Update app.py

app.py

@@ -1,46 +1,53 @@
 import gradio as gr
 import spaces
+import random
 
-@spaces.GPU
-def generate_image(prompt, negative_prompt="", seed=42, width=512, height=512, guidance_scale=3.5, num_inference_steps=20, lora_scale=0.8):
+@spaces.GPU(duration=60)
+def generate_image(prompt, negative_prompt="", seed=-1, width=512, height=512, guidance_scale=7.5, num_inference_steps=50):
     # This function will be called by gr.load(), so we don't need to implement it here
     pass
 
-examples = [
-    ["d3xt3r as a camp counselor in the woods."],
-    ["d3xt3r dressed as batman"],
-    ["d3xt3r in a suit and tie"],
-]
+# Load the pre-configured Flux model interface
+flux_demo = gr.load("GenAIJake/d3xt3r")
 
-demo = gr.load("models/GenAIJake/d3xt3r")
-
-# Customize the interface
-with gr.Blocks() as new_demo:
-    gr.Markdown("# D3XT3R Dachshund Image Generator")
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# D3XT3R Dachshund Image Generator (Extended Version)")
     
     with gr.Row():
         with gr.Column():
-            for component in demo.input_components:
-                component.render()
-            
-            # Add LoRA scale slider
-            lora_scale = gr.Slider(minimum=0.0, maximum=1.0, value=0.8, step=0.05, label="LoRA Influence")
-            
-            generate_btn = gr.Button("Generate")
+            prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
+            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here...")
+            generate_button = gr.Button("Generate Image")
         
         with gr.Column():
-            for component in demo.output_components:
-                component.render()
+            seed = gr.Number(label="Seed", value=-1, precision=0, description="Set to -1 for random seed")
+            width = gr.Slider(256, 1024, value=512, step=64, label="Width")
+            height = gr.Slider(256, 1024, value=512, step=64, label="Height")
+            guidance_scale = gr.Slider(1, 20, value=7.5, step=0.5, label="Guidance Scale")
+            num_inference_steps = gr.Slider(10, 100, value=50, step=1, label="Number of Inference Steps")
+    
+    with gr.Row():
+        image_output = gr.Image(label="Generated Image")
+        seed_output = gr.Number(label="Used Seed", precision=0)
     
-    gr.Examples(examples, inputs=demo.input_components)
+    # Set up the function call using the loaded Flux model interface
+    generate_button.click(
+        flux_demo,
+        inputs=[prompt, negative_prompt, seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[image_output, seed_output]
+    )
     
-    # Update the generate button to include the lora_scale parameter
-    generate_btn.click(
-        fn=generate_image,
-        inputs=demo.input_components + [lora_scale],
-        outputs=demo.output_components
+    # Add examples
+    gr.Examples(
+        examples=[
+            ["d3xt3r as a camp counselor in the woods.", "", -1, 512, 512, 7.5, 50],
+            ["d3xt3r dressed as batman", "blurry, low quality", 42, 640, 640, 8.0, 60],
+            ["d3xt3r in a suit and tie", "", 123, 768, 512, 6.5, 40],
+        ],
+        inputs=[prompt, negative_prompt, seed, width, height, guidance_scale, num_inference_steps],
     )
 
 # Add GPUZero functionality
-new_demo.queue()
-new_demo.launch()
+demo.queue()
+demo.launch()