revert

app.py

@@ -15,7 +15,6 @@ import spaces
 import gc
 import torch
 from PIL import Image
-import hashlib
 from diffusers import (
     ControlNetModel,
     DPMSolverMultistepScheduler,
@@ -240,9 +239,7 @@ def apply_style(style_name):
         p = styles.get(style_name, "boho chic")
     return p
 
-def image_hash(image):
-    return hashlib.md5(image.tobytes()).hexdigest()
-
+    
 css = """
 h1, h2, h3 {
     text-align: center;
@@ -360,40 +357,35 @@ with gr.Blocks(theme="bethecloud/storj_theme", css=css) as demo:
         guidance_scale,
         seed,
     ]
- 
+    
     with gr.Row():
         helper_text = gr.Markdown("## Tap and hold (on mobile) to save the image.", visible=True)
     
-    previous_image_hash = None
-
+    # image processing
     @gr.on(triggers=[image.upload, prompt.submit, run_button.click], inputs=config, outputs=result, show_progress="minimal")
     def auto_process_image(image, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed, progress=gr.Progress(track_tqdm=True)):
-        global previous_image_hash
-        result, new_hash = process_image(image, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed, previous_image_hash)
-        previous_image_hash = new_hash
-        return result
+        return process_image(image, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed)
     
+    # AI image processing
     @gr.on(triggers=[use_ai_button.click], inputs=[result] + config, outputs=[image, result], show_progress="minimal")
     def submit(previous_result, image, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed, progress=gr.Progress(track_tqdm=True)):
-        global previous_image_hash
+        # First, yield the previous result to update the input image immediately
         yield previous_result, gr.update()
-        new_result, new_hash = process_image(previous_result, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed, previous_image_hash)
-        previous_image_hash = new_hash
+        # Then, process the new input image
+        new_result = process_image(previous_result, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed)
+        # Finally, yield the new result
         yield previous_result, new_result
 
+    # Turn off buttons when processing
     @gr.on(triggers=[image.upload, use_ai_button.click, run_button.click], inputs=None, outputs=[run_button, use_ai_button], show_progress="hidden")
     def turn_buttons_off():
         return gr.update(visible=False), gr.update(visible=False)
     
+    # Turn on buttons when processing is complete
     @gr.on(triggers=[result.change], inputs=None, outputs=[use_ai_button, run_button], show_progress="hidden")
     def turn_buttons_on():
         return gr.update(visible=True), gr.update(visible=True)
 
-    @gr.on(triggers=[image.upload], inputs=None, outputs=None)
-    def clear_image_hash():
-        global previous_image_hash
-        previous_image_hash = None
-
 @spaces.GPU(duration=12)
 @torch.inference_mode()
 def process_image(
@@ -408,34 +400,25 @@ def process_image(
     num_steps,
     guidance_scale,
     seed,
-    previous_image_hash=None
 ):
-    current_image_hash = image_hash(image)
-    
+    # torch.cuda.synchronize()
     preprocess_start = time.time()
     print("processing image")
 
     seed = random.randint(0, MAX_SEED)
     generator = torch.cuda.manual_seed(seed)
-    
-    if previous_image_hash != current_image_hash:
-        print("not the same image")
-        preprocessor.load("NormalBae")
-        control_image = preprocessor(
-            image=image,
-            image_resolution=image_resolution,
-            detect_resolution=preprocess_resolution,
-        )
-    else:
-        print("Image unchanged, skipping preprocessing")
-        control_image = image
-
+    preprocessor.load("NormalBae")
+    control_image = preprocessor(
+        image=image,
+        image_resolution=image_resolution,
+        detect_resolution=preprocess_resolution,
+    )
     preprocess_time = time.time() - preprocess_start
     if style_selection is not None or style_selection != "None":
         prompt = "Photo from Pinterest of " + apply_style(style_selection) + " " + prompt + " " + a_prompt
     else:
-        prompt = str(get_prompt(prompt, a_prompt))
-    negative_prompt = str(n_prompt)
+        prompt=str(get_prompt(prompt, a_prompt))
+    negative_prompt=str(n_prompt)
     print(prompt)
     print(f"\n-------------------------Preprocess done in: {preprocess_time:.2f} seconds-------------------------")    
     start = time.time()
@@ -449,8 +432,9 @@ def process_image(
         image=control_image,
     ).images[0]
     print(f"\n-------------------------Inference done in: {time.time() - start:.2f} seconds-------------------------")
+    # torch.cuda.synchronize()
     torch.cuda.empty_cache()
-    return results, current_image_hash
+    return results
 
 if prod:
     demo.queue(max_size=20).launch(server_name="localhost", server_port=port)