Create app.py

app.py

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+import torch
+import gradio as gr
+from diffusers import DiffusionPipeline
+import diffusers
+import numpy as np
+import random
+
+# =========================================================
+# MODEL CONFIGURATION
+# =========================================================
+MAX_SEED = np.iinfo(np.int32).max
+# Turbo model ဖြစ်၍ CPU ပေါ်တွင် 1-4 steps သာ သုံးရန် အကြံပြုပါသည် (မြန်ဆန်စေရန်)
+DEFAULT_STEPS = 4 
+
+# =========================================================
+# LOAD PIPELINE (CPU Optimized)
+# =========================================================
+print("Loading Z-Image-Turbo pipeline to CPU...")
+
+# CPU ပေါ်တွင် Error ကင်းစေရန် float32 သုံးရပါမည်
+pipe = DiffusionPipeline.from_pretrained(
+    "Tongyi-MAI/Z-Image-Turbo",
+    torch_dtype=torch.float32,
+    low_cpu_mem_usage=True
+)
+
+# Memory ချွေတာရန် (CPU အတွက် အရေးကြီးပါသည်)
+pipe.enable_attention_slicing()
+pipe.to("cpu")
+
+# =========================================================
+# PROMPT EXAMPLES (User ပေးထားသော list ထဲမှ အချို့ကို နမူနာယူထားသည်)
+# =========================================================
+prompt_examples = [
+    "Moody mature anime scene of two lovers kissing under neon rain, sensual atmosphere",
+    "A woman in a blue hanbok sits on a wooden floor, gazing out of a window.",
+    "A traditional Japanese onsen, with steam rising, a young woman in a colorful kimono."
+]
+
+def get_random_prompt():
+    return random.choice(prompt_examples)
+
+# =========================================================
+# IMAGE GENERATOR
+# =========================================================
+def generate_image(prompt, height, width, num_inference_steps, seed, randomize_seed, num_images):
+    if not prompt:
+        raise gr.Error("Please enter a prompt.")
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    # CPU ပေါ်တွင် RAM မပြည့်စေရန် ပုံအရေအတွက်ကို ကန့်သတ်ခြင်း
+    num_images = min(max(1, int(num_images)), 2)
+
+    generator = torch.Generator("cpu").manual_seed(int(seed))
+
+    # CPU inference ဖြစ်၍ အချိန်ကြာနိုင်ကြောင်း သတိပြုပါ
+    result = pipe(
+        prompt=prompt,
+        height=int(height),
+        width=int(width),
+        num_inference_steps=int(num_inference_steps),
+        guidance_scale=0.0,
+        generator=generator,
+        max_sequence_length=512, # CPU အတွက် length လျှော့ထားခြင်းက ပိုမြန်စေသည်
+        num_images_per_prompt=num_images,
+        output_type="pil",
+    )
+
+    return result.images, seed
+
+# ============================================
+# 🎨 UI Design (Original CSS and Layout)
+# ============================================
+css = """
+/* User ပေးထားသော CSS ကို ဤနေရာတွင် ထည့်သွင်းထားသည် */
+@import url('https://fonts.googleapis.com/css2?family=Bangers&family=Comic+Neue:wght@400;700&display=swap');
+.gradio-container { background-color: #FEF9C3 !important; font-family: 'Comic Neue', cursive !important; }
+.header-text h1 { font-family: 'Bangers', cursive !important; text-align: center; font-size: 3rem; }
+.warning-box { background: #FEF3C7; border: 3px solid #F59E0B; padding: 10px; text-align: center; }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# 🖼️ AI Image Generator (CPU Version)", elem_classes="header-text")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="✏️ Prompt", lines=3)
+            random_button = gr.Button("🎲 RANDOM PROMPT")
+            
+            with gr.Row():
+                height_input = gr.Slider(256, 1024, 512, step=64, label="Height")
+                width_input = gr.Slider(256, 1024, 512, step=64, label="Width")
+            
+            num_images_input = gr.Slider(1, 2, 1, step=1, label="Images Count")
+            
+            with gr.Accordion("⚙️ Settings", open=False):
+                steps_slider = gr.Slider(1, 10, DEFAULT_STEPS, step=1, label="Steps (Keep low for CPU)")
+                seed_input = gr.Number(value=42, label="Seed")
+                randomize_seed_checkbox = gr.Checkbox(label="Randomize Seed", value=True)
+
+            generate_button = gr.Button("✨ GENERATE", variant="primary")
+            
+        with gr.Column():
+            output_gallery = gr.Gallery(label="Output", columns=1)
+            used_seed_output = gr.Number(label="Seed Used")
+
+    random_button.click(fn=get_random_prompt, outputs=[prompt_input])
+    generate_button.click(
+        fn=generate_image,
+        inputs=[prompt_input, height_input, width_input, steps_slider, seed_input, randomize_seed_checkbox, num_images_input],
+        outputs=[output_gallery, used_seed_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()
+