155 lines
6.1 KiB
Markdown
155 lines
6.1 KiB
Markdown
---
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tags:
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- AnimateDiff
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- AnimateDiffContext
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- Animation
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---
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# AnimateDiff Loader [Legacy] 🎭🅐🅓①
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## Documentation
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- Class name: ADE_AnimateDiffLoaderWithContext
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- Category: Animate Diff 🎭🅐🅓/① Gen1 nodes ①
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- Output node: False
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此节点旨在加载AnimateDiff模型及其特定上下文，有助于集成影响动画过程的额外信息或设置。它在传统AnimateDiff模型与更新的、上下文感知的功能之间架起桥梁，确保兼容性并增强对动画生成的控制。
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## Input types
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### Required
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- model
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- 指定要加载的AnimateDiff模型，作为动画过程的核心组件。
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- Comfy dtype: MODEL
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- Python dtype: str
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- model_name
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- 通过名称识别特定的AnimateDiff模型，允许在可用选项中进行精确选择。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- beta_schedule
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- 调整动画的beta进度，影响扩散过程。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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### Optional
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- context_options
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- 提供上下文特定选项以修改AnimateDiff模型处理动画的方式，允许定制动画行为。
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- Comfy dtype: CONTEXT_OPTIONS
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- Python dtype: str
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- motion_lora
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- 指定运动的LoRA参数，增强模型生成动态动画的能力。
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- Comfy dtype: MOTION_LORA
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- Python dtype: str
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- ad_settings
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- 定义AnimateDiff的设置，调整动画过程以满足特定要求。
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- Comfy dtype: AD_SETTINGS
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- Python dtype: str
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- sample_settings
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- 确定动画的采样设置，影响生成动画的质量和特征。
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- Comfy dtype: SAMPLE_SETTINGS
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- Python dtype: str
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- motion_scale
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- 控制动画中的运动规模，允许更精细地调整运动强度。
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- Comfy dtype: FLOAT
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- Python dtype: str
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- apply_v2_models_properly
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- 确保正确应用版本2模型，优化兼容性和性能。
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- Comfy dtype: BOOLEAN
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- Python dtype: str
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- ad_keyframes
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- 指定动画的关键帧，引导AnimateDiff模型生成目标动画序列。
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- Comfy dtype: AD_KEYFRAMES
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- Python dtype: str
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## Output types
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- model
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- Comfy dtype: MODEL
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- 加载的AnimateDiff模型，准备好生成具有指定上下文和设置的动画。
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- Python dtype: str
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## Usage tips
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- Infra type: CPU
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<!-- - Common nodes:
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- [KSampler](./KSampler.md)
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- [FreeU_V2](./FreeU_V2.md)
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- [KSamplerAdvanced](../../Comfy/Nodes/KSamplerAdvanced.md)
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- [LoraLoaderModelOnly](../../Comfy/Nodes/LoraLoaderModelOnly.md)
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- [LoraLoader](../../Comfy/Nodes/LoraLoader.md)
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- [ToBasicPipe](../../ComfyUI-Impact-Pack/Nodes/ToBasicPipe.md)
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- IPAdapterApply
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- DynamicThresholdingSimple
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- Reroute -->
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## Source code
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```python
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class LegacyAnimateDiffLoaderWithContext:
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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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"model": ("MODEL",),
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"model_name": (get_available_motion_models(),),
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"beta_schedule": (BetaSchedules.ALIAS_LIST, {"default": BetaSchedules.AUTOSELECT}),
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#"apply_mm_groupnorm_hack": ("BOOLEAN", {"default": True}),
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},
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"optional": {
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"context_options": ("CONTEXT_OPTIONS",),
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"motion_lora": ("MOTION_LORA",),
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"ad_settings": ("AD_SETTINGS",),
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"sample_settings": ("SAMPLE_SETTINGS",),
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"motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
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"apply_v2_models_properly": ("BOOLEAN", {"default": True}),
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"ad_keyframes": ("AD_KEYFRAMES",),
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}
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}
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RETURN_TYPES = ("MODEL",)
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CATEGORY = "Animate Diff 🎭🅐🅓/① Gen1 nodes ①"
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FUNCTION = "load_mm_and_inject_params"
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def load_mm_and_inject_params(self,
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model: ModelPatcher,
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model_name: str, beta_schedule: str,# apply_mm_groupnorm_hack: bool,
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context_options: ContextOptionsGroup=None, motion_lora: MotionLoraList=None, ad_settings: AnimateDiffSettings=None, motion_model_settings: AnimateDiffSettings=None,
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sample_settings: SampleSettings=None, motion_scale: float=1.0, apply_v2_models_properly: bool=False, ad_keyframes: ADKeyframeGroup=None,
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):
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if ad_settings is not None:
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motion_model_settings = ad_settings
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# load motion module
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motion_model = load_motion_module_gen1(model_name, model, motion_lora=motion_lora, motion_model_settings=motion_model_settings)
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# set injection params
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params = InjectionParams(
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unlimited_area_hack=False,
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model_name=model_name,
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apply_v2_properly=apply_v2_models_properly,
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)
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if context_options:
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params.set_context(context_options)
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# set motion_scale and motion_model_settings
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if not motion_model_settings:
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motion_model_settings = AnimateDiffSettings()
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motion_model_settings.attn_scale = motion_scale
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params.set_motion_model_settings(motion_model_settings)
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if params.motion_model_settings.mask_attn_scale is not None:
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motion_model.scale_multival = params.motion_model_settings.mask_attn_scale * params.motion_model_settings.attn_scale
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else:
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motion_model.scale_multival = params.motion_model_settings.attn_scale
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motion_model.keyframes = ad_keyframes.clone() if ad_keyframes else ADKeyframeGroup()
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model = ModelPatcherAndInjector.create_from(model, hooks_only=True)
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model.motion_models = MotionModelGroup(motion_model)
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model.sample_settings = sample_settings if sample_settings is not None else SampleSettings()
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model.motion_injection_params = params
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# save model sampling from BetaSchedule as object patch
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# if autoselect, get suggested beta_schedule from motion model
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if beta_schedule == BetaSchedules.AUTOSELECT and not model.motion_models.is_empty():
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beta_schedule = model.motion_models[0].model.get_best_beta_schedule(log=True)
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new_model_sampling = BetaSchedules.to_model_sampling(beta_schedule, model)
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if new_model_sampling is not None:
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model.add_object_patch("model_sampling", new_model_sampling)
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del motion_model
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return (model,) |