Update app.py

app.py

@@ -1,21 +1,12 @@
 import gradio as gr
 import numpy as np
 import random
-import spaces
+import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline, DPMSolverSDEScheduler
 import torch
-from huggingface_hub import hf_hub_download
-from ultralytics import YOLO
-from PIL import Image
-import cv2
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "John6666/wai-ani-nsfw-ponyxl-v8-sdxl"
-adetailer_model_id = "Bingsu/adetailer"  # Your ADetailer model
-
-# Load the YOLO model for face detection
-yolo_model_path = hf_hub_download(adetailer_model_id, "face_yolov8n.pt")
-yolo_model = YOLO(yolo_model_path)
+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
@@ -29,55 +20,25 @@ pipe = pipe.to(device)
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-def correct_anime_face(image):
-    # Convert to OpenCV format
-    img = np.array(image)
-    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-    
-    # Detect faces
-    results = yolo_model(img)
-    
-    for detection in results[0].boxes:
-        x1, y1, x2, y2 = map(int, detection.xyxy[0].tolist())
-        
-        # Crop the face region
-        face = img[y1:y2, x1:x2]
-        face_pil = Image.fromarray(cv2.cvtColor(face, cv2.COLOR_BGR2RGB))
-        
-        # Prompt for the correction model
-        prompt = "Enhance this anime character's face, fix eyes and make features more vivid."
-        
-        # Process the face with the anime correction model
-        corrected_face = pipe(prompt=prompt, image=face_pil).images[0]  # Replace with your correction model
-        
-        # Place the corrected face back into the original image
-        img[y1:y2, x1:x2] = np.array(corrected_face)
-    
-    # Convert back to PIL
-    final_image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-    return final_image
-
-@spaces.GPU
+@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)):
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
     generator = torch.Generator().manual_seed(seed)
     
     image = pipe(
-        prompt=prompt, 
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale, 
-        num_inference_steps=num_inference_steps, 
-        width=width, 
-        height=height,
-        generator=generator
+        prompt = prompt, 
+        negative_prompt = negative_prompt,
+        guidance_scale = guidance_scale, 
+        num_inference_steps = num_inference_steps, 
+        width = width, 
+        height = height,
+        generator = generator
     ).images[0] 
     
-    # Correct anime face in the generated image
-    corrected_image = correct_anime_face(image)
-    
-    return corrected_image, seed
+    return image, seed
 
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
@@ -85,7 +46,7 @@ examples = [
     "A delicious ceviche cheesecake slice",
 ]
 
-css = """
+css="""
 #col-container {
     margin: 0 auto;
     max-width: 640px;
@@ -93,33 +54,90 @@ css = """
 """
 
 with gr.Blocks(css=css) as demo:
+    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("# Text-to-Image Gradio Template")
+        gr.Markdown(f"""
+        # Text-to-Image Gradio Template
+        """)
         
         with gr.Row():
-            prompt = gr.Text(label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False)
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
             run_button = gr.Button("Run", scale=0)
         
         result = gr.Image(label="Result", show_label=False)
 
         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, maximum=MAX_SEED, step=1, value=0)
+            
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=False,
+            )
+            
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                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)
-                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
                 
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    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
+                )
+            
             with gr.Row():
-                guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=0.0)
-                num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=2)
+                
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=0.0, #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=2, #Replace with defaults that work for your model
+                )
         
-        gr.Examples(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])
+        gr.Examples(
+            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]
+    )
 
-demo.queue().launch()
+demo.queue().launch()