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