feat: RIFE and FILM frame interpolation model support (CORE-29)

[kijai]

Adds pytorch only optimized support for RIFE (MIT) and FILM (Apache 2.0) video frame interpolation models:

https://huggingface.co/Comfy-Org/frame_interpolation/tree/main/frame_interpolation

Optional (advanced) torch.compile option is mostly for FILM since it's a considerable speedup.

[coderabbitai]

📝 Walkthrough

Walkthrough

This PR adds frame interpolation to Comfy: a new extension registers two nodes (FrameInterpolationModelLoader, FrameInterpolate) and a custom type FrameInterpolationModel. It introduces FILM and IFNet model implementations, model-loading logic that detects RIFE vs FILM checkpoints, GPU/device and dtype handling, and runtime interpolation code with padding, batching, and OOM mitigation. It also adds a frame_interpolation models folder and updates builtin extra node imports.

🚥 Pre-merge checks | ✅ 2 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 8.20% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and specifically describes the main change: adding support for RIFE and FILM frame interpolation models.
Description check	✅ Passed	The description is directly related to the changeset, explaining the addition of PyTorch-optimized support for RIFE and FILM frame interpolation models with links and implementation details.

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[coderabbitai]

Actionable comments posted: 1

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@comfy_extras/nodes_frame_interpolation.py`:
- Around line 107-110: The padding call on frames using F.pad with
mode="reflect" can fail when pad_h >= H or pad_w >= W (e.g., 16×64 or 32×32);
update the logic around pad_h/pad_w calculation so that before calling F.pad you
choose mode = "reflect" if pad_h < H and pad_w < W, otherwise use mode =
"replicate" (or another non-reflect fallback) and pass that mode into
F.pad(frames, (0, pad_w, 0, pad_h), mode=mode) so the function (and the
variables pad_h, pad_w, H, W and the F.pad call) handles small-frame cases
safely.

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Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes

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ℹ️ Review info

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📥 Commits

Reviewing files that changed from the base of the PR and between 0c63b4f and a859152.

📒 Files selected for processing (4)

• comfy_extras/nodes_frame_interpolation.py
• comfy_extras/rife_model/ifnet.py
• folder_paths.py
• nodes.py

[coderabbitai]

Actionable comments posted: 2

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@comfy_extras/nodes_frame_interpolation.py`:
- Around line 161-167: The multi_fn branch (where multi_fn is used to compute
mids in forward_multi_timestep()) currently asks for all timestep outputs at
once and bypasses the OOM-aware batch-halving fallback; update this branch to
catch CUDA OOM (or memory errors) from multi_fn/forward_multi_timestep and retry
by splitting t_values into smaller chunks (e.g., half the size repeatedly) and
calling multi_fn/forward_multi_timestep per-chunk, copying each chunk into
result as done now, using the same dtype/non_blocking logic and pbar/tqdm
updates; ensure you clear caches between retries (feat_cache or
torch.cuda.empty_cache()) and propagate the original error if even single-step
chunks fail.
- Around line 151-189: The inference loop currently calls inference_model and
inference_model.extract_features while only setting eval(), which still builds
autograd graphs and retains tensors in feat_cache; wrap the loop that iterates
over frame pairs (the try: for i in range(total_pairs): ... finally:) in
torch.inference_mode() so all calls to extract_features, inference_model(...)
and multi_fn(...) run without tracking gradients, preventing unnecessary graph
retention and reducing memory/OOM issues; update any context usage around
feat_cache, multi_fn, and inference_model to execute inside the inference_mode
block.

🪄 Autofix (Beta)

Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes

---

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⚙️ Run configuration

Configuration used: Path: .coderabbit.yaml

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Plan: Pro

Run ID: 43f05f12-4f18-425e-bfd3-3376c4458e9f

📥 Commits

Reviewing files that changed from the base of the PR and between a859152 and 257c531.

📒 Files selected for processing (3)

• comfy_extras/frame_interpolation_models/film_net.py
• comfy_extras/frame_interpolation_models/ifnet.py
• comfy_extras/nodes_frame_interpolation.py

[coderabbitai]

Actionable comments posted: 1

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@comfy_extras/nodes_frame_interpolation.py`:
- Around line 168-180: The OOM fallback in the multi_fn branch incorrectly uses
"continue", which skips writing outputs for the current pair (variables:
multi_fn, result, out_idx, num_interp) and moves to the next pair; instead,
catch model_management.OOM_EXCEPTION, call model_management.soft_empty_cache(),
set multi_fn = None, and do NOT continue so the loop falls through and retries
the same pair using the single-timestep path (i.e., let the subsequent
single-step interpolation logic run for the current i rather than skipping it).

🪄 Autofix (Beta)

Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes

---

ℹ️ Review info

⚙️ Run configuration

Configuration used: Path: .coderabbit.yaml

Review profile: CHILL

Plan: Pro

Run ID: e695b624-086d-4151-9510-1abfe96d97d3

📥 Commits

Reviewing files that changed from the base of the PR and between 257c531 and 3cbd1d5.

📒 Files selected for processing (2)

• comfy_extras/frame_interpolation_models/film_net.py
• comfy_extras/nodes_frame_interpolation.py