113 lines
5.1 KiB
Markdown
113 lines
5.1 KiB
Markdown
# Use Evolved Sampling 🎭🅐🅓②
|
|
## Documentation
|
|
- Class name: ADE_UseEvolvedSampling
|
|
- Category: Animate Diff 🎭🅐🅓/② Gen2 nodes ②
|
|
- Output node: False
|
|
- Repo Ref: https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved.git
|
|
|
|
ADE_UseEvolvedSampling节点将先进的采样技术集成到动画扩散过程中，利用进化模型采样配置来增强生成动画的质量和效率。它根据模型配置和动态条件调整采样过程，旨在优化动画输出的视觉保真度和计算性能。
|
|
|
|
## Input types
|
|
### Required
|
|
- model
|
|
- 指定用于动画扩散过程的模型，影响生成动画的整体质量和特性。
|
|
- Comfy dtype: MODEL
|
|
- Python dtype: Model
|
|
- beta_schedule
|
|
- 决定在采样过程中应用的beta计划，影响动画过渡的平滑度和质量。
|
|
- Comfy dtype: COMBO[STRING]
|
|
- Python dtype: BetaSchedules.ALIAS_LIST
|
|
|
|
### Optional
|
|
- m_models
|
|
- 可选。为动画过程提供额外的模型，可能增强输出的多样性和丰富性。
|
|
- Comfy dtype: M_MODELS
|
|
- Python dtype: list
|
|
- context_options
|
|
- 可选。定义可以修改采样过程行为的上下文特定选项，定制其以满足特定需求或偏好。
|
|
- Comfy dtype: CONTEXT_OPTIONS
|
|
- Python dtype: dict
|
|
- sample_settings
|
|
- 可选。指定与采样过程相关的设置，如分辨率和时间调整，直接影响动画的视觉质量。
|
|
- Comfy dtype: SAMPLE_SETTINGS
|
|
- Python dtype: dict
|
|
|
|
## Output types
|
|
- model
|
|
- Comfy dtype: MODEL
|
|
- 增强的模型实例，配备进化的采样配置以改进动画扩散。
|
|
- Python dtype: Model
|
|
|
|
## Usage tips
|
|
- Infra type: CPU
|
|
<!-- - Common nodes:
|
|
- [KSampler](../../Comfy/Nodes/KSampler.md)
|
|
- [ToBasicPipe](../../ComfyUI-Impact-Pack/Nodes/ToBasicPipe.md)
|
|
- [SamplerCustom](../../Comfy/Nodes/SamplerCustom.md)
|
|
- [LCMScheduler](../../ComfyUI-sampler-lcm-alternative/Nodes/LCMScheduler.md) -->
|
|
|
|
## Source code
|
|
```python
|
|
class UseEvolvedSamplingNode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL",),
|
|
"beta_schedule": (BetaSchedules.ALIAS_LIST, {"default": BetaSchedules.AUTOSELECT}),
|
|
},
|
|
"optional": {
|
|
"m_models": ("M_MODELS",),
|
|
"context_options": ("CONTEXT_OPTIONS",),
|
|
"sample_settings": ("SAMPLE_SETTINGS",),
|
|
#"beta_schedule_override": ("BETA_SCHEDULE",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MODEL",)
|
|
CATEGORY = "Animate Diff 🎭🅐🅓/② Gen2 nodes ②"
|
|
FUNCTION = "use_evolved_sampling"
|
|
|
|
def use_evolved_sampling(self, model: ModelPatcher, beta_schedule: str, m_models: MotionModelGroup=None, context_options: ContextOptionsGroup=None,
|
|
sample_settings: SampleSettings=None, beta_schedule_override=None):
|
|
if m_models is not None:
|
|
m_models = m_models.clone()
|
|
# for each motion model, confirm that it is compatible with SD model
|
|
for motion_model in m_models.models:
|
|
validate_model_compatibility_gen2(model=model, motion_model=motion_model)
|
|
# create injection params
|
|
model_name_list = [motion_model.model.mm_info.mm_name for motion_model in m_models.models]
|
|
model_names = ",".join(model_name_list)
|
|
# TODO: check if any apply_v2_properly is set to False
|
|
params = InjectionParams(unlimited_area_hack=False, model_name=model_names)
|
|
else:
|
|
params = InjectionParams()
|
|
# apply context options
|
|
if context_options:
|
|
params.set_context(context_options)
|
|
# need to use a ModelPatcher that supports injection of motion modules into unet
|
|
model = ModelPatcherAndInjector.create_from(model, hooks_only=True)
|
|
model.motion_models = m_models
|
|
model.sample_settings = sample_settings if sample_settings is not None else SampleSettings()
|
|
model.motion_injection_params = params
|
|
|
|
if model.sample_settings.custom_cfg is not None:
|
|
logger.info("[Sample Settings] custom_cfg is set; will override any KSampler cfg values or patches.")
|
|
|
|
if model.sample_settings.sigma_schedule is not None:
|
|
logger.info("[Sample Settings] sigma_schedule is set; will override beta_schedule.")
|
|
model.add_object_patch("model_sampling", model.sample_settings.sigma_schedule.clone().model_sampling)
|
|
else:
|
|
# save model_sampling from BetaSchedule as object patch
|
|
# if autoselect, get suggested beta_schedule from motion model
|
|
if beta_schedule == BetaSchedules.AUTOSELECT:
|
|
if model.motion_models is None or model.motion_models.is_empty():
|
|
beta_schedule = BetaSchedules.USE_EXISTING
|
|
else:
|
|
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 m_models
|
|
return (model,) |