app.py

import torch
import gradio as gr
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
from PIL import Image

# Model yükleme
print("Modeller yükleniyor...")
model_id = "runwayml/stable-diffusion-v1-5"

# Text-to-Image Pipeline
txt2img_pipe = StableDiffusionPipeline.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    safety_checker=None
)
txt2img_pipe.scheduler = DPMSolverMultistepScheduler.from_config(txt2img_pipe.scheduler.config)

# Image-to-Image Pipeline
img2img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    safety_checker=None
)
img2img_pipe.scheduler = DPMSolverMultistepScheduler.from_config(img2img_pipe.scheduler.config)

if torch.cuda.is_available():
    txt2img_pipe = txt2img_pipe.to("cuda")
    txt2img_pipe.enable_attention_slicing()
    img2img_pipe = img2img_pipe.to("cuda")
    img2img_pipe.enable_attention_slicing()
    print("GPU kullanılıyor ✅")
else:
    print("CPU kullanılıyor (yavaş olabilir)")

# Text-to-Image fonksiyonu
def generate_image(prompt, negative_prompt, num_steps, guidance_scale, width, height, seed):
    """Text'ten görüntü üretir"""
    device = "cuda" if torch.cuda.is_available() else "cpu"
    generator = torch.Generator(device).manual_seed(int(seed))
    
    image = txt2img_pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=int(num_steps),
        guidance_scale=guidance_scale,
        width=int(width),
        height=int(height),
        generator=generator
    ).images[0]
    
    return image

# Image-to-Image fonksiyonu
def transform_image(init_image, prompt, negative_prompt, num_steps, guidance_scale, strength, seed):
    """Mevcut görüntüyü dönüştürür"""
    if init_image is None:
        return None
    
    device = "cuda" if torch.cuda.is_available() else "cpu"
    generator = torch.Generator(device).manual_seed(int(seed))
    
    # Görüntüyü yeniden boyutlandır
    init_image = init_image.resize((512, 512))
    
    image = img2img_pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        image=init_image,
        num_inference_steps=int(num_steps),
        guidance_scale=guidance_scale,
        strength=strength,
        generator=generator
    ).images[0]
    
    return image

# Gradio UI
with gr.Blocks(title="🎨 Stable Diffusion Generator", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🎨 Stable Diffusion Görüntü Üretici
    Text-to-Image ve Image-to-Image modelleri ile hayal gücünüzü görüntüye dönüştürün!
    
    **Model:** Stable Diffusion v1.5  
    **Geliştirici:** Mehmet Tuğrul Kaya
    """)
    
    # TAB YAPISI
    with gr.Tabs():
        # TAB 1: TEXT-TO-IMAGE
        with gr.Tab("📝 Text-to-Image"):
            with gr.Row():
                with gr.Column(scale=1):
                    t2i_prompt = gr.Textbox(
                        label="✍️ Prompt",
                        placeholder="A beautiful sunset over mountains, digital art, highly detailed",
                        lines=3
                    )
                    
                    t2i_negative = gr.Textbox(
                        label="🚫 Negative Prompt",
                        placeholder="blurry, low quality, distorted",
                        lines=2,
                        value="blurry, low quality, ugly, distorted, deformed"
                    )
                    
                    with gr.Accordion("⚙️ Gelişmiş Ayarlar", open=False):
                        t2i_steps = gr.Slider(10, 50, value=25, step=1, label="🔄 Steps")
                        t2i_guidance = gr.Slider(1, 20, value=7.5, step=0.5, label="🎯 Guidance Scale")
                        
                        with gr.Row():
                            t2i_width = gr.Slider(256, 768, value=512, step=64, label="📐 Width")
                            t2i_height = gr.Slider(256, 768, value=512, step=64, label="📏 Height")
                        
                        t2i_seed = gr.Number(label="🌱 Seed", value=42, precision=0)
                    
                    t2i_btn = gr.Button("🎨 Görüntü Üret", variant="primary", size="lg")
                
                with gr.Column(scale=1):
                    t2i_output = gr.Image(label="🖼️ Üretilen Görüntü", type="pil")
            
            gr.Markdown("### 💡 Örnek Promptlar:")
            gr.Examples(
                examples=[
                    ["A majestic lion in savanna at sunset, photorealistic, 8k", "blurry", 25, 7.5, 512, 512, 42],
                    ["Futuristic cyberpunk city, neon lights, night", "blurry, dark", 30, 8.0, 512, 512, 123],
                    ["Cute cat wearing wizard hat, digital art, kawaii", "scary, ugly", 25, 7.5, 512, 512, 456],
                    ["Beautiful Turkish tea glass, Istanbul Bosphorus view, sunset", "blurry", 25, 7.5, 512, 512, 789],
                ],
                inputs=[t2i_prompt, t2i_negative, t2i_steps, t2i_guidance, t2i_width, t2i_height, t2i_seed],
                outputs=t2i_output,
                fn=generate_image,
                cache_examples=False
            )
            
            t2i_btn.click(
                fn=generate_image,
                inputs=[t2i_prompt, t2i_negative, t2i_steps, t2i_guidance, t2i_width, t2i_height, t2i_seed],
                outputs=t2i_output
            )
        
        # TAB 2: IMAGE-TO-IMAGE
        with gr.Tab("🖼️ Image-to-Image"):
            with gr.Row():
                with gr.Column(scale=1):
                    i2i_input = gr.Image(
                        label="📤 Başlangıç Görüntüsü Yükle",
                        type="pil",
                        sources=["upload", "clipboard"]
                    )
                    
                    i2i_prompt = gr.Textbox(
                        label="✍️ Dönüşüm Prompt'u",
                        placeholder="Turn this into a watercolor painting",
                        lines=3
                    )
                    
                    i2i_negative = gr.Textbox(
                        label="🚫 Negative Prompt",
                        placeholder="blurry, low quality",
                        lines=2,
                        value="blurry, low quality, distorted"
                    )
                    
                    with gr.Accordion("⚙️ Gelişmiş Ayarlar", open=False):
                        i2i_steps = gr.Slider(10, 50, value=30, step=1, label="🔄 Steps")
                        i2i_guidance = gr.Slider(1, 20, value=7.5, step=0.5, label="🎯 Guidance Scale")
                        i2i_strength = gr.Slider(
                            0.0, 1.0, value=0.75, step=0.05,
                            label="💪 Strength (Ne kadar değişsin?)"
                        )
                        i2i_seed = gr.Number(label="🌱 Seed", value=42, precision=0)
                    
                    i2i_btn = gr.Button("🔄 Görüntüyü Dönüştür", variant="primary", size="lg")
                
                with gr.Column(scale=1):
                    i2i_output = gr.Image(label="✨ Dönüştürülmüş Görüntü", type="pil")
            
            gr.Markdown("""
            ### 💡 Image-to-Image İpuçları:
            - **Strength 0.3-0.5**: Küçük değişiklikler, orijinale yakın
            - **Strength 0.6-0.8**: Orta seviye değişiklik (önerilen)
            - **Strength 0.9-1.0**: Büyük değişiklikler, orijinalden uzak
            
            ### 🎨 Örnek Kullanımlar:
            - Fotoğrafı resme dönüştür: "oil painting style"
            - Stil değiştir: "anime style", "cyberpunk style"
            - Renklendirme: "colorful, vibrant colors"
            - Sezon değiştir: "winter scene with snow"
            """)
            
            i2i_btn.click(
                fn=transform_image,
                inputs=[i2i_input, i2i_prompt, i2i_negative, i2i_steps, i2i_guidance, i2i_strength, i2i_seed],
                outputs=i2i_output
            )
    
    # GENEL BİLGİLER
    gr.Markdown("""
    ---
    ### 📝 Genel İpuçları:
    - **Prompt**: Detaylı ve açıklayıcı olun (örn: "photorealistic", "8k", "detailed" ekleyin)
    - **Negative Prompt**: İstemediğiniz özellikleri ekleyin
    - **Steps**: 25-30 arası optimal
    - **Guidance Scale**: 7-8 arası genelde en iyi sonuçları verir
    
    ### 🔗 Bağlantılar:
    - [GitHub](https://github.com/mtkaya)
    - [Hugging Face](https://huggingface.co/tugrulkaya)
    """)

if __name__ == "__main__":
    demo.launch()