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Add RIFE v4.25 support, clean dropdown, pin polygraphy

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- Add RIFE v4.25 architecture support in rife_arch.py (Head with
  16/4 channels, 5 IFBlocks with c=192/128/96/64/32, scale_list
  [16,8,4,2,1], no ensemble)
- Add rife425.pth to export_onnx.py with HF fallback URL
- Update load_rife_config.json: dropdown now only shows rife425 and
  rife49 (removed rife47/48/417/426/sudo_rife4 that never worked)
- Set rife425 as default
- Make TORCH_DEVICE fallback to CPU for ONNX export on non-CUDA
- Pin polygraphy==0.49.26 for TensorRT 10.15.1 compatibility

Generated with [Devin](https://devin.ai)

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
huchukato
2026-08-28 02:50:00 +02:00
co-authored by Devin
parent 2132614242
commit 970f51649b
5 changed files with 40 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files
__init__.py
+2 -2
View File
@@ -309,8 +309,8 @@ def load_node_config(config_filename="load_rife_config.json"):
default_config = {
"model": {
"options": ["rife49_ensemble_True_scale_1_sim"],
"default": "rife49_ensemble_True_scale_1_sim",
"options": ["rife425_ensemble_False_scale_1_sim", "rife49_ensemble_True_scale_1_sim"],
"default": "rife425_ensemble_False_scale_1_sim",
"tooltip": "Default model (fallback from code)"
},
"precision": {
export_onnx.py
+6 -1
View File
@@ -24,6 +24,7 @@ CKPT_NAME_VER_DICT = {
"rife48.pth": "4.7",
"rife49.pth": "4.7",
"rife417.pth": "4.17",
"rife425.pth": "4.25",
"rife426.pth": "4.26",
"sudo_rife4_269.662_testV1_scale1.pth": "4.0"
}
@@ -38,6 +39,9 @@ CKPT_FALLBACK_URLS = {
"https://huggingface.co/marduk191/rife/resolve/main/rife417.pth",
"https://huggingface.co/MachineDelusions/RIFE/resolve/main/rife417.pth",
],
"rife425.pth": [
"https://huggingface.co/huchukato/favs/resolve/main/RIFE/rife425.pth",
],
"rife426.pth": [
"https://huggingface.co/marduk191/rife/resolve/main/rife426.pth",
"https://huggingface.co/MachineDelusions/RIFE/resolve/main/rife426.pth",
@@ -60,7 +64,7 @@ CKPT_FALLBACK_URLS = {
"https://huggingface.co/licyk/sd-upscaler-models/resolve/main/ESRGAN/sudo_rife4_269.662_testV1_scale1.pth",
],
}
TORCH_DEVICE = "cuda:0"
TORCH_DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
"""Load file form http url, will download models if necessary.
@@ -188,5 +192,6 @@ def export_onnx(ckpt_name, ensemble, scale_factor):
export_onnx(ckpt_name="rife47.pth", ensemble=True, scale_factor=1)
export_onnx(ckpt_name="rife49.pth", ensemble=True, scale_factor=1)
export_onnx(ckpt_name="rife417.pth", ensemble=True, scale_factor=1)
export_onnx(ckpt_name="rife425.pth", ensemble=False, scale_factor=1)
export_onnx(ckpt_name="rife426.pth", ensemble=False, scale_factor=1)
export_onnx(ckpt_name="sudo_rife4_269.662_testV1_scale1.pth", ensemble=True, scale_factor=1)
load_rife_config.json
+3 -7
View File
@@ -1,15 +1,11 @@
{
"model": {
"options": [
"rife47_ensemble_True_scale_1_sim",
"rife48_ensemble_True_scale_1_sim",
"rife49_ensemble_True_scale_1_sim",
"rife417_ensemble_True_scale_1_sim",
"rife426_ensemble_False_scale_1_sim",
"sudo_rife4_269.662_testV1_scale1_ensemble_True_scale_1_sim"
"rife425_ensemble_False_scale_1_sim"
],
"default": "rife49_ensemble_True_scale_1_sim",
"tooltip": "RIFE models for video frame interpolation. rife49 (v4.7) is the stable default. rife417 (v4.17) and rife426 (v4.26) are newer with improved quality. sudo_rife4 is a community-trained v4.0 variant."
"default": "rife425_ensemble_False_scale_1_sim",
"tooltip": "RIFE models for video frame interpolation. rife425 (v4.25) is the recommended default for diffusion-generated video. rife49 (v4.9) is the stable legacy option."
},
"precision": {
"options": ["fp16", "fp32"],
requirements.txt
+4 -1
View File
@@ -8,7 +8,10 @@
# Base dependencies that work with any CUDA version
einops
colored
polygraphy
# Pin to the Polygraphy version bundled with TensorRT 10.15.1.
# Newer Polygraphy can break trt.OnnxParser init with:
# pybind11::init(): factory function returned nullptr
polygraphy==0.49.26
cuda-python
requests
tqdm
rife_arch.py
+25 -15
View File
@@ -94,7 +94,7 @@ def conv(
),
nn.PReLU(out_planes),
)
if arch_ver in ["4.2", "4.3", "4.5", "4.6", "4.7", "4.10", "4.17", "4.26"]:
if arch_ver in ["4.2", "4.3", "4.5", "4.6", "4.7", "4.10", "4.17", "4.26", "4.25"]:
return nn.Sequential(
nn.Conv2d(
in_planes,
@@ -150,7 +150,7 @@ def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1, arch_ver="
),
nn.PReLU(out_planes),
)
if arch_ver in ["4.2", "4.3", "4.5", "4.6", "4.7", "4.10", "4.17", "4.26"]:
if arch_ver in ["4.2", "4.3", "4.5", "4.6", "4.7", "4.10", "4.17", "4.26", "4.25"]:
return nn.Sequential(
torch.nn.ConvTranspose2d(
in_channels=in_planes,
@@ -219,8 +219,8 @@ class IFBlock(nn.Module):
nn.ConvTranspose2d(c, 4 * 6, 4, 2, 1), nn.PixelShuffle(2)
)
# add 4.26 support
if arch_ver in ["4.26"]:
# add 4.26/4.25 support
if arch_ver in ["4.26", "4.25"]:
self.convblock = nn.Sequential(
ResConv(c),
ResConv(c),
@@ -252,7 +252,7 @@ class IFBlock(nn.Module):
feat = self.conv0(x)
if self.arch_ver == "4.0":
feat = self.convblock(feat) + feat
if self.arch_ver in ["4.2", "4.3", "4.5", "4.6", "4.7", "4.10", "4.17", "4.26"]:
if self.arch_ver in ["4.2", "4.3", "4.5", "4.6", "4.7", "4.10", "4.17", "4.26", "4.25"]:
feat = self.convblock(feat)
tmp = self.lastconv(feat)
@@ -266,7 +266,7 @@ class IFBlock(nn.Module):
tmp, scale_factor=scale, mode="bilinear", align_corners=False
)
flow = tmp[:, :4] * scale
if self.arch_ver in ["4.26"]:
if self.arch_ver in ["4.26", "4.25"]:
tmp = F.interpolate(
tmp, scale_factor=scale, mode="bilinear", align_corners=False
)
@@ -443,6 +443,14 @@ class IFNet(nn.Module):
self.block3 = IFBlock(8+4+16+8, c=64, arch_ver=arch_ver)
self.block4 = IFBlock(8+4+16+8, c=64, arch_ver=arch_ver)
self.encode = Head_417()
# add 4.25 support (same structure as 4.26 but smaller encode head and block4)
if arch_ver in ["4.25"]:
self.block0 = IFBlock(7+8, c=192, arch_ver=arch_ver)
self.block1 = IFBlock(8+4+8+8, c=128, arch_ver=arch_ver)
self.block2 = IFBlock(8+4+8+8, c=96, arch_ver=arch_ver)
self.block3 = IFBlock(8+4+8+8, c=64, arch_ver=arch_ver)
self.block4 = IFBlock(8+4+8+8, c=32, arch_ver=arch_ver)
self.encode = Head()
if arch_ver in ["4.0", "4.2", "4.3"]:
self.contextnet = Contextnet(arch_ver=arch_ver)
@@ -464,6 +472,8 @@ class IFNet(nn.Module):
img1 = torch.clamp(img1, 0, 1)
scale_list = [8,4,2,1]
if self.arch_ver in ["4.25", "4.26"]:
scale_list = [16,8,4,2,1]
training=True
fastmode=True
@@ -492,7 +502,7 @@ class IFNet(nn.Module):
merged = []
mask_list = []
if self.arch_ver in ["4.7", "4.10", "4.17", "4.26"]:
if self.arch_ver in ["4.7", "4.10", "4.17", "4.26", "4.25"]:
f0 = self.encode(img0[:, :3])
f1 = self.encode(img1[:, :3])
@@ -502,7 +512,7 @@ class IFNet(nn.Module):
flow = None
mask = None
block = [self.block0, self.block1, self.block2, self.block3]
if self.arch_ver in ["4.26"]:
if self.arch_ver in ["4.26", "4.25"]:
block.append(self.block4)
for i in range(len(block)):
@@ -526,8 +536,8 @@ class IFNet(nn.Module):
flow = (flow + torch.cat((f_[:, 2:4], f_[:, :2]), 1)) / 2
mask = (mask + (-m_)) / 2
# 4.26
if self.arch_ver in ["4.26"]:
# 4.26 / 4.25
if self.arch_ver in ["4.26", "4.25"]:
flow, mask, feat_out = block[i](
torch.cat((img0[:, :3], img1[:, :3], f0, f1, timestep), 1),
None,
@@ -554,8 +564,8 @@ class IFNet(nn.Module):
)
flow = flow + fd
# 4.26
if self.arch_ver in ["4.26"]:
# 4.26 / 4.25
if self.arch_ver in ["4.26", "4.25"]:
fd, m0, feat_out = block[i](
torch.cat(
(
@@ -628,7 +638,7 @@ class IFNet(nn.Module):
if not ensemble and self.arch_ver in ["4.7", "4.10", "4.17"]:
mask = m0
if self.arch_ver in ["4.26"]:
if self.arch_ver in ["4.26", "4.25"]:
mask = m0
mask_list.append(mask)
@@ -637,8 +647,8 @@ class IFNet(nn.Module):
warped_img1 = warp(img1, flow[:, 2:4])
merged.append((warped_img0, warped_img1))
# add 4.26 support
if self.arch_ver in ["4.26"]:
# add 4.26/4.25 support
if self.arch_ver in ["4.26", "4.25"]:
final_mask = mask_list[-1] if mask_list else mask
final_mask = torch.sigmoid(final_mask)
merged_final = warped_img0 * final_mask + warped_img1 * (1 - final_mask)