Update app.py

app.py

@@ -5,7 +5,7 @@ import numpy as np
 from moviepy.editor import *
 from share_btn import community_icon_html, loading_icon_html, share_js
 
-from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
+from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
 import torch
 from PIL import Image
 import time
@@ -13,7 +13,7 @@ import psutil
 import random
 
 
-pipe = DiffusionPipeline.from_pretrained("timbrooks/instruct-pix2pix", torch_dtype=torch.float16, safety_checker=None)
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix", torch_dtype=torch.float16, safety_checker=None)
 pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
 pipe.enable_xformers_memory_efficient_attention()
 pipe.unet.to(memory_format=torch.channels_last)
@@ -127,13 +127,13 @@ def infer(prompt,video_in, seed_in, trim_value):
     for i in frames_list[0:int(n_frame)]:
         pix2pix_img = pix2pix(prompt,5.5,1.5,i,15,"",512,512,seed_in)
         print(pix2pix_img)
-        #image = Image.open(pix2pix_img)
-        #rgb_im = image.convert("RGB")
+        image = Image.open(pix2pix_img)
+        rgb_im = image.convert("RGB")
   
         # exporting the image
-        #pix2pix_img.save(f"result_img-{i}.jpg")
-        #result_frames.append(f"result_img-{i}.jpg")
-        #print("frame " + i + "/" + str(n_frame) + ": done;")
+        rgb_im.save(f"result_img-{i}.jpg")
+        result_frames.append(f"result_img-{i}.jpg")
+        print("frame " + i + "/" + str(n_frame) + ": done;")
 
     final_vid = create_video(result_frames, fps)
     print("finished !")