Open SSUHan opened 11 months ago
Hi, I'm sincerely glad that you shared your great work!
I tried to reimplement the training logic of CAV but had some troubles.. Can you take a look at what might be the problem?
train.py:
import argparse import datetime import logging import inspect import math import os from typing import Dict, Optional, Tuple from omegaconf import OmegaConf from tqdm.auto import tqdm import time import itertools import imageio import torch import torch.nn.functional as F import torch.utils.checkpoint from einops import rearrange, repeat from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed import diffusers from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler from diffusers.optimization import get_scheduler from diffusers.utils.import_utils import is_xformers_available import transformers from transformers import CLIPTextModel, CLIPTokenizer from transformers import DPTForDepthEstimation from model.video_diffusion.models.unet_3d_condition import UNetPseudo3DConditionModel from model.video_diffusion.models.controlnet3d import ControlNet3DModel from model.video_diffusion.pipelines.pipeline_stable_diffusion_controlnet3d import Controlnet3DStableDiffusionPipeline from model.video_diffusion.dataloader.dataset import ControlAVideoDataset import utils from vis_utils import image_utils as iutils logger = get_logger(__name__, log_level="INFO") def find_trainable_params(name:str, trainable_modules:tuple): _bool = False for tm in trainable_modules: if tm in name: _bool = True break return _bool def get_statedict_for_sanity_check(model, checklist): param_name_to_meanvalue = {} # i = 0 for name, p in model.named_parameters(): if name in checklist: param_name_to_meanvalue[name] = p.mean().item() else: continue return param_name_to_meanvalue def main(config): print(f"config : {config}") accelerator = Accelerator( gradient_accumulation_steps=config.optimizer.gradient_accumulation_steps, mixed_precision=config.mixed_precision, ) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: transformers.utils.logging.set_verbosity_warning() diffusers.utils.logging.set_verbosity_info() else: transformers.utils.logging.set_verbosity_error() diffusers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if config.seed is not None: set_seed(config.seed) # Handle the output folder creation if accelerator.is_main_process: now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") config.output_dir = os.path.join(config.output_dir, f"{now}") os.makedirs(config.output_dir, exist_ok=True) os.makedirs(f"{config.output_dir}/samples", exist_ok=True) os.makedirs(f"{config.output_dir}/inv_latents", exist_ok=True) OmegaConf.save(config, os.path.join(config.output_dir, 'config.yaml')) ### === Load scheduler, tokenizer and models. ============================================================ ### pretrained_model_path = config.pretrained.pretrained_model_path control_mode = config.pretrained.control_mode controlnet_model_path = config.pretrained.controlnet_model_path noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler") tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer") text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder") vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae") unet = UNetPseudo3DConditionModel.from_2d_model(pretrained_model_path, subfolder="unet") controlnet = ControlNet3DModel.from_2d_model(controlnet_model_path) if control_mode == 'depth': annotator_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda") elif control_mode == 'canny': annotator_model = None elif control_mode == 'hed': # firstly download from https://huggingface.co/wf-genius/controlavideo-hed/resolve/main/hed-network.pth annotator_model = HEDNetwork('./pretrained_checkpoints/hed-network.pth').to("cuda") # print(f"unet : {unet}") ### ====================================================================================================== ### ### === Freeze vae and text_encoder vae.requires_grad_(False) text_encoder.requires_grad_(False) annotator_model.requires_grad_(False) unet.requires_grad_(False) trainable_modules = tuple(config.trainable_modules) for name, module in unet.named_modules(): # print(f"name : {name}") # if name.endswith(tuple(config.trainable_modules)): if find_trainable_params(name, trainable_modules): # print(f"---> selected name : {name}") for params in module.parameters(): params.requires_grad = True controlnet.requires_grad_(False) trainable_modules = tuple(config.trainable_modules) for name, module in controlnet.named_modules(): # print(f"controlnet name : {name}") if find_trainable_params(name, trainable_modules): # print(f"---> selected name : {name}") for params in module.parameters(): params.requires_grad = True ### ====================================================================================================== ### # for name, p in controlnet.named_parameters(): # print(f"222 name : {name}") # if find_trainable_params(name, trainable_modules): # print(f"---> 222 selected name : {name}") weight_sanity_checklist = [ "down_blocks.2.resnets.1.conv2.weight", "down_blocks.2.resnets.1.conv2.conv_temporal.weight", "controlnet_cond_embedding.conv_in.weight", "controlnet_cond_embedding.conv_in.conv_temporal.weight", ] ### ====================================================================================================== ### if config.enable_xformers_memory_efficient_attention: if is_xformers_available(): unet.enable_xformers_memory_efficient_attention() else: raise ValueError("xformers is not available. Make sure it is installed correctly") if config.gradient_checkpointing: unet.enable_gradient_checkpointing() ### === Set optimizer === config_optimizer = config.optimizer if config_optimizer.scale_lr: learning_rate = ( config_optimizer.learning_rate * config_optimizer.gradient_accumulation_steps * train_batch_size * accelerator.num_processes ) else: learning_rate = config_optimizer.learning_rate # Initialize the optimizer if config_optimizer.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError( "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" ) optimizer_cls = bnb.optim.AdamW8bit else: optimizer_cls = torch.optim.AdamW optimizer = optimizer_cls( itertools.chain(unet.parameters(), controlnet.parameters()), lr=learning_rate, betas=(config_optimizer.adam_beta1, config_optimizer.adam_beta2), weight_decay=config_optimizer.adam_weight_decay, eps=config_optimizer.adam_epsilon, ) # optimizer = optimizer_cls( # unet.parameters(), # lr=learning_rate, # betas=(config_optimizer.adam_beta1, config_optimizer.adam_beta2), # weight_decay=config_optimizer.adam_weight_decay, # eps=config_optimizer.adam_epsilon, # ) print(f"optimizer : {optimizer}") # Scheduler lr_scheduler = get_scheduler( config_optimizer.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=config_optimizer.lr_warmup_steps * config_optimizer.gradient_accumulation_steps, num_training_steps=config.max_train_steps * config_optimizer.gradient_accumulation_steps, ) print(f"lr_scheduler : {lr_scheduler}") ### ====================================================================================================== ### ### === Get the training dataset pipeline === ### train_dataset = ControlAVideoDataset(**config.train_data) # Preprocessing the dataset train_dataset.prompt_ids = tokenizer( train_dataset.prompt, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" ).input_ids[0] # print(f"prompt_ids : {train_dataset.prompt_ids}") # DataLoaders creation: train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=config.train_batch_size ) ### ====================================================================================================== ### ### === Get the validation dataset pipeline === ### validation_pipeline = Controlnet3DStableDiffusionPipeline( vae=vae, unet=unet, text_encoder=text_encoder, tokenizer=tokenizer, controlnet=controlnet, scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler'), annotator_model=annotator_model, ) ### ====================================================================================================== ### # Prepare everything with our `accelerator`. unet, controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, controlnet, optimizer, train_dataloader, lr_scheduler ) # For mixed precision training we cast the text_encoder and vae weights to half-precision # as these models are only used for inference, keeping weights in full precision is not required. weight_dtype = torch.float32 if accelerator.mixed_precision == "fp16": weight_dtype = torch.float16 elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 print(f" weight dtype : {weight_dtype}") # Move text_encode and vae to gpu and cast to weight_dtype text_encoder.to(accelerator.device, dtype=weight_dtype) vae.to(accelerator.device, dtype=weight_dtype) annotator_model.to(accelerator.device, dtype=weight_dtype) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / config.optimizer.gradient_accumulation_steps) # Afterwards we recalculate our number of training epochs num_train_epochs = math.ceil(config.max_train_steps / num_update_steps_per_epoch) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if accelerator.is_main_process: accelerator.init_trackers("text2video-controlavideo") ### === Logging for training === ### total_batch_size = config.train_batch_size * accelerator.num_processes * config.optimizer.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {num_train_epochs}") logger.info(f" Instantaneous batch size per device = {config.train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {config.optimizer.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {config.max_train_steps}") global_step = 0 first_epoch = 0 w = config.train_data.width h = config.train_data.height n_sample_frames = config.train_data.n_sample_frames ### ====================================================================================================== ### ### === Train === ### # Only show the progress bar once on each machine. progress_bar = tqdm(range(global_step, config.max_train_steps), disable=not accelerator.is_local_main_process) progress_bar.set_description("Steps") for epoch in range(first_epoch, num_train_epochs): unet.train() controlnet.train() train_loss = 0.0 for step, batch in enumerate(train_dataloader): # # Skip steps until we reach the resumed step # if resume_from_checkpoint and epoch == first_epoch and step < resume_step: # if step % gradient_accumulation_steps == 0: # progress_bar.update(1) # continue # start_time = time.time() with accelerator.accumulate(unet): # Convert videos to latent space pixel_values = batch["pixel_values"].to(weight_dtype) # B x F x 3 x 512 x 512 video_length = pixel_values.shape[1] pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w") # torch.Size([24, 3, 512, 512]) ### === Prepare inputs === latents = vae.encode(pixel_values).latent_dist.sample() # print(f"latents : {latents.size()}") # torch.Size([24, 4, 64, 64]) latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length) # torch.Size([1, 4, 24, 64, 64]) latents = latents * 0.18215 control_maps = validation_pipeline.get_depth_map(pixel_values, h, w, return_standard_norm=False)# (b f) 1 h w control_maps = control_maps.to(dtype=controlnet.dtype, device=controlnet.device) # torch.Size([16, 1, 512, 512]) control_maps = F.interpolate(control_maps, size=(h,w), mode='bilinear', align_corners=False) # torch.Size([16, 1, 512, 512]) control_maps = rearrange(control_maps, "(b f) c h w -> b c f h w", f=n_sample_frames) if control_maps.shape[1] == 1: control_maps = repeat(control_maps, 'b c f h w -> b (n c) f h w', n=3) # print(f"control_maps 3: {control_maps.size()}") # torch.Size([1, 3, 16, 512, 512]) if global_step == 0: tmp_images = batch["pixel_values"] tmp_control_maps = rearrange(control_maps, "b c f h w-> b f c h w") tmp = torch.cat([tmp_images, tmp_control_maps], dim=0) print(f"tmp : {tmp.size()}") image_dict = [ {"tensors": tmp, "n_in_row": 4, "pp_type": iutils.PP_RGB}, ] dir_path = utils.mkdir_ifnotexist(os.path.join(config.output_dir, "train")) iutils.save_images_from_dict( image_dict, dir_path=dir_path, file_name=f"inputs_"+str(global_step).zfill(8), n_instance=6, is_save=accelerator.is_main_process, return_images=False ) ### ====================================================================================================== ### # Sample noise that we'll add to the latents bsz = latents.shape[0] # Sample a random timestep for each video timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() # tensor([905], device='cuda:0') # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) first_latent = latents[:, 0:1, :, :, :] first_target = torch.zeros_like(first_latent) rest_latents = latents[:, 1:, :, :, :] rest_noise = torch.randn_like(rest_latents) # rest_noise = torch.randn_like(rest_latents) - first_latent.repeat(1, rest_latents.size(1), 1, 1, 1) noisy_latents = noise_scheduler.add_noise(rest_latents, rest_noise, timesteps) in_latents = torch.cat([first_latent, noisy_latents], dim=1) # torch.Size([1, 4, 16, 64, 64]) # Get the text embedding for conditioning encoder_hidden_states = text_encoder(batch["prompt_ids"])[0] # torch.Size([1, 77, 768]) # Get the target for loss depending on the prediction type if noise_scheduler.prediction_type == "epsilon": # target = torch.cat([first_target, rest_noise], dim=1) target = rest_noise elif noise_scheduler.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(rest_latents, rest_noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.prediction_type}") ### === Forward controlnet down_block_res_samples, mid_block_res_sample = controlnet( in_latents, timesteps, encoder_hidden_states=encoder_hidden_states, controlnet_cond=control_maps, return_dict=False, ) down_block_res_samples = [ down_block_res_sample * config.controlnet.controlnet_conditioning_scale for down_block_res_sample in down_block_res_samples ] mid_block_res_sample *= config.controlnet.controlnet_conditioning_scale # torch.Size([1, 1280, 16, 8, 8]) ### ====================================================================================================== ### ### === Predict the noise residual and compute loss model_pred = unet( in_latents, timesteps, encoder_hidden_states=encoder_hidden_states, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, ).sample # model_pred = unet( # in_latents, # timesteps, # encoder_hidden_states=encoder_hidden_states, # ).sample ### ====================================================================================================== ### ### === Calculate loss loss = F.mse_loss(model_pred[:, 1:, :, :, :].float(), target.float(), reduction="mean") # loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") # Gather the losses across all processes for logging (if we use distributed training). avg_loss = accelerator.gather(loss.repeat(config.train_batch_size)).mean() train_loss += avg_loss.item() / config.optimizer.gradient_accumulation_steps ### ====================================================================================================== ### ### === Backpropagate accelerator.backward(loss) if accelerator.sync_gradients: accelerator.clip_grad_norm_(itertools.chain(unet.parameters(), controlnet.parameters()), config.optimizer.max_grad_norm) # accelerator.clip_grad_norm_(unet.parameters(), config.optimizer.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad() ### ====================================================================================================== ### ### === Log print(f"epoch: {epoch}, global_step: {global_step}, timestep: {timesteps}, train_loss : {train_loss}") param_name_to_meanvalue = get_statedict_for_sanity_check(unet, weight_sanity_checklist) for key, value in param_name_to_meanvalue.items(): print(f" [weight sanity check], key: {key}, value: {value} ") # param_name_to_meanvalue = get_statedict_for_sanity_check(controlnet, weight_sanity_checklist) # for key, value in param_name_to_meanvalue.items(): # print(f" [weight sanity check], key: {key}, value: {value} ") ### ====================================================================================================== ### # print(f"iter time : {time.time() - start_time}") # 1 iter 에 7.3s 정도 걸리네 # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 accelerator.log({"train_loss": train_loss}, step=global_step) train_loss = 0.0 if accelerator.is_main_process and (global_step % config.checkpointing_steps == 0): dir_path = utils.mkdir_ifnotexist(os.path.join(config.output_dir, "checkpoints")) save_path = os.path.join(dir_path, f"checkpoint-{global_step}") accelerator.save_state(save_path) logger.info(f"Saved state to {save_path}") if accelerator.is_main_process and (global_step % config.validation_steps == 0): frames = (pixel_values+1.0) / 2.0 v2v_input_frames = torch.nn.functional.interpolate( pixel_values, size=(h, w), mode="bicubic", antialias=True, ) v2v_input_frames = rearrange(v2v_input_frames, '(b f) c h w -> b c f h w ', f=config.train_data.n_sample_frames) print(f"in validation logic, pixel_values : {pixel_values.size()}, dtype: {pixel_values.dtype}, device: {pixel_values.device}") print(f"in validation logic, v2v_input_frames : {v2v_input_frames.size()}, dtype: {v2v_input_frames.dtype}, device: {v2v_input_frames.device}") out = validation_pipeline( controlnet_hint=control_maps, # controlnet_hint=None, images=v2v_input_frames, first_frame_output=None, prompt=train_dataset.prompt, # num_inference_steps=num_inference_steps, num_inference_steps=config.inference.inference_steps, width=w, height=h, # guidance_scale=guidance_scale, guidance_scale=config.inference.guidance_scale, generator=[torch.Generator(device="cuda").manual_seed(config.seed)], # video_scale = video_scale, # per-frame as negative (>= 1 or set 0) video_scale = config.inference.video_scale, # per-frame as negative (>= 1 or set 0) # init_noise_by_residual_thres = init_noise_thres, # residual-based init. larger thres ==> more smooth. init_noise_by_residual_thres = config.inference.init_noise_thres, # residual-based init. larger thres ==> more smooth. controlnet_conditioning_scale=1.0, fix_first_frame=True, in_domain=True, # whether to use the video model to generate the first frame. ) dir_path = utils.mkdir_ifnotexist(os.path.join(config.output_dir, "samples")) output_filename = f"inputs_{str(global_step).zfill(8)}.gif" # print(f"out : type : {type(out)}") # out : type : <class 'diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput'> out = out.images[0] imageio.mimsave(os.path.join(dir_path, output_filename), out, fps=8) ### ====================================================================================================== ### print(f"End of process..!") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default="./configs/depth.yaml") parser.add_argument("--desc", type=str, default="_") parser.add_argument("--output_dir", type=str, default="./outputs") args = parser.parse_args() config = OmegaConf.load(args.config) ### update config config.output_dir = f"{args.output_dir}/{args.desc}" ### =============================================================== main(config)
where config file contains the followings:
train_batch_size: 1 max_train_steps: 500 checkpointing_steps: 100 validation_steps: 3 trainable_modules: - "_temporal" seed: 33 ### === config for accelerator ====== ### mixed_precision: fp16 ### ================================= ### enable_xformers_memory_efficient_attention: True gradient_checkpointing: True # pretrained_model_path: "runwayml/stable-diffusion-v1-5" pretrained: pretrained_model_path: "./pretrained_checkpoints/stable-diffusion-v1-5" control_mode: "depth" controlnet_model_path: "./pretrained_checkpoints/sd-controlnet-depth" controlnet: controlnet_conditioning_scale: 1.0 train_data: video_path: "./videos/bear.mp4" prompt: "a bear is walking" n_sample_frames: 8 width: 512 height: 512 sample_start_idx: 0 sample_frame_rate: 2 ### === config for optimizer ====== ### optimizer: use_8bit_adam: False scale_lr: False learning_rate: 3e-5 adam_beta1: 0.9 adam_beta2: 0.999 adam_weight_decay: 1e-2 adam_epsilon: 1e-08 max_grad_norm: 1.0 lr_scheduler_type: "constant" lr_warmup_steps: 0 gradient_accumulation_steps: 1 ### ================================= ### inference: inference_steps: 20 guidance_scale: 7.5 video_scale: 1.5 init_noise_thres: 0.1
With the current code, the model outputs at step 3
at step 6
at step9
I suspect that the loss function part or the validation pipeline part may be wrong, but it is difficult to think of a way other than the content of the text.
@SSUHan Hi, did you solve the problem?
Hi, I'm sincerely glad that you shared your great work!
I tried to reimplement the training logic of CAV but had some troubles.. Can you take a look at what might be the problem?
train.py:
where config file contains the followings:
With the current code, the model outputs
at step 3
I suspect that the loss function part or the validation pipeline part may be wrong, but it is difficult to think of a way other than the content of the text.