Update app.py

app.py

@@ -1,154 +1,70 @@
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
-
-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(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).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(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+from diffusers import AutoPipelineForText2Image
+
+def generate_image(prompt, use_tok=True):
+    # Eğer use_tok seçeneği işaretlendiyse, prompt'a TOK ekle
+    if use_tok and "TOK" not in prompt:
+        prompt = f"TOK {prompt}"
+    
+    # Pipeline oluştur
+    try:
+        pipeline = AutoPipelineForText2Image.from_pretrained(
+            "black-forest-labs/FLUX.1-dev", 
+            torch_dtype=torch.float16
+        ).to("cuda" if torch.cuda.is_available() else "cpu")
+        
+        # LoRA ağırlıklarını yükle
+        pipeline.load_lora_weights(
+            "codermert/malikafinal", 
+            weight_name="lora.safetensors"
+        )
+        
+        # Görüntü oluştur
+        image = pipeline(prompt).images[0]
+        return image, f"Oluşturulan prompt: {prompt}"
+    except Exception as e:
+        return None, f"Hata oluştu: {str(e)}"
+
+# Gradio arayüzü
+with gr.Blocks(title="Malika - FLUX Text-to-Image") as demo:
+    gr.Markdown("# Malika - FLUX.1 Text-to-Image Modeliyle Görüntü Oluşturucu")
+    gr.Markdown("Bu uygulama, codermert/malikafinal modelini kullanarak metinden görüntü oluşturur.")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(
+                label="Prompt", 
+                placeholder="Görüntü için prompt yazın...",
+                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,
+            tok_checkbox = gr.Checkbox(
+                label="Otomatik TOK Ekle", 
+                value=True, 
+                info="İşaretliyse prompt'a otomatik olarak TOK ekler"
             )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            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,
-        ],
-        outputs=[result, seed],
+            generate_btn = gr.Button("Görüntü Oluştur", variant="primary")
+        
+        with gr.Column():
+            image_output = gr.Image(label="Oluşturulan Görüntü")
+            prompt_used = gr.Textbox(label="Kullanılan Prompt")
+    
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt_input, tok_checkbox],
+        outputs=[image_output, prompt_used]
     )
-
-if __name__ == "__main__":
-    demo.launch()
+    
+    gr.Markdown("""
+    ## Kullanım Tavsiyeleri
+    - Eğer model için özel bir trigger sözcüğü gerekliyse 'TOK' seçeneğini işaretli bırakın
+    - Daha gerçekçi sonuçlar için "photorealistic, 8K, detailed" gibi ifadeler ekleyebilirsiniz
+    - Örnek: "portrait of a woman with blue eyes, photorealistic, 8K"
+    
+    ## Model Bilgisi
+    Bu uygulama codermert/malikafinal modelini kullanmaktadır.
+    Base model: black-forest-labs/FLUX.1-dev
+    """)
+
+# Arayüzü başlat
+demo.launch()