nodes/er1cw00-ComfyUI-RIFE
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test: add timing log

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test: log more timing

fix: return frames as fp16
This commit is contained in:
er1cw00
2026-09-01 22:05:49 +08:00
parent 9f68ea042e
commit b770d4d7b4
1 changed files with 30 additions and 10 deletions
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rife_node.py
+30 -10
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@@ -171,7 +171,7 @@ class RIFE_VFI:
torch_dtype = DTYPE_MAP[dtype]
device = get_torch_device()
print(f'{time.time():.3f}: device: {device}, dtype: {frames.dtype}')
# 4.26 disable ensemble
if arch_ver == "4.26":
ensemble = False # 4.26 版本不支持 ensemble
@@ -190,10 +190,15 @@ class RIFE_VFI:
if torch_compile:
interpolation_model = torch.compile(interpolation_model)
_model_cache[cache_key] = interpolation_model
print(f"Comfy-VFI: Loaded and cached model {ckpt_name} ({dtype}{'+ torch.compile' if torch_compile else ''})")
print(f"{time.time():.3f}: Comfy-VFI: Loaded and cached model {ckpt_name} ({dtype}{'+ torch.compile' if torch_compile else ''})")
else:
interpolation_model = _model_cache[cache_key]
print(f"Comfy-VFI: Using cached model {ckpt_name} ({dtype}{'+ torch.compile' if torch_compile else ''})")
print(f"{time.time():.3f}: Comfy-VFI: Using cached model {ckpt_name} ({dtype}{'+ torch.compile' if torch_compile else ''})")
print(
f"Comfy-VFI: model device={next(interpolation_model.parameters()).device}, "
f"dtype={next(interpolation_model.parameters()).dtype}"
)
frames = preprocess_frames(frames)
@@ -234,6 +239,10 @@ class RIFE_VFI:
# Opt 5: inference_mode disables autograd tracking entirely — lower overhead than no_grad().
# Opt 7: process batch_size tasks per GPU call. IFNet supports batched tensor timesteps,
# so multiple (pair, timestep) combinations can be inferred in a single forward pass.
print(f"{time.time():.3f}: Comfy-VFI: start inference")
total_inference_time = 0.0
total_gpu_to_cpu_time = 0.0
with torch.inference_mode():
while pos < len(tasks):
batch_tasks = tasks[pos : pos + batch_size]
@@ -249,6 +258,8 @@ class RIFE_VFI:
# Batched timestep tensor: shape [B, 1, 1, 1] — IFNet expands it internally
timestep_tensor = torch.tensor(timestep_list, dtype=torch_dtype, device=device).view(-1, 1, 1, 1)
t_infer = time.perf_counter()
middle_frames = interpolation_model(
frame0_batch,
frame1_batch,
@@ -256,8 +267,16 @@ class RIFE_VFI:
scale_list,
fast_mode,
ensemble,
).clamp(0, 1).detach().cpu()
).clamp(0, 1)
infer_elapsed = time.perf_counter() - t_infer
total_inference_time += infer_elapsed
t_copy = time.perf_counter()
middle_frames = middle_frames.detach().cpu()
copy_elapsed = time.perf_counter() - t_copy
total_gpu_to_cpu_time += copy_elapsed
for idx, (pair_idx, _) in enumerate(batch_tasks):
results[pair_idx].append(middle_frames[idx : idx + 1].to(dtype=torch_dtype))
tasks_remaining_per_pair[pair_idx] -= 1
@@ -279,15 +298,16 @@ class RIFE_VFI:
output_frames.append(mid)
output_frames.append(frames[-1:].to(dtype=torch_dtype))
print("Comfy-VFI: Final clearing cache...", end=' ')
print(f"{time.time():.3f}: Comfy-VFI: Final clearing cache...")
soft_empty_cache()
print("Done cache clearing")
print(f"Comfy-VFI done! {sum(len(v) for v in results.values()) + len(frames)} frames generated")
print(f"{time.time():.3f}: Done cache clearing")
print(f"{time.time():.3f}: Comfy-VFI inference_time: {total_inference_time:.4f}; copy_time: {total_gpu_to_cpu_time:.4f}")
print(f"{time.time():.3f}: Comfy-VFI done! {sum(len(v) for v in results.values()) + len(frames)} frames generated")
# Always return float32 — numpy and all downstream ComfyUI nodes require it
out_tensor = torch.cat(output_frames, dim=0).to(torch.float32)
out_tensor = torch.cat(output_frames, dim=0).to(torch.float16)
stop_time = time.perf_counter()
elapsed_time = stop_time - start_time
print(f"VFI elapsed: {elapsed_time:.4f} s ({elapsed_time * 1000:.2f} ms)")
print(f"{time.time():.3f}: VFI elapsed: {elapsed_time:.3f} s ({elapsed_time * 1000:.2f} ms)\n")
return (postprocess_frames(out_tensor),)