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 = "kaytoo2022/jguan_35-flux"
+lora_repo = "kaytoo2022/jguan_35-flux-2"
 trigger_word = ""  # Leave trigger_word blank if not used.
 pipe.load_lora_weights(lora_repo, adapter_name='jguan')
 
@@ -93,7 +93,7 @@ with gr.Blocks() as app:
             lora_scale_2 = gr.Slider(label="LoRA Scale (GhibSky)", 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 Flux and a text prompt.\nUse `[[just1n]]` in the prompt to activate the LoRA adapter.\n[[non-commercial license, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]")
+            gr.Markdown("Generate images using Flux and a text prompt.\nUse `[Just1nGu4n]` in the prompt to activate the LoRA adapter.\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, lora_scale_2, result])