Update app.py

app.py

@@ -1,36 +1,80 @@
 import gradio as gr
-from diffusers import StableDiffusionPipeline
 import torch
+from diffusers import StableDiffusionPipeline
+from PIL import Image
+import numpy as np
+import cv2
 
-# Configura el modelo, puedes ajustar el modelo a tu necesidad específica.
-MODEL_NAME = "CompVis/stable-diffusion-v1-4"  # Puedes cambiarlo a tu modelo subido en HF
+# Device setup
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-def load_model():
-    # Cargar el modelo de Stable Diffusion
-    pipe = StableDiffusionPipeline.from_pretrained(MODEL_NAME, torch_dtype=torch.float16).to(device)
-    return pipe
+# Load Stable Diffusion model
+model_name = "CompVis/stable-diffusion-v1-4"
+def load_image_model():
+    return StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16).to(device)
 
-# Cargar el modelo al iniciar la aplicación
-model = load_model()
+image_model = load_image_model()
 
 def generate_image(prompt):
-    # Generar imagen usando el modelo cargado
-    if not prompt:
-        return None
     with torch.no_grad():
-        image = model(prompt).images[0]  # Genera la imagen
+        image = image_model(prompt).images[0]
     return image
 
-# Configurar la interfaz de Gradio
+# Face swap function placeholder (replace with actual implementation)
+def swap_faces(image1, image2):
+    # Placeholder implementation, you should replace this with your actual face swapping code
+    return image1
+
+def face_swap(image1, image2):
+    image1 = cv2.cvtColor(np.array(image1), cv2.COLOR_RGB2BGR)
+    image2 = cv2.cvtColor(np.array(image2), cv2.COLOR_RGB2BGR)
+    swapped_image = swap_faces(image1, image2)
+    return cv2.cvtColor(swapped_image, cv2.COLOR_BGR2RGB)
+
+# Upscaling function
+def upscale_image(image, scale_factor=2):
+    image = Image.fromarray(np.array(image))
+    width, height = image.size
+    new_size = (int(width * scale_factor), int(height * scale_factor))
+    upscaled_image = image.resize(new_size, Image.LANCZOS)
+    return upscaled_image
+
+# Gradio interface function
+def process_image(prompt, image1=None, image2=None, scale_factor=2):
+    # Generate image from prompt
+    generated_image = generate_image(prompt)
+    
+    # Perform faceswap if two images are provided
+    swapped_image = None
+    if image1 and image2:
+        swapped_image = face_swap(image1, image2)
+    
+    # Perform upscaling if scale factor is specified
+    upscaled_image = upscale_image(generated_image, scale_factor)
+
+    return {
+        "Generated Image": generated_image,
+        "Swapped Image": swapped_image,
+        "Upscaled Image": upscaled_image
+    }
+
+# Gradio interface setup
 iface = gr.Interface(
-    fn=generate_image,
-    inputs=gr.Textbox(label="Enter your prompt"),
-    outputs=gr.Image(label="Generated Image"),
-    title="AI Image Generator",
-    description="Generate images from text prompts using Stable Diffusion."
+    fn=process_image,
+    inputs=[
+        gr.Textbox(label="Enter your prompt"),
+        gr.Image(label="Image 1 (for faceswap)", type="pil", optional=True),
+        gr.Image(label="Image 2 (for faceswap)", type="pil", optional=True),
+        gr.Slider(label="Upscale Factor", minimum=1, maximum=4, step=1, default=2)
+    ],
+    outputs=[
+        gr.Image(label="Generated Image"),
+        gr.Image(label="Swapped Image"),
+        gr.Image(label="Upscaled Image")
+    ],
+    title="Fooocus Image Processing",
+    description="Generate images from prompts, swap faces, and upscale images."
 )
 
 if __name__ == "__main__":
-    # Lanzar la interfaz
     iface.launch(server_name="0.0.0.0", server_port=7860)