Update app.py

app.py

@@ -15,10 +15,6 @@ torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
-#pipe.load_lora_weights("prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA", weight_name="SD3.5-Turbo-Realism-2.0-LoRA.safetensors")
-#trigger_word = "Turbo Realism"  
-#pipe.fuse_lora(lora_scale=1.0)
-
 # Constants
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
@@ -58,6 +54,7 @@ grid_sizes = {
     "3x2": (3, 2),
     "1x1": (1, 1),
 }
+
 @spaces.GPU
 def infer(
      prompt,
@@ -72,21 +69,17 @@ def infer(
      grid_size="1x1",
      progress=gr.Progress(track_tqdm=True),
  ):
-     # Apply seed
      if randomize_seed:
          seed = random.randint(0, MAX_SEED)
      generator = torch.Generator().manual_seed(seed)
 
-     # Style formatting
      selected_style = next(s for s in style_list if s["name"] == style)
      styled_prompt = selected_style["prompt"].format(prompt=prompt)
      styled_negative = selected_style["negative_prompt"] or negative_prompt
 
-     # Grid calculation
      grid_x, grid_y = grid_sizes.get(grid_size, (1, 1))
      num_images = grid_x * grid_y
 
-     # Inference
      output = pipe(
          prompt=styled_prompt,
          negative_prompt=styled_negative,
@@ -98,7 +91,6 @@ def infer(
          num_images_per_prompt=num_images,
      )
 
-     # Combine into grid
      grid_img = Image.new('RGB', (width * grid_x, height * grid_y))
      for i, img in enumerate(output.images[:num_images]):
          x = (i % grid_x) * width
@@ -116,71 +108,69 @@ examples = [
 
 css = '''
 .gradio-container {
-    max-width: 599px !important;
-    margin: 0 auto !important;
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    justify-content: center;
+    max-width: 100%;
+    margin: 0 auto;
 }
 h1 { text-align: center; }
 footer { visibility: hidden; }
 '''
 
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("## Text to Image SD3.5")
-
-        with gr.Row():
-            prompt = gr.Text(
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            run_button = gr.Button("Run", scale=0, variant="primary")
+    gr.Markdown("## Text to Image SD3.5")
 
-        result = gr.Image(show_label=False)
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Row():
+                prompt = gr.Text(
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+                run_button = gr.Button("Run", scale=0, variant="primary")
+
+            result = gr.Image(show_label=False)
 
-        with gr.Row():
             grid_size_selection = gr.Dropdown(
                 choices=list(grid_sizes.keys()),
                 value="1x1",
                 label="Grid Size"
             )
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
-            )
-            seed = gr.Slider(0, MAX_SEED, value=0, label="Seed")
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(512, MAX_IMAGE_SIZE, step=32, value=1024, label="Width")
-                height = gr.Slider(512, MAX_IMAGE_SIZE, step=32, value=1024, label="Height")
-
-            with gr.Row():
-                guidance_scale = gr.Slider(0.0, 7.5, step=0.1, value=0.0, label="Guidance scale")
-                num_inference_steps = gr.Slider(1, 50, step=1, value=10, label="Number of inference steps")
-
-            style_selection = gr.Radio(
-                choices=STYLE_NAMES,
-                value=DEFAULT_STYLE_NAME,
-                label="Quality Style",
+            with gr.Accordion("Advanced Settings", open=False):
+                negative_prompt = gr.Text(
+                    label="Negative prompt",
+                    max_lines=1,
+                    placeholder="Enter a negative prompt",
+                    value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
+                )
+                seed = gr.Slider(0, MAX_SEED, value=0, label="Seed")
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+                with gr.Row():
+                    width = gr.Slider(512, MAX_IMAGE_SIZE, step=32, value=1024, label="Width")
+                    height = gr.Slider(512, MAX_IMAGE_SIZE, step=32, value=1024, label="Height")
+
+                with gr.Row():
+                    guidance_scale = gr.Slider(0.0, 7.5, step=0.1, value=0.0, label="Guidance scale")
+                    num_inference_steps = gr.Slider(1, 50, step=1, value=10, label="Number of inference steps")
+
+                style_selection = gr.Radio(
+                    choices=STYLE_NAMES,
+                    value=DEFAULT_STYLE_NAME,
+                    label="Quality Style",
+                )
+
+        with gr.Column(scale=1):
+            gr.Examples(
+                examples=examples,
+                inputs=[prompt],
+                outputs=[result, seed],
+                fn=infer,
+                cache_examples=False,
+                label="Prompt Examples"
             )
 
-        gr.Examples(
-            examples=examples,
-            inputs=[prompt],
-            outputs=[result, seed],
-            fn=infer,
-            cache_examples=False
-        )
-
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,