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| import gradio as gr | |
| import torch | |
| from diffusers import AutoPipelineForText2Image | |
| from PIL import Image | |
| import time | |
| # ─── MODEL LOAD ─────────────────────────────────────────────────────────────── | |
| print("Loading SDXL-Turbo...") | |
| pipe = AutoPipelineForText2Image.from_pretrained( | |
| "stabilityai/sdxl-turbo", | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| variant="fp16" if torch.cuda.is_available() else None, | |
| ) | |
| pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu") | |
| # speed optimisation for CPU | |
| if not torch.cuda.is_available(): | |
| pipe.enable_attention_slicing() | |
| print("SDXL-Turbo loaded ✅") | |
| # ─── GENERATION FUNCTION ────────────────────────────────────────────────────── | |
| def generate_image(prompt, steps, width, height, seed): | |
| if not prompt.strip(): | |
| return None, "⚠️ Please enter a prompt first!" | |
| generator = None | |
| if seed != -1: | |
| generator = torch.Generator().manual_seed(int(seed)) | |
| try: | |
| start = time.time() | |
| result = pipe( | |
| prompt=prompt, | |
| num_inference_steps=int(steps), | |
| guidance_scale=0.0, # SDXL-Turbo is guidance-free | |
| width=int(width), | |
| height=int(height), | |
| generator=generator, | |
| ) | |
| elapsed = round(time.time() - start, 1) | |
| image = result.images[0] | |
| info = f"✅ Generated in {elapsed}s | Steps: {steps} | Model: SDXL-Turbo | Seed: {seed if seed != -1 else 'random'}" | |
| return image, info | |
| except Exception as e: | |
| return None, f"❌ Error: {str(e)}" | |
| # ─── EXAMPLE PROMPTS ────────────────────────────────────────────────────────── | |
| examples = [ | |
| ["a lone tree in a golden wheat field at sunset, dramatic lighting, 4k", 4, 512, 512, -1], | |
| ["a futuristic cyberpunk city at night, neon lights, rain, cinematic", 4, 512, 512, -1], | |
| ["a majestic snow-capped mountain reflected in a crystal clear lake, hyperrealistic", 4, 512, 512, -1], | |
| ["portrait of an astronaut on Mars, dramatic lighting, photorealistic", 4, 512, 512, -1], | |
| ["a cozy cafe interior with warm lighting and coffee cups, aesthetic", 4, 512, 512, -1], | |
| ] | |
| # ─── CUSTOM CSS ─────────────────────────────────────────────────────────────── | |
| css = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@700;900&family=Share+Tech+Mono&family=Rajdhani:wght@400;600;700&display=swap'); | |
| body, .gradio-container { | |
| background: #080b0f !important; | |
| font-family: 'Rajdhani', sans-serif !important; | |
| } | |
| .app-header { | |
| text-align: center; | |
| padding: 28px 0 10px; | |
| border-bottom: 1px solid rgba(230,57,70,0.2); | |
| margin-bottom: 24px; | |
| } | |
| .app-title { | |
| font-family: 'Orbitron', monospace; | |
| font-size: 2rem; | |
| font-weight: 900; | |
| color: #fff; | |
| text-shadow: 0 0 30px rgba(230,57,70,0.4); | |
| letter-spacing: 2px; | |
| } | |
| .app-sub { | |
| font-family: 'Share Tech Mono', monospace; | |
| font-size: 0.75rem; | |
| color: #8a9ab0; | |
| letter-spacing: 4px; | |
| text-transform: uppercase; | |
| margin-top: 6px; | |
| } | |
| .gradio-container label { | |
| font-family: 'Share Tech Mono', monospace !important; | |
| font-size: 0.72rem !important; | |
| letter-spacing: 2px !important; | |
| text-transform: uppercase !important; | |
| color: #e63946 !important; | |
| } | |
| textarea, input[type="text"], input[type="number"] { | |
| background: #0f1318 !important; | |
| border: 1px solid rgba(230,57,70,0.25) !important; | |
| border-radius: 6px !important; | |
| color: #d4dde8 !important; | |
| font-family: 'Rajdhani', sans-serif !important; | |
| } | |
| textarea:focus, input:focus { | |
| border-color: #e63946 !important; | |
| box-shadow: 0 0 12px rgba(230,57,70,0.2) !important; | |
| } | |
| button.primary { | |
| background: linear-gradient(135deg, #e63946, #c1121f) !important; | |
| border: none !important; | |
| border-radius: 6px !important; | |
| font-family: 'Share Tech Mono', monospace !important; | |
| font-size: 0.85rem !important; | |
| letter-spacing: 2px !important; | |
| text-transform: uppercase !important; | |
| color: white !important; | |
| box-shadow: 0 0 20px rgba(230,57,70,0.35) !important; | |
| transition: all 0.3s !important; | |
| } | |
| button.primary:hover { | |
| box-shadow: 0 0 35px rgba(230,57,70,0.6) !important; | |
| transform: translateY(-2px) !important; | |
| } | |
| .output-image { | |
| border: 1px solid rgba(230,57,70,0.2) !important; | |
| border-radius: 10px !important; | |
| overflow: hidden !important; | |
| } | |
| input[type="range"] { accent-color: #e63946 !important; } | |
| .info-box textarea { | |
| font-family: 'Share Tech Mono', monospace !important; | |
| font-size: 0.75rem !important; | |
| color: #39ff14 !important; | |
| background: #0a0f0a !important; | |
| border-color: rgba(57,255,20,0.2) !important; | |
| } | |
| .gr-box, .gr-panel { | |
| background: #0f1318 !important; | |
| border: 1px solid rgba(230,57,70,0.12) !important; | |
| border-radius: 10px !important; | |
| } | |
| .app-footer { | |
| text-align: center; | |
| padding: 16px 0 8px; | |
| font-family: 'Share Tech Mono', monospace; | |
| font-size: 0.7rem; | |
| color: #8a9ab0; | |
| letter-spacing: 2px; | |
| border-top: 1px solid rgba(230,57,70,0.1); | |
| margin-top: 20px; | |
| } | |
| .app-footer a { color: #e63946; text-decoration: none; } | |
| .app-footer a:hover { text-decoration: underline; } | |
| """ | |
| # ─── GRADIO UI ──────────────────────────────────────────────────────────────── | |
| with gr.Blocks(css=css, title="Pixora — Sriram") as demo: | |
| gr.HTML(""" | |
| <div class="app-header"> | |
| <div class="app-title">PIXORA</div> | |
| <div class="app-sub">SDXL-Turbo · Built by Sriram Sai</div> | |
| </div> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| prompt = gr.Textbox( | |
| label="PROMPT", | |
| placeholder="describe what you want to generate...", | |
| lines=3, | |
| ) | |
| with gr.Accordion("⚙️ ADVANCED SETTINGS", open=False): | |
| with gr.Row(): | |
| steps = gr.Slider(1, 8, value=4, step=1, label="INFERENCE STEPS (1–4 recommended)") | |
| with gr.Row(): | |
| width = gr.Slider(256, 768, value=512, step=64, label="WIDTH (px)") | |
| height = gr.Slider(256, 768, value=512, step=64, label="HEIGHT (px)") | |
| seed = gr.Number(value=-1, label="SEED (-1 = random)") | |
| generate_btn = gr.Button("▶ GENERATE IMAGE", variant="primary", size="lg") | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[prompt, steps, width, height, seed], | |
| label="💡 EXAMPLE PROMPTS — click to load", | |
| examples_per_page=5, | |
| ) | |
| with gr.Column(scale=1): | |
| output_image = gr.Image( | |
| label="GENERATED IMAGE", | |
| type="pil", | |
| elem_classes=["output-image"], | |
| height=520, | |
| ) | |
| info_text = gr.Textbox( | |
| label="STATUS", | |
| interactive=False, | |
| elem_classes=["info-box"], | |
| lines=1, | |
| ) | |
| generate_btn.click( | |
| fn=generate_image, | |
| inputs=[prompt, steps, width, height, seed], | |
| outputs=[output_image, info_text], | |
| ) | |
| prompt.submit( | |
| fn=generate_image, | |
| inputs=[prompt, steps, width, height, seed], | |
| outputs=[output_image, info_text], | |
| ) | |
| gr.HTML(""" | |
| <div class="app-footer"> | |
| Built by <a href="https://github.com/sriramsai18" target="_blank">Sriram Sai Laggisetti</a> | |
| · | |
| Model: <a href="https://huggingface.co/stabilityai/sdxl-turbo" target="_blank">stabilityai/sdxl-turbo</a> | |
| · | |
| <a href="https://www.linkedin.com/in/sriram-sai-laggisetti/" target="_blank">LinkedIn</a> | |
| </div> | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch() |