--- tags: - AnimateDiff - AnimateDiffContext - Animation --- # AnimateDiff Loader [Legacy] 🎭🅐🅓① ## Documentation - Class name: ADE_AnimateDiffLoaderWithContext - Category: Animate Diff 🎭🅐🅓/① Gen1 nodes ① - Output node: False 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。 ## Input types ### Required - model - 指定要加载的AnimateDiff模型，作为动画过程的核心组件。 - Comfy dtype: MODEL - Python dtype: str - model_name - 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。 - Comfy dtype: COMBO[STRING] - Python dtype: str - beta_schedule - 调整动画的beta进度，影响扩散过程。 - Comfy dtype: COMBO[STRING] - Python dtype: str ### Optional - context_options - 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。 - Comfy dtype: CONTEXT_OPTIONS - Python dtype: str - motion_lora - 指定运动的LoRA参数，增强模型生成动态动画的能力。 - Comfy dtype: MOTION_LORA - Python dtype: str - ad_settings - 定义AnimateDiff的设置，调整动画过程以满足特定要求。 - Comfy dtype: AD_SETTINGS - Python dtype: str - sample_settings - 确定动画的采样设置，影响生成动画的质量和特征。 - Comfy dtype: SAMPLE_SETTINGS - Python dtype: str - motion_scale - 控制动画中的运动规模，允许更精细地调整运动强度。 - Comfy dtype: FLOAT - Python dtype: str - apply_v2_models_properly - 确保正确应用版本2模型，优化兼容性和性能。 - Comfy dtype: BOOLEAN - Python dtype: str - ad_keyframes - 指定动画的关键帧，引导AnimateDiff模型生成目标动画序列。 - Comfy dtype: AD_KEYFRAMES - Python dtype: str ## Output types - model - Comfy dtype: MODEL - 加载的AnimateDiff模型，准备好生成具有指定上下文和设置的动画。 - Python dtype: str ## Usage tips - Infra type: CPU ## Source code ```python class LegacyAnimateDiffLoaderWithContext: @classmethod def INPUT_TYPES(s): return { "required": { "model": ("MODEL",), "model_name": (get_available_motion_models(),), "beta_schedule": (BetaSchedules.ALIAS_LIST, {"default": BetaSchedules.AUTOSELECT}), #"apply_mm_groupnorm_hack": ("BOOLEAN", {"default": True}), }, "optional": { "context_options": ("CONTEXT_OPTIONS",), "motion_lora": ("MOTION_LORA",), "ad_settings": ("AD_SETTINGS",), "sample_settings": ("SAMPLE_SETTINGS",), "motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}), "apply_v2_models_properly": ("BOOLEAN", {"default": True}), "ad_keyframes": ("AD_KEYFRAMES",), } } RETURN_TYPES = ("MODEL",) CATEGORY = "Animate Diff 🎭🅐🅓/① Gen1 nodes ①" FUNCTION = "load_mm_and_inject_params" def load_mm_and_inject_params(self, model: ModelPatcher, model_name: str, beta_schedule: str,# apply_mm_groupnorm_hack: bool, context_options: ContextOptionsGroup=None, motion_lora: MotionLoraList=None, ad_settings: AnimateDiffSettings=None, motion_model_settings: AnimateDiffSettings=None, sample_settings: SampleSettings=None, motion_scale: float=1.0, apply_v2_models_properly: bool=False, ad_keyframes: ADKeyframeGroup=None, ): if ad_settings is not None: motion_model_settings = ad_settings # load motion module motion_model = load_motion_module_gen1(model_name, model, motion_lora=motion_lora, motion_model_settings=motion_model_settings) # set injection params params = InjectionParams( unlimited_area_hack=False, model_name=model_name, apply_v2_properly=apply_v2_models_properly, ) if context_options: params.set_context(context_options) # set motion_scale and motion_model_settings if not motion_model_settings: motion_model_settings = AnimateDiffSettings() motion_model_settings.attn_scale = motion_scale params.set_motion_model_settings(motion_model_settings) if params.motion_model_settings.mask_attn_scale is not None: motion_model.scale_multival = params.motion_model_settings.mask_attn_scale * params.motion_model_settings.attn_scale else: motion_model.scale_multival = params.motion_model_settings.attn_scale motion_model.keyframes = ad_keyframes.clone() if ad_keyframes else ADKeyframeGroup() model = ModelPatcherAndInjector.create_from(model, hooks_only=True) model.motion_models = MotionModelGroup(motion_model) model.sample_settings = sample_settings if sample_settings is not None else SampleSettings() model.motion_injection_params = params # save model sampling from BetaSchedule as object patch # if autoselect, get suggested beta_schedule from motion model if beta_schedule == BetaSchedules.AUTOSELECT and not model.motion_models.is_empty(): beta_schedule = model.motion_models[0].model.get_best_beta_schedule(log=True) new_model_sampling = BetaSchedules.to_model_sampling(beta_schedule, model) if new_model_sampling is not None: model.add_object_patch("model_sampling", new_model_sampling) del motion_model return (model,)