Ethanfel
db64fc195a
Wraps BiM-VFI (CVPR 2025) as a ComfyUI custom node for long video frame interpolation with memory-safe sequential processing. - LoadBIMVFIModel: checkpoint loader with auto-download from Google Drive - BIMVFIInterpolate: 2x/4x/8x recursive interpolation with per-pair GPU processing, configurable VRAM management (all_on_gpu for high-VRAM setups), progress bar, and backwarp cache clearing - Vendored inference-only architecture from KAIST-VICLab/BiM-VFI - Auto-detection of CUDA version for cupy installation Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
400 lines
16 KiB
Python
400 lines
16 KiB
Python
#!/usr/bin/env python
|
|
|
|
import collections
|
|
import cupy
|
|
import os
|
|
import re
|
|
import torch
|
|
import typing
|
|
|
|
|
|
##########################################################
|
|
|
|
|
|
objCudacache = {}
|
|
|
|
|
|
def cuda_int32(intIn:int):
|
|
return cupy.int32(intIn)
|
|
# end
|
|
|
|
|
|
def cuda_float32(fltIn:float):
|
|
return cupy.float32(fltIn)
|
|
# end
|
|
|
|
|
|
def cuda_kernel(strFunction:str, strKernel:str, objVariables:typing.Dict):
|
|
if 'device' not in objCudacache:
|
|
objCudacache['device'] = torch.cuda.get_device_name()
|
|
# end
|
|
|
|
strKey = strFunction
|
|
|
|
for strVariable in objVariables:
|
|
objValue = objVariables[strVariable]
|
|
|
|
strKey += strVariable
|
|
|
|
if objValue is None:
|
|
continue
|
|
|
|
elif type(objValue) == int:
|
|
strKey += str(objValue)
|
|
|
|
elif type(objValue) == float:
|
|
strKey += str(objValue)
|
|
|
|
elif type(objValue) == bool:
|
|
strKey += str(objValue)
|
|
|
|
elif type(objValue) == str:
|
|
strKey += objValue
|
|
|
|
elif type(objValue) == torch.Tensor:
|
|
strKey += str(objValue.dtype)
|
|
strKey += str(objValue.shape)
|
|
strKey += str(objValue.stride())
|
|
|
|
elif True:
|
|
print(strVariable, type(objValue))
|
|
assert(False)
|
|
|
|
# end
|
|
# end
|
|
|
|
strKey += objCudacache['device']
|
|
|
|
if strKey not in objCudacache:
|
|
for strVariable in objVariables:
|
|
objValue = objVariables[strVariable]
|
|
|
|
if objValue is None:
|
|
continue
|
|
|
|
elif type(objValue) == int:
|
|
strKernel = strKernel.replace('{{' + strVariable + '}}', str(objValue))
|
|
|
|
elif type(objValue) == float:
|
|
strKernel = strKernel.replace('{{' + strVariable + '}}', str(objValue))
|
|
|
|
elif type(objValue) == bool:
|
|
strKernel = strKernel.replace('{{' + strVariable + '}}', str(objValue))
|
|
|
|
elif type(objValue) == str:
|
|
strKernel = strKernel.replace('{{' + strVariable + '}}', objValue)
|
|
|
|
elif type(objValue) == torch.Tensor and objValue.dtype == torch.uint8:
|
|
strKernel = strKernel.replace('{{type}}', 'unsigned char')
|
|
|
|
elif type(objValue) == torch.Tensor and objValue.dtype == torch.float16:
|
|
strKernel = strKernel.replace('{{type}}', 'half')
|
|
|
|
elif type(objValue) == torch.Tensor and objValue.dtype == torch.float32:
|
|
strKernel = strKernel.replace('{{type}}', 'float')
|
|
|
|
elif type(objValue) == torch.Tensor and objValue.dtype == torch.float64:
|
|
strKernel = strKernel.replace('{{type}}', 'double')
|
|
|
|
elif type(objValue) == torch.Tensor and objValue.dtype == torch.int32:
|
|
strKernel = strKernel.replace('{{type}}', 'int')
|
|
|
|
elif type(objValue) == torch.Tensor and objValue.dtype == torch.int64:
|
|
strKernel = strKernel.replace('{{type}}', 'long')
|
|
|
|
elif type(objValue) == torch.Tensor:
|
|
print(strVariable, objValue.dtype)
|
|
assert(False)
|
|
|
|
elif True:
|
|
print(strVariable, type(objValue))
|
|
assert(False)
|
|
|
|
# end
|
|
# end
|
|
|
|
while True:
|
|
objMatch = re.search('(SIZE_)([0-4])(\()([^\)]*)(\))', strKernel)
|
|
|
|
if objMatch is None:
|
|
break
|
|
# end
|
|
|
|
intArg = int(objMatch.group(2))
|
|
|
|
strTensor = objMatch.group(4)
|
|
intSizes = objVariables[strTensor].size()
|
|
|
|
strKernel = strKernel.replace(objMatch.group(), str(intSizes[intArg] if torch.is_tensor(intSizes[intArg]) == False else intSizes[intArg].item()))
|
|
# end
|
|
|
|
while True:
|
|
objMatch = re.search('(OFFSET_)([0-4])(\()', strKernel)
|
|
|
|
if objMatch is None:
|
|
break
|
|
# end
|
|
|
|
intStart = objMatch.span()[1]
|
|
intStop = objMatch.span()[1]
|
|
intParentheses = 1
|
|
|
|
while True:
|
|
intParentheses += 1 if strKernel[intStop] == '(' else 0
|
|
intParentheses -= 1 if strKernel[intStop] == ')' else 0
|
|
|
|
if intParentheses == 0:
|
|
break
|
|
# end
|
|
|
|
intStop += 1
|
|
# end
|
|
|
|
intArgs = int(objMatch.group(2))
|
|
strArgs = strKernel[intStart:intStop].split(',')
|
|
|
|
assert(intArgs == len(strArgs) - 1)
|
|
|
|
strTensor = strArgs[0]
|
|
intStrides = objVariables[strTensor].stride()
|
|
|
|
strIndex = []
|
|
|
|
for intArg in range(intArgs):
|
|
strIndex.append('((' + strArgs[intArg + 1].replace('{', '(').replace('}', ')').strip() + ')*' + str(intStrides[intArg] if torch.is_tensor(intStrides[intArg]) == False else intStrides[intArg].item()) + ')')
|
|
# end
|
|
|
|
strKernel = strKernel.replace('OFFSET_' + str(intArgs) + '(' + strKernel[intStart:intStop] + ')', '(' + str.join('+', strIndex) + ')')
|
|
# end
|
|
|
|
while True:
|
|
objMatch = re.search('(VALUE_)([0-4])(\()', strKernel)
|
|
|
|
if objMatch is None:
|
|
break
|
|
# end
|
|
|
|
intStart = objMatch.span()[1]
|
|
intStop = objMatch.span()[1]
|
|
intParentheses = 1
|
|
|
|
while True:
|
|
intParentheses += 1 if strKernel[intStop] == '(' else 0
|
|
intParentheses -= 1 if strKernel[intStop] == ')' else 0
|
|
|
|
if intParentheses == 0:
|
|
break
|
|
# end
|
|
|
|
intStop += 1
|
|
# end
|
|
|
|
intArgs = int(objMatch.group(2))
|
|
strArgs = strKernel[intStart:intStop].split(',')
|
|
|
|
assert(intArgs == len(strArgs) - 1)
|
|
|
|
strTensor = strArgs[0]
|
|
intStrides = objVariables[strTensor].stride()
|
|
|
|
strIndex = []
|
|
|
|
for intArg in range(intArgs):
|
|
strIndex.append('((' + strArgs[intArg + 1].replace('{', '(').replace('}', ')').strip() + ')*' + str(intStrides[intArg] if torch.is_tensor(intStrides[intArg]) == False else intStrides[intArg].item()) + ')')
|
|
# end
|
|
|
|
strKernel = strKernel.replace('VALUE_' + str(intArgs) + '(' + strKernel[intStart:intStop] + ')', strTensor + '[' + str.join('+', strIndex) + ']')
|
|
# end
|
|
|
|
objCudacache[strKey] = {
|
|
'strFunction': strFunction,
|
|
'strKernel': strKernel
|
|
}
|
|
# end
|
|
|
|
return strKey
|
|
# end
|
|
|
|
|
|
@cupy.memoize(for_each_device=True)
|
|
def cuda_launch(strKey:str):
|
|
if 'CUDA_HOME' not in os.environ:
|
|
os.environ['CUDA_HOME'] = '/usr/local/cuda/'
|
|
# end
|
|
|
|
return cupy.RawModule(code=objCudacache[strKey]['strKernel']).get_function(objCudacache[strKey]['strFunction'])
|
|
# end
|
|
|
|
|
|
##########################################################
|
|
|
|
|
|
class costvol_func(torch.autograd.Function):
|
|
@staticmethod
|
|
@torch.amp.custom_fwd(device_type='cuda', cast_inputs=torch.float32)
|
|
def forward(self, tenOne, tenTwo, intKernelSize):
|
|
tenOut = tenOne.new_empty([tenOne.shape[0], intKernelSize ** 2, tenOne.shape[2], tenOne.shape[3]])
|
|
|
|
cuda_launch(cuda_kernel('costvol_out', '''
|
|
extern "C" __global__ void __launch_bounds__(512) costvol_out(
|
|
const int n,
|
|
const {{type}}* __restrict__ tenOne,
|
|
const {{type}}* __restrict__ tenTwo,
|
|
const int intKernelSize,
|
|
{{type}}* __restrict__ tenOut
|
|
) { for (int intIndex = (blockIdx.x * blockDim.x) + threadIdx.x; intIndex < n; intIndex += blockDim.x * gridDim.x) {
|
|
const int intN = ( intIndex / SIZE_3(tenOut) / SIZE_2(tenOut) ) % SIZE_0(tenOut);
|
|
const int intY = ( intIndex / SIZE_3(tenOut) ) % SIZE_2(tenOut);
|
|
const int intX = ( intIndex ) % SIZE_3(tenOut);
|
|
|
|
{{type}} fltOne[{{intChans}}];
|
|
|
|
for (int intValue = 0; intValue < SIZE_1(tenOne); intValue += 1) {
|
|
fltOne[intValue] = VALUE_4(tenOne, intN, intValue, intY, intX);
|
|
}
|
|
|
|
int intOffset = OFFSET_4(tenOut, intN, 0, intY, intX);
|
|
|
|
for (int intOy = intY - (intKernelSize - 1) / 2; intOy <= intY + (intKernelSize - 1) / 2; intOy += 1) {
|
|
for (int intOx = intX - (intKernelSize - 1) / 2; intOx <= intX + (intKernelSize - 1) / 2; intOx += 1) {
|
|
{{type}} fltValue = 0.0f;
|
|
|
|
if ((intOy >= 0) && (intOy < SIZE_2(tenOut)) && (intOx >= 0) && (intOx < SIZE_3(tenOut))) {
|
|
for (int intValue = 0; intValue < SIZE_1(tenOne); intValue += 1) {
|
|
fltValue += (fltOne[intValue] * VALUE_4(tenTwo, intN, intValue, intOy, intOx));
|
|
}
|
|
}
|
|
|
|
tenOut[intOffset] = fltValue;
|
|
intOffset += SIZE_2(tenOut) * SIZE_3(tenOut);
|
|
}
|
|
}
|
|
} }
|
|
''', {
|
|
'intChans': tenOne.shape[1],
|
|
'tenOne': tenOne,
|
|
'tenTwo': tenTwo,
|
|
'intKernelSize': intKernelSize,
|
|
'tenOut': tenOut
|
|
}))(
|
|
grid=tuple([int(((tenOut.shape[0] * tenOut.shape[2] * tenOut.shape[3]) + 512 - 1) / 512), 1, 1]),
|
|
block=tuple([512, 1, 1]),
|
|
args=[cuda_int32(tenOut.shape[0] * tenOut.shape[2] * tenOut.shape[3]), tenOne.data_ptr(), tenTwo.data_ptr(), intKernelSize, tenOut.data_ptr()],
|
|
stream=collections.namedtuple('Stream', 'ptr')(torch.cuda.current_stream().cuda_stream)
|
|
)
|
|
|
|
self.save_for_backward(tenOne, tenTwo)
|
|
self.intKernelSize = intKernelSize
|
|
|
|
return tenOut
|
|
# end
|
|
|
|
@staticmethod
|
|
@torch.amp.custom_bwd(device_type='cuda')
|
|
def backward(self, tenOutgrad):
|
|
tenOne, tenTwo = self.saved_tensors
|
|
intKernelSize = self.intKernelSize
|
|
|
|
tenOutgrad = tenOutgrad.contiguous(); assert(tenOutgrad.is_cuda == True)
|
|
|
|
tenOnegrad = tenOne.new_zeros([tenOne.shape[0], tenOne.shape[1], tenOne.shape[2], tenOne.shape[3]]) if self.needs_input_grad[0] == True else None
|
|
tenTwograd = tenTwo.new_zeros([tenTwo.shape[0], tenTwo.shape[1], tenTwo.shape[2], tenTwo.shape[3]]) if self.needs_input_grad[1] == True else None
|
|
|
|
if tenOnegrad is not None:
|
|
cuda_launch(cuda_kernel('costvol_onegrad', '''
|
|
extern "C" __global__ void __launch_bounds__(512) costvol_onegrad(
|
|
const int n,
|
|
const {{type}}* __restrict__ tenOne,
|
|
const {{type}}* __restrict__ tenTwo,
|
|
const {{type}}* __restrict__ tenOutgrad,
|
|
const int intKernelSize,
|
|
{{type}}* __restrict__ tenOnegrad,
|
|
{{type}}* __restrict__ tenTwograd
|
|
) { for (int intIndex = (blockIdx.x * blockDim.x) + threadIdx.x; intIndex < n; intIndex += blockDim.x * gridDim.x) {
|
|
const int intN = ( intIndex / SIZE_3(tenOnegrad) / SIZE_2(tenOnegrad) ) % SIZE_0(tenOnegrad);
|
|
const int intY = ( intIndex / SIZE_3(tenOnegrad) ) % SIZE_2(tenOnegrad);
|
|
const int intX = ( intIndex ) % SIZE_3(tenOnegrad);
|
|
|
|
int intOffset = OFFSET_4(tenOutgrad, intN, 0, intY, intX);
|
|
|
|
for (int intOy = intY - (intKernelSize - 1) / 2; intOy <= intY + (intKernelSize - 1) / 2; intOy += 1) {
|
|
for (int intOx = intX - (intKernelSize - 1) / 2; intOx <= intX + (intKernelSize - 1) / 2; intOx += 1) {
|
|
if ((intOy >= 0) && (intOy < SIZE_2(tenOutgrad)) && (intOx >= 0) && (intOx < SIZE_3(tenOutgrad))) {
|
|
for (int intValue = 0; intValue < SIZE_1(tenOne); intValue += 1) {
|
|
tenOnegrad[OFFSET_4(tenOnegrad, intN, intValue, intY, intX)] += tenOutgrad[intOffset] * VALUE_4(tenTwo, intN, intValue, intOy, intOx);
|
|
}
|
|
}
|
|
intOffset += SIZE_2(tenOutgrad) * SIZE_3(tenOutgrad);
|
|
}
|
|
}
|
|
} }
|
|
''', {
|
|
'intChans': tenOne.shape[1],
|
|
'tenOne': tenOne,
|
|
'tenTwo': tenTwo,
|
|
'tenOutgrad': tenOutgrad,
|
|
'intKernelSize': intKernelSize,
|
|
'tenOnegrad': tenOnegrad,
|
|
'tenTwograd': tenTwograd
|
|
}))(
|
|
grid=tuple([int(((tenOnegrad.shape[0] * tenOnegrad.shape[2] * tenOnegrad.shape[3]) + 512 - 1) / 512), 1, 1]),
|
|
block=tuple([512, 1, 1]),
|
|
args=[cuda_int32(tenOnegrad.shape[0] * tenOnegrad.shape[2] * tenOnegrad.shape[3]), tenOne.data_ptr(), tenTwo.data_ptr(), tenOutgrad.data_ptr(), intKernelSize, tenOnegrad.data_ptr(), tenTwograd.data_ptr()],
|
|
stream=collections.namedtuple('Stream', 'ptr')(torch.cuda.current_stream().cuda_stream)
|
|
)
|
|
# end
|
|
|
|
if tenTwograd is not None:
|
|
cuda_launch(cuda_kernel('costvol_twograd', '''
|
|
extern "C" __global__ void __launch_bounds__(512) costvol_twograd(
|
|
const int n,
|
|
const {{type}}* __restrict__ tenOne,
|
|
const {{type}}* __restrict__ tenTwo,
|
|
const {{type}}* __restrict__ tenOutgrad,
|
|
const int intKernelSize,
|
|
{{type}}* __restrict__ tenOnegrad,
|
|
{{type}}* __restrict__ tenTwograd
|
|
) { for (int intIndex = (blockIdx.x * blockDim.x) + threadIdx.x; intIndex < n; intIndex += blockDim.x * gridDim.x) {
|
|
const int intN = ( intIndex / SIZE_3(tenTwograd) / SIZE_2(tenTwograd) ) % SIZE_0(tenTwograd);
|
|
const int intY = ( intIndex / SIZE_3(tenTwograd) ) % SIZE_2(tenTwograd);
|
|
const int intX = ( intIndex ) % SIZE_3(tenTwograd);
|
|
|
|
{{type}} fltOne[{{intChans}}];
|
|
|
|
for (int intValue = 0; intValue < SIZE_1(tenOne); intValue += 1) {
|
|
fltOne[intValue] = VALUE_4(tenOne, intN, intValue, intY, intX);
|
|
}
|
|
|
|
int intOffset = OFFSET_4(tenOutgrad, intN, 0, intY, intX);
|
|
|
|
for (int intOy = intY - (intKernelSize - 1) / 2; intOy <= intY + (intKernelSize - 1) / 2; intOy += 1) {
|
|
for (int intOx = intX - (intKernelSize - 1) / 2; intOx <= intX + (intKernelSize - 1) / 2; intOx += 1) {
|
|
if ((intOy >= 0) && (intOy < SIZE_2(tenOutgrad)) && (intOx >= 0) && (intOx < SIZE_3(tenOutgrad))) {
|
|
for (int intValue = 0; intValue < SIZE_1(tenOne); intValue += 1) {
|
|
atomicAdd(&tenTwograd[OFFSET_4(tenTwograd, intN, intValue, intOy, intOx)], tenOutgrad[intOffset] * fltOne[intValue]);
|
|
}
|
|
}
|
|
intOffset += SIZE_2(tenOutgrad) * SIZE_3(tenOutgrad);
|
|
}
|
|
}
|
|
} }
|
|
''', {
|
|
'intChans': tenOne.shape[1],
|
|
'tenOne': tenOne,
|
|
'tenTwo': tenTwo,
|
|
'tenOutgrad': tenOutgrad,
|
|
'intKernelSize': intKernelSize,
|
|
'tenOnegrad': tenOnegrad,
|
|
'tenTwograd': tenTwograd
|
|
}))(
|
|
grid=tuple([int(((tenTwograd.shape[0] * tenTwograd.shape[2] * tenTwograd.shape[3]) + 512 - 1) / 512), 1, 1]),
|
|
block=tuple([512, 1, 1]),
|
|
args=[cuda_int32(tenTwograd.shape[0] * tenTwograd.shape[2] * tenTwograd.shape[3]), tenOne.data_ptr(), tenTwo.data_ptr(), tenOutgrad.data_ptr(), intKernelSize, tenOnegrad.data_ptr(), tenTwograd.data_ptr()],
|
|
stream=collections.namedtuple('Stream', 'ptr')(torch.cuda.current_stream().cuda_stream)
|
|
)
|
|
# end
|
|
|
|
return tenOnegrad, tenTwograd, None, None, None
|
|
# end
|
|
# end
|