Update app.py

app.py

@@ -2,26 +2,28 @@ 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
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+# ✅ WAI Illustrious 1.6 model
+model_repo_id = "WAI-Illustrious/WAI-Illustrious-1.6"
+
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
+# ✅ Performance optimizations
+if torch.cuda.is_available():
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe.enable_model_cpu_offload()
+
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
     negative_prompt,
@@ -38,35 +40,36 @@ def infer(
 
     generator = torch.Generator().manual_seed(seed)
 
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
+    # ✅ Generate 4 images
+    images = pipe(
+        prompt=[prompt] * 4,
+        negative_prompt=[negative_prompt] * 4 if negative_prompt else None,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         width=width,
         height=height,
         generator=generator,
-    ).images[0]
+    ).images
 
-    return image, seed
+    return images, seed
 
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "masterpiece, best quality, anime girl, detailed eyes",
+    "1girl, silver hair, fantasy armor, glowing sword",
+    "anime landscape, sunset, cinematic lighting",
 ]
 
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 720px;
 }
 """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown("# WAI Illustrious 1.6 - Text to Image")
 
         with gr.Row():
             prompt = gr.Text(
@@ -76,17 +79,16 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-
             run_button = gr.Button("Run", scale=0, variant="primary")
 
-        result = gr.Image(label="Result", show_label=False)
+        # ✅ Gallery instead of single image
+        result = gr.Gallery(label="Results", show_label=False, columns=2)
 
         with gr.Accordion("Advanced Settings", open=False):
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
+                placeholder="low quality, bad anatomy",
             )
 
             seed = gr.Slider(
@@ -105,7 +107,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
 
                 height = gr.Slider(
@@ -113,7 +115,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
 
             with gr.Row():
@@ -122,18 +124,19 @@ with gr.Blocks(css=css) as demo:
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=5.0,  # ✅ better default
                 )
 
                 num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
+                    label="Steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=25,  # ✅ better default
                 )
 
         gr.Examples(examples=examples, inputs=[prompt])
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
@@ -151,4 +154,4 @@ with gr.Blocks(css=css) as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()