiffSynth-Studio/General-Image-Encoders",
-            "description": "General Image Encoders (SigLIP2-G384, DINOv3-7B)",
-            "allow_patterns": None,
-        },
-        {
-            "repo_id": "Tongyi-MAI/Z-Image-Turbo",
-            "description": "Z-Image Turbo (text encoder, VAE, tokenizer)",
-            "allow_patterns": [
-                "text_encoder/*.safetensors",
-                "vae/*.safetensors",
-                "tokenizer/*",
-            ],
-        },
-        {
-            "repo_id": "Tongyi-MAI/Z-Image",
-            "description": "Z-Image base model (transformer)",
-            "allow_patterns": ["transformer/*.safetensors"],
-        },
-        {
-            "repo_id": "DiffSynth-Studio/Z-Image-i2L",
-            "description": "Z-Image-i2L (Image to LoRA model)",
-            "allow_patterns": ["*.safetensors"],
-        },
-    ]
-    
-    downloaded_paths = {}
-    
-    for model in models:
-        repo_id = model["repo_id"]
-        local_dir = output_dir / repo_id
-        
-        # Check if already downloaded
-        if local_dir.exists() and any(local_dir.rglob("*.safetensors")):
-            print(f"   ✓ {repo_id} (already downloaded)")
-            downloaded_paths[repo_id] = local_dir
-            continue
-        
-        print(f"   📥 Downloading {repo_id}...")
-        print(f"      {model['description']}")
-        
-        try:
-            result_path = snapshot_download(
-                repo_id=repo_id,
-                local_dir=str(local_dir),
-                allow_patterns=model["allow_patterns"],
-                local_dir_use_symlinks=False,
-                resume_download=True,
-            )
-            downloaded_paths[repo_id] = Path(result_path)
-            print(f"   ✓ {repo_id}")
-        except Exception as e:
-            print(f"   ❌ Error downloading {repo_id}: {e}")
-            raise
-    
-    return downloaded_paths
-
-
-def get_model_files(base_path: Path, pattern: str) -> list:
-    """Get list of files matching a glob pattern."""
-    full_pattern = str(base_path / pattern)
-    files = sorted(glob.glob(full_pattern))
-    return files
-
+os.system("pip install diffsynth==2.0.3")
+from modelscope.hub.api import HubApi
+api = HubApi()
+api.login(os.environ["MODELSCOPE_TOKEN"])
+os.environ["DIFFSYNTH_MODEL_BASE_PATH"] = "/mnt/workspace/models"
+os.environ["DIFFSYNTH_DOWNLOAD_SOURCE"] = "huggingface"
 
-def install_diffsynth_studio():
-    """Clone and install DiffSynth-Studio if not already installed."""
-    try:
-        from diffsynth.pipelines.z_image import ZImagePipeline
-        return True, "✅ DiffSynth-Studio is already installed."
-    except ImportError:
-        pass
-    
-    repo_dir = Path(__file__).parent / "DiffSynth-Studio"
-    
-    try:
-        if not repo_dir.exists():
-            print("📥 Cloning DiffSynth-Studio repository...")
-            subprocess.run(
-                ["git", "clone", "https://github.com/modelscope/DiffSynth-Studio.git", str(repo_dir)],
-                capture_output=True,
-                text=True,
-                check=True
-            )
-            print("✅ Repository cloned successfully.")
-        else:
-            print("📁 DiffSynth-Studio directory already exists, pulling latest...")
-            subprocess.run(
-                ["git", "-C", str(repo_dir), "pull"],
-                capture_output=True,
-                text=True
-            )
-        
-        print("📦 Installing DiffSynth-Studio...")
-        subprocess.run(
-            [sys.executable, "-m", "pip", "install", "-e", str(repo_dir)],
-            capture_output=True,
-            text=True,
-            check=True
-        )
-        print("✅ DiffSynth-Studio installed successfully.")
-        
-        sys.path.insert(0, str(repo_dir))
-        
-        from diffsynth.pipelines.z_image import ZImagePipeline
-        return True, "✅ DiffSynth-Studio installed successfully!"
-        
-    except subprocess.CalledProcessError as e:
-        error_msg = f"❌ Installation failed: {e.stderr}"
-        print(error_msg)
-        return False, error_msg
-    except Exception as e:
-        error_msg = f"❌ Error during installation: {str(e)}"
-        print(error_msg)
-        return False, error_msg
-
-
-# =============================================================================
-# Pipeline Initialization
-# =============================================================================
-
-print("=" * 60)
-print("  Z-Image-i2L Gradio Demo - Initializing")
-print("=" * 60)
-print()
-
-# Step 1: Install DiffSynth-Studio
-print("🔍 Step 1: Checking DiffSynth-Studio installation...")
-success, message = install_diffsynth_studio()
-print(message)
-
-if not success:
-    raise RuntimeError("Failed to install DiffSynth-Studio. Cannot continue.")
-
-# Step 2: Download HuggingFace models
-print()
-print("🔍 Step 2: Downloading models from HuggingFace...")
-print(f"   Models directory: {MODELS_DIR.absolute()}")
-downloaded_paths = download_hf_models(MODELS_DIR)
-
-# Import required modules
+import gradio as gr
+import torch
 from diffsynth.pipelines.z_image import (
     ZImagePipeline, ModelConfig,
     ZImageUnit_Image2LoRAEncode, ZImageUnit_Image2LoRADecode
 )
-from safetensors.torch import save_file, load_file
 
-# Step 3: Configure VRAM settings
-print()
-print("⚙️  Step 3: Configuring VRAM settings...")
+# Use `vram_config` to enable LoRA hot-loading
 vram_config = {
     "offload_dtype": torch.bfloat16,
     "offload_device": "cuda",
@@ -199,276 +25,165 @@ vram_config = {
     "computation_device": "cuda",
 }
 
-# Step 4: Resolve local model paths
-print()
-print("📂 Step 4: Resolving model paths...")
-
-# Z-Image transformer
-zimage_path = MODELS_DIR / "Tongyi-MAI" / "Z-Image"
-zimage_transformer_files = get_model_files(zimage_path, "transformer/*.safetensors")
-
-# Z-Image-Turbo
-zimage_turbo_path = MODELS_DIR / "Tongyi-MAI" / "Z-Image-Turbo"
-text_encoder_files = get_model_files(zimage_turbo_path, "text_encoder/*.safetensors")
-vae_file = get_model_files(zimage_turbo_path, "vae/diffusion_pytorch_model.safetensors")
-tokenizer_path = zimage_turbo_path / "tokenizer"
-
-# General Image Encoders
-encoders_path = MODELS_DIR / "DiffSynth-Studio" / "General-Image-Encoders"
-siglip_file = get_model_files(encoders_path, "SigLIP2-G384/model.safetensors")
-dino_file = get_model_files(encoders_path, "DINOv3-7B/model.safetensors")
-
-# Z-Image-i2L from HuggingFace
-zimage_i2l_path = MODELS_DIR / "DiffSynth-Studio" / "Z-Image-i2L"
-zimage_i2l_file = get_model_files(zimage_i2l_path, "model.safetensors")
-
-print(f"   Z-Image transformer: {len(zimage_transformer_files)} file(s)")
-print(f"   Text encoder: {len(text_encoder_files)} file(s)")
-print(f"   VAE: {len(vae_file)} file(s)")
-print(f"   Tokenizer: {tokenizer_path}")
-print(f"   SigLIP2: {len(siglip_file)} file(s)")
-print(f"   DINOv3: {len(dino_file)} file(s)")
-print(f"   Z-Image-i2L: {len(zimage_i2l_file)} file(s)")
-
-# Validate files
-missing = []
-if not zimage_transformer_files: missing.append("Z-Image transformer")
-if not text_encoder_files: missing.append("Text encoder")
-if not vae_file: missing.append("VAE")
-if not tokenizer_path.exists(): missing.append("Tokenizer")
-if not siglip_file: missing.append("SigLIP2")
-if not dino_file: missing.append("DINOv3")
-if not zimage_i2l_file: missing.append("Z-Image-i2L")
-
-if missing:
-    raise FileNotFoundError(f"Missing model files: {', '.join(missing)}")
-
-# Step 5: Load pipeline
-print()
-print("🚀 Step 5: Loading Z-Image pipeline...")
-print("   All models loaded from HuggingFace local paths")
-
-model_configs = [
-    # All models from HuggingFace - use path= for local files
-    ModelConfig(path=zimage_transformer_files, **vram_config),
-    ModelConfig(path=text_encoder_files),
-    ModelConfig(path=vae_file),
-    ModelConfig(path=siglip_file),
-    ModelConfig(path=dino_file),
-    ModelConfig(path=zimage_i2l_file),
-]
-
+# Load models
 pipe = ZImagePipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
     device="cuda",
-    model_configs=model_configs,
-    tokenizer_config=ModelConfig(path=str(tokenizer_path)),
+    model_configs=[
+        ModelConfig(model_id="Tongyi-MAI/Z-Image", origin_file_pattern="transformer/*.safetensors", **vram_config),
+        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors"),
+        ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors"),
+        ModelConfig(model_id="DiffSynth-Studio/Z-Image-i2L", origin_file_pattern="model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
 )
 
-print()
-print("✅ Pipeline loaded successfully!")
-print("=" * 60)
-print()
-
-
-# =============================================================================
-# Gradio Functions
-# =============================================================================
-
-@spaces.GPU(duration=120)
-def image_to_lora(images, progress=gr.Progress()):
-    """Convert input images to a LoRA model."""
-    if images is None or len(images) == 0:
-        return None, "❌ Please upload at least one image!"
+def run_inference(style_image_1, style_image_2, style_image_3, style_image_4, style_image_5, style_image_6, prompt, negative_prompt, cfg_scale, sigma_shift, seed, num_inference_steps, height, width, progress=gr.Progress()):
+    # Filter out None values and collect valid images
+    style_images = [img for img in [style_image_1, style_image_2, style_image_3, style_image_4, style_image_5, style_image_6] if img is not None]
     
-    try:
-        progress(0.1, desc="Processing images...")
-        
-        pil_images = []
-        for img in images:
-            if isinstance(img, str):
-                pil_images.append(Image.open(img).convert("RGB"))
-            elif isinstance(img, tuple):
-                pil_images.append(Image.open(img[0]).convert("RGB"))
-            else:
-                pil_images.append(Image.fromarray(img).convert("RGB"))
-        
-        progress(0.3, desc="Encoding images to LoRA...")
-        
-        with torch.no_grad():
-            embs = ZImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=pil_images)
-            progress(0.7, desc="Decoding LoRA weights...")
-            lora = ZImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"]
-        
-        progress(0.9, desc="Saving LoRA file...")
-        
-        temp_dir = tempfile.mkdtemp()
-        lora_path = os.path.join(temp_dir, "generated_lora.safetensors")
-        save_file(lora, lora_path)
-        
-        progress(1.0, desc="Done!")
-        
-        return lora_path, f"✅ LoRA generated successfully from {len(pil_images)} image(s)!"
+    # Image to LoRA
+    with torch.no_grad():
+        embs = ZImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=style_images)
+        lora = ZImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"]
+
+    # Generate images
+    progress(0, desc="Generating image")
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        seed=None if seed == -1 else seed,
+        cfg_scale=cfg_scale, num_inference_steps=num_inference_steps,
+        positive_only_lora=lora,
+        sigma_shift=sigma_shift,
+        height=height,
+        width=width,
+        progress_bar_cmd=progress.tqdm
+    )
+    return image
+
+
+with gr.Blocks(title="Z-Image-Omni-Base") as demo:
+    gr.Markdown("Model: https://modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L")
+    gr.Markdown("GitHub: https://github.com/modelscope/DiffSynth-Studio")
+    gr.Markdown("Upload images and generate new images based on text prompts")
     
-    except Exception as e:
-        return None, f"❌ Error generating LoRA: {str(e)}"
-
+    with gr.Row():
+        # 第一列：输入的6张图
+        with gr.Column():
+            gr.Markdown("Input Images (upload 1-6 images)")
+            with gr.Row():
+                style_image_1 = gr.Image(label="Image 1", type="pil")
+                style_image_2 = gr.Image(label="Image 2", type="pil")
+            with gr.Row():
+                style_image_3 = gr.Image(label="Image 3", type="pil")
+                style_image_4 = gr.Image(label="Image 4", type="pil")
+            with gr.Row():
+                style_image_5 = gr.Image(label="Image 5", type="pil")
+                style_image_6 = gr.Image(label="Image 6", type="pil")
+        
+        # 第二列：提示词等控件
+        with gr.Column():
+            gr.Markdown("Settings")
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="Enter your prompt here...",
+                value="a cat",
+                lines=3
+            )
+            
+            seed = gr.Number(
+                label="Seed",
+                value=-1,
+                precision=0
+            )
 
-@spaces.GPU(duration=60)
-def generate_image(
-    lora_file,
-    prompt,
-    negative_prompt,
-    seed,
-    cfg_scale,
-    sigma_shift,
-    num_steps,
-    progress=gr.Progress()
-):
-    """Generate an image using the created LoRA."""
-    if lora_file is None:
-        return None, "❌ Please generate or upload a LoRA file first!"
-    
-    try:
-        progress(0.1, desc="Loading LoRA...")
-        
-        lora = load_file(lora_file)
-        # Move LoRA tensors to CUDA with correct dtype
-        lora = {k: v.to(device="cuda", dtype=torch.bfloat16) for k, v in lora.items()}
-        
-        progress(0.3, desc="Generating image...")
-        
-        image = pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            seed=int(seed),
-            cfg_scale=cfg_scale,
-            num_inference_steps=int(num_steps),
-            positive_only_lora=lora,
-            sigma_shift=sigma_shift
-        )
-        
-        progress(1.0, desc="Done!")
-        
-        return image, "✅ Image generated successfully!"
-    
-    except Exception as e:
-        return None, f"❌ Error generating image: {str(e)}"
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=1536,
+                step=64,
+                value=1024
+            )
 
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=1536,
+                step=64,
+                value=1024
+            )
 
-def create_demo():
-    """Create the Gradio interface."""
-    
-    with gr.Blocks(
-        title="Z-Image-i2L Demo",
-        theme=gr.themes.Soft(),
-        css=".gradio-container { max-width: 1200px !important; margin: 0 auto}"
-    ) as demo:
-        gr.Markdown("""
-        # 🎨 Z-Image-i2L: Image to LoRA Demo
-        
-        > 💡 **Tip**: For best results, use 4-6 images with a consistent artistic style.
-        """)
-        
-        with gr.Tabs():
-            with gr.TabItem("📸 Step 1: Image to LoRA"):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        input_gallery = gr.Gallery(
-                            label="Upload Style Images (1-6 images)",
-                            file_types=["image"],
-                            columns=3,
-                            height=300,
-                            interactive=True
-                        )
-                        
-                        gr.Markdown("""
-                        **Guidelines:**
-                        - Upload 1-6 images with a consistent style
-                        - Higher quality images produce better results
-                        - Mix of subjects helps generalization
-                        """)
-                        
-                        generate_lora_btn = gr.Button("🎯 Generate LoRA", variant="primary")
-                    
-                    with gr.Column(scale=1):
-                        lora_output = gr.File(
-                            label="Generated LoRA File",
-                            file_types=[".safetensors"],
-                            interactive=False
-                        )
-                        lora_status = gr.Textbox(
-                            label="Status",
-                            interactive=False,
-                            lines=2
-                        )
+            num_inference_steps = gr.Slider(
+                label="Inference Steps",
+                minimum=10,
+                maximum=50,
+                step=1,
+                value=30
+            )
             
-            with gr.TabItem("🖼️ Step 2: Generate Images"):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        lora_input = gr.File(
-                            label="LoRA File (from Step 1 or upload)",
-                            file_types=[".safetensors"]
-                        )
-                        
-                        prompt = gr.Textbox(
-                            label="Prompt",
-                            placeholder="Describe what you want to generate...",
-                            value="a cat",
-                            lines=2
-                        )
-                        
-                        with gr.Accordion("Negative Prompt", open=False):
-                            negative_prompt = gr.Textbox(
-                                label="Negative Prompt",
-                                value=NEGATIVE_PROMPT_CN,
-                                lines=3
-                            )
-                            with gr.Row():
-                                use_cn_neg = gr.Button("Use Chinese", size="sm")
-                                use_en_neg = gr.Button("Use English", size="sm")
-                        
-                        with gr.Accordion("Advanced Settings", open=False):
-                            seed = gr.Number(label="Seed", value=0, precision=0)
-                            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=10, value=4, step=0.5)
-                            sigma_shift = gr.Slider(label="Sigma Shift", minimum=1, maximum=15, value=8, step=1)
-                            num_steps = gr.Slider(label="Steps", minimum=20, maximum=100, value=50, step=5)
-                        
-                        generate_btn = gr.Button("✨ Generate Image", variant="primary")
-                    
-                    with gr.Column(scale=1):
-                        output_image = gr.Image(label="Generated Image", type="pil", height=512)
-                        gen_status = gr.Textbox(label="Status", interactive=False, lines=2)
-        
-        gr.Markdown("""
-        ---
-        **Resources:** [Z-Image-i2L (HuggingFace)](https://huggingface.co/DiffSynth-Studio/Z-Image-i2L) | 
-        [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) |
-        **Settings:** CFG=4, Sigma Shift=8, Steps=50
-        """)
-        
-        # Event handlers
-        generate_lora_btn.click(
-            fn=image_to_lora,
-            inputs=[input_gallery],
-            outputs=[lora_output, lora_status]
-        )
-        
-        lora_output.change(fn=lambda x: x, inputs=[lora_output], outputs=[lora_input])
-        
-        generate_btn.click(
-            fn=generate_image,
-            inputs=[lora_input, prompt, negative_prompt, seed, cfg_scale, sigma_shift, num_steps],
-            outputs=[output_image, gen_status]
-        )
-        
-        use_cn_neg.click(fn=lambda: NEGATIVE_PROMPT_CN, outputs=[negative_prompt])
-        use_en_neg.click(fn=lambda: NEGATIVE_PROMPT_EN, outputs=[negative_prompt])
+            # Advanced settings (collapsed by default)
+            with gr.Accordion("Advanced Settings", open=True):
+                negative_prompt = gr.Textbox(
+                    label="Negative Prompt",
+                    placeholder="Enter negative prompt here...",
+                    value="泛黄，发绿，模糊，低分辨率，低质量图像，扭曲的肢体，诡异的外观，丑陋，AI感，噪点，网格感，JPEG压缩条纹，异常的肢体，水印，乱码，意义不明的字符"
+                )
+                cfg_scale = gr.Slider(
+                    label="CFG Scale",
+                    minimum=1.0,
+                    maximum=10.0,
+                    step=0.5,
+                    value=4.0
+                )
+                sigma_shift = gr.Slider(
+                    label="Sigma Shift",
+                    minimum=1.0,
+                    maximum=20.0,
+                    step=0.5,
+                    value=8.0
+                )
+            
+            generate_btn = gr.Button("Generate Image")
+        
+        # 第三列：输出图
+        with gr.Column():
+            gr.Markdown("Output")
+            output_image = gr.Image(
+                label="Generated Image",
+                interactive=False
+            )
     
-    return demo
-
+    generate_btn.click(
+        fn=run_inference,
+        inputs=[style_image_1, style_image_2, style_image_3, style_image_4, style_image_5, style_image_6, prompt, negative_prompt, cfg_scale, sigma_shift, seed, num_inference_steps, height, width],
+        outputs=[output_image],
+    )
+    gr.Examples(
+        examples=[
+            [
+                "assets/style/1/0.jpg", "assets/style/1/1.jpg", "assets/style/1/2.jpg", "assets/style/1/3.jpg", None, None,
+                "a cat", "泛黄，发绿，模糊，低分辨率，低质量图像，扭曲的肢体，诡异的外观，丑陋，AI感，噪点，网格感，JPEG压缩条纹，异常的肢体，水印，乱码，意义不明的字符",
+                4, 8, 0, 30, 1024, 1024
+            ],
+            [
+                "assets/style/4/0.jpg", "assets/style/4/1.jpg", "assets/style/4/2.jpg", "assets/style/4/3.jpg", "assets/style/4/4.jpg", "assets/style/4/5.jpg",
+                "a dog", "泛黄，发绿，模糊，低分辨率，低质量图像，扭曲的肢体，诡异的外观，丑陋，AI感，噪点，网格感，JPEG压缩条纹，异常的肢体，水印，乱码，意义不明的字符",
+                4, 8, 2, 30, 1024, 1024
+            ],
+            [
+                "assets/style/3/0.jpg", "assets/style/3/1.jpg", "assets/style/3/2.jpg", "assets/style/3/3.jpg", None, None,
+                "a girl", "泛黄，发绿，模糊，低分辨率，低质量图像，扭曲的肢体，诡异的外观，丑陋，AI感，噪点，网格感，JPEG压缩条纹，异常的肢体，水印，乱码，意义不明的字符",
+                4, 8, 1, 30, 1024, 1024
+            ],
+        ],
+        fn=run_inference,
+        inputs=[style_image_1, style_image_2, style_image_3, style_image_4, style_image_5, style_image_6, prompt, negative_prompt, cfg_scale, sigma_shift, seed, num_inference_steps, height, width],
+        outputs=[output_image],
+        cache_examples=True
+    )
 
 if __name__ == "__main__":
-    print("Starting Gradio server...")
-    demo = create_demo()
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)