Update app.py

app.py

@@ -10,7 +10,6 @@ from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = int(os.getenv('MAX_IMAGE_SIZE', '1024'))
-SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')
 
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 if torch.cuda.is_available():
@@ -45,12 +44,8 @@ def generate(prompt: str,
              width: int = 1024,
              height: int = 1024,
              guidance_scale: float = 1.0,
-             num_inference_steps: int = 6,
-             secret_token: str = '') -> PIL.Image.Image:
-    if secret_token != SECRET_TOKEN:
-        raise gr.Error(
-            f'Invalid secret token. Please fork the original space if you want to use it for yourself.')
-        
+             num_inference_steps: int = 6) -> PIL.Image.Image:
+
     generator = torch.Generator().manual_seed(seed)
 
     if not use_negative_prompt:
@@ -66,68 +61,63 @@ def generate(prompt: str,
                 output_type='pil').images[0]
 
 with gr.Blocks() as demo:
-    gr.HTML("""
-    <div style="z-index: 100; position: fixed; top: 0px; right: 0px; left: 0px; bottom: 0px; width: 100%; height: 100%; background: white; display: flex; align-items: center; justify-content: center; color: black;">
-        <div style="text-align: center; color: black;">
-            <p style="color: black;">This space is a REST API to programmatically generate images using LCM-SSD-1B.</p>
-            <p style="color: black;">It is not meant to be directly used through a user interface, but using code and an access key.</p>
-        </div>
-    </div>""")
-    secret_token = gr.Text(
-        label='Secret Token',
-        max_lines=1,
-        placeholder='Enter your secret token',
-    )
-    prompt = gr.Text(
-        label='Prompt',
-        show_label=False,
-        max_lines=1,
-        placeholder='Enter your prompt',
-        container=False,
-    )
-    result = gr.Image(label='Result', show_label=False)
+    with gr.Row():
+        with gr.Row():
+            prompt = gr.Text(
+                label='Prompt',
+                show_label=False,
+                max_lines=1,
+                placeholder='Enter your prompt',
+                container=False,
+            )
+            run_button = gr.Button('Run', scale=0)
+        result = gr.Image(label='Result', show_label=False)
+        with gr.Accordion('Advanced options', open=False):
+            with gr.Row():
+                use_negative_prompt = gr.Checkbox(label='Use negative prompt',
+                                                  value=False)
+            negative_prompt = gr.Text(
+                label='Negative prompt',
+                max_lines=1,
+                placeholder='Enter a negative prompt',
+                visible=False,
+            )
 
-    use_negative_prompt = gr.Checkbox(label='Use negative prompt', value=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,
-                    maximum=MAX_SEED,
+            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=1024,
+                )
+                height = gr.Slider(
+                    label='Height',
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label='Guidance scale',
+                    minimum=1,
+                    maximum=20,
+                    step=0.1,
+                    value=5.0)
+                num_inference_steps = gr.Slider(
+                    label='Number of inference steps',
+                    minimum=2,
+                    maximum=50,
                     step=1,
-                    value=0)
-    randomize_seed = gr.Checkbox(label='Randomize seed', value=True)
+                    value=6)
 
-    width = gr.Slider(
-        label='Width',
-        minimum=256,
-        maximum=MAX_IMAGE_SIZE,
-        step=32,
-        value=1024,
-    )
-    height = gr.Slider(
-        label='Height',
-        minimum=256,
-        maximum=MAX_IMAGE_SIZE,
-        step=32,
-        value=1024,
-    )
-    guidance_scale = gr.Slider(
-        label='Guidance scale',
-        minimum=1,
-        maximum=20,
-        step=0.1,
-        value=1.0)
-    num_inference_steps = gr.Slider(
-        label='Number of inference steps',
-        minimum=2,
-        maximum=40,
-        step=1,
-        value=6)
-    
     use_negative_prompt.change(
         fn=lambda x: gr.update(visible=x),
         inputs=use_negative_prompt,
@@ -159,5 +149,29 @@ with gr.Blocks() as demo:
         outputs=result,
         api_name='run',
     )
+    negative_prompt.submit(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=generate,
+        inputs=inputs,
+        outputs=result,
+        api_name=False,
+    )
+    run_button.click(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=generate,
+        inputs=inputs,
+        outputs=result,
+        api_name=False,
+    )
 
 demo.queue(max_size=6).launch()