Update app.py

app.py

@@ -1,68 +1,31 @@
+from diffusers import StableDiffusionImg2ImgPipeline
 import torch
-import cv2
+import gradio as gr
 from PIL import Image
 import numpy as np
-import gradio as gr
-
-from diffusers import (
-    StableDiffusionControlNetImg2ImgPipeline,
-    ControlNetModel,
-    UniPCMultistepScheduler,
-)
-from transformers import AutoTokenizer  # bazı yeni modellerde gerekli olabilir
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-dtype = torch.float16 if device == "cuda" else torch.float32
 
-controlnet = ControlNetModel.from_pretrained(
-    "lllyasviel/sd-controlnet-canny", torch_dtype=dtype
-)
+device = "cpu"
+dtype = torch.float32
 
-pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
-    controlnet=controlnet,
     torch_dtype=dtype,
-    safety_checker=None  # Hata almamak için bazı modellerde gerekir
+    safety_checker=None
 )
-
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 pipe.to(device)
 
-def get_prompt_settings(magnitude):
-    if 3 <= magnitude < 5:
-        return ("building with slight cracks after magnitude 3 to 5 earthquake, minor damage, realistic",
-                "blurry, bad quality, low resolution, destruction, ruins, collapse", 9.0, 0.85, 0.5)
-    elif 5 <= magnitude < 6:
-        return ("building with visible cracks and slight wall damage after magnitude 5 to 6 earthquake, realistic",
-                "blurry, bad quality, low resolution, destroyed building", 10.0, 0.75, 0.6)
-    elif 6 <= magnitude < 7:
-        return ("building with large cracks, broken windows, partial wall destruction after magnitude 6 to 7 earthquake, realistic",
-                "blurry, bad quality, low resolution", 11.0, 0.6, 0.7)
-    elif 7 <= magnitude < 8:
-        return ("building heavily damaged, collapsed sections, broken walls, rubble, after magnitude 7 to 8 earthquake, realistic",
-                "blurry, bad quality, low resolution, intact building", 12.0, 0.27, 0.8)
-    elif 8 <= magnitude <= 9:
-        return ("building completely destroyed, full collapse, ruins, debris, after magnitude above 8 earthquake, realistic",
-                "blurry, bad quality, low resolution, intact building", 13.0, 0.12, 0.9)
-    else:
-        raise ValueError("Magnitude must be between 3 and 9.")
-
 def process(image, magnitude):
-    image = image.resize((512, 512))
-    edges = cv2.Canny(np.array(image), 100, 200)
-    control_image = Image.fromarray(edges)
-
-    prompt, neg_prompt, guidance, cond_scale, strength = get_prompt_settings(magnitude)
-
+    prompt = f"building slightly damaged after magnitude {magnitude} earthquake, realistic"
+    neg_prompt = "blurry, low quality, bad composition"
+    
+    image = image.resize((384, 384))  # küçük boyut daha hızlı
     result = pipe(
         prompt=prompt,
         negative_prompt=neg_prompt,
         image=image,
-        control_image=control_image,
-        strength=strength,
-        guidance_scale=guidance,
-        controlnet_conditioning_scale=cond_scale,
-        num_inference_steps=40
+        strength=0.5,
+        guidance_scale=7.5,
+        num_inference_steps=20  # az adım
     ).images[0]
 
     return result
@@ -70,12 +33,12 @@ def process(image, magnitude):
 demo = gr.Interface(
     fn=process,
     inputs=[
-        gr.Image(type="pil", label="Bina Fotoğrafı Yükle"),
+        gr.Image(type="pil", label="Bina Görseli"),
         gr.Slider(3, 9, step=0.1, label="Deprem Şiddeti")
     ],
-    outputs=gr.Image(type="pil", label="Depremli Görsel"),
-    title="Earthquake Damage Visualizer",
-    description="Yüklediğiniz bina görselini deprem şiddetine göre hasarlı hale getirir."
+    outputs=gr.Image(type="pil"),
+    title="Earthquake Visualizer (CPU Mode)",
+    description="Deprem hasarını CPU üzerinde hızlı simüle eder."
 )
 
 if __name__ == "__main__":