first release

FollowYourPose/followyourpose/pipelines/pipeline_followyourpose.py

@@ -419,7 +419,7 @@ class FollowYourPosePipeline(DiffusionPipeline):
     
     @torch.no_grad()
     def get_skeleton(self,skeleton_path):
-        skeleton_start_end = list(range(0, 120, 5))
+        skeleton_start_end = list(range(0, 96, 4))
         self_transform = transforms.Compose([transforms.Resize(512),
                                         transforms_video.CenterCropVideo(512)])
         

FollowYourPose/test_followyourpose.py

@@ -105,10 +105,6 @@ def test(
     text_encoder.requires_grad_(False)
 
     unet.requires_grad_(False)
-    # for name, module in unet.named_modules():
-    #     if name.endswith(tuple(trainable_modules)):
-    #         for params in module.parameters():
-    #             params.requires_grad = True
 
     if enable_xformers_memory_efficient_attention:
         if is_xformers_available():

app.py

@@ -159,8 +159,7 @@ with gr.Blocks(css='style.css') as demo:
     with gr.Row():
         from example import style_example
         examples = style_example
-
-
+                
     inputs = [
             user_input_video,
             target_prompt,

inference_followyourpose.py

@@ -18,11 +18,11 @@ class merge_config_then_run():
         self.text_encoder = None
         self.vae = None
         self.unet = None
-        
-    def download_model(self):
+    
+    def download_model():
         REPO_ID = 'YueMafighting/FollowYourPose_v1'
-        snapshot_download(repo_id=REPO_ID, local_dir='./FollowYourPose/checkpoints', local_dir_use_symlinks=False)
-                
+        snapshot_download(repo_id=REPO_ID, local_dir='./FollowYourPose/checkpoints', local_dir_use_symlinks=False)    
+ 
             
     def run(
         self,
@@ -40,7 +40,7 @@ class merge_config_then_run():
         bottom_crop=0,
     ):
         self.download_model()
-        default_edit_config='FollowYourPose/configs/pose_sample.yaml'
+        default_edit_config='./FollowYourPose/configs/pose_sample.yaml'
         Omegadict_default_edit_config = OmegaConf.load(default_edit_config)
         
         dataset_time_string = get_time_string()