697 lines
35 KiB
Python
697 lines
35 KiB
Python
from typing import Callable
|
|
|
|
import math
|
|
import torch
|
|
from torch import Tensor
|
|
from torch.nn.functional import group_norm
|
|
from einops import rearrange
|
|
|
|
import comfy.ldm.modules.attention as attention
|
|
from comfy.ldm.modules.diffusionmodules import openaimodel
|
|
import comfy.model_management as model_management
|
|
import comfy.samplers
|
|
import comfy.sample
|
|
import comfy.utils
|
|
from comfy.controlnet import ControlBase
|
|
import comfy.ops
|
|
|
|
from .context import ContextFuseMethod, ContextSchedules, get_context_weights, get_context_windows
|
|
from .sample_settings import IterationOptions, SampleSettings, SeedNoiseGeneration, prepare_mask_ad
|
|
from .utils_model import ModelTypeSD, wrap_function_to_inject_xformers_bug_info
|
|
from .model_injection import InjectionParams, ModelPatcherAndInjector, MotionModelGroup, MotionModelPatcher, LoraHookGroup
|
|
from .motion_module_ad import AnimateDiffFormat, AnimateDiffInfo, AnimateDiffVersion, VanillaTemporalModule
|
|
from .logger import logger
|
|
|
|
|
|
##################################################################################
|
|
######################################################################
|
|
# Global variable to use to more conveniently hack variable access into samplers
|
|
class AnimateDiffHelper_GlobalState:
|
|
def __init__(self):
|
|
self.model_patcher: ModelPatcherAndInjector = None
|
|
self.motion_models: MotionModelGroup = None
|
|
self.params: InjectionParams = None
|
|
self.sample_settings: SampleSettings = None
|
|
self.reset()
|
|
|
|
def initialize(self, model):
|
|
# this function is to be run in sampling func
|
|
if not self.initialized:
|
|
self.initialized = True
|
|
if self.motion_models is not None:
|
|
self.motion_models.initialize_timesteps(model)
|
|
if self.params.context_options is not None:
|
|
self.params.context_options.initialize_timesteps(model)
|
|
if self.sample_settings.custom_cfg is not None:
|
|
self.sample_settings.custom_cfg.initialize_timesteps(model)
|
|
|
|
def reset(self):
|
|
self.initialized = False
|
|
self.start_step: int = 0
|
|
self.last_step: int = 0
|
|
self.current_step: int = 0
|
|
self.total_steps: int = 0
|
|
if self.model_patcher is not None:
|
|
self.model_patcher.unpatch_hooked()
|
|
self.model_patcher.clear_cached_hooked_weights()
|
|
del self.model_patcher
|
|
self.model_patcher = None
|
|
if self.motion_models is not None:
|
|
del self.motion_models
|
|
self.motion_models = None
|
|
if self.params is not None:
|
|
del self.params
|
|
self.params = None
|
|
if self.sample_settings is not None:
|
|
del self.sample_settings
|
|
self.sample_settings = None
|
|
|
|
def update_with_inject_params(self, params: InjectionParams):
|
|
self.params = params
|
|
|
|
def is_using_sliding_context(self):
|
|
return self.params is not None and self.params.is_using_sliding_context()
|
|
|
|
def create_exposed_params(self):
|
|
# This dict will be exposed to be used by other extensions
|
|
# DO NOT change any of the key names
|
|
# or I will find you 👁.👁
|
|
return {
|
|
"full_length": self.params.full_length,
|
|
"context_length": self.params.context_options.context_length,
|
|
"sub_idxs": self.params.sub_idxs,
|
|
}
|
|
|
|
ADGS = AnimateDiffHelper_GlobalState()
|
|
######################################################################
|
|
##################################################################################
|
|
|
|
|
|
##################################################################################
|
|
#### Code Injection ##################################################
|
|
|
|
# refer to forward_timestep_embed in comfy/ldm/modules/diffusionmodules/openaimodel.py
|
|
def forward_timestep_embed_factory() -> Callable:
|
|
def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None):
|
|
for layer in ts:
|
|
if isinstance(layer, openaimodel.VideoResBlock):
|
|
x = layer(x, emb, num_video_frames, image_only_indicator)
|
|
elif isinstance(layer, openaimodel.TimestepBlock):
|
|
x = layer(x, emb)
|
|
elif isinstance(layer, VanillaTemporalModule):
|
|
x = layer(x, context)
|
|
elif isinstance(layer, attention.SpatialVideoTransformer):
|
|
x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options)
|
|
if "transformer_index" in transformer_options:
|
|
transformer_options["transformer_index"] += 1
|
|
if "current_index" in transformer_options: # keep this for backward compat, for now
|
|
transformer_options["current_index"] += 1
|
|
elif isinstance(layer, attention.SpatialTransformer):
|
|
x = layer(x, context, transformer_options)
|
|
if "transformer_index" in transformer_options:
|
|
transformer_options["transformer_index"] += 1
|
|
if "current_index" in transformer_options: # keep this for backward compat, for now
|
|
transformer_options["current_index"] += 1
|
|
elif isinstance(layer, openaimodel.Upsample):
|
|
x = layer(x, output_shape=output_shape)
|
|
else:
|
|
x = layer(x)
|
|
return x
|
|
return forward_timestep_embed
|
|
|
|
|
|
def unlimited_memory_required(*args, **kwargs):
|
|
return 0
|
|
|
|
|
|
def groupnorm_mm_factory(params: InjectionParams, manual_cast=False):
|
|
def groupnorm_mm_forward(self, input: Tensor) -> Tensor:
|
|
# axes_factor normalizes batch based on total conds and unconds passed in batch;
|
|
# the conds and unconds per batch can change based on VRAM optimizations that may kick in
|
|
if not params.is_using_sliding_context():
|
|
batched_conds = input.size(0)//params.full_length
|
|
else:
|
|
batched_conds = input.size(0)//params.context_options.context_length
|
|
|
|
input = rearrange(input, "(b f) c h w -> b c f h w", b=batched_conds)
|
|
if manual_cast:
|
|
weight, bias = comfy.ops.cast_bias_weight(self, input)
|
|
else:
|
|
weight, bias = self.weight, self.bias
|
|
input = group_norm(input, self.num_groups, weight, bias, self.eps)
|
|
input = rearrange(input, "b c f h w -> (b f) c h w", b=batched_conds)
|
|
return input
|
|
return groupnorm_mm_forward
|
|
|
|
|
|
def get_additional_models_factory(orig_get_additional_models: Callable, motion_models: MotionModelGroup):
|
|
def get_additional_models_with_motion(*args, **kwargs):
|
|
models, inference_memory = orig_get_additional_models(*args, **kwargs)
|
|
if motion_models is not None:
|
|
for motion_model in motion_models.models:
|
|
models.append(motion_model)
|
|
# TODO: account for inference memory as well?
|
|
return models, inference_memory
|
|
return get_additional_models_with_motion
|
|
|
|
def apply_model_factory(orig_apply_model: Callable):
|
|
def apply_model_ade_wrapper(self, *args, **kwargs):
|
|
x: Tensor = args[0]
|
|
cond_or_uncond = kwargs["transformer_options"]["cond_or_uncond"]
|
|
ad_params = kwargs["transformer_options"]["ad_params"]
|
|
if ADGS.motion_models is not None:
|
|
for motion_model in ADGS.motion_models.models:
|
|
motion_model.prepare_img_features(x=x, cond_or_uncond=cond_or_uncond, ad_params=ad_params, latent_format=self.latent_format)
|
|
del x
|
|
return orig_apply_model(*args, **kwargs)
|
|
return apply_model_ade_wrapper
|
|
######################################################################
|
|
##################################################################################
|
|
|
|
|
|
def apply_params_to_motion_models(motion_models: MotionModelGroup, params: InjectionParams):
|
|
params = params.clone()
|
|
for context in params.context_options.contexts:
|
|
if context.context_schedule == ContextSchedules.VIEW_AS_CONTEXT:
|
|
context.context_length = params.full_length
|
|
# TODO: check (and message) should be different based on use_on_equal_length setting
|
|
if params.context_options.context_length:
|
|
pass
|
|
|
|
allow_equal = params.context_options.use_on_equal_length
|
|
if params.context_options.context_length:
|
|
enough_latents = params.full_length >= params.context_options.context_length if allow_equal else params.full_length > params.context_options.context_length
|
|
else:
|
|
enough_latents = False
|
|
if params.context_options.context_length and enough_latents:
|
|
logger.info(f"Sliding context window activated - latents passed in ({params.full_length}) greater than context_length {params.context_options.context_length}.")
|
|
else:
|
|
logger.info(f"Regular AnimateDiff activated - latents passed in ({params.full_length}) less or equal to context_length {params.context_options.context_length}.")
|
|
params.reset_context()
|
|
if motion_models is not None:
|
|
# if no context_length, treat video length as intended AD frame window
|
|
if not params.context_options.context_length:
|
|
for motion_model in motion_models.models:
|
|
if not motion_model.model.is_length_valid_for_encoding_max_len(params.full_length):
|
|
raise ValueError(f"Without a context window, AnimateDiff model {motion_model.model.mm_info.mm_name} has upper limit of {motion_model.model.encoding_max_len} frames, but received {params.full_length} latents.")
|
|
motion_models.set_video_length(params.full_length, params.full_length)
|
|
# otherwise, treat context_length as intended AD frame window
|
|
else:
|
|
for motion_model in motion_models.models:
|
|
view_options = params.context_options.view_options
|
|
context_length = view_options.context_length if view_options else params.context_options.context_length
|
|
if not motion_model.model.is_length_valid_for_encoding_max_len(context_length):
|
|
raise ValueError(f"AnimateDiff model {motion_model.model.mm_info.mm_name} has upper limit of {motion_model.model.encoding_max_len} frames for a context window, but received context length of {params.context_options.context_length}.")
|
|
motion_models.set_video_length(params.context_options.context_length, params.full_length)
|
|
# inject model
|
|
module_str = "modules" if len(motion_models.models) > 1 else "module"
|
|
logger.info(f"Using motion {module_str} {motion_models.get_name_string(show_version=True)}.")
|
|
return params
|
|
|
|
|
|
class FunctionInjectionHolder:
|
|
def __init__(self):
|
|
pass
|
|
|
|
def inject_functions(self, model: ModelPatcherAndInjector, params: InjectionParams):
|
|
# Save Original Functions
|
|
self.orig_forward_timestep_embed = openaimodel.forward_timestep_embed # needed to account for VanillaTemporalModule
|
|
self.orig_memory_required = model.model.memory_required # allows for "unlimited area hack" to prevent halving of conds/unconds
|
|
self.orig_groupnorm_forward = torch.nn.GroupNorm.forward # used to normalize latents to remove "flickering" of colors/brightness between frames
|
|
self.orig_groupnorm_manual_cast_forward = comfy.ops.manual_cast.GroupNorm.forward_comfy_cast_weights
|
|
self.orig_sampling_function = comfy.samplers.sampling_function # used to support sliding context windows in samplers
|
|
self.orig_prepare_mask = comfy.sample.prepare_mask
|
|
self.orig_get_additional_models = comfy.sample.get_additional_models
|
|
self.orig_apply_model = model.model.apply_model # TODO: remove this if end up not needing to hack it
|
|
# Inject Functions
|
|
openaimodel.forward_timestep_embed = forward_timestep_embed_factory()
|
|
if params.unlimited_area_hack:
|
|
model.model.memory_required = unlimited_memory_required
|
|
if model.motion_models is not None:
|
|
# only apply groupnorm hack if not [v3 or ([not Hotshot] and SD1.5 and v2 and apply_v2_properly)]
|
|
info: AnimateDiffInfo = model.motion_models[0].model.mm_info
|
|
if not (info.mm_version == AnimateDiffVersion.V3 or
|
|
(info.mm_format not in [AnimateDiffFormat.HOTSHOTXL] and info.sd_type == ModelTypeSD.SD1_5 and info.mm_version == AnimateDiffVersion.V2 and params.apply_v2_properly)):
|
|
torch.nn.GroupNorm.forward = groupnorm_mm_factory(params)
|
|
comfy.ops.manual_cast.GroupNorm.forward_comfy_cast_weights = groupnorm_mm_factory(params, manual_cast=True)
|
|
# if mps device (Apple Silicon), disable batched conds to avoid black images with groupnorm hack
|
|
try:
|
|
if model.load_device.type == "mps":
|
|
model.model.memory_required = unlimited_memory_required
|
|
except Exception:
|
|
pass
|
|
# if img_encoder present, inject apply_model to handle img_latents correctly
|
|
for motion_model in model.motion_models:
|
|
if motion_model.model.img_encoder != None:
|
|
model.model.apply_model = apply_model_factory(self.orig_apply_model).__get__(model.model, type(model.model))
|
|
break
|
|
del info
|
|
comfy.samplers.sampling_function = evolved_sampling_function
|
|
comfy.sample.prepare_mask = prepare_mask_ad
|
|
comfy.sample.get_additional_models = get_additional_models_factory(self.orig_get_additional_models, model.motion_models)
|
|
|
|
def restore_functions(self, model: ModelPatcherAndInjector):
|
|
# Restoration
|
|
try:
|
|
model.model.memory_required = self.orig_memory_required
|
|
openaimodel.forward_timestep_embed = self.orig_forward_timestep_embed
|
|
torch.nn.GroupNorm.forward = self.orig_groupnorm_forward
|
|
comfy.ops.manual_cast.GroupNorm.forward_comfy_cast_weights = self.orig_groupnorm_manual_cast_forward
|
|
comfy.samplers.sampling_function = self.orig_sampling_function
|
|
comfy.sample.prepare_mask = self.orig_prepare_mask
|
|
comfy.sample.get_additional_models = self.orig_get_additional_models
|
|
model.model.apply_model = self.orig_apply_model
|
|
except AttributeError:
|
|
logger.error("Encountered AttributeError while attempting to restore functions - likely, an error occured while trying " + \
|
|
"to save original functions before injection, and a more specific error was thrown by ComfyUI.")
|
|
|
|
|
|
def motion_sample_factory(orig_comfy_sample: Callable, is_custom: bool=False) -> Callable:
|
|
def motion_sample(model: ModelPatcherAndInjector, noise: Tensor, *args, **kwargs):
|
|
# check if model is intended for injecting
|
|
if type(model) != ModelPatcherAndInjector:
|
|
return orig_comfy_sample(model, noise, *args, **kwargs)
|
|
# otherwise, injection time
|
|
latents = None
|
|
cached_latents = None
|
|
cached_noise = None
|
|
function_injections = FunctionInjectionHolder()
|
|
try:
|
|
if model.sample_settings.custom_cfg is not None:
|
|
model = model.sample_settings.custom_cfg.patch_model(model)
|
|
# clone params from model
|
|
params = model.motion_injection_params.clone()
|
|
# get amount of latents passed in, and store in params
|
|
latents: Tensor = args[-1]
|
|
params.full_length = latents.size(0)
|
|
# reset global state
|
|
ADGS.reset()
|
|
|
|
# apply custom noise, if needed
|
|
disable_noise = kwargs.get("disable_noise") or False
|
|
seed = kwargs["seed"]
|
|
|
|
# apply params to motion model
|
|
params = apply_params_to_motion_models(model.motion_models, params)
|
|
|
|
# store and inject functions
|
|
function_injections.inject_functions(model, params)
|
|
|
|
# prepare noise_extra_args for noise generation purposes
|
|
noise_extra_args = {"disable_noise": disable_noise}
|
|
params.set_noise_extra_args(noise_extra_args)
|
|
# if noise is not disabled, do noise stuff
|
|
if not disable_noise:
|
|
noise = model.sample_settings.prepare_noise(seed, latents, noise, extra_args=noise_extra_args, force_create_noise=False)
|
|
|
|
# callback setup
|
|
original_callback = kwargs.get("callback", None)
|
|
def ad_callback(step, x0, x, total_steps):
|
|
if original_callback is not None:
|
|
original_callback(step, x0, x, total_steps)
|
|
# update GLOBALSTATE for next iteration
|
|
ADGS.current_step = ADGS.start_step + step + 1
|
|
kwargs["callback"] = ad_callback
|
|
ADGS.model_patcher = model
|
|
ADGS.motion_models = model.motion_models
|
|
ADGS.sample_settings = model.sample_settings
|
|
|
|
# apply adapt_denoise_steps
|
|
args = list(args)
|
|
if model.sample_settings.adapt_denoise_steps and not is_custom:
|
|
# only applicable when denoise and steps are provided (from simple KSampler nodes)
|
|
denoise = kwargs.get("denoise", None)
|
|
steps = args[0]
|
|
if denoise is not None and type(steps) == int:
|
|
args[0] = max(int(denoise * steps), 1)
|
|
|
|
|
|
iter_opts = IterationOptions()
|
|
if model.sample_settings is not None:
|
|
iter_opts = model.sample_settings.iteration_opts
|
|
iter_opts.initialize(latents)
|
|
# cache initial noise and latents, if needed
|
|
if iter_opts.cache_init_latents:
|
|
cached_latents = latents.clone()
|
|
if iter_opts.cache_init_noise:
|
|
cached_noise = noise.clone()
|
|
# prepare iter opts preprocess kwargs, if needed
|
|
iter_kwargs = {}
|
|
if iter_opts.need_sampler:
|
|
# -5 for sampler_name (not custom) and sampler (custom)
|
|
model_management.load_model_gpu(model)
|
|
if is_custom:
|
|
iter_kwargs[IterationOptions.SAMPLER] = None #args[-5]
|
|
else:
|
|
iter_kwargs[IterationOptions.SAMPLER] = comfy.samplers.KSampler(
|
|
model.model, steps=999, #steps=args[-7],
|
|
device=model.current_device, sampler=args[-5],
|
|
scheduler=args[-4], denoise=kwargs.get("denoise", None),
|
|
model_options=model.model_options)
|
|
|
|
for curr_i in range(iter_opts.iterations):
|
|
# handle GLOBALSTATE vars and step tally
|
|
ADGS.update_with_inject_params(params)
|
|
ADGS.start_step = kwargs.get("start_step") or 0
|
|
ADGS.current_step = ADGS.start_step
|
|
ADGS.last_step = kwargs.get("last_step") or 0
|
|
if iter_opts.iterations > 1:
|
|
logger.info(f"Iteration {curr_i+1}/{iter_opts.iterations}")
|
|
# perform any iter_opts preprocessing on latents
|
|
latents, noise = iter_opts.preprocess_latents(curr_i=curr_i, model=model, latents=latents, noise=noise,
|
|
cached_latents=cached_latents, cached_noise=cached_noise,
|
|
seed=seed,
|
|
sample_settings=model.sample_settings, noise_extra_args=noise_extra_args,
|
|
**iter_kwargs)
|
|
args[-1] = latents
|
|
|
|
if model.motion_models is not None:
|
|
model.motion_models.pre_run(model)
|
|
if model.sample_settings is not None:
|
|
model.sample_settings.pre_run(model)
|
|
latents = wrap_function_to_inject_xformers_bug_info(orig_comfy_sample)(model, noise, *args, **kwargs)
|
|
return latents
|
|
finally:
|
|
del latents
|
|
del noise
|
|
del cached_latents
|
|
del cached_noise
|
|
# reset global state
|
|
ADGS.reset()
|
|
# clean motion_models
|
|
if model.motion_models is not None:
|
|
model.motion_models.cleanup()
|
|
# restore injected functions
|
|
function_injections.restore_functions(model)
|
|
del function_injections
|
|
return motion_sample
|
|
|
|
|
|
def evolved_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options: dict={}, seed=None):
|
|
ADGS.initialize(model)
|
|
if ADGS.motion_models is not None:
|
|
ADGS.motion_models.prepare_current_keyframe(t=timestep)
|
|
if ADGS.params.context_options is not None:
|
|
ADGS.params.context_options.prepare_current_context(t=timestep)
|
|
if ADGS.sample_settings.custom_cfg is not None:
|
|
ADGS.sample_settings.custom_cfg.prepare_current_keyframe(t=timestep)
|
|
|
|
# never use cfg1 optimization if using custom_cfg (since can have timesteps and such)
|
|
if ADGS.sample_settings.custom_cfg is None and math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
|
|
uncond_ = None
|
|
else:
|
|
uncond_ = uncond
|
|
|
|
# add AD/evolved-sampling params to model_options (transformer_options)
|
|
model_options = model_options.copy()
|
|
if "tranformer_options" not in model_options:
|
|
model_options["tranformer_options"] = {}
|
|
model_options["transformer_options"]["ad_params"] = ADGS.create_exposed_params()
|
|
|
|
if not ADGS.is_using_sliding_context():
|
|
cond_pred, uncond_pred = calc_cond_uncond_batch_wrapper(model, cond, uncond_, x, timestep, model_options)
|
|
else:
|
|
cond_pred, uncond_pred = sliding_calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
|
|
|
|
if "sampler_cfg_function" in model_options:
|
|
args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
|
|
"cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
|
|
cfg_result = x - model_options["sampler_cfg_function"](args)
|
|
else:
|
|
cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
|
|
|
|
for fn in model_options.get("sampler_post_cfg_function", []):
|
|
args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
|
|
"sigma": timestep, "model_options": model_options, "input": x}
|
|
cfg_result = fn(args)
|
|
|
|
return cfg_result
|
|
|
|
|
|
# sliding_calc_cond_uncond_batch inspired by ashen's initial hack for 16-frame sliding context:
|
|
# https://github.com/comfyanonymous/ComfyUI/compare/master...ashen-sensored:ComfyUI:master
|
|
def sliding_calc_cond_uncond_batch(model, cond, uncond, x_in: Tensor, timestep, model_options):
|
|
def prepare_control_objects(control: ControlBase, full_idxs: list[int]):
|
|
if control.previous_controlnet is not None:
|
|
prepare_control_objects(control.previous_controlnet, full_idxs)
|
|
control.sub_idxs = full_idxs
|
|
control.full_latent_length = ADGS.params.full_length
|
|
control.context_length = ADGS.params.context_options.context_length
|
|
|
|
def get_resized_cond(cond_in, full_idxs: list[int], context_length: int) -> list:
|
|
# reuse or resize cond items to match context requirements
|
|
resized_cond = []
|
|
# cond object is a list containing a dict - outer list is irrelevant, so just loop through it
|
|
for actual_cond in cond_in:
|
|
resized_actual_cond = actual_cond.copy()
|
|
# now we are in the inner dict - "pooled_output" is a tensor, "control" is a ControlBase object, "model_conds" is dictionary
|
|
for key in actual_cond:
|
|
try:
|
|
cond_item = actual_cond[key]
|
|
if isinstance(cond_item, Tensor):
|
|
# check that tensor is the expected length - x.size(0)
|
|
if cond_item.size(0) == x_in.size(0):
|
|
# if so, it's subsetting time - tell controls the expected indeces so they can handle them
|
|
actual_cond_item = cond_item[full_idxs]
|
|
resized_actual_cond[key] = actual_cond_item
|
|
else:
|
|
resized_actual_cond[key] = cond_item
|
|
# look for control
|
|
elif key == "control":
|
|
control_item = cond_item
|
|
if hasattr(control_item, "sub_idxs"):
|
|
prepare_control_objects(control_item, full_idxs)
|
|
else:
|
|
raise ValueError(f"Control type {type(control_item).__name__} may not support required features for sliding context window; \
|
|
use Control objects from Kosinkadink/ComfyUI-Advanced-ControlNet nodes, or make sure Advanced-ControlNet is updated.")
|
|
resized_actual_cond[key] = control_item
|
|
del control_item
|
|
elif isinstance(cond_item, dict):
|
|
new_cond_item = cond_item.copy()
|
|
# when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor)
|
|
for cond_key, cond_value in new_cond_item.items():
|
|
if isinstance(cond_value, Tensor):
|
|
if cond_value.size(0) == x_in.size(0):
|
|
new_cond_item[cond_key] = cond_value[full_idxs]
|
|
# if has cond that is a Tensor, check if needs to be subset
|
|
elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, Tensor):
|
|
if cond_value.cond.size(0) == x_in.size(0):
|
|
new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond[full_idxs])
|
|
elif cond_key == "num_video_frames": # for SVD
|
|
new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond)
|
|
new_cond_item[cond_key].cond = context_length
|
|
resized_actual_cond[key] = new_cond_item
|
|
else:
|
|
resized_actual_cond[key] = cond_item
|
|
finally:
|
|
del cond_item # just in case to prevent VRAM issues
|
|
resized_cond.append(resized_actual_cond)
|
|
return resized_cond
|
|
|
|
# get context windows
|
|
ADGS.params.context_options.step = ADGS.current_step
|
|
context_windows = get_context_windows(ADGS.params.full_length, ADGS.params.context_options)
|
|
# figure out how input is split
|
|
batched_conds = x_in.size(0)//ADGS.params.full_length
|
|
|
|
if ADGS.motion_models is not None:
|
|
ADGS.motion_models.set_view_options(ADGS.params.context_options.view_options)
|
|
|
|
# prepare final cond, uncond, and out_count
|
|
cond_final = torch.zeros_like(x_in)
|
|
uncond_final = torch.zeros_like(x_in)
|
|
out_count_final = torch.zeros((x_in.shape[0], 1, 1, 1), device=x_in.device)
|
|
bias_final = [0.0] * x_in.shape[0]
|
|
|
|
# perform calc_cond_uncond_batch per context window
|
|
for ctx_idxs in context_windows:
|
|
ADGS.params.sub_idxs = ctx_idxs
|
|
if ADGS.motion_models is not None:
|
|
ADGS.motion_models.set_sub_idxs(ctx_idxs)
|
|
ADGS.motion_models.set_video_length(len(ctx_idxs), ADGS.params.full_length)
|
|
# update exposed params
|
|
model_options["transformer_options"]["ad_params"]["sub_idxs"] = ctx_idxs
|
|
model_options["transformer_options"]["ad_params"]["context_length"] = len(ctx_idxs)
|
|
# account for all portions of input frames
|
|
full_idxs = []
|
|
for n in range(batched_conds):
|
|
for ind in ctx_idxs:
|
|
full_idxs.append((ADGS.params.full_length*n)+ind)
|
|
# get subsections of x, timestep, cond, uncond, cond_concat
|
|
sub_x = x_in[full_idxs]
|
|
sub_timestep = timestep[full_idxs]
|
|
sub_cond = get_resized_cond(cond, full_idxs, len(ctx_idxs)) if cond is not None else None
|
|
sub_uncond = get_resized_cond(uncond, full_idxs, len(ctx_idxs)) if uncond is not None else None
|
|
|
|
sub_cond_out, sub_uncond_out = calc_cond_uncond_batch_wrapper(model, sub_cond, sub_uncond, sub_x, sub_timestep, model_options)
|
|
|
|
if ADGS.params.context_options.fuse_method == ContextFuseMethod.RELATIVE:
|
|
full_length = ADGS.params.full_length
|
|
for pos, idx in enumerate(ctx_idxs):
|
|
# bias is the influence of a specific index in relation to the whole context window
|
|
bias = 1 - abs(idx - (ctx_idxs[0] + ctx_idxs[-1]) / 2) / ((ctx_idxs[-1] - ctx_idxs[0] + 1e-2) / 2)
|
|
bias = max(1e-2, bias)
|
|
# take weighted average relative to total bias of current idx
|
|
# and account for batched_conds
|
|
for n in range(batched_conds):
|
|
bias_total = bias_final[(full_length*n)+idx]
|
|
prev_weight = (bias_total / (bias_total + bias))
|
|
new_weight = (bias / (bias_total + bias))
|
|
cond_final[(full_length*n)+idx] = cond_final[(full_length*n)+idx] * prev_weight + sub_cond_out[(full_length*n)+pos] * new_weight
|
|
uncond_final[(full_length*n)+idx] = uncond_final[(full_length*n)+idx] * prev_weight + sub_uncond_out[(full_length*n)+pos] * new_weight
|
|
bias_final[(full_length*n)+idx] = bias_total + bias
|
|
else:
|
|
# add conds and counts based on weights of fuse method
|
|
weights = get_context_weights(len(ctx_idxs), ADGS.params.context_options.fuse_method) * batched_conds
|
|
weights_tensor = torch.Tensor(weights).to(device=x_in.device).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
|
|
cond_final[full_idxs] += sub_cond_out * weights_tensor
|
|
uncond_final[full_idxs] += sub_uncond_out * weights_tensor
|
|
out_count_final[full_idxs] += weights_tensor
|
|
|
|
if ADGS.params.context_options.fuse_method == ContextFuseMethod.RELATIVE:
|
|
# already normalized, so return as is
|
|
del out_count_final
|
|
return cond_final, uncond_final
|
|
else:
|
|
# normalize cond and uncond via division by context usage counts
|
|
cond_final /= out_count_final
|
|
uncond_final /= out_count_final
|
|
del out_count_final
|
|
return cond_final, uncond_final
|
|
|
|
|
|
def calc_cond_uncond_batch_wrapper(model, cond, uncond, x_in, timestep, model_options):
|
|
# check if conds or unconds contain lora_hook
|
|
contains_lora_hooks = False
|
|
for cond_uncond in [cond, uncond]:
|
|
if cond_uncond is None:
|
|
continue
|
|
for t in cond_uncond:
|
|
if "lora_hook" in t:
|
|
contains_lora_hooks = True
|
|
break
|
|
if contains_lora_hooks:
|
|
break
|
|
if contains_lora_hooks:
|
|
return calc_cond_uncond_batch_lora_hook(model, cond, uncond, x_in, timestep, model_options)
|
|
return comfy.samplers.calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options)
|
|
|
|
|
|
def calc_cond_uncond_batch_lora_hook(model, cond, uncond, x_in, timestep, model_options):
|
|
out_cond = torch.zeros_like(x_in)
|
|
out_count = torch.ones_like(x_in) * 1e-37
|
|
|
|
out_uncond = torch.zeros_like(x_in)
|
|
out_uncond_count = torch.ones_like(x_in) * 1e-37
|
|
|
|
COND = 0
|
|
UNCOND = 1
|
|
|
|
# separate conds and unconds by matching lora_hooks
|
|
hooked_to_run = {}
|
|
for x in cond:
|
|
p = comfy.samplers.get_area_and_mult(x, x_in, timestep)
|
|
if p is None:
|
|
continue
|
|
hook: LoraHookGroup = x.get("lora_hook", None)
|
|
hooked_to_run.setdefault(hook, list())
|
|
hooked_to_run[hook] += [(p, COND)]
|
|
if uncond is not None:
|
|
for x in uncond:
|
|
p = comfy.samplers.get_area_and_mult(x, x_in, timestep)
|
|
if p is None:
|
|
continue
|
|
hook: LoraHookGroup = x.get("lora_hook", None)
|
|
hooked_to_run.setdefault(hook, list())
|
|
hooked_to_run[hook] += [(p, UNCOND)]
|
|
|
|
# run every hooked_to_run separately
|
|
for lora_hooks, to_run in hooked_to_run.items():
|
|
while len(to_run) > 0:
|
|
first = to_run[0]
|
|
first_shape = first[0][0].shape
|
|
to_batch_temp = []
|
|
for x in range(len(to_run)):
|
|
if comfy.samplers.can_concat_cond(to_run[x][0], first[0]):
|
|
to_batch_temp += [x]
|
|
|
|
to_batch_temp.reverse()
|
|
to_batch = to_batch_temp[:1]
|
|
|
|
free_memory = model_management.get_free_memory(x_in.device)
|
|
for i in range(1, len(to_batch_temp) + 1):
|
|
batch_amount = to_batch_temp[:len(to_batch_temp)//i]
|
|
input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
|
|
if model.memory_required(input_shape) < free_memory:
|
|
to_batch = batch_amount
|
|
break
|
|
ADGS.model_patcher.apply_lora_hooks(lora_hooks=lora_hooks)
|
|
|
|
input_x = []
|
|
mult = []
|
|
c = []
|
|
cond_or_uncond = []
|
|
area = []
|
|
control = None
|
|
patches = None
|
|
for x in to_batch:
|
|
o = to_run.pop(x)
|
|
p = o[0]
|
|
input_x.append(p.input_x)
|
|
mult.append(p.mult)
|
|
c.append(p.conditioning)
|
|
area.append(p.area)
|
|
cond_or_uncond.append(o[1])
|
|
control = p.control
|
|
patches = p.patches
|
|
|
|
batch_chunks = len(cond_or_uncond)
|
|
input_x = torch.cat(input_x)
|
|
c = comfy.samplers.cond_cat(c)
|
|
timestep_ = torch.cat([timestep] * batch_chunks)
|
|
|
|
if control is not None:
|
|
c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
|
|
|
|
transformer_options = {}
|
|
if 'transformer_options' in model_options:
|
|
transformer_options = model_options['transformer_options'].copy()
|
|
|
|
if patches is not None:
|
|
if "patches" in transformer_options:
|
|
cur_patches = transformer_options["patches"].copy()
|
|
for p in patches:
|
|
if p in cur_patches:
|
|
cur_patches[p] = cur_patches[p] + patches[p]
|
|
else:
|
|
cur_patches[p] = patches[p]
|
|
transformer_options["patches"] = cur_patches
|
|
else:
|
|
transformer_options["patches"] = patches
|
|
|
|
transformer_options["cond_or_uncond"] = cond_or_uncond[:]
|
|
transformer_options["sigmas"] = timestep
|
|
|
|
c['transformer_options'] = transformer_options
|
|
|
|
if 'model_function_wrapper' in model_options:
|
|
output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
|
|
else:
|
|
output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
|
|
del input_x
|
|
|
|
for o in range(batch_chunks):
|
|
if cond_or_uncond[o] == COND:
|
|
out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
|
|
out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
|
|
else:
|
|
out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
|
|
out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
|
|
del mult
|
|
|
|
out_cond /= out_count
|
|
del out_count
|
|
out_uncond /= out_uncond_count
|
|
del out_uncond_count
|
|
return out_cond, out_uncond
|