diff --git a/predict.py b/predict.py
index 8450cd7..eba4904 100644
--- a/predict.py
+++ b/predict.py
@@ -50,17 +50,7 @@ def predict(
             default=None,
         ),
         image: Path = Input(
-            description="Inital image to generate variations of.",
-        ),
-        width: int = Input(
-            description="Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
-            choices=[128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024],
-            default=512,
-        ),
-        height: int = Input(
-            description="Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
-            choices=[128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024],
-            default=512,
+            description="A starting image from which to generate variations (aka 'img2img')"
         ),
         prompt_strength: float = Input(
             description="Prompt strength when providing the image. 1.0 corresponds to full destruction of information in init image",
@@ -80,7 +70,14 @@ def predict(
         ),
         scheduler: str = Input(
             default="DPMSolverMultistep",
-            choices=["DDIM", "K_EULER", "DPMSolverMultistep", "K_EULER_ANCESTRAL", "PNDM", "KLMS"],
+            choices=[
+                "DDIM",
+                "K_EULER",
+                "DPMSolverMultistep",
+                "K_EULER_ANCESTRAL",
+                "PNDM",
+                "KLMS",
+            ],
             description="Choose a scheduler.",
         ),
         seed: int = Input(
@@ -102,6 +99,9 @@ def predict(
         generator = torch.Generator("cuda").manual_seed(seed)
         output = pipe(
             prompt=[prompt] * num_outputs if prompt is not None else None,
+            negative_prompt=[negative_prompt] * num_outputs
+            if negative_prompt is not None
+            else None,
             guidance_scale=guidance_scale,
             generator=generator,
             num_inference_steps=num_inference_steps,