Update app.py

app.py

@@ -6,7 +6,7 @@ from transformers import CLIPVisionModelWithProjection
 import numpy as np
 import spaces
 
-
+# Initialize the image encoder and pipeline outside the function
 image_encoder = CLIPVisionModelWithProjection.from_pretrained(
     "h94/IP-Adapter",
     subfolder="models/image_encoder",
@@ -19,30 +19,26 @@ pipeline = AutoPipelineForText2Image.from_pretrained(
     image_encoder=image_encoder,
 )
 pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
-
 pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name=["ip-adapter-plus_sdxl_vit-h.safetensors", "ip-adapter-plus-face_sdxl_vit-h.safetensors"])
-pipeline.set_ip_adapter_scale([soy_strength, anon_strength])
-
 pipeline.enable_model_cpu_offload()
 
 @spaces.GPU
-def transform_image(face_image):
+def transform_image(face_image, soy_strength, face_strength):
     generator = torch.Generator(device="cpu").manual_seed(0)
 
-    # Check if the input is already a PIL Image
     if isinstance(face_image, Image.Image):
         processed_face_image = face_image
-    # If the input is a NumPy array, convert it to a PIL Image
     elif isinstance(face_image, np.ndarray):
         processed_face_image = Image.fromarray(face_image)
     else:
         raise ValueError("Unsupported image format")
 
-    # Load the style image from the local path
     style_image_path = "examples/soyjak2.jpg"
     style_image = Image.open(style_image_path)
 
-    # Perform the transformation
+    # Set the IP adapter scale dynamically based on the sliders
+    pipeline.set_ip_adapter_scale([soy_strength, face_strength])
+
     image = pipeline(
         prompt="soyjak",
         ip_adapter_image=[style_image, processed_face_image],
@@ -53,18 +49,18 @@ def transform_image(face_image):
 
     return image
 
-# Gradio interface setup
+# Gradio interface setup with dynamic sliders
 demo = gr.Interface(
     fn=transform_image,
     inputs=[
         gr.Image(label="Upload your face image"),
         gr.Slider(minimum=0, maximum=1, step=0.05, value=0.7, label="Soy Strength"),
-        gr.Slider(minimum=0, maximum=1, step=0.05, value=0.5, label="Face Strength")
+        gr.Slider(minimum=0, maximum=1, step=0.05, value=0.5, label="Face Strength")  # Renamed to Face Strength
     ],
     outputs=gr.Image(label="Your Soyjak"),
     title="InstaSoyjak - turn anyone into a Soyjak",
-    description="All you need to do is upload an image. **Please use responsibly.** Please follow me on Twitter if you like this space: https://twitter.com/angrypenguinPNG. Idea from Yacine, please give him a follow: https://twitter.com/yacineMTB.",
+    description="All you need to do is upload an image and adjust the strengths. **Please use responsibly.**",
 )
 
-demo.queue(max_size=20)  # Configures the queue with a maximum size of 20
-demo.launch()
+demo.queue(max_size=20)
+demo.launch()