fix the white result

libs/magicanimate/pipelines/pipeline_animation.py

@@ -353,7 +353,7 @@ class AnimationPipeline(DiffusionPipeline):
 
     def prepare_condition(self, condition, num_videos_per_prompt, device, dtype, do_classifier_free_guidance):
         # prepare conditions for controlnet
-        condition = torch.from_numpy(condition.copy()).to(device=device, dtype=dtype) #/ 255.0
+        condition = torch.from_numpy(condition.copy()).to(device=device, dtype=dtype) / 255.0
         condition = torch.stack([condition for _ in range(num_videos_per_prompt)], dim=0)
         condition = rearrange(condition, 'b f h w c -> (b f) c h w').clone()
         if do_classifier_free_guidance:
@@ -389,7 +389,7 @@ class AnimationPipeline(DiffusionPipeline):
         Convert RGB image to VAE latents
         """
         device = self._execution_device
-        images = torch.from_numpy(images).float().to(dtype) # / 127.5 - 1
+        images = torch.from_numpy(images).float().to(dtype) / 127.5 - 1
         images = rearrange(images, "f h w c -> f c h w").to(device)
         latents = []
         for frame_idx in range(images.shape[0]):

nodes.py

@@ -231,7 +231,8 @@ class MagicAnimate:
             image = self.resize_image_frame(image, size)
             # print(image.shape)
             H, W, C = image.shape
-        
+            
+        image = image * 255 
         prompt = ""
         n_prompt = ""
         control = pose_video.detach().cpu().numpy() # (num_frames, H, W, C)
@@ -246,7 +247,7 @@ class MagicAnimate:
         original_length = control.shape[0]
         if control.shape[0] % config.L > 0:
             control = np.pad(control, ((0, config.L-control.shape[0] % config.L), (0, 0), (0, 0), (0, 0)), mode='edge')
-        
+        control = control * 255
         self.generator.manual_seed(seed)
 
         dist_kwargs = {"rank":0, "world_size":1, "dist":False}