Create distill_hunyuan_dmd_exp2.sh

fastvideo/dataset/latent_datasets.py

@@ -107,7 +107,7 @@ def latent_collate_function(batch):
 
 
 if __name__ == "__main__":
-    dataset = LatentDataset("data/Mochi-Synthetic-Data/merge.txt", num_latent_t=28)
+    dataset = LatentDataset("data/HD-Mixkit-Finetune-Hunyuan/videos2caption.json", num_latent_t=8, cfg_rate=6)
     dataloader = torch.utils.data.DataLoader(
         dataset, batch_size=2, shuffle=False, collate_fn=latent_collate_function
     )

fastvideo/distill/discriminator.py

@@ -24,7 +24,7 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
 class DiscriminatorHead(nn.Module):
-    def __init__(self, input_channel, output_channel=1):
+    def __init__(self, input_channel, output_channel=1, args=None):
         super().__init__()
         inner_channel = 1024
         self.conv1 = nn.Sequential(
@@ -43,13 +43,21 @@ class DiscriminatorHead(nn.Module):
         )
 
         self.conv_out = nn.Conv2d(inner_channel, output_channel, 1, 1, 0)
+        
+        vae_spatial_scale_factor = 8
+        
+        self.patch_height = args.num_height // vae_spatial_scale_factor // 2
+        self.patch_width = args.num_width // vae_spatial_scale_factor // 2
+        print("## DiscriminatorHead: patch_height: ", self.patch_height)
+        print("## DiscriminatorHead: patch_width: ", self.patch_width)
 
     def forward(self, x):
         b, twh, c = x.shape
-        t = twh // (30 * 53)
-        x = x.view(-1, 30 * 53, c)
+        
+        t = twh // (self.patch_height * self.patch_width)
+        x = x.view(-1, self.patch_height * self.patch_width, c)
         x = x.permute(0, 2, 1)
-        x = x.view(b * t, c, 30, 53)
+        x = x.view(b * t, c, self.patch_height, self.patch_width)
         x = self.conv1(x)
         x = self.conv2(x) + x
         x = self.conv_out(x)
@@ -58,7 +66,12 @@ class DiscriminatorHead(nn.Module):
 
 class Discriminator(nn.Module):
     def __init__(
-        self, stride=8, num_h_per_head=1, adapter_channel_dims=[3072], total_layers=48,
+        self,
+        stride=8,
+        num_h_per_head=1,
+        adapter_channel_dims=[3072],
+        total_layers = 48,
+        args=None,
     ):
         super().__init__()
         adapter_channel_dims = adapter_channel_dims * (total_layers // stride)
@@ -69,7 +82,10 @@ class Discriminator(nn.Module):
             [
                 nn.ModuleList(
                     [
-                        DiscriminatorHead(adapter_channel)
+                        DiscriminatorHead(
+                            adapter_channel, 
+                            args=args
+                        )
                         for _ in range(self.num_h_per_head)
                     ]
                 )
@@ -97,3 +113,25 @@ class Discriminator(nn.Module):
                 out = h(features[i])
                 outputs.append(out)
         return outputs
+
+
+class DMDiscriminator(nn.Module):
+    
+    def __init__(self):
+        super().__init__()
+    
+        self.cls_pred_branch = nn.Sequential(
+            nn.Conv2d(kernel_size=4, in_channels=1280, out_channels=1280, stride=2, padding=1), # 8x8 -> 4x4 
+            nn.GroupNorm(num_groups=32, num_channels=1280),
+            nn.SiLU(),
+            nn.Conv2d(kernel_size=4, in_channels=1280, out_channels=1280, stride=4, padding=0), # 4x4 -> 1x1
+            nn.GroupNorm(num_groups=32, num_channels=1280),
+            nn.SiLU(),
+            nn.Conv2d(kernel_size=1, in_channels=1280, out_channels=1, stride=1, padding=0), # 1x1 -> 1x1
+        )
+
+        self.cls_pred_branch.requires_grad_(True)
+        
+    def forward(self, features):
+        print("## features shape: ", features.shape)
+        return self.cls_pred_branch(features)

fastvideo/distill_adv.py

@@ -22,7 +22,7 @@ from torch.distributed.fsdp import (
     StateDictType,
     FullStateDictConfig,
 )
-from fastvideo.utils.load import load_transformer
+from fastvideo.utils.load import  load_transformer
 
 from fastvideo.models.mochi_hf.pipeline_mochi import linear_quadratic_schedule
 import json
@@ -37,7 +37,9 @@ from fastvideo.utils.fsdp_util import (
     get_discriminator_fsdp_kwargs,
 )
 import diffusers
-from diffusers import FlowMatchEulerDiscreteScheduler
+from diffusers import (
+    FlowMatchEulerDiscreteScheduler,
+)
 from fastvideo.distill.discriminator import Discriminator
 from fastvideo.distill.solver import EulerSolver, extract_into_tensor
 from copy import deepcopy
@@ -47,16 +49,45 @@ from diffusers.utils import check_min_version
 from fastvideo.dataset.latent_datasets import LatentDataset, latent_collate_function
 import torch.distributed as dist
 from peft import LoraConfig
-from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.distributed.fsdp import (
+    FullyShardedDataParallel as FSDP,
+)
 from fastvideo.utils.checkpoint import (
-    save_checkpoint,
     save_lora_checkpoint,
     resume_lora_optimizer,
     resume_training,
     save_checkpoint_generator_discriminator,
     resume_training_generator_discriminator,
 )
+# from fastvideo.utils.checkpoint import save_checkpoint
 from fastvideo.utils.logging_ import main_print
+from torch.distributed.fsdp import FullOptimStateDictConfig
+from safetensors.torch import save_file
+
+def save_checkpoint(model, rank, output_dir, step, discriminator=False):
+    with FSDP.state_dict_type(
+        model,
+        StateDictType.FULL_STATE_DICT,
+        FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
+        FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
+    ):
+        cpu_state = model.state_dict()
+
+    # todo move to get_state_dict
+    save_dir = os.path.join(output_dir, f"checkpoint-{step}")
+    os.makedirs(save_dir, exist_ok=True)
+    # save using safetensors
+    if rank <= 0 and not discriminator:
+        weight_path = os.path.join(save_dir, "diffusion_pytorch_model.safetensors")
+        save_file(cpu_state, weight_path)
+        config_dict = dict(model.config)
+        config_path = os.path.join(save_dir, "config.json")
+        # save dict as json
+        with open(config_path, "w") as f:
+            json.dump(config_dict, f, indent=4)
+    else:
+        weight_path = os.path.join(save_dir, "discriminator_pytorch_model.safetensors")
+        save_file(cpu_state, weight_path)
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.31.0")
@@ -73,7 +104,7 @@ def gan_d_loss(
     encoder_hidden_states,
     encoder_attention_mask,
     weight,
-    discriminator_head_stride,
+    discriminator_head_stride
 ):
     loss = 0.0
     # collate sample_fake and sample_real
@@ -115,7 +146,7 @@ def gan_g_loss(
     encoder_hidden_states,
     encoder_attention_mask,
     weight,
-    discriminator_head_stride,
+    discriminator_head_stride
 ):
     loss = 0.0
     features = teacher_transformer(
@@ -127,7 +158,9 @@ def gan_g_loss(
         output_features_stride=discriminator_head_stride,
         return_dict=False,
     )[1]
-    fake_outputs = discriminator(features,)
+    fake_outputs = discriminator(
+        features,
+    )
     for fake_output in fake_outputs:
         loss += torch.mean(weight * torch.relu(1 - fake_output.float())) / (
             discriminator.head_num * discriminator.num_h_per_head
@@ -156,7 +189,7 @@ def distill_one_step_adv(
     not_apply_cfg_solver,
     distill_cfg,
     adv_weight,
-    discriminator_head_stride,
+    discriminator_head_stride
 ):
     optimizer.zero_grad()
     discriminator_optimizer.zero_grad()
@@ -272,7 +305,7 @@ def distill_one_step_adv(
 
     huber_c = 0.001
     g_loss = torch.mean(
-        torch.sqrt((model_pred.float() - target.float()) ** 2 + huber_c ** 2) - huber_c
+        torch.sqrt((model_pred.float() - target.float()) ** 2 + huber_c**2) - huber_c
     )
     discriminator.requires_grad_(False)
     with torch.autocast("cuda", dtype=torch.bfloat16):
@@ -284,7 +317,7 @@ def distill_one_step_adv(
             encoder_hidden_states.float(),
             encoder_attention_mask,
             1.0,
-            discriminator_head_stride,
+            discriminator_head_stride
         )
     g_loss += g_gan_loss
     g_loss.backward()
@@ -356,10 +389,7 @@ def main(args):
         torch.float32 if args.master_weight_type == "fp32" else torch.bfloat16,
     )
     teacher_transformer = deepcopy(transformer)
-    discriminator = Discriminator(
-        args.discriminator_head_stride,
-        total_layers=48 if args.model_type == "mochi" else 40,
-    )
+    discriminator = Discriminator(args.discriminator_head_stride, total_layers = 48 if args.model_type =="mochi" else 40)
 
     if args.use_lora:
         transformer.requires_grad_(False)
@@ -397,9 +427,18 @@ def main(args):
         transformer._no_split_modules = no_split_modules
         fsdp_kwargs["auto_wrap_policy"] = fsdp_kwargs["auto_wrap_policy"](transformer)
 
-    transformer = FSDP(transformer, **fsdp_kwargs,)
-    teacher_transformer = FSDP(teacher_transformer, **fsdp_kwargs,)
-    discriminator = FSDP(discriminator, **discriminator_fsdp_kwargs,)
+    transformer = FSDP(
+        transformer,
+        **fsdp_kwargs,
+    )
+    teacher_transformer = FSDP(
+        teacher_transformer,
+        **fsdp_kwargs,
+    )
+    discriminator = FSDP(
+        discriminator,
+        **discriminator_fsdp_kwargs,
+    )
     main_print(f"--> model loaded")
 
     if args.gradient_checkpointing:
@@ -570,7 +609,6 @@ def main(args):
             if step <= int(phase_step):
                 return int(phase)
         return phase
-
     for i in range(init_steps):
         _ = next(loader)
     for step in range(init_steps + 1, args.max_train_steps + 1):
@@ -603,7 +641,7 @@ def main(args):
             args.not_apply_cfg_solver,
             args.distill_cfg,
             args.adv_weight,
-            args.discriminator_head_stride,
+            args.discriminator_head_stride
         )
 
         step_time = time.time() - start_time
@@ -642,7 +680,7 @@ def main(args):
                 )
             else:
                 # Your existing checkpoint saving code
-                # TODO
+                # TODO 
                 # save_checkpoint_generator_discriminator(
                 #     transformer,
                 #     optimizer,
@@ -652,9 +690,7 @@ def main(args):
                 #     args.output_dir,
                 #     step,
                 # )
-                save_checkpoint(
-                    transformer, rank, args.output_dir, args.max_train_steps
-                )
+                save_checkpoint(transformer, rank, args.output_dir, step, discriminator)
             main_print(f"--> checkpoint saved at step {step}")
             dist.barrier()
         if args.log_validation and step % args.validation_steps == 0:
@@ -671,6 +707,7 @@ def main(args):
                 linear_range=args.linear_range,
                 ema=False,
             )
+            
 
     if args.use_lora:
         save_lora_checkpoint(
@@ -691,6 +728,8 @@ if __name__ == "__main__":
     )
     # dataset & dataloader
     parser.add_argument("--data_json_path", type=str, required=True)
+    parser.add_argument("--num_height", type=int, default=480)
+    parser.add_argument("--num_width", type=int, default=848)
     parser.add_argument("--num_frames", type=int, default=163)
     parser.add_argument(
         "--dataloader_num_workers",
@@ -928,6 +967,15 @@ if __name__ == "__main__":
         default=0.025,
         help="The threshold of the linear quadratic scheduler.",
     )
+    parser.add_argument(
+        "--linear_range",
+        type=float,
+        default=0.5,
+        help="Range for linear quadratic scheduler.",
+    )
+    parser.add_argument(
+        "--weight_decay", type=float, default=0.001, help="Weight decay to apply."
+    )
     parser.add_argument(
         "--master_weight_type",
         type=str,
@@ -935,4 +983,4 @@ if __name__ == "__main__":
         help="Weight type to use - fp32 or bf16.",
     )
     args = parser.parse_args()
-    main(args)
+    main(args)

fastvideo/utils/checkpoint.py

@@ -49,7 +49,7 @@ def save_checkpoint(model, optimizer, rank, output_dir, step, discriminator=Fals
         save_file(cpu_state, weight_path)
         optimizer_path = os.path.join(save_dir, "discriminator_optimizer.pt")
         torch.save(optim_state, optimizer_path)
-
+        
 
 def save_checkpoint_generator_discriminator(
     model, optimizer, discriminator, discriminator_optimizer, rank, output_dir, step,
@@ -183,6 +183,36 @@ def resume_training_generator_discriminator(
     )
     return model, optimizer, discriminator, discriminator_optimizer, step
 
+def resume_training_generator_fake_transformer(
+    model, 
+    optimizer, 
+    fake_transformer,
+    guidance_optimizer,
+    discriminator,
+    discriminator_optimizer,
+    checkpoint_dir, 
+    rank,
+):
+    step = int(checkpoint_dir.split("-")[-1])
+    model_weight_dir = os.path.join(checkpoint_dir, "model_weights_state")
+    model_optimizer_dir = os.path.join(checkpoint_dir, "model_optimizer_state")
+    fake_model_weight_dir = os.path.join(checkpoint_dir, "fake_model_weights_state")
+    fake_model_optimizer_dir = os.path.join(checkpoint_dir, "fake_model_optimizer_state")
+    model, optimizer = load_sharded_model(
+        model, optimizer, model_weight_dir, model_optimizer_dir
+    )
+    fake_transformer, guidance_optimizer = load_sharded_model(
+        fake_transformer, guidance_optimizer, fake_model_weight_dir, fake_model_optimizer_dir
+    )
+    discriminator_ckpt_file = os.path.join(
+        checkpoint_dir, "discriminator_fsdp_state", "discriminator_state.pt"
+    )
+    discriminator, discriminator_optimizer = load_full_state_model(
+        discriminator, discriminator_optimizer, discriminator_ckpt_file, rank
+    )
+    return model, optimizer, fake_transformer, guidance_optimizer, discriminator, discriminator_optimizer, step
+
+
 
 def resume_training(model, optimizer, checkpoint_dir, discriminator=False):
     weight_path = os.path.join(checkpoint_dir, "diffusion_pytorch_model.safetensors")

fastvideo/utils/load.py

@@ -274,6 +274,8 @@ def load_transformer(
             in_channels=16, out_channels=16, **hunyuan_config, dtype=master_weight_type,
         )
         transformer = load_hunyuan_state_dict(transformer, dit_model_name_or_path)
+        if master_weight_type == torch.bfloat16:
+            transformer = transformer.bfloat16()
     else:
         raise ValueError(f"Unsupported model type: {model_type}")
     return transformer

fastvideo/utils/optimizer.py

@@ -1,20 +1,23 @@
-from accelerate.logging import get_logger
 import torch
 
-logger = get_logger(__name__)
-
-
-def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False):
+def get_optimizer(
+    params_to_optimize,
+    args,
+    lr=1e-5,
+    betas=(0.9, 0.999),
+    weight_decay=1e-3,
+    eps=1e-8,
+):
     # Optimizer creation
-    supported_optimizers = ["adam", "adamw", "prodigy"]
+    supported_optimizers = ["adam", "adamw"]
     if args.optimizer not in supported_optimizers:
-        logger.warning(
+        print(
             f"Unsupported choice of optimizer: {args.optimizer}. Supported optimizers include {supported_optimizers}. Defaulting to AdamW"
         )
         args.optimizer = "adamw"
-
+        
     if args.use_8bit_adam and not (args.optimizer.lower() not in ["adam", "adamw"]):
-        logger.warning(
+        print(
             f"use_8bit_adam is ignored when optimizer is not set to 'Adam' or 'AdamW'. Optimizer was "
             f"set to {args.optimizer.lower()}"
         )
@@ -34,44 +37,20 @@ def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False):
 
         optimizer = optimizer_class(
             params_to_optimize,
-            betas=(args.adam_beta1, args.adam_beta2),
-            eps=args.adam_epsilon,
-            weight_decay=args.adam_weight_decay,
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
         )
     elif args.optimizer.lower() == "adam":
         optimizer_class = bnb.optim.Adam8bit if args.use_8bit_adam else torch.optim.Adam
 
         optimizer = optimizer_class(
             params_to_optimize,
-            betas=(args.adam_beta1, args.adam_beta2),
-            eps=args.adam_epsilon,
-            weight_decay=args.adam_weight_decay,
-        )
-    elif args.optimizer.lower() == "prodigy":
-        try:
-            import prodigyopt
-        except ImportError:
-            raise ImportError(
-                "To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`"
-            )
-
-        optimizer_class = prodigyopt.Prodigy
-
-        if args.learning_rate <= 0.1:
-            logger.warning(
-                "Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
-            )
-
-        optimizer = optimizer_class(
-            params_to_optimize,
-            lr=args.learning_rate,
-            betas=(args.adam_beta1, args.adam_beta2),
-            beta3=args.prodigy_beta3,
-            weight_decay=args.adam_weight_decay,
-            eps=args.adam_epsilon,
-            decouple=args.prodigy_decouple,
-            use_bias_correction=args.prodigy_use_bias_correction,
-            safeguard_warmup=args.prodigy_safeguard_warmup,
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
         )
 
     return optimizer

fastvideo/utils/validation.py

@@ -46,6 +46,7 @@ def prepare_latents(
     return latents
 
 
+
 def sample_validation_video(
     transformer,
     vae,

scripts/distill/distill_hunyuan_adv.sh

@@ -0,0 +1,44 @@
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export WANDB_API_KEY=4f6de3765d6464f43e0506ec7d785641af645e73
+
+DATA_DIR=./data
+
+torchrun --nnodes 1 --nproc_per_node 4\
+    fastvideo/distill_adv.py\
+    --seed 42\
+    --pretrained_model_name_or_path $DATA_DIR/hunyuan\
+    --dit_model_name_or_path $DATA_DIR/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt\
+    --model_type "hunyuan" \
+    --cache_dir "$DATA_DIR/.cache"\
+    --data_json_path "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/videos2caption.json"\
+    --validation_prompt_dir "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/validation"\
+    --gradient_checkpointing\
+    --train_batch_size=1\
+    --num_latent_t 8\
+    --sp_size 4 \
+    --train_sp_batch_size 1\
+    --dataloader_num_workers 4\
+    --gradient_accumulation_steps=1\
+    --max_train_steps=2000\
+    --learning_rate=1e-6\
+    --mixed_precision="bf16"\
+    --checkpointing_steps=64\
+    --validation_steps 64\
+    --validation_sampling_steps "2,4,8" \
+    --checkpoints_total_limit 3\
+    --allow_tf32\
+    --ema_start_step 0\
+    --cfg 0.0\
+    --log_validation\
+    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_8_adv_HD"\
+    --tracker_project_name Hunyuan_Distill \
+    --num_height 720 \
+    --num_width 1280 \
+    --num_frames  125 \
+    --shift 17 \
+    --validation_guidance_scale "1.0" \
+    --num_euler_timesteps 50 \
+    --multi_phased_distill_schedule "4000-1" \
+    --not_apply_cfg_solver \
+    --master_weight_type "bf16"

scripts/distill/distill_hunyuan_dmd.sh

@@ -0,0 +1,48 @@
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export WANDB_API_KEY=7abd9763ff10869f9e88526d919c2639766139a8
+
+DATA_DIR=./data
+
+torchrun --nnodes 1 --nproc_per_node 4\
+    fastvideo/distill_dmd.py\
+    --seed 42\
+    --pretrained_model_name_or_path $DATA_DIR/hunyuan\
+    --dit_model_name_or_path $DATA_DIR/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt\
+    --model_type "hunyuan" \
+    --cache_dir "$DATA_DIR/.cache"\
+    --data_json_path "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/videos2caption.json"\
+    --validation_prompt_dir "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/validation"\
+    --gradient_checkpointing\
+    --train_batch_size=1\
+    --num_latent_t 8\
+    --sp_size 4\
+    --train_sp_batch_size 1\
+    --dataloader_num_workers 4\
+    --gradient_accumulation_steps=1\
+    --max_train_steps=30000\
+    --learning_rate=1e-5\
+    --mixed_precision="bf16"\
+    --checkpointing_steps=64\
+    --validation_steps 64\
+    --validation_sampling_steps "2,4,8" \
+    --num_inference_steps 4 \
+    --checkpoints_total_limit 3\
+    --allow_tf32\
+    --ema_start_step 0\
+    --cfg 0.0\
+    --log_validation\
+    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_32"\
+    --tracker_project_name Hunyuan_Distill \
+    --num_height 720 \
+    --num_width 1280 \
+    --num_frames 125 \
+    --shift 17 \
+    --validation_guidance_scale "1.0" \
+    --num_euler_timesteps 50 \
+    --multi_phased_distill_schedule "4000-1" \
+    --not_apply_cfg_solver \
+    --master_weight_type "bf16" \
+    --optimizer "AdamW" \
+    --use_8bit_adam \
+    --generator_update_steps 3

scripts/distill/distill_hunyuan_dmd_exp2.sh

@@ -0,0 +1,48 @@
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export WANDB_API_KEY=7abd9763ff10869f9e88526d919c2639766139a8
+
+DATA_DIR=./data
+
+torchrun --nnodes 1 --nproc_per_node 4\
+    fastvideo/distill_dmd.py\
+    --seed 42\
+    --pretrained_model_name_or_path $DATA_DIR/hunyuan\
+    --dit_model_name_or_path $DATA_DIR/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt\
+    --model_type "hunyuan" \
+    --cache_dir "$DATA_DIR/.cache"\
+    --data_json_path "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/videos2caption.json"\
+    --validation_prompt_dir "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/validation"\
+    --gradient_checkpointing\
+    --train_batch_size=1\
+    --num_latent_t 8\
+    --sp_size 4\
+    --train_sp_batch_size 1\
+    --dataloader_num_workers 4\
+    --gradient_accumulation_steps=1\
+    --max_train_steps=30000\
+    --learning_rate=1e-5\
+    --mixed_precision="bf16"\
+    --checkpointing_steps=64\
+    --validation_steps 64\
+    --validation_sampling_steps "2,4,8" \
+    --num_inference_steps 8 \
+    --checkpoints_total_limit 3\
+    --allow_tf32\
+    --ema_start_step 0\
+    --cfg 0.0\
+    --log_validation\
+    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_32"\
+    --tracker_project_name Hunyuan_Distill \
+    --num_height 720 \
+    --num_width 1280 \
+    --num_frames 125 \
+    --shift 17 \
+    --validation_guidance_scale "1.0" \
+    --num_euler_timesteps 50 \
+    --multi_phased_distill_schedule "4000-1" \
+    --not_apply_cfg_solver \
+    --master_weight_type "bf16" \
+    --optimizer "AdamW" \
+    --use_8bit_adam \
+    --generator_update_steps 3

scripts/distill/distill_hunyuan_dmd_runpod.sh

@@ -0,0 +1,49 @@
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export WANDB_API_KEY=7abd9763ff10869f9e88526d919c2639766139a8
+
+DATA_DIR=./data
+
+torchrun --nnodes 1 --nproc_per_node 4\
+    fastvideo/distill_dmd.py\
+    --seed 42\
+    --pretrained_model_name_or_path $DATA_DIR/hunyuan\
+    --dit_model_name_or_path $DATA_DIR/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt\
+    --model_type "hunyuan" \
+    --cache_dir "$DATA_DIR/.cache"\
+    --data_json_path "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/videos2caption.json"\
+    --validation_prompt_dir "$DATA_DIR/HD-Mixkit-Finetune-Hunyuan/validation"\
+    --gradient_checkpointing\
+    --train_batch_size=1\
+    --num_latent_t 8\
+    --sp_size 4\
+    --train_sp_batch_size 1\
+    --dataloader_num_workers 4\
+    --gradient_accumulation_steps=1\
+    --max_train_steps=30000\
+    --learning_rate=1e-5\
+    --mixed_precision="bf16"\
+    --checkpointing_steps=1\
+    --validation_steps 1\
+    --validation_sampling_steps "2,4,8" \
+    --num_inference_steps 4 \
+    --checkpoints_total_limit 3\
+    --allow_tf32\
+    --ema_start_step 0\
+    --cfg 0.0\
+    --log_validation\
+    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_32"\
+    --tracker_project_name Hunyuan_Distill \
+    --num_height 784 \
+    --num_width 1280 \
+    --num_frames 29 \
+    --shift 17 \
+    --validation_guidance_scale "1.0" \
+    --num_euler_timesteps 50 \
+    --multi_phased_distill_schedule "4000-1" \
+    --not_apply_cfg_solver \
+    --master_weight_type "bf16" \
+    --optimizer "AdamW" \
+    --use_8bit_adam \
+    --generator_update_steps 5 \
+    --run_pod

scripts/distill/distill_mochi_dmd.sh

@@ -0,0 +1,39 @@
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+
+torchrun --nnodes 1 --nproc_per_node 4 \
+    fastvideo/distill_dmd.py \
+    --seed 42 \
+    --pretrained_model_name_or_path data/mochi \
+    --model_type "mochi" \
+    --cache_dir data/.cache \
+    --data_json_path data/Mochi-425-Data/videos2caption.json \
+    --validation_prompt_dir data/Mochi-425-Data/validation \
+    --gradient_checkpointing \
+    --train_batch_size=1 \
+    --num_latent_t 16 \
+    --sp_size 4 \
+    --train_sp_batch_size 1 \
+    --generator_update_steps=1 \
+    --dataloader_num_workers 4 \
+    --gradient_accumulation_steps=1 \
+    --max_train_steps=4000 \
+    --learning_rate=1e-6 \
+    --mixed_precision=bf16 \
+    --checkpointing_steps=64 \
+    --validation_steps=64 \
+    --validation_sampling_steps 6 \
+    --checkpoints_total_limit 3 \
+    --allow_tf32 \
+    --ema_start_step 0 \
+    --cfg 0.0 \
+    --log_validation \
+    --output_dir="data/outputs/lq_euler_50_thres0.1_lrg_0.75_phase1_lr1e-6_repro" \
+    --tracker_project_name PCM \
+    --num_frames 93 \
+    --scheduler_type pcm_linear_quadratic \
+    --validation_guidance_scale 0.5,1.5,2.5 \
+    --num_euler_timesteps 50 \
+    --linear_quadratic_threshold 0.1 \
+    --linear_range 0.75 \
+    --multi_phased_distill_schedule 4000-1