Update app.py

app.py

@@ -13,7 +13,7 @@ torch.backends.cuda.matmul.allow_tf32 = True
 base_model = "black-forest-labs/FLUX.1-dev"
 pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
 
-lora_repo = "XLabs-AI/flux-RealismLora"
+lora_repo = "strangerzonehf/Flux-Pixel-Background-LoRA"
 trigger_word = ""  # Leave trigger_word blank if not used.
 pipe.load_lora_weights(lora_repo)
 
@@ -55,11 +55,7 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
 
 # Example cached image and settings
 example_image_path = "example0.webp"  # Replace with the actual path to the example image
-example_prompt = """A Jelita Sukawati speaker is captured mid-speech. She has long, dark brown hair that cascades over her shoulders, framing her radiant, smiling face. Her Latina features are highlighted by warm, sun-kissed skin and bright, expressive eyes. She gestures with her left hand, displaying a delicate ring on her pinky finger, as she speaks passionately.
-
-The woman is wearing a colorful, patterned dress with a green lanyard featuring multiple badges and logos hanging around her neck. The lanyard prominently displays the "CagliostroLab" text.
-
-Behind her, there is a blurred background with a white banner containing logos and text, indicating a professional or conference setting. The overall scene captures the energy and vibrancy of her presentation."""
+example_prompt = """Pixel Background, a silhouette of a surfer is seen riding a wave on a red surfboard. The surfers shadow is cast on the left side of the image, adding a touch of depth to the composition. The background is a vibrant orange, pink, and blue, with a sun setting in the upper right corner of the frame. The silhouette of the surfer, a palm tree casts a shadow onto the wave, adding depth and contrast to the scene."""
 example_cfg_scale = 3.2
 example_steps = 32
 example_width = 1152
@@ -87,7 +83,6 @@ with gr.Blocks() as app:
             lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=example_lora_scale)
         with gr.Column(scale=1):
             result = gr.Image(label="Generated Image")
-            gr.Markdown("Generate images using RealismLora and a text prompt.\n[[non-commercial license, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]")
 
     # Automatically load example data and image when the interface is launched
     app.load(load_example, inputs=[], outputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, result])