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import torch
from torch.utils.data import DataLoader
import pathlib
from vfi_utils import load_file_from_github_release, preprocess_frames, postprocess_frames, generic_frame_loop, InterpolationStateList
import typing
from comfy.model_management import get_torch_device
MODEL_TYPE = pathlib.Path(__file__).parent.name
CKPT_NAMES = ["pretrained_cain.pth"]
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(type(self).__name__, frames, clear_cache_after_n_frames, multiplier, return_middle_frame, *args,
interpolation_states=optional_interpolation_states, use_timestep=False, dtype=torch.float32)
)
return (out,) |