| 344 | self.copy_to(c) |
| 345 | self.copy_to_advanced(c) |
| 346 | return c |
| 347 | |
| 348 | |
| 349 | class SparseCtrlAdvanced(ControlNetAdvanced): |
| 350 | def __init__(self, control_model: SparseControlNet, motion_model: InterfaceAnimateDiffModel, |
| 351 | timestep_keyframes: TimestepKeyframeGroup, sparse_settings: SparseSettings=None, global_average_pooling=False, load_device=None, manual_cast_dtype=None): |
| 352 | super().__init__(control_model=None, timestep_keyframes=timestep_keyframes, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype) |
| 353 | self.control_model = control_model |
| 354 | if control_model is not None: |
| 355 | self.control_model_wrapped: ModelPatcher = create_sparse_modelpatcher(self.control_model, motion_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device()) |
| 356 | self.prepare_conditioning_info() |
| 357 | self.add_compatible_weight(ControlWeightType.SPARSECTRL) |
| 358 | self.postpone_condhint_latents_check = True |
| 359 | self.sparse_settings = sparse_settings if sparse_settings is not None else SparseSettings.default() |
| 360 | self.model_latent_format = None # latent format for active SD model, NOT controlnet |
| 361 | self.preprocessed = False |
| 362 | |
| 363 | def prepare_conditioning_info(self): |
| 364 | if self.control_model.use_simplified_conditioning_embedding: |
| 365 | # TODO: allow vae_optional to be used instead of preprocessor |
| 366 | #self.require_vae = True |
| 367 | self.allow_condhint_latents = True |
| 368 | |
| 369 | @property |
| 370 | def motion_model(self) -> InterfaceAnimateDiffModel: |
| 371 | motion_models = self.control_model_wrapped.get_additional_models_with_key(WrapperConsts.ACN) |
| 372 | if len(motion_models) == 0: |
| 373 | return None |
| 374 | return motion_models[0].model |
| 375 | |
| 376 | def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number: int, transformer_options): |
| 377 | # normal ControlNet stuff |
| 378 | control_prev = None |
| 379 | if self.previous_controlnet is not None: |
| 380 | control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options) |
| 381 | |
| 382 | if self.timestep_range is not None: |
| 383 | if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: |
| 384 | if control_prev is not None: |
| 385 | return control_prev |
| 386 | else: |
| 387 | return None |
| 388 | |
| 389 | dtype = self.control_model.dtype |
| 390 | if self.manual_cast_dtype is not None: |
| 391 | dtype = self.manual_cast_dtype |
| 392 | output_dtype = x_noisy.dtype |
| 393 | # set actual input length on motion model |
| 394 | actual_length = x_noisy.size(0)//batched_number |
| 395 | full_length = actual_length if self.sub_idxs is None else self.full_latent_length |
| 396 | if self.motion_model is not None: |
| 397 | self.motion_model.set_video_length(video_length=actual_length, full_length=full_length) |
| 398 | # prepare cond_hint, if needed |
| 399 | dim_mult = 1 if self.control_model.use_simplified_conditioning_embedding else 8 |
| 400 | if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2]*dim_mult != self.cond_hint.shape[2] or x_noisy.shape[3]*dim_mult != self.cond_hint.shape[3]: |
| 401 | # clear out cond_hint and conditioning_mask |
| 402 | if self.cond_hint is not None: |
| 403 | del self.cond_hint |
no outgoing calls
no test coverage detected