Update app.py

app.py

@@ -23,7 +23,7 @@ pipe = PixArtSigmaPipeline.from_pretrained(
 )
 pipe.to(device)
 
-@spaces.GPU(duration=120)
+@spaces.GPU(duration=90)
 def generate(prompt, negative_prompt, num_inference_steps, guidance_scale, height, width):
     image = pipe(
         prompt,
@@ -40,14 +40,19 @@ interface = gr.Interface(
     inputs=[
         gr.Text(label="Prompt"),
         gr.Text(label="Negative Prompt"),
-        gr.Slider(minimum=1, maximum=500, value=100, step=1, label="Number of Inference Steps"),
-        gr.Slider(minimum=1, maximum=20, value=4.5, step=0.1, label="Guidance Scale"),
-        gr.Slider(minimum=64, maximum=1024, value=512, step=64, label="Height"),
-        gr.Slider(minimum=64, maximum=1024, value=512, step=64, label="Width"),
+        gr.Slider(minimum=10, maximum=100, value=30, step=1, label="Number of Inference Steps"),
+        gr.Slider(minimum=1, maximum=20, value=6, step=0.1, label="Guidance Scale"),
+        gr.Slider(minimum=64, maximum=1024, value=1024, step=64, label="Height"),
+        gr.Slider(minimum=64, maximum=1024, value=1024, step=64, label="Width"),
     ],
     outputs=gr.Image(label="Generated Image"),
     title="PixArt Sigma Image Generation",
-    description="Generate images using the PixArt Sigma model.",
+    description="""Generate high-fidelity 4K images from text prompts using PixArt-Sigma, a state-of-the-art diffusion model.
+
+PixArt-Sigma achieves superior image quality and alignment with prompts compared to previous models like [PixArt-alpha](https://github.com/PixArt-alpha/PixArt-sigma). It does so efficiently, evolving from PixArt-alpha through a process termed weak-to-strong training - leveraging higher quality data and an improved attention mechanism.
+
+With just 0.6 billion parameters, PixArt-Sigma reaches new heights in text-to-image generation. Output stunning, intricate 4K images for posters, wallpapers, concept art, and more. Guide the model with descriptive prompts and fine-tune parameters like guidance scale and number of inference steps.
+""",
 )
 
 interface.launch()