Update app.py

app.py

@@ -1,11 +1,17 @@
 import torch
 import random
+import numpy as np
 import gradio as gr
-from diffusers import StableDiffusionControlNetPipeline
+from pytorch_lightning import seed_everything
 from annotator.util import resize_image, HWC3
+from diffusers import StableDiffusionControlNetPipeline
 
 # Load the pipeline
-pipe = StableDiffusionControlNetPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to("cuda")
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    controlnet="path/to/controlnet/model",
+    image_encoder="path/to/image_encoder/model"
+).to("cuda")
 
 def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, low_threshold, high_threshold):
     with torch.no_grad():
@@ -13,9 +19,10 @@ def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resoluti
         
         if seed == -1:
             seed = random.randint(0, 65535)
-        generator = torch.manual_seed(seed)
+        seed_everything(seed)
 
         # Generate images using the pipeline
+        generator = torch.Generator("cuda").manual_seed(seed)
         images = pipe(prompt=prompt + ', ' + a_prompt, num_inference_steps=ddim_steps, guidance_scale=scale, generator=generator, num_images_per_prompt=num_samples).images
 
         results = [np.array(image) for image in images]