Update app.py

app.py

@@ -12,15 +12,8 @@ import numpy as np
 from diffusers.models.attention_processor import AttnProcessor2_0
 import gradio as gr
 
-# Constants
-SD15_WEIGHTS = "weights"
-CONTROLNET_CACHE = "controlnet-cache"
-SCHEDULERS = {
-    "DDIM": DDIMScheduler,
-    "DPMSolverMultistep": DPMSolverMultistepScheduler,
-    "K_EULER_ANCESTRAL": EulerAncestralDiscreteScheduler,
-    "K_EULER": EulerDiscreteScheduler,
-}
+USE_TORCH_COMPILE = 0
+ENABLE_CPU_OFFLOAD = 0
 
 # Function to download files
 def download_file(url, folder_path, filename):
@@ -42,12 +35,7 @@ def download_file(url, folder_path, filename):
 
 # Download necessary models and files
 
-@spaces.GPU
-def gradio_process_image(input_image, resolution, num_inference_steps, strength, hdr, guidance_scale):
-    prompt = "masterpiece, best quality, highres"
-    negative_prompt = "low quality, normal quality, ugly, blurry, blur, lowres, bad anatomy, bad hands, cropped, worst quality, verybadimagenegative_v1.3, JuggernautNegative-neg"
-    result = process_image(input_image, prompt, negative_prompt, resolution, num_inference_steps, guidance_scale, strength, hdr)
-    return result
+
     
 # MODEL
 download_file(
@@ -147,8 +135,6 @@ pipe.load_lora_weights("models/Lora/more_details.safetensors")
 pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
 
 # Move the pipeline to the device and enable memory efficient attention
-pipe = pipe.to(device)
-pipe.unet.set_attn_processor(AttnProcessor2_0())
 
 # Enable FreeU
 pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.3, b2=1.4)
@@ -250,6 +236,15 @@ def process_image(input_image, prompt, negative_prompt, resolution=2048, num_inf
 
     return result
 
+@spaces.GPU
+def gradio_process_image(input_image, resolution, num_inference_steps, strength, hdr, guidance_scale):
+    pipe = pipe.to(device)
+    pipe.unet.set_attn_processor(AttnProcessor2_0())
+    prompt = "masterpiece, best quality, highres"
+    negative_prompt = "low quality, normal quality, ugly, blurry, blur, lowres, bad anatomy, bad hands, cropped, worst quality, verybadimagenegative_v1.3, JuggernautNegative-neg"
+    result = process_image(input_image, prompt, negative_prompt, resolution, num_inference_steps, guidance_scale, strength, hdr)
+    return result
+    
 # Simple options
 simple_options = [
     gr.Image(type="pil", label="Input Image"),