Update app.py

app.py

@@ -1,39 +1,127 @@
 import gradio as gr
+import numpy as np
+import random
+import spaces
 import torch
-from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
-from safetensors.torch import load_file
+from diffusers import DiffusionPipeline
 
-model_id = "runwayml/stable-diffusion-v1-5"
-lora_path = "https://huggingface.co/codermert/model_malika/resolve/main/sarah-lora.safetensors"
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = DiffusionPipeline.from_pretrained("codermert/zehra_flux", torch_dtype=dtype).to(device)
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
 
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda")
+@spaces.GPU()
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, num_images=4, progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    generator = torch.Generator().manual_seed(seed)
+    images = []
+    
+    for _ in range(num_images):
+        image = pipe(
+            prompt=prompt,
+            width=width,
+            height=height,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
+            guidance_scale=0.0
+        ).images[0]
+        images.append(image)
+        # Her görsel için farklı seed kullan
+        seed = random.randint(0, MAX_SEED)
+        generator = torch.Generator().manual_seed(seed)
+    
+    return images, seed
 
-# LoRA dosyasını yükle
-state_dict = load_file(lora_path)
-pipe.unet.load_attn_procs(state_dict)
+examples = [
+    "a tiny astronaut hatching from an egg on the moon",
+    "a cat holding a sign that says hello world",
+    "an anime illustration of a wiener schnitzel",
+]
 
-def generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps):
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps
-    ).images[0]
-    return image
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 900px;
+}
+.generated-images {
+    display: grid;
+    grid-template-columns: repeat(2, 1fr);
+    gap: 10px;
+}
+"""
 
-iface = gr.Interface(
-    fn=generate_image,
-    inputs=[
-        gr.Textbox(label="Prompt"),
-        gr.Textbox(label="Negative Prompt"),
-        gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale", value=7.5),
-        gr.Slider(minimum=1, maximum=100, step=1, label="Number of Inference Steps", value=50)
-    ],
-    outputs=gr.Image(label="Generated Image"),
-    title="Stable Diffusion with LoRA",
-    description="Generate images using Stable Diffusion v1.5 with a custom LoRA model."
-)
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("""# Zehra Flux Image Generator
+        4 farklı görsel üreten AI görsel oluşturucu
+        """)
+        
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Görseliniz için prompt girin",
+                container=False,
+            )
+            run_button = gr.Button("Oluştur", scale=0)
+        
+        # 4 görsel için grid layout
+        with gr.Row(elem_classes="generated-images"):
+            results = [gr.Image(label=f"Sonuç {i+1}", show_label=True) for i in range(4)]
+        
+        with gr.Accordion("Gelişmiş Ayarlar", open=False):
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            
+            randomize_seed = gr.Checkbox(label="Rastgele seed", value=True)
+            
+            with gr.Row():
+                width = gr.Slider(
+                    label="Genişlik",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+                
+                height = gr.Slider(
+                    label="Yükseklik",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+            
+            num_inference_steps = gr.Slider(
+                label="Inference adım sayısı",
+                minimum=1,
+                maximum=50,
+                step=1,
+                value=4,
+            )
+        
+        gr.Examples(
+            examples=examples,
+            fn=infer,
+            inputs=[prompt],
+            outputs=[*results, seed],
+            cache_examples="lazy"
+        )
+        
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
+        outputs=[*results, seed]
+    )
 
-iface.launch()
+demo.launch()