Add application file

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+from diffusers import StableDiffusionPipeline
+import torch
+from huggingface_hub import HfFolder
+
+def generate_image(prompt, guidance_scale, num_steps, lora_scale):
+    # Lade das Base Model
+    pipe = StableDiffusionPipeline.from_pretrained(
+        "runwayml/stable-diffusion-v1-5",
+        torch_dtype=torch.float16
+    ).to("cuda")
+    
+    # Lade dein LoRA
+    pipe.load_lora_weights(
+        "Scalino84/my-flux-face-v2",
+        weight_name="flux_train_replicate.safetensors"
+    )
+    
+    # Generiere das Bild
+    image = pipe(
+        prompt=prompt,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        cross_attention_kwargs={"scale": lora_scale}
+    ).images[0]
+    
+    return image
+
+# Erstelle das Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Flux Face Generator")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+                value="a photo of xyz person, professional headshot",
+                lines=3
+            )
+            guidance = gr.Slider(
+                label="Guidance Scale",
+                minimum=1,
+                maximum=20,
+                value=7.5,
+                step=0.5
+            )
+            steps = gr.Slider(
+                label="Inference Steps",
+                minimum=20,
+                maximum=100,
+                value=30,
+                step=1
+            )
+            lora_scale = gr.Slider(
+                label="LoRA Scale",
+                minimum=0.1,
+                maximum=1.0,
+                value=0.8,
+                step=0.1
+            )
+            generate = gr.Button("Generate Image")
+        
+        with gr.Column():
+            output = gr.Image(label="Generated Image")
+    
+    generate.click(
+        fn=generate_image,
+        inputs=[prompt, guidance, steps, lora_scale],
+        outputs=output
+    )
+
+demo.launch()
+