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| import gradio as gr | |
| import torch | |
| from transformers import AutoModelForCausalLM | |
| import os | |
| # Load the model | |
| model_id = "tencent/HunyuanImage-3.0" | |
| print("Loading HunyuanImage-3.0 model...") | |
| print("Note: This is a very large model (80B params) and requires significant GPU memory.") | |
| print("For production use, consider using the FAL API or other inference providers.") | |
| # For demo purposes, we'll use inference API | |
| def generate_image(prompt, seed=42, diff_infer_steps=50, image_size="auto"): | |
| """ | |
| Generate image using HunyuanImage-3.0 | |
| Note: Direct model loading requires 3x80GB GPU memory. | |
| For Spaces, consider using Inference API or providers like FAL. | |
| """ | |
| try: | |
| # This is a placeholder - actual implementation would require | |
| # either very large GPU or using Inference API | |
| from PIL import Image | |
| import numpy as np | |
| # Create a placeholder image with text | |
| img = Image.new('RGB', (1024, 1024), color=(240, 240, 245)) | |
| return img, seed | |
| except Exception as e: | |
| print(f"Error: {e}") | |
| return None, seed | |
| def infer(prompt, seed, randomize_seed, diff_infer_steps, image_size): | |
| import random | |
| if randomize_seed: | |
| seed = random.randint(0, 2**32 - 1) | |
| image, used_seed = generate_image(prompt, seed, diff_infer_steps, image_size) | |
| return image, used_seed | |
| # Gradio Interface | |
| examples = [ | |
| "A brown and white dog is running on the grass", | |
| "A futuristic city at sunset with flying cars", | |
| "A serene mountain landscape with a crystal clear lake", | |
| ] | |
| css = """ | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 800px; | |
| } | |
| .note { | |
| background: #fff3cd; | |
| padding: 15px; | |
| border-radius: 8px; | |
| margin: 10px 0; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown("# 🎨 HunyuanImage-3.0 Text-to-Image") | |
| gr.Markdown( | |
| """### Tencent HunyuanImage-3.0 - A Powerful Native Multimodal Model | |
| **⚠️ Important Note:** This model requires 3×80GB GPU memory for direct inference. | |
| For production use, please: | |
| 1. Use the Inference API endpoint | |
| 2. Use inference providers like FAL AI | |
| 3. Deploy on appropriate hardware | |
| This demo shows the interface structure. For actual inference, configure with appropriate resources. | |
| """, | |
| elem_classes="note" | |
| ) | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=True, | |
| max_lines=3, | |
| placeholder="Enter your prompt for image generation...", | |
| ) | |
| run_button = gr.Button("🎨 Generate Image", variant="primary") | |
| result = gr.Image(label="Generated Image", show_label=True) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=2**32 - 1, | |
| step=1, | |
| value=42, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| diff_infer_steps = gr.Slider( | |
| label="Diffusion inference steps", | |
| minimum=10, | |
| maximum=100, | |
| step=10, | |
| value=50, | |
| ) | |
| image_size = gr.Radio( | |
| label="Image Size", | |
| choices=["auto", "1024x1024", "1280x768", "768x1280"], | |
| value="auto", | |
| ) | |
| gr.Examples(examples=examples, inputs=[prompt]) | |
| run_button.click( | |
| fn=infer, | |
| inputs=[prompt, seed, randomize_seed, diff_infer_steps, image_size], | |
| outputs=[result, seed], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |