syn

fastvideo/models/flash_attn_no_pad.py

@@ -1,36 +1,48 @@
-from einops import rearrange
+try: 
+    from flash_attn_interface import flash_attn_func as flash_attn_func_v3 
+    has_v3 = True
+except: 
+    pass
 from flash_attn import flash_attn_varlen_qkvpacked_func
 from flash_attn.bert_padding import pad_input, unpad_input
+from einops import rearrange
 
 
-def flash_attn_no_pad(qkv,
-                      key_padding_mask,
-                      causal=False,
-                      dropout_p=0.0,
-                      softmax_scale=None):
+def flash_attn_no_pad(
+    qkv, key_padding_mask, causal=False, dropout_p=0.0, softmax_scale=None, attn_impl="fa3",
+):
     # adapted from https://github.com/Dao-AILab/flash-attention/blob/13403e81157ba37ca525890f2f0f2137edf75311/flash_attn/flash_attention.py#L27
     batch_size = qkv.shape[0]
     seqlen = qkv.shape[1]
     nheads = qkv.shape[-2]
     x = rearrange(qkv, "b s three h d -> b s (three h d)")
     x_unpad, indices, cu_seqlens, max_s, used_seqlens_in_batch = unpad_input(
-        x, key_padding_mask)
-
-    x_unpad = rearrange(x_unpad,
-                        "nnz (three h d) -> nnz three h d",
-                        three=3,
-                        h=nheads)
-    output_unpad = flash_attn_varlen_qkvpacked_func(
-        x_unpad,
-        cu_seqlens,
-        max_s,
-        dropout_p,
-        softmax_scale=softmax_scale,
-        causal=causal,
+        x, key_padding_mask
     )
+
+    x_unpad = rearrange(x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads)
+    if attn_impl == "fa3":
+        assert has_v3
+        q, k, v = x_unpad[:, 0].unsqueeze(0), x_unpad[:, 1].unsqueeze(0), x_unpad[:, 2].unsqueeze(0)
+        assert dropout_p == 0.0
+        output_unpad = flash_attn_func_v3(
+            q,
+            k,
+            v,
+        )[0].squeeze(0)
+    else:
+        output_unpad = flash_attn_varlen_qkvpacked_func(
+            x_unpad,
+            cu_seqlens,
+            max_s,
+            dropout_p,
+            softmax_scale=softmax_scale,
+            causal=causal,
+        )
     output = rearrange(
-        pad_input(rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices,
-                  batch_size, seqlen),
+        pad_input(
+            rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, batch_size, seqlen
+        ),
         "b s (h d) -> b s h d",
         h=nheads,
     )

fastvideo/models/hunyuan/diffusion/pipelines/pipeline_hunyuan_video.py

@@ -606,6 +606,7 @@ class HunyuanVideoPipeline(DiffusionPipeline):
         enable_vae_sp: bool = False,
         n_tokens: Optional[int] = None,
         embedded_guidance_scale: Optional[float] = None,
+        only_save_latent: bool = False,
         **kwargs,
     ):
         r"""
@@ -978,8 +979,13 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                 latents = (latents / self.vae.config.scaling_factor +
                            self.vae.config.shift_factor)
             else:
-                latents = latents / self.vae.config.scaling_factor
-
+                latents = latents / self.vae.config.scaling_factor # 1, 16, 32, 90, 160
+                
+            if only_save_latent:
+                latents = latents.cpu().float()
+                self.maybe_free_model_hooks()
+                return latents
+            
             with torch.autocast(device_type="cuda",
                                 dtype=vae_dtype,
                                 enabled=vae_autocast_enabled):
@@ -993,7 +999,6 @@ class HunyuanVideoPipeline(DiffusionPipeline):
 
             if expand_temporal_dim or image.shape[2] == 1:
                 image = image.squeeze(2)
-
         else:
             image = latents
 

fastvideo/models/hunyuan/inference.py

@@ -369,6 +369,7 @@ class HunyuanVideoSampler(Inference):
         embedded_guidance_scale=None,
         batch_size=1,
         num_videos_per_prompt=1,
+        only_save_latent=False,
         **kwargs,
     ):
         """
@@ -524,7 +525,10 @@ class HunyuanVideoSampler(Inference):
             vae_ver=self.args.vae,
             enable_tiling=self.args.vae_tiling,
             enable_vae_sp=self.args.vae_sp,
+            only_save_latent=only_save_latent,
         )[0]
+        if only_save_latent:
+            return samples
         out_dict["samples"] = samples
         out_dict["prompts"] = prompt
 

fastvideo/sample/generate_synthetic_hunyuan.py

@@ -0,0 +1,286 @@
+import argparse
+import os
+from pathlib import Path
+
+import imageio
+import numpy as np
+import torch
+import torch.distributed as dist
+import torchvision
+from einops import rearrange
+import json
+from fastvideo.models.hunyuan.inference import HunyuanVideoSampler
+from fastvideo.utils.parallel_states import (
+    initialize_sequence_parallel_state, nccl_info)
+
+
+def initialize_distributed():
+    local_rank = int(os.getenv("RANK", 0))
+    world_size = int(os.getenv("WORLD_SIZE", 1))
+    print("world_size", world_size)
+    torch.cuda.set_device(local_rank)
+    dist.init_process_group(backend="nccl",
+                            init_method="env://",
+                            world_size=world_size,
+                            rank=local_rank)
+    initialize_sequence_parallel_state(world_size)
+
+
+def main(args):
+    print(nccl_info.sp_size)
+    print(args)
+    models_root_path = Path(args.model_path)
+    if not models_root_path.exists():
+        raise ValueError(f"`models_root` not exists: {models_root_path}")
+
+    save_path = args.output_path
+    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+
+    # Handle both JSON and TXT files
+    prompts = []
+    if args.prompt_json.endswith('.json'):
+        with open(args.prompt_json) as f:
+            prompt_json = json.load(f)
+            prompts = [(item["caption"], item["latent_path"]) for item in prompt_json]
+    else:
+        with open(args.prompt_json) as f:
+            for line in f:
+                prompt = line.strip()
+                if prompt:
+                    # Use first 50 chars of prompt as filename
+                    file_name = "".join(c for c in prompt[:50] if c.isalnum() or c.isspace())
+                    file_name = file_name.strip().replace(" ", "_")
+                    prompts.append((prompt, file_name))
+
+    # Filter for unprocessed prompts in latent-only mode
+    if args.only_save_latent:
+        filtered_prompts = []
+        for prompt, file_name in prompts:
+            latent_path = os.path.join(args.output_path, "latent", f"{file_name}.pt")
+            if not os.path.exists(latent_path):
+                filtered_prompts.append((prompt, file_name))
+            else:
+                print(f"Latent file exists for {file_name}, skipping...")
+        
+        prompts = filtered_prompts
+        if not prompts:
+            print("All prompts processed. Exiting...")
+            return
+
+    hunyuan_video_sampler = HunyuanVideoSampler.from_pretrained(
+        models_root_path, args=args)
+    args = hunyuan_video_sampler.args
+
+    prompt_start_end_idx = [int(i) for i in args.prompt_start_end_idx.split(",")]
+    prompts = prompts[prompt_start_end_idx[0]:prompt_start_end_idx[1]]
+    
+    for prompt, file_name in prompts:
+        outputs = hunyuan_video_sampler.predict(
+            prompt=prompt,
+            height=args.height,
+            width=args.width,
+            video_length=args.num_frames,
+            seed=args.seed,
+            negative_prompt=args.neg_prompt,
+            infer_steps=args.num_inference_steps,
+            guidance_scale=args.guidance_scale,
+            num_videos_per_prompt=args.num_videos,
+            flow_shift=args.flow_shift,
+            batch_size=args.batch_size,
+            embedded_guidance_scale=args.embedded_cfg_scale,
+            only_save_latent=args.only_save_latent,
+        )
+        if args.only_save_latent:
+            os.makedirs(os.path.join(args.output_path, "latent"), exist_ok=True)
+            latent_path = os.path.join(args.output_path, "latent", f"{file_name}.pt")
+            torch.save(outputs.to(torch.bfloat16), latent_path)
+        else:
+            videos = rearrange(outputs["samples"], "b c t h w -> t b c h w")
+            outputs = []
+            for x in videos:
+                x = torchvision.utils.make_grid(x, nrow=6)
+                x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+                outputs.append((x * 255).numpy().astype(np.uint8))
+            os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
+            imageio.mimsave(os.path.join(args.output_path, f"{file_name}.mp4"),
+                            outputs,
+                            fps=args.fps)
+
+#                 
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    # Basic parameters
+    parser.add_argument("--prompt_json", type=str, required=True, help="prompt file for inference")
+    parser.add_argument("--num_frames", type=int, default=16)
+    parser.add_argument("--height", type=int, default=256)
+    parser.add_argument("--width", type=int, default=256)
+    parser.add_argument("--num_inference_steps", type=int, default=50)
+    parser.add_argument("--model_path", type=str, default="data/hunyuan")
+    parser.add_argument("--output_path", type=str, default="./outputs/video")
+    parser.add_argument("--fps", type=int, default=24)
+
+    # Additional parameters
+    parser.add_argument(
+        "--prompt_start_end_idx",
+        type=str,
+        default="0,99",
+        help=" 0 stands for starting layer index, 9 for stride",
+    )
+    parser.add_argument(
+        "--only_save_latent",
+        type=bool,
+        default=False,
+        help="Whether save latent or not.",
+    )
+    parser.add_argument(
+        "--denoise-type",
+        type=str,
+        default="flow",
+        help="Denoise type for noised inputs.",
+    )
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="Seed for evaluation.")
+    parser.add_argument("--neg_prompt",
+                        type=str,
+                        default=None,
+                        help="Negative prompt for sampling.")
+    parser.add_argument(
+        "--guidance_scale",
+        type=float,
+        default=1.0,
+        help="Classifier free guidance scale.",
+    )
+    parser.add_argument(
+        "--embedded_cfg_scale",
+        type=float,
+        default=6.0,
+        help="Embedded classifier free guidance scale.",
+    )
+    parser.add_argument("--flow_shift",
+                        type=int,
+                        default=7,
+                        help="Flow shift parameter.")
+    parser.add_argument("--batch_size",
+                        type=int,
+                        default=1,
+                        help="Batch size for inference.")
+    parser.add_argument(
+        "--num_videos",
+        type=int,
+        default=1,
+        help="Number of videos to generate per prompt.",
+    )
+    parser.add_argument(
+        "--load-key",
+        type=str,
+        default="module",
+        help=
+        "Key to load the model states. 'module' for the main model, 'ema' for the EMA model.",
+    )
+    parser.add_argument(
+        "--use-cpu-offload",
+        action="store_true",
+        help="Use CPU offload for the model load.",
+    )
+    parser.add_argument(
+        "--dit-weight",
+        type=str,
+        default=
+        "data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt",
+    )
+    parser.add_argument(
+        "--reproduce",
+        action="store_true",
+        help=
+        "Enable reproducibility by setting random seeds and deterministic algorithms.",
+    )
+    parser.add_argument(
+        "--disable-autocast",
+        action="store_true",
+        help=
+        "Disable autocast for denoising loop and vae decoding in pipeline sampling.",
+    )
+
+    # Flow Matching
+    parser.add_argument(
+        "--flow-reverse",
+        action="store_true",
+        help="If reverse, learning/sampling from t=1 -> t=0.",
+    )
+    parser.add_argument("--flow-solver",
+                        type=str,
+                        default="euler",
+                        help="Solver for flow matching.")
+    parser.add_argument(
+        "--use-linear-quadratic-schedule",
+        action="store_true",
+        help=
+        "Use linear quadratic schedule for flow matching. Following MovieGen (https://ai.meta.com/static-resource/movie-gen-research-paper)",
+    )
+    parser.add_argument(
+        "--linear-schedule-end",
+        type=int,
+        default=25,
+        help="End step for linear quadratic schedule for flow matching.",
+    )
+
+    # Model parameters
+    parser.add_argument("--model", type=str, default="HYVideo-T/2-cfgdistill")
+    parser.add_argument("--latent-channels", type=int, default=16)
+    parser.add_argument("--precision",
+                        type=str,
+                        default="bf16",
+                        choices=["fp32", "fp16", "bf16"])
+    parser.add_argument("--rope-theta",
+                        type=int,
+                        default=256,
+                        help="Theta used in RoPE.")
+
+    parser.add_argument("--vae", type=str, default="884-16c-hy")
+    parser.add_argument("--vae-precision",
+                        type=str,
+                        default="fp16",
+                        choices=["fp32", "fp16", "bf16"])
+    parser.add_argument("--vae-tiling", action="store_true", default=True)
+    parser.add_argument("--vae-sp", action="store_true", default=False)
+
+    parser.add_argument("--text-encoder", type=str, default="llm")
+    parser.add_argument(
+        "--text-encoder-precision",
+        type=str,
+        default="fp16",
+        choices=["fp32", "fp16", "bf16"],
+    )
+    parser.add_argument("--text-states-dim", type=int, default=4096)
+    parser.add_argument("--text-len", type=int, default=256)
+    parser.add_argument("--tokenizer", type=str, default="llm")
+    parser.add_argument("--prompt-template",
+                        type=str,
+                        default="dit-llm-encode")
+    parser.add_argument("--prompt-template-video",
+                        type=str,
+                        default="dit-llm-encode-video")
+    parser.add_argument("--hidden-state-skip-layer", type=int, default=2)
+    parser.add_argument("--apply-final-norm", action="store_true")
+
+    parser.add_argument("--text-encoder-2", type=str, default="clipL")
+    parser.add_argument(
+        "--text-encoder-precision-2",
+        type=str,
+        default="fp16",
+        choices=["fp32", "fp16", "bf16"],
+    )
+    parser.add_argument("--text-states-dim-2", type=int, default=768)
+    parser.add_argument("--tokenizer-2", type=str, default="clipL")
+    parser.add_argument("--text-len-2", type=int, default=77)
+
+    args = parser.parse_args()
+    # process for vae sequence parallel
+    if args.vae_sp and not args.vae_tiling:
+        raise ValueError(
+            "Currently enabling vae_sp requires enabling vae_tiling, please set --vae-tiling to True."
+        )
+    main(args)

fastvideo/sample/generate_synthetic_mochi.py

@@ -1,16 +1,16 @@
-import argparse
 import json
-import os
 
-import torch
 import torch.distributed as dist
-from diffusers.utils import export_to_video
-
+import torch
 from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
+import os
+from diffusers.utils import export_to_video
+import argparse
 
 
-def generate_video_and_latent(pipe, prompt, height, width, num_frames,
-                              num_inference_steps, guidance_scale):
+def generate_video_and_latent(
+    pipe, prompt, height, width, num_frames, num_inference_steps, guidance_scale
+):
     # Set the random seed for reproducibility
     generator = torch.Generator("cuda").manual_seed(12345)
     # Generate videos from the input prompt
@@ -25,8 +25,7 @@ def generate_video_and_latent(pipe, prompt, height, width, num_frames,
         output_type="latent_and_video",
     )
     # prompt_embed has negative prompt at index 0
-    return noise[0], video[0], latent[0], prompt_embed[
-        1], prompt_attention_mask[1]
+    return noise[0], video[0], latent[0], prompt_embed[1], prompt_attention_mask[1]
 
     # return dummy tensor to debug first
     # return torch.zeros(1, 3, 480, 848), torch.zeros(1, 256, 16, 16)
@@ -40,22 +39,19 @@ if __name__ == "__main__":
     parser.add_argument("--num_inference_steps", type=int, default=64)
     parser.add_argument("--guidance_scale", type=float, default=4.5)
     parser.add_argument("--model_path", type=str, default="data/mochi")
-    parser.add_argument("--prompt_path",
-                        type=str,
-                        default="data/dummyVid/videos2caption.json")
-    parser.add_argument("--dataset_output_dir",
-                        type=str,
-                        default="data/dummySynthetic")
+    parser.add_argument(
+        "--prompt_path", type=str, default="data/dummyVid/videos2caption.json"
+    )
+    parser.add_argument("--dataset_output_dir", type=str, default="data/dummySynthetic")
     args = parser.parse_args()
 
     local_rank = int(os.getenv("RANK", 0))
     world_size = int(os.getenv("WORLD_SIZE", 1))
     print("world_size", world_size, "local rank", local_rank)
     torch.cuda.set_device(local_rank)
-    dist.init_process_group(backend="nccl",
-                            init_method="env://",
-                            world_size=world_size,
-                            rank=local_rank)
+    dist.init_process_group(
+        backend="nccl", init_method="env://", world_size=world_size, rank=local_rank
+    )
 
     if not isinstance(args.prompt_path, list):
         args.prompt_path = [args.prompt_path]
@@ -63,8 +59,7 @@ if __name__ == "__main__":
         text_prompt = open(args.prompt_path[0], "r").readlines()
         text_prompt = [i.strip() for i in text_prompt]
 
-    pipe = MochiPipeline.from_pretrained(args.model_path,
-                                         torch_dtype=torch.bfloat16)
+    pipe = MochiPipeline.from_pretrained(args.model_path, torch_dtype=torch.bfloat16)
     pipe.enable_vae_tiling()
     pipe.enable_model_cpu_offload(gpu_id=local_rank)
     # make dir if not exist
@@ -73,10 +68,10 @@ if __name__ == "__main__":
     os.makedirs(os.path.join(args.dataset_output_dir, "noise"), exist_ok=True)
     os.makedirs(os.path.join(args.dataset_output_dir, "video"), exist_ok=True)
     os.makedirs(os.path.join(args.dataset_output_dir, "latent"), exist_ok=True)
-    os.makedirs(os.path.join(args.dataset_output_dir, "prompt_embed"),
-                exist_ok=True)
-    os.makedirs(os.path.join(args.dataset_output_dir, "prompt_attention_mask"),
-                exist_ok=True)
+    os.makedirs(os.path.join(args.dataset_output_dir, "prompt_embed"), exist_ok=True)
+    os.makedirs(
+        os.path.join(args.dataset_output_dir, "prompt_attention_mask"), exist_ok=True
+    )
     data = []
     for i, prompt in enumerate(text_prompt):
         if i % world_size != local_rank:
@@ -98,17 +93,17 @@ if __name__ == "__main__":
         )
         # save latent
         video_name = str(i)
-        noise_path = os.path.join(args.dataset_output_dir, "noise",
-                                  video_name + ".pt")
-        latent_path = os.path.join(args.dataset_output_dir, "latent",
-                                   video_name + ".pt")
-        prompt_embed_path = os.path.join(args.dataset_output_dir,
-                                         "prompt_embed", video_name + ".pt")
-        video_path = os.path.join(args.dataset_output_dir, "video",
-                                  video_name + ".mp4")
-        prompt_attention_mask_path = os.path.join(args.dataset_output_dir,
-                                                  "prompt_attention_mask",
-                                                  video_name + ".pt")
+        noise_path = os.path.join(args.dataset_output_dir, "noise", video_name + ".pt")
+        latent_path = os.path.join(
+            args.dataset_output_dir, "latent", video_name + ".pt"
+        )
+        prompt_embed_path = os.path.join(
+            args.dataset_output_dir, "prompt_embed", video_name + ".pt"
+        )
+        video_path = os.path.join(args.dataset_output_dir, "video", video_name + ".mp4")
+        prompt_attention_mask_path = os.path.join(
+            args.dataset_output_dir, "prompt_attention_mask", video_name + ".pt"
+        )
         # save latent
         torch.save(noise, noise_path)
         torch.save(latent, latent_path)
@@ -132,6 +127,7 @@ if __name__ == "__main__":
     # save json
     if local_rank == 0:
         all_data = [item for sublist in gathered_data for item in sublist]
-        with open(os.path.join(args.dataset_output_dir, "videos2caption.json"),
-                  "w") as f:
+        with open(
+            os.path.join(args.dataset_output_dir, "videos2caption.json"), "w"
+        ) as f:
             json.dump(all_data, f, indent=4)

local_scripts/fastvideo/Hunyuan_lora.slurm

@@ -0,0 +1,62 @@
+#!/bin/bash
+#SBATCH --job-name=fastvideo
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log/slurm_lora_1e4.out
+#SBATCH --error=slurm_log/slurm_lora_1e4.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+export MASTER_PORT=29803
+torchrun --nnodes 1 --nproc_per_node 8 --master_port $MASTER_PORT \
+    fastvideo/train.py \
+    --seed 1024 \
+    --pretrained_model_name_or_path ~/data/hunyuan_diffusers \
+    --model_type hunyuan_hf \
+    --cache_dir data/.cache \
+    --data_json_path ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/videos2caption.json \
+    --validation_prompt_dir ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/validation \
+    --gradient_checkpointing \
+    --train_batch_size 8 \
+    --num_latent_t 32 \
+    --sp_size 8 \
+    --train_sp_batch_size 1 \
+    --dataloader_num_workers 4 \
+    --gradient_accumulation_steps 1 \
+    --max_train_steps 8000 \
+    --learning_rate 1e-4 \
+    --mixed_precision bf16 \
+    --checkpointing_steps 100 \
+    --validation_steps 100 \
+    --validation_sampling_steps 50 \
+    --checkpoints_total_limit 3 \
+    --allow_tf32 \
+    --ema_start_step 0 \
+    --cfg 0.0 \
+    --ema_decay 0.999 \
+    --log_validation \
+    --output_dir data/outputs/SBA_lora_1e4_r32 \
+    --tracker_project_name SBA \
+    --num_frames 125 \
+    --num_width 1280 \
+    --num_height 768 \
+    --validation_guidance_scale "1.0" \
+    --shift 7 \
+    --use_lora \
+    --lora_rank 32 \
+    --lora_alpha 32

local_scripts/fastvideo/Hunyuan_ori_finetune.slurm

@@ -0,0 +1,100 @@
+#!/bin/bash
+#SBATCH --job-name=fv_syn_hunyuan
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log/slurm_ori_ft_syn.out
+#SBATCH --error=slurm_log/slurm_ori_ft_syn.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+export MASTER_PORT=29803
+torchrun --nnodes 1 --nproc_per_node 6 \
+    fastvideo/train.py \
+    --seed 42 \
+    --pretrained_model_name_or_path data/hunyuan \
+    --dit_model_name_or_path data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt\
+    --model_type "hunyuan" \
+    --cache_dir data/.cache \
+    --data_json_path ../FastVideo-Internal/data/synthetic_MixKit/videos2caption.json \
+    --validation_prompt_dir ../FastVideo-Internal/data/synthetic_MixKit/validation \
+    --gradient_checkpointing \
+    --train_batch_size=1 \
+    --num_latent_t 30 \
+    --sp_size 6 \
+    --train_sp_batch_size 1 \
+    --dataloader_num_workers 4 \
+    --gradient_accumulation_steps=2 \
+    --max_train_steps=2000 \
+    --learning_rate=1e-5 \
+    --mixed_precision=bf16 \
+    --checkpointing_steps=200 \
+    --validation_steps 100 \
+    --validation_sampling_steps 50 \
+    --checkpoints_total_limit 3 \
+    --allow_tf32 \
+    --ema_start_step 0 \
+    --cfg 0.0 \
+    --ema_decay 0.999 \
+    --log_validation \
+    --output_dir=data/outputs/SBA_hunyuan_ori_ft_1e5 \
+    --tracker_project_name SBA-Syn \
+    --num_frames 117 \
+    --num_height 768 \
+    --num_width 1280 \
+    --shift 7 \
+    --validation_guidance_scale "1.0" \
+    --training_guidance "6.0" \
+    --run_name "SBA_hunyuan_ori_ft_1e5_g6" \
+
+torchrun --nnodes 1 --nproc_per_node 6 \
+    fastvideo/train.py \
+    --seed 42 \
+    --pretrained_model_name_or_path data/hunyuan \
+    --dit_model_name_or_path data/hunyuan/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt\
+    --model_type "hunyuan" \
+    --cache_dir data/.cache \
+    --data_json_path ../FastVideo-Internal/data/synthetic_MixKit/videos2caption.json \
+    --validation_prompt_dir ../FastVideo-Internal/data/synthetic_MixKit/validation \
+    --gradient_checkpointing \
+    --train_batch_size=1 \
+    --num_latent_t 30 \
+    --sp_size 6 \
+    --train_sp_batch_size 1 \
+    --dataloader_num_workers 4 \
+    --gradient_accumulation_steps=2 \
+    --max_train_steps=2000 \
+    --learning_rate=1e-5 \
+    --mixed_precision=bf16 \
+    --checkpointing_steps=200 \
+    --validation_steps 100 \
+    --validation_sampling_steps 50 \
+    --checkpoints_total_limit 3 \
+    --allow_tf32 \
+    --ema_start_step 0 \
+    --cfg 0.0 \
+    --ema_decay 0.999 \
+    --log_validation \
+    --output_dir=data/outputs/SBA_hunyuan_ori_ft_1e5 \
+    --tracker_project_name SBA-Syn \
+    --num_frames 117 \
+    --num_height 768 \
+    --num_width 1280 \
+    --shift 7 \
+    --validation_guidance_scale "1.0" \
+    --training_guidance "1.0" \
+    --run_name "SBA_hunyuan_ori_ft_1e5_g1" \

local_scripts/get_videos2caption.py

@@ -0,0 +1,12 @@
+import json
+src_json = "../HD-MixKit-Hunyuan-Distill-125x768x1280/videos2caption.json"
+des_json = "./videos2caption.json"
+syn_num = 8
+with open(src_json) as f:
+    data = json.load(f)
+data = data[:syn_num]
+# from IPython import embed
+# embed()
+
+with open(des_json, "w") as f2:
+    json.dump(data, f2, indent=4, ensure_ascii=False)

local_scripts/gpu_reservation.slurm

@@ -0,0 +1,65 @@
+#!/bin/bash
+#SBATCH --job-name=hyhf-lora-5e6-r32
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=4
+#SBATCH --mem=512G
+#SBATCH --output=slurm_log/slurm.out
+#SBATCH --error=slurm_log/slurm.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+cd ~/yongqi/FastVideo
+export MASTER_PORT=29803
+torchrun --nnodes 1 --nproc_per_node 8 --master_port $MASTER_PORT \
+    fastvideo/train.py \
+    --seed 1024 \
+    --pretrained_model_name_or_path ~/data/hunyuan_diffusers \
+    --model_type hunyuan_hf \
+    --cache_dir data/.cache \
+    --data_json_path ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/videos2caption.json \
+    --validation_prompt_dir ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/validation \
+    --gradient_checkpointing \
+    --train_batch_size 4 \
+    --num_latent_t 32 \
+    --sp_size 8 \
+    --train_sp_batch_size 1 \
+    --dataloader_num_workers 4 \
+    --gradient_accumulation_steps 1 \
+    --max_train_steps 8000 \
+    --learning_rate 5e-6 \
+    --mixed_precision bf16 \
+    --checkpointing_steps 200 \
+    --validation_steps 100 \
+    --validation_sampling_steps 50 \
+    --checkpoints_total_limit 3 \
+    --allow_tf32 \
+    --ema_start_step 0 \
+    --cfg 0.0 \
+    --ema_decay 0.999 \
+    --log_validation \
+    --output_dir data/outputs/SBA_lora_5e6_r32 \
+    --tracker_project_name SBA \
+    --num_frames 125 \
+    --num_width 1280 \
+    --num_height 768 \
+    --validation_guidance_scale "1.0" \
+    --use_lora \
+    --lora_rank 32 \
+    --lora_alpha 64
+
+echo "Run completed at:- "
+date

local_scripts/synthetic_inference/exp-1.slurm

@@ -0,0 +1,60 @@
+#!/bin/bash
+#SBATCH --job-name=syn-1
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-1.out
+#SBATCH --error=slurm_log_last_dance/syn-1.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+
+# Calculate prompts per GPU
+prompt_start_idx_all=0
+num_promtps_all=200
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ./assets/prompt_vb.txt \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

local_scripts/synthetic_inference/exp-10.slurm

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=syn-10
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-10.out
+#SBATCH --error=slurm_log_last_dance/syn-10.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+# Calculate prompts per GPU
+prompt_start_idx_all=2160
+num_promtps_all=240
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/videos2caption.json \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

local_scripts/synthetic_inference/exp-11.slurm

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=syn-11
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-11.out
+#SBATCH --error=slurm_log_last_dance/syn-11.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+# Calculate prompts per GPU
+prompt_start_idx_all=2400
+num_promtps_all=240
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/videos2caption.json \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

local_scripts/synthetic_inference/exp-2.slurm

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=syn-2
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-2.out
+#SBATCH --error=slurm_log_last_dance/syn-2.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+# Calculate prompts per GPU
+prompt_start_idx_all=198
+num_promtps_all=200
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ./assets/prompt_vb.txt \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

local_scripts/synthetic_inference/exp-3.slurm

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=syn-3
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-3.out
+#SBATCH --error=slurm_log_last_dance/syn-3.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+# Calculate prompts per GPU
+prompt_start_idx_all=396
+num_promtps_all=200
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ./assets/prompt_vb.txt \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

local_scripts/synthetic_inference/exp-4.slurm

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=syn-4
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-4.out
+#SBATCH --error=slurm_log_last_dance/syn-4.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+# Calculate prompts per GPU
+prompt_start_idx_all=594
+num_promtps_all=200
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ./assets/prompt_vb.txt \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

local_scripts/synthetic_inference/exp-5.slurm

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=syn-5
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-5.out
+#SBATCH --error=slurm_log_last_dance/syn-5.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+# Calculate prompts per GPU
+prompt_start_idx_all=960
+num_promtps_all=240
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/videos2caption.json \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

local_scripts/synthetic_inference/exp-6.slurm

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=syn-6
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-6.out
+#SBATCH --error=slurm_log_last_dance/syn-6.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+# Calculate prompts per GPU
+prompt_start_idx_all=1200
+num_promtps_all=240
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/videos2caption.json \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

local_scripts/synthetic_inference/exp-7.slurm

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=syn-7
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-7.out
+#SBATCH --error=slurm_log_last_dance/syn-7.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+# Calculate prompts per GPU
+prompt_start_idx_all=1440
+num_promtps_all=240
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/videos2caption.json \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

local_scripts/synthetic_inference/exp-8.slurm

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=syn-8
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-8.out
+#SBATCH --error=slurm_log_last_dance/syn-8.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+# Calculate prompts per GPU
+prompt_start_idx_all=1680
+num_promtps_all=240
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/videos2caption.json \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

local_scripts/synthetic_inference/exp-9.slurm

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=syn-9
+#SBATCH --partition=mbzuai
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=960G
+#SBATCH --output=slurm_log_last_dance/syn-9.out
+#SBATCH --error=slurm_log_last_dance/syn-9.err
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+conda init
+source ~/conda/miniconda/bin/activate
+PYTHON_VIRTUAL_ENVIRONMENT=fastvideo
+conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+cd /mbz/users/hao.zhang/yongqi/FastVideo
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices"
+echo ""
+echo " Run started at:- "
+date
+
+# Calculate prompts per GPU
+prompt_start_idx_all=1920
+num_promtps_all=240
+num_gpus=8
+prompts_per_gpu=$((num_promtps_all / num_gpus))
+export MODEL_BASE=data/hunyuan
+
+for gpu_id in $(seq 0 $((num_gpus-1))); do
+    start_idx=$((prompt_start_idx_all + gpu_id * prompts_per_gpu))
+    end_idx=$((start_idx + prompts_per_gpu))
+    prompt_start_end_idx_single_gpu="${start_idx},${end_idx}"
+    
+    CUDA_VISIBLE_DEVICES=$gpu_id python fastvideo/sample/generate_synthetic_hunyuan.py \
+        --height 768 \
+        --width 1280 \
+        --num_frames 117 \
+        --num_inference_steps 50 \
+        --guidance_scale 1 \
+        --embedded_cfg_scale 6 \
+        --flow_shift 7 \
+        --flow-reverse \
+        --prompt_json ~/data/HD-MixKit-Hunyuan-Distill-125x768x1280/videos2caption.json \
+        --seed 1024 \
+        --output_path outputs_latents/ \
+        --model_path $MODEL_BASE \
+        --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+        --only_save_latent True \
+        --prompt_start_end_idx ${prompt_start_end_idx_single_gpu} &
+done
+
+# Wait for all background jobs to complete
+wait

scripts/inference/inference_hunyuan.sh

@@ -18,3 +18,22 @@ torchrun --nnodes=1 --nproc_per_node=$num_gpus --master_port 29503 \
     --model_path $MODEL_BASE \
     --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
     --vae-sp
+
+num_gpus=6
+export MODEL_BASE=data/hunyuan
+torchrun --nnodes=1 --nproc_per_node=$num_gpus --master_port 29503 \
+    fastvideo/sample/sample_t2v_hunyuan.py \
+    --height 768 \
+    --width 1280 \
+    --num_frames 117 \
+    --num_inference_steps 50 \
+    --guidance_scale 1 \
+    --embedded_cfg_scale 8 \
+    --flow_shift 7 \
+    --flow-reverse \
+    --prompt ./assets/prompt_mixkit.txt \
+    --seed 1024 \
+    --output_path outputs_video/mixkit-8/ \
+    --model_path $MODEL_BASE \
+    --dit-weight ${MODEL_BASE}/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt \
+    --vae-sp