Update app.py

app.py

@@ -74,7 +74,6 @@ def find_best_bucket(h, w, options):
     return best_bucket
 
 @spaces.GPU()
-@torch.inference_mode()
 def encode_cropped_prompt_77tokens(txt: str):
     memory_management.load_models_to_gpu(text_encoder)
     cond_ids = tokenizer(txt,
@@ -86,7 +85,6 @@ def encode_cropped_prompt_77tokens(txt: str):
     return text_cond
 
 @spaces.GPU()
-@torch.inference_mode()
 def pytorch2numpy(imgs):
     results = []
     for x in imgs:
@@ -97,7 +95,6 @@ def pytorch2numpy(imgs):
     return results
 
 @spaces.GPU()
-@torch.inference_mode()
 def numpy2pytorch(imgs):
     h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.5 - 1.0
     h = h.movedim(-1, 1)
@@ -110,12 +107,10 @@ def resize_without_crop(image, target_width, target_height):
     return np.array(resized_image)
 
 @spaces.GPU()
-@torch.inference_mode()
 def interrogator_process(x):
     return wd14tagger.default_interrogator(x)
 
 @spaces.GPU()
-@torch.inference_mode()
 def process(input_fg, prompt, input_undo_steps, image_width, image_height, seed, steps, n_prompt, cfg,
             progress=gr.Progress()):
     rng = torch.Generator(device=memory_management.gpu).manual_seed(int(seed))
@@ -155,7 +150,6 @@ def process(input_fg, prompt, input_undo_steps, image_width, image_height, seed,
     return pixels
 
 @spaces.GPU()
-@torch.inference_mode()
 def process_video_inner(image_1, image_2, prompt, seed=123, steps=25, cfg_scale=7.5, fs=3, progress_tqdm=None):
     random.seed(seed)
     np.random.seed(seed)
@@ -211,7 +205,6 @@ def process_video_inner(image_1, image_2, prompt, seed=123, steps=25, cfg_scale=
     return video, image_1, image_2
 
 @spaces.GPU
-@torch.inference_mode()
 def process_video(keyframes, prompt, steps, cfg, fps, seed, progress=gr.Progress()):
     result_frames = []
     cropped_images = []