Update bg_input_test.py

bg_input_test.py

@@ -1,3 +1,4 @@
+import spaces
 import os
 import math
 import gradio as gr
@@ -128,7 +129,7 @@ i2i_pipe = StableDiffusionImg2ImgPipeline(
     image_encoder=None
 )
 
-
+@spaces.GPU(duration=59)
 @torch.inference_mode()
 def encode_prompt_inner(txt: str):
     max_length = tokenizer.model_max_length
@@ -149,7 +150,7 @@ def encode_prompt_inner(txt: str):
 
     return conds
 
-
+@spaces.GPU(duration=59)
 @torch.inference_mode()
 def encode_prompt_pair(positive_prompt, negative_prompt):
     c = encode_prompt_inner(positive_prompt)
@@ -170,7 +171,7 @@ def encode_prompt_pair(positive_prompt, negative_prompt):
 
     return c, uc
 
-
+@spaces.GPU(duration=59)
 @torch.inference_mode()
 def pytorch2numpy(imgs, quant=True):
     results = []
@@ -187,7 +188,7 @@ def pytorch2numpy(imgs, quant=True):
         results.append(y)
     return results
 
-
+@spaces.GPU(duration=59)
 @torch.inference_mode()
 def numpy2pytorch(imgs):
     h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0  # so that 127 must be strictly 0.0
@@ -215,7 +216,7 @@ def resize_without_crop(image, target_width, target_height):
     resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
     return np.array(resized_image)
 
-
+@spaces.GPU(duration=59)
 @torch.inference_mode()
 def run_rmbg(img, sigma=0.0):
     H, W, C = img.shape
@@ -335,7 +336,7 @@ def process(input_fg, prompt, image_width, image_height, num_samples, seed, step
 
     return pytorch2numpy(pixels)
 
-
+@spaces.GPU(duration=59)
 @torch.inference_mode()
 def process_relight(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source):
     input_fg, matting = run_rmbg(input_fg)