Update app.py

app.py

@@ -49,15 +49,15 @@ prompt = gr.Textbox(label="Prompt", interactive=True)
 
 stable_diffusion_model = gr.Dropdown(["2", "xl"], interactive=True, label="Stable Diffusion Model", value="xl", info="Choose which Stable Diffusion Model to use, xl understands prompts better")
 
-num_inference_steps = gr.Number(value=50, minimum=1, precision=0, interactive=True, label="Inference Steps", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference")
+num_inference_steps = gr.Number(value=50, minimum=1, interactive=True, label="Inference Steps", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference")
 
 guidance_scale = gr.Number(value=7.5, minimum=0.1, interactive=True, label="Guidance Scale", info="How closely the generated image adheres to the prompt")
 
-num_images_per_prompt = gr.Number(value=1, minimum=1, precision=0, interactive=True, label="Images Per Prompt", info="The number of images to make with the prompt")
+num_images_per_prompt = gr.Number(value=1, minimum=1, interactive=True, label="Images Per Prompt", info="The number of images to make with the prompt")
 
 model_precision_type = gr.Dropdown(["fp16", "fp32"], value="fp16", interactive=True, label="Model Precision Type", info="The precision type to load the model, like fp16 which is faster, or fp32 which gives better results")
 
-seed = gr.Number(value=42, interactive=True, precision=0, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
+seed = gr.Number(value=42, interactive=True, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
 
 output_image_name = gr.Textbox(label="Name of Generated Skin Output", interactive=True, value="output.png")