CAIN VFI¶
Documentation¶
- Class name:
CAIN VFI
- Category:
ComfyUI-Frame-Interpolation/VFI
- Output node:
False
The CAIN_VFI node is designed for video frame interpolation, leveraging deep learning models to predict intermediate frames between existing ones in a video sequence. It focuses on enhancing video fluidity and frame rate by generating high-quality intermediate frames.
Input types¶
Required¶
ckpt_name
- Specifies the checkpoint name for loading the pre-trained CAIN model, which is essential for initializing the model with learned weights for frame interpolation.
- Comfy dtype:
COMBO[STRING]
- Python dtype:
str
frames
- The input frames to be interpolated. This tensor contains the consecutive frames between which the intermediate frames are to be generated.
- Comfy dtype:
IMAGE
- Python dtype:
torch.Tensor
clear_cache_after_n_frames
- Determines after how many frames the cache should be cleared to manage memory usage efficiently during the interpolation process.
- Comfy dtype:
INT
- Python dtype:
int
multiplier
- Defines the factor by which the frame rate is to be increased, indicating how many intermediate frames are to be generated between each pair of original frames.
- Comfy dtype:
INT
- Python dtype:
int
Optional¶
optional_interpolation_states
- Allows for the passing of optional states that can influence the interpolation process, providing flexibility in handling different interpolation scenarios.
- Comfy dtype:
INTERPOLATION_STATES
- Python dtype:
InterpolationStateList
Output types¶
image
- Comfy dtype:
IMAGE
- The output tensor containing the interpolated frames, enhancing the video's fluidity by filling in the gaps between original frames.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU
- Common nodes: unknown
Source code¶
class CAIN_VFI:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"ckpt_name": (CKPT_NAMES, ),
"frames": ("IMAGE", ),
"clear_cache_after_n_frames": ("INT", {"default": 10, "min": 1, "max": 1000}),
"multiplier": ("INT", {"default": 2, "min": 2, "max": 1000})
},
"optional": {
"optional_interpolation_states": ("INTERPOLATION_STATES", )
}
}
RETURN_TYPES = ("IMAGE", )
FUNCTION = "vfi"
CATEGORY = "ComfyUI-Frame-Interpolation/VFI"
def vfi(
self,
ckpt_name: typing.AnyStr,
frames: torch.Tensor,
clear_cache_after_n_frames: typing.SupportsInt = 1,
multiplier: typing.SupportsInt = 2,
optional_interpolation_states: InterpolationStateList = None,
**kwargs
):
from .cain_arch import CAIN
model_path = load_file_from_github_release(MODEL_TYPE, ckpt_name)
sd = torch.load(model_path)["state_dict"]
sd = {key.replace('module.', ''): value for key, value in sd.items()}
global interpolation_model
interpolation_model = CAIN(depth=3)
interpolation_model.load_state_dict(sd)
interpolation_model.eval().to(get_torch_device())
del sd
frames = preprocess_frames(frames)
def return_middle_frame(frame_0, frame_1, timestep, model):
#CAIN does some direct modifications to input frame tensors so we need to clone them
return model(frame_0.detach().clone(), frame_1.detach().clone())[0]
args = [interpolation_model]
out = postprocess_frames(
generic_frame_loop(frames, clear_cache_after_n_frames, multiplier, return_middle_frame, *args,
interpolation_states=optional_interpolation_states, use_timestep=False, dtype=torch.float32)
)
return (out,)