diff --git a/IPAdapterPlus.py b/IPAdapterPlus.py
index aad4bbb..0f43a60 100644
--- a/IPAdapterPlus.py
+++ b/IPAdapterPlus.py
@@ -210,13 +210,13 @@ class IPAdapter(nn.Module):
 
 class CrossAttentionPatch:
     # forward for patching
-    def __init__(self, weight, ipadapter, dtype, number, cond, uncond, weight_type, mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False):
+    def __init__(self, weight, ipadapter, device, dtype, number, cond, uncond, weight_type, mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False):
         self.weights = [weight]
         self.ipadapters = [ipadapter]
         self.conds = [cond]
         self.unconds = [uncond]
         self.dtype = dtype
-        self.device = 'cuda'
+        self.device = device
         self.number = number
         self.weight_type = [weight_type]
         self.masks = [mask]
@@ -227,15 +227,15 @@ class CrossAttentionPatch:
         self.k_key = str(self.number*2+1) + "_to_k_ip"
         self.v_key = str(self.number*2+1) + "_to_v_ip"
     
-    def set_new_condition(self, weight, ipadapter, cond, uncond, dtype, number, weight_type, mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False):
+    def set_new_condition(self, weight, ipadapter, device, dtype, number, cond, uncond, weight_type, mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False):
         self.weights.append(weight)
         self.ipadapters.append(ipadapter)
         self.conds.append(cond)
         self.unconds.append(uncond)
         self.masks.append(mask)
         self.dtype = dtype
+        self.device = device
         self.weight_type.append(weight_type)
-        self.device = 'cuda'
         self.sigma_start.append(sigma_start)
         self.sigma_end.append(sigma_end)
         self.unfold_batch.append(unfold_batch)
@@ -245,7 +245,10 @@ class CrossAttentionPatch:
         cond_or_uncond = extra_options["cond_or_uncond"]
         sigma = extra_options["sigmas"][0].item() if 'sigmas' in extra_options else 999999999.9
 
-        with torch.autocast(device_type=self.device, dtype=self.dtype):
+        # extra options for AnimateDiff
+        ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None
+
+        with torch.autocast(device_type=self.device.type, dtype=self.dtype):
             q = n
             k = context_attn2
             v = value_attn2
@@ -255,9 +258,6 @@ class CrossAttentionPatch:
             out = optimized_attention(q, k, v, extra_options["n_heads"])
             _, _, lh, lw = extra_options["original_shape"]
             
-            # extra options for AnimateDiff
-            ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None
-
             for weight, cond, uncond, ipadapter, mask, weight_type, sigma_start, sigma_end, unfold_batch in zip(self.weights, self.conds, self.unconds, self.ipadapters, self.masks, self.weight_type, self.sigma_start, self.sigma_end, self.unfold_batch):
                 if sigma > sigma_start or sigma < sigma_end:
                     continue
@@ -492,6 +492,7 @@ class IPAdapterApply:
             "number": 0,
             "weight": self.weight,
             "ipadapter": self.ipadapter,
+            "device": self.device,
             "dtype": self.dtype,
             "cond": image_prompt_embeds,
             "uncond": uncond_image_prompt_embeds,
diff --git a/README.md b/README.md
index 3023c98..508bbf9 100644
--- a/README.md
+++ b/README.md
@@ -45,6 +45,8 @@ The IPAdapter are very powerful models for image-to-image conditioning. Given a
 
 **:japanese_goblin: [Attention Masking video](https://www.youtube.com/watch?v=vqG1VXKteQg)**
 
+**:movie_camera: [Animation Features video](https://www.youtube.com/watch?v=ddYbhv3WgWw)**
+
 ## Installation
 
 Download or git clone this repository inside `ComfyUI/custom_nodes/` directory.
@@ -200,7 +202,8 @@ If you are interested I've also implemented the same features for [Huggingface D
 
 ## IPAdapter in the wild
 
-Let me know if you spot the IPAdapter in the wild!
+Let me know if you spot the IPAdapter in the wild or tag @latentvision in the video description!
 
 - For German speakers you can find interesting YouTube tutorials on [A Latent Place](https://www.youtube.com/watch?v=rAWn_0YOBU0).
+- In Chinese [Introversify](https://www.youtube.com/watch?v=xl8f3oxZgY8)
 - [Scott Detweiler](https://www.youtube.com/watch?v=xzGdynQDzsM) covered this extension.