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| import os | |
| import gradio as gr | |
| from PIL import Image | |
| import io | |
| import tempfile | |
| import torch | |
| from diffusers import StableDiffusionXLPipeline | |
| # ============================================ | |
| # SEO Configuration for Hugging Face Spaces | |
| # ============================================ | |
| TITLE = "FreeAI PicGen - Free Unlimited AI Image Generator" | |
| DESCRIPTION = """ | |
| 🎨 **FreeAI PicGen** - Generate stunning AI images for FREE using Stable Diffusion XL! | |
| ### ✨ Features | |
| - 📝 **Text-to-Image**: Create images from text descriptions (512x512 default) | |
| - 💡 **Smart Prompt Helper**: Auto-optimize your prompts for better results | |
| - ⚡ **Unlimited Free Generations**: No limits, powered by Hugging Face Inference API | |
| - 💾 **Instant Download**: One-click download for all generated images | |
| ### 🔒 Privacy First | |
| Your images are processed securely and not stored permanently. | |
| 🚀 **Powered by**: [Hugging Face Diffusers](https://huggingface.co/docs/diffusers) | [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | |
| """ | |
| # ============================================ | |
| # Configuration | |
| # ============================================ | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| if not HF_TOKEN: | |
| print("⚠️ Warning: HF_TOKEN environment variable not set. Please set it in your Space secrets.") | |
| # Load the model pipeline | |
| MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0" | |
| pipe = StableDiffusionXLPipeline.from_pretrained( | |
| MODEL_ID, | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| token=HF_TOKEN | |
| ) | |
| # pipe.enable_model_cpu_offload() # Save VRAM | |
| # Prompt optimization templates | |
| PROMPT_TEMPLATES = { | |
| "quality": "masterpiece, best quality, highly detailed, {prompt}, 8k uhd, dslr, soft lighting, film grain, sharp focus", | |
| "cinematic": "cinematic shot, {prompt}, depth of field, motion blur, cinematic lighting, film grain, 35mm, unreal engine 5, octane render", | |
| "anime": "anime style, {prompt}, studio ghibli, detailed face, vibrant colors, anime screencap, cel shaded", | |
| "digital_art": "digital art, {prompt}, concept art, sharp focus, illustration, trending on artstation, by artgerm and greg rutkowski", | |
| "photorealistic": "professional photography, {prompt}, photorealistic, hyperrealistic, detailed skin texture, professional lighting, RAW photo, 8k uhd" | |
| } | |
| # ============================================ | |
| # Core Functions | |
| # ============================================ | |
| def optimize_prompt(prompt, style="quality"): | |
| """Optimize user prompt using templates""" | |
| if not prompt or not prompt.strip(): | |
| return "" | |
| template = PROMPT_TEMPLATES.get(style, PROMPT_TEMPLATES["quality"]) | |
| return template.format(prompt=prompt.strip()) | |
| def save_image_to_temp(image, prefix="generated"): | |
| """Save PIL image to temporary file and return path""" | |
| if image is None: | |
| return None | |
| temp_dir = tempfile.gettempdir() | |
| temp_path = os.path.join(temp_dir, f"{prefix}_{hash(str(image))}.png") | |
| image.save(temp_path, format='PNG') | |
| return temp_path | |
| def generate_text2image(prompt, negative_prompt, width, height, steps, cfg_scale, seed): | |
| """Generate image from text using local SDXL pipeline""" | |
| try: | |
| if not prompt or not prompt.strip(): | |
| return None, "❌ Error: Please enter a prompt!", None | |
| # Generate image | |
| generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu") | |
| if seed != -1: | |
| generator.manual_seed(int(seed)) | |
| image = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=int(width), | |
| height=int(height), | |
| num_inference_steps=int(steps), | |
| guidance_scale=float(cfg_scale), | |
| generator=generator | |
| ).images[0] | |
| # Save to temp file for download | |
| download_path = save_image_to_temp(image, "txt2img") | |
| return image, "✅ Image generated successfully!", download_path | |
| except Exception as e: | |
| error_str = str(e) | |
| if "out of memory" in error_str.lower(): | |
| return None, "⏳ GPU out of memory. Try lower resolution or fewer steps.", None | |
| elif "cuda" in error_str.lower(): | |
| return None, "⏳ CUDA error. Ensure GPU is available.", None | |
| else: | |
| return None, f"❌ Error: {error_str}", None | |
| # def generate_img2img(prompt, input_image, strength, negative_prompt, steps, cfg_scale, seed): | |
| # """Generate image from image using HF Inference API""" | |
| # try: | |
| # if not prompt or not prompt.strip(): | |
| # return None, "❌ Error: Please enter a prompt!", None | |
| # if input_image is None: | |
| # return None, "❌ Error: Please upload an image!", None | |
| # # Try to generate image from image | |
| # try: | |
| # image = client.image_to_image( | |
| # image=input_image, | |
| # prompt=prompt, | |
| # negative_prompt=negative_prompt, | |
| # model=MODEL_ID, | |
| # strength=float(strength), | |
| # num_inference_steps=int(steps), | |
| # guidance_scale=float(cfg_scale), | |
| # seed=None if seed == -1 else int(seed) | |
| # ) | |
| # # Save to temp file for download | |
| # download_path = save_image_to_temp(image, "img2img") | |
| # return image, "✅ Image transformed successfully!", download_path | |
| # except Exception as api_error: | |
| # error_msg = str(api_error) | |
| # if "not supported" in error_msg.lower() or "task" in error_msg.lower(): | |
| # return None, "⚠️ Image-to-Image requires Hugging Face Pro Inference API. Text-to-Image is available for free!", None | |
| # raise api_error | |
| # except Exception as e: | |
| # error_str = str(e) | |
| # if "rate limit" in error_str.lower(): | |
| # return None, "⏳ Rate limit reached. Please wait 30 seconds and try again.", None | |
| # elif "401" in error_str or "unauthorized" in error_str.lower(): | |
| # return None, "🔑 Authentication error. Please set HF_TOKEN environment variable.", None | |
| # elif "503" in error_str or "loading" in error_str.lower(): | |
| # return None, "⏳ Model is loading. Please wait a moment and retry.", None | |
| # else: | |
| # return None, f"❌ Error: {error_str}", None | |
| # ============================================ | |
| # Custom CSS with AdSense Placeholder | |
| # ============================================ | |
| CUSTOM_CSS = """ | |
| /* ========================================= | |
| GOOGLE ADSENSE PLACEHOLDER | |
| Uncomment below and add your publisher ID | |
| ========================================= */ | |
| /* | |
| <script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-YOUR_PUBLISHER_ID" crossorigin="anonymous"></script> | |
| <!-- Ad Unit --> | |
| <ins class="adsbygoogle" | |
| style="display:block" | |
| data-ad-client="ca-pub-YOUR_PUBLISHER_ID" | |
| data-ad-slot="YOUR_AD_SLOT_ID" | |
| data-ad-format="auto" | |
| data-full-width-responsive="true"></ins> | |
| <script> | |
| (adsbygoogle = window.adsbygoogle || []).push({}); | |
| </script> | |
| */ | |
| .gradio-container { | |
| font-family: 'Inter', system-ui, -apple-system, sans-serif; | |
| } | |
| .title-container { | |
| text-align: center; | |
| margin-bottom: 2rem; | |
| } | |
| .title-container h1 { | |
| color: #6366f1; | |
| font-size: 2.5rem; | |
| margin-bottom: 0.5rem; | |
| } | |
| .title-container p { | |
| color: #6b7280; | |
| font-size: 1.1rem; | |
| } | |
| .generate-btn { | |
| background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%) !important; | |
| border: none !important; | |
| color: white !important; | |
| font-weight: 600 !important; | |
| } | |
| .generate-btn:hover { | |
| transform: translateY(-1px); | |
| box-shadow: 0 4px 12px rgba(99, 102, 241, 0.4); | |
| } | |
| .pro-badge { | |
| background: linear-gradient(135deg, #f59e0b 0%, #d97706 100%); | |
| color: white; | |
| padding: 2px 8px; | |
| border-radius: 4px; | |
| font-size: 0.75rem; | |
| font-weight: 600; | |
| margin-left: 8px; | |
| } | |
| """ | |
| # ============================================ | |
| # Gradio Interface | |
| # ============================================ | |
| with gr.Blocks() as demo: | |
| # Header | |
| with gr.Column(elem_classes="title-container"): | |
| gr.Markdown("# 🎨 FreeAI PicGen") | |
| gr.Markdown(DESCRIPTION) | |
| # Google AdSense Banner | |
| gr.HTML(""" | |
| <!-- Google AdSense Placeholder --> | |
| <div style="text-align: center; margin: 20px 0;"> | |
| <script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-YOUR_PUBLISHER_ID" crossorigin="anonymous"></script> | |
| <ins class="adsbygoogle" | |
| style="display:block" | |
| data-ad-client="ca-pub-YOUR_PUBLISHER_ID" | |
| data-ad-slot="YOUR_AD_SLOT_ID" | |
| data-ad-format="auto" | |
| data-full-width-responsive="true"></ins> | |
| <script> | |
| (adsbygoogle = window.adsbygoogle || []).push({}); | |
| </script> | |
| </div> | |
| """) | |
| # Main Tabs | |
| with gr.Tabs(): | |
| # ======================================== | |
| # Tab 1: Text-to-Image | |
| # ======================================== | |
| with gr.Tab("📝 Text to Image"): | |
| with gr.Row(): | |
| # Left Column - Inputs | |
| with gr.Column(scale=1): | |
| t2i_prompt = gr.Textbox( | |
| label="Prompt", | |
| placeholder="A serene lake surrounded by mountains at sunset...", | |
| lines=3, | |
| max_lines=5 | |
| ) | |
| t2i_negative = gr.Textbox( | |
| label="Negative Prompt (Optional)", | |
| placeholder="blur, low quality, distorted, watermark, signature...", | |
| lines=2 | |
| ) | |
| # Prompt Helper Section | |
| with gr.Row(): | |
| style_dropdown = gr.Dropdown( | |
| choices=list(PROMPT_TEMPLATES.keys()), | |
| value="quality", | |
| label="Style Preset", | |
| info="Select a style to optimize your prompt" | |
| ) | |
| optimize_btn = gr.Button("✨ Optimize Prompt", variant="secondary", size="sm") | |
| # Advanced Settings | |
| with gr.Accordion("⚙️ Advanced Settings", open=False): | |
| with gr.Row(): | |
| t2i_width = gr.Slider( | |
| minimum=256, maximum=1024, value=512, step=64, | |
| label="Width" | |
| ) | |
| t2i_height = gr.Slider( | |
| minimum=256, maximum=1024, value=512, step=64, | |
| label="Height" | |
| ) | |
| with gr.Row(): | |
| t2i_steps = gr.Slider( | |
| minimum=10, maximum=50, value=30, step=1, | |
| label="Inference Steps" | |
| ) | |
| t2i_cfg = gr.Slider( | |
| minimum=1.0, maximum=15.0, value=7.5, step=0.5, | |
| label="CFG Scale" | |
| ) | |
| t2i_seed = gr.Number( | |
| value=-1, | |
| label="Seed (-1 for random)", | |
| precision=0 | |
| ) | |
| t2i_generate = gr.Button( | |
| "🎨 Generate Image", | |
| variant="primary", | |
| elem_classes="generate-btn", | |
| size="lg" | |
| ) | |
| # Right Column - Outputs | |
| with gr.Column(scale=1): | |
| t2i_output = gr.Image( | |
| label="Generated Image", | |
| type="pil" | |
| ) | |
| t2i_status = gr.Textbox( | |
| label="Status", | |
| interactive=False | |
| ) | |
| t2i_download = gr.File( | |
| label="Download Image (PNG)", | |
| file_types=[".png"] | |
| ) | |
| # Event Handlers | |
| optimize_btn.click( | |
| fn=optimize_prompt, | |
| inputs=[t2i_prompt, style_dropdown], | |
| outputs=t2i_prompt | |
| ) | |
| t2i_generate.click( | |
| fn=generate_text2image, | |
| inputs=[t2i_prompt, t2i_negative, t2i_width, t2i_height, t2i_steps, t2i_cfg, t2i_seed], | |
| outputs=[t2i_output, t2i_status, t2i_download] | |
| ) | |
| # ======================================== | |
| # Tab 2: Image-to-Image (Pro Feature) | |
| # ======================================== | |
| # with gr.Tab("🔄 Image to Image"): | |
| # gr.Markdown(""" | |
| # ### 🔄 Image-to-Image Transformation | |
| # <span class="pro-badge">PRO FEATURE</span> | |
| # Transform existing images using AI. This feature requires a Hugging Face Pro Inference API subscription. | |
| # Free tier users can use Text-to-Image which is unlimited! | |
| # """) | |
| # with gr.Row(): | |
| # # Left Column | |
| # with gr.Column(scale=1): | |
| # i2i_prompt = gr.Textbox( | |
| # label="Prompt", | |
| # placeholder="Describe what you want to transform the image into...", | |
| # lines=3 | |
| # ) | |
| # i2i_negative = gr.Textbox( | |
| # label="Negative Prompt (Optional)", | |
| # placeholder="blur, low quality, distorted...", | |
| # lines=2 | |
| # ) | |
| # i2i_input = gr.Image( | |
| # label="Upload Source Image", | |
| # type="pil", | |
| # sources=["upload", "clipboard"] | |
| # ) | |
| # i2i_strength = gr.Slider( | |
| # minimum=0.0, maximum=1.0, value=0.75, step=0.05, | |
| # label="Denoising Strength", | |
| # info="0.0 = original image, 1.0 = completely new image" | |
| # ) | |
| # with gr.Accordion("⚙️ Advanced Settings", open=False): | |
| # with gr.Row(): | |
| # i2i_steps = gr.Slider( | |
| # minimum=10, maximum=50, value=30, step=1, | |
| # label="Inference Steps" | |
| # ) | |
| # i2i_cfg = gr.Slider( | |
| # minimum=1.0, maximum=15.0, value=7.5, step=0.5, | |
| # label="CFG Scale" | |
| # ) | |
| # i2i_seed = gr.Number( | |
| # value=-1, | |
| # label="Seed (-1 for random)", | |
| # precision=0 | |
| # ) | |
| # i2i_generate = gr.Button( | |
| # "🔄 Transform Image", | |
| # variant="primary", | |
| # elem_classes="generate-btn", | |
| # size="lg" | |
| # ) | |
| # # Right Column | |
| # with gr.Column(scale=1): | |
| # i2i_output = gr.Image( | |
| # label="Transformed Image", | |
| # type="pil" | |
| # ) | |
| # i2i_status = gr.Textbox( | |
| # label="Status", | |
| # interactive=False | |
| # ) | |
| # i2i_download = gr.File( | |
| # label="Download Image (PNG)", | |
| # file_types=[".png"] | |
| # ) | |
| # # Event Handlers | |
| # i2i_generate.click( | |
| # fn=generate_img2img, | |
| # inputs=[i2i_prompt, i2i_input, i2i_strength, i2i_negative, i2i_steps, i2i_cfg, i2i_seed], | |
| # outputs=[i2i_output, i2i_status, i2i_download] | |
| # ) | |
| # ======================================== | |
| # Tab 3: Prompt Helper | |
| # ======================================== | |
| with gr.Tab("💡 Prompt Helper"): | |
| gr.Markdown(""" | |
| ## 🎯 Prompt Optimization Guide | |
| **Good prompts are specific and detailed.** Use these templates to improve your results: | |
| ### 📋 Available Styles | |
| - **Quality**: Best for general high-quality images | |
| - **Cinematic**: Movie-like scenes with dramatic lighting | |
| - **Anime**: Japanese animation style | |
| - **Digital Art**: Concept art and illustrations | |
| - **Photorealistic**: Ultra-realistic photography | |
| ### 🏷️ Useful Keywords | |
| **Quality Tags**: `masterpiece, best quality, highly detailed, 8k uhd, sharp focus` | |
| **Lighting**: `cinematic lighting, golden hour, volumetric lighting, soft shadows, rim light` | |
| **Camera**: `dslr, 35mm film, wide angle, bokeh, depth of field, macro lens` | |
| **Style**: `oil painting, watercolor, digital art, 3d render, pencil sketch, line art` | |
| **Negative Prompts**: `blur, low quality, distorted, watermark, signature, ugly, duplicate, morbid` | |
| """) | |
| with gr.Row(): | |
| helper_input = gr.Textbox( | |
| label="Your Simple Prompt", | |
| placeholder="A cat sitting on a windowsill", | |
| lines=2 | |
| ) | |
| helper_style = gr.Dropdown( | |
| choices=list(PROMPT_TEMPLATES.keys()), | |
| value="quality", | |
| label="Style" | |
| ) | |
| helper_output = gr.Textbox( | |
| label="Optimized Prompt", | |
| lines=3, | |
| interactive=False | |
| ) | |
| helper_btn = gr.Button("✨ Generate Optimized Prompt", variant="primary") | |
| helper_btn.click( | |
| fn=optimize_prompt, | |
| inputs=[helper_input, helper_style], | |
| outputs=helper_output | |
| ) | |
| # Footer | |
| gr.Markdown(""" | |
| --- | |
| Made with ❤️ using [Gradio](https://gradio.app) & [Hugging Face](https://huggingface.co) | |
| """) | |
| # ============================================ | |
| # Launch Configuration | |
| # ============================================ | |
| if __name__ == "__main__": | |
| demo.queue(max_size=10).launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=False, | |
| show_error=True, | |
| favicon_path=None, | |
| css=CUSTOM_CSS | |
| ) |