""" Z-Image-i2L Gradio Demo ======================== A web interface for the Z-Image-i2L model that converts images to LoRA models. Setup Instructions: 1. Install dependencies: pip install -r requirements.txt 2. Run this demo (DiffSynth-Studio will be auto-installed): python app.py Note: This requires a GPU with sufficient VRAM (recommended 24GB+) """ import spaces import gradio as gr import torch from PIL import Image import os import sys import subprocess import tempfile from pathlib import Path # 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" def install_diffsynth_studio(): """Clone and install DiffSynth-Studio if not already installed.""" # Check if already installed try: from diffsynth.pipelines.z_image import ZImagePipeline return True, "✅ DiffSynth-Studio is already installed." except ImportError: pass # Define paths repo_dir = Path(__file__).parent / "DiffSynth-Studio" try: # Clone the repository if it doesn't exist if not repo_dir.exists(): print("📥 Cloning DiffSynth-Studio repository...") result = 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...") result = subprocess.run( ["git", "-C", str(repo_dir), "pull"], capture_output=True, text=True ) # Install in editable mode print("📦 Installing DiffSynth-Studio...") result = subprocess.run( [sys.executable, "-m", "pip", "install", "-e", str(repo_dir)], capture_output=True, text=True, check=True ) print("✅ DiffSynth-Studio installed successfully.") # Add to path and try importing again 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 (runs at module load time) # ============================================================================= print("=" * 50) print(" Z-Image-i2L Gradio Demo - Initializing") print("=" * 50) print() # Ensure DiffSynth-Studio is installed print("🔍 Checking DiffSynth-Studio installation...") success, message = install_diffsynth_studio() print(message) if not success: raise RuntimeError("Failed to install DiffSynth-Studio. Cannot continue.") # Import required modules from diffsynth.pipelines.z_image import ( ZImagePipeline, ModelConfig, ZImageUnit_Image2LoRAEncode, ZImageUnit_Image2LoRADecode ) from safetensors.torch import save_file, load_file # Configure VRAM settings print("⚙️ Configuring VRAM settings...") vram_config = { "offload_dtype": torch.bfloat16, "offload_device": "cuda", "onload_dtype": torch.bfloat16, "onload_device": "cuda", "preparing_dtype": torch.bfloat16, "preparing_device": "cuda", "computation_dtype": torch.bfloat16, "computation_device": "cuda", } # Load the pipeline print("🚀 Loading Z-Image pipeline...") print(" This may take a few minutes on first run (downloading models)...") pipe = ZImagePipeline.from_pretrained( torch_dtype=torch.bfloat16, device="cuda", 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("✅ Pipeline loaded successfully!") 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!" try: progress(0.1, desc="Processing images...") # Convert uploaded images to PIL pil_images = [] for img in images: if isinstance(img, str): pil_images.append(Image.open(img).convert("RGB")) elif isinstance(img, tuple): # Gradio gallery returns tuples (filepath, caption) 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...") # Save to temporary 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)} images!" except Exception as e: return None, f"❌ Error generating LoRA: {str(e)}" @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) 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)}" 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; } .status-box { padding: 10px; border-radius: 5px; margin: 10px 0; } """ ) as demo: gr.Markdown(""" # 🎨 Z-Image-i2L: Image to LoRA Demo Convert your images into style LoRA models and generate new images with that style! **How it works:** 1. **Upload Images**: Add 1-6 images with a consistent style 2. **Generate LoRA**: Convert your images into a LoRA model 3. **Generate Images**: Use the LoRA to create new images with your style > 💡 **Tip**: For best results, use 4-6 images with a consistent artistic style. ✅ **Pipeline is pre-loaded and ready to use!** """) with gr.Tabs(): # Tab 1: Image to LoRA 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 for input images:** - Upload 1-6 images with a consistent style - Higher quality images produce better results - Mix of subjects (people, objects, scenes) 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="LoRA Generation Status", interactive=False, lines=2 ) # Tab 2: Generate Images 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="Number of 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="Generation Status", interactive=False, lines=2 ) gr.Markdown(""" --- ### 📚 Resources - [Z-Image-i2L Model](https://modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L) - [DiffSynth-Studio GitHub](https://github.com/modelscope/DiffSynth-Studio) - [Online Demo](https://modelscope.cn/studios/DiffSynth-Studio/Z-Image-i2L) ### ⚙️ Recommended Settings - **CFG Scale**: 4 - **Sigma Shift**: 8 - **Steps**: 50 - Use negative prompts for better quality """) # Event handlers generate_lora_btn.click( fn=image_to_lora, inputs=[input_gallery], outputs=[lora_output, lora_status] ) # Auto-update lora_input when lora is generated 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] ) # Negative prompt presets use_cn_neg.click( fn=lambda: NEGATIVE_PROMPT_CN, outputs=[negative_prompt] ) use_en_neg.click( fn=lambda: NEGATIVE_PROMPT_EN, outputs=[negative_prompt] ) return demo if __name__ == "__main__": print("Starting Gradio server...") print() demo = create_demo() demo.launch( server_name="0.0.0.0", server_port=7860, share=False )