Update app.py

app.py

@@ -34,6 +34,14 @@ def infer(
     height,
     guidance_scale,
     num_inference_steps,
+    lscale,
+    controlnet_enabled,
+    control_strength,
+    control_mode,
+    control_image,
+    ip_adapter_enabled,
+    ip_adapter_scale,
+    ip_adapter_image,
     progress=gr.Progress(track_tqdm=True),
 ):
     if randomize_seed:
@@ -42,7 +50,9 @@ def infer(
     generator = torch.Generator().manual_seed(seed)
     
     pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
-    if (model_repo_id=="stable-diffusion-v1-5/stable-diffusion-v1-5"): pipe.unet = PeftModel.from_pretrained(pipe.unet,"um235/cartoon_cat_stickers")
+    if (model_repo_id=="stable-diffusion-v1-5/stable-diffusion-v1-5"): 
+        pipe.unet = PeftModel.from_pretrained(pipe.unet,"um235/cartoon_cat_stickers")
+        pipe.scale_lora(lscale)
     pipe = pipe.to(device)
 
     image = pipe(
@@ -81,7 +91,14 @@ with gr.Blocks(css=css) as demo:
                 show_label=True,
                 placeholder="Enter model",
             )
-        
+        with gr.Row():
+                lscale = gr.Slider(
+                    label="Lora scale",
+                    minimum=0,
+                    maximum=2,
+                    step=0.05,
+                    value=1,  # Replace with defaults that work for your model
+                )
         
         
         with gr.Row():
@@ -92,7 +109,45 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
+            with gr.Accordion("ControlNet Settings", open=False):
+            controlnet_enabled = gr.Checkbox(label="Enable ControlNet", value=False)
+
+        with gr.Row():
+                control_strength = gr.Slider(
+                    label="ControlNet Strength",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.05,
+                    value=0.75,
+                    visible=False,
+                )
+
+                control_mode = gr.Dropdown(
+                    label="ControlNet Mode",
+                    choices=["edge_detection", "pose_estimation", "depth_estimation"],
+                    value="edge_detection",
+                    visible=False,
+            )
 
+            control_image = gr.Image(label="ControlNet Image", type="pil", visible=False)
+
+        with gr.Accordion("IP-Adapter Settings", open=False):
+            ip_adapter_enabled = gr.Checkbox(label="Enable IP-Adapter", value=False)
+
+            with gr.Row():
+                ip_adapter_scale = gr.Slider(
+                    label="IP-Adapter Scale",
+                    minimum=0.0,
+                    maximum=2.0,
+                    step=0.05,
+                    value=1.0,
+                    visible=False,
+                )
+
+            ip_adapter_image = gr.Image(label="IP-Adapter Image", type="pil", visible=False)
+
+        with gr.Row():
+            
             run_button = gr.Button("Run", scale=0, variant="primary")
 
         result = gr.Image(label="Result", show_label=False)
@@ -102,7 +157,7 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
+                visible=True,
             )
 
             seed = gr.Slider(
@@ -163,6 +218,14 @@ with gr.Blocks(css=css) as demo:
             height,
             guidance_scale,
             num_inference_steps,
+            lscale,
+            controlnet_enabled,
+            control_strength,
+            control_mode,
+            control_image,
+            ip_adapter_enabled,
+            ip_adapter_scale,
+            ip_adapter_image,
         ],
         outputs=[result, seed],
     )