FineTrainers-Conditioning/training/mochi-1/text_to_video_lora.py

# Copyright 2024 The HuggingFace Team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import gc
import random
from glob import glob
import math
import os
import torch.nn.functional as F
import numpy as np
from pathlib import Path
from typing import Any, Dict, Tuple, List

import torch
import wandb
from diffusers import FlowMatchEulerDiscreteScheduler, MochiPipeline, MochiTransformer3DModel
from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
from diffusers.training_utils import cast_training_params
from diffusers.utils import export_to_video
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from huggingface_hub import create_repo, upload_folder
from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
from torch.utils.data import DataLoader
from tqdm.auto import tqdm


from args import get_args  # isort:skip
from dataset_simple import LatentEmbedDataset

import sys


sys.path.append("..")

from utils import print_memory, reset_memory  # isort:skip


# Taken from
# https://github.com/genmoai/mochi/blob/aba74c1b5e0755b1fa3343d9e4bd22e89de77ab1/demos/fine_tuner/train.py#L139
def get_cosine_annealing_lr_scheduler(
    optimizer: torch.optim.Optimizer,
    warmup_steps: int,
    total_steps: int,
):
    def lr_lambda(step):
        if step < warmup_steps:
            return float(step) / float(max(1, warmup_steps))
        else:
            return 0.5 * (1 + np.cos(np.pi * (step - warmup_steps) / (total_steps - warmup_steps)))

    return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)


def save_model_card(
    repo_id: str,
    videos=None,
    base_model: str = None,
    validation_prompt=None,
    repo_folder=None,
    fps=30,
):
    widget_dict = []
    if videos is not None and len(videos) > 0:
        for i, video in enumerate(videos):
            export_to_video(video, os.path.join(repo_folder, f"final_video_{i}.mp4"), fps=fps)
            widget_dict.append(
                {
                    "text": validation_prompt if validation_prompt else " ",
                    "output": {"url": f"final_video_{i}.mp4"},
                }
            )

    model_description = f"""
# Mochi-1 Preview LoRA Finetune

<Gallery />

## Model description

This is a lora finetune of the Mochi-1 preview model `{base_model}`.

The model was trained using [CogVideoX Factory](https://github.com/a-r-r-o-w/cogvideox-factory) - a repository containing memory-optimized training scripts for the CogVideoX and Mochi family of models using [TorchAO](https://github.com/pytorch/ao) and [DeepSpeed](https://github.com/microsoft/DeepSpeed). The scripts were adopted from [CogVideoX Diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/cogvideo/train_cogvideox_lora.py).

## Download model

[Download LoRA]({repo_id}/tree/main) in the Files & Versions tab.

## Usage

Requires the [🧨 Diffusers library](https://github.com/huggingface/diffusers) installed.

```py
from diffusers import MochiPipeline
from diffusers.utils import export_to_video
import torch 

pipe = MochiPipeline.from_pretrained("genmo/mochi-1-preview")
pipe.load_lora_weights("CHANGE_ME")
pipe.enable_model_cpu_offload()

with torch.autocast("cuda", torch.bfloat16):
    video = pipe(
        prompt="CHANGE_ME",
        guidance_scale=6.0,
        num_inference_steps=64,
        height=480,
        width=848,
        max_sequence_length=256,
        output_type="np"
    ).frames[0]
export_to_video(video)
```

For more details, including weighting, merging and fusing LoRAs, check the [documentation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) on loading LoRAs in diffusers.

"""
    model_card = load_or_create_model_card(
        repo_id_or_path=repo_id,
        from_training=True,
        license="apache-2.0",
        base_model=base_model,
        prompt=validation_prompt,
        model_description=model_description,
        widget=widget_dict,
    )
    tags = [
        "text-to-video",
        "diffusers-training",
        "diffusers",
        "lora",
        "mochi-1-preview",
        "mochi-1-preview-diffusers",
        "template:sd-lora",
    ]

    model_card = populate_model_card(model_card, tags=tags)
    model_card.save(os.path.join(repo_folder, "README.md"))


def log_validation(
    pipe: MochiPipeline,
    args: Dict[str, Any],
    pipeline_args: Dict[str, Any],
    epoch,
    wandb_run: str = None,
    is_final_validation: bool = False,
):
    print(
        f"Running validation... \n Generating {args.num_validation_videos} videos with prompt: {pipeline_args['prompt']}."
    )
    phase_name = "test" if is_final_validation else "validation"

    if not args.enable_model_cpu_offload:
        pipe = pipe.to("cuda")

    # run inference
    generator = torch.manual_seed(args.seed) if args.seed else None

    videos = []
    with torch.autocast("cuda", torch.bfloat16, cache_enabled=False):
        for _ in range(args.num_validation_videos):
            video = pipe(**pipeline_args, generator=generator, output_type="np").frames[0]
            videos.append(video)

    video_filenames = []
    for i, video in enumerate(videos):
        prompt = (
            pipeline_args["prompt"][:25]
            .replace(" ", "_")
            .replace(" ", "_")
            .replace("'", "_")
            .replace('"', "_")
            .replace("/", "_")
        )
        filename = os.path.join(args.output_dir, f"{phase_name}_video_{i}_{prompt}.mp4")
        export_to_video(video, filename, fps=30)
        video_filenames.append(filename)

    if wandb_run:
        wandb.log(
            {
                phase_name: [
                    wandb.Video(filename, caption=f"{i}: {pipeline_args['prompt']}", fps=30)
                    for i, filename in enumerate(video_filenames)
                ]
            }
        )

    return videos


# Adapted from the original code:
# https://github.com/genmoai/mochi/blob/aba74c1b5e0755b1fa3343d9e4bd22e89de77ab1/src/genmo/mochi_preview/pipelines.py#L578
def cast_dit(model, dtype):
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear):
            assert any(
                n in name for n in ["time_embed", "proj_out", "blocks", "norm_out"]
            ), f"Unexpected linear layer: {name}"
            module.to(dtype=dtype)
        elif isinstance(module, torch.nn.Conv2d):
            module.to(dtype=dtype)
    return model


def save_checkpoint(model, optimizer, lr_scheduler, global_step, checkpoint_path):
    lora_state_dict = get_peft_model_state_dict(model)
    torch.save(
        {
            "state_dict": lora_state_dict,
            "optimizer": optimizer.state_dict(),
            "lr_scheduler": lr_scheduler.state_dict(),
            "global_step": global_step,
        },
        checkpoint_path,
    )


class CollateFunction:
    def __init__(self, caption_dropout: float = None) -> None:
        self.caption_dropout = caption_dropout

    def __call__(self, samples: List[Tuple[dict, torch.Tensor]]) -> Dict[str, torch.Tensor]:
        ldists = torch.cat([data[0]["ldist"] for data in samples], dim=0)
        z = DiagonalGaussianDistribution(ldists).sample()
        assert torch.isfinite(z).all()

        # Sample noise which we will add to the samples.
        eps = torch.randn_like(z)
        sigma = torch.rand(z.shape[:1], device="cpu", dtype=torch.float32)

        prompt_embeds = torch.cat([data[1]["prompt_embeds"] for data in samples], dim=0)
        prompt_attention_mask = torch.cat([data[1]["prompt_attention_mask"] for data in samples], dim=0)
        if self.caption_dropout and random.random() < self.caption_dropout:
            prompt_embeds.zero_()
            prompt_attention_mask = prompt_attention_mask.long()
            prompt_attention_mask.zero_()
            prompt_attention_mask = prompt_attention_mask.bool()

        return dict(
            z=z, eps=eps, sigma=sigma, prompt_embeds=prompt_embeds, prompt_attention_mask=prompt_attention_mask
        )


def main(args):
    if not torch.cuda.is_available():
        raise ValueError("Not supported without CUDA.")

    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
            " Please use `huggingface-cli login` to authenticate with the Hub."
        )

    # Handle the repository creation
    if args.output_dir is not None:
        os.makedirs(args.output_dir, exist_ok=True)

    if args.push_to_hub:
        repo_id = create_repo(
            repo_id=args.hub_model_id or Path(args.output_dir).name,
            exist_ok=True,
        ).repo_id

    # Prepare models and scheduler
    transformer = MochiTransformer3DModel.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="transformer",
        revision=args.revision,
        variant=args.variant,
    )
    scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="scheduler"
    )

    transformer.requires_grad_(False)
    transformer.to("cuda")
    if args.gradient_checkpointing:
        transformer.enable_gradient_checkpointing()
    if args.cast_dit:
        transformer = cast_dit(transformer, torch.bfloat16)
    if args.compile_dit:
        transformer.compile()

    # now we will add new LoRA weights to the attention layers
    transformer_lora_config = LoraConfig(
        r=args.rank,
        lora_alpha=args.lora_alpha,
        init_lora_weights="gaussian",
        target_modules=args.target_modules,
    )
    transformer.add_adapter(transformer_lora_config)

    # Enable TF32 for faster training on Ampere GPUs,
    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
    if args.allow_tf32 and torch.cuda.is_available():
        torch.backends.cuda.matmul.allow_tf32 = True

    if args.scale_lr:
        args.learning_rate = args.learning_rate * args.train_batch_size
    # only upcast trainable parameters (LoRA) into fp32
    cast_training_params([transformer], dtype=torch.float32)

    # Prepare optimizer
    transformer_lora_parameters = list(filter(lambda p: p.requires_grad, transformer.parameters()))
    num_trainable_parameters = sum(param.numel() for param in transformer_lora_parameters)
    optimizer = torch.optim.AdamW(transformer_lora_parameters, lr=args.learning_rate, weight_decay=args.weight_decay)

    # Dataset and DataLoader
    train_vids = list(sorted(glob(f"{args.data_root}/*.mp4")))
    train_vids = [v for v in train_vids if not v.endswith(".recon.mp4")]
    print(f"Found {len(train_vids)} training videos in {args.data_root}")
    assert len(train_vids) > 0, f"No training data found in {args.data_root}"

    collate_fn = CollateFunction(caption_dropout=args.caption_dropout)
    train_dataset = LatentEmbedDataset(train_vids, repeat=1)
    train_dataloader = DataLoader(
        train_dataset,
        collate_fn=collate_fn,
        batch_size=args.train_batch_size,
        num_workers=args.dataloader_num_workers,
        pin_memory=args.pin_memory,
    )

    # LR scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = len(train_dataloader)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_cosine_annealing_lr_scheduler(
        optimizer, warmup_steps=args.lr_warmup_steps, total_steps=args.max_train_steps
    )

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = len(train_dataloader)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.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.
    wandb_run = None
    if args.report_to == "wandb":
        tracker_name = args.tracker_name or "mochi-1-lora"
        wandb_run = wandb.init(project=tracker_name, config=vars(args))

    # Resume from checkpoint if specified
    if args.resume_from_checkpoint:
        checkpoint = torch.load(args.resume_from_checkpoint, map_location="cpu", weights_only=True)
        if "global_step" in checkpoint:
            global_step = checkpoint["global_step"]
        if "optimizer" in checkpoint:
            optimizer.load_state_dict(checkpoint["optimizer"])
        if "lr_scheduler" in checkpoint:
            lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])

        set_peft_model_state_dict(transformer, checkpoint["state_dict"])

        print(f"Resuming from checkpoint: {args.resume_from_checkpoint}")
        print(f"Resuming from global step: {global_step}")
    else:
        global_step = 0

    print("===== Memory before training =====")
    reset_memory("cuda")
    print_memory("cuda")

    # Train!
    total_batch_size = args.train_batch_size
    print("***** Running training *****")
    print(f"  Num trainable parameters = {num_trainable_parameters}")
    print(f"  Num examples = {len(train_dataset)}")
    print(f"  Num batches each epoch = {len(train_dataloader)}")
    print(f"  Num epochs = {args.num_train_epochs}")
    print(f"  Instantaneous batch size per device = {args.train_batch_size}")
    print(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    print(f"  Total optimization steps = {args.max_train_steps}")

    first_epoch = 0
    progress_bar = tqdm(
        range(0, args.max_train_steps),
        initial=global_step,
        desc="Steps",
    )
    for epoch in range(first_epoch, args.num_train_epochs):
        transformer.train()

        for step, batch in enumerate(train_dataloader):
            with torch.no_grad():
                z = batch["z"].to("cuda")
                eps = batch["eps"].to("cuda")
                sigma = batch["sigma"].to("cuda")
                prompt_embeds = batch["prompt_embeds"].to("cuda")
                prompt_attention_mask = batch["prompt_attention_mask"].to("cuda")

                sigma_bcthw = sigma[:, None, None, None, None]  # [B, 1, 1, 1, 1]
                # Add noise according to flow matching.
                # zt = (1 - texp) * x + texp * z1
                z_sigma = (1 - sigma_bcthw) * z + sigma_bcthw * eps
                ut = z - eps

                # (1 - sigma) because of
                # https://github.com/genmoai/mochi/blob/aba74c1b5e0755b1fa3343d9e4bd22e89de77ab1/src/genmo/mochi_preview/dit/joint_model/asymm_models_joint.py#L656
                # Also, we operate on the scaled version of the `timesteps` directly in the `diffusers` implementation.
                timesteps = (1 - sigma) * scheduler.config.num_train_timesteps

            with torch.autocast("cuda", torch.bfloat16):
                model_pred = transformer(
                    hidden_states=z_sigma,
                    encoder_hidden_states=prompt_embeds,
                    encoder_attention_mask=prompt_attention_mask,
                    timestep=timesteps,
                    return_dict=False,
                )[0]
            assert model_pred.shape == z.shape
            loss = F.mse_loss(model_pred.float(), ut.float())
            loss.backward()

            optimizer.step()
            optimizer.zero_grad()
            lr_scheduler.step()

            progress_bar.update(1)
            global_step += 1

            last_lr = lr_scheduler.get_last_lr()[0] if lr_scheduler is not None else args.learning_rate
            logs = {"loss": loss.detach().item(), "lr": last_lr}
            progress_bar.set_postfix(**logs)
            if wandb_run:
                wandb_run.log(logs, step=global_step)

            if args.checkpointing_steps is not None and global_step % args.checkpointing_steps == 0:
                print(f"Saving checkpoint at step {global_step}")
                checkpoint_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.pt")
                save_checkpoint(
                    transformer,
                    optimizer,
                    lr_scheduler,
                    global_step,
                    checkpoint_path,
                )

            if global_step >= args.max_train_steps:
                break

        if global_step >= args.max_train_steps:
            break

        if args.validation_prompt is not None and (epoch + 1) % args.validation_epochs == 0:
            print("===== Memory before validation =====")
            print_memory("cuda")

            transformer.eval()
            pipe = MochiPipeline.from_pretrained(
                args.pretrained_model_name_or_path,
                transformer=transformer,
                scheduler=scheduler,
                revision=args.revision,
                variant=args.variant,
            )

            if args.enable_slicing:
                pipe.vae.enable_slicing()
            if args.enable_tiling:
                pipe.vae.enable_tiling()
            if args.enable_model_cpu_offload:
                pipe.enable_model_cpu_offload()

            validation_prompts = args.validation_prompt.split(args.validation_prompt_separator)
            for validation_prompt in validation_prompts:
                pipeline_args = {
                    "prompt": validation_prompt,
                    "guidance_scale": 6.0,
                    "num_inference_steps": 64,
                    "height": args.height,
                    "width": args.width,
                    "max_sequence_length": 256,
                }
                log_validation(
                    pipe=pipe,
                    args=args,
                    pipeline_args=pipeline_args,
                    epoch=epoch,
                    wandb_run=wandb_run,
                )

            print("===== Memory after validation =====")
            print_memory("cuda")
            reset_memory("cuda")

            del pipe.text_encoder
            del pipe.vae
            del pipe
            gc.collect()
            torch.cuda.empty_cache()

            transformer.train()

    transformer.eval()
    transformer_lora_layers = get_peft_model_state_dict(transformer)
    MochiPipeline.save_lora_weights(save_directory=args.output_dir, transformer_lora_layers=transformer_lora_layers)

    # Cleanup trained models to save memory
    del transformer

    gc.collect()
    torch.cuda.empty_cache()

    # Final test inference
    validation_outputs = []
    if args.validation_prompt and args.num_validation_videos > 0:
        print("===== Memory before testing =====")
        print_memory("cuda")
        reset_memory("cuda")

        pipe = MochiPipeline.from_pretrained(
            args.pretrained_model_name_or_path,
            revision=args.revision,
            variant=args.variant,
        )

        if args.enable_slicing:
            pipe.vae.enable_slicing()
        if args.enable_tiling:
            pipe.vae.enable_tiling()
        if args.enable_model_cpu_offload:
            pipe.enable_model_cpu_offload()

        # Load LoRA weights
        lora_scaling = args.lora_alpha / args.rank
        pipe.load_lora_weights(args.output_dir, adapter_name="mochi-lora")
        pipe.set_adapters(["mochi-lora"], [lora_scaling])

        # Run inference
        validation_prompts = args.validation_prompt.split(args.validation_prompt_separator)
        for validation_prompt in validation_prompts:
            pipeline_args = {
                "prompt": validation_prompt,
                "guidance_scale": 6.0,
                "num_inference_steps": 64,
                "height": args.height,
                "width": args.width,
                "max_sequence_length": 256,
            }

            video = log_validation(
                pipe=pipe,
                args=args,
                pipeline_args=pipeline_args,
                epoch=epoch,
                wandb_run=wandb_run,
                is_final_validation=True,
            )
            validation_outputs.extend(video)

        print("===== Memory after testing =====")
        print_memory("cuda")
        reset_memory("cuda")
        torch.cuda.synchronize("cuda")

    if args.push_to_hub:
        save_model_card(
            repo_id,
            videos=validation_outputs,
            base_model=args.pretrained_model_name_or_path,
            validation_prompt=args.validation_prompt,
            repo_folder=args.output_dir,
            fps=args.fps,
        )
        upload_folder(
            repo_id=repo_id,
            folder_path=args.output_dir,
            commit_message="End of training",
            ignore_patterns=["*.bin"],
        )
        print(f"Params pushed to {repo_id}.")


if __name__ == "__main__":
    args = get_args()
    main(args)