Add application file

app.py

@@ -1,73 +1,48 @@
 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
+from diffusers import StableDiffusionPipeline
 
-# 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")
+def generate_image(prompt, guidance_scale=7.5, num_steps=30, lora_scale=0.8):
+    try:
+        # Initialize the pipeline
+        pipe = StableDiffusionPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5",
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+        )
+        
+        # Load LoRA weights
+        pipe.load_lora_weights("Scalino84/my-flux-face-v2")
+        
+        if torch.cuda.is_available():
+            pipe = pipe.to("cuda")
         
-        with gr.Column():
-            output = gr.Image(label="Generated Image")
+        # Generate image
+        image = pipe(
+            prompt=prompt,
+            num_inference_steps=int(num_steps),
+            guidance_scale=float(guidance_scale),
+        ).images[0]
+        
+        return image
     
-    generate.click(
-        fn=generate_image,
-        inputs=[prompt, guidance, steps, lora_scale],
-        outputs=output
-    )
+    except Exception as e:
+        print(f"Error: {str(e)}")
+        raise gr.Error(str(e))
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Prompt", value="a photo of xyz person, professional headshot"),
+        gr.Slider(minimum=1, maximum=20, value=7.5, label="Guidance Scale"),
+        gr.Slider(minimum=20, maximum=100, value=30, label="Number of Steps"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.8, label="LoRA Scale")
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="Flux Face Generator",
+    description="Generate images using your custom LoRA model"
+)
 
-demo.launch()
+# Launch the interface
+interface.launch()