image hash pt 2

app.py

@@ -15,6 +15,7 @@ import spaces
 import gc
 import torch
 from PIL import Image
+import hashlib
 from diffusers import (
     ControlNetModel,
     DPMSolverMultistepScheduler,
@@ -239,7 +240,9 @@ 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;
@@ -357,35 +360,40 @@ 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)
     
-    # image processing
+    previous_image_hash = None
+
     @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)):
-        return process_image(image, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed)
+        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
     
-    # 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)):
-        # First, yield the previous result to update the input image immediately
+        global previous_image_hash
         yield previous_result, gr.update()
-        # 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
+        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
         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(
@@ -400,25 +408,34 @@ def process_image(
     num_steps,
     guidance_scale,
     seed,
+    previous_image_hash=None
 ):
-    # torch.cuda.synchronize()
+    current_image_hash = image_hash(image)
+    
     preprocess_start = time.time()
     print("processing image")
 
     seed = random.randint(0, MAX_SEED)
     generator = torch.cuda.manual_seed(seed)
-    preprocessor.load("NormalBae")
-    control_image = preprocessor(
-        image=image,
-        image_resolution=image_resolution,
-        detect_resolution=preprocess_resolution,
-    )
+    
+    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
+
     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()
@@ -432,9 +449,8 @@ 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
+    return results, current_image_hash
 
 if prod:
     demo.queue(max_size=20).launch(server_name="localhost", server_port=port)