Update app.py

app.py

@@ -26,25 +26,6 @@ def run_inference(prompt, stable_diffusion_model, num_inference_steps, guidance_
 
     return output_image_name
 
-
-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, interactive=True, label="Inference Steps",)
-
-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, 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, 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")
-
-verbose = gr.Checkbox(label="Verbose Output", interactive=True, value=False, info="Produce verbose output while running")
-
     
 # Define Gradio UI components
 prompt_input = gr.Textbox(label="Your Prompt", info="What the Minecraft Skin should look like")
@@ -53,6 +34,7 @@ num_inference_steps_input = gr.Number(label="Number of Inference Steps", precisi
 guidance_scale_input = gr.Number(minimum=0.1, value=7.5, label="Guidance Scale", 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_images_per_prompt_input = gr.Number(minimum=1, value=1, precision=0, label="Number of Images per Prompt", info="The number of images to make with the prompt")
 model_precision_type_input = gr.Dropdown(["fp16", "fp32"], value="fp16", label="Model Precision Type", info="The precision type to load the model, like fp16 which is faster, or fp32 which gives better results")
+seed_input = gr.Number(value=42, label="Seed", info="A starting point to initiate generation, put 0 for a random one")
 output_image_name_input = gr.Textbox(label="Output Image Name", info="The name of the file of the output image skin, keep the .png", value="output-skin.png")
 verbose_input = gr.Checkbox(label="Verbose Output", info="Produce more detailed output while running", value=False)