Update app.py

app.py

@@ -38,7 +38,8 @@ torch.backends.cudnn.benchmark = False
 
 hftoken = os.getenv("HF_TOKEN") 
 
-image_encoder_path = "google/siglip-so400m-patch14-384"
+#image_encoder_path = "google/siglip-so400m-patch14-384"
+#image_encoder_path_b = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
 ipadapter_path = hf_hub_download(repo_id="InstantX/SD3.5-Large-IP-Adapter", filename="ip-adapter.bin")
 model_path = 'ford442/stable-diffusion-3.5-large-bf16'
 
@@ -100,6 +101,7 @@ def infer(
     num_inference_steps,
     latent_file,  # Add latents file input
     ip_scale,
+    image_encoder_path,
     progress=gr.Progress(track_tqdm=True),
 ):
     upscaler_2.to(torch.device('cpu'))
@@ -226,6 +228,10 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
         result = gr.Image(label="Result", show_label=False)
         with gr.Accordion("Advanced Settings", open=True):
             latent_file = gr.File(label="Image File (optional)")  # Add latents file input
+            image_encoder_path = gr.Dropdown(
+                ["google/siglip-so400m-patch14-384", "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"], 
+                label="CLIP Model", 
+            )
             ip_scale = gr.Slider(
                 label="Image Prompt Scale",
                 minimum=0.0,
@@ -298,6 +304,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             num_inference_steps,
             latent_file,  # Add latent_file to the inputs
             ip_scale,
+            image_encoder_path,
         ],
         outputs=[result, expanded_prompt_output],
         )