v2 app.py

app.py

@@ -12,11 +12,11 @@ from PIL import Image, PngImagePlugin
 from datetime import datetime
 from diffusers.models import AutoencoderKL
 from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
+import random
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-
 if not torch.cuda.is_available():
     DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>"
 IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1"
@@ -247,6 +247,14 @@ def generate_and_update_history(*args, **kwargs):
         generation_history.pop()
     return images[0], json.dumps(metadata, indent=2), update_history_list()
 
+# Load the character list
+with open('characterfull.txt', 'r') as f:
+    characters = [line.strip() for line in f.readlines()]
+
+# Function to get a random character
+def get_random_character():
+    return random.choice(characters)
+
 if torch.cuda.is_available():
     pipe = load_pipeline(MODEL)
     logger.info("Loaded on Device!")
@@ -254,8 +262,6 @@ else:
     pipe = None
 
 with gr.Blocks(css="style.css") as demo:
-    
-    
     gr.Markdown(DESCRIPTION)
 
     gr.DuplicateButton(
@@ -360,6 +366,9 @@ with gr.Blocks(css="style.css") as demo:
         json_input = gr.TextArea(label="Edit/Paste JSON Parameters", placeholder="Paste or edit JSON parameters here")
         generate_from_json = gr.Button("Generate from JSON")
 
+    with gr.Accordion("Randomize", open=False):
+        random_character_button = gr.Button("Random Character")
+
     with gr.Accordion("Generation History", open=False) as history_accordion:
         history_gallery = gr.Gallery(
             label="History",
@@ -454,5 +463,17 @@ with gr.Blocks(css="style.css") as demo:
         inputs=inputs,
         outputs=[result, gr_metadata, history_gallery],
     )
+
+    random_character_button.click(
+        fn=get_random_character,
+        inputs=[],
+        outputs=[prompt]
+    )
+
+    history_gallery.select(
+        fn=handle_image_click,
+        inputs=[],
+        outputs=[selected_image, selected_metadata]
+    )
     
 demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)