Update app.py

app.py

@@ -1,3 +1,4 @@
+# ...
 import os
 import random
 import uuid
@@ -13,7 +14,7 @@ DESCRIPTIONx = """## REALVISXL V5 🦉
 """
 
 css = '''
-.gradio-container{max-width: 550px !important}
+.gradio-container{max-width: 560px !important}
 h1{text-align:center}
 footer {
     visibility: hidden
@@ -25,12 +26,28 @@ examples = [
     "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K", 
 ]
 
-MODEL_ID = os.getenv("MODEL_VAL_PATH", "SG161222/RealVisXL_V4.0_Lightning") 
+
+#examples = [
+  #  ["file/1.png", "3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)"],
+   # ["file/2.png", "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K"],
+    #["file/3.png", "Vector illustration of a horse, vector graphic design with flat colors on a brown background in the style of vector art, using simple shapes and graphics with simple details, professionally designed as a tshirt logo ready for print on a white background. --ar 89:82 --v 6.0 --style raw"],
+    #["file/4.png", "Man in brown leather jacket posing for the camera, in the style of sleek and stylized, clockpunk, subtle shades, exacting precision, ferrania p30  --ar 67:101 --v 5"],
+    #["file/5.png", "Commercial photography, giant burger, white lighting, studio light, 8k octane rendering, high resolution photography, insanely detailed, fine details, on a white isolated plain, 8k, commercial photography, stock photo, professional color grading, --v 4 --ar 9:16"]
+#]
+
+
+#Set an os.Getenv variable
+#set VAR_NAME=”VALUE”
+#Fetch an environment variable
+#echo %VAR_NAME%
+
+MODEL_ID = os.getenv("MODEL_VAL_PATH") #Use SDXL Model as "MODEL_REPO" --------->>> ”VALUE”. 
 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
 
+#Load model outside of function
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 pipe = StableDiffusionXLPipeline.from_pretrained(
     MODEL_ID,
@@ -44,6 +61,7 @@ pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.conf
 if USE_TORCH_COMPILE:
     pipe.compile()
 
+# Offloading capacity (RAM)
 if ENABLE_CPU_OFFLOAD:
     pipe.enable_model_cpu_offload()
 
@@ -69,7 +87,7 @@ def set_wallpaper_size(size):
     elif size == "Headers (1080x512)":
         return 1080, 512
     else:
-        return 1024, 1024  # Default return if none of the conditions are met
+        return 1024, 1024 
 
 @spaces.GPU(duration=60, enable_queue=True)
 def generate(
@@ -77,6 +95,8 @@ def generate(
     negative_prompt: str = "",
     use_negative_prompt: bool = False,
     seed: int = 1,
+    width: int = 1024,
+    height: int = 1024,
     wallpaper_size: str = "Default (1024x1024)",
     guidance_scale: float = 3,
     num_inference_steps: int = 25,
@@ -87,9 +107,9 @@ def generate(
 ):
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator(device=device).manual_seed(seed)
-
     width, height = set_wallpaper_size(wallpaper_size)
 
+    
     options = {
         "prompt": [prompt] * num_images,
         "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
@@ -100,10 +120,12 @@ def generate(
         "generator": generator,
         "output_type": "pil",
     }
-    
+
+    #VRAM usage Lesser
     if use_resolution_binning:
         options["use_resolution_binning"] = True
 
+    #Images potential batches
     images = []
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
@@ -116,20 +138,26 @@ def generate(
     return image_paths, seed
 
 with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
-
     gr.Markdown(DESCRIPTIONx)  
-    with gr.Row():
-        prompt = gr.Text(
-            label="Prompt",
-            show_label=False,
-            max_lines=1,
-            placeholder="Enter your prompt",
-            container=False,
-        )
-        run_button = gr.Button("Run ⚡", scale=0)
-    result = gr.Gallery(label="Result", columns=1, show_label=False) 
-        
-    with gr.Accordion("Advanced options", open=False, visible=True):
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Gallery(label="Result", columns=1, show_label=False) 
+
+        with gr.Row(visible=True):
+            wallpaper_size = gr.Radio(
+                choices=["Mobile (1080x1920)", "Desktop (1920x1080)", "Extented (1920x512)", "Headers (1080x512)", "Default (1024x1024)"],
+                label="Pixel Size(x*y)",
+                value="Default (1024x1024)"
+            )
+    with gr.Accordion("Advanced options", open=False, visible=False):
         num_images = gr.Slider(
             label="Number of Images",
             minimum=1,
@@ -156,10 +184,19 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
         )
         randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
         with gr.Row(visible=True):
-            wallpaper_size = gr.Radio(
-                choices=["Mobile (1080x1920)", "Desktop (1920x1080)", "Extented (1920x512)", "Headers (1080x512)", "Default (1024x1024)"],
-                label="Pixel Size(x*y)",
-                value="Default (1024x1024)"
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=64,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=64,
+                value=1024,
             )
         with gr.Row():
             guidance_scale = gr.Slider(
@@ -201,7 +238,8 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
             negative_prompt,
             use_negative_prompt,
             seed,
-            wallpaper_size,
+            width,
+            height,
             guidance_scale,
             num_inference_steps,
             randomize_seed,
@@ -210,6 +248,6 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
         outputs=[result, seed],
         api_name="run",
     )   
-    
+
 if __name__ == "__main__":
     demo.queue(max_size=40).launch()