flf Tiled

__init__.py

@@ -1,9 +1,11 @@
-from .nodes_wan import WanImageToVideoTiledVAE
+from .nodes_wan import WanImageToVideoTiledVAE,WanFirstLastFrameToVideoTiledVAE
 
 NODE_CLASS_MAPPINGS = {
     "WanImageToVideoTiledVAE": WanImageToVideoTiledVAE,
+    "WanFirstLastFrameToVideoTiledVAE": WanFirstLastFrameToVideoTiledVAE
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "WanImageToVideoTiledVAE": "WanImageToVideo (Tiled VAE encode)",
+    "WanFirstLastFrameToVideoTiledVAE": "WanFirstLastFrameToVideo (Tiled VAE encode)",
 }

nodes_wan.py

@@ -22,6 +22,7 @@ class WanImageToVideoTiledVAE:
                              "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                              "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+
                              "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": "Amount of frames to encode at a time."}),
@@ -58,3 +59,75 @@ class WanImageToVideoTiledVAE:
         out_latent = {}
         out_latent["samples"] = latent
         return (positive, negative, out_latent)
+
+
+class WanFirstLastFrameToVideoTiledVAE:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE", ),
+                             "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+
+                             "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": "Amount of frames to encode at a time."}),
+                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Amount of frames to overlap."})
+                },
+                "optional": {"clip_vision_start_image": ("CLIP_VISION_OUTPUT", ),
+                             "clip_vision_end_image": ("CLIP_VISION_OUTPUT", ),
+                             "start_image": ("IMAGE", ),
+                             "end_image": ("IMAGE", ),
+                }}
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/video_models"
+
+    def encode(self, positive, negative, vae, width, height, length, batch_size,  tile_size, overlap, temporal_size=64, temporal_overlap=8, start_image=None, end_image=None, clip_vision_start_image=None, clip_vision_end_image=None):
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+        if end_image is not None:
+            end_image = comfy.utils.common_upscale(end_image[-length:].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+
+        image = torch.ones((length, height, width, 3)) * 0.5
+        mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1]))
+
+        if start_image is not None:
+            image[:start_image.shape[0]] = start_image
+            mask[:, :, :start_image.shape[0] + 3] = 0.0
+
+        if end_image is not None:
+            image[-end_image.shape[0]:] = end_image
+            mask[:, :, -end_image.shape[0]:] = 0.0
+
+        concat_latent_image = vae.encode_tiled(image[:, :, :, :3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
+        mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2)
+        positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
+        negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask})
+
+        clip_vision_output = None
+        if clip_vision_start_image is not None:
+            clip_vision_output = clip_vision_start_image
+
+        if clip_vision_end_image is not None:
+            if clip_vision_output is not None:
+                states = torch.cat([clip_vision_output.penultimate_hidden_states, clip_vision_end_image.penultimate_hidden_states], dim=-2)
+                clip_vision_output = comfy.clip_vision.Output()
+                clip_vision_output.penultimate_hidden_states = states
+            else:
+                clip_vision_output = clip_vision_end_image
+
+        if clip_vision_output is not None:
+            positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
+            negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return (positive, negative, out_latent)