import torch from ..models import SDUNet, SDMotionModel, SDXLUNet, SDXLMotionModel from ..models.sd_unet import PushBlock, PopBlock from ..controlnets import MultiControlNetManager def lets_dance( unet: SDUNet, motion_modules: SDMotionModel = None, controlnet: MultiControlNetManager = None, sample = None, timestep = None, encoder_hidden_states = None, ipadapter_kwargs_list = {}, controlnet_frames = None, unet_batch_size = 1, controlnet_batch_size = 1, cross_frame_attention = False, tiled=False, tile_size=64, tile_stride=32, device = "cuda", vram_limit_level = 0, ): # 1. ControlNet # This part will be repeated on overlapping frames if animatediff_batch_size > animatediff_stride. # I leave it here because I intend to do something interesting on the ControlNets. controlnet_insert_block_id = 30 if controlnet is not None and controlnet_frames is not None: res_stacks = [] # process controlnet frames with batch for batch_id in range(0, sample.shape[0], controlnet_batch_size): batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0]) res_stack = controlnet( sample[batch_id: batch_id_], timestep, encoder_hidden_states[batch_id: batch_id_], controlnet_frames[:, batch_id: batch_id_], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride ) if vram_limit_level >= 1: res_stack = [res.cpu() for res in res_stack] res_stacks.append(res_stack) # concat the residual additional_res_stack = [] for i in range(len(res_stacks[0])): res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0) additional_res_stack.append(res) else: additional_res_stack = None # 2. time time_emb = unet.time_proj(timestep[None]).to(sample.dtype) time_emb = unet.time_embedding(time_emb) # 3. pre-process height, width = sample.shape[2], sample.shape[3] hidden_states = unet.conv_in(sample) text_emb = encoder_hidden_states res_stack = [hidden_states.cpu() if vram_limit_level>=1 else hidden_states] # 4. blocks for block_id, block in enumerate(unet.blocks): # 4.1 UNet if isinstance(block, PushBlock): hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) if vram_limit_level>=1: res_stack[-1] = res_stack[-1].cpu() elif isinstance(block, PopBlock): if vram_limit_level>=1: res_stack[-1] = res_stack[-1].to(device) hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) else: hidden_states_input = hidden_states hidden_states_output = [] for batch_id in range(0, sample.shape[0], unet_batch_size): batch_id_ = min(batch_id + unet_batch_size, sample.shape[0]) hidden_states, _, _, _ = block( hidden_states_input[batch_id: batch_id_], time_emb, text_emb[batch_id: batch_id_], res_stack, cross_frame_attention=cross_frame_attention, ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride ) hidden_states_output.append(hidden_states) hidden_states = torch.concat(hidden_states_output, dim=0) # 4.2 AnimateDiff if motion_modules is not None: if block_id in motion_modules.call_block_id: motion_module_id = motion_modules.call_block_id[block_id] hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id]( hidden_states, time_emb, text_emb, res_stack, batch_size=1 ) # 4.3 ControlNet if block_id == controlnet_insert_block_id and additional_res_stack is not None: hidden_states += additional_res_stack.pop().to(device) if vram_limit_level>=1: res_stack = [(res.to(device) + additional_res.to(device)).cpu() for res, additional_res in zip(res_stack, additional_res_stack)] else: res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)] # 5. output hidden_states = unet.conv_norm_out(hidden_states) hidden_states = unet.conv_act(hidden_states) hidden_states = unet.conv_out(hidden_states) return hidden_states def lets_dance_xl( unet: SDXLUNet, motion_modules: SDXLMotionModel = None, controlnet: MultiControlNetManager = None, sample = None, add_time_id = None, add_text_embeds = None, timestep = None, encoder_hidden_states = None, ipadapter_kwargs_list = {}, controlnet_frames = None, unet_batch_size = 1, controlnet_batch_size = 1, cross_frame_attention = False, tiled=False, tile_size=64, tile_stride=32, device = "cuda", vram_limit_level = 0, ): # 2. time t_emb = unet.time_proj(timestep[None]).to(sample.dtype) t_emb = unet.time_embedding(t_emb) time_embeds = unet.add_time_proj(add_time_id) time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) add_embeds = add_embeds.to(sample.dtype) add_embeds = unet.add_time_embedding(add_embeds) time_emb = t_emb + add_embeds # 3. pre-process height, width = sample.shape[2], sample.shape[3] hidden_states = unet.conv_in(sample) text_emb = encoder_hidden_states if unet.text_intermediate_proj is None else unet.text_intermediate_proj(encoder_hidden_states) res_stack = [hidden_states] # 4. blocks for block_id, block in enumerate(unet.blocks): hidden_states, time_emb, text_emb, res_stack = block( hidden_states, time_emb, text_emb, res_stack, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}) ) # 4.2 AnimateDiff if motion_modules is not None: if block_id in motion_modules.call_block_id: motion_module_id = motion_modules.call_block_id[block_id] hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id]( hidden_states, time_emb, text_emb, res_stack, batch_size=1 ) # 5. output hidden_states = unet.conv_norm_out(hidden_states) hidden_states = unet.conv_act(hidden_states) hidden_states = unet.conv_out(hidden_states) return hidden_states