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from tqdm import tqdm
from typing_extensions import override
import comfy.model_patcher
import comfy.utils
import folder_paths
from comfy import model_management
from comfy_extras.frame_interpolation_models.ifnet import IFNet, detect_rife_config
from comfy_extras.frame_interpolation_models.film_net import FILMNet
from comfy_api.latest import ComfyExtension, io
FrameInterpolationModel = io.Custom("INTERP_MODEL")
class FrameInterpolationModelLoader(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="FrameInterpolationModelLoader",
display_name="Load Frame Interpolation Model",
category="model/loaders",
inputs=[
io.Combo.Input("model_name", options=folder_paths.get_filename_list("frame_interpolation"),
tooltip="Select a frame interpolation model to load. Models must be placed in the 'frame_interpolation' folder."),
],
outputs=[
FrameInterpolationModel.Output(),
],
)
@classmethod
def execute(cls, model_name) -> io.NodeOutput:
model_path = folder_paths.get_full_path_or_raise("frame_interpolation", model_name)
sd = comfy.utils.load_torch_file(model_path, safe_load=True)
model = cls._detect_and_load(sd)
dtype = torch.float16 if model_management.should_use_fp16(model_management.get_torch_device()) else torch.float32
model.eval().to(dtype)
patcher = comfy.model_patcher.CoreModelPatcher(
model,
load_device=model_management.get_torch_device(),
offload_device=model_management.unet_offload_device(),
)
return io.NodeOutput(patcher)
@classmethod
def _detect_and_load(cls, sd):
# Try FILM
if "extract.extract_sublevels.convs.0.0.conv.weight" in sd:
model = FILMNet()
model.load_state_dict(sd)
return model
# Try RIFE (needs key remapping for raw checkpoints)
sd = comfy.utils.state_dict_prefix_replace(sd, {"module.": "", "flownet.": ""})
key_map = {}
for k in sd:
for i in range(5):
if k.startswith(f"block{i}."):
key_map[k] = f"blocks.{i}.{k[len(f'block{i}.'):]}"
if key_map:
sd = {key_map.get(k, k): v for k, v in sd.items()}
sd = {k: v for k, v in sd.items() if not k.startswith(("teacher.", "caltime."))}
try:
head_ch, channels = detect_rife_config(sd)
except (KeyError, ValueError):
raise ValueError("Unrecognized frame interpolation model format")
model = IFNet(head_ch=head_ch, channels=channels)
model.load_state_dict(sd)
return model
class FrameInterpolate(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="FrameInterpolate",
display_name="Run Frame Interpolation Model",
category="video",
search_aliases=["rife", "film", "frame interpolation", "slow motion", "interpolate frames", "vfi"],
inputs=[
FrameInterpolationModel.Input("interp_model"),
io.Image.Input("images"),
io.Int.Input("multiplier", default=2, min=2, max=16),
],
outputs=[
io.Image.Output(),
],
)
@classmethod
def execute(cls, interp_model, images, multiplier) -> io.NodeOutput:
offload_device = model_management.intermediate_device()
num_frames = images.shape[0]
if num_frames < 2 or multiplier < 2:
return io.NodeOutput(images)
device = interp_model.load_device
dtype = interp_model.model_dtype()
inference_model = interp_model.model
activation_mem = inference_model.memory_used_forward(images.shape, dtype)
model_management.load_models_gpu([interp_model], memory_required=activation_mem)
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
def prepare_frame(idx):
frame = images[idx:idx + 1].movedim(-1, 1).to(dtype=dtype, device=device)
if align > 1:
from comfy.ldm.common_dit import pad_to_patch_size
frame = pad_to_patch_size(frame, (align, align), padding_mode="reflect")
return frame
# Count total interpolation passes for progress bar
total_pairs = num_frames - 1
num_interp = multiplier - 1
total_steps = total_pairs * num_interp
pbar = comfy.utils.ProgressBar(total_steps)
tqdm_bar = tqdm(total=total_steps, desc="Frame interpolation")
batch = num_interp # reduced on OOM and persists across pairs (same resolution = same limit)
t_values = [t / multiplier for t in range(1, multiplier)]
out_dtype = model_management.intermediate_dtype()
total_out_frames = total_pairs * multiplier + 1
result = torch.empty((total_out_frames, 3, H, W), dtype=out_dtype, device=offload_device)
result[0] = images[0].movedim(-1, 0).to(out_dtype)
out_idx = 1
# Pre-compute timestep tensor on device (padded dimensions needed)
sample = prepare_frame(0)
pH, pW = sample.shape[2], sample.shape[3]
ts_full = torch.tensor(t_values, device=device, dtype=dtype).reshape(num_interp, 1, 1, 1)
ts_full = ts_full.expand(-1, 1, pH, pW)
del sample
multi_fn = getattr(inference_model, "forward_multi_timestep", None)
feat_cache = {}
prev_frame = None
try:
for i in range(total_pairs):
img0_single = prev_frame if prev_frame is not None else prepare_frame(i)
img1_single = prepare_frame(i + 1)
prev_frame = img1_single
# Cache features: img1 of pair N becomes img0 of pair N+1
feat_cache["img0"] = feat_cache.pop("next") if "next" in feat_cache else inference_model.extract_features(img0_single)
feat_cache["img1"] = inference_model.extract_features(img1_single)
feat_cache["next"] = feat_cache["img1"]
used_multi = False
if multi_fn is not None:
# Models with timestep-independent flow can compute it once for all timesteps
try:
mids = multi_fn(img0_single, img1_single, t_values, cache=feat_cache)
result[out_idx:out_idx + num_interp] = mids[:, :, :H, :W].to(out_dtype)
out_idx += num_interp
pbar.update(num_interp)
tqdm_bar.update(num_interp)
used_multi = True
except model_management.OOM_EXCEPTION:
model_management.soft_empty_cache()
multi_fn = None # fall through to single-timestep path
if not used_multi:
j = 0
while j < num_interp:
b = min(batch, num_interp - j)
try:
img0 = img0_single.expand(b, -1, -1, -1)
img1 = img1_single.expand(b, -1, -1, -1)
mids = inference_model(img0, img1, timestep=ts_full[j:j + b], cache=feat_cache)
result[out_idx:out_idx + b] = mids[:, :, :H, :W].to(out_dtype)
out_idx += b
pbar.update(b)
tqdm_bar.update(b)
j += b
except model_management.OOM_EXCEPTION:
if batch <= 1:
raise
batch = max(1, batch // 2)
model_management.soft_empty_cache()
result[out_idx] = images[i + 1].movedim(-1, 0).to(out_dtype)
out_idx += 1
finally:
tqdm_bar.close()
# BCHW -> BHWC
result = result.movedim(1, -1).clamp_(0.0, 1.0)
return io.NodeOutput(result)
class FrameInterpolationExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
FrameInterpolationModelLoader,
FrameInterpolate,
]
async def comfy_entrypoint() -> FrameInterpolationExtension:
return FrameInterpolationExtension()
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