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update TeaCache docs

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kijai 2025-03-08 12:09:02 +02:00
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commit 28d1fbda34
1 changed files with 24 additions and 5 deletions
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nodes/model_optimization_nodes.py
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@ -813,7 +813,7 @@ class WanVideoTeaCacheKJ:
return { return {
"required": { "required": {
"model": ("MODEL",), "model": ("MODEL",),
"rel_l1_thresh": ("FLOAT", {"default": 0.275, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Threshold for to determine when to apply the cache, compromise between speed and accuracy. When using coefficients a good value range is something between 0.2-0.4, and without it shold be about 10 times smaller."}), "rel_l1_thresh": ("FLOAT", {"default": 0.275, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Threshold for to determine when to apply the cache, compromise between speed and accuracy. When using coefficients a good value range is something between 0.2-0.4 for all but 1.3B model, which should be about 10 times smaller, same as when not using coefficients."}),
"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."}), "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."}), "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"}), "cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
@ -826,10 +826,29 @@ class WanVideoTeaCacheKJ:
FUNCTION = "patch_teacache" FUNCTION = "patch_teacache"
CATEGORY = "KJNodes/teacache" CATEGORY = "KJNodes/teacache"
DESCRIPTION = """ DESCRIPTION = """
Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and applying it instead of doing the step. Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and
Best results are achieved by choosing the appropriate coefficients for the model. applying it instead of doing the step. Best results are achieved by choosing the
Early steps should never be skipped, with too aggressive values this can happen and the motion suffers. Starting later can help with that too. appropriate coefficients for the model. Early steps should never be skipped, with too
When NOT using coefficients the threshold value should be about 10 times smaller than the value used with coefficients. 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.
Official recommended values https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4Wan2.1:
<pre style='font-family:monospace'>
+-------------------+--------+---------+--------+
| Model | Low | Medium | High |
+-------------------+--------+---------+--------+
| Wan2.1 t2v 1.3B | 0.05 | 0.07 | 0.08 |
| Wan2.1 t2v 14B | 0.14 | 0.15 | 0.20 |
| Wan2.1 i2v 480P | 0.13 | 0.19 | 0.26 |
| Wan2.1 i2v 720P | 0.18 | 0.20 | 0.30 |
+-------------------+--------+---------+--------+
</pre>
""" """
EXPERIMENTAL = True EXPERIMENTAL = True