Update app.py

app.py

@@ -1,154 +1,83 @@
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+from diffusers import AutoPipelineForText2Image
 import torch
 
+# Model ve pipeline kurulumu
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
+pipeline = AutoPipelineForText2Image.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    torch_dtype=torch.float16
+).to(device)
+
+# LoRA modelini yükle
+pipeline.load_lora_weights("codermert/gamzekocc_fluxx", weight_name="lora.safetensors")
+
+def generate_image(prompt, negative_prompt, guidance_scale):
+    # TOK trigger'ını otomatik ekle
+    if not prompt.startswith("TOK"):
+        prompt = "TOK, " + prompt
+    
+    # Görseli oluştur
+    image = pipeline(
         prompt=prompt,
         negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
+        guidance_scale=float(guidance_scale)
     ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
+    
+    return image
+
+# Gradio arayüzü
+with gr.Blocks(title="Mert Baba'nın Görsel Oluşturucusu") as demo:
+    gr.Markdown("""
+    # 🎨 Mert Baba'nın AI Görsel Oluşturucusu
+    FLUX LoRA modeli ile özel görseller oluşturun!
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
                 label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+                placeholder="Görsel için açıklama girin...",
+                lines=3
             )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                value="blurry, bad quality, worst quality, jpeg artifacts",
+                lines=2
             )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+            guidance_scale = gr.Slider(
+                minimum=1,
+                maximum=20,
+                value=7.5,
+                step=0.5,
+                label="Guidance Scale"
             )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
+            generate_btn = gr.Button("Görsel Oluştur 🎨")
+        
+        with gr.Column():
+            output_image = gr.Image(label="Oluşturulan Görsel")
+    
+    # Örnek promptlar
+    gr.Examples(
+        examples=[
+            ["A striking woman lit with bi-color directional lighting poses", 
+             "blurry, bad quality, worst quality, jpeg artifacts",
+             7.5],
+            ["A beautiful portrait photo in a city", 
+             "blurry, bad quality",
+             7.5],
         ],
-        outputs=[result, seed],
+        inputs=[prompt, negative_prompt, guidance_scale],
+        outputs=output_image,
+        fn=generate_image,
+        cache_examples=True,
+    )
+    
+    # Butona tıklayınca çalışacak fonksiyon
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt, negative_prompt, guidance_scale],
+        outputs=output_image
     )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()