[Fix] Save CK, Dataset bug fix

README.md

@@ -149,6 +149,10 @@ For Image-Video Mixture Fine-tuning, make sure to enable the --group_frame optio
   - [ ] fp8 support
   - [ ] faster load model and save model support
 
+## Contributing
+
+We welcome all contributions. Please run bash format.sh before submitting a pull request.
+
 ## Acknowledgement
 We learned and reused code from the following projects: [PCM](https://github.com/G-U-N/Phased-Consistency-Model), [diffusers](https://github.com/huggingface/diffusers), [OpenSoraPlan](https://github.com/PKU-YuanGroup/Open-Sora-Plan), and [xDiT](https://github.com/xdit-project/xDiT).
 

fastvideo/distill.py

@@ -631,6 +631,8 @@ def get_num_phases(multi_phased_distill_schedule, step):
 
     # 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",

fastvideo/distill_adv.py

@@ -425,15 +425,15 @@ def main(args):
         params_to_optimize,
         lr=args.learning_rate,
         betas=(0.9, 0.999),
-        weight_decay=1e-3,
+        weight_decay=args.weight_decay,
         eps=1e-8,
     )
 
     discriminator_optimizer = torch.optim.AdamW(
         discriminator.parameters(),
         lr=args.discriminator_learning_rate,
         betas=(0, 0.999),
-        weight_decay=1e-3,
+        weight_decay=args.weight_decay,
         eps=1e-8,
     )
 
@@ -634,8 +634,7 @@ def get_num_phases(multi_phased_distill_schedule, step):
                 #     args.output_dir,
                 #     step,
                 # )
-                save_checkpoint(transformer, rank, args.output_dir,
-                                args.max_train_steps)
+                save_checkpoint(transformer, rank, args.output_dir, step)
             main_print(f"--> checkpoint saved at step {step}")
             dist.barrier()
         if args.log_validation and step % args.validation_steps == 0:
@@ -673,6 +672,8 @@ def get_num_phases(multi_phased_distill_schedule, step):
                         help="The type of model to train.")
     # 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",
@@ -922,6 +923,16 @@ def get_num_phases(multi_phased_distill_schedule, step):
         default=2,
         help="The stride of the discriminator head.",
     )
+    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(
         "--linear_quadratic_threshold",
         type=float,

fastvideo/train.py

@@ -472,6 +472,8 @@ def main(args):
                         help="The type of model to train.")
     # 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",

fastvideo/utils/checkpoint.py

@@ -19,12 +19,12 @@
 from fastvideo.utils.logging_ import main_print
 
 
-def save_checkpoint(model,
-                    optimizer,
-                    rank,
-                    output_dir,
-                    step,
-                    discriminator=False):
+def save_checkpoint_optimizer(model,
+                              optimizer,
+                              rank,
+                              output_dir,
+                              step,
+                              discriminator=False):
     with FSDP.state_dict_type(
             model,
             StateDictType.FULL_STATE_DICT,
@@ -60,6 +60,32 @@ def save_checkpoint(model,
         torch.save(optim_state, optimizer_path)
 
 
+def save_checkpoint(transformer, rank, output_dir, step):
+    main_print(f"--> saving checkpoint at step {step}")
+    with FSDP.state_dict_type(
+            transformer,
+            StateDictType.FULL_STATE_DICT,
+            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
+    ):
+        cpu_state = transformer.state_dict()
+    # todo move to get_state_dict
+    if rank <= 0:
+        save_dir = os.path.join(output_dir, f"checkpoint-{step}")
+        os.makedirs(save_dir, exist_ok=True)
+        # save using safetensors
+        weight_path = os.path.join(save_dir,
+                                   "diffusion_pytorch_model.safetensors")
+        save_file(cpu_state, weight_path)
+        config_dict = dict(transformer.config)
+        if "dtype" in config_dict:
+            del config_dict["dtype"]  # TODO
+        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)
+    main_print(f"--> checkpoint saved at step {step}")
+
+
 def save_checkpoint_generator_discriminator(
     model,
     optimizer,

fastvideo/utils/load.py

@@ -274,6 +274,8 @@ def load_transformer(
         )
         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/validation.py

@@ -4,14 +4,14 @@
 
 import numpy as np
 import torch
-import wandb
 from diffusers import FlowMatchEulerDiscreteScheduler
 from diffusers.utils import export_to_video
 from diffusers.utils.torch_utils import randn_tensor
 from diffusers.video_processor import VideoProcessor
 from einops import rearrange
 from tqdm import tqdm
 
+import wandb
 from fastvideo.distill.solver import PCMFMScheduler
 from fastvideo.models.mochi_hf.pipeline_mochi import (
     linear_quadratic_schedule, retrieve_timesteps)
@@ -294,8 +294,8 @@ def log_validation(
                     scheduler_type=scheduler_type,
                     num_frames=args.num_frames,
                     # Peiyuan TODO: remove hardcode
-                    height=480,
-                    width=848,
+                    height=args.num_height,
+                    width=args.num_width,
                     num_inference_steps=validation_sampling_step,
                     guidance_scale=validation_guidance_scale,
                     generator=generator,

scripts/distill/distill_hunyuan.sh

@@ -2,24 +2,69 @@ export WANDB_BASE_URL="https://api.wandb.ai"
 export WANDB_MODE=online
 
 DATA_DIR=./data
+IP=[MASTER NODE IP]
 
+torchrun --nnodes 4 --nproc_per_node 8\
+    --node_rank=0 \
+    --rdzv_id=456 \
+    --rdzv_backend=c10d \
+    --rdzv_endpoint=$IP:29500 \
+    fastvideo/distill.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 32 \
+    --sp_size 2 \
+    --train_sp_batch_size 1\
+    --dataloader_num_workers 4\
+    --gradient_accumulation_steps=1\
+    --max_train_steps=320\
+    --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_16_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 
+
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
 torchrun --nnodes 1 --nproc_per_node 8\
     fastvideo/distill.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 "$DATA_DIR/Hunyuan-30K-Distill-Data/videos2caption.json"\
-    --validation_prompt_dir "$DATA_DIR/Hunyuan-Distill-Data/validation"\
+    --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 24\
-    --sp_size 1\
+    --num_latent_t 32 \
+    --sp_size 2 \
     --train_sp_batch_size 1\
     --dataloader_num_workers 4\
     --gradient_accumulation_steps=1\
-    --max_train_steps=2000\
+    --max_train_steps=320\
     --learning_rate=1e-6\
     --mixed_precision="bf16"\
     --checkpointing_steps=64\
@@ -30,9 +75,11 @@ torchrun --nnodes 1 --nproc_per_node 8\
     --ema_start_step 0\
     --cfg 0.0\
     --log_validation\
-    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_32"\
+    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_16_HD"\
     --tracker_project_name Hunyuan_Distill \
-    --num_frames  93 \
+    --num_height 720 \
+    --num_width 1280 \
+    --num_frames  125 \
     --shift 17 \
     --validation_guidance_scale "1.0" \
     --num_euler_timesteps 50 \

scripts/finetune/finetune_hunyuan.sh

@@ -32,5 +32,7 @@ torchrun --nnodes 1 --nproc_per_node 8 \
     --output_dir=data/outputs/HSH-Taylor-Finetune-Hunyuan \
     --tracker_project_name HSH-Taylor-Finetune-Hunyuan \
     --num_frames 93 \
+    --num_height 720 \
+    --num_width 1280 \
     --validation_guidance_scale "1.0" \
     --group_frame