Update app.py

app.py

@@ -23,7 +23,7 @@ os.environ["HF_HOME"] = cache_path
 
 torch.backends.cuda.matmul.allow_tf32 = True
 
-pipe = FluxPipeline.from_pretrained("AlekseyCalvin/PixelwaveFluxSchnell_Diffusers", torch_dtype=torch.bfloat16)
+pipe = FluxPipeline.from_pretrained("AlekseyCalvin/HistoricColorSoonr_v2_FluxSchnell_Diffusers", torch_dtype=torch.bfloat16)
 pipe.to(device="cuda", dtype=torch.bfloat16)
 
 # Load LoRAs from JSON file
@@ -138,7 +138,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
     )
     	    # Info blob stating what the app is running
     info_blob = gr.HTML(
-        """<div id="info_blob"> Activist & Futurealist LoRa-stocked Img Manufactory (currently on Flux Schnell Pixelwave (2-8 steps))</div>"""
+        """<div id="info_blob"> Activist & Futurealist LoRa-stocked Img Manufactory (currently on our Historic Color Soon®v.2 Flux Schnell (2-8 steps) model checkpoint (at AlekseyCalvin/HistoricColorSoonrFluxV2) )</div>"""
     )
 
         # Info blob stating what the app is running
@@ -169,7 +169,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
         with gr.Accordion("Advanced Settings", open=True):
             with gr.Column():
                 with gr.Row():
-                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
+                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=1.0)
                     steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=6)
                 
                 with gr.Row():
@@ -179,7 +179,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
                 with gr.Row():
                     randomize_seed = gr.Checkbox(True, label="Randomize seed")
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
-                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1.5, step=0.01, value=0.9)
+                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=2.0, step=0.01, value=0.6)
 
     gallery.select(
         update_selection,