jinx/ComfyUI
1
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2025-08-02 23:14:49 +08:00
Code Issues Activity

Add temporal tiling to VAE Decode (Tiled) node.

Browse Source 
You can now do tiled VAE decoding on the temporal direction for videos.
This commit is contained in:
comfyanonymous
2024-12-23 20:03:37 -05:00
parent f18ebbd316
commit bc6dac4327
3 changed files with 56 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
nodes.py
View File

@@ -293,17 +293,27 @@ class VAEDecodeTiled:
return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ),
"tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 32}),
"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 decode 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."}),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "decode"
CATEGORY = "_for_testing"
def decode(self, vae, samples, tile_size, overlap=64):
def decode(self, vae, samples, tile_size, overlap=64, temporal_size=64, temporal_overlap=8):
if tile_size < overlap * 4:
overlap = tile_size // 4
temporal_compression = vae.temporal_compression_decode()
if temporal_compression is not None:
temporal_size = max(2, temporal_size // temporal_compression)
temporal_overlap = min(1, temporal_size // 2, temporal_overlap // temporal_compression)
else:
temporal_size = None
temporal_overlap = None
compression = vae.spacial_compression_decode()
images = vae.decode_tiled(samples["samples"], tile_x=tile_size // compression, tile_y=tile_size // compression, overlap=overlap // compression)
images = vae.decode_tiled(samples["samples"], tile_x=tile_size // compression, tile_y=tile_size // compression, overlap=overlap // compression, tile_t=temporal_size, overlap_t=temporal_overlap)
if len(images.shape) == 5: #Combine batches
images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
return (images, )