Update app.py

app.py

@@ -12,15 +12,11 @@ from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 #Load the HTML content
 #html_file_url = "https://prithivmlmods-hamster-static.static.hf.space/index.html"
 #html_content = f'<iframe src="{html_file_url}" style="width:100%; height:180px; border:none;"></iframe>'
-
-
 html_file_url = "https://prithivmlmods-hamster-static.static.hf.space/index.html"
 html_content = f'<iframe src="{html_file_url}" style="width:100%; height:200px; border:none"></iframe>'
 
-
 DESCRIPTIONx = """## STABLE HAMSTER
 """
-
 css = '''
 .gradio-container{max-width: 560px !important}
 h1{text-align:center}
@@ -38,15 +34,17 @@ examples = [
     "Kids going to school, Anime style"
 ]
 
-
-#Use environment variables for flexibility
+#Set an os.Getenv variable
+#set VAR_NAME=”VALUE”
+#Fetch an environment variable
+#echo %VAR_NAME%
 MODEL_ID = os.getenv("MODEL_REPO")
 MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
 USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
 ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
 BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))  # Allow generating multiple images at once
 
-# Determine device and load model outside of function for efficiency
+#Load model outside of function
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 pipe = StableDiffusionXLPipeline.from_pretrained(
     MODEL_ID,
@@ -56,11 +54,11 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
 ).to(device)
 pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
 
-# Torch compile for potential speedup (experimental)
+# <compile speedup >
 if USE_TORCH_COMPILE:
     pipe.compile()
 
-# CPU offloading for larger RAM capacity (experimental)
+# Offloading capacity (RAM)
 if ENABLE_CPU_OFFLOAD:
     pipe.enable_model_cpu_offload()
 
@@ -76,7 +74,7 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
         seed = random.randint(0, MAX_SEED)
     return seed
 
-@spaces.GPU(duration=35, enable_queue=True)
+@spaces.GPU(duration=60, enable_queue=True)
 def generate(
     prompt: str,
     negative_prompt: str = "",
@@ -85,7 +83,7 @@ def generate(
     width: int = 1024,
     height: int = 1024,
     guidance_scale: float = 3,
-    num_inference_steps: int = 30,
+    num_inference_steps: int = 25,
     randomize_seed: bool = False,
     use_resolution_binning: bool = True, 
     num_images: int = 1,  # Number of images to generate
@@ -94,7 +92,7 @@ def generate(
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator(device=device).manual_seed(seed)
 
-    # Improved options handling
+    #Options 
     options = {
         "prompt": [prompt] * num_images,
         "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
@@ -106,11 +104,11 @@ def generate(
         "output_type": "pil",
     }
 
-    # Use resolution binning for faster generation with less VRAM usage
+    #VRAM usage Lesser
     if use_resolution_binning:
         options["use_resolution_binning"] = True
 
-    # Generate images potentially in batches
+    #Images potential batches
     images = []
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
@@ -121,8 +119,7 @@ def generate(
 
     image_paths = [save_image(img) for img in images]
     return image_paths, seed
-
-
+#Main gr.Block
 with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
     gr.Markdown(DESCRIPTIONx)
     with gr.Group():
@@ -188,7 +185,7 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
             num_inference_steps = gr.Slider(
                 label="Number of inference steps",
                 minimum=1,
-                maximum=15,
+                maximum=25,
                 step=1,
                 value=8,
             )
@@ -227,9 +224,7 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
         ],
         outputs=[result, seed],
         api_name="run",
-    )
-
-    
+    )   
     gr.HTML(html_content)
 if __name__ == "__main__":
     demo.queue(max_size=50).launch()