Update app.py

app.py

@@ -21,7 +21,7 @@ from insightface.app import FaceAnalysis
 from style_template import styles
 from pipeline_stable_diffusion_xl_instantid_full import StableDiffusionXLInstantIDPipeline, draw_kps
 
-from controlnet_aux import OpenposeDetector
+# from controlnet_aux import OpenposeDetector
 
 import gradio as gr
 
@@ -58,7 +58,7 @@ app = FaceAnalysis(
 )
 app.prepare(ctx_id=0, det_size=(640, 640))
 
-openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+# openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
 
 depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(device).eval()
 
@@ -86,13 +86,13 @@ controlnet_identitynet = ControlNetModel.from_pretrained(
 )
 
 # controlnet-pose/canny/depth
-controlnet_pose_model = "thibaud/controlnet-openpose-sdxl-1.0"
+# controlnet_pose_model = "thibaud/controlnet-openpose-sdxl-1.0"
 controlnet_canny_model = "diffusers/controlnet-canny-sdxl-1.0"
 controlnet_depth_model = "diffusers/controlnet-depth-sdxl-1.0-small"
 
-controlnet_pose = ControlNetModel.from_pretrained(
-    controlnet_pose_model, torch_dtype=dtype
-).to(device)
+# controlnet_pose = ControlNetModel.from_pretrained(
+#     controlnet_pose_model, torch_dtype=dtype
+# ).to(device)
 controlnet_canny = ControlNetModel.from_pretrained(
     controlnet_canny_model, torch_dtype=dtype
 ).to(device)
@@ -127,12 +127,12 @@ def get_canny_image(image, t1=100, t2=200):
     return Image.fromarray(edges, "L")
 
 controlnet_map = {
-    "pose": controlnet_pose,
+    # "pose": controlnet_pose,
     "canny": controlnet_canny,
     "depth": controlnet_depth,
 }
 controlnet_map_fn = {
-    "pose": openpose,
+    # "pose": openpose,
     "canny": get_canny_image,
     "depth": get_depth_map,
 }
@@ -230,10 +230,10 @@ def run_for_examples(face_file, pose_file, prompt, style, negative_prompt):
         20,  # num_steps
         0.8,  # identitynet_strength_ratio
         0.8,  # adapter_strength_ratio
-        0.4,  # pose_strength
+        # 0.4,  # pose_strength
         0.3,  # canny_strength
         0.5,  # depth_strength
-        ["pose", "canny"],  # controlnet_selection
+        ["depth", "canny"],  # controlnet_selection
         5.0,  # guidance_scale
         42,  # seed
         "EulerDiscreteScheduler",  # scheduler
@@ -294,7 +294,7 @@ def generate_image(
     num_steps,
     identitynet_strength_ratio,
     adapter_strength_ratio,
-    pose_strength,
+    # pose_strength,
     canny_strength,
     depth_strength,
     controlnet_selection,
@@ -383,7 +383,7 @@ def generate_image(
 
     if len(controlnet_selection) > 0:
         controlnet_scales = {
-            "pose": pose_strength,
+            # "pose": pose_strength,
             "canny": canny_strength,
             "depth": depth_strength,
         }
@@ -526,16 +526,16 @@ with gr.Blocks(css=css) as demo:
             )
             with gr.Accordion("Controlnet"):
                 controlnet_selection = gr.CheckboxGroup(
-                    ["pose", "canny", "depth"], label="Controlnet", value=["pose"],
+                    ["canny", "depth"], label="Controlnet", value=["canny"],
                     info="Use pose for skeleton inference, canny for edge detection, and depth for depth map estimation. You can try all three to control the generation process"
                 )
-                pose_strength = gr.Slider(
-                    label="Pose strength",
-                    minimum=0,
-                    maximum=1.5,
-                    step=0.05,
-                    value=0.40,
-                )
+                # pose_strength = gr.Slider(
+                #     label="Pose strength",
+                #     minimum=0,
+                #     maximum=1.5,
+                #     step=0.05,
+                #     value=0.40,
+                # )
                 canny_strength = gr.Slider(
                     label="Canny strength",
                     minimum=0,
@@ -619,7 +619,7 @@ with gr.Blocks(css=css) as demo:
                 num_steps,
                 identitynet_strength_ratio,
                 adapter_strength_ratio,
-                pose_strength,
+                # pose_strength,
                 canny_strength,
                 depth_strength,
                 controlnet_selection,