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fix: Proper memory estimation for frame interpolation when not using dynamic VRAM (#13698)

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Jukka Seppänen 2026-05-04 20:20:40 +03:00 committed by GitHub
parent f3ea976cba
commit c33d26c283
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3 changed files with 10 additions and 7 deletions
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3
comfy_extras/frame_interpolation_models/film_net.py
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@ -199,6 +199,9 @@ class FILMNet(nn.Module):
def get_dtype(self): def get_dtype(self):
return self.extract.extract_sublevels.convs[0][0].conv.weight.dtype return self.extract.extract_sublevels.convs[0][0].conv.weight.dtype
def memory_used_forward(self, shape, dtype):
return 1700 * shape[1] * shape[2] * dtype.itemsize
def _build_warp_grids(self, H, W, device): def _build_warp_grids(self, H, W, device):
"""Pre-compute warp grids for all pyramid levels.""" """Pre-compute warp grids for all pyramid levels."""
if (H, W) in self._warp_grids: if (H, W) in self._warp_grids:

3
comfy_extras/frame_interpolation_models/ifnet.py
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@ -74,6 +74,9 @@ class IFNet(nn.Module):
def get_dtype(self): def get_dtype(self):
return self.encode.cnn0.weight.dtype return self.encode.cnn0.weight.dtype
def memory_used_forward(self, shape, dtype):
return 300 * shape[1] * shape[2] * dtype.itemsize
def _build_warp_grids(self, H, W, device): def _build_warp_grids(self, H, W, device):
if (H, W) in self._warp_grids: if (H, W) in self._warp_grids:
return return

11
comfy_extras/nodes_frame_interpolation.py
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@ -37,7 +37,7 @@ class FrameInterpolationModelLoader(io.ComfyNode):
model = cls._detect_and_load(sd) model = cls._detect_and_load(sd)
dtype = torch.float16 if model_management.should_use_fp16(model_management.get_torch_device()) else torch.float32 dtype = torch.float16 if model_management.should_use_fp16(model_management.get_torch_device()) else torch.float32
model.eval().to(dtype) model.eval().to(dtype)
patcher = comfy.model_patcher.ModelPatcher( patcher = comfy.model_patcher.CoreModelPatcher(
model, model,
load_device=model_management.get_torch_device(), load_device=model_management.get_torch_device(),
offload_device=model_management.unet_offload_device(), offload_device=model_management.unet_offload_device(),
@ -98,16 +98,13 @@ class FrameInterpolate(io.ComfyNode):
if num_frames < 2 or multiplier < 2: if num_frames < 2 or multiplier < 2:
return io.NodeOutput(images) return io.NodeOutput(images)
model_management.load_model_gpu(interp_model)
device = interp_model.load_device device = interp_model.load_device
dtype = interp_model.model_dtype() dtype = interp_model.model_dtype()
inference_model = interp_model.model inference_model = interp_model.model
activation_mem = inference_model.memory_used_forward(images.shape, dtype)
# Free VRAM for inference activations (model weights + ~20x a single frame's worth) model_management.load_models_gpu([interp_model], memory_required=activation_mem)
H, W = images.shape[1], images.shape[2]
activation_mem = H * W * 3 * images.element_size() * 20
model_management.free_memory(activation_mem, device)
align = getattr(inference_model, "pad_align", 1) align = getattr(inference_model, "pad_align", 1)
H, W = images.shape[1], images.shape[2]
# Prepare a single padded frame on device for determining output dimensions # Prepare a single padded frame on device for determining output dimensions
def prepare_frame(idx): def prepare_frame(idx):