Update app.py

app.py

@@ -15,7 +15,7 @@ pipeline = FluxPipeline.from_pretrained(
 
 
 @spaces.GPU()
-def generate_image(image, height, width, prompt, guidance_scale, num_steps, lora_name):
+def generate_image(image, prompt, guidance_scale, num_steps, lora_name):
     # Load the model
     
     # Load and fuse base LoRA weights
@@ -27,6 +27,10 @@ def generate_image(image, height, width, prompt, guidance_scale, num_steps, lora
     pipeline.load_lora_weights("nicolaus-huang/PhotoDoodle", weight_name=f"{lora_name}.safetensors")
     pipeline.fuse_lora()
     
+    height=768
+    width=512
+
+    
     # Prepare the input image
     condition_image = image.resize((height, width)).convert("RGB")
     
@@ -40,16 +44,18 @@ def generate_image(image, height, width, prompt, guidance_scale, num_steps, lora
         num_inference_steps=num_steps,
         max_sequence_length=512
     ).images[0]
+
+    final_image  =  image.resize(image.size)
     
-    return result
+    return final_image
 
 # Create Gradio interface with sliders for numeric inputs
 iface = gr.Interface(
     fn=generate_image,
     inputs=[
         gr.Image(label="Input Image", type="pil", value=default_image),
-        gr.Slider(label="Height", value=768, minimum=256, maximum=1024, step=64),
-        gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=64),
+        # gr.Slider(label="Height", value=768, minimum=256, maximum=1024, step=64),
+        # gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=64),
         gr.Textbox(label="Prompt", value="add a halo and wings for the cat by sksmagiceffects"),
         gr.Slider(label="Guidance Scale", value=3.5, minimum=1.0, maximum=10.0, step=0.1),
         gr.Slider(label="Number of Steps", value=20, minimum=1, maximum=100, step=1),