update wan2.2-fun

Author: lzw478614@alibaba-inc.com

diffsynth/models/wan_video_dit.py

@@ -294,6 +294,7 @@ def __init__(
     ):
         super().__init__()
         self.dim = dim
+        self.in_dim = dim
         self.freq_dim = freq_dim
         self.has_image_input = has_image_input
         self.patch_size = patch_size

diffsynth/pipelines/wan_video_new.py

@@ -663,25 +663,23 @@ def process(self, pipe: WanVideoPipeline, input_image, latents, height, width, t
 class WanVideoUnit_FunControl(PipelineUnit):
     def __init__(self):
         super().__init__(
-            input_params=("control_video", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride", "clip_feature", "y"),
+            input_params=("control_video", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride", "clip_feature", "y", "latents"),
             onload_model_names=("vae",)
         )
 
-    def process(self, pipe: WanVideoPipeline, control_video, num_frames, height, width, tiled, tile_size, tile_stride, clip_feature, y):
+    def process(self, pipe: WanVideoPipeline, control_video, num_frames, height, width, tiled, tile_size, tile_stride, clip_feature, y, latents):
         if control_video is None:
             return {}
         pipe.load_models_to_device(self.onload_model_names)
         control_video = pipe.preprocess_video(control_video)
         control_latents = pipe.vae.encode(control_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
         control_latents = control_latents.to(dtype=pipe.torch_dtype, device=pipe.device)
+        y_dim = pipe.dit.in_dim-control_latents.shape[1]-latents.shape[1]
         if clip_feature is None or y is None:
             clip_feature = torch.zeros((1, 257, 1280), dtype=pipe.torch_dtype, device=pipe.device)
-            y = torch.zeros((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device)
-            if pipe.dit2 is not None:
-                y = torch.zeros((1, 20, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device)
+            y = torch.zeros((1, y_dim, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device)
         else:
-            if pipe.dit2 is None:
-                y = y[:, -16:]
+            y = y[:, -y_dim:]
         y = torch.concat([control_latents, y], dim=1)
         return {"clip_feature": clip_feature, "y": y}
 
@@ -734,13 +732,19 @@ def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_cont
         control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
         control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
         control_camera_latents_input = control_camera_latents.to(device=pipe.device, dtype=pipe.torch_dtype)
-
-        image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
-        vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
-        y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+        
+        input_image = input_image.resize((width, height))
+        input_latents = pipe.preprocess_video([input_image])
+        input_latents = pipe.vae.encode(input_latents, device=pipe.device)
+        y = torch.zeros_like(latents).to(pipe.device)
+        y[:, :, :1] = input_latents
         y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
 
-        if pipe.dit2 is not None:
+        if y.shape[1] != pipe.dit.in_dim - latents.shape[1]:
+            image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
+            vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
+            y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+            y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
             msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
             msk[:, 1:] = 0
             msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
@@ -1061,7 +1065,7 @@ def model_fn_wan_video(
     if clip_feature is not None and dit.require_clip_embedding:
         clip_embdding = dit.img_emb(clip_feature)
         context = torch.cat([clip_embdding, context], dim=1)
-    
+
     # Add camera control
     x, (f, h, w) = dit.patchify(x, control_camera_latents_input)