Update app.py

Update Interface

app.py

@@ -1,27 +1,24 @@
 import gradio as gr
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+model_repo_id = "Grandediw/lora_model"  # Use the fine-tuned model
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+# Adjust torch data type based on device
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
+# Load the model pipeline
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
+# Constants
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
+# Inference function
 def infer(
     prompt,
     negative_prompt,
@@ -31,13 +28,13 @@ def infer(
     height,
     guidance_scale,
     num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
 ):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    generator = torch.Generator(device).manual_seed(seed)
 
+    # Generate the image
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -50,92 +47,109 @@ def infer(
 
     return image, seed
 
-
+# Example prompts
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
     "A delicious ceviche cheesecake slice",
 ]
 
+# Improved CSS for better styling
 css = """
-#col-container {
+#interface-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 700px;
+    padding: 10px;
+    box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
+    border-radius: 10px;
+    background-color: #f9f9f9;
+}
+#header {
+    text-align: center;
+    font-size: 1.5em;
+    margin-bottom: 20px;
+    color: #333;
+}
+#advanced-settings {
+    background-color: #f1f1f1;
+    padding: 10px;
+    border-radius: 8px;
 }
 """
 
+# Gradio interface
 with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+    with gr.Box(elem_id="interface-container"):
+        gr.Markdown(
+            """
+            <div id="header">🖼️ Text-to-Image Generator</div>
+            Generate high-quality images from your text prompts with the fine-tuned LoRA model.
+            """
+        )
+
+        # Main input row
         with gr.Row():
-            prompt = gr.Text(
+            prompt = gr.Textbox(
                 label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+                placeholder="Describe the image you want to create...",
+                lines=2,
             )
+            run_button = gr.Button("Generate Image", variant="primary")
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
+        # Output image display
+        result = gr.Image(label="Generated Image").style(height="512px")
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
+        # Advanced settings
+        with gr.Accordion("Advanced Settings", open=False, elem_id="advanced-settings"):
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                placeholder="What to exclude from the image...",
             )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+            seed = gr.Number(label="Seed", value=0, interactive=True)
 
             with gr.Row():
                 width = gr.Slider(
-                    label="Width",
+                    label="Image Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    step=64,
+                    value=512,
                 )
-
                 height = gr.Slider(
-                    label="Height",
+                    label="Image Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    step=64,
+                    value=512,
                 )
 
             with gr.Row():
                 guidance_scale = gr.Slider(
-                    label="Guidance scale",
+                    label="Guidance Scale",
                     minimum=0.0,
-                    maximum=10.0,
+                    maximum=20.0,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=7.5,
                 )
-
                 num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    label="Steps",
+                    minimum=10,
+                    maximum=100,
+                    step=5,
+                    value=50,
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
+        # Examples
+        gr.Examples(
+            examples=examples,
+            inputs=[prompt],
+            outputs=[result],
+            label="Try these prompts",
+        )
+
+    # Event handler
+    run_button.click(
         fn=infer,
         inputs=[
             prompt,