Update app.py

app.py

@@ -28,6 +28,7 @@ pipe.enable_xformers_memory_efficient_attention()
 
 def super_esr(source_image,prompt,negative_prompt,strength,seed,num_inference_steps):
     condition_image = resize_for_condition_image(source_image, 1024)
+    generator =  torch.Generator(device="cuda").manual_seed(seed)
     
     image = pipe(prompt=prompt,#"best quality", 
                 negative_prompt="blur, lowres, bad anatomy, bad hands, cropped, worst quality", 
@@ -36,13 +37,13 @@ def super_esr(source_image,prompt,negative_prompt,strength,seed,num_inference_st
                 width=condition_image.size[0],
                 height=condition_image.size[1],
                 strength=1.0,
-                generator=seed,
+                generator=generator,
                 num_inference_steps=num_inference_steps,
-                ).image
+                ).images[0]
     # print(source_image,prompt,negative_prompt,strength,seed,num_inference_steps)
     return image
 
-#define laund take input nsame as super_esr function
+# define and take input the same as the super_esr function
 
 inputs=[
     gr.inputs.Image(type="pil",label="Source Image"),
@@ -61,7 +62,7 @@ examples=[
     ["https://i.imgur.com/9IqyX1F.png","best quality","blur, lowres, bad anatomy, bad hands, cropped, worst quality",1.0,0,100],
     ["https://i.imgur.com/9IqyX1F.png","best quality","blur, lowres, bad anatomy, bad hands, cropped, worst quality",1.0,0,100],
 ]
-#create queue the rerquests
+# create a queue of the requests
 x=gr.Interface(fn=super_esr,inputs=inputs,outputs=outputs,title=title,description=description,examples=examples)
 x.launch()