commenting out code for enhanced garment net generated from claude as it was failing the flow

app.py

@@ -53,8 +53,9 @@ unet = UNet2DConditionModel.from_pretrained(
 )
 unet.requires_grad_(False)
 
-enhancedGarmentNet = EnhancedGarmentNetWithTimestep()
-enhancedGarmentNet.to(dtype=torch.float16)
+# This is suggestion from Claude for enhanced garment net
+#enhancedGarmentNet = EnhancedGarmentNetWithTimestep()
+#enhancedGarmentNet.to(dtype=torch.float16)
 
 tokenizer_one = AutoTokenizer.from_pretrained(
     base_path,
@@ -128,7 +129,7 @@ pipe = TryonPipeline.from_pretrained(
         torch_dtype=torch.float16,
 )
 pipe.unet_encoder = UNet_Encoder
-pipe.garment_net = enhancedGarmentNet
+# pipe.garment_net = enhancedGarmentNet
 
 # Standard size of shein images
 #WIDTH = int(4160/5)
@@ -159,7 +160,7 @@ def start_tryon(human_img_dict,garm_img,garment_des, background_img, is_checked,
     openpose_model.preprocessor.body_estimation.model.to(device)
     pipe.to(device)
     pipe.unet_encoder.to(device)
-    pipe.garment_net.to(device)
+    # pipe.garment_net.to(device)
 
     human_img_orig = human_img_dict["background"].convert("RGB")   # ImageEditor
     #human_img_orig = human_img_dict.convert("RGB")     # Image

src/tryon_pipeline.py

@@ -1789,8 +1789,8 @@ class StableDiffusionXLInpaintPipeline(
                     added_cond_kwargs["image_embeds"] = image_embeds
                 print("Calling unet encoder for garment feature extraction")
                 # down,reference_features = self.UNet_Encoder(cloth,t, text_embeds_cloth,added_cond_kwargs= {"text_embeds": pooled_prompt_embeds_c, "time_ids": add_time_ids},return_dict=False)
-                #down,reference_features = self.unet_encoder(cloth,t, text_embeds_cloth,return_dict=False)                
-                garment_out, reference_features = self.garment_net(cloth, t, text_embeds_cloth)
+                down,reference_features = self.unet_encoder(cloth,t, text_embeds_cloth,return_dict=False)                
+                # garment_out, reference_features = self.garment_net(cloth, t, text_embeds_cloth)
                 print(type(reference_features))
                 print(reference_features)
                 reference_features = list(reference_features)