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Update model_optimization_nodes.py

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kijai 2025-03-07 01:38:40 +02:00
parent d835ac9604
commit 8ba503c0a5
1 changed files with 8 additions and 3 deletions
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11
nodes/model_optimization_nodes.py
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@ -805,8 +805,8 @@ class WanVideoTeaCacheKJ:
return {
"required": {
"model": ("MODEL",),
"rel_l1_thresh": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Threshold for to determine when to apply the cache, compromise between speed and accuracy"}),
"start_percent": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of the steps to use with TeaCache."}),
"rel_l1_thresh": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Threshold for to determine when to apply the cache, compromise between speed and accuracy."}),
"start_percent": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of the steps to use with TeaCache."}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The end percentage of the steps to use with TeaCache."}),
"cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
"coefficients": (["disabled", "1.3B", "14B", "i2v_480", "i2v_720"],),
@ -817,7 +817,12 @@ class WanVideoTeaCacheKJ:
RETURN_NAMES = ("model",)
FUNCTION = "patch_teacache"
CATEGORY = "KJNodes/teacache"
DESCRIPTION = "Patch WanVideo model to use TeaCache. Speeds up inference by caching the output of the model and applying it based on the input/output difference. Currently doesn't use coefficients for caching, will be imporoved in the future"
DESCRIPTION = """
Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and applying it instead of doing the step.
Best results are achieved by choosing the appropriate coefficients for the model.
Early steps should never be skipped, with too aggressive values this can happen and the motion suffers. Starting later can help with that too.
When NOT using coefficients the threshold value should be about 10 times smaller than the value used with coefficients.
"""
EXPERIMENTAL = True
def patch_teacache(self, model, rel_l1_thresh, start_percent, end_percent, cache_device, coefficients):