Update app.py

app.py

@@ -16,7 +16,7 @@ def read_content(file_path: str) -> str:
 
     return content
 
-def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
+def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=30, strength=1.0, scheduler="DPMSolverMultistepScheduler-Karras"):
     if negative_prompt == "":
         negative_prompt = None
     scheduler_class_name = scheduler.split("-")[0]
@@ -34,9 +34,7 @@ def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, s
     mask = dict["mask"]
     
     output = pipe(prompt = prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength, clip_skip=1)
-    output_image = output.images[0]  # Assuming the output dictionary has an 'images' key
-    output_image = torch.nn.functional.interpolate(output_image.unsqueeze(0), size=(1024,832))[0]
-    return output.images, gr.update(visible=True)
+    return output.images[0], gr.update(visible=True)
 
 
 css = '''