Update app.py

app.py

@@ -5,7 +5,6 @@ import random
 import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
-from io import BytesIO
 
 token = os.getenv('HUGGINGFACE_TOKEN')
 
@@ -41,13 +40,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         generator = generator
     ).images[0] 
     
-    # Convert image to base64
-    buffered = BytesIO()
-    image.save(buffered, format="WEBP")  # Save image to buffer in WebP format
-    image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")  # Convert buffer to base64 string
-    
-    # Return the base64 image string and seed
-    return f"data:image/webp;base64,{image_base64}", seed
+    return image, seed
 
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
@@ -63,12 +56,14 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
+    
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
         # Text-to-Image Gradio Template
         """)
         
         with gr.Row():
+            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -76,18 +71,20 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
+            
             run_button = gr.Button("Run", scale=0)
         
-        # Use gr.Textbox for base64-encoded image output
-        result = gr.Textbox(label="Result (Base64 Image)", show_label=False, interactive=False)
+        result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
+            
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
                 visible=False,
             )
+            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -95,48 +92,54 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
+            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            
             with gr.Row():
+                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Default values
+                    value=1024, #Replace with defaults that work for your model
                 )
+                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Default values
+                    value=1024, #Replace with defaults that work for your model
                 )
+            
             with gr.Row():
+                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=7.5,  # Default value
+                    value=0.0, #Replace with defaults that work for your model
                 )
+                
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=35,  # Default value
+                    value=2, #Replace with defaults that work for your model
                 )
         
         gr.Examples(
-            examples=examples,
-            inputs=[prompt]
+            examples = examples,
+            inputs = [prompt]
         )
-
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result, seed]  # Seed is optional, result now holds base64 image string
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result, seed]
     )
 
 demo.queue().launch()