Update app.py

app.py

@@ -6,41 +6,26 @@ from optimum.intel.openvino import OVStableDiffusionPipeline
 import torch
 
 
-
-
 model_id = "helenai/Linaqruf-anything-v3.0-ov"
 
 pipe = OVStableDiffusionPipeline.from_pretrained(model_id, compile=False)
 pipe.reshape( batch_size=1, height=256, width=256, num_images_per_prompt=1)
 pipe.compile()
 
+def infer(prompt, negative_prompt):
 
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 256
-
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
-    #if randomize_seed:
-    #    seed = random.randint(0, MAX_SEED)
-        
-    #generator = torch.Generator().manual_seed(seed)
-    
     image = pipe(
         prompt = prompt, 
         negative_prompt = negative_prompt,
-        #guidance_scale = guidance_scale, 
-        #num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        #generator = generator
+        width = 256, 
+        height = 256,
     ).images[0] 
     
     return image
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
+    "A cute kitten, Japanese cartoon style.",
+    "A sweet family, dad stands next to mom, mom holds baby girl.",
     "A delicious ceviche cheesecake slice",
 ]
 
@@ -76,61 +61,6 @@ with gr.Blocks(css=css) as demo:
         
         result = gr.Image(label="Result", show_label=False)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=256,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=256,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=3.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=25,
-                    step=1,
-                    value=25,
-                )
-        
         gr.Examples(
             examples = examples,
             inputs = [prompt]
@@ -138,7 +68,7 @@ with gr.Blocks(css=css) as demo:
 
     run_button.click(
         fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs = [prompt, negative_prompt],
         outputs = [result]
     )