Update app.py

app.py

@@ -16,16 +16,16 @@ sd_2_1_4x = StableDiffusionUpscalePipeline.from_pretrained(model_4x, torch_dtype
 
 # Define the function that will be called when the interface is used
 
-def upscale_image(model, input_image, prompt, negative_prompt, guidance):
+def upscale_image(model, input_image, prompt, guidance):
     # Convert the image 
     generator = torch.manual_seed(999999)
     input_image = Image.open(input_image).convert("RGB")
     # Upscale the image using the selected model
     if model == "SD 2.1 4x Upscaler":
         low_res_img = input_image.resize((128, 128))
-        upscaled_image = sd_2_1_4x(prompt, negative_prompt, image=low_res_img, num_inference_steps=5, guidance_scale=guidance).images[0]
+        upscaled_image = sd_2_1_4x(prompt, image=low_res_img, num_inference_steps=5, guidance_scale=guidance).images[0]
     else:
-        upscaled_image = sd_2_0_2x(prompt, negative_prompt, image=input_image, num_inference_steps=5, guidance_scale=guidance).images[0]
+        upscaled_image = sd_2_0_2x(prompt, image=input_image, num_inference_steps=5, guidance_scale=guidance).images[0]
         # Return the upscaled image
     return upscaled_image
 
@@ -35,7 +35,6 @@ gr.Interface(
     inputs=[gr.Radio(["SD 2.0 2x Latent Upscaler", "SD 2.1 4x Upscaler"], label="Models:"), 
             gr.Image(type="filepath", label = "Raw Image"),
             gr.Textbox(label='Guide the AI Upscaling'),
-            gr.Textbox(label='What you do not want AI to generate during Upscaling'),
             gr.Slider(minimum=0, value=0, maximum=3, label='Guidance Scale')],
     outputs=gr.Image(type="filepath", label = "Upscaled Image"),
     title="SD Image Upscaler",