460 lines
20 KiB
Python
460 lines
20 KiB
Python
import json
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import os
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import shutil
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import subprocess
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from typing import Dict, List
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import numpy as np
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import torch
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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import folder_paths
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from comfy.model_patcher import ModelPatcher
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from .ad_settings import AnimateDiffSettings, AdjustGroup, AdjustPE, AdjustWeight
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from .context import ContextOptionsGroup, ContextOptions, ContextSchedules
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from .logger import logger
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from .utils_model import Folders, BetaSchedules, get_available_motion_models
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from .model_injection import ModelPatcherAndInjector, InjectionParams, MotionModelGroup, load_motion_module_gen1
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class AnimateDiffLoader_Deprecated:
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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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"latents": ("LATENT",),
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"model_name": (get_available_motion_models(),),
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"unlimited_area_hack": ("BOOLEAN", {"default": False},),
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"beta_schedule": (BetaSchedules.get_alias_list_with_first_element(BetaSchedules.SQRT_LINEAR),),
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},
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"optional": {"deprecation_warning": ("ADEWARN", {"text": "Deprecated"})},
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}
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RETURN_TYPES = ("MODEL", "LATENT")
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CATEGORY = ""
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FUNCTION = "load_mm_and_inject_params"
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def load_mm_and_inject_params(
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self,
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model: ModelPatcher,
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latents: Dict[str, torch.Tensor],
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model_name: str, unlimited_area_hack: bool, beta_schedule: str,
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):
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# load motion module
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motion_model = load_motion_module_gen1(model_name, model)
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# get total frames
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init_frames_len = len(latents["samples"]) # deprecated - no longer used for anything lol
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# set injection params
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params = InjectionParams(
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unlimited_area_hack=unlimited_area_hack,
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apply_mm_groupnorm_hack=True,
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model_name=model_name,
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apply_v2_properly=False,
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)
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# inject for use in sampling code
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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.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, latents)
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class AnimateDiffLoaderAdvanced_Deprecated:
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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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"latents": ("LATENT",),
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"model_name": (get_available_motion_models(),),
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"unlimited_area_hack": ("BOOLEAN", {"default": False},),
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"context_length": ("INT", {"default": 16, "min": 0, "max": 1000}),
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"context_stride": ("INT", {"default": 1, "min": 1, "max": 1000}),
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"context_overlap": ("INT", {"default": 4, "min": 0, "max": 1000}),
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"context_schedule": (ContextSchedules.LEGACY_UNIFORM_SCHEDULE_LIST,),
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"closed_loop": ("BOOLEAN", {"default": False},),
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"beta_schedule": (BetaSchedules.get_alias_list_with_first_element(BetaSchedules.SQRT_LINEAR),),
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},
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"optional": {"deprecation_warning": ("ADEWARN", {"text": "Deprecated"})},
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}
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RETURN_TYPES = ("MODEL", "LATENT")
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CATEGORY = ""
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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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latents: Dict[str, torch.Tensor],
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model_name: str, unlimited_area_hack: bool,
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context_length: int, context_stride: int, context_overlap: int, context_schedule: str, closed_loop: bool,
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beta_schedule: str,
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):
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# load motion module
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motion_model = load_motion_module_gen1(model_name, model)
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# get total frames
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init_frames_len = len(latents["samples"]) # deprecated - no longer used for anything lol
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# set injection params
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params = InjectionParams(
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unlimited_area_hack=unlimited_area_hack,
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apply_mm_groupnorm_hack=True,
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model_name=model_name,
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apply_v2_properly=False,
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)
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context_group = ContextOptionsGroup()
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context_group.add(
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ContextOptions(
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context_length=context_length,
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context_stride=context_stride,
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context_overlap=context_overlap,
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context_schedule=context_schedule,
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closed_loop=closed_loop,
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)
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)
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# set context settings
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params.set_context(context_options=context_group)
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# inject for use in sampling code
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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.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, latents)
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class AnimateDiffCombine_Deprecated:
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ffmpeg_warning_already_shown = False
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@classmethod
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def INPUT_TYPES(s):
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ffmpeg_path = shutil.which("ffmpeg")
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#Hide ffmpeg formats if ffmpeg isn't available
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if ffmpeg_path is not None:
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ffmpeg_formats = ["video/"+x[:-5] for x in folder_paths.get_filename_list(Folders.VIDEO_FORMATS)]
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else:
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ffmpeg_formats = []
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if not s.ffmpeg_warning_already_shown:
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# Deprecated node are now hidden, so no need to show warning unless node is used.
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# logger.warning("This warning can be ignored, you should not be using the deprecated AnimateDiff Combine node anyway. If you are, use Video Combine from ComfyUI-VideoHelperSuite instead. ffmpeg could not be found. Outputs that require it have been disabled")
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s.ffmpeg_warning_already_shown = True
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return {
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"required": {
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"images": ("IMAGE",),
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"frame_rate": (
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"INT",
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{"default": 8, "min": 1, "max": 24, "step": 1},
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),
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"loop_count": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
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"filename_prefix": ("STRING", {"default": "AnimateDiff"}),
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"format": (["image/gif", "image/webp"] + ffmpeg_formats,),
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"pingpong": ("BOOLEAN", {"default": False}),
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"save_image": ("BOOLEAN", {"default": True}),
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},
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"optional": {"deprecation_warning": ("ADEWARN", {"text": "Deprecated. Use VHS Video Combine"})},
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"hidden": {
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"prompt": "PROMPT",
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"extra_pnginfo": "EXTRA_PNGINFO",
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},
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}
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RETURN_TYPES = ("GIF",)
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OUTPUT_NODE = True
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CATEGORY = ""
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FUNCTION = "generate_gif"
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def generate_gif(
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self,
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images,
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frame_rate: int,
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loop_count: int,
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filename_prefix="AnimateDiff",
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format="image/gif",
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pingpong=False,
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save_image=True,
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prompt=None,
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extra_pnginfo=None,
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):
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logger.warning("Do not use AnimateDiff Combine node, it is deprecated. Use Video Combine node from ComfyUI-VideoHelperSuite instead. Video nodes from VideoHelperSuite are actively maintained, more feature-rich, and also automatically attempts to get ffmpeg.")
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# convert images to numpy
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frames: List[Image.Image] = []
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for image in images:
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img = 255.0 * image.cpu().numpy()
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img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
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frames.append(img)
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# get output information
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output_dir = (
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folder_paths.get_output_directory()
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if save_image
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else folder_paths.get_temp_directory()
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)
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(
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full_output_folder,
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filename,
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counter,
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subfolder,
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_,
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) = folder_paths.get_save_image_path(filename_prefix, output_dir)
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metadata = PngInfo()
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if prompt is not None:
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metadata.add_text("prompt", json.dumps(prompt))
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if extra_pnginfo is not None:
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for x in extra_pnginfo:
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metadata.add_text(x, json.dumps(extra_pnginfo[x]))
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# save first frame as png to keep metadata
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file = f"{filename}_{counter:05}_.png"
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file_path = os.path.join(full_output_folder, file)
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frames[0].save(
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file_path,
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pnginfo=metadata,
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compress_level=4,
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)
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if pingpong:
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frames = frames + frames[-2:0:-1]
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format_type, format_ext = format.split("/")
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file = f"{filename}_{counter:05}_.{format_ext}"
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file_path = os.path.join(full_output_folder, file)
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if format_type == "image":
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# Use pillow directly to save an animated image
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frames[0].save(
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file_path,
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format=format_ext.upper(),
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save_all=True,
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append_images=frames[1:],
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duration=round(1000 / frame_rate),
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loop=loop_count,
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compress_level=4,
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)
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else:
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# Use ffmpeg to save a video
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ffmpeg_path = shutil.which("ffmpeg")
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if ffmpeg_path is None:
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#Should never be reachable
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raise ProcessLookupError("Could not find ffmpeg")
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video_format_path = folder_paths.get_full_path("video_formats", format_ext + ".json")
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with open(video_format_path, 'r') as stream:
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video_format = json.load(stream)
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file = f"{filename}_{counter:05}_.{video_format['extension']}"
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file_path = os.path.join(full_output_folder, file)
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dimensions = f"{frames[0].width}x{frames[0].height}"
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args = [ffmpeg_path, "-v", "error", "-f", "rawvideo", "-pix_fmt", "rgb24",
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"-s", dimensions, "-r", str(frame_rate), "-i", "-"] \
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+ video_format['main_pass'] + [file_path]
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env=os.environ.copy()
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if "environment" in video_format:
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env.update(video_format["environment"])
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with subprocess.Popen(args, stdin=subprocess.PIPE, env=env) as proc:
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for frame in frames:
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proc.stdin.write(frame.tobytes())
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previews = [
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{
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"filename": file,
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"subfolder": subfolder,
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"type": "output" if save_image else "temp",
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"format": format,
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}
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]
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return {"ui": {"gifs": previews}}
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class AnimateDiffModelSettings:
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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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"min_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
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"max_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
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},
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"optional": {
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"mask_motion_scale": ("MASK",),
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"deprecation_warning": ("ADEWARN", {"text": "Deprecated"}),
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}
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}
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RETURN_TYPES = ("AD_SETTINGS",)
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CATEGORY = "" #"Animate Diff 🎭🅐🅓/① Gen1 nodes ①/motion settings"
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FUNCTION = "get_motion_model_settings"
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def get_motion_model_settings(self, mask_motion_scale: torch.Tensor=None, min_motion_scale: float=1.0, max_motion_scale: float=1.0):
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motion_model_settings = AnimateDiffSettings(
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mask_attn_scale=mask_motion_scale,
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mask_attn_scale_min=min_motion_scale,
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mask_attn_scale_max=max_motion_scale,
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)
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return (motion_model_settings,)
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class AnimateDiffModelSettingsSimple:
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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_pe_stretch": ("INT", {"default": 0, "min": 0, "step": 1}),
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},
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"optional": {
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"mask_motion_scale": ("MASK",),
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"min_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
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"max_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
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"deprecation_warning": ("ADEWARN", {"text": "Deprecated"}),
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}
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}
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RETURN_TYPES = ("AD_SETTINGS",)
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CATEGORY = "" #"Animate Diff 🎭🅐🅓/① Gen1 nodes ①/motion settings/experimental"
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FUNCTION = "get_motion_model_settings"
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def get_motion_model_settings(self, motion_pe_stretch: int,
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mask_motion_scale: torch.Tensor=None, min_motion_scale: float=1.0, max_motion_scale: float=1.0):
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adjust_pe = AdjustGroup(AdjustPE(motion_pe_stretch=motion_pe_stretch))
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motion_model_settings = AnimateDiffSettings(
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adjust_pe=adjust_pe,
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mask_attn_scale=mask_motion_scale,
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mask_attn_scale_min=min_motion_scale,
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mask_attn_scale_max=max_motion_scale,
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)
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return (motion_model_settings,)
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class AnimateDiffModelSettingsAdvanced:
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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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"pe_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"attn_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"other_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"motion_pe_stretch": ("INT", {"default": 0, "min": 0, "step": 1}),
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"cap_initial_pe_length": ("INT", {"default": 0, "min": 0, "step": 1}),
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"interpolate_pe_to_length": ("INT", {"default": 0, "min": 0, "step": 1}),
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"initial_pe_idx_offset": ("INT", {"default": 0, "min": 0, "step": 1}),
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"final_pe_idx_offset": ("INT", {"default": 0, "min": 0, "step": 1}),
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},
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"optional": {
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"mask_motion_scale": ("MASK",),
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"min_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
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"max_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
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"deprecation_warning": ("ADEWARN", {"text": "Deprecated"}),
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}
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}
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RETURN_TYPES = ("AD_SETTINGS",)
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CATEGORY = "" #"Animate Diff 🎭🅐🅓/① Gen1 nodes ①/motion settings/experimental"
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FUNCTION = "get_motion_model_settings"
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def get_motion_model_settings(self, pe_strength: float, attn_strength: float, other_strength: float,
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motion_pe_stretch: int,
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cap_initial_pe_length: int, interpolate_pe_to_length: int,
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initial_pe_idx_offset: int, final_pe_idx_offset: int,
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mask_motion_scale: torch.Tensor=None, min_motion_scale: float=1.0, max_motion_scale: float=1.0):
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adjust_pe = AdjustGroup(AdjustPE(motion_pe_stretch=motion_pe_stretch,
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cap_initial_pe_length=cap_initial_pe_length, interpolate_pe_to_length=interpolate_pe_to_length,
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initial_pe_idx_offset=initial_pe_idx_offset, final_pe_idx_offset=final_pe_idx_offset))
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adjust_weight = AdjustGroup(AdjustWeight(
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pe_MULT=pe_strength,
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attn_MULT=attn_strength,
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other_MULT=other_strength,
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))
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motion_model_settings = AnimateDiffSettings(
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adjust_pe=adjust_pe,
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adjust_weight=adjust_weight,
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mask_attn_scale=mask_motion_scale,
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mask_attn_scale_min=min_motion_scale,
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mask_attn_scale_max=max_motion_scale,
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)
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return (motion_model_settings,)
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class AnimateDiffModelSettingsAdvancedAttnStrengths:
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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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"pe_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"attn_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"attn_q_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"attn_k_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"attn_v_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"attn_out_weight_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"attn_out_bias_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"other_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
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"motion_pe_stretch": ("INT", {"default": 0, "min": 0, "step": 1}),
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"cap_initial_pe_length": ("INT", {"default": 0, "min": 0, "step": 1}),
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"interpolate_pe_to_length": ("INT", {"default": 0, "min": 0, "step": 1}),
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"initial_pe_idx_offset": ("INT", {"default": 0, "min": 0, "step": 1}),
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"final_pe_idx_offset": ("INT", {"default": 0, "min": 0, "step": 1}),
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},
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"optional": {
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"mask_motion_scale": ("MASK",),
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"min_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
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"max_motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
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"deprecation_warning": ("ADEWARN", {"text": "Deprecated"}),
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}
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}
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RETURN_TYPES = ("AD_SETTINGS",)
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CATEGORY = "" #"Animate Diff 🎭🅐🅓/① Gen1 nodes ①/motion settings/experimental"
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FUNCTION = "get_motion_model_settings"
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def get_motion_model_settings(self, pe_strength: float, attn_strength: float,
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attn_q_strength: float,
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attn_k_strength: float,
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attn_v_strength: float,
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attn_out_weight_strength: float,
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attn_out_bias_strength: float,
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other_strength: float,
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motion_pe_stretch: int,
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cap_initial_pe_length: int, interpolate_pe_to_length: int,
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initial_pe_idx_offset: int, final_pe_idx_offset: int,
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mask_motion_scale: torch.Tensor=None, min_motion_scale: float=1.0, max_motion_scale: float=1.0):
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adjust_pe = AdjustGroup(AdjustPE(motion_pe_stretch=motion_pe_stretch,
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cap_initial_pe_length=cap_initial_pe_length, interpolate_pe_to_length=interpolate_pe_to_length,
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initial_pe_idx_offset=initial_pe_idx_offset, final_pe_idx_offset=final_pe_idx_offset))
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adjust_weight = AdjustGroup(AdjustWeight(
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pe_MULT=pe_strength,
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|
attn_MULT=attn_strength,
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|
attn_q_MULT=attn_q_strength,
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|
attn_k_MULT=attn_k_strength,
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|
attn_v_MULT=attn_v_strength,
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|
attn_out_weight_MULT=attn_out_weight_strength,
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|
attn_out_bias_MULT=attn_out_bias_strength,
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|
other_MULT=other_strength,
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))
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|
motion_model_settings = AnimateDiffSettings(
|
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adjust_pe=adjust_pe,
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adjust_weight=adjust_weight,
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|
mask_attn_scale=mask_motion_scale,
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|
mask_attn_scale_min=min_motion_scale,
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|
mask_attn_scale_max=max_motion_scale,
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|
)
|
|
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return (motion_model_settings,)
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|
|