split encodevideo into two nodes

comfy_extras/nodes_video.py

@@ -16,41 +16,7 @@ from comfy_api.latest import ComfyExtension, io, ui
 from comfy.cli_args import args
 import comfy.utils
 
-class EncodeVideo(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="EncodeVideo",
-            display_name="Encode Video",
-            category="image/video",
-            description="Encode a video using an image encoder.",
-            inputs=[
-                io.Video.Input("video", tooltip="The video to be encoded."),
-                io.Int.Input(
-                    "processing_batch_size", default=-1, min=-1,
-                    tooltip=(
-                        "Number of frames/segments to process at a time during encoding.\n"
-                        "-1 means process all at once. Smaller values reduce GPU memory usage."
-                    ),
-                ),
-                io.Int.Input("step_size", default=8, min=1, max=32,
-                    tooltip=(
-                        "Stride (in frames) between the start of consecutive segments.\n"
-                        "Smaller step = more overlap and smoother temporal coverage "
-                        "but higher compute cost. Larger step = faster but may miss detail."
-                    ),
-                ),
-                io.Vae.Input("vae", optional=True),
-                io.ClipVision.Input("clip_vision", optional=True),
-            ],
-            outputs=[
-                io.Conditioning.Output(display_name="encoded_video"),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, video, processing_batch_size, step_size, vae = None, clip_vision = None):
-
+def encode_video(vae, model, video, step_size, processing_batch_size):
     video = video.images
     if not isinstance(video, torch.Tensor):
         video = torch.from_numpy(video)
@@ -60,20 +26,9 @@ class EncodeVideo(io.ComfyNode):
     # channel last
     if rest[-1] in (1, 3, 4) and rest[0] not in (1, 3, 4):
         video = video.permute(0, 3, 1, 2)
-
-        t, c, h, w = video.shape
-        device = video.device
     b = 1
-        batch_size = b * t
-
-        if vae is not None and clip_vision is not None:
-            raise ValueError("Must either have vae or clip_vision.")
-        elif vae is None and clip_vision is None:
-            raise ValueError("Can't have VAE and Clip Vision passed at the same time!")
-        model = vae.first_stage_model if vae is not None else clip_vision.model
-        vae = vae if vae is not None else clip_vision
-
-
+    t, c, h, w = video.shape
+    batch_size = video.shape[0]
     if hasattr(model, "video_encoding"):
         data, num_segments, output_fn = model.video_encoding(video, step_size)
         batch_size = b * num_segments
@@ -83,14 +38,13 @@ class EncodeVideo(io.ComfyNode):
 
     if processing_batch_size != -1:
         batch_size = processing_batch_size
-
     outputs = None
     total = data.shape[0]
     pbar = comfy.utils.ProgressBar(total/batch_size)
     model_dtype = next(model.parameters()).dtype
     with torch.inference_mode():
         for i in range(0, total, batch_size):
-                chunk = data[i : i + batch_size].to(device, non_blocking = True)
+            chunk = data[i : i + batch_size].to(next(model.parameters()).device, non_blocking = True)
             chunk = chunk.to(model_dtype)
             if hasattr(vae, "encode"):
                 try:
@@ -118,7 +72,69 @@ class EncodeVideo(io.ComfyNode):
             torch.cuda.empty_cache()
             pbar.update(1)
 
-        return io.NodeOutput(output_fn(outputs))
+    return output_fn(outputs)
+
+encode_video_inputs = [
+    io.Video.Input("video", tooltip="The video to be encoded."),
+    io.Int.Input(
+        "processing_batch_size", default=-1, min=-1,
+        tooltip=(
+            "Number of frames/segments to process at a time during encoding.\n"
+            "-1 means process all at once. Smaller values reduce GPU memory usage."
+        ),
+    ),
+    io.Int.Input("step_size", default=8, min=1, max=32,
+        tooltip=(
+            "Stride (in frames) between the start of consecutive segments.\n"
+            "Smaller step = more overlap and smoother temporal coverage "
+            "but higher compute cost. Larger step = faster but may miss detail."
+        ),
+    ),
+]
+class EncodeVideoVAE(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EncodeVideoVAE",
+            display_name="Encode Video VAE",
+            category="image/video",
+            description="Encode a video using a VAE.",
+            inputs=[
+                *encode_video_inputs,
+                io.Vae.Input("vae"),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="encoded_video"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, video, processing_batch_size, step_size, vae):
+        model = vae.first_stage_model
+        model = model.to(vae.device)
+        return io.NodeOutput(encode_video(vae, model, video, step_size, processing_batch_size))
+    
+class EncodeVideoCLIP(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EncodeVideoCLIP",
+            display_name="Encode Video CLIP",
+            category="image/video",
+            description="Encode a video using a CLIP Vision Model.",
+            inputs=[
+                *encode_video_inputs,
+                io.ClipVision.Input("clip_vision"),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="encoded_video"),
+            ],
+        )
+    
+    @classmethod
+    def execute(cls, video, processing_batch_size, step_size, clip_vision):
+        model = clip_vision.model
+        return io.NodeOutput(encode_video(clip_vision, model, video, step_size, processing_batch_size))
 
 class ResampleVideo(io.ComfyNode):
     @classmethod
@@ -373,8 +389,9 @@ class VideoExtension(ComfyExtension):
             CreateVideo,
             GetVideoComponents,
             LoadVideo,
-            EncodeVideo,
             ResampleVideo,
+            EncodeVideoVAE,
+            EncodeVideoCLIP
         ]
 
 async def comfy_entrypoint() -> VideoExtension: