Update app.py

app.py

@@ -1,14 +1,8 @@
 import gradio as gr
 import numpy as np
 import random
-from diffusers import DiffusionPipeline
 import torch
-
-# Load the pipeline
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
-pipe.load_lora_weights("EvanZhouDev/open-genmoji")
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-pipe = pipe.to(device)
+from diffusers import DiffusionPipeline
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
@@ -22,7 +16,17 @@ def infer(
     height,
     guidance_scale,
     num_inference_steps,
+    user_info,
 ):
+    if user_info is None:
+        return "Please log in to generate images.", None
+
+    # Load the pipeline only when this function is called
+    pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
+    pipe.load_lora_weights("EvanZhouDev/open-genmoji")
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    pipe = pipe.to(device)
+
     # Handle seed randomization
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -60,8 +64,6 @@ with gr.Blocks(css=css) as demo:
 
         # Add the LoginButton
         login_button = gr.LoginButton()
-
-        # Placeholder for user profile information
         user_info = gr.State()
 
         # Function to update user_info upon login
@@ -73,14 +75,8 @@ with gr.Blocks(css=css) as demo:
         # Update user_info when login_button is clicked
         login_button.click(update_user_info, inputs=None, outputs=user_info)
 
-        # Check if user is authenticated before displaying the main interface
-        def check_auth(user_info):
-            if user_info is None:
-                return gr.Error("Please log in to access the application.")
-            return gr.update(visible=True)
-
-        # Main interface components
-        with gr.Row(visible=False) as main_interface:
+        # Main interface
+        with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -91,56 +87,53 @@ with gr.Blocks(css=css) as demo:
             run_button = gr.Button("Run", scale=0, variant="primary")
             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=True,
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=True,
+            )
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                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=512,
                 )
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=7.5,
+                )
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
                     step=1,
-                    value=0,
+                    value=25,
                 )
-                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=512,
-                    )
-                    height = gr.Slider(
-                        label="Height",
-                        minimum=256,
-                        maximum=MAX_IMAGE_SIZE,
-                        step=32,
-                        value=512,
-                    )
-                with gr.Row():
-                    guidance_scale = gr.Slider(
-                        label="Guidance scale",
-                        minimum=0.0,
-                        maximum=10.0,
-                        step=0.1,
-                        value=7.5,
-                    )
-                    num_inference_steps = gr.Slider(
-                        label="Number of inference steps",
-                        minimum=1,
-                        maximum=50,
-                        step=1,
-                        value=25,
-                    )
-
-            gr.Examples(examples=examples, inputs=[prompt])
-
-        # Display main interface if authenticated
-        user_info.change(check_auth, inputs=user_info, outputs=main_interface)
+
+        gr.Examples(examples=examples, inputs=[prompt])
 
         # Run inference when run_button is clicked
         run_button.click(
@@ -154,6 +147,7 @@ with gr.Blocks(css=css) as demo:
                 height,
                 guidance_scale,
                 num_inference_steps,
+                user_info,
             ],
             outputs=[result, seed],
         )