Update app.py

app.py

@@ -64,16 +64,6 @@ pipe.load_lora_weights(
     use_auth_token=HF_TOKEN,
 )
 
-## Load papercut LoRA
-#pipe.load_lora_weights(
-#    "TheLastBen/Papercut_SDXL",
-#    weight_name="papercut.safetensors",
-#    adapter_name="papercut",
-#)
-
-# Mix the LoRAs
-#pipe.set_adapters(["lcm", "papercut"], adapter_weights=[1.0, 0.8])
-
 compel_proc = Compel(
     tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
     text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
@@ -123,7 +113,7 @@ css = """
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="container"):
         gr.Markdown(
-            """# Ultra-Fast SDXL with Latent Consistency LoRA
+            """# SDXL in 4 steps with Latent Consistency LoRAs
             SDXL is loaded with a LCM-LoRA, giving it the super power of doing inference in as little as 4 steps. [Learn more on our blog](#) or [technical report](#).
             """,
             elem_id="intro",
@@ -145,12 +135,16 @@ with gr.Blocks(css=css) as demo:
                 randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1
             )
         with gr.Group():
-            gr.Markdown('''## Using it with `diffusers`
+            gr.Markdown('''## Running LCM-LoRAs it with `diffusers`
+            ```bash
+            pip install diffusers==0.23.0
+            ```
+            
             ```py
             from diffusers import DiffusionPipeline, LCMScheduler
             pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0").to("cuda") 
             pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
-            pipe.load_lora_weights("lcm-sd/lcm-sdxl-lora")
+            pipe.load_lora_weights("lcm-sd/lcm-sdxl-lora") #yes, it is a real LoRA that gives superpowers to SDXL!
 
             results = pipe(
                 prompt="The spirit of a tamagotchi wandering in the city of Vienna",