add multiple attention masks

IPAdapterPlus.py

@@ -241,6 +241,7 @@ def set_new_condition(self, weight, ipadapter, cond, uncond, dtype, number, weig
     def __call__(self, n, context_attn2, value_attn2, extra_options):
         org_dtype = n.dtype
         cond_or_uncond = extra_options["cond_or_uncond"]
+
         with torch.autocast(device_type=self.device, dtype=self.dtype):
             q = n
             k = context_attn2
@@ -287,9 +288,40 @@ def __call__(self, n, context_attn2, value_attn2, extra_options):
 
                         if mask_h*mask_w == qs:
                             break
+
+                    # check if using AnimateDiff and sliding context window
+                    if (mask.shape[0] > 1 and hasattr(cond_or_uncond, "params") and cond_or_uncond.params["sub_idxs"] is not None):
+                        # if mask length matches or exceeds full_length, just get sub_idx masks, resize, and continue
+                        if mask.shape[0] >= cond_or_uncond.params["full_length"]:
+                            mask_downsample = torch.Tensor(mask[cond_or_uncond.params["sub_idxs"]])
+                            mask_downsample = F.interpolate(mask_downsample.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)
+                        # otherwise, need to do more to get proper sub_idxs masks
+                        else:
+                            # first, resize to needed attention size (to save on needed memory for other operations)
+                            mask_downsample = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)
+                            # check if mask length matches full_length - if not, make it match
+                            if mask_downsample.shape[0] < cond_or_uncond.params["full_length"]:
+                                mask_downsample = torch.cat((mask_downsample, mask_downsample[-1:].repeat((cond_or_uncond.params["full_length"]-mask_downsample.shape[0], 1, 1))), dim=0)
+                            # if we have too many remove the excess (should not happen, but just in case)
+                            if mask_downsample.shape[0] > cond_or_uncond.params["full_length"]:
+                                mask_downsample = mask_downsample[:cond_or_uncond.params["full_length"]]
+                            # now, select sub_idxs masks
+                            mask_downsample = mask_downsample[cond_or_uncond.params["sub_idxs"]]
+                    # otherwise, perform usual mask interpolation
+                    else:
+                        mask_downsample = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bicubic").squeeze(1)
+
+                    # if we don't have enough masks repeat the last one until we reach the right size
+                    if mask_downsample.shape[0] < batch_prompt:
+                        mask_downsample = torch.cat((mask_downsample, mask_downsample[-1:, :, :].repeat((batch_prompt-mask_downsample.shape[0], 1, 1))), dim=0)
+                    # if we have too many remove the exceeding
+                    elif mask_downsample.shape[0] > batch_prompt:
+                        mask_downsample = mask_downsample[:batch_prompt, :, :]
 
-                    mask_downsample = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(mask_h, mask_w), mode="bilinear").squeeze(0)
-                    mask_downsample = mask_downsample.view(1, -1, 1).repeat(out.shape[0], 1, out.shape[2])
+                    # repeat the masks
+                    mask_downsample = mask_downsample.repeat(len(cond_or_uncond), 1, 1)
+                    mask_downsample = mask_downsample.view(mask_downsample.shape[0], -1, 1).repeat(1, 1, out.shape[2])
+
                     out_ip = out_ip * mask_downsample
 
                 out = out + out_ip
@@ -410,7 +442,7 @@ def apply_ipadapter(self, ipadapter, model, weight, clip_vision=None, image=None
         work_model = model.clone()
 
         if attn_mask is not None:
-            attn_mask = attn_mask.squeeze().to(self.device)
+            attn_mask = attn_mask.to(self.device)
 
         patch_kwargs = {
             "number": 0,

README.md

@@ -5,6 +5,8 @@ IPAdapter implementation that follows the ComfyUI way of doing things. The code
 
 ## Important updates
 
+**2023/11/24**: Support for multiple attention masks.
+
 **2023/11/23**: Small but important update: the new default location for the IPAdapter models is `ComfyUI/models/ipadapter`. **No panic**: the legacy `ComfyUI/custom_nodes/ComfyUI_IPAdapter_plus/models` location still works and nothing will break.
 
 **2023/11/08**: Added [attention masking](#attention-masking).
@@ -119,7 +121,7 @@ IPAdapter offers an interesting model for a kind of "face swap" effect. [The wor
 
 **Note:** there's a new `full-face` model available that's arguably better.
 
-### Masking
+### Masking (Inpainting)
 
 The most effective way to apply the IPAdapter to a region is by an [inpainting workflow](./examples/IPAdapter_inpaint.json). Remeber to use a specific checkpoint for inpainting otherwise it won't work. Even if you are inpainting a face I find that the *IPAdapter-Plus* (not the *face* one), works best.
 
@@ -167,6 +169,8 @@ In the picture below I use two reference images masked one on the left and the o
 
 <img src="./examples/masking.jpg" width="512" alt="masking" />
 
+It is also possible to send a batch of masks that will be applied to a batch of latents, one per frame. The size should be the same but if needed some normalization will be performed to avoid errors. This feature also supports (experimentally) AnimateDiff including context sliding.
+
 In the examples directory you'll find a couple of masking workflows: [simple](examples/IPAdapter_mask.json) and [two masks](examples/IPAdapter_2_masks.json).
 
 ## Troubleshooting