from typing import Union from torch import Tensor from .freeinit import FreeInitFilter from .sample_settings import (FreeInitOptions, IterationOptions, NoiseLayerAdd, NoiseLayerAddWeighted, NoiseLayerGroup, NoiseLayerReplace, NoiseLayerType, SeedNoiseGeneration, SampleSettings, CustomCFGKeyframeGroup, CustomCFGKeyframe) from .utils_model import BIGMIN, BIGMAX, SigmaSchedule class SampleSettingsNode: @classmethod def INPUT_TYPES(s): return { "required": { "batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}), "noise_type": (NoiseLayerType.LIST,), "seed_gen": (SeedNoiseGeneration.LIST,), "seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}), }, "optional": { "noise_layers": ("NOISE_LAYERS",), "iteration_opts": ("ITERATION_OPTS",), "seed_override": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}), "adapt_denoise_steps": ("BOOLEAN", {"default": False},), "custom_cfg": ("CUSTOM_CFG",), "sigma_schedule": ("SIGMA_SCHEDULE",), } } RETURN_TYPES = ("SAMPLE_SETTINGS",) RETURN_NAMES = ("settings",) CATEGORY = "Animate Diff 🎭🅐🅓" FUNCTION = "create_settings" def create_settings(self, batch_offset: int, noise_type: str, seed_gen: str, seed_offset: int, noise_layers: NoiseLayerGroup=None, iteration_opts: IterationOptions=None, seed_override: int=None, adapt_denoise_steps=False, custom_cfg: CustomCFGKeyframeGroup=None, sigma_schedule: SigmaSchedule=None): sampling_settings = SampleSettings(batch_offset=batch_offset, noise_type=noise_type, seed_gen=seed_gen, seed_offset=seed_offset, noise_layers=noise_layers, iteration_opts=iteration_opts, seed_override=seed_override, adapt_denoise_steps=adapt_denoise_steps, custom_cfg=custom_cfg, sigma_schedule=sigma_schedule) return (sampling_settings,) class NoiseLayerReplaceNode: @classmethod def INPUT_TYPES(s): return { "required": { "batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}), "noise_type": (NoiseLayerType.LIST,), "seed_gen_override": (SeedNoiseGeneration.LIST_WITH_OVERRIDE,), "seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}), }, "optional": { "prev_noise_layers": ("NOISE_LAYERS",), "mask_optional": ("MASK",), "seed_override": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}), } } RETURN_TYPES = ("NOISE_LAYERS",) CATEGORY = "Animate Diff 🎭🅐🅓/noise layers" FUNCTION = "create_layers" def create_layers(self, batch_offset: int, noise_type: str, seed_gen_override: str, seed_offset: int, prev_noise_layers: NoiseLayerGroup=None, mask_optional: Tensor=None, seed_override: int=None,): # prepare prev_noise_layers if prev_noise_layers is None: prev_noise_layers = NoiseLayerGroup() prev_noise_layers = prev_noise_layers.clone() # create layer layer = NoiseLayerReplace(noise_type=noise_type, batch_offset=batch_offset, seed_gen_override=seed_gen_override, seed_offset=seed_offset, seed_override=seed_override, mask=mask_optional) prev_noise_layers.add_to_start(layer) return (prev_noise_layers,) class NoiseLayerAddNode: @classmethod def INPUT_TYPES(s): return { "required": { "batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}), "noise_type": (NoiseLayerType.LIST,), "seed_gen_override": (SeedNoiseGeneration.LIST_WITH_OVERRIDE,), "seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}), "noise_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.001}), }, "optional": { "prev_noise_layers": ("NOISE_LAYERS",), "mask_optional": ("MASK",), "seed_override": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}), } } RETURN_TYPES = ("NOISE_LAYERS",) CATEGORY = "Animate Diff 🎭🅐🅓/noise layers" FUNCTION = "create_layers" def create_layers(self, batch_offset: int, noise_type: str, seed_gen_override: str, seed_offset: int, noise_weight: float, prev_noise_layers: NoiseLayerGroup=None, mask_optional: Tensor=None, seed_override: int=None,): # prepare prev_noise_layers if prev_noise_layers is None: prev_noise_layers = NoiseLayerGroup() prev_noise_layers = prev_noise_layers.clone() # create layer layer = NoiseLayerAdd(noise_type=noise_type, batch_offset=batch_offset, seed_gen_override=seed_gen_override, seed_offset=seed_offset, seed_override=seed_override, mask=mask_optional, noise_weight=noise_weight) prev_noise_layers.add_to_start(layer) return (prev_noise_layers,) class NoiseLayerAddWeightedNode: @classmethod def INPUT_TYPES(s): return { "required": { "batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}), "noise_type": (NoiseLayerType.LIST,), "seed_gen_override": (SeedNoiseGeneration.LIST_WITH_OVERRIDE,), "seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}), "noise_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.001}), "balance_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}), }, "optional": { "prev_noise_layers": ("NOISE_LAYERS",), "mask_optional": ("MASK",), "seed_override": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}), } } RETURN_TYPES = ("NOISE_LAYERS",) CATEGORY = "Animate Diff 🎭🅐🅓/noise layers" FUNCTION = "create_layers" def create_layers(self, batch_offset: int, noise_type: str, seed_gen_override: str, seed_offset: int, noise_weight: float, balance_multiplier: float, prev_noise_layers: NoiseLayerGroup=None, mask_optional: Tensor=None, seed_override: int=None,): # prepare prev_noise_layers if prev_noise_layers is None: prev_noise_layers = NoiseLayerGroup() prev_noise_layers = prev_noise_layers.clone() # create layer layer = NoiseLayerAddWeighted(noise_type=noise_type, batch_offset=batch_offset, seed_gen_override=seed_gen_override, seed_offset=seed_offset, seed_override=seed_override, mask=mask_optional, noise_weight=noise_weight, balance_multiplier=balance_multiplier) prev_noise_layers.add_to_start(layer) return (prev_noise_layers,) class IterationOptionsNode: @classmethod def INPUT_TYPES(s): return { "required": { "iterations": ("INT", {"default": 1, "min": 1}), }, "optional": { "iter_batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}), "iter_seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}), } } RETURN_TYPES = ("ITERATION_OPTS",) CATEGORY = "Animate Diff 🎭🅐🅓/iteration opts" FUNCTION = "create_iter_opts" def create_iter_opts(self, iterations: int, iter_batch_offset: int=0, iter_seed_offset: int=0): iter_opts = IterationOptions(iterations=iterations, iter_batch_offset=iter_batch_offset, iter_seed_offset=iter_seed_offset) return (iter_opts,) class FreeInitOptionsNode: @classmethod def INPUT_TYPES(s): return { "required": { "iterations": ("INT", {"default": 2, "min": 1}), "filter": (FreeInitFilter.LIST,), "d_s": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.001}), "d_t": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.001}), "n_butterworth": ("INT", {"default": 4, "min": 1, "max": 100},), "sigma_step": ("INT", {"default": 999, "min": 1, "max": 999}), "apply_to_1st_iter": ("BOOLEAN", {"default": False}), "init_type": (FreeInitOptions.LIST,) }, "optional": { "iter_batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}), "iter_seed_offset": ("INT", {"default": 1, "min": BIGMIN, "max": BIGMAX}), } } RETURN_TYPES = ("ITERATION_OPTS",) CATEGORY = "Animate Diff 🎭🅐🅓/iteration opts" FUNCTION = "create_iter_opts" def create_iter_opts(self, iterations: int, filter: str, d_s: float, d_t: float, n_butterworth: int, sigma_step: int, apply_to_1st_iter: bool, init_type: str, iter_batch_offset: int=0, iter_seed_offset: int=1): # init_type does nothing for now, not until I add more methods of applying low+high freq noise iter_opts = FreeInitOptions(iterations=iterations, step=sigma_step, apply_to_1st_iter=apply_to_1st_iter, filter=filter, d_s=d_s, d_t=d_t, n=n_butterworth, init_type=init_type, iter_batch_offset=iter_batch_offset, iter_seed_offset=iter_seed_offset) return (iter_opts,) class CustomCFGNode: @classmethod def INPUT_TYPES(s): return { "required": { "cfg_multival": ("MULTIVAL",), } } RETURN_TYPES = ("CUSTOM_CFG",) CATEGORY = "Animate Diff 🎭🅐🅓/sample settings" FUNCTION = "create_custom_cfg" def create_custom_cfg(self, cfg_multival: Union[float, Tensor]): keyframe = CustomCFGKeyframe(cfg_multival=cfg_multival) cfg_custom = CustomCFGKeyframeGroup() cfg_custom.add(keyframe) return (cfg_custom,) class CustomCFGKeyframeNode: @classmethod def INPUT_TYPES(s): return { "required": { "cfg_multival": ("MULTIVAL",), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}), }, "optional": { "prev_custom_cfg": ("CUSTOM_CFG",), } } RETURN_TYPES = ("CUSTOM_CFG",) CATEGORY = "Animate Diff 🎭🅐🅓/sample settings" FUNCTION = "create_custom_cfg" def create_custom_cfg(self, cfg_multival: Union[float, Tensor], start_percent: float=0.0, guarantee_steps: int=1, prev_custom_cfg: CustomCFGKeyframeGroup=None): if not prev_custom_cfg: prev_custom_cfg = CustomCFGKeyframeGroup() prev_custom_cfg = prev_custom_cfg.clone() keyframe = CustomCFGKeyframe(cfg_multival=cfg_multival, start_percent=start_percent, guarantee_steps=guarantee_steps) prev_custom_cfg.add(keyframe) return (prev_custom_cfg,)