local

app.py

@@ -366,23 +366,6 @@ with gr.Blocks(theme="bethecloud/storj_theme", css=css) as demo:
     @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)
-
-    # AI Image Processing
-    @gr.on(triggers=[use_ai_button.click], inputs=config, outputs=result, show_progress="minimal")
-    def submit(result, 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(result, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed)
-
-    # # Change input to result
-    # @gr.on(triggers=[use_ai_button.click], inputs=result, outputs=image, show_progress="hidden")
-    # def update_input(result):
-    #     try:
-    #         print("Updating image to AI Temp Image")
-    #         return result
-    #         # ai_temp_image = Image.open(os.path.join("/data", "temp_image.jpg"))
-    #         # return ai_temp_image
-    #     except FileNotFoundError:
-    #         print("No AI Image Available")
-    #         return None
     
     @gr.on(triggers=[use_ai_button.click], inputs=[result] + config, outputs=[image, result], show_progress="full")
     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)):

local_app.py

@@ -333,36 +333,36 @@ 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
     @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)
 
-    # AI Image Processing
-    @gr.on(triggers=[use_ai_button.click], inputs=config, outputs=result, show_progress="minimal")
-    def submit(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)
-
-    # Change input to result
-    @gr.on(triggers=[use_ai_button.click], inputs=None, outputs=image, show_progress="hidden")
-    def update_input():
-        try:
-            print("Updating image to AI Temp Image")
-            ai_temp_image = Image.open("temp_image.jpg")
-            return ai_temp_image
-        except FileNotFoundError:
-            print("No AI Image Available")
-            return None
+    # # AI Image Processing
+    # @gr.on(triggers=[use_ai_button.click], inputs=config, outputs=result, show_progress="minimal")
+    # def submit(result, 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(result, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed)
+        
+    @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
+        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
+        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():
@@ -385,7 +385,9 @@ def process_image(
     # torch.cuda.synchronize()
     preprocess_start = time.time()
     print("processing image")
-    preprocessor.load("NormalBae")
+
+    # global preprocessor
+    # preprocessor.load("NormalBae")
     
     seed = random.randint(0, MAX_SEED)
     generator = torch.cuda.manual_seed(seed)
@@ -413,7 +415,7 @@ def process_image(
         image=control_image,
     ).images[0]
     print(f"\n-------------------------Inference done in: {time.time() - start:.2f} seconds-------------------------")
-    results.save("temp_image.jpg")
+    # results.save(os.path.join("/data", "temp_image.jpg"))
     # torch.cuda.synchronize()
     # torch.cuda.empty_cache()
     return results