Update app.py

app.py

@@ -7,9 +7,9 @@ import numpy as np
 pipeline = LatentDiffusionUncondPipeline.from_pretrained("CompVis/latent-diffusion-celeba-256")
 
 
-def predict(seed):
+def predict(steps=1, seed):
     generator = torch.manual_seed(seed)
-    image = pipeline(generator=generator, num_inference_steps=1)["sample"]
+    image = pipeline(generator=generator, num_inference_steps=steps)["sample"]
     image_processed = image.cpu().permute(0, 2, 3, 1)
     image_processed = (image_processed + 1.0) * 127.5
     image_processed = image_processed.clamp(0, 255).numpy().astype(np.uint8)
@@ -18,6 +18,7 @@ def predict(seed):
 gr.Interface(
     predict,
     inputs=[
+        gr.inputs.Slider(1, 10, label='Inference Steps', default=1),
         gr.inputs.Slider(0, 1000, label='Seed', default=42),
     ],
     outputs="image",