increasing guidance scale range

app.py

@@ -270,7 +270,7 @@ with gr.Blocks(analytics_enabled=False) as demo:
     with gr.Row():
         in_inference_steps = gr.Number(label="Inference steps", value=config.value["inference_steps"], info="Each step improves the final result but also results in higher computation")
         in_manual_seed = gr.Number(label="Manual seed", value=config.value["manual_seed"], info="Set this to -1 or leave it empty to randomly generate an image. A fixed value will result in a similar image for every run")
-        in_guidance_scale = gr.Slider(minimum=0, maximum=1, step=0.01, label="Guidance Scale", value=config.value["guidance_scale"], info="A low guidance scale leads to a faster inference time, with the drawback that negative prompts don’t have any effect on the denoising process.")
+        in_guidance_scale = gr.Slider(minimum=0, maximum=100, step=0.1, label="Guidance Scale", value=config.value["guidance_scale"], info="A low guidance scale leads to a faster inference time, with the drawback that negative prompts don’t have any effect on the denoising process.")
     with gr.Row():
         gr.Markdown("**VAE** stands for Variational Autoencoders. An 'autoencoder' is an artificial neural network that is able to encode input data and decode to output data to bascially recreate the input. The VAE whereas adds a couple of additional layers of complexity to create new and unique output.")
     with gr.Row():

config.py

@@ -50,7 +50,7 @@ def get_initial_config():
         "enable_vae_tiling": "True",
         "manual_seed": 42,
         "inference_steps": 10,
-        "guidance_scale": 0.5,
+        "guidance_scale": 5,
         "prompt": 'A white rabbit',
         "trigger_token": '',
         "negative_prompt": 'lowres, cropped, worst quality, low quality',