Update app.py

app.py

@@ -28,7 +28,7 @@ def error_str(error, title="Error"):
             {error}"""  if error else ""
 
 @spaces.GPU
-def inference(prompt, guidance, steps, width=768, height=768, seed=0, img=None, strength=0.5, neg_prompt=""):
+def inference(prompt, guidance, steps, width=640, height=640, seed=0, img=None, strength=0.5, neg_prompt=""):
 
   if torch.cuda.is_available():
     generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
@@ -108,27 +108,27 @@ with gr.Blocks(css=css) as demo:
                 prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="[your prompt]")
                 generate = gr.Button(value="Generate")
 
-              image_out = gr.Image(height=768)
+              image_out = gr.Image(height=640)
           error_output = gr.Markdown()
 
         with gr.Column(scale=45):
           with gr.Tab("Options"):
             with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")              
+              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image", value="deformed, extra limbs, grainy, unfocused, blurry, NSFW, nude, naked")              
 
               with gr.Row():
                 guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
                 steps = gr.Slider(label="Steps", value=24, minimum=4, maximum=50, step=1)
 
               with gr.Row():
-                width = gr.Slider(label="Width", value=768, minimum=64, maximum=1024, step=64)
-                height = gr.Slider(label="Height", value=768, minimum=64, maximum=1024, step=64)
+                width = gr.Slider(label="Width", value=640, minimum=64, maximum=1024, step=64)
+                height = gr.Slider(label="Height", value=640, minimum=64, maximum=1024, step=64)
 
               seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
 
           with gr.Tab("Image to image"):
               with gr.Group():
-                image = gr.Image(label="Image", height=768, type="pil")
+                image = gr.Image(label="Image", height=640, type="pil")
                 strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
 
     inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]