106 lines
4.4 KiB
Markdown
106 lines
4.4 KiB
Markdown
# Apply AnimateDiff Model (Adv.) 🎭🅐🅓②
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## Documentation
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- Class name: ADE_ApplyAnimateDiffModel
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- Category: Animate Diff 🎭🅐🅓/② Gen2 nodes ②
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- Output node: False
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- Repo Ref: https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved.git
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ADE_ApplyAnimateDiffModel节点旨在应用高级AnimateDiff模型配置以生成图像中的运动。它利用一套全面的参数来微调动画过程，适应各种运动效果和风格。
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## Input types
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### Required
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- motion_model
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- 指定用于动画的运动模型。它对于定义动画的行为和特性至关重要。
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- Comfy dtype: MOTION_MODEL_ADE
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- Python dtype: MotionModelPatcher
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- start_percent
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- 定义动画效果的起始百分比，标记运动应用的开始。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- end_percent
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- 指定动画效果的结束百分比，确定运动停止的点。
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- Comfy dtype: FLOAT
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- Python dtype: float
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### Optional
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- motion_lora
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- 可选参数，允许使用LoRA（低秩适应）技术调整运动，增强动画质量。
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- Comfy dtype: MOTION_LORA
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- Python dtype: MotionLoraList
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- scale_multival
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- 可选参数，影响动画效果的规模，允许微调动画的强度。
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- Comfy dtype: MULTIVAL
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- Python dtype: float
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- effect_multival
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- 可选参数，调整动画的整体效果，允许自定义视觉结果。
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- Comfy dtype: MULTIVAL
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- Python dtype: float
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- ad_keyframes
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- 可选参数，指定动画的关键帧，允许精确控制运动的时间和序列。
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- Comfy dtype: AD_KEYFRAMES
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- Python dtype: ADKeyframeGroup
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- prev_m_models
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- 可选参数，包括要在当前动画过程中考虑的先前运动模型，允许累积效果。
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- Comfy dtype: M_MODELS
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- Python dtype: M_MODELS
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## Output types
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- m_models
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- Comfy dtype: M_MODELS
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- 输出动画过程中使用的运动模型，包含所有调整和配置。
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- Python dtype: M_MODELS
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## Usage tips
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- Infra type: GPU
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<!-- - Common nodes:
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- [ADE_UseEvolvedSampling](../../ComfyUI-AnimateDiff-Evolved/Nodes/ADE_UseEvolvedSampling.md) -->
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## Source code
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```python
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class ApplyAnimateDiffModelNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"motion_model": ("MOTION_MODEL_ADE",),
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"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
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"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
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},
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"optional": {
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"motion_lora": ("MOTION_LORA",),
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"scale_multival": ("MULTIVAL",),
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"effect_multival": ("MULTIVAL",),
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"ad_keyframes": ("AD_KEYFRAMES",),
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"prev_m_models": ("M_MODELS",),
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}
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}
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RETURN_TYPES = ("M_MODELS",)
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CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②"
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FUNCTION = "apply_motion_model"
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def apply_motion_model(self, motion_model: MotionModelPatcher, start_percent: float=0.0, end_percent: float=1.0,
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motion_lora: MotionLoraList=None, ad_keyframes: ADKeyframeGroup=None,
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scale_multival=None, effect_multival=None,
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prev_m_models: MotionModelGroup=None,):
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# set up motion models list
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if prev_m_models is None:
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prev_m_models = MotionModelGroup()
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prev_m_models = prev_m_models.clone()
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motion_model = motion_model.clone()
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# check if internal motion model already present in previous model - create new if so
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for prev_model in prev_m_models.models:
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if motion_model.model is prev_model.model:
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# need to create new internal model based on same state_dict
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motion_model = create_fresh_motion_module(motion_model)
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# apply motion model to loaded_mm
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if motion_lora is not None:
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for lora in motion_lora.loras:
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load_motion_lora_as_patches(motion_model, lora)
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motion_model.scale_multival = scale_multival
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motion_model.effect_multival = effect_multival
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motion_model.keyframes = ad_keyframes.clone() if ad_keyframes else ADKeyframeGroup()
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motion_model.timestep_percent_range = (start_percent, end_percent)
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# add to beginning, so that after injection, it will be the earliest of prev_m_models to be run
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prev_m_models.add_to_start(mm=motion_model)
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return (prev_m_models,) |