Update app.py

app.py

@@ -4,10 +4,12 @@ import random
 import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline, DPMSolverSDEScheduler
 import torch
+from transformers import AutoModelForObjectDetection, AutoImageProcessor
 
 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"  # Your diffusion model
 
+# Load your main diffusion pipeline
 if torch.cuda.is_available():
     torch_dtype = torch.float16
 else:
@@ -20,7 +22,28 @@ pipe = pipe.to(device)
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-@spaces.GPU #[uncomment to use ZeroGPU]
+# Load ADetailer model (from Hugging Face)
+adetailer_model_id = "Bingsu/adetailer"
+adetailer_model = AutoModelForObjectDetection.from_pretrained(adetailer_model_id)
+adetailer_processor = AutoImageProcessor.from_pretrained(adetailer_model_id)
+
+def fix_eyes_with_adetailer(image):
+    # Convert image to format for ADetailer
+    pixel_values = adetailer_processor(images=image, return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device)
+    
+    # Run ADetailer on the image
+    with torch.no_grad():
+        outputs = adetailer_model(pixel_values=pixel_values)
+    
+    # Post-process the outputs and apply the fixes (if any)
+    corrected_image = image  # Placeholder for the actual post-processing
+    # Apply fixes based on the detection and correction model outputs
+    # This step requires actual ADetailer implementation details for correcting eyes.
+
+    return corrected_image  # Return the corrected image
+
+@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:
@@ -29,16 +52,19 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     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] 
+
+    # Apply ADetailer to fix eyes after generating the image
+    corrected_image = fix_eyes_with_adetailer(image)
     
-    return image, seed
+    return corrected_image, seed
 
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
@@ -46,12 +72,7 @@ examples = [
     "A delicious ceviche cheesecake slice",
 ]
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+css="""#col-container {margin: 0 auto; max-width: 640px;}"""
 
 with gr.Blocks(css=css) as demo:
     
@@ -118,26 +139,3 @@ with gr.Blocks(css=css) as demo:
                     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]
-    )
-
-demo.queue().launch()