Update app.py

app.py

@@ -13,15 +13,17 @@ else:
 
 # Initialize the base model and specific LoRA
 base_model = "black-forest-labs/FLUX.1-dev"
+lora_model = "sagar007/sagar_flux"
+
+# Load the base model
 pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.float16)
 
 # Check if CUDA is available and move the model to GPU if possible
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 pipe = pipe.to(device)
 
-lora_repo = "sagar007/sagar_flux"
-trigger_word = "sagar"
-pipe.load_lora_weights(lora_repo)
+# Load the LoRA weights
+pipe.load_lora_weights(lora_model)
 
 MAX_SEED = 2**32-1
 
@@ -31,7 +33,7 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
     generator = torch.Generator(device=device).manual_seed(seed)
     progress(0, f"Starting image generation (using {device})...")
     image = pipe(
-        prompt=f"{prompt} {trigger_word}",
+        prompt=prompt,  # Remove the trigger word as it's part of the LoRA model
         num_inference_steps=steps,
         guidance_scale=cfg_scale,
         width=width,