Add HunyuanVideoEncodeKeyframesToCond

to use native hunyuan dashtoon keyframe LoRA:

https://huggingface.co/Kijai/HunyuanVideo_comfy/blob/main/HunyuanVideo_dashtoon_keyframe_lora_converted_comfy_bf16.safetensors

__init__.py

@@ -186,6 +186,7 @@ NODE_CONFIG = {
     "WanVideoTeaCacheKJ": {"class": WanVideoTeaCacheKJ, "name": "WanVideo Tea Cache (native)"},
     "WanVideoEnhanceAVideoKJ": {"class": WanVideoEnhanceAVideoKJ, "name": "WanVideo Enhance A Video (native)"},
     "TimerNodeKJ": {"class": TimerNodeKJ, "name": "Timer Node KJ"},
+    "HunyuanVideoEncodeKeyframesToCond": {"class": HunyuanVideoEncodeKeyframesToCond, "name": "HunyuanVideo Encode Keyframes To Cond"},
 
     #instance diffusion
     "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking},

nodes/nodes.py

@@ -2644,4 +2644,68 @@ class TimerNodeKJ:
             timer.start_time = None
             return (any_input, timer, timer.elapsed)
 
-        
+class HunyuanVideoEncodeKeyframesToCond:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "model": ("MODEL",),
+                    "positive": ("CONDITIONING", ),
+                    "vae": ("VAE", ),
+                    "start_frame": ("IMAGE", ),
+                    "end_frame": ("IMAGE", ),
+                    "num_frames": ("INT", {"default": 33, "min": 2, "max": 4096, "step": 1}),
+                    "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
+                    "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
+                    "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}),
+                    "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}),
+                    },
+                    "optional": {
+                        "negative": ("CONDITIONING", ),
+                    }
+                }
+
+    RETURN_TYPES = ("MODEL", "CONDITIONING","CONDITIONING","LATENT")
+    RETURN_NAMES = ("model", "positive", "negative", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "KJNodes/videomodels"
+
+    def encode(self, model, positive, start_frame, end_frame, num_frames, vae, tile_size, overlap, temporal_size, temporal_overlap, negative=None):
+
+        model_clone = model.clone()
+
+        model_clone.add_object_patch("concat_keys", ("concat_image",))
+
+       
+        x = (start_frame.shape[1] // 8) * 8
+        y = (start_frame.shape[2] // 8) * 8
+
+        if start_frame.shape[1] != x or start_frame.shape[2] != y:
+            x_offset = (start_frame.shape[1] % 8) // 2
+            y_offset = (start_frame.shape[2] % 8) // 2
+            start_frame = start_frame[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+        if end_frame.shape[1] != x or end_frame.shape[2] != y:
+            x_offset = (start_frame.shape[1] % 8) // 2
+            y_offset = (start_frame.shape[2] % 8) // 2
+            end_frame = end_frame[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+
+        video_frames = torch.zeros(num_frames-2, start_frame.shape[1], start_frame.shape[2], start_frame.shape[3], device=start_frame.device, dtype=start_frame.dtype)
+        video_frames = torch.cat([start_frame, video_frames, end_frame], dim=0)
+
+        concat_latent = vae.encode_tiled(video_frames[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
+
+        out_latent = {}
+        out_latent["samples"] = torch.zeros_like(concat_latent)
+
+        out = []
+        for conditioning in [positive, negative if negative is not None else []]:
+            c = []
+            for t in conditioning:
+                d = t[1].copy()
+                d["concat_latent_image"] = concat_latent
+                n = [t[0], d]
+                c.append(n)
+            out.append(c)
+        if len(out) == 1:
+            out.append(out[0])
+        return (model_clone, out[0], out[1], out_latent)