Create app_1.py

app_1.py

@@ -0,0 +1,68 @@
+import torch
+from diffusers import FluxPipeline
+import gradio as gr
+
+def generate_flux_image(
+    prompt, 
+    width=512, 
+    height=512, 
+    num_inference_steps=50,
+    guidance_scale=7.5,
+    seed=None
+):
+    # Zufallsgenerator-Initialisierung
+    if seed is None:
+        generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu")
+    else:
+        generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
+    
+    # Pipeline laden
+    pipeline = FluxPipeline.from_pretrained(
+        "improvements/flux",  # Passen Sie den Pfad an
+        torch_dtype=torch.float16
+    )
+    
+    # Überprüfen und ggf. auf GPU verschieben
+    if torch.cuda.is_available():
+        pipeline = pipeline.to("cuda")
+    
+    # Bildgenerierung
+    image = pipeline(
+        prompt=prompt,
+        width=width,
+        height=height,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator=generator
+    ).images[0]
+    
+    return image
+
+# Gradio-Interface
+def create_gradio_interface():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            prompt = gr.Textbox(label="Bildprompt")
+            width = gr.Slider(minimum=64, maximum=2048, value=512, label="Breite")
+            height = gr.Slider(minimum=64, maximum=2048, value=512, label="Höhe")
+        
+        with gr.Row():
+            steps = gr.Slider(minimum=10, maximum=100, value=50, label="Inference Steps")
+            guidance = gr.Slider(minimum=1, maximum=15, value=7.5, label="Guidance Scale")
+            seed = gr.Number(label="Seed (optional)", precision=0)
+        
+        generate_btn = gr.Button("Bild generieren")
+        output_image = gr.Image(label="Generiertes Bild")
+        
+        generate_btn.click(
+            fn=generate_flux_image, 
+            inputs=[prompt, width, height, steps, guidance, seed],
+            outputs=output_image
+        )
+    
+    return demo
+
+# Interface starten
+if __name__ == "__main__":
+    interface = create_gradio_interface()
+    interface.launch(share=True)