Update app.py

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
 import numpy as np
 import random
-import spaces #[uncomment to use ZeroGPU]
+import spaces  # [uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline, DPMSolverSDEScheduler
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl" #Replace to the model you would like to use
+model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl"  # Replace to the model you would like to use
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
@@ -20,24 +20,28 @@ pipe = pipe.to(device)
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+@spaces.GPU  # [uncomment to use ZeroGPU]
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, tag_selection, progress=gr.Progress(track_tqdm=True)):
+
+    # Combine selected tags with the user input prompt
+    tags_text = ', '.join(tag_selection)
+    final_prompt = f'score_9, score_8_up, score_7_up,source_anime, {tags_text}, {prompt}'
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
+
     generator = torch.Generator().manual_seed(seed)
-    
+
     image = pipe(
-        prompt = 'score_9, score_8_up, score_7_up,source_anime, ' + prompt, 
-        negative_prompt = 'worst quality, bad quality, jpeg artifacts, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark, ' + negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
+        prompt=final_prompt,
+        negative_prompt='worst quality, bad quality, jpeg artifacts, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark, ' + negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator
+    ).images[0]
+
     return image, seed
 
 examples = [
@@ -46,22 +50,27 @@ examples = [
     "A delicious ceviche cheesecake slice",
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 640px;
 }
 """
 
+# Define a list of example tags
+tag_options = [
+    "fantasy", "sci-fi", "realistic", "cyberpunk", "noir", "surreal", 
+    "colorful", "detailed", "high resolution", "anime style"
+]
+
 with gr.Blocks(css=css) as demo:
-    
+
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
+        gr.Markdown("""
         # Text-to-Image Gradio Template
         """)
-        
+
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -69,20 +78,23 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
+
             run_button = gr.Button("Run", scale=0)
-        
+
         result = gr.Image(label="Result", show_label=False)
 
+        with gr.Row():
+            # Checkbox group for selectable tags
+            tag_selection = gr.CheckboxGroup(choices=tag_options, label="Select Tags")
+
         with gr.Accordion("Advanced Settings", open=False):
-            
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
                 visible=False,
             )
-            
+
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -90,54 +102,52 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
+
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
+
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=1024,  # Replace with defaults that work for your model
                 )
-                
+
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=1024,  # Replace with defaults that work for your model
                 )
-            
+
             with gr.Row():
-                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=7, #Replace with defaults that work for your model
+                    value=7,  # Replace with defaults that work for your model
                 )
-                
+
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=35, #Replace with defaults that work for your model
+                    value=35,  # Replace with defaults that work for your model
                 )
-        
+
         gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+            examples=examples,
+            inputs=[prompt]
         )
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, tag_selection],
+        outputs=[result, seed]
     )
 
-demo.queue().launch()
+demo.queue().launch()