Update app.py

app.py

@@ -21,19 +21,25 @@ if not os.path.exists(model_path):
     with open(model_path, 'wb') as f:
         f.write(response.content)
 
-# Configuration for the model
+# Source: https://github.com/XLabs-AI/x-flux.git
 name = "flux-dev"
 device = torch.device("cuda")
+offload = False
 is_schnell = name == "flux-schnell"
 
 def preprocess_image(image, target_width, target_height, crop=True):
     if crop:
         image = c_crop(image)  # Crop the image to square
         original_width, original_height = image.size
+
+        # Resize to match the target size without stretching
         scale = max(target_width / original_width, target_height / original_height)
         resized_width = int(scale * original_width)
         resized_height = int(scale * original_height)
+
         image = image.resize((resized_width, resized_height), Image.LANCZOS)
+        
+        # Center crop to match the target dimensions
         left = (resized_width - target_width) // 2
         top = (resized_height - target_height) // 2
         image = image.crop((left, top, left + target_width, top + target_height))
@@ -50,12 +56,13 @@ def preprocess_canny_image(image, target_width, target_height, crop=True):
 @spaces.GPU(duration=120)
 def generate_image(prompt, control_image, num_steps=50, guidance=4, width=512, height=512, seed=42, random_seed=False):
     if random_seed:
-        seed = np.random.randint(0, 999999999)
+        seed = np.random.randint(0, 10000)
     
     if not os.path.isdir("./controlnet_results/"):
         os.makedirs("./controlnet_results/")
 
     torch_device = torch.device("cuda")
+    
     torch.cuda.empty_cache()  # Clear GPU cache
     
     model = load_flow_model(name, device=torch_device)
@@ -92,26 +99,22 @@ def generate_image(prompt, control_image, num_steps=50, guidance=4, width=512, h
     
     return [processed_input, output_img]  # Return both images for slider
 
-def update_value(name, value):
-    return f"{name}: {value}"
-
 interface = gr.Interface(
     fn=generate_image,
     inputs=[
         gr.Textbox(label="Prompt"),
         gr.Image(type="pil", label="Control Image"),
-        gr.Slider(step=1, minimum=1, maximum=64, value=28, label="Num Steps", interactive=True, info="Steps for the generation"),
-        gr.Slider(minimum=0.1, maximum=10, value=4, label="Guidance", interactive=True, info="Guidance scale"),
-        gr.Slider(minimum=128, maximum=1024, step=128, value=512, label="Width", interactive=True, info="Image width"),
-        gr.Slider(minimum=128, maximum=1024, step=128, value=512, label="Height", interactive=True, info="Image height"),
-        gr.Slider(value=42, minimum=0, maximum=99999999, step=1, label="Seed", interactive=True, info="Random seed"),
+        gr.Slider(step=1, minimum=1, maximum=64, value=28, label="Num Steps"),
+        gr.Slider(minimum=0.1, maximum=10, value=4, label="Guidance"),
+        gr.Slider(minimum=128, maximum=1024, step=128, value=512, label="Width"),
+        gr.Slider(minimum=128, maximum=1024, step=128, value=512, label="Height"),
+        gr.Number(value=42, label="Seed"),
         gr.Checkbox(label="Random Seed")
     ],
     outputs=ImageSlider(label="Before / After"),  # Use ImageSlider as the output
     title="FLUX.1 Controlnet Canny",
-    description="Generate images using ControlNet and a text prompt.\n[[non-commercial license, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]",
-    live=True,
+    description="Generate images using ControlNet and a text prompt.\n[[non-commercial license, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]"
 )
 
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch()