nodes/stduhpf-ComfyUI--WanImageToVideoTiled
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Wan phantom tiled encode

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Stéphane du Hamel
2025-08-03 21:21:11 +02:00
parent 7a5b2a6f15
commit 91d47400be
2 changed files with 45 additions and 0 deletions
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__init__.py
+2
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@@ -7,6 +7,7 @@ NODE_CLASS_MAPPINGS = {
"WanFunInpaintToVideoTiledVAE": WanFunInpaintToVideoTiledVAE,
"WanVaceToVideoTiledVAE": WanVaceToVideoTiledVAE,
"WanCameraImageToVideoTiledVAE":WanCameraImageToVideoTiledVAE,
"WanPhantomSubjectToVideoTiledVAE": WanPhantomSubjectToVideoTiledVAE,
}
NODE_DISPLAY_NAME_MAPPINGS = {
@@ -16,4 +17,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"WanFunInpaintToVideoTiledVAE": "WanFunInpaintToVideo (Tiled VAE encode)",
"WanVaceToVideoTiledVAE": "WanVaceToVideo (Tiled VAE encode)",
"WanCameraImageToVideoTiledVAE": "WanCameraImageToVideo (Tiled VAE encode)",
"WanPhantomSubjectToVideoTiledVAE": "WanPhantomSubjectToVideo (Tiled VAE encode)",
}
nodes_wan.py
+43
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@@ -357,3 +357,46 @@ class WanCameraImageToVideoTiledVAE:
out_latent = {}
out_latent["samples"] = latent
return (positive, negative, out_latent)
class WanPhantomSubjectToVideoTiledVAE:
@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": {"images": ("IMAGE", ),
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative_text", "negative_img_text", "latent")
FUNCTION = "encode"
CATEGORY = "conditioning/video_models"
def encode(self, positive, negative, vae, width, height, length, batch_size, tile_size, overlap, temporal_size, temporal_overlap, images):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
cond2 = negative
if images is not None:
images = comfy.utils.common_upscale(images[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
latent_images = []
for i in images:
latent_images += [vae.encode_tiled(i.unsqueeze(0)[:, :, :, :3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)]
concat_latent_image = torch.cat(latent_images, dim=2)
positive = node_helpers.conditioning_set_values(positive, {"time_dim_concat": concat_latent_image})
cond2 = node_helpers.conditioning_set_values(negative, {"time_dim_concat": concat_latent_image})
negative = node_helpers.conditioning_set_values(negative, {"time_dim_concat": comfy.latent_formats.Wan21().process_out(torch.zeros_like(concat_latent_image))})
out_latent = {}
out_latent["samples"] = latent
return (positive, cond2, negative, out_latent)