adding logging
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@@ -18,6 +18,10 @@ import torch
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import comfy.model_management as mm
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from pathlib import Path
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# Use polygraphy TensorRTX backend
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os.environ["POLYGRAPHY_USE_TENSORRT_RTX"] = "1"
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from polygraphy import cuda
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from .vfi_utilities import preprocess_frames, postprocess_frames, generate_frames_rife
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from .trt_utilities import Engine
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+2
-3
@@ -297,11 +297,10 @@ class Engine:
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if not enable_all_tactics:
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config_kwargs["tactic_sources"] = []
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# Load ONNX file then convert the model to fp16
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# Probably might cause issues if onnx file given is quantized or whatever
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# Load ONNX file
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onnx_bytes = onnx.load(onnx_path)
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onnx_precision = onnx_bytes.graph.input[0].type.tensor_type.elem_type
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onnx_simp, check = simplify(float16.convert_float_to_float16(onnx_bytes) if onnx_precision == onnx.TensorProto.FLOAT else onnx_bytes)
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onnx_simp, check = simplify(onnx_bytes)
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if not check:
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error(f"Simplified ONNX model: {onnx_path} could not be validated")
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+5
-1
@@ -11,6 +11,7 @@ import os
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import torch
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import typing
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import logging
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import numpy as np
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from comfy.model_management import soft_empty_cache, get_torch_device
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@@ -73,6 +74,8 @@ def generate_frames_rife(
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cache_counter = 0
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pbar = ProgressBar(len(frames))
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logging.info(f"Interpolating frames")
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for frame_itr in range(len(frames) - 1):
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@@ -96,13 +99,14 @@ def generate_frames_rife(
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cache_counter += 1
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if cache_counter >= clear_cache_after_n_frames:
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soft_empty_cache()
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logging.info(f"Clearing cache...")
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cache_counter = 0
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pbar.update(1)
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# Append final frame
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output_frames[out_len] = frames[-1:]
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logger(f"done! - {(len(frames) -1) * (multiplier-1)} new frames generated at resolution: {output_frames[0].shape}")
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logging.info(f"done! - {(len(frames) -1) * (multiplier-1)} new frames generated at resolution: {output_frames[0].shape}")
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out_len += 1
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# clear cache for courtesy
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