Update app.py

app.py

@@ -3,6 +3,9 @@ import gradio as gr
 from diffusers import AnimateDiffPipeline, MotionAdapter, DPMSolverMultistepScheduler, AutoencoderKL, SparseControlNetModel, EulerAncestralDiscreteScheduler
 from diffusers.utils import export_to_gif, load_image
 from realesrgan import RealESRGAN
+from PIL import Image
+import cv2
+import numpy as np
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -14,6 +17,13 @@ def enhance_quality(image_path):
     sr_image.save('enhanced_' + image_path)
     return 'enhanced_' + image_path
 
+def denoise_image(image_path):
+    image = cv2.imread(image_path)
+    denoised_image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
+    denoised_path = 'denoised_' + image_path
+    cv2.imwrite(denoised_path, denoised_image)
+    return denoised_path
+
 def generate_video(prompt, negative_prompt, num_inference_steps, conditioning_frame_indices, controlnet_conditioning_scale):
     motion_adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3", torch_dtype=torch.float16).to(device)
     controlnet = SparseControlNetModel.from_pretrained("guoyww/animatediff-sparsectrl-scribble", torch_dtype=torch.float16).to(device)
@@ -51,7 +61,8 @@ def generate_video(prompt, negative_prompt, num_inference_steps, conditioning_fr
     
     export_to_gif(video, "output.gif")
     enhanced_gif = enhance_quality("output.gif")
-    return enhanced_gif
+    denoised_gif = denoise_image(enhanced_gif)
+    return denoised_gif
 
 def generate_simple_video(prompt):
     adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16).to(device)
@@ -76,7 +87,8 @@ def generate_simple_video(prompt):
     
     export_to_gif(frames, "simple_output.gif")
     enhanced_gif = enhance_quality("simple_output.gif")
-    return enhanced_gif
+    denoised_gif = denoise_image(enhanced_gif)
+    return denoised_gif
 
 demo1 = gr.Interface(
     fn=generate_video,
@@ -102,6 +114,5 @@ demo2 = gr.Interface(
 
 demo = gr.TabbedInterface([demo1, demo2], ["Advanced Video Generation", "Simple Video Generation"])
 
-
 demo.launch()
 #demo.launch(server_name="0.0.0.0", server_port=7910)