657 lines
32 KiB
Python
657 lines
32 KiB
Python
from typing import Callable
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import math
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import torch
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from torch import Tensor
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from torch.nn.functional import group_norm
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from einops import rearrange
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import comfy.ldm.modules.attention as attention
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from comfy.ldm.modules.diffusionmodules import openaimodel
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import comfy.model_management as model_management
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import comfy.samplers
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import comfy.sample
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import comfy.utils
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from comfy.controlnet import ControlBase
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from .context import get_context_scheduler
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from .sample_settings import IterationOptions, SeedNoiseGeneration, prepare_mask_ad
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from .motion_utils import GroupNormAD
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from .model_utils import ModelTypeSD, wrap_function_to_inject_xformers_bug_info
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from .model_injection import InjectionParams, ModelPatcherAndInjector, MotionModelPatcher
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from .motion_module_ad import AnimateDiffFormat, AnimateDiffInfo, AnimateDiffVersion, VanillaTemporalModule
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from .logger import logger
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##################################################################################
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######################################################################
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# Global variable to use to more conveniently hack variable access into samplers
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class AnimateDiffHelper_GlobalState:
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def __init__(self):
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self.motion_model: MotionModelPatcher = None
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self.params: InjectionParams = None
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self.reset()
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def reset(self):
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self.start_step: int = 0
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self.last_step: int = 0
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self.current_step: int = 0
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self.total_steps: int = 0
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if self.motion_model is not None:
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del self.motion_model
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self.motion_model = None
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if self.params is not None:
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del self.params
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self.params = None
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def update_with_inject_params(self, params: InjectionParams):
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self.params = params
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def is_using_sliding_context(self):
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return self.params is not None and self.params.context_length is not None
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def create_exposed_params(self):
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# This dict will be exposed to be used by other extensions
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# DO NOT change any of the key names
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# or I will find you 👁.👁
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return {
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"full_length": self.params.video_length,
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"context_length": self.params.context_length,
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"sub_idxs": self.params.sub_idxs,
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}
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ADGS = AnimateDiffHelper_GlobalState()
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######################################################################
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##################################################################################
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##################################################################################
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#### Code Injection ##################################################
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# refer to forward_timestep_embed in comfy/ldm/modules/diffusionmodules/openaimodel.py
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def forward_timestep_embed_factory() -> Callable:
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if hasattr(attention, "SpatialVideoTransformer"):
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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):
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for layer in ts:
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if isinstance(layer, openaimodel.VideoResBlock):
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x = layer(x, emb, num_video_frames, image_only_indicator)
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elif isinstance(layer, openaimodel.TimestepBlock):
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x = layer(x, emb)
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elif isinstance(layer, VanillaTemporalModule):
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x = layer(x, context)
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elif isinstance(layer, attention.SpatialVideoTransformer):
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x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options)
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if "transformer_index" in transformer_options:
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transformer_options["transformer_index"] += 1
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if "current_index" in transformer_options: # keep this for backward compat, for now
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transformer_options["current_index"] += 1
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elif isinstance(layer, attention.SpatialTransformer):
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x = layer(x, context, transformer_options)
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if "transformer_index" in transformer_options:
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transformer_options["transformer_index"] += 1
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if "current_index" in transformer_options: # keep this for backward compat, for now
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transformer_options["current_index"] += 1
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elif isinstance(layer, openaimodel.Upsample):
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x = layer(x, output_shape=output_shape)
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else:
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x = layer(x)
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return x
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# keep old version for backwards compatibility (TODO: remove at end of 2023)
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else:
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def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None):
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for layer in ts:
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if isinstance(layer, openaimodel.TimestepBlock):
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x = layer(x, emb)
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elif isinstance(layer, VanillaTemporalModule):
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x = layer(x, context)
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elif isinstance(layer, attention.SpatialTransformer):
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x = layer(x, context, transformer_options)
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if "current_index" in transformer_options:
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transformer_options["current_index"] += 1
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elif isinstance(layer, openaimodel.Upsample):
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x = layer(x, output_shape=output_shape)
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else:
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x = layer(x)
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return x
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return forward_timestep_embed
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def unlimited_memory_required(*args, **kwargs):
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return 0
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def groupnorm_mm_factory(params: InjectionParams):
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def groupnorm_mm_forward(self, input: Tensor) -> Tensor:
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# axes_factor normalizes batch based on total conds and unconds passed in batch;
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# the conds and unconds per batch can change based on VRAM optimizations that may kick in
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if not ADGS.is_using_sliding_context():
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axes_factor = input.size(0)//params.video_length
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else:
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axes_factor = input.size(0)//params.context_length
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input = rearrange(input, "(b f) c h w -> b c f h w", b=axes_factor)
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input = group_norm(input, self.num_groups, self.weight, self.bias, self.eps)
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input = rearrange(input, "b c f h w -> (b f) c h w", b=axes_factor)
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return input
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return groupnorm_mm_forward
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def get_additional_models_factory(orig_get_additional_models: Callable, motion_model: MotionModelPatcher):
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def get_additional_models_with_motion(*args, **kwargs):
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models, inference_memory = orig_get_additional_models(*args, **kwargs)
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models.append(motion_model)
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# TODO: account for inference memory as well?
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return models, inference_memory
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return get_additional_models_with_motion
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######################################################################
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##################################################################################
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def apply_params_to_motion_model(motion_model: MotionModelPatcher, params: InjectionParams):
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if params.context_length and params.video_length > params.context_length:
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logger.info(f"Sliding context window activated - latents passed in ({params.video_length}) greater than context_length {params.context_length}.")
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else:
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logger.info(f"Regular AnimateDiff activated - latents passed in ({params.video_length}) less or equal to context_length {params.context_length}.")
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params.reset_context()
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# if no context_length, treat video length as intended AD frame window
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if not params.context_length:
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if params.video_length > motion_model.model.encoding_max_len:
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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.video_length} latents.")
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motion_model.model.set_video_length(params.video_length, params.full_length)
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# otherwise, treat context_length as intended AD frame window
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else:
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if params.context_length > motion_model.model.encoding_max_len:
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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_length}.")
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motion_model.model.set_video_length(params.context_length, params.full_length)
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# inject model
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logger.info(f"Using motion module {motion_model.model.mm_info.mm_name} version {motion_model.model.mm_info.mm_version}.")
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class FunctionInjectionHolder:
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def __init__(self):
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pass
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def inject_functions(self, model: ModelPatcherAndInjector, params: InjectionParams):
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# Save Original Functions
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self.orig_forward_timestep_embed = openaimodel.forward_timestep_embed # needed to account for VanillaTemporalModule
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self.orig_memory_required = model.model.memory_required # allows for "unlimited area hack" to prevent halving of conds/unconds
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self.orig_groupnorm_forward = torch.nn.GroupNorm.forward # used to normalize latents to remove "flickering" of colors/brightness between frames
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self.orig_groupnormad_forward = GroupNormAD.forward
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self.orig_sampling_function = comfy.samplers.sampling_function # used to support sliding context windows in samplers
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self.orig_prepare_mask = comfy.sample.prepare_mask
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self.orig_get_additional_models = comfy.sample.get_additional_models
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# Inject Functions
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openaimodel.forward_timestep_embed = forward_timestep_embed_factory()
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if params.unlimited_area_hack:
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model.model.memory_required = unlimited_memory_required
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# only apply groupnorm hack if not [v3 or (AnimateDiff SD1.5 and v2 and should apply v2 properly)]
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info: AnimateDiffInfo = model.motion_model.model.mm_info
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if not (info.mm_version == AnimateDiffVersion.V3 or (info.mm_format == AnimateDiffFormat.ANIMATEDIFF and info.sd_type == ModelTypeSD.SD1_5 and
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info.mm_version == AnimateDiffVersion.V2 and params.apply_v2_models_properly)):
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torch.nn.GroupNorm.forward = groupnorm_mm_factory(params)
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if params.apply_mm_groupnorm_hack:
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GroupNormAD.forward = groupnorm_mm_factory(params)
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comfy.samplers.sampling_function = sliding_sampling_function
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comfy.sample.prepare_mask = prepare_mask_ad
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comfy.sample.get_additional_models = get_additional_models_factory(self.orig_get_additional_models, model.motion_model)
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del info
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def restore_functions(self, model: ModelPatcherAndInjector):
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# Restoration
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try:
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model.model.memory_required = self.orig_memory_required
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openaimodel.forward_timestep_embed = self.orig_forward_timestep_embed
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torch.nn.GroupNorm.forward = self.orig_groupnorm_forward
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GroupNormAD.forward = self.orig_groupnormad_forward
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comfy.samplers.sampling_function = self.orig_sampling_function
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comfy.sample.prepare_mask = self.orig_prepare_mask
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comfy.sample.get_additional_models = self.orig_get_additional_models
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except AttributeError:
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logger.error("Encountered AttributeError while attempting to restore functions - likely, an error occured while trying " + \
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"to save original functions before injection, and a more specific error was thrown by ComfyUI.")
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def motion_sample_factory(orig_comfy_sample: Callable, is_custom: bool=False) -> Callable:
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def motion_sample(model: ModelPatcherAndInjector, noise: Tensor, *args, **kwargs):
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# check if model is intended for injecting
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if type(model) != ModelPatcherAndInjector:
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return orig_comfy_sample(model, noise, *args, **kwargs)
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# otherwise, injection time
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latents = None
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cached_latents = None
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cached_noise = None
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function_injections = FunctionInjectionHolder()
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try:
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# clone params from model
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params = model.motion_injection_params.clone()
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# get amount of latents passed in, and store in params
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latents: Tensor = args[-1]
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params.video_length = latents.size(0)
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params.full_length = latents.size(0)
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# reset global state
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ADGS.reset()
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# store and inject functions
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function_injections.inject_functions(model, params)
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# apply custom noise, if needed
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disable_noise = kwargs.get("disable_noise") or False
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seed = kwargs["seed"]
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# apply params to motion model
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apply_params_to_motion_model(model.motion_model, params)
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# prepare noise_extra_args for noise generation purposes
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noise_extra_args = {"disable_noise": disable_noise}
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params.set_noise_extra_args(noise_extra_args)
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# if noise is not disabled, do noise stuff
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if not disable_noise:
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noise = model.sample_settings.prepare_noise(seed, latents, noise, extra_args=noise_extra_args, force_create_noise=False)
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# callback setup
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original_callback = kwargs.get("callback", None)
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def ad_callback(step, x0, x, total_steps):
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if original_callback is not None:
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original_callback(step, x0, x, total_steps)
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# update GLOBALSTATE for next iteration
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ADGS.current_step = ADGS.start_step + step + 1
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kwargs["callback"] = ad_callback
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ADGS.motion_model = model.motion_model
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iter_opts = IterationOptions()
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if model.sample_settings is not None:
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iter_opts = model.sample_settings.iteration_opts
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iter_opts.initialize(latents)
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# cache initial noise and latents, if needed
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if iter_opts.cache_init_latents:
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cached_latents = latents.clone()
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if iter_opts.cache_init_noise:
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cached_noise = noise.clone()
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# prepare iter opts preprocess kwargs, if needed
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iter_kwargs = {}
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if iter_opts.need_sampler:
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# -5 for sampler_name (not custom) and sampler (custom)
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if is_custom:
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iter_kwargs[IterationOptions.SAMPLER] = args[-5]
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else:
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iter_kwargs[IterationOptions.SAMPLER] = comfy.samplers.KSampler(
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model.model, steps=args[-7],
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device=model.current_device, sampler=args[-5],
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scheduler=args[-4], denoise=kwargs["denoise"],
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model_options=model.model_options)
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args = list(args)
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for curr_i in range(iter_opts.iterations):
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# handle GLOBALSTATE vars and step tally
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ADGS.update_with_inject_params(params)
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ADGS.start_step = kwargs.get("start_step") or 0
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ADGS.current_step = ADGS.start_step
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ADGS.last_step = kwargs.get("last_step") or 0
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if iter_opts.iterations > 1:
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logger.info(f"Iteration {curr_i+1}/{iter_opts.iterations}")
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# perform any iter_opts preprocessing on latents
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latents, noise = iter_opts.preprocess_latents(curr_i=curr_i, model=model, latents=latents, noise=noise,
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cached_latents=cached_latents, cached_noise=cached_noise,
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seed=seed,
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sample_settings=model.sample_settings, noise_extra_args=noise_extra_args)
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args[-1] = latents
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model.motion_model.pre_run()
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latents = wrap_function_to_inject_xformers_bug_info(orig_comfy_sample)(model, noise, *args, **kwargs)
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return latents
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finally:
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del latents
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del noise
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del cached_latents
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del cached_noise
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# reset global state
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ADGS.reset()
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# restore injected functions
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function_injections.restore_functions(model)
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del function_injections
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return motion_sample
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def sliding_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
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def get_area_and_mult(conds, x_in, timestep_in):
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area = (x_in.shape[2], x_in.shape[3], 0, 0)
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strength = 1.0
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if 'timestep_start' in conds:
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timestep_start = conds['timestep_start']
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if timestep_in[0] > timestep_start:
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return None
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if 'timestep_end' in conds:
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timestep_end = conds['timestep_end']
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if timestep_in[0] < timestep_end:
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return None
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if 'area' in conds:
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area = conds['area']
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if 'strength' in conds:
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strength = conds['strength']
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input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
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if 'mask' in conds:
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# Scale the mask to the size of the input
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# The mask should have been resized as we began the sampling process
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mask_strength = 1.0
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if "mask_strength" in conds:
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mask_strength = conds["mask_strength"]
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mask = conds['mask']
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assert(mask.shape[1] == x_in.shape[2])
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assert(mask.shape[2] == x_in.shape[3])
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mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength
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mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
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else:
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mask = torch.ones_like(input_x)
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mult = mask * strength
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if 'mask' not in conds:
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rr = 8
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if area[2] != 0:
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for t in range(rr):
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mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1))
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if (area[0] + area[2]) < x_in.shape[2]:
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for t in range(rr):
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mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1))
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if area[3] != 0:
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for t in range(rr):
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mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1))
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if (area[1] + area[3]) < x_in.shape[3]:
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for t in range(rr):
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mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1))
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conditionning = {}
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model_conds = conds["model_conds"]
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for c in model_conds:
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conditionning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
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control = None
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if 'control' in conds:
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control = conds['control']
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patches = None
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if 'gligen' in conds:
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gligen = conds['gligen']
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patches = {}
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gligen_type = gligen[0]
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gligen_model = gligen[1]
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if gligen_type == "position":
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gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)
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else:
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gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)
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patches['middle_patch'] = [gligen_patch]
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return (input_x, mult, conditionning, area, control, patches)
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def cond_equal_size(c1, c2):
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if c1 is c2:
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return True
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if c1.keys() != c2.keys():
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return False
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for k in c1:
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if not c1[k].can_concat(c2[k]):
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return False
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return True
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def can_concat_cond(c1, c2):
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if c1[0].shape != c2[0].shape:
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return False
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#control
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if (c1[4] is None) != (c2[4] is None):
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return False
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if c1[4] is not None:
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if c1[4] is not c2[4]:
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return False
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#patches
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if (c1[5] is None) != (c2[5] is None):
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return False
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if (c1[5] is not None):
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if c1[5] is not c2[5]:
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return False
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return cond_equal_size(c1[2], c2[2])
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def cond_cat(c_list):
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c_crossattn = []
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c_concat = []
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c_adm = []
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crossattn_max_len = 0
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temp = {}
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for x in c_list:
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for k in x:
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cur = temp.get(k, [])
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cur.append(x[k])
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temp[k] = cur
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out = {}
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for k in temp:
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conds = temp[k]
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out[k] = conds[0].concat(conds[1:])
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|
|
|
return out
|
|
|
|
def calc_cond_uncond_batch(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
|
|
|
|
to_run = []
|
|
for x in cond:
|
|
p = get_area_and_mult(x, x_in, timestep)
|
|
if p is None:
|
|
continue
|
|
|
|
to_run += [(p, COND)]
|
|
if uncond is not None:
|
|
for x in uncond:
|
|
p = get_area_and_mult(x, x_in, timestep)
|
|
if p is None:
|
|
continue
|
|
|
|
to_run += [(p, UNCOND)]
|
|
|
|
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 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
|
|
|
|
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 += [p[0]]
|
|
mult += [p[1]]
|
|
c += [p[2]]
|
|
area += [p[3]]
|
|
cond_or_uncond += [o[1]]
|
|
control = p[4]
|
|
patches = p[5]
|
|
|
|
batch_chunks = len(cond_or_uncond)
|
|
input_x = torch.cat(input_x)
|
|
c = 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]
|
|
else:
|
|
transformer_options["patches"] = patches
|
|
|
|
transformer_options["cond_or_uncond"] = cond_or_uncond
|
|
transformer_options["sigmas"] = timestep
|
|
transformer_options["ad_params"] = ADGS.create_exposed_params()
|
|
|
|
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
|
|
|
|
# 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, timestep, model_options):
|
|
# get context scheduler
|
|
context_scheduler = get_context_scheduler(ADGS.params.context_schedule)
|
|
# figure out how input is split
|
|
axes_factor = x_in.size(0)//ADGS.params.video_length
|
|
|
|
# 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)
|
|
|
|
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.video_length
|
|
control.context_length = ADGS.params.context_length
|
|
|
|
def get_resized_cond(cond_in, full_idxs) -> 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])
|
|
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
|
|
|
|
# perform calc_cond_uncond_batch per context window
|
|
for ctx_idxs in context_scheduler(ADGS.current_step, ADGS.total_steps, ADGS.params.video_length, ADGS.params.context_length, ADGS.params.context_stride, ADGS.params.context_overlap, ADGS.params.closed_loop):
|
|
ADGS.params.sub_idxs = ctx_idxs
|
|
ADGS.motion_model.model.set_sub_idxs(ctx_idxs)
|
|
# account for all portions of input frames
|
|
full_idxs = []
|
|
for n in range(axes_factor):
|
|
for ind in ctx_idxs:
|
|
full_idxs.append((ADGS.params.video_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) if cond is not None else None
|
|
sub_uncond = get_resized_cond(uncond, full_idxs) if uncond is not None else None
|
|
|
|
sub_cond_out, sub_uncond_out = calc_cond_uncond_batch(model, sub_cond, sub_uncond, sub_x, sub_timestep, model_options)
|
|
|
|
cond_final[full_idxs] += sub_cond_out
|
|
uncond_final[full_idxs] += sub_uncond_out
|
|
out_count_final[full_idxs] += 1 # increment which indeces were used
|
|
|
|
# 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
|
|
|
|
|
|
if math.isclose(cond_scale, 1.0):
|
|
uncond = None
|
|
|
|
if not ADGS.is_using_sliding_context():
|
|
cond, uncond = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options)
|
|
else:
|
|
cond, uncond = sliding_calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options)
|
|
if "sampler_cfg_function" in model_options:
|
|
args = {"cond": x - cond, "uncond": x - uncond, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep}
|
|
return x - model_options["sampler_cfg_function"](args)
|
|
else:
|
|
return uncond + (cond - uncond) * cond_scale
|