update

Updated the number of GPUs used for training from 1 to 4.

.github/workflows/fastvideo-kernel-publish.yml

@@ -156,23 +156,8 @@ jobs:
           
           # Fix the wheel to be manylinux compliant
           pip install auditwheel
-          # Point auditwheel at torch libs, but do not vendor them into the wheel.
-          TORCH_LIB_DIR=$(python - <<'PY'
-          import os
-          import torch
-
-          print(os.path.join(os.path.dirname(torch.__file__), "lib"))
-          PY
-          )
-          export LD_LIBRARY_PATH="${TORCH_LIB_DIR}:${LD_LIBRARY_PATH}"
           # Target manylinux_2_35 (Ubuntu 22.04 native)
-          auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist \
-            --exclude libtorch_cuda.so \
-            --exclude libtorch_cpu.so \
-            --exclude libtorch.so \
-            --exclude libc10.so \
-            --exclude libc10_cuda.so \
-            --exclude libtorch_python.so
+          auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist
           # Move fixed wheels back to dist for upload consistency
           rm dist/*.whl
           mv fixed_dist/*.whl dist/

examples/distill/SFHunyuan1.5-T2V/distill_dmd.slurm

@@ -1,169 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=dmd_3333
-#SBATCH --partition=main
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=dmd_3333_output/dmd_3333_%j.out
-#SBATCH --error=dmd_3333_output/dmd_3333_%j.err
-#SBATCH --exclusive
-
-# Basic Info
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-export NCCL_DEBUG_SUBSYS=INIT,NET
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export TOKENIZERS_PARALLELISM=false
-# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
-export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
-# export WANDB_API_KEY='your_wandb_api_key_here'
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-
-source ~/conda/miniconda/bin/activate
-conda activate wei-fv-distill
-export HOME="/mnt/weka/home/hao.zhang/wei"
-
-# Configs
-NUM_GPUS=8
-NUM_TOTAL_GPUS=32
-
-# Model paths for Self-Forcing DMD distillation with Hunyuan1.5:
-GENERATOR_MODEL_PATH="weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v"  # Updated to Wan2.2
-REAL_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v" # Teacher model
-FAKE_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v"
-# REAL_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled"
-# FAKE_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled" # Critic model
-
-# DATA_DIR="data/test-text-preprocessing/Node_0_GPU_1_File_1/combined_parquet_dataset/"
-# DATA_DIR="/mnt/weka/home/hao.zhang/matthew/FastVideo/data/test-text-preprocessing"
-DATA_DIR="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/vidprom_16k_text_embed"
-DATA_DIR_2="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/fv-ode-preprocessing-16k-hy15-121"
-# DATA_DIR=data/crush-smol_processed_t2v/combined_parquet_dataset
-# DATA_DIR="/mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn-upload/latents_i2v/train/"
-# VALIDATION_DATASET_FILE="data/crush-smol-single_processed_t2v/validation.json"
-VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
-
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-training_args=(
-  --tracker_project_name SFhy1.5_t2v_distill_self_forcing_dmd  # Updated for Wan2.2
-  --output_dir "/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_3333_1e-5_1e-5_cfg6_corrected_scheduler"
-  --wandb_run_name "self_forcing_3333_context_forcing"
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 848
-  --enable_gradient_checkpointing_type "full"
-  --simulate_generator_forward
-  --log_visualization
-  --num_frames 81
-  --num_frame_per_block 3  # Frame generation block size for self-forcing
-  # --resume-from-checkpoint "/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_1333/checkpoint-300"
-  --init_weights_from_safetensors /mnt/weka/home/hao.zhang/wei/hy15_worldplay_df_init_3333/checkpoint-2000/transformer/diffusion_pytorch_model.safetensors
-  # --init_weights_from_safetensors /mnt/weka/home/hao.zhang/wei/hy15_distilled_ode_init_3333/checkpoint-2400/transformer/diffusion_pytorch_model.safetensors
-)
-
-parallel_args=(
-  --num_gpus $NUM_TOTAL_GPUS # 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim $NUM_TOTAL_GPUS
-)
-
-model_args=(
-  --model_path $GENERATOR_MODEL_PATH
-  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-dataset_args=(
-  --data_path "$DATA_DIR"
-  # --data_path_2 "$DATA_DIR_2"
-  --dataloader_num_workers 4
-)
-
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "8"
-  --validation_guidance_scale "1.0" # not used for dmd inference
-  --text-encoder-cpu-offload
-  # --vae_cpu_offload True
-)
-
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 100
-  --weight_only_checkpointing_steps 500
-  --weight_decay 0.01
-  --betas '0.0,0.999'
-  --max_grad_norm 1.0
-)
-
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --flow_shift 5
-  --seed 1000
-  --use_ema True
-  --ema_decay 0.99
-  --ema_start_step 200
-)
-
-dmd_args=(
-  --warp_denoising_step
-  --dmd_denoising_steps '1000,875,750,625,500,375,250,125'
-  # --dmd_denoising_steps '1000,760,520,280'
-  # --dmd_denoising_steps '1000,750,500,250'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --dfake_gen_update_ratio 5
-  --real_score_guidance_scale 3.5
-  --fake_score_learning_rate 5e-6
-  --fake_score_betas '0.0,0.999'
-)
-
-self_forcing_args=(
-  --independent_first_frame False  # Whether to treat first frame independently
-  --same_step_across_blocks True  # Whether to use same denoising step across all blocks
-  --last_step_only False  # Whether to only use the last denoising step
-  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
-  # --use-context-forcing True
-)
-
-srun torchrun \
---nnodes $SLURM_JOB_NUM_NODES \
---nproc_per_node $NUM_GPUS \
---node_rank $SLURM_PROCID \
---rdzv_backend=c10d \
---rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/training/hy15_self_forcing_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"

examples/inference/basic/basic_hy15.py

@@ -2,14 +2,14 @@ from fastvideo import VideoGenerator
 import json
 # from fastvideo.configs.sample import SamplingParam
 
-OUTPUT_PATH = "video_samples_hy15_t2v_distilled"
+OUTPUT_PATH = "video_samples_hy15"
 def main():
     # FastVideo will automatically use the optimal default arguments for the
     # model.
     # If a local path is provided, FastVideo will make a best effort
     # attempt to identify the optimal arguments.
     generator = VideoGenerator.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled",
+        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
         use_fsdp_inference=False, # set to True if GPU is out of memory
@@ -18,35 +18,15 @@ def main():
         text_encoder_cpu_offload=True,
         pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
         # image_encoder_cpu_offload=False,
-        # init_weights_from_safetensors="/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_1333_5e-5_2e-6_cfg3.5/checkpoint-500/ema/generator_ema.safetensors"
     )
 
-    # json_path = "/mnt/weka/home/hao.zhang/wei/FastVideo/data/mixkit_i2v_full_720p.json"
-    # with open(json_path, 'r') as f:
-    #     data_list = json.load(f)["data"]
-    
-    # # Now you can index into data_list however you like
-    # # For example: data_list[0], data_list[1:3], etc.
-    # for data in data_list:
-    #     prompt = data["prompt"]
-    #     image_path = data["image_path"]
-    #     generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, image_path=image_path, num_frames=121, fps=24)
-    # return
-
-    # prompt = (
-    #     "In a brightly lit studio, a photographer wearing a denim jacket focuses intently, capturing shots with a professional camera. Facing him, a model stands gracefully, adjusting her long, flowing hair with delicate movements. The scene is characterized by strong contrasts; the model's soft pink attire and gentle gestures complement the rugged, precise demeanor of the photographer. Positioned against a minimalist backdrop, the pair work seamlessly, with the camera\u2019s lens pointed directly at the model, capturing her elegance. The soft, diffused lighting casts a gentle glow on both subjects, creating an airy and ethereal atmosphere perfect for a high-fashion photo shoot."
-    # )
-
-    # video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, negative_prompt="", num_frames=121, fps=24, image_path="data/1.png")
-    # return
-
     prompt = (
         "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
         "wide with interest. The playful yet serene atmosphere is complemented by soft "
         "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
     )
 
-    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True)
+    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, negative_prompt="", num_frames=81, fps=16)
 
     prompt2 = (
         "A majestic lion strides across the golden savanna, its powerful frame "
@@ -55,7 +35,7 @@ def main():
         "embodying the raw energy of the wild. Low angle, steady tracking shot, "
         "cinematic.")
 
-    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)
+    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True, negative_prompt="", num_frames=81, fps=16)
 
 
 if __name__ == "__main__":

examples/inference/basic/basic_hyworld.py

@@ -1,224 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Basic example for HYWorld (HY-WorldPlay) video generation using FastVideo.
-
-This example replicates the same functionality as HY-WorldPlay/run.sh,
-demonstrating image-to-video generation with camera trajectory control.
-"""
-
-import time
-import math
-import numpy as np
-import imageio
-import torchvision
-from einops import rearrange
-
-from fastvideo import VideoGenerator
-from fastvideo.pipelines import ForwardBatch
-from fastvideo.utils import shallow_asdict, align_to
-from fastvideo.logger import init_logger
-
-from fastvideo.models.dits.hyworld.pose import pose_to_input, compute_latent_num
-from fastvideo.models.dits.hyworld.resolution_utils import get_resolution_from_image
-
-logger = init_logger(__name__)
-
-class HYWorldVideoGenerator(VideoGenerator):
-    """Extended VideoGenerator that adds HYWorld-specific parameters to batch.extra."""
-
-    def _generate_single_video(self, prompt: str, sampling_param=None, **kwargs):
-        """Override to add viewmats, Ks, and action to batch.extra."""
-        fastvideo_args = self.fastvideo_args
-        pipeline_config = fastvideo_args.pipeline_config
-
-        if sampling_param is None:
-            from fastvideo.configs.sample import SamplingParam
-            sampling_param = SamplingParam.from_pretrained(fastvideo_args.model_path)
-
-        # Update sampling param with kwargs
-        if kwargs:
-            for key, value in kwargs.items():
-                if hasattr(sampling_param, key):
-                    setattr(sampling_param, key, value)
-
-        # Get pose string from sampling_param or kwargs
-        pose = kwargs.get('pose', getattr(sampling_param, 'POSE', 'w-31'))
-        num_frames = kwargs.get('num_frames', getattr(sampling_param, 'num_frames', 125))
-
-        # Calculate number of latents
-        latent_num = compute_latent_num(num_frames)
-
-        # Convert pose to viewmats, Ks, and action
-        viewmats, Ks, action = pose_to_input(pose, latent_num)
-
-        # Convert to tensors and add batch dimension
-        viewmats = viewmats.unsqueeze(0)  # (1, T, 4, 4)
-        Ks = Ks.unsqueeze(0)  # (1, T, 3, 3)
-        action = action.unsqueeze(0)  # (1, T)
-
-        # Validate inputs
-        prompt = prompt.strip()
-        sampling_param = sampling_param.__class__(**shallow_asdict(sampling_param))
-        output_path = kwargs.get("output_path", sampling_param.output_path)
-        sampling_param.prompt = prompt
-
-        if sampling_param.negative_prompt is not None:
-            sampling_param.negative_prompt = sampling_param.negative_prompt.strip()
-
-        # Validate dimensions
-        if (sampling_param.height <= 0 or sampling_param.width <= 0 or
-            sampling_param.num_frames <= 0):
-            raise ValueError(
-                f"Height, width, and num_frames must be positive integers")
-
-        temporal_scale_factor = pipeline_config.vae_config.arch_config.temporal_compression_ratio
-        num_frames = sampling_param.num_frames
-        num_gpus = fastvideo_args.num_gpus
-        use_temporal_scaling_frames = pipeline_config.vae_config.use_temporal_scaling_frames
-
-        # Adjust number of frames based on number of GPUs
-        if use_temporal_scaling_frames:
-            orig_latent_num_frames = (num_frames - 1) // temporal_scale_factor + 1
-        else:
-            orig_latent_num_frames = sampling_param.num_frames // 17 * 3
-
-        if orig_latent_num_frames % fastvideo_args.num_gpus != 0:
-            if use_temporal_scaling_frames:
-                new_num_frames = (orig_latent_num_frames - 1) * temporal_scale_factor + 1
-            else:
-                divisor = math.lcm(3, num_gpus)
-                orig_latent_num_frames = (
-                    (orig_latent_num_frames + divisor - 1) // divisor) * divisor
-                new_num_frames = orig_latent_num_frames // 3 * 17
-
-            logger.info(
-                "Adjusting number of frames from %s to %s based on number of GPUs (%s)",
-                sampling_param.num_frames, new_num_frames, fastvideo_args.num_gpus)
-            sampling_param.num_frames = new_num_frames
-
-        # Calculate sizes
-        target_height = align_to(sampling_param.height, 16)
-        target_width = align_to(sampling_param.width, 16)
-
-        # Calculate latent sizes
-        latents_size = [(sampling_param.num_frames - 1) // 4 + 1,
-                        sampling_param.height // 8, sampling_param.width // 8]
-        n_tokens = latents_size[0] * latents_size[1] * latents_size[2]
-
-        # Prepare batch
-        batch = ForwardBatch(
-            **shallow_asdict(sampling_param),
-            eta=0.0,
-            n_tokens=n_tokens,
-            VSA_sparsity=fastvideo_args.VSA_sparsity,
-        )
-
-        # Add HYWorld-specific parameters to batch.extra
-        batch.extra['viewmats'] = viewmats
-        batch.extra['Ks'] = Ks
-        batch.extra['action'] = action
-        batch.extra['chunk_latent_frames'] = 16  # For bidirectional model
-
-        # Run inference
-        start_time = time.perf_counter()
-        output_batch = self.executor.execute_forward(batch, fastvideo_args)
-        samples = output_batch.output
-        logging_info = output_batch.logging_info
-
-        gen_time = time.perf_counter() - start_time
-        logger.info("Generated successfully in %.2f seconds", gen_time)
-
-        # Process outputs
-        videos = rearrange(samples, "b c t h w -> t b c h w")
-        frames = []
-        for x in videos:
-            x = torchvision.utils.make_grid(x, nrow=6)
-            x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
-            frames.append((x * 255).numpy().astype(np.uint8))
-
-        # Save video if requested
-        if batch.save_video:
-            imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
-            logger.info("Saved video to %s", output_path)
-
-        if batch.return_frames:
-            return frames
-        else:
-            return {
-                "samples": samples,
-                "frames": frames,
-                "prompts": prompt,
-                "size": (target_height, target_width, batch.num_frames),
-                "generation_time": gen_time,
-                "logging_info": logging_info,
-                "trajectory": output_batch.trajectory_latents,
-                "trajectory_timesteps": output_batch.trajectory_timesteps,
-                "trajectory_decoded": output_batch.trajectory_decoded,
-            }
-
-# Default prompt from HY-WorldPlay run.sh
-DEFAULT_PROMPT = 'A paved pathway leads towards a stone arch bridge spanning a calm body of water.  Lush green trees and foliage line the path and the far bank of the water. A traditional-style pavilion with a tiered, reddish-brown roof sits on the far shore. The water reflects the surrounding greenery and the sky.  The scene is bathed in soft, natural light, creating a tranquil and serene atmosphere. The pathway is composed of large, rectangular stones, and the bridge is constructed of light gray stone.  The overall composition emphasizes the peaceful and harmonious nature of the landscape.'
-DEFAULT_IMAGE = 'https://raw.githubusercontent.com/Tencent-Hunyuan/HY-WorldPlay/main/assets/img/test.png'
-
-def main():
-    import argparse
-
-    # pose: (a, w, s, d) - (15, 31)
-    # num_frames: (61, 125)
-    parser = argparse.ArgumentParser(description="HYWorld video generation with FastVideo")
-    parser.add_argument("--prompt", type=str, default=DEFAULT_PROMPT, help="Text prompt for video generation")
-    parser.add_argument("--image", type=str, default=DEFAULT_IMAGE, help="Path or URL to input image")
-    parser.add_argument("--pose", type=str, default='w-31', help="Pose string (e.g., 'a-31', 'w-31', 's-31', 'd-31')")
-    parser.add_argument("--output_path", type=str, default='video_samples_hyworld', help="Output video path")
-    parser.add_argument("--num-frames", type=int, default=125, help="Number of frames")
-    parser.add_argument("--seed", type=int, default=1, help="Random seed")
-    parser.add_argument("--resolution", type=str, default="480p", help="Only support 480p for now")
-    args = parser.parse_args()
-
-    # Automatically determine resolution from input image
-    HEIGHT, WIDTH = get_resolution_from_image(args.image, args.resolution)
-    print(f"Image: {args.image}")
-    print(f"Pose: {args.pose}")
-    print(f"Resolution: {HEIGHT}x{WIDTH} (from {args.resolution} buckets)")
-    print(f"Num frames: {args.num_frames}")
-    print(f"Output path: {args.output_path}")
-
-    # Initialize generator
-    print("\nInitializing VideoGenerator for HYWorld...")
-    
-    generator = HYWorldVideoGenerator.from_pretrained(
-        "FastVideo/HY-WorldPlay-Bidirectional-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True,
-        image_encoder_cpu_offload=True,
-    )
-
-    # Generate video
-    print("\nGenerating video...")
-    start_time = time.time()
-    video = generator.generate_video(
-        prompt=args.prompt,
-        image_path=args.image,
-        output_path=args.output_path,
-        save_video=True,
-        negative_prompt="",
-        num_frames=args.num_frames,
-        fps=24,
-        height=HEIGHT,
-        width=WIDTH,
-        seed=args.seed,
-        pose=args.pose,
-    )
-    elapsed = time.time() - start_time
-
-    print(f"\nVideo generated successfully!")
-    print(f"Saved to: {args.output_path}")
-    print(f"Time: {elapsed:.2f}s")
-
-
-if __name__ == "__main__":
-    main()

examples/inference/basic/basic_ltx2.py

@@ -1,34 +0,0 @@
-from fastvideo import VideoGenerator
-
-PROMPT = (
-    "A warm sunny backyard. The camera starts in a tight cinematic close-up "
-    "of a woman and a man in their 30s, facing each other with serious "
-    "expressions. The woman, emotional and dramatic, says softly, \"That's "
-    "it... Dad's lost it. And we've lost Dad.\" The man exhales, slightly "
-    "annoyed: \"Stop being so dramatic, Jess.\" A beat. He glances aside, "
-    "then mutters defensively, \"He's just having fun.\" The camera slowly "
-    "pans right, revealing the grandfather in the garden wearing enormous "
-    "butterfly wings, waving his arms in the air like he's trying to take "
-    "off. He shouts, \"Wheeeew!\" as he flaps his wings with full commitment. "
-    "The woman covers her face, on the verge of tears. The tone is deadpan, "
-    "absurd, and quietly tragic."
-)
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LTX2-Distilled-Diffusers",
-        num_gpus=1,
-    )
-
-    output_path = "outputs_video/ltx2_basic/output_ltx2_distilled_t2v.mp4"
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path=output_path,
-        save_video=True,
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()

examples/training/consistency_finetune/causal_ode_init/finetune_ode_init.sh

@@ -1,38 +1,36 @@
 #!/bin/bash
 
 export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=offline
+export WANDB_MODE=online
 export TOKENIZERS_PARALLELISM=false
 
-MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v"
-DATA_DIR="data/ode-preprocessing-hy15-test/"
-VALIDATION_DATASET_FILE="data/validation_64.json"
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init/"
+VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
 NUM_GPUS=1
 # IP=[MASTER NODE IP]
 
 # Training arguments
 training_args=(
   --tracker_project_name "wan_ode_init"
-  --output_dir "ode_init_hy15_test"
-  --wandb_run_name "vidprom_bz128_1e-5"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  # --warp_denoising_step
-  # --log_visualization
-  --max_train_steps 6001
+  --output_dir "wan_ode_init_crush_smol"
+  --override_transformer_cls_name "CausalWanTransformer3DModel"
+  --wandb_run_name "wan_ode_init_crush_smol"
+  --max_train_steps 6000
   --train_batch_size 1
   --train_sp_batch_size 1
   --gradient_accumulation_steps 1
-  --num_latent_t 19
+  --num_latent_t 21
   --num_height 480
   --num_width 832
-  --num_frames 73
-  --dmd_denoising_steps "1000,750,500,250"
+  --num_frames 77
+  --warp_denoising_step
   --enable_gradient_checkpointing_type "full"
 )
 
 # Parallel arguments
 parallel_args=(
-  --num_gpus 1
+  --num_gpus $NUM_GPUS
   --sp_size 1
   --tp_size 1
   --hsdp_replicate_dim 1
@@ -53,17 +51,18 @@ dataset_args=(
 
 # Validation arguments
 validation_args=(
-  # --log_validation
+  --log_validation
   --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 200
+  --validation_steps 50
   --validation_sampling_steps "50"
   --validation_guidance_scale "6.0"
 )
 
 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 1e-5
+  --learning_rate 6e-6
   --mixed_precision "bf16"
+  --weight_only_checkpointing_steps 1000
   --training_state_checkpointing_steps 1000
   --weight_decay 1e-4
   --max_grad_norm 1.0
@@ -79,7 +78,6 @@ miscellaneous_args=(
   --dit_precision "fp32"
   --num_euler_timesteps 50
   --ema_start_step 0
-  # --enable_gradient_checkpointing_type "full"
 )
 
 # If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t

examples/training/consistency_finetune/causal_ode_init/hy15_finetune_ode_init.slurm

@@ -1,132 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=hy15_ode_3333
-#SBATCH --partition=main
-#SBATCH --nodes=8
-#SBATCH --ntasks=8
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=hy15_ode_3333_output/hy15_ode_3333_%j.out
-#SBATCH --error=hy15_ode_3333_output/hy15_ode_3333_%j.err
-#SBATCH --exclusive
-
-# Basic Info
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-export NCCL_DEBUG_SUBSYS=INIT,NET
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export TOKENIZERS_PARALLELISM=false
-# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
-export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
-# export WANDB_API_KEY='your_wandb_api_key_here'
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-
-source ~/conda/miniconda/bin/activate
-conda activate wei-fv-distill
-export HOME="/mnt/weka/home/hao.zhang/wei"
-
-# Configs
-NUM_GPUS=8
-NUM_TOTAL_GPUS=64
-
-MODEL_PATH="weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v"
-DATA_DIR="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/fv-ode-preprocessing-16k-hy15-121"
-VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "hy15_ode_init"
-  --output_dir "/mnt/weka/home/hao.zhang/wei/hy15_ode_init_1333_new"
-  --wandb_run_name "hy15_ode_init_1333_new"
-  --warp_denoising_step
-  --dmd_denoising_steps '1000,760,520,280,0'
-  --log_visualization
-  --visualization_steps 100
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 2
-  --num_latent_t 31
-  --num_height 480
-  --num_width 848
-  --num_frames 121
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_TOTAL_GPUS # 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim $NUM_TOTAL_GPUS
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-  --text-encoder-cpu-offload
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 4
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 400
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-validation_args=(
-  # --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 10
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "1.0" # not used for dmd inference
-  # --vae_cpu_offload True
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 6
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-srun torchrun \
---nnodes $SLURM_JOB_NUM_NODES \
---nproc_per_node $NUM_GPUS \
---node_rank $SLURM_PROCID \
---rdzv_backend=c10d \
---rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/training/ode_causal_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"

examples/training/finetune/hunyuan15_t2v/download_dataset.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"

examples/training/finetune/hunyuan15_t2v/preprocess_hunyuan_data_t2v.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v"
+DATASET_PATH="data/crush-smol"
+OUTPUT_DIR="data/crush-smol_processed_t2v_hunyuan15/"
+
+torchrun --nproc_per_node=$GPU_NUM --master_port=29513 \
+    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
+    --model_path $MODEL_PATH \
+    --mode preprocess \
+    --workload_type t2v \
+    --preprocess.video_loader_type torchvision \
+    --preprocess.dataset_type merged \
+    --preprocess.dataset_path $DATASET_PATH \
+    --preprocess.dataset_output_dir $OUTPUT_DIR \
+    --preprocess.preprocess_video_batch_size 2 \
+    --preprocess.dataloader_num_workers 0 \
+    --preprocess.max_height 480 \
+    --preprocess.max_width 832 \
+    --preprocess.num_frames 61 \
+    --preprocess.train_fps 15 \
+    --preprocess.samples_per_file 8 \
+    --preprocess.flush_frequency 8 \
+    --preprocess.video_length_tolerance_range 5

examples/training/finetune/hunyuan_t2v/download_dataset.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"

examples/training/finetune/hunyuan_t2v/preprocess_hunyuan_data_t2v.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="hunyuanvideo-community/HunyuanVideo"
+DATASET_PATH="data/crush-smol"
+OUTPUT_DIR="data/crush-smol_processed_t2v_hunyuan/"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
+    --model_path $MODEL_PATH \
+    --mode preprocess \
+    --workload_type t2v \
+    --preprocess.video_loader_type torchvision \
+    --preprocess.dataset_type merged \
+    --preprocess.dataset_path $DATASET_PATH \
+    --preprocess.dataset_output_dir $OUTPUT_DIR \
+    --preprocess.preprocess_video_batch_size 2 \
+    --preprocess.dataloader_num_workers 0 \
+    --preprocess.max_height 480 \
+    --preprocess.max_width 832 \
+    --preprocess.num_frames 77 \
+    --preprocess.train_fps 16 \
+    --preprocess.samples_per_file 8 \
+    --preprocess.flush_frequency 8 \
+    --preprocess.video_length_tolerance_range 5

fastvideo-kernel/README.md

@@ -40,17 +40,6 @@ out = video_sparse_attn(q, k, v, block_sizes, block_sizes, topk=5)
 out = moba_attn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_k, ...)
 ```
 
-## Benchmark
-
-### VSA (block-sparse) TFLOPs
-
-After building/installing `fastvideo-kernel`, run:
-
-```bash
-cd fastvideo-kernel
-python benchmarks/bench_vsa.py --batch_size 1 --num_heads 16 --head_dim 128 --q_seq_lens 49152 --topk 64
-```
-
 ### TurboDiffusion Kernels
 
 This package also includes kernels from [TurboDiffusion](https://github.com/thu-ml/TurboDiffusion), including INT8 GEMM, Quantization, RMSNorm and LayerNorm.

fastvideo-kernel/benchmarks/bench_vsa.py

@@ -1,166 +0,0 @@
-#!/usr/bin/env python3
-"""
-Benchmark VSA *wrapper* performance (forward + backward) and report TFLOPs.
-
-This script benchmarks the autograd-enabled wrapper:
-  - fastvideo_kernel.block_sparse_attn.block_sparse_attn
-
-So measured time includes wrapper overhead (map->index conversion, dispatch) plus kernel time.
-"""
-
-from __future__ import annotations
-
-import argparse
-import os
-import random
-from typing import Tuple, Callable
-
-import numpy as np
-import torch
-
-try:
-    from triton.testing import do_bench
-except Exception as e:  # pragma: no cover
-    raise ImportError("This benchmark requires triton (for triton.testing.do_bench).") from e
-
-
-BLOCK_M = 64
-BLOCK_N = 64
-
-
-def set_seed(seed: int = 42) -> None:
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-
-def parse_arguments() -> argparse.Namespace:
-    p = argparse.ArgumentParser(description="Benchmark FastVideo VSA block-sparse attention")
-    p.add_argument("--batch_size", type=int, default=1)
-    p.add_argument("--num_heads", type=int, default=12)
-    p.add_argument("--head_dim", type=int, default=128, choices=[64, 128])
-    p.add_argument("--topk", type=int, default=None, help="KV blocks per Q block (default: ~90%% sparsity)")
-    p.add_argument("--q_seq_lens", type=int, nargs="+", default=[49152], help="Q sequence lengths (must be /64)")
-    p.add_argument("--kv_seq_lens", type=int, nargs="+", default=None, help="KV sequence lengths (defaults to q_seq_len)")
-    p.add_argument("--warmup", type=int, default=5)
-    p.add_argument("--rep", type=int, default=20)
-    p.add_argument("--seed", type=int, default=42)
-    p.add_argument("--dtype", type=str, default="bf16", choices=["bf16", "fp16"])
-    p.add_argument("--force_triton", action="store_true", help="Force wrapper to use Triton path (if supported by shapes).")
-    return p.parse_args()
-
-
-def create_qkv(batch: int, heads: int, q_len: int, kv_len: int, d: int, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    q = torch.randn(batch, heads, q_len, d, dtype=dtype, device="cuda")
-    k = torch.randn(batch, heads, kv_len, d, dtype=dtype, device="cuda")
-    v = torch.randn(batch, heads, kv_len, d, dtype=dtype, device="cuda")
-    return q, k, v
-
-
-def make_block_map(bs: int, h: int, num_q_blocks: int, num_kv_blocks: int, topk: int) -> torch.Tensor:
-    # block_map: [bs, h, num_q_blocks, num_kv_blocks] bool
-    scores = torch.rand(bs, h, num_q_blocks, num_kv_blocks, device="cuda")
-    topk = min(max(1, topk), num_kv_blocks)
-    idx = torch.topk(scores, topk, dim=-1).indices
-    block_map = torch.zeros(bs, h, num_q_blocks, num_kv_blocks, dtype=torch.bool, device="cuda")
-    block_map.scatter_(-1, idx, True)
-    return block_map
-
-
-def flops_sparse_attention(bs: int, h: int, d: int, q_len: int, topk_blocks: int, block_n: int) -> float:
-    # Approx: QK^T + PV, each is ~2*bs*h*q_len*(topk_blocks*block_n)*d
-    return 4.0 * bs * h * d * q_len * (topk_blocks * block_n)
-
-
-def bench_ms(fn: Callable[[], object], warmup: int, rep: int) -> float:
-    return do_bench(fn, warmup=warmup, rep=rep, quantiles=None)
-
-
-def main() -> None:
-    args = parse_arguments()
-    set_seed(args.seed)
-
-    dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float16
-
-    if args.force_triton:
-        os.environ["FASTVIDEO_KERNEL_VSA_FORCE_TRITON"] = "1"
-
-    from fastvideo_kernel.block_sparse_attn import block_sparse_attn
-
-    bs, h, d = args.batch_size, args.num_heads, args.head_dim
-    kv_seq_lens = args.kv_seq_lens
-    if kv_seq_lens is None:
-        kv_seq_lens = args.q_seq_lens
-    if len(kv_seq_lens) != len(args.q_seq_lens):
-        raise ValueError("kv_seq_lens must have the same number of entries as q_seq_lens (or be omitted).")
-
-    print("VSA Block-Sparse Attention Benchmark (WRAPPER)")
-    print(f"device: {torch.cuda.get_device_name(0)}")
-    print(f"batch={bs}, heads={h}, head_dim={d}, dtype={args.dtype}")
-    print(f"BLOCK_M={BLOCK_M}, BLOCK_N={BLOCK_N}")
-    print("NOTE: timings include wrapper overhead (map->index + dispatch).")
-    if args.force_triton:
-        print("dispatch: forced Triton (FASTVIDEO_KERNEL_VSA_FORCE_TRITON=1)")
-    else:
-        print("dispatch: SM90 if available, else Triton")
-
-    for q_len, kv_len in zip(args.q_seq_lens, kv_seq_lens):
-        if q_len % BLOCK_M != 0 or kv_len % BLOCK_N != 0:
-            print(f"[skip] q_len={q_len}, kv_len={kv_len} must be divisible by 64")
-            continue
-
-        num_q_blocks = q_len // BLOCK_M
-        num_kv_blocks = kv_len // BLOCK_N
-        topk = args.topk if args.topk is not None else max(1, num_kv_blocks // 10)
-        topk = min(topk, num_kv_blocks)
-
-        print("\n" + "=" * 80)
-        print(f"q_len={q_len}, kv_len={kv_len}, num_q_blocks={num_q_blocks}, num_kv_blocks={num_kv_blocks}, topk={topk}")
-
-        q, k, v = create_qkv(bs, h, q_len, kv_len, d, dtype)
-        block_map = make_block_map(bs, h, num_q_blocks, num_kv_blocks, topk)
-
-        # Variable block sizes: default full blocks (64 tokens per KV block)
-        variable_block_sizes = torch.full((num_kv_blocks,), BLOCK_N, dtype=torch.int32, device="cuda")
-
-        def _fwd():
-            return block_sparse_attn(q, k, v, block_map, variable_block_sizes)
-
-        fwd_ms = bench_ms(_fwd, warmup=args.warmup, rep=args.rep)
-
-        # Backward benchmark (wrapper autograd). We build the graph once, then repeatedly run backward
-        # on the retained graph so bwd timing excludes the forward compute.
-        q_ = q.detach().requires_grad_(True)
-        k_ = k.detach().requires_grad_(True)
-        v_ = v.detach().requires_grad_(True)
-        o_, _aux_ = block_sparse_attn(q_, k_, v_, block_map, variable_block_sizes)
-        og = torch.randn_like(o_)
-        loss = (o_ * og).sum()
-
-        for _ in range(max(1, args.warmup // 2)):
-            torch.autograd.grad(loss, (q_, k_, v_), retain_graph=True)
-        torch.cuda.synchronize()
-
-        bwd_ms = bench_ms(
-            lambda: torch.autograd.grad(loss, (q_, k_, v_), retain_graph=True),
-            warmup=0,
-            rep=max(5, args.rep // 2),
-        )
-
-        flops = flops_sparse_attention(bs, h, d, q_len, topk, BLOCK_N)
-        fwd_tflops = flops / fwd_ms * 1e-12 * 1e3
-        # Rough backward multiplier (attention backward typically ~2-3x forward)
-        bwd_tflops = (2.5 * flops) / bwd_ms * 1e-12 * 1e3
-
-        print(f"fwd(wrapper): {fwd_ms:.3f} ms  | {fwd_tflops:.2f} TFLOPs (approx)")
-        print(f"bwd(wrapper): {bwd_ms:.3f} ms  | {bwd_tflops:.2f} TFLOPs (approx)")
-
-
-if __name__ == "__main__":
-    if not torch.cuda.is_available():
-        raise RuntimeError("CUDA is required for this benchmark.")
-    main()
-
-

fastvideo-kernel/csrc/turbodiffusion/norm/layernorm.cu

@@ -4,7 +4,6 @@
 #include <torch/all.h>
 #include <torch/python.h>
 #include <cutlass/cutlass.h>
-#include <cutlass/numeric_types.h>
 #include "common/common.hpp"
 #include "norm/layernorm.hpp"
 
@@ -15,6 +14,10 @@ auto layer_norm(
   std::optional<at::Tensor const> const B,
   std::optional<at::Tensor> Output
 ) {
+  using ElementIn = float;
+  using ElementOut = float;
+  using ElementWeight = float;
+
   int64_t const m = Input.size(0);
   int64_t const n = Input.size(1);
   torch::Device const input_device = Input.device();
@@ -23,70 +26,31 @@ auto layer_norm(
     Output.emplace(
       torch::empty(
         {m, n},
-        torch::TensorOptions().device(input_device).dtype(Input.scalar_type())
+        torch::TensorOptions().device(input_device).dtype(torch::kFloat32)
       )
     );
   }
 
-  TORCH_CHECK(Output.value().scalar_type() == Input.scalar_type(),
-              "Output dtype must match Input dtype. Got Output=",
-              Output.value().scalar_type(), ", Input=", Input.scalar_type());
-  if (W.has_value()) {
-    TORCH_CHECK(W.value().scalar_type() == Input.scalar_type(),
-                "W dtype must match Input dtype. Got W=",
-                W.value().scalar_type(), ", Input=", Input.scalar_type());
-  }
-  if (B.has_value()) {
-    TORCH_CHECK(B.value().scalar_type() == Input.scalar_type(),
-                "B dtype must match Input dtype. Got B=",
-                B.value().scalar_type(), ", Input=", Input.scalar_type());
-  }
 
   void *Iptr = Input.data_ptr();
   void *Wptr = W.has_value() ? W.value().data_ptr() : nullptr;
   void *Bptr = B.has_value() ? B.value().data_ptr() : nullptr;
   void *Optr = Output.value().data_ptr();
 
-  auto stream = at::cuda::getCurrentCUDAStream().stream();
-  if (Input.scalar_type() == at::kHalf) {
-    using ElementIn = cutlass::half_t;
-    using ElementOut = cutlass::half_t;
-    using ElementWeight = cutlass::half_t;
-    BOOL_SWITCH(B.has_value(), BIAS, [&]{
-      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
-        CONFIG_SWITCH(n, [&]{
-          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
-        });
+  BOOL_SWITCH(B.has_value(), BIAS, [&]{
+    BOOL_SWITCH(W.has_value(), AFFINE, [&]{
+    CONFIG_SWITCH(n, [&]{
+    layernorm<
+    ElementIn, ElementOut, ElementWeight,
+    AFFINE, BIAS,
+    MAX_HIDDEN_SIZE, NUM_THR_PER_CTA> (
+      Iptr, Wptr, Bptr,
+      Optr, eps, m, n,
+      at::cuda::getCurrentCUDAStream().stream()
+        );
       });
     });
-  } else if (Input.scalar_type() == at::kBFloat16) {
-    using ElementIn = cutlass::bfloat16_t;
-    using ElementOut = cutlass::bfloat16_t;
-    using ElementWeight = cutlass::bfloat16_t;
-    BOOL_SWITCH(B.has_value(), BIAS, [&]{
-      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
-        CONFIG_SWITCH(n, [&]{
-          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
-        });
-      });
-    });
-  } else if (Input.scalar_type() == at::kFloat) {
-    using ElementIn = float;
-    using ElementOut = float;
-    using ElementWeight = float;
-    BOOL_SWITCH(B.has_value(), BIAS, [&]{
-      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
-        CONFIG_SWITCH(n, [&]{
-          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
-        });
-      });
-    });
-  } else {
-    TORCH_CHECK(false, "Unsupported dtype for layer_norm_cuda: ", Input.scalar_type());
-  }
+  });
   
     
 

fastvideo-kernel/csrc/turbodiffusion/norm/layernorm.hpp

@@ -68,22 +68,9 @@ public:
     // mean reduction
     float u = _reduce_sum(x, shared_data) / params.n;
 
-    // IMPORTANT:
-    // Loader pads out-of-range lanes with 0. That is OK for the sum, but after
-    // subtracting mean, those padded lanes become -u and would incorrectly
-    // contribute to the variance. Mask them back to 0 before variance reduction.
-    // We launch exactly NumThrPerCta threads for a 1xMaxHiddenSize tile,
-    // so each thread is responsible for a contiguous chunk in N.
-    int thr_n_offset = tidx * NumElementPerThread;
     CUTLASS_PRAGMA_UNROLL
-    for (int i = 0; i < NumElementPerThread; ++i) {
-      int idx = thr_n_offset + i;
-      if (idx < params.n) {
-        x[i] -= u;
-      } else {
-        x[i] = 0.f;
-      }
-    }
+    for (int i = 0; i < NumElementPerThread; ++i)
+      x[i] -= u;
 
     __syncthreads();
     // var reduction

fastvideo-kernel/csrc/turbodiffusion/norm/rmsnorm.cu

@@ -4,7 +4,6 @@
 #include <torch/all.h>
 #include <torch/python.h>
 #include <cutlass/cutlass.h>
-#include <cutlass/numeric_types.h>
 #include <pybind11/pybind11.h>
 
 #include "common/common.hpp"
@@ -17,6 +16,10 @@ auto rms_norm(
   std::optional<at::Tensor>& Output
 ) {
   
+  using ElementIn = float;
+  using ElementOut = float;
+  using ElementWeight = float;
+  
   int64_t const m = Input.size(0);
   int64_t const n = Input.size(1);
   torch::Device const input_device = Input.device();
@@ -25,51 +28,27 @@ auto rms_norm(
     Output.emplace(
       torch::empty(
         {m, n},
-        torch::TensorOptions().device(input_device).dtype(Input.scalar_type())
+        torch::TensorOptions().device(input_device).dtype(torch::kFloat32)
       )
     );
   }
 
-  TORCH_CHECK(Output.value().scalar_type() == Input.scalar_type(),
-              "Output dtype must match Input dtype. Got Output=",
-              Output.value().scalar_type(), ", Input=", Input.scalar_type());
-  if (Weight.has_value()) {
-    TORCH_CHECK(Weight.value().scalar_type() == Input.scalar_type(),
-                "Weight dtype must match Input dtype. Got Weight=",
-                Weight.value().scalar_type(), ", Input=", Input.scalar_type());
-  }
-
   void *Iptr = Input.data_ptr();
   void *Wptr = Weight.has_value() ? Weight.value().data_ptr() : nullptr;
   void *Optr = Output.value().data_ptr();
 
-  if (Input.scalar_type() == at::kHalf) {
-    using ElementIn = cutlass::half_t;
-    using ElementOut = cutlass::half_t;
-    using ElementWeight = cutlass::half_t;
-    CONFIG_SWITCH(n, [&]{
-      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
-    });
-  } else if (Input.scalar_type() == at::kBFloat16) {
-    using ElementIn = cutlass::bfloat16_t;
-    using ElementOut = cutlass::bfloat16_t;
-    using ElementWeight = cutlass::bfloat16_t;
-    CONFIG_SWITCH(n, [&]{
-      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
-    });
-  } else if (Input.scalar_type() == at::kFloat) {
-    using ElementIn = float;
-    using ElementOut = float;
-    using ElementWeight = float;
-    CONFIG_SWITCH(n, [&]{
-      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
-    });
-  } else {
-    TORCH_CHECK(false, "Unsupported dtype for rms_norm_cuda: ", Input.scalar_type());
-  }
+
+  CONFIG_SWITCH(n, [&]{
+    rmsnorm<
+    ElementIn, ElementOut, ElementWeight,
+    MAX_HIDDEN_SIZE, NUM_THR_PER_CTA
+    > (
+      Iptr, Wptr,
+      Optr, 
+      eps, m, n, 
+      at::cuda::getCurrentCUDAStream().stream()
+    );
+});
   
 
   return Output;

fastvideo-kernel/pyproject.toml

@@ -9,7 +9,7 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "fastvideo-kernel"
-version = "0.2.5"
+version = "0.2.3"
 description = "Unified CUDA kernels for FastVideo"
 readme = "README.md"
 requires-python = ">=3.10"

fastvideo-kernel/python/fastvideo_kernel/block_sparse_attn.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import os
 from typing import Tuple
 
 import torch
@@ -24,18 +23,13 @@ def _is_sm90() -> bool:
     return major == 9 and minor == 0
 
 
-def _force_triton() -> bool:
-    # Force Triton even on SM90 and even if the compiled extension is available.
-    # Useful for CI / debugging / parity testing.
-    return os.environ.get("FASTVIDEO_KERNEL_VSA_FORCE_TRITON", "0") == "1"
-
-
-def _map_to_index(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+def _map_to_index_torch(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
     """
-    Preferred map->index conversion used by the wrapper.
-
-    This wrapper **requires** the Triton implementation.
-    If Triton (or the Triton map_to_index module) is not available, it raises.
+    Pure-torch (no triton) conversion:
+      block_map: [B, H, Q, KV] bool  (or [H, Q, KV] which will be treated as B=1)
+      returns:
+        index: [B, H, Q, KV] int32 (packed KV indices, -1 padding)
+        num:   [B, H, Q] int32 (#kv blocks per q block)
     """
     if block_map.dim() == 3:
         block_map = block_map.unsqueeze(0)
@@ -44,17 +38,20 @@ def _map_to_index(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
     if block_map.dtype != torch.bool:
         block_map = block_map.to(torch.bool)
 
-    if not block_map.is_cuda:
-        raise RuntimeError("block_map must be a CUDA tensor (Triton map_to_index required).")
+    B, H, Q, KV = block_map.shape
+    index = torch.full((B, H, Q, KV), -1, dtype=torch.int32, device=block_map.device)
+    num = torch.zeros((B, H, Q), dtype=torch.int32, device=block_map.device)
 
-    try:
-        from fastvideo_kernel.triton_kernels.index import map_to_index as triton_map_to_index  # local import
-    except Exception as e:
-        raise ImportError(
-            "Triton map_to_index is required but not available. "
-            "Ensure Triton is installed and fastvideo_kernel.triton_kernels.index is importable."
-        ) from e
-    return triton_map_to_index(block_map)
+    # Small sizes in practice (B=1, H<=16, Q/KV<=64), so a Python loop is fine.
+    for b in range(B):
+        for h in range(H):
+            for q in range(Q):
+                kv_idx = torch.nonzero(block_map[b, h, q], as_tuple=False).flatten().to(torch.int32)
+                n = int(kv_idx.numel())
+                if n:
+                    index[b, h, q, :n] = kv_idx
+                num[b, h, q] = n
+    return index, num
 
 
 @torch.library.custom_op(
@@ -73,7 +70,7 @@ def block_sparse_attn_triton(
     k = k.contiguous()
     v = v.contiguous()
     block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index(block_map)
+    q2k_idx, q2k_num = _map_to_index_torch(block_map)
 
     from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
         triton_block_sparse_attn_forward,
@@ -83,7 +80,6 @@ def block_sparse_attn_triton(
     return o, M
 
 
-
 @torch.library.register_fake("fastvideo_kernel::block_sparse_attn_triton")
 def _block_sparse_attn_triton_fake(
     q: torch.Tensor,
@@ -114,8 +110,8 @@ def block_sparse_attn_backward_triton(
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     grad_output = grad_output.contiguous()
     block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index(block_map)
-    k2q_idx, k2q_num = _map_to_index(block_map.transpose(-1, -2).contiguous())
+    q2k_idx, q2k_num = _map_to_index_torch(block_map)
+    k2q_idx, k2q_num = _map_to_index_torch(block_map.transpose(-1, -2).contiguous())
 
     from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
         triton_block_sparse_attn_backward,
@@ -179,7 +175,7 @@ def block_sparse_attn_sm90(
     k_padded = k_padded.contiguous()
     v_padded = v_padded.contiguous()
     block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index(block_map)
+    q2k_idx, q2k_num = _map_to_index_torch(block_map)
 
     o_padded, lse_padded = block_sparse_fwd(
         q_padded, k_padded, v_padded, q2k_idx, q2k_num, variable_block_sizes.int()
@@ -221,7 +217,7 @@ def block_sparse_attn_backward_sm90(
 
     grad_output_padded = grad_output_padded.contiguous()
     block_map = block_map.to(torch.bool)
-    k2q_idx, k2q_num = _map_to_index(block_map.transpose(-1, -2).contiguous())
+    k2q_idx, k2q_num = _map_to_index_torch(block_map.transpose(-1, -2).contiguous())
 
     dq, dk, dv = block_sparse_bwd(
         q_padded,
@@ -285,7 +281,7 @@ def block_sparse_attn(
     - Otherwise: uses Triton implementation (requires q/k/v to have same padded length today).
     """
     block_sparse_fwd, block_sparse_bwd = _get_sm90_ops()
-    if (not _force_triton()) and _is_sm90() and (block_sparse_fwd is not None) and (block_sparse_bwd is not None):
+    if _is_sm90() and (block_sparse_fwd is not None) and (block_sparse_bwd is not None):
         return block_sparse_attn_sm90(q, k, v, block_map, variable_block_sizes)
     # Triton path: generally assumes q/k/v share the same padded length
     if q.shape[2] != k.shape[2] or q.shape[2] != v.shape[2]:

fastvideo-kernel/python/fastvideo_kernel/ops.py

@@ -46,22 +46,14 @@ def sliding_tile_attention(
     flag = shape_map[seq_shape]
 
     for head_idx, (t, h, w) in enumerate(window_size):
-        # Per-head slices are not contiguous in the batch dimension when batch>1
-        # (they keep the original head-stride). The TK kernel assumes contiguous
-        # [B, H, S, D] layout, so we materialize a contiguous [B,1,S,D] view.
-        q_h = q[:, head_idx:head_idx + 1].contiguous()
-        k_h = k[:, head_idx:head_idx + 1].contiguous()
-        v_h = v[:, head_idx:head_idx + 1].contiguous()
-        o_h = torch.empty_like(q_h)
         sta_fwd(
-            q_h, k_h,
-            v_h, o_h,
+            q[:, head_idx:head_idx + 1], k[:, head_idx:head_idx + 1],
+            v[:, head_idx:head_idx + 1], output[:, head_idx:head_idx + 1],
             t, h, w, text_length, False, has_text, flag
         )
-        output[:, head_idx:head_idx + 1] = o_h
 
     if has_text:
-        sta_fwd(q.contiguous(), k.contiguous(), v.contiguous(), output, 3, 3, 3, text_length, True, True, flag)
+        sta_fwd(q, k, v, output, 3, 3, 3, text_length, True, True, flag)
 
     return output[:, :, :seq_length]
 

fastvideo-kernel/python/fastvideo_kernel/version.py

@@ -1 +1 @@
-__version__ = "0.2.5"
+__version__ = "0.2.3"

fastvideo/configs/models/__init__.py

@@ -2,16 +2,5 @@ from fastvideo.configs.models.base import ModelConfig
 from fastvideo.configs.models.dits.base import DiTConfig
 from fastvideo.configs.models.encoders.base import EncoderConfig
 from fastvideo.configs.models.vaes.base import VAEConfig
-from fastvideo.configs.models.audio import (LTX2AudioDecoderConfig,
-                                            LTX2AudioEncoderConfig,
-                                            LTX2VocoderConfig)
 
-__all__ = [
-    "ModelConfig",
-    "VAEConfig",
-    "DiTConfig",
-    "EncoderConfig",
-    "LTX2AudioEncoderConfig",
-    "LTX2AudioDecoderConfig",
-    "LTX2VocoderConfig",
-]
+__all__ = ["ModelConfig", "VAEConfig", "DiTConfig", "EncoderConfig"]

fastvideo/configs/models/audio/__init__.py

@@ -1,13 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from fastvideo.configs.models.audio.ltx2_audio_vae import (
-    LTX2AudioDecoderConfig,
-    LTX2AudioEncoderConfig,
-    LTX2VocoderConfig,
-)
-
-__all__ = [
-    "LTX2AudioEncoderConfig",
-    "LTX2AudioDecoderConfig",
-    "LTX2VocoderConfig",
-]

fastvideo/configs/models/audio/ltx2_audio_vae.py

@@ -1,31 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LTX-2 audio VAE and vocoder configuration.
-"""
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.base import ArchConfig, ModelConfig
-
-
-@dataclass
-class LTX2AudioArchConfig(ArchConfig):
-    architectures: list[str] = field(default_factory=list)
-
-
-@dataclass
-class LTX2AudioEncoderConfig(ModelConfig):
-    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
-        architectures=["LTX2AudioEncoder"]))
-
-
-@dataclass
-class LTX2AudioDecoderConfig(ModelConfig):
-    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
-        architectures=["LTX2AudioDecoder"]))
-
-
-@dataclass
-class LTX2VocoderConfig(ModelConfig):
-    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
-        architectures=["LTX2Vocoder"]))

fastvideo/configs/models/dits/__init__.py

@@ -3,13 +3,11 @@ from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
 from fastvideo.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
 from fastvideo.configs.models.dits.hunyuanvideo15 import HunyuanVideo15Config
 from fastvideo.configs.models.dits.longcat import LongCatVideoConfig
-from fastvideo.configs.models.dits.ltx2 import LTX2VideoConfig
 from fastvideo.configs.models.dits.stepvideo import StepVideoConfig
 from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
-from fastvideo.configs.models.dits.hyworld import HYWorldConfig
 
 __all__ = [
     "HunyuanVideoConfig", "HunyuanVideo15Config", "WanVideoConfig",
     "StepVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig",
-    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig"
+    "LongCatVideoConfig"
 ]

fastvideo/configs/models/dits/hunyuanvideo15.py

@@ -26,9 +26,6 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
 
     param_names_mapping: dict = field(
         default_factory=lambda: {
-            r"^cond_type_embed\.(.*)$":
-            r"cond_type_embed.\1",
-
             # 1. context_embedder.time_text_embed submodules (specific rules, applied first):
             r"^context_embedder\.time_text_embed\.timestep_embedder\.linear_1\.(.*)$":
             r"txt_in.t_embedder.mlp.fc_in.\1",
@@ -58,16 +55,6 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
             r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
             r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.norm_out\.linear\.(.*)$":
             r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
-            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.self_attn_qkv\.(.*)$":
-            r"txt_in.refiner_blocks.\1.self_attn_qkv.\2",
-            r"^image_embedder\.linear_1\.(.*)$":
-            r"image_embedder.linear_1.\1",
-            r"^image_embedder\.linear_2\.(.*)$":
-            r"image_embedder.linear_2.\1",
-            r"^image_embedder\.norm_in\.(.*)$":
-            r"image_embedder.norm_in.\1",
-            r"^image_embedder\.norm_out\.(.*)$":
-            r"image_embedder.norm_out.\1",
 
             # 2. txt_in_2 mapping:
             r"^context_embedder_2\.(.*)$":
@@ -157,12 +144,6 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
     exclude_lora_layers: list[str] = field(
         default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in"])
 
-    # Causal HunyuanVideo1.5
-    local_attn_size: int = -1  # Window size for temporal local attention (-1 indicates global attention)
-    sink_size: int = 0  # Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache
-    num_frames_per_block: int = 3
-    sliding_window_num_frames: int = 31
-
     def __post_init__(self):
         super().__post_init__()
         self.hidden_size: int = self.attention_head_dim * self.num_attention_heads

fastvideo/configs/models/dits/hyworld.py

@@ -1,207 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-
-
-def is_double_block(n: str, m) -> bool:
-    return "double" in n and str.isdigit(n.split(".")[-1])
-
-
-def is_single_block(n: str, m) -> bool:
-    return "single" in n and str.isdigit(n.split(".")[-1])
-
-
-# def is_refiner_block(n: str, m) -> bool:
-#     return "refiner" in n and str.isdigit(n.split(".")[-1])
-
-
-def is_txt_in(n: str, m) -> bool:
-    return n.split(".")[-1] == "txt_in"
-
-
-@dataclass
-class HYWorldArchConfig(DiTArchConfig):
-    _fsdp_shard_conditions: list = field(
-        default_factory=lambda: [is_double_block, is_single_block])
-
-    _compile_conditions: list = field(
-        default_factory=lambda: [is_double_block, is_single_block, is_txt_in])
-
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            # 1. txt_in submodules (text embedder, refiner blocks):
-            r"^txt_in\.t_embedder\.mlp\.0\.(.*)$":
-            r"txt_in.t_embedder.mlp.fc_in.\1",
-            r"^txt_in\.t_embedder\.mlp\.2\.(.*)$":
-            r"txt_in.t_embedder.mlp.fc_out.\1",
-            r"^txt_in\.c_embedder\.linear_1\.(.*)$":
-            r"txt_in.c_embedder.fc_in.\1",
-            r"^txt_in\.c_embedder\.linear_2\.(.*)$":
-            r"txt_in.c_embedder.fc_out.\1",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.norm1\.(.*)$":
-            r"txt_in.refiner_blocks.\1.norm1.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.norm2\.(.*)$":
-            r"txt_in.refiner_blocks.\1.norm2.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.self_attn_qkv\.(.*)$":
-            r"txt_in.refiner_blocks.\1.self_attn_qkv.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.self_attn_proj\.(.*)$":
-            r"txt_in.refiner_blocks.\1.self_attn_proj.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.mlp\.fc1\.(.*)$":
-            r"txt_in.refiner_blocks.\1.mlp.fc_in.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.mlp\.fc2\.(.*)$":
-            r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.adaLN_modulation\.1\.(.*)$":
-            r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
-
-            # 2. time_in mappings (HYWorld uses TimestepEmbedder directly,
-            #    but FastVideo model inherits HunyuanVideo15TimeEmbedding with timestep_embedder):
-            r"^time_in\.mlp\.0\.(.*)$":
-            r"time_in.timestep_embedder.mlp.fc_in.\1",
-            r"^time_in\.mlp\.2\.(.*)$":
-            r"time_in.timestep_embedder.mlp.fc_out.\1",
-
-            # 3. action_in mappings:
-            r"^action_in\.mlp\.0\.(.*)$":
-            r"action_in.mlp.fc_in.\1",
-            r"^action_in\.mlp\.2\.(.*)$":
-            r"action_in.mlp.fc_out.\1",
-
-            # 4. byt5_in -> txt_in_2 mappings:
-            r"^byt5_in\.layernorm\.(.*)$":
-            r"txt_in_2.norm.\1",
-            r"^byt5_in\.fc1\.(.*)$":
-            r"txt_in_2.linear_1.\1",
-            r"^byt5_in\.fc2\.(.*)$":
-            r"txt_in_2.linear_2.\1",
-            r"^byt5_in\.fc3\.(.*)$":
-            r"txt_in_2.linear_3.\1",
-
-            # 5. cond_type_embedding -> cond_type_embed:
-            r"^cond_type_embedding\.(.*)$":
-            r"cond_type_embed.\1",
-
-            # 6. vision_in -> image_embedder mappings:
-            r"^vision_in\.proj\.0\.(.*)$":
-            r"image_embedder.norm_in.\1",
-            r"^vision_in\.proj\.1\.(.*)$":
-            r"image_embedder.linear_1.\1",
-            r"^vision_in\.proj\.3\.(.*)$":
-            r"image_embedder.linear_2.\1",
-            r"^vision_in\.proj\.4\.(.*)$":
-            r"image_embedder.norm_out.\1",
-
-            # 7. double_blocks mapping:
-            r"^double_blocks\.(\d+)\.img_attn_q\.(.*)$":
-            (r"double_blocks.\1.img_attn_qkv.\2", 0, 3),
-            r"^double_blocks\.(\d+)\.img_attn_k\.(.*)$":
-            (r"double_blocks.\1.img_attn_qkv.\2", 1, 3),
-            r"^double_blocks\.(\d+)\.img_attn_v\.(.*)$":
-            (r"double_blocks.\1.img_attn_qkv.\2", 2, 3),
-            r"^double_blocks\.(\d+)\.txt_attn_q\.(.*)$":
-            (r"double_blocks.\1.txt_attn_qkv.\2", 0, 3),
-            r"^double_blocks\.(\d+)\.txt_attn_k\.(.*)$":
-            (r"double_blocks.\1.txt_attn_qkv.\2", 1, 3),
-            r"^double_blocks\.(\d+)\.txt_attn_v\.(.*)$":
-            (r"double_blocks.\1.txt_attn_qkv.\2", 2, 3),
-            r"^double_blocks\.(\d+)\.img_mlp\.fc1\.(.*)$":
-            r"double_blocks.\1.img_mlp.fc_in.\2",
-            r"^double_blocks\.(\d+)\.img_mlp\.fc2\.(.*)$":
-            r"double_blocks.\1.img_mlp.fc_out.\2",
-            r"^double_blocks\.(\d+)\.txt_mlp\.fc1\.(.*)$":
-            r"double_blocks.\1.txt_mlp.fc_in.\2",
-            r"^double_blocks\.(\d+)\.txt_mlp\.fc2\.(.*)$":
-            r"double_blocks.\1.txt_mlp.fc_out.\2",
-
-            # 8. Final layer mapping:
-            r"^final_layer\.adaLN_modulation\.1\.(.*)$":
-            r"final_layer.adaLN_modulation.linear.\1",
-        })
-
-    # Reverse mapping for saving checkpoints: custom -> hf
-    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
-
-    # Parameters from HY-WorldPlay config.json (loaded from checkpoint)
-    patch_size: list | tuple | int = field(default_factory=lambda: [1, 1, 1])
-    # Base latent channels - will be expanded in __post_init__ if concat_condition=True
-    in_channels: int = 32
-    concat_condition: bool = True
-    out_channels: int = 32
-    hidden_size: int = 2048
-    heads_num: int = 16
-    mlp_width_ratio: float = 4.0
-    mlp_act_type: str = "gelu_tanh"
-    mm_double_blocks_depth: int = 54
-    mm_single_blocks_depth: int = 0
-    rope_dim_list: list | tuple = field(default_factory=lambda: [16, 56, 56])
-    qkv_bias: bool = True
-    qk_norm: bool | str = True
-    qk_norm_type: str = "rms"
-    guidance_embed: bool = False
-    use_meanflow: bool = False
-    text_projection: str = "single_refiner"
-    use_attention_mask: bool = True
-    text_states_dim: int = 3584
-    text_states_dim_2: int | None = None
-    text_pool_type: str | None = None
-    rope_theta: float = 256.0
-    attn_mode: str = "flash"
-    attn_param: str | None = None
-    glyph_byT5_v2: bool = True
-    vision_projection: str = "linear"
-    vision_states_dim: int = 1152
-    is_reshape_temporal_channels: bool = False
-    use_cond_type_embedding: bool = True
-    ideal_resolution: str = "480p"
-    ideal_task: str = "i2v"
-    task_type: str = "i2v"
-    exclude_lora_layers: list[str] = field(
-        default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in"])
-
-    def __post_init__(self):
-        super().__post_init__()
-        # Convert HY-WorldPlay naming to FastVideo naming conventions
-        self.num_attention_heads: int = self.heads_num
-        self.attention_head_dim: int = self.hidden_size // self.heads_num
-        self.num_layers: int = self.mm_double_blocks_depth
-        self.num_single_layers: int = self.mm_single_blocks_depth
-        self.num_refiner_layers: int = 2  # Default for HYWorld
-        self.mlp_ratio: float = float(self.mlp_width_ratio)
-        self.text_embed_dim: int = self.text_states_dim
-        self.text_embed_2_dim: int = self.text_states_dim_2 if self.text_states_dim_2 else 1472
-        self.image_embed_dim: int = self.vision_states_dim
-        self.rope_axes_dim: tuple[int, ...] = tuple(self.rope_dim_list)
-        self.num_channels_latents: int = self.out_channels
-        self.target_size: int = 640
-
-        # Handle concat_condition: when True, actual in_channels = base * 2 + 1
-        # (base latent + condition latent + mask channel)
-        # config.json has base in_channels (32), but img_in needs full (65)
-        if self.concat_condition and self.in_channels == 32:
-            if self.is_reshape_temporal_channels:
-                self.in_channels = self.in_channels + self.in_channels // 2 + 1
-            else:
-                self.in_channels = self.in_channels * 2 + 1  # 32 * 2 + 1 = 65
-
-        # Handle patch_size (can be list/tuple or int)
-        if isinstance(self.patch_size, list | tuple):
-            self.patch_size_t: int = self.patch_size[0]
-            # assume square patch size for height and width
-            patch_size_hw: int = self.patch_size[1]
-            object.__setattr__(self, 'patch_size', patch_size_hw)
-        else:
-            self.patch_size_t = 1
-
-        # Convert qk_norm to string format
-        if isinstance(self.qk_norm, bool):
-            if self.qk_norm:
-                self.qk_norm = "rms_norm" if self.qk_norm_type == "rms" else self.qk_norm_type
-            else:
-                self.qk_norm = "none"
-
-
-@dataclass
-class HYWorldConfig(DiTConfig):
-    arch_config: DiTArchConfig = field(default_factory=HYWorldArchConfig)
-
-    prefix: str = "HYWorld"

fastvideo/configs/models/dits/ltx2.py

@@ -1,84 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LTX-2 Transformer configuration for native FastVideo integration.
-"""
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-
-
-def is_ltx2_blocks(name: str, _module) -> bool:
-    """FSDP shard condition for LTX-2 transformer blocks."""
-    return "transformer_blocks" in name
-
-
-@dataclass
-class LTX2VideoArchConfig(DiTArchConfig):
-    """Architecture configuration for LTX-2 video transformer."""
-
-    _fsdp_shard_conditions: list = field(
-        default_factory=lambda: [is_ltx2_blocks])
-    _compile_conditions: list = field(default_factory=lambda: [is_ltx2_blocks])
-
-    # Parameter name mapping for weight conversion (hf/comfy -> FastVideo)
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            r"^model\.diffusion_model\.(.*)$": r"model.\1",
-            r"^diffusion_model\.(.*)$": r"model.\1",
-            r"^model\.(.*)$": r"model.\1",
-            r"^(.*)$": r"model.\1",
-        })
-
-    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
-    lora_param_names_mapping: dict = field(default_factory=lambda: {})
-
-    # Core transformer settings (defaults from LTX-2 metadata)
-    num_attention_heads: int = 32
-    attention_head_dim: int = 128
-    num_layers: int = 48
-    cross_attention_dim: int = 4096
-    caption_channels: int = 3840
-    norm_eps: float = 1e-6
-    attention_type: str = "default"
-    rope_type: str = "split"
-    double_precision_rope: bool = True
-
-    positional_embedding_theta: float = 10000.0
-    positional_embedding_max_pos: list[int] = field(
-        default_factory=lambda: [20, 2048, 2048])
-    timestep_scale_multiplier: int = 1000
-    use_middle_indices_grid: bool = True
-
-    # Patchification (video-only path)
-    patch_size: tuple[int, int, int] = (1, 1, 1)
-    num_channels_latents: int = 128
-    in_channels: int | None = None
-    out_channels: int | None = None
-
-    # Audio defaults (reserved for joint AV ports)
-    audio_num_attention_heads: int = 32
-    audio_attention_head_dim: int = 64
-    audio_in_channels: int = 128
-    audio_out_channels: int = 128
-    audio_cross_attention_dim: int = 2048
-    audio_positional_embedding_max_pos: list[int] = field(
-        default_factory=lambda: [20])
-    av_ca_timestep_scale_multiplier: int = 1
-
-    def __post_init__(self):
-        super().__post_init__()
-        patch_volume = self.patch_size[0] * self.patch_size[
-            1] * self.patch_size[2]
-        if self.in_channels is None:
-            self.in_channels = self.num_channels_latents * patch_volume
-        if self.out_channels is None:
-            self.out_channels = self.in_channels
-
-
-@dataclass
-class LTX2VideoConfig(DiTConfig):
-    """Main configuration for LTX-2 transformer."""
-
-    arch_config: DiTArchConfig = field(default_factory=LTX2VideoArchConfig)
-    prefix: str = "ltx2"

fastvideo/configs/models/encoders/__init__.py

@@ -7,14 +7,11 @@ from fastvideo.configs.models.encoders.clip import (
 from fastvideo.configs.models.encoders.llama import LlamaConfig
 from fastvideo.configs.models.encoders.t5 import T5Config, T5LargeConfig
 from fastvideo.configs.models.encoders.qwen2_5 import Qwen2_5_VLConfig
-from fastvideo.configs.models.encoders.siglip import SiglipVisionConfig
 from fastvideo.configs.models.encoders.reason1 import Reason1ArchConfig, Reason1Config
-from fastvideo.configs.models.encoders.gemma import LTX2GemmaConfig
 
 __all__ = [
     "EncoderConfig", "TextEncoderConfig", "ImageEncoderConfig",
     "BaseEncoderOutput", "CLIPTextConfig", "CLIPVisionConfig",
     "WAN2_1ControlCLIPVisionConfig", "LlamaConfig", "T5Config", "T5LargeConfig",
-    "Qwen2_5_VLConfig", "Reason1ArchConfig", "Reason1Config", "LTX2GemmaConfig",
-    "SiglipVisionConfig"
+    "Qwen2_5_VLConfig", "Reason1ArchConfig", "Reason1Config"
 ]

fastvideo/configs/models/encoders/clip.py

@@ -44,6 +44,15 @@ class CLIPTextArchConfig(TextEncoderArchConfig):
     _fsdp_shard_conditions: list = field(
         default_factory=lambda: [_is_transformer_layer, _is_embeddings])
 
+    tokenizer_kwargs: dict = field(
+        default_factory=lambda: {
+            "padding": "max_length",
+            "truncation": True,
+            "max_length": 77,
+            "return_tensors": "pt",
+        }
+    )
+
 
 @dataclass
 class CLIPVisionArchConfig(ImageEncoderArchConfig):

fastvideo/configs/models/encoders/gemma.py

@@ -1,48 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.encoders.base import (
-    TextEncoderArchConfig,
-    TextEncoderConfig,
-)
-
-
-@dataclass
-class LTX2GemmaArchConfig(TextEncoderArchConfig):
-    architectures: list[str] = field(
-        default_factory=lambda: ["LTX2GemmaTextEncoderModel"])
-    hidden_size: int = 3840
-    num_hidden_layers: int = 48
-    num_attention_heads: int = 30
-    text_len: int = 1024
-    pad_token_id: int = 0
-    eos_token_id: int = 2
-
-    gemma_model_path: str = ""
-    gemma_dtype: str = "bfloat16"
-    padding_side: str = "left"
-
-    feature_extractor_in_features: int = 3840 * 49
-    feature_extractor_out_features: int = 3840
-
-    connector_num_attention_heads: int = 30
-    connector_attention_head_dim: int = 128
-    connector_num_layers: int = 2
-    connector_positional_embedding_theta: float = 10000.0
-    connector_positional_embedding_max_pos: list[int] = field(
-        default_factory=lambda: [4096])
-    connector_rope_type: str = "split"
-    connector_double_precision_rope: bool = False
-    connector_num_learnable_registers: int | None = 128
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.tokenizer_kwargs["padding"] = "max_length"
-
-
-@dataclass
-class LTX2GemmaConfig(TextEncoderConfig):
-    arch_config: TextEncoderArchConfig = field(
-        default_factory=LTX2GemmaArchConfig)
-
-    prefix: str = "ltx2_gemma"

fastvideo/configs/models/encoders/llama.py

@@ -56,6 +56,15 @@ class LlamaArchConfig(TextEncoderArchConfig):
         default_factory=lambda:
         [_is_transformer_layer, _is_embeddings, _is_final_norm])
 
+    tokenizer_kwargs: dict = field(
+        default_factory=lambda: {
+            "padding": "max_length",
+            "truncation": True,
+            "max_length": 256,
+            "return_tensors": "pt",
+        }
+    )
+
 
 @dataclass
 class LlamaConfig(TextEncoderConfig):

fastvideo/configs/models/encoders/siglip.py

@@ -1,53 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""SigLIP vision encoder configuration for FastVideo."""
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.encoders.base import (ImageEncoderArchConfig,
-                                                    ImageEncoderConfig)
-
-
-@dataclass
-class SiglipVisionArchConfig(ImageEncoderArchConfig):
-    """Architecture configuration for SigLIP vision encoder.
-    
-    Fields match the config.json from HuggingFace SigLIP checkpoints.
-    """
-
-    # From config.json
-    architectures: list[str] = field(
-        default_factory=lambda: ["SiglipVisionModel"])
-    attention_dropout: float = 0.0
-    dtype: str | None = None
-    hidden_act: str = "gelu_pytorch_tanh"
-    hidden_size: int = 1152
-    image_size: int = 384
-    intermediate_size: int = 4304
-    layer_norm_eps: float = 1e-6
-    model_type: str = "siglip_vision_model"
-    num_attention_heads: int = 16
-    num_channels: int = 3
-    num_hidden_layers: int = 27
-    patch_size: int = 14
-
-    # FastVideo specific - QKV fusion mapping
-    stacked_params_mapping: list = field(default_factory=lambda: [
-        ("qkv_proj", "q_proj", "q"),
-        ("qkv_proj", "k_proj", "k"),
-        ("qkv_proj", "v_proj", "v"),
-    ])
-
-
-@dataclass
-class SiglipVisionConfig(ImageEncoderConfig):
-    """Configuration for SigLIP vision encoder."""
-
-    arch_config: ImageEncoderArchConfig = field(
-        default_factory=SiglipVisionArchConfig)
-
-    # FastVideo specific
-    num_hidden_layers_override: int | None = None
-    require_post_norm: bool | None = None
-    enable_scale: bool = True
-    is_causal: bool = False
-    prefix: str = "siglip"

fastvideo/configs/models/vaes/__init__.py

@@ -2,7 +2,6 @@ from fastvideo.configs.models.vaes.cosmosvae import CosmosVAEConfig
 from fastvideo.configs.models.vaes.cosmos2_5vae import Cosmos25VAEConfig
 from fastvideo.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
 from fastvideo.configs.models.vaes.hunyuan15vae import Hunyuan15VAEConfig
-from fastvideo.configs.models.vaes.ltx2vae import LTX2VAEConfig
 from fastvideo.configs.models.vaes.stepvideovae import StepVideoVAEConfig
 from fastvideo.configs.models.vaes.wanvae import WanVAEConfig
 
@@ -13,5 +12,4 @@ __all__ = [
     "CosmosVAEConfig",
     "Cosmos25VAEConfig",
     "Hunyuan15VAEConfig",
-    "LTX2VAEConfig",
 ]

fastvideo/configs/models/vaes/ltx2vae.py

@@ -1,45 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LTX-2 VAE configuration.
-"""
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
-
-
-@dataclass
-class LTX2VAEArchConfig(VAEArchConfig):
-    # Mirrors LTX-2 safetensors metadata config under "vae"
-    _class_name: str = "CausalVideoAutoencoder"
-    dims: int = 3
-    in_channels: int = 3
-    out_channels: int = 3
-    latent_channels: int = 128
-    encoder_blocks: list = field(default_factory=list)
-    decoder_blocks: list = field(default_factory=list)
-    patch_size: int = 4
-    norm_layer: str = "pixel_norm"
-    latent_log_var: str = "uniform"
-    encoder_spatial_padding_mode: str = "zeros"
-    decoder_spatial_padding_mode: str = "reflect"
-    causal_decoder: bool = False
-    timestep_conditioning: bool = True
-    use_quant_conv: bool = False
-    scaling_factor: float = 1.0
-    normalize_latent_channels: bool = False
-
-    # Match FastVideo naming for compression ratios (LTX-2 default)
-    temporal_compression_ratio: int = 8
-    spatial_compression_ratio: int = 32
-
-
-@dataclass
-class LTX2VAEConfig(VAEConfig):
-    arch_config: VAEArchConfig = field(default_factory=LTX2VAEArchConfig)
-
-    # LTX-2 tiling defaults (match ltx_core.video_vae.TilingConfig.default()).
-    ltx2_spatial_tile_size_in_pixels: int = 512
-    ltx2_spatial_tile_overlap_in_pixels: int = 64
-    ltx2_temporal_tile_size_in_frames: int = 64
-    ltx2_temporal_tile_overlap_in_frames: int = 24

fastvideo/configs/pipelines/__init__.py

@@ -4,8 +4,6 @@ from fastvideo.configs.pipelines.cosmos import CosmosConfig
 from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
-from fastvideo.configs.pipelines.hyworld import HYWorldConfig
-from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
 from fastvideo.configs.pipelines.registry import (
     get_pipeline_config_cls_from_name)
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
@@ -18,6 +16,5 @@ __all__ = [
     "Hunyuan15T2V480PConfig", "Hunyuan15T2V720PConfig", "SlidingTileAttnConfig",
     "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig",
     "WanI2V720PConfig", "StepVideoT2VConfig", "SelfForcingWanT2V480PConfig",
-    "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig", "HYWorldConfig",
-    "get_pipeline_config_cls_from_name"
+    "CosmosConfig", "Cosmos25Config", "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/hunyuan15.py

@@ -127,29 +127,13 @@ class Hunyuan15T2V480PConfig(PipelineConfig):
     vae_tiling: bool = True
 
     def __post_init__(self):
-        self.vae_config.load_encoder = True
+        self.vae_config.load_encoder = False
         self.vae_config.load_decoder = True
 
 
-@dataclass
-class Hunyuan15DistilledI2V480PConfig(Hunyuan15T2V480PConfig):
-    flow_shift: int = 7
-
-
 @dataclass
 class Hunyuan15T2V720PConfig(Hunyuan15T2V480PConfig):
     """Base configuration for HunYuan pipeline architecture."""
 
     # HunyuanConfig-specific parameters with defaults
     flow_shift: int = 9
-
-
-@dataclass
-class SelfForcingHunyuan15T2V480PConfig(Hunyuan15T2V480PConfig):
-    flow_shift: int = 5
-    is_causal: bool = True
-    # dmd_denoising_steps: list[int] | None = field(
-    #     default_factory=lambda: [1000, 750, 500, 250])
-    dmd_denoising_steps: list[int] | None = field(
-        default_factory=lambda: [1000, 875, 750, 625, 500, 375, 250, 125])
-    warp_denoising_step: bool = True

fastvideo/configs/pipelines/hyworld.py

@@ -1,29 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig
-from fastvideo.configs.models.dits import HYWorldConfig as HYWorldDiTConfig
-from fastvideo.configs.models.encoders import SiglipVisionConfig
-from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig
-
-
-@dataclass
-class HYWorldConfig(Hunyuan15T2V480PConfig):
-    """Base configuration for HYWorld pipeline architecture."""
-
-    # HYWorldConfig-specific parameters with defaults
-    dit_config: DiTConfig = field(default_factory=HYWorldDiTConfig)
-
-    # SigLIP image encoder for I2V
-    image_encoder_config: EncoderConfig = field(
-        default_factory=SiglipVisionConfig)
-    image_encoder_precision: str = "fp16"
-    # vae_precision: str = "fp32"
-
-    # Text encoding
-    text_encoder_precisions: tuple[str, ...] = field(
-        default_factory=lambda: ("fp16", "fp32"))
-
-    def __post_init__(self):
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True

fastvideo/configs/pipelines/ltx2.py

@@ -1,50 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from collections.abc import Callable
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
-                                      LTX2AudioDecoderConfig, LTX2VocoderConfig,
-                                      VAEConfig)
-from fastvideo.configs.models.dits import LTX2VideoConfig
-from fastvideo.configs.models.encoders import BaseEncoderOutput, LTX2GemmaConfig
-from fastvideo.configs.models.vaes import LTX2VAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig, preprocess_text
-
-
-def ltx2_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
-    return outputs.last_hidden_state
-
-
-@dataclass
-class LTX2T2VConfig(PipelineConfig):
-    """Configuration for LTX-2 T2V pipeline."""
-
-    dit_config: DiTConfig = field(default_factory=LTX2VideoConfig)
-    vae_config: VAEConfig = field(default_factory=LTX2VAEConfig)
-    vae_tiling: bool = True
-    vae_sp: bool = False
-
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(
-        default_factory=lambda: (LTX2GemmaConfig(), ))
-    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
-        default_factory=lambda: (preprocess_text, ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda:
-                                               (ltx2_postprocess_text, ))
-
-    dit_precision: str = "bf16"
-    vae_precision: str = "bf16"
-    text_encoder_precisions: tuple[str, ...] = field(
-        default_factory=lambda: ("bf16", ))
-
-    audio_decoder_config: ModelConfig = field(
-        default_factory=LTX2AudioDecoderConfig)
-    vocoder_config: ModelConfig = field(default_factory=LTX2VocoderConfig)
-    audio_decoder_precision: str = "bf16"
-    vocoder_precision: str = "bf16"
-
-    def __post_init__(self) -> None:
-        self.vae_config.load_encoder = False
-        self.vae_config.load_decoder = True

fastvideo/configs/pipelines/registry.py

@@ -8,9 +8,7 @@ from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
 from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
-from fastvideo.configs.pipelines.hyworld import HYWorldConfig
-from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
-from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig, SelfForcingHunyuan15T2V480PConfig, Hunyuan15DistilledI2V480PConfig
+from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 from fastvideo.configs.pipelines.longcat import LongCatT2V480PConfig
 from fastvideo.configs.pipelines.turbodiffusion import (
@@ -35,15 +33,10 @@ logger = init_logger(__name__)
 PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
     "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
-    "weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v":
-    SelfForcingHunyuan15T2V480PConfig,
-    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled":
-    Hunyuan15DistilledI2V480PConfig,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
     Hunyuan15T2V480PConfig,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
     Hunyuan15T2V720PConfig,
-    "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorldConfig,
     "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
     "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": WanI2V480PConfig,
     "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers": WANV2VConfig,
@@ -71,9 +64,6 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "FastVideo/LongCat-Video-T2V-Diffusers": LongCatT2V480PConfig,
     "FastVideo/LongCat-Video-I2V-Diffusers": LongCatT2V480PConfig,
     "FastVideo/LongCat-Video-VC-Diffusers": LongCatT2V480PConfig,
-    # LTX-2 models
-    "Lightricks/LTX-2": LTX2T2VConfig,
-    "converted/ltx2_diffusers": LTX2T2VConfig,
     # TurboDiffusion models
     "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers": TurboDiffusionT2V_1_3B_Config,
     "loayrashid/TurboWan2.1-T2V-14B-Diffusers": TurboDiffusionT2V_14B_Config,
@@ -93,8 +83,6 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "hunyuan" in id.lower(),
     "hunyuan15":
     lambda id: "hunyuan15" in id.lower(),
-    "hyworld":
-    lambda id: "hyworld" in id.lower(),
     "matrixgame":
     lambda id: "matrix-game" in id.lower() or "matrixgame" in id.lower(),
     "wanpipeline":
@@ -114,8 +102,6 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "cosmos25" in id.lower(),
     "turbodiffusion":
     lambda id: "turbodiffusion" in id.lower() or "turbowan" in id.lower(),
-    "ltx2":
-    lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
@@ -130,8 +116,6 @@ PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
     "matrixgame": MatrixGameI2V480PConfig,
     "hunyuan15":
     Hunyuan15T2V480PConfig,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
-    "hyworld":
-    HYWorldConfig,  # HYWorld-specific config as fallback for any HYWorld variant
     "wanpipeline":
     WanT2V480PConfig,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V480PConfig,
@@ -139,7 +123,6 @@ PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
     "wancausaldmdpipeline": SelfForcingWanT2V480PConfig,
     "stepvideo": StepVideoT2VConfig,
     "turbodiffusion": TurboDiffusionT2V_1_3B_Config,
-    "ltx2": LTX2T2VConfig,
     # Other fallbacks by architecture
 }
 

fastvideo/configs/sample/hunyuan15.py

@@ -17,24 +17,11 @@ class Hunyuan15_480P_SamplingParam(SamplingParam):
     guidance_scale: float = 6.0
     prompt_attention_mask: list = field(default_factory=list)
     negative_attention_mask: list = field(default_factory=list)
-    # sigmas: list[float] | None = field(
-    #     default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
+    sigmas: list[float] | None = field(
+        default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
 
     negative_prompt: str = ""
 
-    def __post_init__(self):
-        super().__post_init__()
-        self.sigmas = list(
-            np.linspace(1.0, 0.0, self.num_inference_steps + 1)[:-1])
-
-
-@dataclass
-class Hunyuan15_480P_Distilled_SamplingParam(Hunyuan15_480P_SamplingParam):
-    num_inference_steps: int = 8
-    fps: int = 24
-
-    guidance_scale: float = 1.0
-
 
 @dataclass
 class Hunyuan15_720P_SamplingParam(Hunyuan15_480P_SamplingParam):

fastvideo/configs/sample/hyworld.py

@@ -1,26 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.sample.base import SamplingParam
-import numpy as np
-
-
-@dataclass
-class HYWorld_SamplingParam(SamplingParam):
-    num_inference_steps: int = 50
-
-    num_frames: int = 125
-    height: int = 480
-    width: int = 832
-    fps: int = 24
-
-    # Camera trajectory: pose string (e.g., 'w-31' means generating [1 + 31] latents) or JSON file path
-    pose: str = 'w-31'
-
-    guidance_scale: float = 6.0
-    prompt_attention_mask: list = field(default_factory=list)
-    negative_attention_mask: list = field(default_factory=list)
-    sigmas: list[float] | None = field(
-        default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
-
-    negative_prompt: str = ""

fastvideo/configs/sample/ltx2.py

@@ -1,20 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass
-
-from fastvideo.configs.sample.base import SamplingParam
-
-
-@dataclass
-class LTX2SamplingParam(SamplingParam):
-    """Default sampling parameters for LTX-2 distilled T2V.
-    """
-
-    seed: int = 10
-    num_frames: int = 121
-    height: int = 1024
-    width: int = 1536
-    fps: int = 24
-    num_inference_steps: int = 8
-    guidance_scale: float = 1.0
-    # No default negative_prompt for distilled models
-    negative_prompt: str = ""

fastvideo/configs/sample/registry.py

@@ -5,13 +5,11 @@ from typing import Any
 
 from fastvideo.configs.sample.hunyuan import (FastHunyuanSamplingParam,
                                               HunyuanSamplingParam)
-from fastvideo.configs.sample.hyworld import HYWorld_SamplingParam
-from fastvideo.configs.sample.hunyuan15 import Hunyuan15_480P_SamplingParam, Hunyuan15_720P_SamplingParam, Hunyuan15_480P_Distilled_SamplingParam
+from fastvideo.configs.sample.hunyuan15 import Hunyuan15_480P_SamplingParam, Hunyuan15_720P_SamplingParam
 from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
 
 from fastvideo.configs.sample.cosmos import Cosmos_Predict2_2B_Video2World_SamplingParam
 from fastvideo.configs.sample.cosmos2_5 import Cosmos_Predict2_5_2B_Diffusers_SamplingParam
-from fastvideo.configs.sample.ltx2 import LTX2SamplingParam
 
 # isort: off
 from fastvideo.configs.sample.wan import (
@@ -42,41 +40,48 @@ from fastvideo.utils import (maybe_download_model_index,
 logger = init_logger(__name__)
 # Registry maps specific model weights to their config classes
 SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
-    "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
-    "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
-    "weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v":
-    Hunyuan15_480P_SamplingParam,
-    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled":
-    Hunyuan15_480P_Distilled_SamplingParam,
+    "FastVideo/FastHunyuan-diffusers":
+    FastHunyuanSamplingParam,
+    "hunyuanvideo-community/HunyuanVideo":
+    HunyuanSamplingParam,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
     Hunyuan15_480P_SamplingParam,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
     Hunyuan15_720P_SamplingParam,
-    "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorld_SamplingParam,
-    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
+    "FastVideo/stepvideo-t2v-diffusers":
+    StepVideoT2VSamplingParam,
 
     # Wan2.1
-    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V_1_3B_SamplingParam,
-    "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParam,
-    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
-    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
+    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers":
+    WanT2V_1_3B_SamplingParam,
+    "Wan-AI/Wan2.1-T2V-14B-Diffusers":
+    WanT2V_14B_SamplingParam,
+    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers":
+    WanI2V_14B_480P_SamplingParam,
+    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers":
+    WanI2V_14B_720P_SamplingParam,
     "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
     Wan2_1_Fun_1_3B_InP_SamplingParam,
     "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers":
     Wan2_1_Fun_1_3B_Control_SamplingParam,
 
     # Wan2.2
-    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
+    "Wan-AI/Wan2.2-TI2V-5B-Diffusers":
+    Wan2_2_TI2V_5B_SamplingParam,
     "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers":
     Wan2_2_TI2V_5B_SamplingParam,
-    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_SamplingParam,
-    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_SamplingParam,
+    "Wan-AI/Wan2.2-T2V-A14B-Diffusers":
+    Wan2_2_T2V_A14B_SamplingParam,
+    "Wan-AI/Wan2.2-I2V-A14B-Diffusers":
+    Wan2_2_I2V_A14B_SamplingParam,
 
     # FastWan2.1
-    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480P_SamplingParam,
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers":
+    FastWanT2V480P_SamplingParam,
 
     # FastWan2.2
-    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
+    "FastVideo/FastWan2.2-TI2V-5B-Diffusers":
+    Wan2_2_TI2V_5B_SamplingParam,
 
     # Causal Self-Forcing Wan2.1
     "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers":
@@ -97,9 +102,12 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
 
     # MatrixGame2.0 models
-    "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGame2_SamplingParam,
-    "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGame2_SamplingParam,
-    "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-Base-Diffusers":
+    MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-GTA-Diffusers":
+    MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers":
+    MatrixGame2_SamplingParam,
 
     # TurboDiffusion models
     "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers":
@@ -109,10 +117,6 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     "loayrashid/TurboWan2.2-I2V-A14B-Diffusers":
     TurboDiffusionI2V_A14B_SamplingParam,
 
-    # LTX-2 models
-    "Lightricks/LTX-2": LTX2SamplingParam,
-    "FastVideo/LTX2-Distilled-Diffusers": LTX2SamplingParam,
-
     # Add other specific weight variants
 }
 
@@ -122,8 +126,6 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "hunyuan" in id.lower(),
     "hunyuan15":
     lambda id: "hunyuan15" in id.lower(),
-    "hyworld":
-    lambda id: "hyworld" in id.lower(),
     "wanpipeline":
     lambda id: "wanpipeline" in id.lower(),
     "wanimagetovideo":
@@ -142,8 +144,6 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "cosmos2_5" in id.lower(),
     "cosmos":
     lambda id: "cosmos" in id.lower() and "2_5" not in id.lower(),
-    "ltx2":
-    lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
@@ -153,8 +153,6 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     HunyuanSamplingParam,  # Base Hunyuan config as fallback for any Hunyuan variant
     "hunyuan15":
     Hunyuan15_480P_SamplingParam,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
-    "hyworld":
-    HYWorld_SamplingParam,  # HYWorld-specific config as fallback for any HYWorld variant
     "wanpipeline":
     WanT2V_1_3B_SamplingParam,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V_14B_480P_SamplingParam,
@@ -166,13 +164,13 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     TurboDiffusionT2V_1_3B_SamplingParam,  # Default to T2V for fallback
     "cosmos25": Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
     "cosmos": Cosmos_Predict2_2B_Video2World_SamplingParam,
-    "ltx2": LTX2SamplingParam,
     # Other fallbacks by architecture
 }
 
 
 def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
     """Get the appropriate sampling param for specific pretrained weights."""
+
     # First try exact match for specific weights
     if pipeline_name_or_path in SAMPLING_PARAM_REGISTRY:
         return SAMPLING_PARAM_REGISTRY[pipeline_name_or_path]

fastvideo/dataset/dataloader/record_schema.py

@@ -72,9 +72,16 @@ def i2v_record_creator(batch: PreprocessBatch) -> list[dict[str, Any]]:
     """Create a record for the Parquet dataset with CLIP features."""
     records = basic_t2v_record_creator(batch)
 
-    assert len(
-        batch.image_embeds) == 1, "image embedding should be a single tensor"
-    image_embeds = batch.image_embeds[0]
+    # Adapt for model doesn't have image encoder, e.g., Hunyuan
+    if len(batch.image_embeds) == 0:
+        image_embeds = None
+    elif len(batch.image_embeds) == 1:
+        image_embeds = batch.image_embeds[0]
+    else:
+        raise ValueError(
+            "Unexpected number of image_embeds in batch: expected 0 or 1, got {}".format(
+                len(batch.image_embeds)))
+        
     image_latent = batch.image_latent
     pil_image = batch.pil_image
 
@@ -127,10 +134,7 @@ def i2v_record_creator(batch: PreprocessBatch) -> list[dict[str, Any]]:
 def ode_text_only_record_creator(
         video_name: str, text_embedding: np.ndarray, caption: str,
         trajectory_latents: np.ndarray,
-        trajectory_timesteps: np.ndarray,
-        text_mask: np.ndarray | None = None,
-        text_embedding_2: np.ndarray | None = None,
-        text_mask_2: np.ndarray | None = None) -> dict[str, Any]:
+        trajectory_timesteps: np.ndarray) -> dict[str, Any]:
     """Create a text-only ODE trajectory record matching pyarrow_schema_ode_trajectory_text_only.
 
     Args:
@@ -168,25 +172,6 @@ def ode_text_only_record_creator(
         "trajectory_timesteps_dtype": str(trajectory_timesteps.dtype),
     })
 
-    if text_embedding_2 is not None:
-        record.update({
-            "text_embedding_2_bytes": text_embedding_2.tobytes(),
-            "text_embedding_2_shape": list(text_embedding_2.shape),
-            "text_embedding_2_dtype": str(text_embedding_2.dtype),
-        })
-    if text_mask is not None:
-        record.update({
-            "text_mask_bytes": text_mask.tobytes(),
-            "text_mask_shape": list(text_mask.shape),
-            "text_mask_dtype": str(text_mask.dtype),
-        })
-    if text_mask_2 is not None:
-        record.update({
-            "text_mask_2_bytes": text_mask_2.tobytes(),
-            "text_mask_2_shape": list(text_mask_2.shape),
-            "text_mask_2_dtype": str(text_mask_2.dtype),
-        })
-        
     return record
 
 
@@ -209,4 +194,4 @@ def text_only_record_creator(text_name: str, text_embedding: np.ndarray,
         "text_embedding_dtype": str(text_embedding.dtype),
         "caption": caption,
     }
-    return record
+    return record

fastvideo/dataset/dataloader/schema.py

@@ -90,15 +90,6 @@ pyarrow_schema_ode_trajectory_text_only = pa.schema([
     pa.field("text_embedding_shape", pa.list_(pa.int64())),
     # e.g., 'bfloat16' or 'float32'
     pa.field("text_embedding_dtype", pa.string()),
-    pa.field("text_embedding_2_bytes", pa.binary()),
-    pa.field("text_embedding_2_shape", pa.list_(pa.int64())),
-    pa.field("text_embedding_2_dtype", pa.string()),
-    pa.field("text_mask_bytes", pa.binary()),
-    pa.field("text_mask_shape", pa.list_(pa.int64())),
-    pa.field("text_mask_dtype", pa.string()),
-    pa.field("text_mask_2_bytes", pa.binary()),
-    pa.field("text_mask_2_shape", pa.list_(pa.int64())),
-    pa.field("text_mask_2_dtype", pa.string()),
     # --- ODE Trajectory ---
     pa.field("trajectory_latents_bytes", pa.binary()),
     pa.field("trajectory_latents_shape", pa.list_(pa.int64())),
@@ -124,4 +115,4 @@ pyarrow_schema_text_only = pa.schema([
     pa.field("text_embedding_dtype", pa.string()),
     # --- Metadata ---
     pa.field("caption", pa.string()),
-])
+])

fastvideo/dataset/preprocessing_datasets.py

@@ -17,7 +17,6 @@ from PIL import Image
 from transformers import AutoTokenizer
 
 from fastvideo.logger import init_logger
-from fastvideo.utils import maybe_download_model
 
 logger = init_logger(__name__)
 
@@ -656,7 +655,7 @@ class TextDataset(torch.utils.data.IterableDataset,
         self.seed = seed
 
         # Initialize tokenizer
-        tokenizer_path = os.path.join(maybe_download_model(args.model_path), "tokenizer")
+        tokenizer_path = os.path.join(args.model_path, "tokenizer")
         tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
                                                   cache_dir=args.cache_dir)
 
@@ -759,4 +758,4 @@ class TextDataset(torch.utils.data.IterableDataset,
 
     def load_state_dict(self, state_dict: dict[str, Any]) -> None:
         """Load state dict from checkpoint."""
-        self.processed_batches = state_dict["processed_batches"]
+        self.processed_batches = state_dict["processed_batches"]

fastvideo/dataset/utils.py

@@ -30,12 +30,13 @@ def get_torch_tensors_from_row_dict(row_dict,
     """
     return_dict = {}
     for key in keys:
-        shape, bytes = None, None
+        shape, bytes, dtype_str = None, None, None
         if isinstance(key, tuple):
             for k in key:
                 try:
                     shape = row_dict[f"{k}_shape"]
                     bytes = row_dict[f"{k}_bytes"]
+                    dtype_str = row_dict.get(f"{k}_dtype", 'float32')
                 except KeyError:
                     continue
             key = key[0]
@@ -44,13 +45,25 @@ def get_torch_tensors_from_row_dict(row_dict,
         else:
             shape = row_dict[f"{key}_shape"]
             bytes = row_dict[f"{key}_bytes"]
+            dtype_str = row_dict.get(f"{key}_dtype", 'float32')
+
+        # Convert dtype string to numpy dtype
+        if 'float16' in dtype_str:
+            np_dtype = np.float16
+        elif 'float32' in dtype_str:
+            np_dtype = np.float32
+        elif 'float64' in dtype_str:
+            np_dtype = np.float64
+        else:
+            # Default to float32 if dtype is unrecognized
+            np_dtype = np.float32
 
         # TODO (peiyuan): read precision
         if key == 'text_embedding' and (rng.random()
                                         if rng else random.random()) < cfg_rate:
             data = np.zeros((512, 4096), dtype=np.float32)
         else:
-            data = np.frombuffer(bytes, dtype=np.float32).reshape(shape).copy()
+            data = np.frombuffer(bytes, dtype=np_dtype).reshape(shape).copy()
         data = torch.from_numpy(data)
         if len(data.shape) == 3:
             B, L, D = data.shape
@@ -141,27 +154,35 @@ def collate_rows_from_parquet_schema(rows,
             # Get tensor data from row using the existing helper function pattern
             shape_key = f"{tensor_name}_shape"
             bytes_key = f"{tensor_name}_bytes"
+            dtype_key = f"{tensor_name}_dtype"
 
             if shape_key in row and bytes_key in row:
                 shape = row[shape_key]
                 bytes_data = row[bytes_key]
+                
+                # Get dtype from row, default to float32 for backward compatibility
+                dtype_str = row.get(dtype_key, 'float32')
+                # Convert dtype string to numpy dtype
+                if 'float16' in dtype_str:
+                    np_dtype = np.float16
+                elif 'float32' in dtype_str:
+                    np_dtype = np.float32
+                elif 'float64' in dtype_str:
+                    np_dtype = np.float64
+                else:
+                    # Default to float32 if dtype is unrecognized
+                    np_dtype = np.float32
 
                 if len(bytes_data) == 0:
                     tensor = torch.zeros(0, dtype=torch.bfloat16)
                 else:
-                    # Convert bytes to tensor using float32 as default
+                    # Convert bytes to tensor using the correct dtype
                     if tensor_name == 'text_embedding' and (rng.random(
                     ) if rng else random.random()) < cfg_rate:
                         data = np.zeros((512, 4096), dtype=np.float32)
                     else:
-                        if row[f"{tensor_name}_dtype"] == "float32":
-                            data = np.frombuffer(
-                                bytes_data, dtype=np.float32).reshape(shape).copy()
-                        elif row[f"{tensor_name}_dtype"] == "int64":
-                            data = np.frombuffer(
-                                bytes_data, dtype=np.int64).reshape(shape).copy()
-                        else:
-                            raise ValueError(f"Unsupported dtype: {row[f"{tensor_name}_dtype"]}")
+                        data = np.frombuffer(
+                            bytes_data, dtype=np_dtype).reshape(shape).copy()
                     tensor = torch.from_numpy(data)
                     # if len(data.shape) == 3:
                     #     B, L, D = tensor.shape
@@ -174,7 +195,7 @@ def collate_rows_from_parquet_schema(rows,
                 tensor_list.append(torch.zeros(0, dtype=torch.bfloat16))
 
         # Stack tensors with special handling for text embeddings
-        if tensor_name == 'null':
+        if tensor_name == 'text_embedding':
             # Handle text embeddings with padding
             padded_tensors = []
             attention_masks = []

fastvideo/distributed/communication_op.py

@@ -53,6 +53,9 @@ def sequence_model_parallel_all_gather_with_unpad(
         Tensor: Gathered and unpadded tensor
     """
 
+    # NCCL all_gather expects contiguous inputs.
+    if not input_.is_contiguous():
+        input_ = input_.contiguous()
     # First gather across all ranks
     gathered = get_sp_group().all_gather(input_, dim)
 

fastvideo/distributed/device_communicators/base_device_communicator.py

@@ -57,9 +57,6 @@ class DistributedAutograd:
             ctx.dim = dim
             ctx.input_shape = input_.shape
 
-            # NCCL all_gather_into_tensor requires contiguous tensors.
-            if not input_.is_contiguous():
-                input_ = input_.contiguous()
             input_size = input_.size()
             output_size = (input_size[0] * world_size, ) + input_size[1:]
             output_tensor = torch.empty(output_size,

fastvideo/entrypoints/video_generator.py

@@ -18,8 +18,6 @@ import numpy as np
 import torch
 import torchvision
 from einops import rearrange
-import shutil
-import tempfile
 
 from fastvideo.configs.sample import SamplingParam
 from fastvideo.fastvideo_args import FastVideoArgs
@@ -391,11 +389,6 @@ class VideoGenerator:
         if batch.save_video:
             imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
             logger.info("Saved video to %s", output_path)
-            audio = output_batch.extra.get("audio")
-            audio_sample_rate = output_batch.extra.get("audio_sample_rate")
-            if (audio is not None and audio_sample_rate is not None and
-                    not self._mux_audio(output_path, audio, audio_sample_rate)):
-                logger.warning("Audio mux failed; saved video without audio.")
 
         if batch.return_frames:
             return frames
@@ -403,7 +396,6 @@ class VideoGenerator:
             return {
                 "samples": samples,
                 "frames": frames,
-                "audio": output_batch.extra.get("audio"),
                 "prompts": prompt,
                 "size": (target_height, target_width, batch.num_frames),
                 "generation_time": gen_time,
@@ -413,98 +405,6 @@ class VideoGenerator:
                 "trajectory_decoded": output_batch.trajectory_decoded,
             }
 
-    @staticmethod
-    def _mux_audio(
-        video_path: str,
-        audio: torch.Tensor | np.ndarray,
-        sample_rate: int,
-    ) -> bool:
-        """Mux audio into video using PyAV."""
-        try:
-            import av
-        except ImportError:
-            logger.warning("PyAV not installed; cannot mux audio. "
-                           "Install with: pip install av")
-            return False
-
-        if torch.is_tensor(audio):
-            audio_np = audio.detach().cpu().float().numpy()
-        else:
-            audio_np = np.asarray(audio, dtype=np.float32)
-
-        if audio_np.ndim == 1:
-            audio_np = audio_np[:, None]
-        elif audio_np.ndim == 2:
-            if audio_np.shape[0] <= 8 and audio_np.shape[1] > audio_np.shape[0]:
-                audio_np = audio_np.T
-        else:
-            logger.warning("Unexpected audio shape %s; skipping mux.",
-                           audio_np.shape)
-            return False
-
-        audio_np = np.clip(audio_np, -1.0, 1.0)
-        audio_int16 = (audio_np * 32767.0).astype(np.int16)
-        num_channels = audio_int16.shape[1]
-        layout = "stereo" if num_channels == 2 else "mono"
-
-        try:
-            import wave
-            with tempfile.TemporaryDirectory() as tmpdir:
-                out_path = os.path.join(tmpdir, "muxed.mp4")
-                wav_path = os.path.join(tmpdir, "audio.wav")
-
-                # Write audio to WAV file
-                with wave.open(wav_path, "wb") as wav_file:
-                    wav_file.setnchannels(num_channels)
-                    wav_file.setsampwidth(2)
-                    wav_file.setframerate(sample_rate)
-                    wav_file.writeframes(audio_int16.tobytes())
-
-                # Open input video and audio
-                input_video = av.open(video_path)
-                input_audio = av.open(wav_path)
-
-                # Create output with both streams
-                output = av.open(out_path, mode="w")
-
-                # Add video stream (copy codec from input)
-                in_video_stream = input_video.streams.video[0]
-                out_video_stream = output.add_stream(
-                    codec_name=in_video_stream.codec_context.name,
-                    rate=in_video_stream.average_rate,
-                )
-                out_video_stream.width = in_video_stream.width
-                out_video_stream.height = in_video_stream.height
-                out_video_stream.pix_fmt = in_video_stream.pix_fmt
-
-                # Add audio stream (AAC)
-                out_audio_stream = output.add_stream("aac", rate=sample_rate)
-                out_audio_stream.layout = layout
-
-                # Remux video (decode and re-encode to be safe)
-                for frame in input_video.decode(video=0):
-                    for packet in out_video_stream.encode(frame):
-                        output.mux(packet)
-                for packet in out_video_stream.encode():
-                    output.mux(packet)
-
-                # Encode audio
-                for frame in input_audio.decode(audio=0):
-                    frame.pts = None  # Let encoder assign PTS
-                    for packet in out_audio_stream.encode(frame):
-                        output.mux(packet)
-                for packet in out_audio_stream.encode():
-                    output.mux(packet)
-
-                input_video.close()
-                input_audio.close()
-                output.close()
-                shutil.move(out_path, video_path)
-            return True
-        except Exception as e:
-            logger.warning("Audio mux failed: %s", e)
-            return False
-
     def set_lora_adapter(self,
                          lora_nickname: str,
                          lora_path: str | None = None) -> None:

fastvideo/fastvideo_args.py

@@ -133,7 +133,7 @@ class FastVideoArgs:
     # CPU offload parameters
     dit_cpu_offload: bool = True
     use_fsdp_inference: bool = False
-    dit_layerwise_offload: bool = True
+    dit_layerwise_offload: bool = False
     text_encoder_cpu_offload: bool = True
     image_encoder_cpu_offload: bool = True
     vae_cpu_offload: bool = True
@@ -166,14 +166,6 @@ class FastVideoArgs:
     # Prompt text file for batch processing
     prompt_txt: str | None = None
 
-    # LTX-2 VAE tiling overrides
-    ltx2_vae_tiling: bool | None = None
-    ltx2_vae_spatial_tile_size_in_pixels: int | None = None
-    ltx2_vae_spatial_tile_overlap_in_pixels: int | None = None
-    ltx2_vae_temporal_tile_size_in_frames: int | None = None
-    ltx2_vae_temporal_tile_overlap_in_frames: int | None = None
-    ltx2_initial_latent_path: str | None = None
-
     # model paths for correct deallocation
     model_paths: dict[str, str] = field(default_factory=dict)
     model_loaded: dict[str, bool] = field(default_factory=lambda: {
@@ -211,44 +203,8 @@ class FastVideoArgs:
                 logger.error("Failed to load V-MoBA config from %s: %s",
                              self.moba_config_path, e)
                 raise
-        self._apply_ltx2_vae_overrides()
         self.check_fastvideo_args()
 
-    def _apply_ltx2_vae_overrides(self) -> None:
-        if self.pipeline_config is None:
-            return
-        vae_config = self.pipeline_config.vae_config
-        has_any = any(value is not None for value in (
-            self.ltx2_vae_spatial_tile_size_in_pixels,
-            self.ltx2_vae_spatial_tile_overlap_in_pixels,
-            self.ltx2_vae_temporal_tile_size_in_frames,
-            self.ltx2_vae_temporal_tile_overlap_in_frames,
-        ))
-        if self.ltx2_vae_tiling is not None and hasattr(self.pipeline_config,
-                                                        "vae_tiling"):
-            self.pipeline_config.vae_tiling = self.ltx2_vae_tiling
-        elif has_any and hasattr(self.pipeline_config, "vae_tiling"):
-            self.pipeline_config.vae_tiling = True
-
-        if hasattr(vae_config, "ltx2_spatial_tile_size_in_pixels"
-                   ) and self.ltx2_vae_spatial_tile_size_in_pixels is not None:
-            vae_config.ltx2_spatial_tile_size_in_pixels = (
-                self.ltx2_vae_spatial_tile_size_in_pixels)
-        if hasattr(
-                vae_config, "ltx2_spatial_tile_overlap_in_pixels"
-        ) and self.ltx2_vae_spatial_tile_overlap_in_pixels is not None:
-            vae_config.ltx2_spatial_tile_overlap_in_pixels = (
-                self.ltx2_vae_spatial_tile_overlap_in_pixels)
-        if hasattr(vae_config, "ltx2_temporal_tile_size_in_frames"
-                   ) and self.ltx2_vae_temporal_tile_size_in_frames is not None:
-            vae_config.ltx2_temporal_tile_size_in_frames = (
-                self.ltx2_vae_temporal_tile_size_in_frames)
-        if hasattr(
-                vae_config, "ltx2_temporal_tile_overlap_in_frames"
-        ) and self.ltx2_vae_temporal_tile_overlap_in_frames is not None:
-            vae_config.ltx2_temporal_tile_overlap_in_frames = (
-                self.ltx2_vae_temporal_tile_overlap_in_frames)
-
     @staticmethod
     def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
         # Model and path configuration
@@ -369,44 +325,6 @@ class FastVideoArgs:
             "Path to a text file containing prompts (one per line) for batch processing",
         )
 
-        # LTX-2 VAE tiling overrides
-        parser.add_argument(
-            "--ltx2-vae-tiling",
-            action=StoreBoolean,
-            default=FastVideoArgs.ltx2_vae_tiling,
-            help="Enable LTX-2 VAE tiling overrides.",
-        )
-        parser.add_argument(
-            "--ltx2-vae-spatial-tile-size-in-pixels",
-            type=int,
-            default=FastVideoArgs.ltx2_vae_spatial_tile_size_in_pixels,
-            help="LTX-2 VAE spatial tile size in pixels.",
-        )
-        parser.add_argument(
-            "--ltx2-vae-spatial-tile-overlap-in-pixels",
-            type=int,
-            default=FastVideoArgs.ltx2_vae_spatial_tile_overlap_in_pixels,
-            help="LTX-2 VAE spatial tile overlap in pixels.",
-        )
-        parser.add_argument(
-            "--ltx2-vae-temporal-tile-size-in-frames",
-            type=int,
-            default=FastVideoArgs.ltx2_vae_temporal_tile_size_in_frames,
-            help="LTX-2 VAE temporal tile size in frames.",
-        )
-        parser.add_argument(
-            "--ltx2-vae-temporal-tile-overlap-in-frames",
-            type=int,
-            default=FastVideoArgs.ltx2_vae_temporal_tile_overlap_in_frames,
-            help="LTX-2 VAE temporal tile overlap in frames.",
-        )
-        parser.add_argument(
-            "--ltx2-initial-latent-path",
-            type=str,
-            default=FastVideoArgs.ltx2_initial_latent_path,
-            help="Path to load/save a precomputed LTX-2 initial latent.",
-        )
-
         # LoRA parameters (inference-time adapter loading)
         parser.add_argument(
             "--lora-path",
@@ -830,7 +748,6 @@ class TrainingArgs(FastVideoArgs):
     precedence.
     """
     data_path: str = ""
-    data_path_2: str | None = None
     dataloader_num_workers: int = 0
     num_height: int = 0
     num_width: int = 0
@@ -860,7 +777,6 @@ class TrainingArgs(FastVideoArgs):
     validation_sampling_steps: str = ""
     validation_guidance_scale: str = ""
     validation_steps: float = 0.0
-    visualization_steps: float = 0.0
     log_validation: bool = False
     trackers: list[str] = dataclasses.field(default_factory=list)
     tracker_project_name: str = ""
@@ -876,7 +792,6 @@ class TrainingArgs(FastVideoArgs):
     num_train_epochs: int = 0
     max_train_steps: int = 0
     gradient_accumulation_steps: int = 0
-    optimizer_type: str = "adamw"
     learning_rate: float = 0.0
     scale_lr: bool = False
     lr_scheduler: str = "constant"
@@ -946,8 +861,6 @@ class TrainingArgs(FastVideoArgs):
     same_step_across_blocks: bool = False  # Use same exit timestep for all blocks
     last_step_only: bool = False  # Only use the last timestep for training
     context_noise: int = 0  # Context noise level for cache updates
-    use_context_forcing: bool = False
-    use_ode_init: bool = False
 
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace) -> "TrainingArgs":
@@ -1003,10 +916,6 @@ class TrainingArgs(FastVideoArgs):
                             type=str,
                             required=True,
                             help="Path to parquet files")
-        parser.add_argument("--data-path-2",
-                            type=str,
-                            required=False,
-                            help="Path to parquet files")
         parser.add_argument("--dataloader-num-workers",
                             type=int,
                             required=True,
@@ -1100,9 +1009,6 @@ class TrainingArgs(FastVideoArgs):
         parser.add_argument("--validation-steps",
                             type=float,
                             help="Number of validation steps")
-        parser.add_argument("--visualization-steps",
-                            type=float,
-                            help="Number of visualization steps")
         parser.add_argument("--log-validation",
                             action=StoreBoolean,
                             help="Whether to log validation results")
@@ -1151,10 +1057,6 @@ class TrainingArgs(FastVideoArgs):
         parser.add_argument("--gradient-accumulation-steps",
                             type=int,
                             help="Number of steps to accumulate gradients")
-        parser.add_argument("--optimizer-type",
-                            type=str,
-                            choices=["adamw", "muon"],
-                            help="Optimizer type")
         parser.add_argument("--learning-rate",
                             type=float,
                             required=True,
@@ -1381,12 +1283,6 @@ class TrainingArgs(FastVideoArgs):
                             type=int,
                             default=TrainingArgs.context_noise,
                             help="Context noise level for cache updates")
-        parser.add_argument("--use-context-forcing",
-                            action=StoreBoolean,
-                            help="Whether to use context forcing")
-        parser.add_argument("--use-ode-init",
-                            action=StoreBoolean,
-                            help="Whether to use ODE init")
 
         return parser
 

fastvideo/hooks/hooks.py

@@ -1,122 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import functools
-from typing import Any
-from torch import nn
-
-
-class ForwardHook:
-    """
-    Base class for forward hooks.
-    Hooks are used in the way:
-        modified_args, modified_kwargs = hook.pre_forward(module, *args, **kwargs)
-        output = module.forward(*modified_args, **modified_kwargs)
-        modified_output = hook.post_forward(module, output)
-    """
-
-    @classmethod
-    def name(cls) -> str:
-        raise NotImplementedError
-
-    def on_attach(self, module: nn.Module):  # noqa: B027
-        """Called once when the hook is attached to the module."""
-        pass
-
-    def on_detach(self, module: nn.Module):  # noqa: B027
-        """
-        Called once when the hook is detached from the module.
-        Note: this function is not guaranteed to be called if the module is
-              deleted before the hook is detached.
-        """
-        pass
-
-    def pre_forward(self, module: nn.Module, *args,
-                    **kwargs) -> tuple[tuple[Any, ...], dict[str, Any]]:
-        """Called before the module's forward method is executed."""
-        return args, kwargs
-
-    def post_forward(self, module: nn.Module, output: Any) -> Any:
-        """Called after the module's forward method is executed."""
-        return output
-
-
-class ModuleHookManager:
-    module_hook_attribute = "_hook_manager"
-
-    def __init__(self, module: nn.Module):
-        self.module = module
-        self.forward_hooks: dict[str, ForwardHook] = {}
-        self.original_forward = module.forward
-
-    @classmethod
-    def get_from(cls, module: nn.Module) -> "ModuleHookManager | None":
-        if hasattr(module, cls.module_hook_attribute):
-            return getattr(module, cls.module_hook_attribute)
-        return None
-
-    @classmethod
-    def get_from_or_default(cls, module: nn.Module) -> "ModuleHookManager":
-        if not hasattr(module, cls.module_hook_attribute):
-            setattr(module, cls.module_hook_attribute, cls(module))
-
-            def forward_hook_wrapper(mod: nn.Module, *args, **kwargs):
-                manager: ModuleHookManager = getattr(mod,
-                                                     cls.module_hook_attribute)
-                for hook in manager.forward_hooks.values():
-                    args, kwargs = hook.pre_forward(mod, *args, **kwargs)
-                output = manager.original_forward(*args, **kwargs)
-                for hook in reversed(manager.forward_hooks.values()):
-                    output = hook.post_forward(mod, output)
-                return output
-
-            module.forward = functools.partial(forward_hook_wrapper, module)
-
-        return getattr(module, cls.module_hook_attribute)
-
-    @staticmethod
-    def remove_from_manager(module: nn.Module) -> None:
-        if hasattr(module, ModuleHookManager.module_hook_attribute):
-            manager: ModuleHookManager = getattr(
-                module, ModuleHookManager.module_hook_attribute)
-            module.forward = manager.original_forward
-            delattr(module, ModuleHookManager.module_hook_attribute)
-
-    def _check_manager_attached(self) -> None:
-        if not hasattr(self.module, self.module_hook_attribute):
-            raise ValueError("ModuleHookManager is not attached to the module.")
-        if getattr(self.module, self.module_hook_attribute) is not self:
-            raise ValueError(
-                "ModuleHookManager attached to the module is different.")
-
-    def append_forward_hook(self, hook: ForwardHook):
-        self._check_manager_attached()
-        if hook.name() in self.forward_hooks:
-            raise ValueError(
-                f"Hook with name {hook.name()} is already registered.")
-        # after python 3.7, dicts maintain insertion order
-        self.forward_hooks[hook.name()] = hook
-        hook.on_attach(self.module)
-
-    def replace_forward_hook(self,
-                             hook_name: str,
-                             new_hook: ForwardHook,
-                             run_on_attach: bool = True):
-        self._check_manager_attached()
-        if hook_name not in self.forward_hooks:
-            raise ValueError(f"No hook with name {hook_name} found.")
-        old_hook = self.forward_hooks[hook_name]
-        if run_on_attach:
-            old_hook.on_detach(self.module)
-        self.forward_hooks[hook_name] = new_hook
-        new_hook.on_attach(self.module)
-
-    def remove_forward_hook(self, hook_name: str, run_detach: bool = True):
-        self._check_manager_attached()
-        if hook_name not in self.forward_hooks:
-            raise ValueError(f"No hook with name {hook_name} found.")
-        if run_detach:
-            self.forward_hooks[hook_name].on_detach(self.module)
-        del self.forward_hooks[hook_name]
-
-    def get_forward_hook(self, hook_name: str) -> ForwardHook | None:
-        return self.forward_hooks.get(hook_name, None)

fastvideo/hooks/layerwise_offload.py

@@ -1,164 +0,0 @@
-from contextlib import contextmanager
-from typing import Any
-import torch
-from torch import nn
-from fastvideo.hooks.hooks import ForwardHook, ModuleHookManager
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-def _tensor_placeholder(tensor: torch.Tensor,
-                        device: torch.device) -> torch.Tensor:
-    """Create a rank-preserving empty placeholder on the specified device."""
-    shape = (0, ) if tensor.ndim <= 0 else (0, ) * tensor.ndim
-    return torch.empty(shape, device=device, dtype=tensor.dtype)
-
-
-class LayerwiseOffloadState:
-
-    def __init__(
-        self,
-        async_copy_stream: torch.cuda.Stream,
-        device: torch.device,
-        next_state: "LayerwiseOffloadState | None" = None,
-    ) -> None:
-        self.async_copy_stream = async_copy_stream
-        self.next_state = next_state
-        self.gpu_named_parameters: dict[str, torch.Tensor] = {}
-        self.cpu_named_parameters: dict[str, torch.Tensor] = {}
-        self.module_ref: nn.Module = None  # type: ignore
-        self.device: torch.device = device
-
-    def _will_offload(self, name: str) -> bool:
-        return True
-
-    @torch.compiler.disable
-    def on_init(self, module: nn.Module):
-        self.module_ref = module
-        for name, param in self.module_ref.named_parameters():
-            if self._will_offload(name):
-                self.cpu_named_parameters[name] = (
-                    param.data.detach().to("cpu").pin_memory())
-                param.data = _tensor_placeholder(param.data, self.device)
-
-    @torch.compiler.disable
-    def wait_and_replace_params(self):
-        torch.cuda.current_stream().wait_stream(self.async_copy_stream)
-        # now gpu_named_parameters are ready
-        for name, param in self.module_ref.named_parameters():
-            if not self._will_offload(name):
-                continue
-            if name not in self.gpu_named_parameters:
-                # first load with blocking load
-                self.gpu_named_parameters[name] = self.cpu_named_parameters[
-                    name].to(self.device)
-            param.data = self.gpu_named_parameters[name]
-
-    @torch.compiler.disable
-    def prefetch_params(self):
-        compute_stream = torch.cuda.current_stream()
-        with torch.cuda.stream(self.async_copy_stream):
-            for name, param in self.module_ref.named_parameters():
-                if not self._will_offload(name):
-                    continue
-                assert name not in self.gpu_named_parameters
-                gpu_param = self.cpu_named_parameters[name].to(
-                    self.device, non_blocking=True)
-                gpu_param.record_stream(
-                    compute_stream
-                )  # ensure tensor will not be freed until forward is completed
-                self.gpu_named_parameters[name] = gpu_param
-
-    @torch.compiler.disable
-    def release_gpu_params(self):
-        for name, param in self.module_ref.named_parameters():
-            if self._will_offload(name):
-                param.data = _tensor_placeholder(param.data, self.device)
-                del self.gpu_named_parameters[name]
-        assert len(self.gpu_named_parameters) == 0
-
-
-class LayerwiseOffloadHook(ForwardHook):
-    """A hook that enables layerwise CPU offloading during forward pass."""
-
-    def __init__(self, state: LayerwiseOffloadState) -> None:
-        self.state = state
-
-    def on_attach(self, module: nn.Module):
-        self.state.on_init(module)  # pyright: ignore
-
-    def on_detach(self, module: nn.Module):
-        named_parameters = dict(module.named_parameters())
-        for name, cpu_tensor in self.state.cpu_named_parameters.items():
-            if name not in self.state.gpu_named_parameters:
-                if name in named_parameters:
-                    named_parameters[name].data = cpu_tensor.to(
-                        device=self.state.device)
-                else:
-                    logger.warning(
-                        "Parameter {} not found in module during detachment.",
-                        name,
-                    )
-
-    @classmethod
-    def name(cls) -> str:
-        return "LayerwiseOffloadHook"
-
-    def pre_forward(self, module: nn.Module, *args, **kwargs):
-        self.state.wait_and_replace_params()  # pyright: ignore
-        if self.state.next_state is not None:
-            self.state.next_state.prefetch_params()  # pyright: ignore
-        return args, kwargs
-
-    def post_forward(self, module: torch.nn.Module, output: Any):
-        self.state.release_gpu_params()  # pyright: ignore
-        return output
-
-    @contextmanager
-    def mutate_params_scope(self):
-        try:
-            # load params to GPU and keep them there
-            self.state.wait_and_replace_params()  # pyright: ignore
-            yield
-        finally:
-            # instead of releasing, we should overwrite the original params since they have been modified
-            self.state.cpu_named_parameters.clear()
-            self.state.gpu_named_parameters.clear()
-            self.state.on_init(self.state.module_ref)  # pyright: ignore
-
-
-def enable_layerwise_offload(model: nn.Module, is_replace: bool = False):
-    if torch.cuda.is_available():
-        device = torch.device("cuda", torch.cuda.current_device())
-    else:
-        logger.warning(
-            "CUDA is not available. Layerwise offloading is disabled.")
-        return
-    state_list = []
-    async_stream = torch.cuda.Stream()
-    for name, submodule in model.named_children():
-        if isinstance(submodule, nn.ModuleList):
-            for idx, module_entry in enumerate(submodule):
-                state = LayerwiseOffloadState(async_copy_stream=async_stream,
-                                              device=device)
-                state_list.append(state)
-                hook_mgr = ModuleHookManager.get_from_or_default(module_entry)
-                hook = LayerwiseOffloadHook(state)
-                if is_replace:
-                    existing_hook = hook_mgr.forward_hooks.get(hook.name())
-                    if existing_hook is not None:
-                        hook_mgr.replace_forward_hook(hook.name(), hook)
-                    else:
-                        raise AssertionError(
-                            f"Expect hook exists in {name} for replacement.")
-                else:
-                    hook_mgr.append_forward_hook(hook)
-            break
-    if len(state_list) == 0:
-        raise ValueError(
-            "No nn.ModuleList found in the model for layerwise offloading.")
-
-    # circular linking of states
-    for i in range(len(state_list)):
-        state_list[i].next_state = state_list[(i + 1) % len(state_list)]

fastvideo/layers/layernorm.py

@@ -168,15 +168,9 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
         else:
             raise NotImplementedError(f"Norm type {norm_type} not implemented")
 
-    def forward(
-        self,
-        residual: torch.Tensor,
-        x: torch.Tensor,
-        gate: torch.Tensor | int,
-        shift: torch.Tensor,
-        scale: torch.Tensor,
-        convert_modulation_dtype: bool = False
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    def forward(self, residual: torch.Tensor, x: torch.Tensor,
+                gate: torch.Tensor | int, shift: torch.Tensor,
+                scale: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Apply gated residual connection, followed by layernorm and 
         scale/shift in a single fused operation.
@@ -211,11 +205,6 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
 
         # Apply normalization
         normalized = self.norm(residual_output)
-
-        if convert_modulation_dtype:
-            scale = scale.to(normalized.dtype)
-            shift = shift.to(normalized.dtype)
-
         # Apply scale and shift
         if isinstance(scale, torch.Tensor) and scale.dim() == 4:
             # scale.shape: [batch_size, num_frames, 1, inner_dim]
@@ -265,21 +254,14 @@ class LayerNormScaleShift(nn.Module):
         else:
             raise NotImplementedError(f"Norm type {norm_type} not implemented")
 
-    def forward(self,
-                x: torch.Tensor,
-                shift: torch.Tensor,
-                scale: torch.Tensor,
-                convert_modulation_dtype: bool = False) -> torch.Tensor:
+    def forward(self, x: torch.Tensor, shift: torch.Tensor,
+                scale: torch.Tensor) -> torch.Tensor:
         """Apply ln followed by scale and shift in a single fused operation."""
         # x.shape: [batch_size, seq_len, inner_dim]
         normalized = self.norm(x)
         if self.compute_dtype == torch.float32:
             normalized = normalized.float()
 
-        if convert_modulation_dtype:
-            scale = scale.to(normalized.dtype)
-            shift = shift.to(normalized.dtype)
-
         if scale.dim() == 4:
             # scale.shape: [batch_size, num_frames, 1, inner_dim]
             num_frames = scale.shape[1]

fastvideo/layers/rotary_embedding.py

@@ -194,14 +194,14 @@ class RotaryEmbedding(CustomOp):
         cos, sin = cos_sin.chunk(2, dim=-1)
 
         query_shape = query.shape
-        query = query.view(num_tokens, -1, self.head_size)
+        query = query.reshape(num_tokens, -1, self.head_size)
         query_rot = query[..., :self.rotary_dim]
         query_pass = query[..., self.rotary_dim:]
         query_rot = _apply_rotary_emb(query_rot, cos, sin, self.is_neox_style)
         query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
 
         key_shape = key.shape
-        key = key.view(num_tokens, -1, self.head_size)
+        key = key.reshape(num_tokens, -1, self.head_size)
         key_rot = key[..., :self.rotary_dim]
         key_pass = key[..., self.rotary_dim:]
         key_rot = _apply_rotary_emb(key_rot, cos, sin, self.is_neox_style)

fastvideo/models/audio/__init__.py

@@ -1,9 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from fastvideo.models.audio.ltx2_audio_vae import (
-    LTX2AudioDecoder,
-    LTX2AudioEncoder,
-    LTX2Vocoder,
-)
-
-__all__ = ["LTX2AudioEncoder", "LTX2AudioDecoder", "LTX2Vocoder"]

fastvideo/models/dits/causal_hunyuanvideo15.py

@@ -1,808 +0,0 @@
-# Copyright 2025 The Hunyuan Team and 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.
-
-from typing import Any, Dict, Optional, List
-import math
-
-import torch
-# import torch._dynamo
-# torch._dynamo.config.cache_size_limit = 128
-# try:
-#     torch._dynamo.config.recompile_limit = 128
-# except AttributeError:
-#     pass
-import torch.nn as nn
-import torch.nn.functional as F
-
-from torch.nn.attention.flex_attention import create_block_mask, flex_attention
-from torch.nn.attention.flex_attention import BlockMask
-flex_attention = torch.compile(
-    flex_attention, dynamic=False, mode="default")
-import torch.distributed as dist
-
-from fastvideo.attention import LocalAttention
-from fastvideo.forward_context import set_forward_context
-from fastvideo.configs.models.dits import HunyuanVideo15Config
-from fastvideo.layers.layernorm import (LayerNormScaleShift, ScaleResidual,
-                                        ScaleResidualLayerNormScaleShift)
-from fastvideo.layers.linear import ReplicatedLinear
-# TODO(will-PY-refactor): RMSNorm ....
-from fastvideo.layers.mlp import MLP
-from fastvideo.layers.rotary_embedding import get_rotary_pos_embed, _apply_rotary_emb
-from fastvideo.layers.visual_embedding import (ModulateProjection, PatchEmbed,
-                                               unpatchify)
-from fastvideo.models.dits.base import CachableDiT
-from fastvideo.platforms import AttentionBackendEnum
-from fastvideo.logger import init_logger
-
-from fastvideo.models.dits.hunyuanvideo15 import (
-    HunyuanRMSNorm, 
-    HunyuanVideo15TimeEmbedding, 
-    HunyuanVideo15ByT5TextProjection,
-    HunyuanVideo15ImageProjection,
-    SingleTokenRefiner,
-    FinalLayer)
-
-logger = init_logger(__name__)
-
-class MMDoubleStreamBlock(nn.Module):
-    """
-    A multimodal DiT block with separate modulation for text and image/video,
-    using distributed attention and linear layers.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_attention_heads: int,
-        mlp_ratio: float,
-        local_attn_size: int = -1,
-        sink_size: int = 0,
-        dtype: torch.dtype | None = None,
-        supported_attention_backends: tuple[AttentionBackendEnum, ...]
-        | None = None,
-        prefix: str = "",
-    ):
-        super().__init__()
-
-        self.deterministic = False
-        self.num_attention_heads = num_attention_heads
-        self.head_dim = hidden_size // num_attention_heads
-        self.local_attn_size = local_attn_size
-        self.sink_size = sink_size
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-
-        # Image modulation components
-        self.img_mod = ModulateProjection(
-            hidden_size,
-            factor=6,
-            act_layer="silu",
-            dtype=dtype,
-            prefix=f"{prefix}.img_mod",
-        )
-
-        # Fused operations for image stream
-        self.img_attn_norm = LayerNormScaleShift(hidden_size,
-                                                 norm_type="layer",
-                                                 elementwise_affine=False,
-                                                 dtype=dtype)
-        self.img_attn_residual_mlp_norm = ScaleResidualLayerNormScaleShift(
-            hidden_size,
-            norm_type="layer",
-            elementwise_affine=False,
-            dtype=dtype)
-        self.img_mlp_residual = ScaleResidual()
-
-        # Image attention components
-        self.img_attn_qkv = ReplicatedLinear(hidden_size,
-                                             hidden_size * 3,
-                                             bias=True,
-                                             params_dtype=dtype,
-                                             prefix=f"{prefix}.img_attn_qkv")
-
-        self.img_attn_q_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
-        self.img_attn_k_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
-
-        self.img_attn_proj = ReplicatedLinear(hidden_size,
-                                              hidden_size,
-                                              bias=True,
-                                              params_dtype=dtype,
-                                              prefix=f"{prefix}.img_attn_proj")
-
-        self.img_mlp = MLP(hidden_size,
-                           mlp_hidden_dim,
-                           bias=True,
-                           dtype=dtype,
-                           prefix=f"{prefix}.img_mlp")
-
-        # Text modulation components
-        self.txt_mod = ModulateProjection(
-            hidden_size,
-            factor=6,
-            act_layer="silu",
-            dtype=dtype,
-            prefix=f"{prefix}.txt_mod",
-        )
-
-        # Fused operations for text stream
-        self.txt_attn_norm = LayerNormScaleShift(hidden_size,
-                                                 norm_type="layer",
-                                                 elementwise_affine=False,
-                                                 dtype=dtype)
-        self.txt_attn_residual_mlp_norm = ScaleResidualLayerNormScaleShift(
-            hidden_size,
-            norm_type="layer",
-            elementwise_affine=False,
-            dtype=dtype)
-        self.txt_mlp_residual = ScaleResidual()
-
-        # Text attention components
-        self.txt_attn_qkv = ReplicatedLinear(hidden_size,
-                                             hidden_size * 3,
-                                             bias=True,
-                                             params_dtype=dtype)
-
-        # QK norm layers for text
-        self.txt_attn_q_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
-        self.txt_attn_k_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
-
-        self.txt_attn_proj = ReplicatedLinear(hidden_size,
-                                              hidden_size,
-                                              bias=True,
-                                              params_dtype=dtype)
-
-        self.txt_mlp = MLP(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype)
-
-        self.max_attention_size = 21 * 1590 if local_attn_size == -1 else local_attn_size * 1590
-
-        self.attn = LocalAttention(
-            num_heads=self.num_attention_heads,
-            head_size=self.head_dim,
-            supported_attention_backends=supported_attention_backends,
-            prefix=f"{prefix}.attn"
-        )
-
-    def forward_txt(
-        self,
-        txt: torch.Tensor,
-        vec: torch.Tensor,
-        cache_txt: bool = False,
-    ):
-        txt_mod_outputs = self.txt_mod(vec)
-        (
-            txt_attn_shift,
-            txt_attn_scale,
-            txt_attn_gate,
-            txt_mlp_shift,
-            txt_mlp_scale,
-            txt_mlp_gate,
-        ) = torch.chunk(txt_mod_outputs, 6, dim=-1)
-
-        # Prepare text for attention using fused operation
-        txt_attn_input = self.txt_attn_norm(txt, txt_attn_shift, txt_attn_scale)
-
-        # Get QKV for text
-        txt_qkv, _ = self.txt_attn_qkv(txt_attn_input)
-        batch_size, text_seq_len = txt_qkv.shape[0], txt_qkv.shape[1]
-
-        # Split QKV
-        txt_qkv = txt_qkv.view(batch_size, text_seq_len, 3,
-                               self.num_attention_heads, -1)
-        txt_q, txt_k, txt_v = txt_qkv[:, :, 0], txt_qkv[:, :, 1], txt_qkv[:, :,
-                                                                          2]
-        # Apply QK-Norm if needed
-        txt_q = self.txt_attn_q_norm(txt_q).to(txt_q.dtype)
-        txt_k = self.txt_attn_k_norm(txt_k).to(txt_k.dtype)
-
-        t_kv = {}
-        if cache_txt:
-            t_kv["k_txt"] = txt_k
-            t_kv["v_txt"] = txt_v
-
-        txt_attn = self.attn(txt_q, txt_k, txt_v)
-        # Process text attention output
-        txt_attn_out, _ = self.txt_attn_proj(
-            txt_attn.reshape(batch_size, text_seq_len, -1))
-
-        # Use fused operation for residual connection, normalization, and modulation
-        txt_mlp_input, txt_residual = self.txt_attn_residual_mlp_norm(
-            txt, txt_attn_out, txt_attn_gate, txt_mlp_shift, txt_mlp_scale)
-
-        # Process text MLP
-        txt_mlp_out = self.txt_mlp(txt_mlp_input)
-        txt = self.txt_mlp_residual(txt_residual, txt_mlp_out, txt_mlp_gate)
-
-        return txt, t_kv
-
-    def forward_vision(
-        self,
-        img: torch.Tensor,
-        vec: torch.Tensor,
-        freqs_cis: tuple,
-        block_mask: BlockMask,
-        kv_cache: dict | None = None,
-        txt_kv_cache: list | None = None,
-        current_start: int = 0,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        # Process modulation vectors
-        if vec.dim() == 3:
-            img_mod_outputs = self.img_mod(vec).unflatten(dim=-1, sizes=(6, -1))
-            (
-                img_attn_shift,
-                img_attn_scale,
-                img_attn_gate,
-                img_mlp_shift,
-                img_mlp_scale,
-                img_mlp_gate,
-            ) = torch.chunk(img_mod_outputs, 6, dim=2)
-        else:
-            img_mod_outputs = self.img_mod(vec)
-            (
-                img_attn_shift,
-                img_attn_scale,
-                img_attn_gate,
-                img_mlp_shift,
-                img_mlp_scale,
-                img_mlp_gate,
-            ) = torch.chunk(img_mod_outputs, 6, dim=-1)
-
-        # Prepare image for attention using fused operation
-        img_attn_input = self.img_attn_norm(img, img_attn_shift, img_attn_scale)
-        # Get QKV for image
-        img_qkv, _ = self.img_attn_qkv(img_attn_input)
-        batch_size, image_seq_len = img_qkv.shape[0], img_qkv.shape[1]
-
-        # Split QKV
-        img_qkv = img_qkv.view(batch_size, image_seq_len, 3,
-                               self.num_attention_heads, -1)
-        img_q, img_k, img_v = img_qkv[:, :, 0], img_qkv[:, :, 1], img_qkv[:, :,
-                                                                          2]
-
-        # Apply QK-Norm if needed
-        img_q = self.img_attn_q_norm(img_q).to(img_v)
-        img_k = self.img_attn_k_norm(img_k).to(img_v)
-
-        # Apply rotary embeddings
-        cos, sin = freqs_cis
-        img_q = _apply_rotary_emb(img_q, cos, sin, is_neox_style=False)
-        img_k = _apply_rotary_emb(img_k, cos, sin, is_neox_style=False)
-
-        # Apply flex_attention
-        # Does not support SP padding for now
-        if kv_cache is None:
-            q = img_q
-            k = torch.cat([img_k, txt_kv_cache["k_txt"]], dim=1)
-            v = torch.cat([img_v, txt_kv_cache["v_txt"]], dim=1)
-            # Padding for flex attention
-            padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1]
-            padded_kv_length = math.ceil(k.shape[1] / 128) * 128 - k.shape[1]
-            padded_roped_query = torch.cat(
-                [q,
-                    torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]],
-                                device=q.device, dtype=v.dtype)],
-                dim=1
-            )
-
-            padded_roped_key = torch.cat(
-                [k, torch.zeros([k.shape[0], padded_kv_length, k.shape[2], k.shape[3]],
-                                        device=k.device, dtype=v.dtype)],
-                dim=1
-            )
-
-            padded_v = torch.cat(
-                [v, torch.zeros([v.shape[0], padded_kv_length, v.shape[2], v.shape[3]],
-                                device=v.device, dtype=v.dtype)],
-                dim=1
-            )
-
-            img_attn = flex_attention(
-                query=padded_roped_query.transpose(2, 1),
-                key=padded_roped_key.transpose(2, 1),
-                value=padded_v.transpose(2, 1),
-                block_mask=block_mask
-            )[:, :, :-padded_length].transpose(2, 1)
-
-            assert img_attn.shape[1] == image_seq_len
-            updated_kv_cache = None
-        else:
-            current_end = current_start + img_q.shape[1]
-            num_new_tokens = img_q.shape[1]
-            sink_tokens = self.sink_size * 1590
-            # If we are using local attention and the current KV cache size is larger than the local attention size, we need to truncate the KV cache
-            kv_cache_size = self.max_attention_size
-            
-            # Clone cache to avoid in-place modification during gradient checkpointing
-            k_cache = kv_cache["k"].clone()
-            v_cache = kv_cache["v"].clone()
-            
-            if self.local_attn_size != -1 and (current_end > kv_cache["global_end_index"].item()) and (
-                    num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size):
-                # Calculate the number of new tokens added in this step
-                # Shift existing cache content left to discard oldest tokens
-                # Clone the source slice to avoid overlapping memory error
-                num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
-                num_rolled_tokens = kv_cache_size - num_new_tokens - sink_tokens
-                k_cache[:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                    k_cache[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-                v_cache[:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                    v_cache[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-                # Insert the new keys/values at the end
-                local_end_index = kv_cache["local_end_index"].item() + current_end - \
-                    kv_cache["global_end_index"].item() - num_evicted_tokens
-                local_start_index = local_end_index - num_new_tokens
-                assert local_end_index == self.max_attention_size
-            else:
-                # Assign new keys/values directly up to current_end
-                local_end_index = kv_cache["local_end_index"].item() + current_end - kv_cache["global_end_index"].item()
-                local_start_index = local_end_index - num_new_tokens
-
-            assert local_start_index >= 0
-            q = img_q
-            k = torch.cat([k_cache[:, :local_start_index], img_k, txt_kv_cache["k_txt"]], dim=1)
-            v = torch.cat([v_cache[:, :local_start_index], img_v, txt_kv_cache["v_txt"]], dim=1)
-            img_attn = self.attn(q, k, v)
-
-            k_cache[:, local_start_index:local_end_index] = img_k
-            v_cache[:, local_start_index:local_end_index] = img_v
-            
-            updated_kv_cache = {
-                "k": k_cache,
-                "v": v_cache,
-                "global_end_index": torch.tensor([current_end], dtype=torch.long, device=k_cache.device),
-                "local_end_index": torch.tensor([local_end_index], dtype=torch.long, device=k_cache.device)
-            }
-        
-        img_attn_out, _ = self.img_attn_proj(
-            img_attn.view(batch_size, image_seq_len, -1))
-        # Use fused operation for residual connection, normalization, and modulation
-        img_mlp_input, img_residual = self.img_attn_residual_mlp_norm(
-            img, img_attn_out, img_attn_gate, img_mlp_shift, img_mlp_scale)
-
-        # Process image MLP
-        img_mlp_out = self.img_mlp(img_mlp_input)
-        img = self.img_mlp_residual(img_residual, img_mlp_out, img_mlp_gate)
-
-        return img, updated_kv_cache
-
-    def forward(
-        self,
-        txt_inference=False,
-        vision_inference=False,
-        **kwargs
-    ):
-        if txt_inference:
-            return self.forward_txt(**kwargs)
-        elif vision_inference:
-            return self.forward_vision(**kwargs)
-        else:
-            raise ValueError("txt_inference and vision_inference cannot be both False")
-
-
-class CausalHunyuanVideo15Transformer3DModel(CachableDiT):
-    r"""
-    A Transformer model for video-like data used in [HunyuanVideo1.5](https://huggingface.co/tencent/HunyuanVideo1.5).
-    """
-
-    # shard single stream, double stream blocks, and refiner_blocks
-    _fsdp_shard_conditions = HunyuanVideo15Config()._fsdp_shard_conditions
-    _compile_conditions = HunyuanVideo15Config()._compile_conditions
-    _supported_attention_backends = HunyuanVideo15Config(
-    )._supported_attention_backends
-    param_names_mapping = HunyuanVideo15Config().param_names_mapping
-    reverse_param_names_mapping = HunyuanVideo15Config(
-    ).reverse_param_names_mapping
-    lora_param_names_mapping = HunyuanVideo15Config().lora_param_names_mapping
-
-    def __init__(
-        self,
-        config: HunyuanVideo15Config,
-        hf_config: dict[str, Any],
-    ) -> None:
-        super().__init__(config=config, hf_config=hf_config)
-
-        self.hidden_size = config.hidden_size
-        self.num_attention_heads = config.num_attention_heads
-        self.num_channels_latents = config.num_channels_latents
-        self.out_channels = config.out_channels or config.in_channels
-        self.patch_size = (config.patch_size_t, config.patch_size, config.patch_size)
-
-        # 1. Latent and condition embedders
-        self.img_in = PatchEmbed(self.patch_size, 
-                                    config.in_channels, 
-                                    self.hidden_size,
-                                    prefix=f"{config.prefix}.img_in")
-        self.image_embedder = HunyuanVideo15ImageProjection(config.image_embed_dim, self.hidden_size)
-
-        self.txt_in = SingleTokenRefiner(config.text_embed_dim,
-                                                   self.hidden_size,
-                                                   config.num_attention_heads,
-                                                   depth=config.num_refiner_layers,
-                                                   dtype=None,
-                                                   prefix=f"{config.prefix}.txt_in")
-
-        self.txt_in_2 = HunyuanVideo15ByT5TextProjection(config.text_embed_2_dim, 2048, self.hidden_size)
-
-        self.time_in = HunyuanVideo15TimeEmbedding(self.hidden_size, use_meanflow=config.use_meanflow)
-
-        self.cond_type_embed = nn.Embedding(3, self.hidden_size)
-
-        # 3. Dual stream transformer blocks
-
-        self.double_blocks = nn.ModuleList(
-            [
-                MMDoubleStreamBlock(
-                    hidden_size=self.hidden_size,
-                    num_attention_heads=config.num_attention_heads,
-                    mlp_ratio=config.mlp_ratio,
-                    local_attn_size=config.local_attn_size,
-                    sink_size=config.sink_size,
-                    dtype=None,
-                    supported_attention_backends=self._supported_attention_backends,
-                    prefix=f"{config.prefix}.double_blocks.{i}"
-                )
-                for i in range(config.num_layers)
-            ]
-        )
-
-        # 5. Output projection
-        self.final_layer = FinalLayer(self.hidden_size,
-                                self.patch_size,
-                                self.out_channels,
-                                prefix=f"{config.prefix}.final_layer")
-
-        self.gradient_checkpointing = False
-
-        self.num_frame_per_block = config.num_frames_per_block
-        self.local_attn_size = config.local_attn_size
-        self.block_mask = None
-
-        self.__post_init__()
-
-    def get_text_and_mask(
-        self,
-        encoder_hidden_states: torch.Tensor,
-        encoder_hidden_states_2: torch.Tensor,
-        encoder_attention_mask: torch.Tensor,
-        encoder_attention_mask_2: torch.Tensor,
-        encoder_hidden_states_image: torch.Tensor,
-        timestep: torch.Tensor,
-    ):
-        batch_size, txt_seq_len = encoder_hidden_states.shape[0], encoder_hidden_states.shape[1]
-        # qwen text embedding
-        encoder_hidden_states = self.txt_in(encoder_hidden_states, timestep, encoder_attention_mask)
-
-        encoder_hidden_states_cond_emb = self.cond_type_embed(
-            torch.zeros_like(encoder_hidden_states[:, :, 0], dtype=torch.long)
-        )
-        encoder_hidden_states = encoder_hidden_states + encoder_hidden_states_cond_emb
-
-        # byt5 text embedding
-        encoder_hidden_states_2 = self.txt_in_2(encoder_hidden_states_2)
-
-        encoder_hidden_states_2_cond_emb = self.cond_type_embed(
-            torch.ones_like(encoder_hidden_states_2[:, :, 0], dtype=torch.long)
-        )
-        encoder_hidden_states_2 = encoder_hidden_states_2 + encoder_hidden_states_2_cond_emb
-
-        # image embed
-        encoder_hidden_states_3 = self.image_embedder(encoder_hidden_states_image)
-        is_t2v = torch.all(encoder_hidden_states_image == 0)
-        if is_t2v:
-            encoder_hidden_states_3 = encoder_hidden_states_3 * 0.0
-            encoder_attention_mask_3 = torch.zeros(
-                (batch_size, encoder_hidden_states_3.shape[1]),
-                dtype=encoder_attention_mask.dtype,
-                device=encoder_attention_mask.device,
-            )
-        else:
-            encoder_attention_mask_3 = torch.ones(
-                (batch_size, encoder_hidden_states_3.shape[1]),
-                dtype=encoder_attention_mask.dtype,
-                device=encoder_attention_mask.device,
-            )
-        encoder_hidden_states_3_cond_emb = self.cond_type_embed(
-            2
-            * torch.ones_like(
-                encoder_hidden_states_3[:, :, 0],
-                dtype=torch.long,
-            )
-        )
-        encoder_hidden_states_3 = encoder_hidden_states_3 + encoder_hidden_states_3_cond_emb
-
-        # reorder and combine text tokens: combine valid tokens first, then padding
-        encoder_attention_mask = encoder_attention_mask.bool()
-        encoder_attention_mask_2 = encoder_attention_mask_2.bool()
-        encoder_attention_mask_3 = encoder_attention_mask_3.bool()
-        new_encoder_hidden_states = []
-        new_encoder_attention_mask = []
-
-        for text, text_mask, text_2, text_mask_2, image, image_mask in zip(
-            encoder_hidden_states,
-            encoder_attention_mask,
-            encoder_hidden_states_2,
-            encoder_attention_mask_2,
-            encoder_hidden_states_3,
-            encoder_attention_mask_3,
-        ):
-            # Concatenate: [valid_image, valid_byt5, valid_mllm, invalid_image, invalid_byt5, invalid_mllm]
-            new_encoder_hidden_states.append(
-                torch.cat(
-                    [
-                        image[image_mask],  # valid image
-                        text_2[text_mask_2],  # valid byt5
-                        text[text_mask],  # valid mllm
-                        image[~image_mask],  # invalid image (zeroed)
-                        torch.zeros_like(text_2[~text_mask_2]),  # invalid byt5 (zeroed)
-                        torch.zeros_like(text[~text_mask]),  # invalid mllm (zeroed)
-                    ],
-                    dim=0,
-                )
-            )
-            # Apply same reordering to attention masks
-            new_encoder_attention_mask.append(
-                torch.cat(
-                    [
-                        image_mask[image_mask],
-                        text_mask_2[text_mask_2],
-                        text_mask[text_mask],
-                        image_mask[~image_mask],
-                        text_mask_2[~text_mask_2],
-                        text_mask[~text_mask],
-                    ],
-                    dim=0,
-                )
-            )
-
-        encoder_hidden_states = torch.stack(new_encoder_hidden_states)
-        encoder_attention_mask = torch.stack(new_encoder_attention_mask)
-        assert encoder_hidden_states.shape[0] == 1
-        return encoder_hidden_states, encoder_attention_mask
-
-    
-    @staticmethod
-    def _prepare_blockwise_causal_attn_mask(
-        device: torch.device | str, num_frames: int = 21,
-        frame_seqlen: int = 1560, num_frame_per_block=1, local_attn_size=-1,
-        text_seq_len: int = 0
-    ) -> BlockMask:
-        """
-        we will divide the token sequence into the following format
-        [1 latent frame] [1 latent frame] ... [1 latent frame]
-        We use flexattention to construct the attention mask
-        """
-        total_length = num_frames * frame_seqlen
-        total_kv_length = total_length + text_seq_len
-
-        total_length_tensor = torch.tensor(total_length, device=device)
-        total_kv_length_tensor = torch.tensor(total_kv_length, device=device)
-
-        # we do right padding to get to a multiple of 128
-        padded_length = math.ceil(total_length / 128) * 128 - total_length
-        kv_padded_length = math.ceil(total_kv_length / 128) * 128 - total_kv_length
-
-        ends = torch.zeros(total_length + padded_length,
-                           device=device, dtype=torch.long)
-
-        # Block-wise causal mask will attend to all elements that are before the end of the current chunk
-        frame_indices = torch.arange(
-            # start=frame_seqlen,
-            start=0,
-            end=total_length,
-            step=frame_seqlen * num_frame_per_block,
-            device=device
-        )
-        # frame_indices = torch.cat([torch.tensor([0], device=device), frame_indices])
-
-        for i, tmp in enumerate(frame_indices):
-            # if i == 0:
-            #     ends[tmp:tmp + frame_seqlen] = tmp + frame_seqlen
-            # else:
-            ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + \
-                frame_seqlen * num_frame_per_block
-
-        def attention_mask(b, h, q_idx, kv_idx):
-            if local_attn_size == -1:
-                return (kv_idx < ends[q_idx]) | (q_idx == kv_idx) | ((kv_idx >= total_length_tensor) & (kv_idx < total_kv_length_tensor))
-            else:
-                return ((kv_idx < ends[q_idx]) & (kv_idx >= (ends[q_idx] - local_attn_size * frame_seqlen))) | (q_idx == kv_idx) | ((kv_idx >= total_length_tensor) & (kv_idx < total_kv_length_tensor))
-            # return ((kv_idx < total_length) & (q_idx < total_length))  | (q_idx == kv_idx) # bidirectional mask
-
-        block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length,
-                                       KV_LEN=total_kv_length + kv_padded_length, _compile=False, device=device)
-
-        # if not dist.is_initialized() or dist.get_rank() == 0:
-        #     print(
-        #         f" cache a block wise causal mask with block size of {num_frame_per_block} frames")
-        #     print(block_mask)
-
-        # import imageio
-        # import numpy as np
-        # from torch.nn.attention.flex_attention import create_mask
-
-        # mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length +
-        #                    padded_length, KV_LEN=total_length + padded_length, device=device)
-        # import cv2
-        # mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024))
-        # imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask))
-
-        return block_mask
-
-    def forward_txt(
-        self,
-        encoder_hidden_states: List[torch.Tensor],
-        timestep: torch.LongTensor,
-        encoder_hidden_states_image: Optional[List[torch.Tensor]] = None,
-        encoder_attention_mask: Optional[List[torch.Tensor]] = None,
-        guidance: Optional[torch.Tensor] = None,
-        timestep_r: Optional[torch.LongTensor] = None,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        cache_txt: bool = False,
-        **kwargs
-    ):
-        # Check that the timestep is only consisted of 0s
-        assert torch.all(timestep == 0), "Timestep for txt must be only consisted of 0s"
-
-        if cache_txt:
-            _kv_cache_new = []
-            transformer_num_layers = len(self.double_blocks)
-            for _ in range(transformer_num_layers):
-                _kv_cache_new.append(
-                    {"k_vision": None, "v_vision": None, "k_txt": None, "v_txt": None}
-                )
-
-        encoder_hidden_states_image = encoder_hidden_states_image[0]
-        encoder_hidden_states, encoder_hidden_states_2 = encoder_hidden_states
-        encoder_attention_mask, encoder_attention_mask_2 = encoder_attention_mask
-
-        # 2. Conditional embeddings
-        if timestep.dim() == 2:
-            ts_seq_len = timestep.shape[1]
-            temb = self.time_in(timestep.flatten(), timestep_r=timestep_r, timestep_seq_len=ts_seq_len)
-        else:
-            temb = self.time_in(timestep, timestep_r=timestep_r)
-        # temb is [bs, seq_len, inner_dim] if ts_seq_len is not None, otherwise [bs, inner_dim]
-
-        encoder_hidden_states, encoder_attention_mask = self.get_text_and_mask(
-            encoder_hidden_states,
-            encoder_hidden_states_2,
-            encoder_attention_mask,
-            encoder_attention_mask_2,
-            encoder_hidden_states_image,
-            timestep
-        )
-        
-        encoder_hidden_states = encoder_hidden_states[encoder_attention_mask.bool().to(encoder_hidden_states.device)].unsqueeze(0)
-
-        # 4. Transformer blocks
-        for index, block in enumerate(self.double_blocks):
-            encoder_hidden_states, t_kv = block(
-                txt_inference=True,
-                vision_inference=False,
-                txt=encoder_hidden_states,
-                vec=temb,
-                cache_txt=cache_txt,
-            )
-
-            if cache_txt:
-                _kv_cache_new[index]["k_txt"] = t_kv["k_txt"]
-                _kv_cache_new[index]["v_txt"] = t_kv["v_txt"]
-
-        if cache_txt:
-            return _kv_cache_new
-
-    def forward_vision(
-        self,
-        hidden_states: torch.Tensor,
-        timestep: torch.LongTensor,
-        guidance: Optional[torch.Tensor] = None,
-        timestep_r: Optional[torch.LongTensor] = None,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        kv_cache: dict | None = None,
-        txt_kv_cache: list | None = None,
-        current_start: int = 0,
-        rope_start_idx: int = 0,
-    ):
-        assert txt_kv_cache is not None, "txt_kv_cache must be provided"
-
-        batch_size, num_channels, num_frames, height, width = hidden_states.shape
-        p_t, p_h, p_w = self.config.patch_size_t, self.config.patch_size, self.config.patch_size
-        post_patch_num_frames = num_frames // p_t
-        post_patch_height = height // p_h
-        post_patch_width = width // p_w
-
-        # 1. RoPE
-        # Get rotary embeddings
-        freqs_cos, freqs_sin = get_rotary_pos_embed(
-            (post_patch_num_frames, post_patch_height, post_patch_width), self.hidden_size,
-            self.num_attention_heads, self.config.rope_axes_dim, self.config.rope_theta, start_frame=rope_start_idx)
-        freqs_cos = freqs_cos.to(hidden_states.device)
-        freqs_sin = freqs_sin.to(hidden_states.device)
-        freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
-
-        # 2. Conditional embeddings
-        if timestep.dim() == 2:
-            ts_seq_len = timestep.shape[1]
-            temb = self.time_in(timestep.flatten(), timestep_r=timestep_r, timestep_seq_len=ts_seq_len)
-        else:
-            temb = self.time_in(timestep, timestep_r=timestep_r)
-        # temb is [bs, seq_len, inner_dim] if ts_seq_len is not None, otherwise [bs, inner_dim]
-
-        hidden_states = self.img_in(hidden_states)
-
-        # Prepare block-wise causal attention mask
-        if kv_cache is None:
-            self.block_mask = self._prepare_blockwise_causal_attn_mask(
-                device=hidden_states.device,
-                num_frames=num_frames,
-                frame_seqlen=post_patch_height * post_patch_width,
-                num_frame_per_block=self.num_frame_per_block,
-                local_attn_size=self.local_attn_size,
-                text_seq_len=txt_kv_cache[0]["k_txt"].shape[1]
-            )
-
-        # 4. Transformer blocks
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for block_index, block in enumerate(self.double_blocks):
-                hidden_states, new_cache = self._gradient_checkpointing_func(
-                    block,
-                    txt_inference=False,
-                    vision_inference=True,
-                    img=hidden_states,
-                    vec=temb,
-                    freqs_cis=freqs_cis,
-                    block_mask=self.block_mask,
-                    kv_cache=kv_cache[block_index] if kv_cache is not None else None,
-                    txt_kv_cache=txt_kv_cache[block_index],
-                    current_start=current_start
-                )
-                if new_cache is not None and kv_cache is not None:
-                    for k in new_cache.keys():
-                        kv_cache[block_index][k] = new_cache[k].clone()
-
-        else:
-            for block_index, block in enumerate(self.double_blocks):
-                hidden_states, new_cache = block(
-                    txt_inference=False,
-                    vision_inference=True,
-                    img=hidden_states,
-                    vec=temb,
-                    freqs_cis=freqs_cis,
-                    block_mask=self.block_mask,
-                    kv_cache=kv_cache[block_index] if kv_cache is not None else None,
-                    txt_kv_cache=txt_kv_cache[block_index],
-                    current_start=current_start
-                )
-                if new_cache is not None and kv_cache is not None:
-                    for k in new_cache.keys():
-                        kv_cache[block_index][k] = new_cache[k].clone()
-
-        # Final layer processing
-        hidden_states = self.final_layer(hidden_states, temb)
-        # Unpatchify to get original shape
-        hidden_states = unpatchify(hidden_states, post_patch_num_frames, post_patch_height, post_patch_width, self.patch_size, self.out_channels)
-
-        return hidden_states, kv_cache
-
-    def forward(
-        self,
-        txt_inference=False,
-        vision_inference=False,
-        **kwargs,
-    ):
-        if txt_inference:
-            return self.forward_txt(**kwargs)
-        elif vision_inference:
-            return self.forward_vision(**kwargs)
-        else:
-            raise ValueError("txt_inference and vision_inference cannot be both False")

fastvideo/models/dits/hunyuanvideo15.py

@@ -127,9 +127,8 @@ class HunyuanVideo15TimeEmbedding(nn.Module):
         self,
         timestep: torch.Tensor,
         timestep_r: Optional[torch.Tensor] = None,
-        timestep_seq_len: int | None = None,
     ) -> torch.Tensor:
-        timesteps_emb = self.timestep_embedder(timestep, timestep_seq_len)
+        timesteps_emb = self.timestep_embedder(timestep)
 
         if timestep_r is not None:
             timesteps_emb_r = self.timestep_embedder_r(timestep_r)
@@ -474,7 +473,6 @@ class HunyuanVideo15Transformer3DModel(CachableDiT):
         guidance: Optional[torch.Tensor] = None,
         timestep_r: Optional[torch.LongTensor] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-        **kwargs
     ):
         encoder_hidden_states_image = encoder_hidden_states_image[0]
         encoder_hidden_states, encoder_hidden_states_2 = encoder_hidden_states
@@ -496,12 +494,7 @@ class HunyuanVideo15Transformer3DModel(CachableDiT):
         freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
 
         # 2. Conditional embeddings
-        if timestep.dim() == 2:
-            ts_seq_len = timestep.shape[1]
-            temb = self.time_in(timestep.flatten(), timestep_r=timestep_r, timestep_seq_len=ts_seq_len)
-        else:
-            temb = self.time_in(timestep, timestep_r=timestep_r)
-        # temb is [bs, seq_len, inner_dim] if ts_seq_len is not None, otherwise [bs, inner_dim]
+        temb = self.time_in(timestep, timestep_r=timestep_r)
 
         hidden_states = self.img_in(hidden_states)
         hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
@@ -633,8 +626,6 @@ class HunyuanVideo15Transformer3DModel(CachableDiT):
                 )
 
         # Final layer processing
-        if get_sp_world_size() > 1:
-            hidden_states = hidden_states.contiguous()
         hidden_states = sequence_model_parallel_all_gather_with_unpad(hidden_states, original_seq_len, dim=1)
         hidden_states = self.final_layer(hidden_states, temb)
         # Unpatchify to get original shape
@@ -699,13 +690,15 @@ class SingleTokenRefiner(nn.Module):
         timestep_aware_representations = self.t_embedder(t)
 
         # Get context-aware representations
-        original_dtype = x.dtype
+        target_dtype = self.c_embedder.fc_in.weight.dtype
+        if x.dtype != target_dtype:
+            x = x.to(dtype=target_dtype)
         if mask is None:
             context_aware_representations = x.mean(dim=1)
         else:
-            mask_float = mask.float().unsqueeze(-1)  # [B, L, 1]
-            context_aware_representations = (x * mask_float).sum(dim=1) / mask_float.sum(dim=1)
-        context_aware_representations = context_aware_representations.to(original_dtype)
+            mask_float = mask.to(dtype=target_dtype).unsqueeze(-1)  # [B, L, 1]
+            context_aware_representations = (
+                x * mask_float).sum(dim=1) / mask_float.sum(dim=1)
 
         context_aware_representations = self.c_embedder(
             context_aware_representations)
@@ -858,13 +851,6 @@ class FinalLayer(nn.Module):
     def forward(self, x, c):
         # What the heck HF? Why you change the scale and shift order here???
         scale, shift = self.adaLN_modulation(c).chunk(2, dim=-1)
-        if c.dim() == 3:
-            # [bs, seq_len, inner_dim]
-            num_frames = scale.shape[1]
-            frame_seqlen = x.shape[1] // num_frames
-            x = (self.norm_final(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1.0 + scale.unsqueeze(2)) + shift.unsqueeze(2)).flatten(1, 2)
-        else:
-            # [bs, inner_dim]
-            x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
         x, _ = self.linear(x)
-        return x
+        return x

fastvideo/models/dits/hyworld/__init__.py

@@ -1,24 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-HYWorld (HY-WorldPlay) model components for FastVideo.
-
-This module provides:
-- HYWorldTransformer3DModel: The main transformer model with ProPE and action conditioning
-- HYWorldVideoGenerator: Extended VideoGenerator for HYWorld inference
-- Utilities for pose processing and camera trajectory generation
-"""
-
-from .hyworld import HYWorldTransformer3DModel, HYWorldDoubleStreamBlock
-
-# Inference utilities (used by examples)
-from .resolution_utils import (
-    get_resolution_from_image,
-)
-
-__all__ = [
-    # Model (used by model registry)
-    "HYWorldTransformer3DModel",
-    "HYWorldDoubleStreamBlock",
-    # Inference utilities (used by examples)
-    "get_resolution_from_image",
-]

fastvideo/models/dits/hyworld/camera_rope.py

@@ -1,261 +0,0 @@
-# HY-WorldPlay/hyvideo/prope/camera_rope.py
-
-# MIT License
-#
-# Copyright (c) Authors of
-# "PRoPE: Projective Positional Encoding for Multiview Transformers"
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# How to use PRoPE attention for self-attention:
-#
-# 1. Easiest way (fast):
-#    attn = PropeDotProductAttention(...)
-#    o = attn(q, k, v, viewmats, Ks)
-#
-# 2. More flexible way (fast):
-#    attn = PropeDotProductAttention(...)
-#    attn._precompute_and_cache_apply_fns(viewmats, Ks)
-#    q = attn._apply_to_q(q)
-#    k = attn._apply_to_kv(k)
-#    v = attn._apply_to_kv(v)
-#    o = F.scaled_dot_product_attention(q, k, v, **kwargs)
-#    o = attn._apply_to_o(o)
-#
-# 3. The most flexible way (but slower because repeated computation of RoPE coefficients):
-#    o = prope_dot_product_attention(q, k, v, ...)
-#
-# How to use PRoPE attention for cross-attention:
-#
-#    attn_src = PropeDotProductAttention(...)
-#    attn_tgt = PropeDotProductAttention(...)
-#    attn_src._precompute_and_cache_apply_fns(viewmats_src, Ks_src)
-#    attn_tgt._precompute_and_cache_apply_fns(viewmats_tgt, Ks_tgt)
-#    q_src = attn_src._apply_to_q(q_src)
-#    k_tgt = attn_tgt._apply_to_kv(k_tgt)
-#    v_tgt = attn_tgt._apply_to_kv(v_tgt)
-#    o_src = F.scaled_dot_product_attention(q_src, k_tgt, v_tgt, **kwargs)
-#    o_src = attn_src._apply_to_o(o_src)
-
-from functools import partial
-from typing import Callable, Optional, Tuple, List
-
-import torch
-import torch.nn.functional as F
-
-
-def prope_qkv(
-    q: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
-    k: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
-    v: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
-    *,
-    viewmats: torch.Tensor,  # (batch, cameras, 4, 4)
-    Ks: Optional[torch.Tensor],  # (batch, cameras, 3, 3)
-    patches_x: int = None,  # How many patches wide is each image?
-    patches_y: int = None,  # How many patches tall is each image?
-    image_width: int = None,  # Width of the image. Used to normalize intrinsics.
-    image_height: int = None,  # Height of the image. Used to normalize intrinsics.
-    coeffs_x: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    coeffs_y: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    mask: Optional[torch.Tensor] = None,
-    kv_cache=None,
-    is_cache: bool = False,
-    **kwargs,
-) -> torch.Tensor:
-    """Similar to torch.nn.functional.scaled_dot_product_attention, but applies PRoPE-style
-    positional encoding.
-
-    Currently, we assume that the sequence length is equal to:
-
-        cameras * patches_x * patches_y
-
-    And token ordering allows the `(seqlen,)` axis to be reshaped into
-    `(cameras, patches_x, patches_y)`.
-    """
-    # We're going to assume self-attention: all inputs are the same shape.
-    (batch, num_heads, seqlen, head_dim) = q.shape
-    cameras = viewmats.shape[1]
-    assert q.shape == k.shape == v.shape
-    assert viewmats.shape == (batch, cameras, 4, 4)
-    assert Ks is None or Ks.shape == (batch, cameras, 3, 3)
-    # assert seqlen == cameras * patches_x * patches_y
-
-    apply_fn_q, apply_fn_kv, apply_fn_o = _prepare_apply_fns_all_dim(
-        head_dim=head_dim,
-        viewmats=viewmats,
-        Ks=Ks,
-        patches_x=patches_x,
-        patches_y=patches_y,
-        image_width=image_width,
-        image_height=image_height,
-        coeffs_x=coeffs_x,
-        coeffs_y=coeffs_y,
-    )
-
-    query = apply_fn_q(q)
-    key = apply_fn_kv(k)
-    value = apply_fn_kv(v)
-
-    return query, key, value, apply_fn_o
-
-
-def _prepare_apply_fns_all_dim(
-    head_dim: int,  # Q/K/V will have this last dimension
-    viewmats: torch.Tensor,  # (batch, cameras, 4, 4)
-    Ks: Optional[torch.Tensor],  # (batch, cameras, 3, 3)
-    patches_x: int,  # How many patches wide is each image?
-    patches_y: int,  # How many patches tall is each image?
-    image_width: int,  # Width of the image. Used to normalize intrinsics.
-    image_height: int,  # Height of the image. Used to normalize intrinsics.
-    coeffs_x: Optional[torch.Tensor] = None,
-    coeffs_y: Optional[torch.Tensor] = None,
-) -> Tuple[
-    Callable[[torch.Tensor], torch.Tensor],
-    Callable[[torch.Tensor], torch.Tensor],
-    Callable[[torch.Tensor], torch.Tensor],
-]:
-    """Prepare transforms for PRoPE-style positional encoding."""
-    device = viewmats.device
-    (batch, cameras, _, _) = viewmats.shape
-
-    # Normalize camera intrinsics.
-    if Ks is not None:
-        Ks_norm = torch.zeros_like(Ks)
-        Ks_norm[..., 0, 0] = Ks[..., 0, 0]
-        Ks_norm[..., 1, 1] = Ks[..., 1, 1]
-        Ks_norm[..., 0, 2] = 0
-        Ks_norm[..., 1, 2] = 0
-        Ks_norm[..., 2, 2] = 1.0
-        Ks_norm = Ks_norm.to(dtype=Ks.dtype)
-        del Ks
-
-        # Compute the camera projection matrices we use in PRoPE.
-        # - K is an `image<-camera` transform.
-        # - viewmats is a `camera<-world` transform.
-        # - P = lift(K) @ viewmats is an `image<-world` transform.
-        P = torch.einsum("...ij,...jk->...ik", _lift_K(Ks_norm), viewmats)
-        P_T = P.transpose(-1, -2).to(dtype=viewmats.dtype)
-        P_inv = torch.einsum(
-            "...ij,...jk->...ik",
-            _invert_SE3(viewmats),
-            _lift_K(_invert_K(Ks_norm)),
-        ).to(dtype=viewmats.dtype)
-
-    else:
-        # GTA formula. P is `camera<-world` transform.
-        P = viewmats
-        P_T = P.transpose(-1, -2)
-        P_inv = _invert_SE3(viewmats)
-
-    assert P.shape == P_inv.shape == (batch, cameras, 4, 4)
-
-    # Block-diagonal transforms to the inputs and outputs of the attention operator.
-    assert head_dim % 4 == 0
-    transforms_q = [
-        (partial(_apply_tiled_projmat, matrix=P_T), head_dim),
-    ]
-    transforms_kv = [
-        (partial(_apply_tiled_projmat, matrix=P_inv), head_dim),
-    ]
-    transforms_o = [
-        (partial(_apply_tiled_projmat, matrix=P), head_dim),
-    ]
-
-    apply_fn_q = partial(_apply_block_diagonal, func_size_pairs=transforms_q)
-    apply_fn_kv = partial(_apply_block_diagonal, func_size_pairs=transforms_kv)
-    apply_fn_o = partial(_apply_block_diagonal, func_size_pairs=transforms_o)
-    return apply_fn_q, apply_fn_kv, apply_fn_o
-
-
-def _apply_tiled_projmat(
-    feats: torch.Tensor,  # (batch, num_heads, seqlen, feat_dim)
-    matrix: torch.Tensor,  # (batch, cameras, D, D)
-) -> torch.Tensor:
-    """Apply projection matrix to features."""
-    # - seqlen => (cameras, patches_x * patches_y)
-    # - feat_dim => (feat_dim // 4, 4)
-    (batch, num_heads, seqlen, feat_dim) = feats.shape
-    cameras = matrix.shape[1]
-    assert seqlen >= cameras and seqlen % cameras == 0
-    D = matrix.shape[-1]
-    assert matrix.shape == (batch, cameras, D, D)
-    assert feat_dim % D == 0
-    return torch.einsum(
-        "bcij,bncpkj->bncpki",
-        matrix,
-        feats.reshape((batch, num_heads, cameras, -1, feat_dim // D, D)),
-    ).reshape(feats.shape)
-
-
-def _apply_block_diagonal(
-    feats: torch.Tensor,  # (..., dim)
-    func_size_pairs: List[Tuple[Callable[[torch.Tensor], torch.Tensor], int]],
-) -> torch.Tensor:
-    """Apply a block-diagonal function to an input array.
-
-    Each function is specified as a tuple with form:
-
-        ((Tensor) -> Tensor, int)
-
-    Where the integer is the size of the input to the function.
-    """
-    funcs, block_sizes = zip(*func_size_pairs)
-    assert feats.shape[-1] == sum(block_sizes)
-    x_blocks = torch.split(feats, block_sizes, dim=-1)
-    out = torch.cat(
-        [f(x_block) for f, x_block in zip(funcs, x_blocks)],
-        dim=-1,
-    )
-    assert out.shape == feats.shape, "Input/output shapes should match."
-    return out
-
-
-def _invert_SE3(transforms: torch.Tensor) -> torch.Tensor:
-    """Invert a 4x4 SE(3) matrix."""
-    assert transforms.shape[-2:] == (4, 4)
-    Rinv = transforms[..., :3, :3].transpose(-1, -2)
-    out = torch.zeros_like(transforms)
-    out[..., :3, :3] = Rinv
-    out[..., :3, 3] = -torch.einsum("...ij,...j->...i", Rinv, transforms[..., :3, 3])
-    out[..., 3, 3] = 1.0
-    out = out.to(dtype=transforms.dtype)
-    return out
-
-
-def _lift_K(Ks: torch.Tensor) -> torch.Tensor:
-    """Lift 3x3 matrices to homogeneous 4x4 matrices."""
-    assert Ks.shape[-2:] == (3, 3)
-    out = torch.zeros(Ks.shape[:-2] + (4, 4), device=Ks.device)
-    out[..., :3, :3] = Ks
-    out[..., 3, 3] = 1.0
-    out = out.to(dtype=Ks.dtype)
-    return out
-
-
-def _invert_K(Ks: torch.Tensor) -> torch.Tensor:
-    """Invert 3x3 intrinsics matrices. Assumes no skew."""
-    assert Ks.shape[-2:] == (3, 3)
-    out = torch.zeros_like(Ks)
-    out[..., 0, 0] = 1.0 / Ks[..., 0, 0]
-    out[..., 1, 1] = 1.0 / Ks[..., 1, 1]
-    out[..., 0, 2] = -Ks[..., 0, 2] / Ks[..., 0, 0]
-    out[..., 1, 2] = -Ks[..., 1, 2] / Ks[..., 1, 1]
-    out[..., 2, 2] = 1.0
-    out = out.to(dtype=Ks.dtype)
-    return out

fastvideo/models/dits/hyworld/data_utils.py

@@ -1,76 +0,0 @@
-# HY-WorldPlay/hyvideo/utils/data_utils.py
-
-# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
-#
-# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
-# output and results therefrom are provided "AS IS" without any express or implied warranties of
-# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
-# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
-# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
-# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
-# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
-# of rights and permissions under this agreement.
-# See the License for the specific language governing permissions and limitations under the License.
-
-import numpy as np
-from PIL import Image
-
-
-def resize_and_center_crop(image, target_width, target_height):
-    if target_height == image.shape[0] and target_width == image.shape[1]:
-        return image
-
-    pil_image = Image.fromarray(image)
-    original_width, original_height = pil_image.size
-    scale_factor = max(target_width / original_width, target_height / original_height)
-    resized_width = int(round(original_width * scale_factor))
-    resized_height = int(round(original_height * scale_factor))
-    resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
-    left = (resized_width - target_width) / 2
-    top = (resized_height - target_height) / 2
-    right = (resized_width + target_width) / 2
-    bottom = (resized_height + target_height) / 2
-    cropped_image = resized_image.crop((left, top, right, bottom))
-    return np.array(cropped_image)
-
-
-def get_closest_ratio(height: float, width: float, ratios: list, buckets: list):
-    """
-    Get the closest ratio in the buckets.
-
-    Args:
-        height (float): video height
-        width (float): video width
-        ratios (list): video aspect ratio
-        buckets (list): buckets generated by `generate_crop_size_list`
-
-    Returns:
-        the closest size in the buckets and the corresponding ratio
-    """
-    aspect_ratio = float(height) / float(width)
-
-    ratios_array = np.array(ratios)
-    closest_ratio_id = np.abs(ratios_array - aspect_ratio).argmin()
-    closest_size = buckets[closest_ratio_id]
-    closest_ratio = ratios_array[closest_ratio_id]
-
-    return closest_size, closest_ratio
-
-
-def generate_crop_size_list(base_size=256, patch_size=16, max_ratio=4.0):
-    num_patches = round((base_size / patch_size) ** 2)
-    assert max_ratio >= 1.0
-    crop_size_list = []
-    wp, hp = num_patches, 1
-    while wp > 0:
-        if max(wp, hp) / min(wp, hp) <= max_ratio:
-            crop_size_list.append((wp * patch_size, hp * patch_size))
-        if (hp + 1) * wp <= num_patches:
-            hp += 1
-        else:
-            wp -= 1
-    return crop_size_list

fastvideo/models/dits/hyworld/hyworld.py

@@ -1,569 +0,0 @@
-# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
-#
-# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
-# output and results therefrom are provided "AS IS" without any express or implied warranties of
-# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
-# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
-# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
-# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
-# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
-# of rights and permissions under this agreement.
-# See the License for the specific language governing permissions and limitations under the License.
-
-from typing import Any, Optional
-
-import torch
-import torch.nn as nn
-from einops import rearrange, repeat
-
-from fastvideo.distributed.communication_op import (
-    sequence_model_parallel_all_gather_with_unpad,
-    sequence_model_parallel_shard)
-from fastvideo.configs.models.dits import HYWorldConfig
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
-from fastvideo.layers.visual_embedding import TimestepEmbedder, unpatchify
-from fastvideo.models.dits.hunyuanvideo15 import (
-    MMDoubleStreamBlock,
-    HunyuanVideo15Transformer3DModel,
-)
-from fastvideo.platforms import AttentionBackendEnum
-from fastvideo.logger import init_logger
-from fastvideo.forward_context import set_forward_context
-from fastvideo.distributed.parallel_state import get_sp_world_size
-from fastvideo.distributed.utils import create_attention_mask_for_padding
-
-from .camera_rope import prope_qkv
-
-logger = init_logger(__name__)
-
-
-class HYWorldDoubleStreamBlock(MMDoubleStreamBlock):
-    """
-    Extended MMDoubleStreamBlock with ProPE (Projective Positional Encoding) support
-    for camera-aware attention in HY-World/WorldPlay models.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_attention_heads: int,
-        mlp_ratio: float,
-        dtype: torch.dtype | None = None,
-        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
-        prefix: str = "",
-    ):
-        super().__init__(
-            hidden_size=hidden_size,
-            num_attention_heads=num_attention_heads,
-            mlp_ratio=mlp_ratio,
-            dtype=dtype,
-            supported_attention_backends=supported_attention_backends,
-            prefix=prefix,
-        )
-        self.hidden_size = hidden_size
-
-        # Add ProPE projection layer for camera-aware attention
-        self.img_attn_prope_proj = ReplicatedLinear(
-            hidden_size,
-            hidden_size,
-            bias=True,
-            params_dtype=dtype,
-            prefix=f"{prefix}.img_attn_prope_proj"
-        )
-        # Zero-initialize ProPE projection (starts as identity)
-        nn.init.zeros_(self.img_attn_prope_proj.weight)
-        if self.img_attn_prope_proj.bias is not None:
-            nn.init.zeros_(self.img_attn_prope_proj.bias)
-
-    def forward(
-        self,
-        img: torch.Tensor,
-        txt: torch.Tensor,
-        encoder_attention_mask: torch.Tensor,
-        vec: torch.Tensor,
-        vec_txt: torch.Tensor,
-        freqs_cis: tuple,
-        seq_attention_mask: torch.Tensor,
-        viewmats: torch.Tensor,
-        Ks: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        Forward pass with ProPE camera conditioning.
-
-        Args:
-            img: Image/video tokens
-            txt: Text tokens
-            encoder_attention_mask: Text attention mask
-            vec: Modulation vector
-            freqs_cis: Rotary embedding frequencies
-            seq_attention_mask: Sequence attention mask
-            viewmats: Camera view matrices for ProPE [B, T, 4, 4]
-            Ks: Camera intrinsics for ProPE [B, T, 3, 3]
-
-        Returns:
-            Tuple of (img, txt) output tokens
-        """
-        # Process modulation vectors (inherited from parent)
-        img_mod_outputs = self.img_mod(vec)
-        (
-            img_attn_shift,
-            img_attn_scale,
-            img_attn_gate,
-            img_mlp_shift,
-            img_mlp_scale,
-            img_mlp_gate,
-        ) = torch.chunk(img_mod_outputs, 6, dim=-1)
-
-        txt_mod_outputs = self.txt_mod(vec_txt)
-        (
-            txt_attn_shift,
-            txt_attn_scale,
-            txt_attn_gate,
-            txt_mlp_shift,
-            txt_mlp_scale,
-            txt_mlp_gate,
-        ) = torch.chunk(txt_mod_outputs, 6, dim=-1)
-
-        # Prepare image for attention using fused operation
-        img_attn_input = self.img_attn_norm(img, img_attn_shift, img_attn_scale, convert_modulation_dtype=True)
-        # Get QKV for image
-        img_qkv, _ = self.img_attn_qkv(img_attn_input)
-        batch_size, image_seq_len = img_qkv.shape[0], img_qkv.shape[1]
-
-        # Split QKV
-        img_qkv = img_qkv.view(batch_size, image_seq_len, 3,
-                               self.num_attention_heads, -1)
-        img_q, img_k, img_v = img_qkv[:, :, 0], img_qkv[:, :, 1], img_qkv[:, :,
-                                                                          2]
-
-        # Apply QK-Norm if needed
-        img_q = self.img_attn_q_norm(img_q).to(img_v)
-        img_k = self.img_attn_k_norm(img_k).to(img_v)
-
-        # Prepare text for attention using fused operation
-        txt_attn_input = self.txt_attn_norm(txt, txt_attn_shift, txt_attn_scale, convert_modulation_dtype=True)
-
-        # Get QKV for text
-        txt_qkv, _ = self.txt_attn_qkv(txt_attn_input)
-        batch_size, text_seq_len = txt_qkv.shape[0], txt_qkv.shape[1]
-
-        # Split QKV
-        txt_qkv = txt_qkv.view(batch_size, text_seq_len, 3,
-                               self.num_attention_heads, -1)
-        txt_q, txt_k, txt_v = txt_qkv[:, :, 0], txt_qkv[:, :, 1], txt_qkv[:, :,
-                                                                          2]
-        # Apply QK-Norm if needed
-        txt_q = self.txt_attn_q_norm(txt_q).to(txt_q.dtype)
-        txt_k = self.txt_attn_k_norm(txt_k).to(txt_k.dtype)
-
-        # begin hyworld: add camera pose through prope
-        img_q_prope, img_k_prope, img_v_prope, apply_fn_o = prope_qkv(
-            img_q.permute(0, 2, 1, 3),
-            img_k.permute(0, 2, 1, 3),
-            img_v.permute(0, 2, 1, 3),
-            viewmats=viewmats,
-            Ks=Ks,
-        )  # [batch, num_heads, seqlen, head_dim]
-        img_q_prope = img_q_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
-        img_k_prope = img_k_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
-        img_v_prope = img_v_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
-        # end hyworld
-
-        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
-        attn_metadata = FlashAttnMetadataBuilder().build(
-            current_timestep=0,
-            attn_mask=encoder_attention_mask,
-        )
-        # Run distributed attention
-        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata):
-            img_attn, txt_attn = self.attn(img_q, img_k, img_v, txt_q, txt_k, txt_v, freqs_cis=freqs_cis, attention_mask=seq_attention_mask)
-        
-        # begin hyworld
-        # attention with prope
-        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
-        attn_metadata_prope = FlashAttnMetadataBuilder().build(
-            current_timestep=0,
-            attn_mask=encoder_attention_mask,
-        )       
-        # NOTE: Do NOT pass freqs_cis to prope attention - HY-WorldPlay does not apply RoPE to prope
-        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata_prope):
-            img_attn_prope, _ = self.attn(
-                img_q_prope, img_k_prope, img_v_prope, txt_q, txt_k, txt_v, 
-                freqs_cis=None, attention_mask=seq_attention_mask  # No RoPE for prope attention
-            )
-            img_attn_prope = img_attn_prope.reshape(batch_size, image_seq_len, -1)
-            img_attn_prope = rearrange(
-                img_attn_prope, "B L (H D) -> B H L D", H=self.num_attention_heads
-            )
-            img_attn_prope = apply_fn_o(img_attn_prope) # [batch, num_heads, seqlen, head_dim]
-            img_attn_prope = rearrange(img_attn_prope, "B H L D -> B L (H D)")
-
-        # add prope to img_attn
-        img_attn_out, _ = self.img_attn_proj(img_attn.view(batch_size, image_seq_len, -1))
-        img_attn_prope_out, _ = self.img_attn_prope_proj(img_attn_prope)
-        img_attn_out = img_attn_out + img_attn_prope_out
-        # end hyworld
-
-        # Use fused operation for residual connection, normalization, and modulation
-        img_mlp_input, img_residual = self.img_attn_residual_mlp_norm(
-            img, img_attn_out, img_attn_gate, img_mlp_shift, img_mlp_scale, convert_modulation_dtype=True)
-
-        # Process image MLP
-        img_mlp_out = self.img_mlp(img_mlp_input)
-        img = self.img_mlp_residual(img_residual, img_mlp_out, img_mlp_gate)
-
-        # Process text attention output
-        txt_attn_out, _ = self.txt_attn_proj(
-            txt_attn.reshape(batch_size, text_seq_len, -1))
-
-        # Use fused operation for residual connection, normalization, and modulation
-        txt_mlp_input, txt_residual = self.txt_attn_residual_mlp_norm(
-            txt, txt_attn_out, txt_attn_gate, txt_mlp_shift, txt_mlp_scale, convert_modulation_dtype=True)
-
-        # Process text MLP
-        txt_mlp_out = self.txt_mlp(txt_mlp_input)
-        txt = self.txt_mlp_residual(txt_residual, txt_mlp_out, txt_mlp_gate)
-
-        return img, txt
-
-
-class HYWorldFinalLayer(nn.Module):
-    """
-    Final layer for HYWorld that uses modulate() to handle per-token conditioning.
-    This matches HY-WorldPlay's FinalLayer behavior.
-    """
-
-    def __init__(self,
-                 hidden_size,
-                 patch_size,
-                 out_channels,
-                 dtype=None,
-                 prefix: str = "") -> None:
-        super().__init__()
-        from fastvideo.layers.linear import ReplicatedLinear
-        from fastvideo.layers.visual_embedding import ModulateProjection
-        from fastvideo.layers.layernorm import LayerNormScaleShift
-
-        self.norm_final = LayerNormScaleShift(
-            hidden_size,
-            norm_type="layer",
-            eps=1e-6,
-            elementwise_affine=False,
-            dtype=dtype,
-            prefix=f"{prefix}.norm_final")
-
-        output_dim = patch_size[0] * patch_size[1] * patch_size[2] * out_channels
-
-        self.linear = ReplicatedLinear(hidden_size,
-                                       output_dim,
-                                       bias=True,
-                                       params_dtype=dtype,
-                                       prefix=f"{prefix}.linear")
-
-        # Modulation projection to get shift/scale
-        self.adaLN_modulation = ModulateProjection(
-            hidden_size,
-            factor=2,
-            act_layer="silu",
-            dtype=dtype,
-            prefix=f"{prefix}.adaLN_modulation")
-
-    def forward(self, x, c):
-        shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
-        x = self.norm_final(x, shift, scale, convert_modulation_dtype=True)
-        x, _ = self.linear(x)
-        return x
-
-
-class HYWorldTransformer3DModel(HunyuanVideo15Transformer3DModel):
-    r"""
-    HY-World Transformer extending HunyuanVideo15 with:
-    - ProPE (Projective Positional Encoding) for camera-aware attention
-    - Action conditioning for interactive video generation
-    """
-    
-    # Class attributes for weight loading - use HYWorld-specific mapping
-    _fsdp_shard_conditions = HYWorldConfig().arch_config._fsdp_shard_conditions
-    _compile_conditions = HYWorldConfig().arch_config._compile_conditions
-    param_names_mapping = HYWorldConfig().arch_config.param_names_mapping
-    reverse_param_names_mapping = HYWorldConfig().arch_config.reverse_param_names_mapping
-
-    def __init__(
-        self,
-        config: HYWorldConfig,
-        hf_config: dict[str, Any],
-    ) -> None:
-        super().__init__(config=config, hf_config=hf_config)
-
-        # Replace double_blocks with HY-World version that supports ProPE
-        self.double_blocks = nn.ModuleList([
-            HYWorldDoubleStreamBlock(
-                hidden_size=self.hidden_size,
-                num_attention_heads=self.num_attention_heads,
-                mlp_ratio=config.arch_config.mlp_ratio,
-                dtype=None,
-                supported_attention_backends=self._supported_attention_backends,
-                prefix=f"{config.prefix}.double_blocks.{i}"
-            )
-            for i in range(config.arch_config.num_layers)
-        ])
-
-        # Add action conditioning module
-        self.action_in = TimestepEmbedder(
-            self.hidden_size,
-            act_layer="silu",
-            dtype=None,
-            prefix=f"{config.prefix}.action_in"
-        )
-        # Zero-initialize action embedding (starts with no effect)
-        nn.init.zeros_(self.action_in.mlp.fc_out.weight)
-        if self.action_in.mlp.fc_out.bias is not None:
-            nn.init.zeros_(self.action_in.mlp.fc_out.bias)
-
-        # Override final_layer with HYWorld version that uses per-token modulate()
-        self.final_layer = HYWorldFinalLayer(
-            hidden_size=self.hidden_size,
-            patch_size=self.patch_size,
-            out_channels=self.out_channels,
-            dtype=None,
-            prefix=f"{config.prefix}.final_layer"
-        )
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: list[torch.Tensor],
-        timestep: torch.LongTensor,
-        encoder_hidden_states_image: list[torch.Tensor],
-        encoder_attention_mask: list[torch.Tensor],
-        action: torch.Tensor,
-        viewmats: torch.Tensor,
-        Ks: torch.Tensor,
-        timestep_txt: torch.LongTensor,
-        guidance: Optional[torch.Tensor] = None,
-        timestep_r: Optional[torch.LongTensor] = None,
-        attention_kwargs: Optional[dict[str, Any]] = None,
-    ):
-        """
-        Forward pass with action and camera conditioning.
-
-        Args:
-            action: Action tensor for action conditioning [B, T] or [B*T]
-            viewmats: Camera view matrices [B, T, 4, 4]
-            Ks: Camera intrinsics [B, T, 3, 3]
-            ... (other args same as parent)
-        """
-        encoder_hidden_states_image = encoder_hidden_states_image[0]
-        encoder_hidden_states, encoder_hidden_states_2 = encoder_hidden_states
-        encoder_attention_mask, encoder_attention_mask_2 = encoder_attention_mask
-
-        batch_size, num_channels, num_frames, height, width = hidden_states.shape
-        p_t, p_h, p_w = self.config.patch_size_t, self.config.patch_size, self.config.patch_size
-        post_patch_num_frames = num_frames // p_t
-        post_patch_height = height // p_h
-        post_patch_width = width // p_w
-
-        # 1. RoPE
-        # Get rotary embeddings
-        freqs_cos, freqs_sin = get_rotary_pos_embed(
-            (post_patch_num_frames, post_patch_height, post_patch_width),
-            self.hidden_size,
-            self.num_attention_heads,
-            self.config.rope_axes_dim,
-            self.config.rope_theta
-        )
-        freqs_cos = freqs_cos.to(hidden_states.device)
-        freqs_sin = freqs_sin.to(hidden_states.device)
-        # NOTE: freqs_cis does NOT need sharding because FastVideo's DistributedAttention
-        # uses all-to-all to gather the full sequence before applying RoPE
-        freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
-
-        # 2. Conditional embeddings
-        temb = self.time_in(timestep, timestep_r=timestep_r)
-        temb_txt = self.time_in(timestep_txt, timestep_r=timestep_r)
-
-        # Add action conditioning if provided
-        # temb shape: [B*T, C] where T = num_frames
-        temb = temb + self.action_in(action.reshape(-1))
-        
-        # Broadcast timestep embedding for transformer blocks (one per spatial token)
-        # [B*T, C] -> [B, T*H*W, C] -> [B*T*H*W, C]
-        temb = repeat(temb, "(B T) C -> B (T H W) C", B=batch_size, H=post_patch_height, W=post_patch_width)
-
-        hidden_states = self.img_in(hidden_states)
-        hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
-
-        current_seq_len = hidden_states.shape[1]
-        sp_world_size = get_sp_world_size()
-        padded_seq_len = current_seq_len * sp_world_size
-
-        if padded_seq_len > original_seq_len:
-            seq_attention_mask = create_attention_mask_for_padding(
-                seq_len=original_seq_len,
-                padded_seq_len=padded_seq_len,
-                batch_size=batch_size,
-                device=hidden_states.device,
-            )
-        else:
-            seq_attention_mask = None
-
-        viewmats_seq = repeat(
-            viewmats, "B T M N->B (T H W) M N",
-            H=post_patch_height,
-            W=post_patch_width
-        )
-        Ks_seq = repeat(
-            Ks, "B T M N->B (T H W) M N",
-            H=post_patch_height,
-            W=post_patch_width
-        )
-        
-        # Shard viewmats, Ks, and temb for sequence parallelism (shard along sequence dim=1)
-        # Note that temb in HY1.5 does not need sharding because it is per-sample modulation
-        # In HYWorld, temb is per-token modulation.
-        if sp_world_size > 1:
-            viewmats_seq, _ = sequence_model_parallel_shard(viewmats_seq, dim=1)
-            Ks_seq, _ = sequence_model_parallel_shard(Ks_seq, dim=1)
-            temb, _ = sequence_model_parallel_shard(temb, dim=1)
-        
-        # Rearrange temb after sharding to match expected shape
-        temb = rearrange(temb, "B S C -> (B S) C")
-
-        # qwen text embedding
-        encoder_hidden_states = self.txt_in(encoder_hidden_states, timestep_txt, encoder_attention_mask)
-
-        encoder_hidden_states_cond_emb = self.cond_type_embed(
-            torch.zeros_like(encoder_hidden_states[:, :, 0], dtype=torch.long)
-        )
-        encoder_hidden_states = encoder_hidden_states + encoder_hidden_states_cond_emb
-
-        # byt5 text embedding
-        encoder_hidden_states_2 = self.txt_in_2(encoder_hidden_states_2)
-
-        encoder_hidden_states_2_cond_emb = self.cond_type_embed(
-            torch.ones_like(encoder_hidden_states_2[:, :, 0], dtype=torch.long)
-        )
-        encoder_hidden_states_2 = encoder_hidden_states_2 + encoder_hidden_states_2_cond_emb
-
-        # image embed
-        encoder_hidden_states_3 = self.image_embedder(encoder_hidden_states_image)
-        is_t2v = torch.all(encoder_hidden_states_image == 0)
-        if is_t2v:
-            encoder_hidden_states_3 = encoder_hidden_states_3 * 0.0
-            encoder_attention_mask_3 = torch.zeros(
-                (batch_size, encoder_hidden_states_3.shape[1]),
-                dtype=encoder_attention_mask.dtype,
-                device=encoder_attention_mask.device,
-            )
-        else:
-            encoder_attention_mask_3 = torch.ones(
-                (batch_size, encoder_hidden_states_3.shape[1]),
-                dtype=encoder_attention_mask.dtype,
-                device=encoder_attention_mask.device,
-            )
-        encoder_hidden_states_3_cond_emb = self.cond_type_embed(
-            2
-            * torch.ones_like(
-                encoder_hidden_states_3[:, :, 0],
-                dtype=torch.long,
-            )
-        )
-        encoder_hidden_states_3 = encoder_hidden_states_3 + encoder_hidden_states_3_cond_emb
-
-        # reorder and combine text tokens: combine valid tokens first, then padding
-        encoder_attention_mask = encoder_attention_mask.bool()
-        encoder_attention_mask_2 = encoder_attention_mask_2.bool()
-        encoder_attention_mask_3 = encoder_attention_mask_3.bool()
-        new_encoder_hidden_states = []
-        new_encoder_attention_mask = []
-
-        for text, text_mask, text_2, text_mask_2, image, image_mask in zip(
-            encoder_hidden_states,
-            encoder_attention_mask,
-            encoder_hidden_states_2,
-            encoder_attention_mask_2,
-            encoder_hidden_states_3,
-            encoder_attention_mask_3,
-        ):
-            # Concatenate: [valid_image, valid_byt5, valid_mllm, invalid_image, invalid_byt5, invalid_mllm]
-            new_encoder_hidden_states.append(
-                torch.cat(
-                    [
-                        image[image_mask],  # valid image
-                        text_2[text_mask_2],  # valid byt5
-                        text[text_mask],  # valid mllm
-                        image[~image_mask],  # invalid image (zeroed)
-                        torch.zeros_like(text_2[~text_mask_2]),  # invalid byt5 (zeroed)
-                        torch.zeros_like(text[~text_mask]),  # invalid mllm (zeroed)
-                    ],
-                    dim=0,
-                )
-            )
-            # Apply same reordering to attention masks
-            new_encoder_attention_mask.append(
-                torch.cat(
-                    [
-                        image_mask[image_mask],
-                        text_mask_2[text_mask_2],
-                        text_mask[text_mask],
-                        image_mask[~image_mask],
-                        text_mask_2[~text_mask_2],
-                        text_mask[~text_mask],
-                    ],
-                    dim=0,
-                )
-            )
-
-        encoder_hidden_states = torch.stack(new_encoder_hidden_states)
-        encoder_attention_mask = torch.stack(new_encoder_attention_mask)
-
-        # 4. Transformer blocks
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for block in self.double_blocks:
-                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
-                    block,
-                    hidden_states,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    temb,
-                    temb_txt,
-                    freqs_cis,
-                    seq_attention_mask,
-                    viewmats_seq, # hyworld
-                    Ks_seq, # hyworld
-                )
-        else:
-            for block in self.double_blocks:
-                hidden_states, encoder_hidden_states = block(
-                    hidden_states,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    temb,
-                    temb_txt,
-                    freqs_cis,
-                    seq_attention_mask,
-                    viewmats=viewmats_seq, # hyworld
-                    Ks=Ks_seq, # hyworld
-                )
-
-
-        # Final layer processing (per-token conditioning via HYWorldFinalLayer)
-        # Apply final_layer on sharded data first, then gather
-        hidden_states = self.final_layer(hidden_states, temb)
-        
-        # Gather the output from all ranks
-        if get_sp_world_size() > 1:
-            hidden_states = hidden_states.contiguous()
-        hidden_states = sequence_model_parallel_all_gather_with_unpad(hidden_states, original_seq_len, dim=1)
-        
-        # Unpatchify to get original shape
-        hidden_states = unpatchify(hidden_states, post_patch_num_frames, post_patch_height, post_patch_width, self.patch_size, self.out_channels)
-
-        return hidden_states

fastvideo/models/dits/hyworld/pose.py

@@ -1,413 +0,0 @@
-# Some functions from HY-WorldPlay/hyvideo/generate.py
-
-"""
-Pose processing utilities for HYWorld video generation.
-
-This module provides functions to convert camera poses to model input tensors,
-including viewmats, intrinsics, and action labels.
-
-Adapted from HY-WorldPlay: https://github.com/Tencent-Hunyuan/HY-WorldPlay
-"""
-
-import json
-import numpy as np
-import torch
-from scipy.spatial.transform import Rotation as R
-from typing import Union, Optional
-
-from .trajectory import generate_camera_trajectory_local
-
-
-# Mapping from one-hot action encoding to single label
-mapping = {
-    (0, 0, 0, 0): 0,
-    (1, 0, 0, 0): 1,
-    (0, 1, 0, 0): 2,
-    (0, 0, 1, 0): 3,
-    (0, 0, 0, 1): 4,
-    (1, 0, 1, 0): 5,
-    (1, 0, 0, 1): 6,
-    (0, 1, 1, 0): 7,
-    (0, 1, 0, 1): 8,
-}
-
-# Default camera intrinsic matrix (for 1920x1080 resolution)
-DEFAULT_INTRINSIC = [
-    [969.6969696969696, 0.0, 960.0],
-    [0.0, 969.6969696969696, 540.0],
-    [0.0, 0.0, 1.0],
-]
-
-# Default movement speeds
-DEFAULT_FORWARD_SPEED = 0.08  # units per frame
-DEFAULT_YAW_SPEED = np.deg2rad(3)  # radians per frame
-DEFAULT_PITCH_SPEED = np.deg2rad(3)  # radians per frame
-
-
-def one_hot_to_one_dimension(one_hot: torch.Tensor) -> torch.Tensor:
-    """Convert one-hot action encoding to single dimension labels."""
-    return torch.tensor([mapping[tuple(row.tolist())] for row in one_hot])
-
-
-def parse_pose_string(
-    pose_string: str,
-    forward_speed: float = DEFAULT_FORWARD_SPEED,
-    yaw_speed: float = DEFAULT_YAW_SPEED,
-    pitch_speed: float = DEFAULT_PITCH_SPEED,
-) -> list[dict]:
-    """
-    Parse pose string to motions list.
-    
-    Format: "w-3, right-0.5, d-4"
-    - w: forward movement
-    - s: backward movement
-    - a: left movement
-    - d: right movement
-    - up: pitch up rotation
-    - down: pitch down rotation
-    - left: yaw left rotation
-    - right: yaw right rotation
-    - number after dash: duration in frames/latents
-    
-    Args:
-        pose_string: Comma-separated pose commands
-        forward_speed: Movement amount per frame
-        yaw_speed: Yaw rotation amount per frame (radians)
-        pitch_speed: Pitch rotation amount per frame (radians)
-        
-    Returns:
-        List of motion dictionaries for generate_camera_trajectory_local
-    """
-    motions = []
-    commands = [cmd.strip() for cmd in pose_string.split(",")]
-
-    for cmd in commands:
-        if not cmd:
-            continue
-
-        parts = cmd.split("-")
-        if len(parts) != 2:
-            raise ValueError(
-                f"Invalid pose command: {cmd}. Expected format: 'action-duration'"
-            )
-
-        action = parts[0].strip()
-        try:
-            duration = float(parts[1].strip())
-        except ValueError:
-            raise ValueError(f"Invalid duration in command: {cmd}")
-
-        num_frames = int(duration)
-
-        # Parse action and create motion dicts
-        if action == "w":
-            # Forward
-            for _ in range(num_frames):
-                motions.append({"forward": forward_speed})
-        elif action == "s":
-            # Backward
-            for _ in range(num_frames):
-                motions.append({"forward": -forward_speed})
-        elif action == "a":
-            # Left
-            for _ in range(num_frames):
-                motions.append({"right": -forward_speed})
-        elif action == "d":
-            # Right
-            for _ in range(num_frames):
-                motions.append({"right": forward_speed})
-        elif action == "up":
-            # Pitch up
-            for _ in range(num_frames):
-                motions.append({"pitch": pitch_speed})
-        elif action == "down":
-            # Pitch down
-            for _ in range(num_frames):
-                motions.append({"pitch": -pitch_speed})
-        elif action == "left":
-            # Yaw left
-            for _ in range(num_frames):
-                motions.append({"yaw": -yaw_speed})
-        elif action == "right":
-            # Yaw right
-            for _ in range(num_frames):
-                motions.append({"yaw": yaw_speed})
-        else:
-            raise ValueError(
-                f"Unknown action: {action}. "
-                f"Supported actions: w, s, a, d, up, down, left, right"
-            )
-
-    return motions
-
-def pose_string_to_json(
-    pose_string: str,
-    intrinsic: Optional[list[list[float]]] = None,
-) -> dict:
-    """
-    Convert pose string to pose JSON format.
-    
-    Args:
-        pose_string: Comma-separated pose commands
-        intrinsic: Camera intrinsic matrix (default: DEFAULT_INTRINSIC from trajectory)
-        
-    Returns:
-        Dict with frame indices as keys, containing extrinsic and K (intrinsic) matrices
-    """
-    if intrinsic is None:
-        intrinsic = DEFAULT_INTRINSIC
-    
-    motions = parse_pose_string(pose_string)
-    poses = generate_camera_trajectory_local(motions)
-
-    pose_json = {}
-    for i, p in enumerate(poses):
-        pose_json[str(i)] = {"extrinsic": p.tolist(), "K": intrinsic}
-
-    return pose_json
-
-def pose_to_input(
-    pose_data: Union[str, dict],
-    latent_num: int,
-    tps: bool = False,
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """
-    Convert pose data to model input tensors.
-    
-    Args:
-        pose_data: One of:
-            - str ending with '.json': path to JSON file
-            - str: pose string (e.g., "w-3, right-0.5, d-4")
-            - dict: pose JSON data
-        latent_num: Number of latents (frames in latent space)
-        tps: Third-person mode flag
-        
-    Returns:
-        Tuple of (viewmats, intrinsics, action_labels):
-            - viewmats: World-to-camera matrices [T, 4, 4]
-            - intrinsics: Normalized camera intrinsics [T, 3, 3]
-            - action_labels: Action labels for each frame [T]
-    """
-    # Handle different input types
-    if isinstance(pose_data, str):
-        if pose_data.endswith(".json"):
-            # Load from JSON file
-            with open(pose_data, "r") as f:
-                pose_json = json.load(f)
-        else:
-            # Parse pose string
-            pose_json = pose_string_to_json(pose_data)
-    elif isinstance(pose_data, dict):
-        pose_json = pose_data
-    else:
-        raise ValueError(
-            f"Invalid pose_data type: {type(pose_data)}. Expected str or dict."
-        )
-
-    pose_keys = list(pose_json.keys())
-    latent_num_from_pose = len(pose_keys)
-    assert latent_num_from_pose == latent_num, (
-        f"pose corresponds to {latent_num_from_pose * 4 - 3} frames, num_frames "
-        f"must be set to {latent_num_from_pose * 4 - 3} to ensure alignment."
-    )
-
-    intrinsic_list = []
-    w2c_list = []
-    for i in range(latent_num):
-        t_key = pose_keys[i]
-        c2w = np.array(pose_json[t_key]["extrinsic"])
-        w2c = np.linalg.inv(c2w)
-        w2c_list.append(w2c)
-        
-        # Normalize intrinsics
-        intrinsic = np.array(pose_json[t_key]["K"])
-        intrinsic[0, 0] /= intrinsic[0, 2] * 2
-        intrinsic[1, 1] /= intrinsic[1, 2] * 2
-        intrinsic[0, 2] = 0.5
-        intrinsic[1, 2] = 0.5
-        intrinsic_list.append(intrinsic)
-
-    w2c_list = np.array(w2c_list)
-    intrinsic_list = torch.tensor(np.array(intrinsic_list))
-
-    # Compute relative camera-to-world transforms
-    c2ws = np.linalg.inv(w2c_list)
-    C_inv = np.linalg.inv(c2ws[:-1])
-    relative_c2w = np.zeros_like(c2ws)
-    relative_c2w[0, ...] = c2ws[0, ...]
-    relative_c2w[1:, ...] = C_inv @ c2ws[1:, ...]
-    
-    # Initialize one-hot action encodings
-    trans_one_hot = np.zeros((relative_c2w.shape[0], 4), dtype=np.int32)
-    rotate_one_hot = np.zeros((relative_c2w.shape[0], 4), dtype=np.int32)
-
-    move_norm_valid = 0.0001
-    for i in range(1, relative_c2w.shape[0]):
-        move_dirs = relative_c2w[i, :3, 3]  # direction vector
-        move_norms = np.linalg.norm(move_dirs)
-        
-        if move_norms > move_norm_valid:  # threshold for movement
-            move_norm_dirs = move_dirs / move_norms
-            angles_rad = np.arccos(move_norm_dirs.clip(-1.0, 1.0))
-            trans_angles_deg = angles_rad * (180.0 / np.pi)  # convert to degrees
-        else:
-            trans_angles_deg = np.zeros(3)
-
-        R_rel = relative_c2w[i, :3, :3]
-        r = R.from_matrix(R_rel)
-        rot_angles_deg = r.as_euler("xyz", degrees=True)
-
-        # Determine movement and rotation actions
-        if move_norms > move_norm_valid:  # threshold for movement
-            if (not tps) or (
-                tps and abs(rot_angles_deg[1]) < 5e-2 and abs(rot_angles_deg[0]) < 5e-2
-            ):
-                if trans_angles_deg[2] < 60:
-                    trans_one_hot[i, 0] = 1  # forward
-                elif trans_angles_deg[2] > 120:
-                    trans_one_hot[i, 1] = 1  # backward
-
-                if trans_angles_deg[0] < 60:
-                    trans_one_hot[i, 2] = 1  # right
-                elif trans_angles_deg[0] > 120:
-                    trans_one_hot[i, 3] = 1  # left
-
-        if rot_angles_deg[1] > 5e-2:
-            rotate_one_hot[i, 0] = 1  # right
-        elif rot_angles_deg[1] < -5e-2:
-            rotate_one_hot[i, 1] = 1  # left
-
-        if rot_angles_deg[0] > 5e-2:
-            rotate_one_hot[i, 2] = 1  # up
-        elif rot_angles_deg[0] < -5e-2:
-            rotate_one_hot[i, 3] = 1  # down
-
-    trans_one_hot = torch.tensor(trans_one_hot)
-    rotate_one_hot = torch.tensor(rotate_one_hot)
-
-    # Convert one-hot to single-dimension labels
-    trans_one_label = one_hot_to_one_dimension(trans_one_hot)
-    rotate_one_label = one_hot_to_one_dimension(rotate_one_hot)
-    action_one_label = trans_one_label * 9 + rotate_one_label
-
-    return (
-        torch.as_tensor(w2c_list),
-        torch.as_tensor(intrinsic_list),
-        action_one_label,
-    )
-
-
-def camera_center_normalization(w2c: np.ndarray) -> np.ndarray:
-    """Normalize camera centers relative to the first camera."""
-    c2w = np.linalg.inv(w2c)
-    C0_inv = np.linalg.inv(c2w[0])
-    c2w_aligned = np.array([C0_inv @ C for C in c2w])
-    return np.linalg.inv(c2w_aligned)
-
-
-
-def parse_pose_string_to_actions(pose_string: str, fps: int = 24) -> list[dict]:
-    """
-    Parse pose string to frame-level action timeline.
-    
-    Format: pose string uses latent counts, where:
-    - 1 latent = 4 frames
-    - Special rule: first frame of entire video is extra (frame 0)
-    - Example: "w-4,d-4" means:
-      - w-4: forward for frames 0-16 (17 frames total: 1 extra + 4*4)
-      - d-4: right for frames 17-32 (16 frames total: 4*4)
-    
-    Args:
-        pose_string: Comma-separated pose commands (e.g., "w-4,d-4")
-        fps: Frames per second for video (default: 24)
-        
-    Returns:
-        List of dicts with action values for each frame
-    """
-    commands = [cmd.strip() for cmd in pose_string.split(",")]
-
-    frame_actions = []
-    is_first_command = True
-
-    for cmd in commands:
-        if not cmd:
-            continue
-
-        parts = cmd.split("-")
-        if len(parts) != 2:
-            raise ValueError(
-                f"Invalid pose command: {cmd}. Expected format: 'action-duration'"
-            )
-
-        action = parts[0].strip()
-        try:
-            num_latents = int(parts[1].strip())
-        except ValueError:
-            raise ValueError(f"Invalid duration in command: {cmd}")
-
-        # Convert latents to frames
-        # First command gets 1 extra frame (the special frame 0)
-        if is_first_command:
-            num_frames = 1 + num_latents * 4
-            is_first_command = False
-        else:
-            num_frames = num_latents * 4
-
-        # Map action to action values
-        action_values = {"forward": 0, "left": 0, "yaw": 0, "pitch": 0}
-
-        if action == "w":
-            action_values["forward"] = 1
-        elif action == "s":
-            action_values["forward"] = -1
-        elif action == "a":
-            action_values["left"] = 1
-        elif action == "d":
-            action_values["left"] = -1
-        elif action == "up":
-            action_values["pitch"] = 1
-        elif action == "down":
-            action_values["pitch"] = -1
-        elif action == "left":
-            action_values["yaw"] = -1
-        elif action == "right":
-            action_values["yaw"] = 1
-        else:
-            raise ValueError(f"Unknown action: {action}")
-
-        # Add frame-level actions
-        for _ in range(num_frames):
-            frame_actions.append(action_values.copy())
-
-    return frame_actions
-
-
-def compute_latent_num(num_frames: int) -> int:
-    """
-    Compute the number of latents from number of frames.
-    
-    Formula: num_frames = (latent_num - 1) * 4 + 1
-    So: latent_num = (num_frames - 1) // 4 + 1
-    
-    Args:
-        num_frames: Number of video frames
-        
-    Returns:
-        Number of latents
-    """
-    return (num_frames - 1) // 4 + 1
-
-
-def compute_num_frames(latent_num: int) -> int:
-    """
-    Compute the number of frames from number of latents.
-    
-    Formula: num_frames = (latent_num - 1) * 4 + 1
-    
-    Args:
-        latent_num: Number of latents
-        
-    Returns:
-        Number of video frames
-    """
-    return (latent_num - 1) * 4 + 1

fastvideo/models/dits/hyworld/resolution_utils.py

@@ -1,64 +0,0 @@
-import numpy as np
-from PIL import Image
-import requests
-from io import BytesIO
-
-from fastvideo.models.dits.hyworld.data_utils import generate_crop_size_list
-
-# Target resolution configs (matching HY-WorldPlay)
-TARGET_SIZE_CONFIG = {
-    "360p": {"bucket_hw_base_size": 480, "bucket_hw_bucket_stride": 16},
-    "480p": {"bucket_hw_base_size": 640, "bucket_hw_bucket_stride": 16},
-    "720p": {"bucket_hw_base_size": 960, "bucket_hw_bucket_stride": 16},
-    "1080p": {"bucket_hw_base_size": 1440, "bucket_hw_bucket_stride": 16},
-}
-
-
-def get_closest_resolution(image_height, image_width, target_resolution="480p"):
-    """
-    Get closest supported resolution for given image dimensions.
-    
-    Args:
-        image_height: Height of input image
-        image_width: Width of input image  
-        target_resolution: Target resolution string (e.g., "480p", "720p")
-        
-    Returns:
-        tuple[int, int]: (height, width) of closest supported resolution
-    """
-    config = TARGET_SIZE_CONFIG[target_resolution]
-    bucket_hw_base_size = config["bucket_hw_base_size"]
-    bucket_hw_bucket_stride = config["bucket_hw_bucket_stride"]
-    
-    crop_size_list = generate_crop_size_list(bucket_hw_base_size, bucket_hw_bucket_stride)
-    aspect_ratios = np.array([round(float(h) / float(w), 5) for h, w in crop_size_list])
-    
-    # Find closest aspect ratio
-    image_ratio = float(image_height) / float(image_width)
-    closest_idx = np.abs(aspect_ratios - image_ratio).argmin()
-    closest_size = crop_size_list[closest_idx]
-    
-    return closest_size[0], closest_size[1]  # (height, width)
-
-
-def get_resolution_from_image(image_path, target_resolution="480p"):
-    """
-    Automatically determine resolution from input image.
-    
-    Args:
-        image_path: Path or URL to input image
-        target_resolution: Target resolution tier ("480p", "720p", etc.)
-        
-    Returns:
-        tuple[int, int]: (height, width) matching HY-WorldPlay's bucket selection
-    """
-    # Handle URL inputs
-    if isinstance(image_path, str) and image_path.startswith(('http://', 'https://')):
-        response = requests.get(image_path)
-        response.raise_for_status()
-        img = Image.open(BytesIO(response.content))
-    else:
-        img = Image.open(image_path)
-    img_width, img_height = img.size
-    return get_closest_resolution(img_height, img_width, target_resolution)
-

fastvideo/models/dits/hyworld/retrieval_context.py

@@ -1,316 +0,0 @@
-# HY-WorldPlay/hyvideo/utils/retrieval_context.py
-
-# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
-#
-# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
-# output and results therefrom are provided "AS IS" without any express or implied warranties of
-# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
-# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
-# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
-# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
-# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
-# of rights and permissions under this agreement.
-# See the License for the specific language governing permissions and limitations under the License.
-
-import torch
-import numpy as np
-from typing import List, Tuple, Dict
-import math
-
-
-def generate_points_in_sphere(n_points: int, radius: float) -> torch.Tensor:
-    """
-    Uniformly sample points within a sphere of a specified radius.
-
-    :param n_points: The number of points to generate.
-    :param radius: The radius of the sphere.
-    :return: A tensor of shape (n_points, 3), representing the (x, y, z) coordinates of the points.
-    """
-    samples_r = torch.rand(n_points)
-    samples_phi = torch.rand(n_points)
-    samples_u = torch.rand(n_points)
-
-    r = radius * torch.pow(samples_r, 1 / 3)
-    phi = 2 * math.pi * samples_phi
-    theta = torch.acos(1 - 2 * samples_u)
-
-    # transfer the coordinates from spherical to cartesian
-    x = r * torch.sin(theta) * torch.cos(phi)
-    y = r * torch.sin(theta) * torch.sin(phi)
-    z = r * torch.cos(theta)
-
-    points = torch.stack((x, y, z), dim=1)
-    return points
-
-
-def rotation_matrix_to_angles(R: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Estimate the Pitch and Yaw angles from a 3x3 rotation matrix R in the camera coordinate system.
-
-    Assumed Camera Coordinate System: X=Right, Y=Up, Z=Backward
-    (or NeRF style: X=Right, Y=Down, Z=Forward).
-    Here we adopt the common Computer Vision convention: Z-axis is Forward.
-
-    Note: The angle calculations here are directly based on the conventions of your `is_inside_fov_3d_hv` function:
-    - Yaw/Azimuth angle is in the XZ plane (atan2(x, z)).
-    - Pitch/Elevation angle is relative to the horizontal plane (atan2(y, sqrt(x^2 + z^2))).
-
-    For the third column R[:, 2] of the W2C matrix R (the direction of the World Z-axis in the Camera frame),
-    this typically corresponds to the direction the camera is looking
-    (the representation of the world Z-axis in the camera frame).
-
-    To simplify and match your `is_inside_fov` logic, we directly use the camera's Z-axis vector:
-    Camera Z-axis direction in World Frame (Forward Vector): fwd = R_w2c_inv @ [0, 0, 1]
-    More simply, the Z-axis vector of the C2W matrix is the camera's forward vector in the world frame.
-    C2W = W2C_inv
-    """
-
-    R_c2w = R.T
-    fwd = R_c2w[:, 2]
-
-    x = fwd[0]
-    y = fwd[1]
-    z = fwd[2]
-
-    # compute yaw and pitch
-    yaw_rad = torch.atan2(x, z)
-    yaw_deg = yaw_rad * (180.0 / math.pi)
-    pitch_rad = torch.atan2(y, torch.sqrt(x**2 + z**2))
-    pitch_deg = pitch_rad * (180.0 / math.pi)
-
-    return pitch_deg, yaw_deg
-
-
-def is_inside_fov_3d_hv(
-    points: torch.Tensor,
-    center: torch.Tensor,
-    center_pitch: torch.Tensor,
-    center_yaw: torch.Tensor,
-    fov_half_h: torch.Tensor,
-    fov_half_v: torch.Tensor,
-) -> torch.Tensor:
-    """
-    Check whether points are inside a 3D view frustum defined by a center coordinate, pitch angle, and yaw angle.
-
-    :param points: Tensor of shape (N, 3) or (N, B, 3) representing the coordinates of the sampled points.
-    :param center: Tensor of shape (3) or (B, 3) representing the camera center coordinates.
-    :param center_pitch: Tensor of shape (1) or (B) representing the pitch angle of center view direction.
-    :param center_yaw: Tensor of shape (1) or (B) representing the yaw angle of the center view direction.
-    :param fov_half_h: The horizontal half field-of-view angle (in degrees).
-    :param fov_half_v: The vertical half field-of-view angle (in degrees).
-    :return: Boolean tensor of shape (N) or (N, B), indicating whether each point is inside the FOV.
-    """
-    if points.ndim == 2:  # N, 3
-        vectors = points - center[None, :]
-        C = 1
-    elif points.ndim == 3:  # N, B, 3
-        vectors = points - center[None, ...]
-        center_pitch = center_pitch[None, :] if center_pitch.ndim == 1 else center_pitch
-        center_yaw = center_yaw[None, :] if center_yaw.ndim == 1 else center_yaw
-    else:
-        raise ValueError("points' shape should be (N, 3) or (N, B, 3)")
-
-    x = vectors[..., 0]
-    y = vectors[..., 1]
-    z = vectors[..., 2]
-
-    # Calculate the horizontal angle (yaw/azimuth), assuming the Z-axis is forward.
-    azimuth = torch.atan2(x, z) * (180 / math.pi)
-
-    # Calculate the vertical angle (pitch/elevation).
-    elevation = torch.atan2(y, torch.sqrt(x**2 + z**2)) * (180 / math.pi)
-
-    # Calculate the angular difference from the center view direction (handling angle wrapping).
-    diff_azimuth = azimuth - center_yaw
-    diff_azimuth = torch.remainder(diff_azimuth + 180, 360) - 180
-
-    diff_elevation = elevation - center_pitch
-    diff_elevation = torch.remainder(diff_elevation + 180, 360) - 180
-
-    # Check if within FOV
-    in_fov_h = diff_azimuth.abs() < fov_half_h
-    in_fov_v = diff_elevation.abs() < fov_half_v
-
-    return in_fov_h & in_fov_v
-
-
-def calculate_fov_overlap_similarity(
-    w2c_matrix_curr: torch.Tensor,
-    w2c_matrix_hist: torch.Tensor,
-    fov_h_deg: float = 105.0,
-    fov_v_deg: float = 75.0,
-    device=None,
-    points_local=None,
-) -> float:
-    """
-    Calculate the Field-of-View (FOV) overlap similarity between two W2C poses using Monte Carlo sampling.
-
-    Similarity = (Number of points in Curr_FOV ∩ Hist_FOV) / (Number of points in Curr_FOV).
-
-    :param w2c_matrix_curr: The (4, 4) W2C matrix for the current frame.
-    :param w2c_matrix_hist: The (4, 4) W2C matrix for the historical frame.
-    :param num_samples, radius, fov_h_deg, fov_v_deg: Sampling and FOV parameters.
-    :return: The overlap ratio (a float between 0.0 and 1.0).
-    """
-    w2c_matrix_curr = torch.tensor(w2c_matrix_curr, device=device)
-    w2c_matrix_hist = torch.tensor(w2c_matrix_hist, device=device)
-
-    c2w_matrix_curr = torch.linalg.inv(w2c_matrix_curr)
-    c2w_matrix_hist = torch.linalg.inv(w2c_matrix_hist)
-    C_inv = w2c_matrix_curr
-
-    w2c_matrix_curr = torch.linalg.inv(C_inv @ c2w_matrix_curr)
-    w2c_matrix_hist = torch.linalg.inv(C_inv @ c2w_matrix_hist)
-
-    R_curr, t_curr = w2c_matrix_curr[:3, :3], w2c_matrix_curr[:3, 3]
-    R_hist, t_hist = w2c_matrix_hist[:3, :3], w2c_matrix_hist[:3, 3]
-    P_w_curr = -R_curr.T @ t_curr
-    P_w_hist = -R_hist.T @ t_hist
-
-    # pitch, yaw
-    pitch_curr, yaw_curr = rotation_matrix_to_angles(R_curr)
-    pitch_hist, yaw_hist = rotation_matrix_to_angles(R_hist)
-
-    fov_half_h = torch.tensor(fov_h_deg / 2.0, device=device)
-    fov_half_v = torch.tensor(fov_v_deg / 2.0, device=device)
-
-    # move to P_w_curr (N, 3)
-    points_world = points_local + P_w_curr[None, :]
-
-    in_fov_curr = is_inside_fov_3d_hv(
-        points_world,
-        P_w_curr[None, :],
-        pitch_curr[None],
-        yaw_curr[None],
-        fov_half_h,
-        fov_half_v,
-    )
-
-    # compute based on angle
-    in_fov_hist = is_inside_fov_3d_hv(
-        points_world,
-        P_w_hist[None, :],
-        pitch_hist[None],
-        yaw_hist[None],
-        fov_half_h,
-        fov_half_v,
-    )
-
-    # compute based on distance
-    dist = torch.norm(points_world - P_w_hist.reshape(1, -1), dim=1) < 8.0
-    in_fov_hist = in_fov_hist.bool() & dist.reshape(1, -1).bool()
-
-    overlap_count = (in_fov_curr.bool() & in_fov_hist.bool()).sum().float()
-    fov_curr_count = in_fov_curr.sum().float()
-
-    if fov_curr_count == 0:
-        return 0.0
-
-    overlap_ratio = overlap_count / fov_curr_count
-
-    return overlap_ratio.item()
-
-
-def select_aligned_memory_frames(
-    w2c_list: List[np.ndarray],
-    current_frame_idx: int,
-    memory_frames: int,
-    temporal_context_size: int,
-    pred_latent_size: int,
-    pos_weight: float = 1.0,
-    ang_weight: float = 1.0,
-    device=None,
-    points_local=None,
-) -> List[int]:
-    """
-    Selects memory and context frames for a given frame based on a four-frame segment distance calculation.
-
-    :param w2c_list: List of all N 4x4 World-to-Camera (W2C) extrinsic matrices (np.ndarray).
-    :param current_frame_idx: The index of the current frame to be processed.
-    :param memory_frames: The total number of memory frames to select.
-    :param context_size: The total number of context frames to select.
-    :param pos_weight: The weight applied to the spatial (position) distance component.
-    :param ang_weight: The weight applied to the angular distance component.
-
-    :return: List[int]: A list containing the indices of the selected memory frames and context frames.
-    """
-    if current_frame_idx <= memory_frames:
-        return list(range(0, current_frame_idx))
-
-    num_total_frames = len(w2c_list)
-    if current_frame_idx >= num_total_frames or current_frame_idx < 3:
-        raise ValueError(
-            f"The current frame index must be within the valid range of w2c_list and must be at least 3."
-            f"{current_frame_idx}, {len(w2c_list)}"
-        )
-
-    start_context_idx = max(0, current_frame_idx - temporal_context_size)
-    context_frames_indices = list(range(start_context_idx, current_frame_idx))
-
-    candidate_distances = []
-    query_clip_indices = list(
-        range(
-            current_frame_idx,
-            (
-                current_frame_idx + pred_latent_size
-                if current_frame_idx + pred_latent_size <= num_total_frames
-                else num_total_frames
-            ),
-        )
-    )
-
-    historical_clip_indices = list(
-        range(4, current_frame_idx - temporal_context_size, 4)
-    )
-
-    memory_frames_indices = [0, 1, 2, 3]  # add the first chunk as context
-    memory_frames = memory_frames - temporal_context_size
-
-    for hist_idx in historical_clip_indices:
-        total_dist = 0
-        hist_w2c_1 = w2c_list[hist_idx]
-        hist_w2c_2 = w2c_list[hist_idx + 2]
-        for query_idx in query_clip_indices:
-            dist_1_for_query_idx = 1.0 - calculate_fov_overlap_similarity(
-                w2c_list[query_idx],
-                hist_w2c_1,
-                fov_h_deg=60.0,
-                fov_v_deg=35.0,
-                device=device,
-                points_local=points_local,
-            )
-            dist_2_for_query_idx = 1.0 - calculate_fov_overlap_similarity(
-                w2c_list[query_idx],
-                hist_w2c_2,
-                fov_h_deg=60.0,
-                fov_v_deg=35.0,
-                device=device,
-                points_local=points_local,
-            )
-            dist_for_query_idx = (dist_1_for_query_idx + dist_2_for_query_idx) / 2.0
-            total_dist += dist_for_query_idx
-
-        final_clip_distance = total_dist / len(query_clip_indices)
-        candidate_distances.append((hist_idx, final_clip_distance))
-
-    candidate_distances.sort(key=lambda x: x[1])
-
-    for start_idx, _ in candidate_distances:
-        # check the memory frame number
-        if len(memory_frames_indices) >= memory_frames:
-            break
-
-        if start_idx not in memory_frames_indices:
-            memory_frames_indices.extend(range(start_idx, start_idx + 4))
-
-    # exclude the repeated frames
-    selected_frames_set = set(context_frames_indices)
-    selected_frames_set.update(memory_frames_indices)
-
-    final_selected_frames = sorted(list(selected_frames_set))
-
-    return final_selected_frames

fastvideo/models/dits/hyworld/trajectory.py

@@ -1,112 +0,0 @@
-# HY-WorldPlay/hyvideo/generate_custom_trajectory.py
-
-import numpy as np
-import json
-
-
-def rot_x(theta):
-    c, s = np.cos(theta), np.sin(theta)
-    return np.array([[1, 0, 0], [0, c, -s], [0, s, c]])
-
-
-def rot_y(theta):
-    c, s = np.cos(theta), np.sin(theta)
-    return np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]])
-
-
-def rot_z(theta):
-    c, s = np.cos(theta), np.sin(theta)
-    return np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])
-
-
-def generate_camera_trajectory_local(motions):
-    """
-    motions: list of dict
-             {"forward": 1.0}, {"yaw": np.pi/2}, {"pitch": np.pi/6}, {"right": 1.0}
-             - forward: Translation (Forward or Backward)
-             - yaw:   Rotate (Left or Right)
-             - pitch: Rotate (Up or Down)
-             - right: Translation (Right or Left)
-             - third_yaw: Third Perspective Rotate (Left or Right)
-    """
-
-    poses = []
-    T = np.eye(4)
-    poses.append(T.copy())
-
-    for move in motions:
-        # Rotate (Left or Right)
-        if "yaw" in move:
-            R = rot_y(move["yaw"])
-            T[:3, :3] = T[:3, :3] @ R
-
-        # Rotate (Up or Down)
-        if "pitch" in move:
-            R = rot_x(move["pitch"])
-            T[:3, :3] = T[:3, :3] @ R
-
-        # Translation (Z-direction of the camera's local coordinate system)
-        forward = move.get("forward", 0.0)
-        if forward != 0:
-            local_t = np.array([0, 0, forward])
-            world_t = T[:3, :3] @ local_t
-            T[:3, 3] += world_t
-
-        # Translation (Z-direction of the camera's local coordinate system)
-        right = move.get("right", 0.0)
-        if right != 0:
-            local_t = np.array([right, 0, 0])
-            world_t = T[:3, :3] @ local_t
-            T[:3, 3] += world_t
-
-        # Third Perspective Rotate (Left or Right)
-        third_yaw = move.get("third_yaw", 0.0)
-        if third_yaw != 0:
-            theta = -third_yaw
-            C = np.array([[1, 0.0, 0, 0], [0, 1, 0, 0], [0, 0, 1, -1.0], [0, 0, 0, 1]])
-            c_origin = C.copy()
-            # Rotation around the Y-axis
-            R_y = np.array(
-                [
-                    [np.cos(theta), 0, np.sin(theta)],
-                    [0, 1, 0],
-                    [-np.sin(theta), 0, np.cos(theta)],
-                ]
-            )
-            # Translation
-            C[:3, :3] = C[:3, :3] @ R_y
-            C[:3, 3] = R_y @ C[:3, 3]
-            c_inv = np.linalg.inv(c_origin)
-            c_relative = c_inv @ C
-            T = T @ c_relative
-
-        poses.append(T.copy())
-
-    return poses
-
-
-if __name__ == "__main__":
-    # Examples: Forward 0.08 * 16 -> Right Rotate 3 degree * 16
-    motions = []
-    for i in range(15):
-        motions.append({"forward": 0.08})
-
-    for i in range(16):
-        motions.append({"yaw": np.deg2rad(3)})
-
-    intrinsic = [
-        [969.6969696969696, 0.0, 960.0],
-        [0.0, 969.6969696969696, 540.0],
-        [0.0, 0.0, 1.0],
-    ]
-
-    poses = generate_camera_trajectory_local(motions)
-    custom_c2w = {}
-    for i, p in enumerate(poses):
-        custom_c2w[str(i)] = {"extrinsic": p.tolist(), "K": intrinsic}
-        json.dump(
-            custom_c2w,
-            open("./assets/pose/pose.json", "w"),
-            indent=4,
-            ensure_ascii=False,
-        )

fastvideo/models/dits/wanvideo.py

@@ -734,8 +734,18 @@ class WanTransformer3DModel(CachableDiT):
                         block, hidden_states, encoder_hidden_states,
                         timestep_proj, freqs_cis, attention_mask)
             else:
-                for block in self.blocks:
-                    hidden_states = block(hidden_states, encoder_hidden_states,
+                offload_mgr = getattr(self, "_layerwise_offload_manager", None)
+                use_offload = offload_mgr is not None and getattr(offload_mgr, "enabled", False)
+
+                for i, block in enumerate(self.blocks):
+                    scope = offload_mgr.layer_scope(
+                        prefetch_layer_idx=i + 1 if i + 1 < len(self.blocks) else None,
+                        release_layer_idx=i,
+                        non_blocking=True,
+                    ) if use_offload else nullcontext()
+                    
+                    with scope:
+                        hidden_states = block(hidden_states, encoder_hidden_states,
                                               timestep_proj, freqs_cis, attention_mask)
             # if teacache is enabled, we need to cache the original hidden states
 

fastvideo/models/encoders/gemma.py

@@ -1,563 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from dataclasses import dataclass
-import os
-from typing import Iterable
-
-import torch
-from torch import nn
-from transformers import Gemma3ForConditionalGeneration
-
-from fastvideo.configs.models.encoders import BaseEncoderOutput, TextEncoderConfig
-from fastvideo.models.encoders.base import TextEncoder
-from fastvideo.models.dits.ltx2 import (
-    FeedForward,
-    LTXRopeType,
-    apply_ltx_rotary_emb,
-    generate_ltx_freq_grid_np,
-    generate_ltx_freq_grid_pytorch,
-    precompute_ltx_freqs_cis,
-)
-from fastvideo.models.loader.weight_utils import default_weight_loader
-from fastvideo.platforms import AttentionBackendEnum
-
-
-def _debug_log_line(message: str) -> None:
-    if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") != "1":
-        return
-    log_path = os.getenv("LTX2_PIPELINE_DEBUG_PATH", "")
-    if not log_path:
-        return
-    log_dir = os.path.dirname(log_path)
-    if log_dir:
-        os.makedirs(log_dir, exist_ok=True)
-    with open(log_path, "a", encoding="utf-8") as f:
-        f.write(message + "\n")
-
-
-def _debug_gemma_log_line(message: str) -> None:
-    log_path = os.getenv("LTX2_FASTVIDEO_GEMMA_LOG", "")
-    if not log_path:
-        return
-    log_dir = os.path.dirname(log_path)
-    if log_dir:
-        os.makedirs(log_dir, exist_ok=True)
-    with open(log_path, "a", encoding="utf-8") as f:
-        f.write(message + "\n")
-
-
-@dataclass(frozen=True)
-class GemmaConnectorConfig:
-    num_attention_heads: int
-    attention_head_dim: int
-    num_layers: int
-    positional_embedding_theta: float
-    positional_embedding_max_pos: list[int]
-    rope_type: LTXRopeType
-    double_precision_rope: bool
-    num_learnable_registers: int | None
-
-
-class GemmaFeaturesExtractorProjLinear(nn.Module):
-    """Linear projection that aggregates stacked Gemma hidden states."""
-
-    def __init__(self, in_features: int, out_features: int) -> None:
-        super().__init__()
-        self.aggregate_embed = nn.Linear(in_features, out_features, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.aggregate_embed(x)
-
-
-class _BasicTransformerBlock1D(nn.Module):
-    """1D transformer block for connector processing."""
-
-    def __init__(
-        self,
-        dim: int,
-        heads: int,
-        dim_head: int,
-        rope_type: LTXRopeType,
-        norm_eps: float = 1e-6,
-    ) -> None:
-        super().__init__()
-        self.attn1 = _GemmaAttention(
-            query_dim=dim,
-            context_dim=None,
-            heads=heads,
-            dim_head=dim_head,
-            norm_eps=norm_eps,
-            rope_type=rope_type,
-        )
-        self.ff = FeedForward(dim, dim_out=dim)
-        self.norm_eps = norm_eps
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-        pe: tuple[torch.Tensor, torch.Tensor] | None = None,
-    ) -> torch.Tensor:
-        norm_hidden_states = torch.nn.functional.rms_norm(
-            hidden_states, (hidden_states.shape[-1],), eps=self.norm_eps
-        )
-        if norm_hidden_states.ndim == 4:
-            norm_hidden_states = norm_hidden_states.squeeze(1)
-
-        attn_output = self.attn1(
-            norm_hidden_states,
-            mask=attention_mask,
-            pe=pe,
-        )
-        hidden_states = attn_output + hidden_states
-        if hidden_states.ndim == 4:
-            hidden_states = hidden_states.squeeze(1)
-
-        norm_hidden_states = torch.nn.functional.rms_norm(
-            hidden_states, (hidden_states.shape[-1],), eps=self.norm_eps
-        )
-        ff_output = self.ff(norm_hidden_states)
-        hidden_states = ff_output + hidden_states
-        if hidden_states.ndim == 4:
-            hidden_states = hidden_states.squeeze(1)
-        return hidden_states
-
-
-class _GemmaAttention(nn.Module):
-    """Attention implementation aligned with LTX-2 text encoder."""
-
-    def __init__(
-        self,
-        query_dim: int,
-        context_dim: int | None,
-        heads: int,
-        dim_head: int,
-        norm_eps: float,
-        rope_type: LTXRopeType,
-    ) -> None:
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = query_dim if context_dim is None else context_dim
-
-        self.heads = heads
-        self.dim_head = dim_head
-        self.rope_type = rope_type
-
-        self.q_norm = torch.nn.RMSNorm(inner_dim, eps=norm_eps)
-        self.k_norm = torch.nn.RMSNorm(inner_dim, eps=norm_eps)
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=True)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=True)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=True)
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim, bias=True), nn.Identity())
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        context: torch.Tensor | None = None,
-        mask: torch.Tensor | None = None,
-        pe: tuple[torch.Tensor, torch.Tensor] | None = None,
-        k_pe: tuple[torch.Tensor, torch.Tensor] | None = None,
-    ) -> torch.Tensor:
-        q = self.to_q(x)
-        context = x if context is None else context
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        if pe is not None:
-            q = apply_ltx_rotary_emb(q, pe, self.rope_type)
-            k = apply_ltx_rotary_emb(k, pe if k_pe is None else k_pe, self.rope_type)
-
-        b, q_len, _ = q.shape
-        k_len = k.shape[1]
-        q = q.view(b, q_len, self.heads, self.dim_head).transpose(1, 2)
-        k = k.view(b, k_len, self.heads, self.dim_head).transpose(1, 2)
-        v = v.view(b, k_len, self.heads, self.dim_head).transpose(1, 2)
-
-        if mask is not None:
-            if mask.ndim == 2:
-                mask = mask.unsqueeze(0)
-            if mask.ndim == 3:
-                mask = mask.unsqueeze(1)
-
-        out = torch.nn.functional.scaled_dot_product_attention(
-            q,
-            k,
-            v,
-            attn_mask=mask,
-            dropout_p=0.0,
-            is_causal=False,
-        )
-        out = out.transpose(1, 2).reshape(b, q_len, -1)
-        return self.to_out(out)
-
-
-class Embeddings1DConnector(nn.Module):
-    """Transformer connector that refines Gemma embeddings for LTX-2."""
-
-    _supports_gradient_checkpointing = True
-
-    def __init__(self, config: GemmaConnectorConfig) -> None:
-        super().__init__()
-        self.num_attention_heads = config.num_attention_heads
-        self.inner_dim = config.num_attention_heads * config.attention_head_dim
-        self.positional_embedding_theta = config.positional_embedding_theta
-        self.positional_embedding_max_pos = config.positional_embedding_max_pos
-        self.rope_type = config.rope_type
-        self.double_precision_rope = config.double_precision_rope
-        self.transformer_1d_blocks = nn.ModuleList(
-            [
-                _BasicTransformerBlock1D(
-                    dim=self.inner_dim,
-                    heads=config.num_attention_heads,
-                    dim_head=config.attention_head_dim,
-                    rope_type=config.rope_type,
-                )
-                for _ in range(config.num_layers)
-            ]
-        )
-        self.num_learnable_registers = config.num_learnable_registers
-        if self.num_learnable_registers:
-            self.learnable_registers = nn.Parameter(
-                torch.rand(
-                    self.num_learnable_registers,
-                    self.inner_dim,
-                    dtype=torch.bfloat16,
-                )
-                * 2.0
-                - 1.0
-            )
-
-    def _replace_padded_with_learnable_registers(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        assert hidden_states.shape[1] % self.num_learnable_registers == 0, (
-            f"Hidden states sequence length {hidden_states.shape[1]} must be divisible by "
-            f"num_learnable_registers {self.num_learnable_registers}."
-        )
-
-        num_registers_duplications = (
-            hidden_states.shape[1] // self.num_learnable_registers
-        )
-        learnable_registers = torch.tile(
-            self.learnable_registers, (num_registers_duplications, 1)
-        )
-        attention_mask_binary = (
-            attention_mask.squeeze(1).squeeze(1).unsqueeze(-1) >= -9000.0
-        ).int()
-
-        non_zero_hidden_states = hidden_states[
-            :, attention_mask_binary.squeeze().bool(), :
-        ]
-        non_zero_nums = non_zero_hidden_states.shape[1]
-        pad_length = hidden_states.shape[1] - non_zero_nums
-        adjusted_hidden_states = torch.nn.functional.pad(
-            non_zero_hidden_states, pad=(0, 0, 0, pad_length), value=0
-        )
-        flipped_mask = torch.flip(attention_mask_binary, dims=[1])
-        hidden_states = flipped_mask * adjusted_hidden_states + (
-            1 - flipped_mask
-        ) * learnable_registers
-
-        attention_mask = torch.full_like(
-            attention_mask,
-            0.0,
-            dtype=attention_mask.dtype,
-            device=attention_mask.device,
-        )
-
-        return hidden_states, attention_mask
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if self.num_learnable_registers:
-            hidden_states, attention_mask = (
-                self._replace_padded_with_learnable_registers(
-                    hidden_states, attention_mask
-                )
-            )
-
-        indices_grid = torch.arange(
-            hidden_states.shape[1],
-            dtype=torch.float32,
-            device=hidden_states.device,
-        )
-        indices_grid = indices_grid[None, None, :]
-        freq_grid_generator = (
-            generate_ltx_freq_grid_np
-            if self.double_precision_rope
-            else generate_ltx_freq_grid_pytorch
-        )
-        freqs_cis = precompute_ltx_freqs_cis(
-            indices_grid=indices_grid,
-            dim=self.inner_dim,
-            out_dtype=hidden_states.dtype,
-            theta=self.positional_embedding_theta,
-            max_pos=self.positional_embedding_max_pos,
-            num_attention_heads=self.num_attention_heads,
-            rope_type=self.rope_type,
-            freq_grid_generator=freq_grid_generator,
-        )
-
-        for block in self.transformer_1d_blocks:
-            hidden_states = block(
-                hidden_states, attention_mask=attention_mask, pe=freqs_cis
-            )
-
-        hidden_states = torch.nn.functional.rms_norm(
-            hidden_states, (hidden_states.shape[-1],), eps=1e-6
-        )
-
-        return hidden_states, attention_mask
-
-
-class LTX2GemmaTextEncoderModel(TextEncoder):
-    _supported_attention_backends = (
-        AttentionBackendEnum.FLASH_ATTN,
-        AttentionBackendEnum.TORCH_SDPA,
-    )
-
-    def __init__(self, config: TextEncoderConfig) -> None:
-        super().__init__(config)
-        arch = config.arch_config
-
-        self.feature_extractor_linear = GemmaFeaturesExtractorProjLinear(
-            in_features=arch.feature_extractor_in_features,
-            out_features=arch.feature_extractor_out_features,
-        )
-
-        connector_config = GemmaConnectorConfig(
-            num_attention_heads=arch.connector_num_attention_heads,
-            attention_head_dim=arch.connector_attention_head_dim,
-            num_layers=arch.connector_num_layers,
-            positional_embedding_theta=arch.connector_positional_embedding_theta,
-            positional_embedding_max_pos=arch.connector_positional_embedding_max_pos,
-            rope_type=LTXRopeType(arch.connector_rope_type),
-            double_precision_rope=arch.connector_double_precision_rope,
-            num_learnable_registers=arch.connector_num_learnable_registers,
-        )
-        self.embeddings_connector = Embeddings1DConnector(connector_config)
-        self.audio_embeddings_connector = Embeddings1DConnector(connector_config)
-
-        self.gemma_model_path = arch.gemma_model_path
-        self.gemma_dtype = arch.gemma_dtype
-        self.padding_side = arch.padding_side
-        self._gemma_model: Gemma3ForConditionalGeneration | None = None
-
-    def named_parameters(self, prefix: str = "", recurse: bool = True):
-        for name, param in super().named_parameters(
-            prefix=prefix, recurse=recurse
-        ):
-            if name.startswith("gemma_model."):
-                continue
-            yield name, param
-
-    @property
-    def gemma_model(self) -> Gemma3ForConditionalGeneration:
-        if self._gemma_model is None:
-            gemma_path = self.gemma_model_path
-            if not gemma_path:
-                raise ValueError(
-                    "gemma_model_path must be set (expected text_encoder/gemma)."
-                )
-            dtype = getattr(torch, self.gemma_dtype, torch.bfloat16)
-            self._gemma_model = Gemma3ForConditionalGeneration.from_pretrained(
-                gemma_path,
-                local_files_only=True,
-                torch_dtype=dtype,
-            )
-            # Configure model-level attention implementation when using TORCH_SDPA.
-            # Note: torch.backends.cuda.enable_*_sdp() settings should be configured
-            # at application/pipeline initialization level, not here, to avoid
-            # unexpected side effects across the application.
-            if os.getenv("FASTVIDEO_ATTENTION_BACKEND") == "TORCH_SDPA":
-                if hasattr(self._gemma_model.config, "attn_implementation"):
-                    self._gemma_model.config.attn_implementation = "sdpa"
-                if hasattr(self._gemma_model.config, "_attn_implementation"):
-                    self._gemma_model.config._attn_implementation = "sdpa"
-            device = next(self.feature_extractor_linear.parameters()).device
-            self._gemma_model.to(device=device)
-            self._gemma_model.eval()
-        return self._gemma_model
-
-    def _run_feature_extractor(
-        self,
-        hidden_states: tuple[torch.Tensor, ...],
-        attention_mask: torch.Tensor,
-        padding_side: str,
-    ) -> torch.Tensor:
-        encoded_text_features = torch.stack(hidden_states, dim=-1)
-        if os.getenv("LTX2_FASTVIDEO_GEMMA_LOG", ""):
-            for idx, layer in enumerate(hidden_states):
-                _debug_gemma_log_line(
-                    f"fastvideo:gemma_hidden_state_{idx}"
-                    f":sum={layer.float().sum().item():.6f}"
-                )
-            _debug_gemma_log_line(
-                "fastvideo:gemma_hidden_states_stack"
-                f":sum={encoded_text_features.float().sum().item():.6f}"
-            )
-        encoded_text_features_dtype = encoded_text_features.dtype
-        sequence_lengths = attention_mask.sum(dim=-1)
-        normed_text_features = _norm_and_concat_padded_batch(
-            encoded_text_features, sequence_lengths, padding_side=padding_side
-        )
-        return self.feature_extractor_linear(
-            normed_text_features.to(encoded_text_features_dtype)
-        )
-
-    def _convert_to_additive_mask(
-        self, attention_mask: torch.Tensor, dtype: torch.dtype
-    ) -> torch.Tensor:
-        return (attention_mask - 1).to(dtype).reshape(
-            (attention_mask.shape[0], 1, -1, attention_mask.shape[-1])
-        ) * torch.finfo(dtype).max
-
-    def _run_connectors(
-        self,
-        encoded_input: torch.Tensor,
-        attention_mask: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        connector_attention_mask = self._convert_to_additive_mask(
-            attention_mask, encoded_input.dtype
-        )
-        encoded, encoded_connector_attention_mask = self.embeddings_connector(
-            encoded_input, connector_attention_mask
-        )
-
-        attention_mask = (encoded_connector_attention_mask < 0.000001).to(
-            torch.int64
-        )
-        attention_mask = attention_mask.reshape(
-            [encoded.shape[0], encoded.shape[1], 1]
-        )
-        encoded = encoded * attention_mask
-
-        encoded_for_audio, _ = self.audio_embeddings_connector(
-            encoded_input, connector_attention_mask
-        )
-
-        return encoded, encoded_for_audio, attention_mask.squeeze(-1)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor | None,
-        position_ids: torch.Tensor | None = None,
-        attention_mask: torch.Tensor | None = None,
-        inputs_embeds: torch.Tensor | None = None,
-        output_hidden_states: bool | None = None,
-        **kwargs,
-    ) -> BaseEncoderOutput:
-        if input_ids is None:
-            raise ValueError("input_ids is required for Gemma text encoding.")
-        if attention_mask is None:
-            attention_mask = torch.ones_like(input_ids)
-
-        model = self.gemma_model
-        input_ids = input_ids.to(device=model.device)
-        attention_mask = attention_mask.to(device=model.device)
-        outputs = model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            output_hidden_states=True,
-            return_dict=True,
-        )
-
-        encoded_inputs = self._run_feature_extractor(
-            outputs.hidden_states,
-            attention_mask,
-            padding_side=self.padding_side,
-        )
-        if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") == "1":
-            _debug_log_line(
-                "fastvideo:gemma_feature"
-                f":sum={encoded_inputs.float().sum().item():.6f} "
-                f"shape={tuple(encoded_inputs.shape)}"
-            )
-        video_encoding, audio_encoding, attention_mask = self._run_connectors(
-            encoded_inputs, attention_mask
-        )
-        if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") == "1":
-            _debug_log_line(
-                "fastvideo:gemma_video_encoding"
-                f":sum={video_encoding.float().sum().item():.6f} "
-                f"shape={tuple(video_encoding.shape)}"
-            )
-            _debug_log_line(
-                "fastvideo:gemma_audio_encoding"
-                f":sum={audio_encoding.float().sum().item():.6f} "
-                f"shape={tuple(audio_encoding.shape)}"
-            )
-
-        hidden_states = (audio_encoding, ) if output_hidden_states else None
-        return BaseEncoderOutput(
-            last_hidden_state=video_encoding,
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-        )
-
-    def load_weights(
-        self, weights: Iterable[tuple[str, torch.Tensor]]
-    ) -> set[str]:
-        params_dict = dict(self.named_parameters())
-        loaded_params: set[str] = set()
-        for name, loaded_weight in weights:
-            if name == "aggregate_embed.weight":
-                name = "feature_extractor_linear.aggregate_embed.weight"
-            if name not in params_dict:
-                continue
-            param = params_dict[name]
-            weight_loader = getattr(param, "weight_loader", default_weight_loader)
-            weight_loader(param, loaded_weight)
-            loaded_params.add(name)
-        return loaded_params
-
-
-def _norm_and_concat_padded_batch(
-    encoded_text: torch.Tensor,
-    sequence_lengths: torch.Tensor,
-    padding_side: str = "right",
-) -> torch.Tensor:
-    b, t, d, l = encoded_text.shape
-    device = encoded_text.device
-
-    token_indices = torch.arange(t, device=device)[None, :]
-    if padding_side == "right":
-        mask = token_indices < sequence_lengths[:, None]
-    elif padding_side == "left":
-        start_indices = t - sequence_lengths[:, None]
-        mask = token_indices >= start_indices
-    else:
-        raise ValueError(
-            f"padding_side must be 'left' or 'right', got {padding_side}"
-        )
-
-    mask = mask.reshape(b, t, 1, 1)
-    eps = 1e-6
-
-    masked = encoded_text.masked_fill(~mask, 0.0)
-    denom = (sequence_lengths * d).view(b, 1, 1, 1)
-    mean = masked.sum(dim=(1, 2), keepdim=True) / (denom + eps)
-
-    x_min = encoded_text.masked_fill(~mask, float("inf")).amin(
-        dim=(1, 2), keepdim=True
-    )
-    x_max = encoded_text.masked_fill(~mask, float("-inf")).amax(
-        dim=(1, 2), keepdim=True
-    )
-    range_ = x_max - x_min
-
-    normed = 8 * (encoded_text - mean) / (range_ + eps)
-    normed = normed.reshape(b, t, -1)
-
-    mask_flattened = mask.reshape(b, t, 1).expand(-1, -1, d * l)
-    normed = normed.masked_fill(~mask_flattened, 0.0)
-    return normed

fastvideo/models/encoders/siglip.py

@@ -1,419 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-SigLIP Vision Encoder for FastVideo.
-
-SigLIP (Sigmoid Loss for Language-Image Pre-training) is similar to CLIP
-but uses sigmoid loss instead of contrastive loss, and doesn't use a CLS token.
-"""
-
-from collections.abc import Iterable
-
-import torch
-import torch.nn as nn
-
-from fastvideo.attention import LocalAttention
-from fastvideo.configs.models.encoders import BaseEncoderOutput
-from fastvideo.configs.models.encoders.siglip import SiglipVisionArchConfig, SiglipVisionConfig
-from fastvideo.distributed import divide, get_tp_world_size
-from fastvideo.layers.activation import get_act_fn
-from fastvideo.layers.linear import (ColumnParallelLinear, QKVParallelLinear,
-                                     RowParallelLinear)
-from fastvideo.layers.quantization import QuantizationConfig
-from fastvideo.logger import init_logger
-from fastvideo.models.encoders.base import ImageEncoder
-from fastvideo.models.loader.weight_utils import default_weight_loader
-
-logger = init_logger(__name__)
-
-
-class SiglipVisionEmbeddings(nn.Module):
-    """
-    SigLIP vision embeddings - similar to CLIP but without class embedding.
-    """
-
-    def __init__(self, arch_config: SiglipVisionArchConfig):
-        super().__init__()
-        self.arch_config = arch_config
-        self.embed_dim = arch_config.hidden_size
-        self.image_size = arch_config.image_size
-        self.patch_size = arch_config.patch_size
-        # SigLIP uses valid padding, so non-divisible sizes work (edge pixels are ignored)
-
-        self.patch_embedding = nn.Conv2d(
-            in_channels=arch_config.num_channels,
-            out_channels=self.embed_dim,
-            kernel_size=self.patch_size,
-            stride=self.patch_size,
-            padding="valid",  # SigLIP uses valid padding
-        )
-
-        # Integer division - with valid padding, edge pixels are ignored
-        self.num_patches = (self.image_size // self.patch_size) ** 2
-        self.num_positions = self.num_patches
-        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
-        self.register_buffer(
-            "position_ids",
-            torch.arange(self.num_positions).expand((1, -1)),
-            persistent=False,
-        )
-
-    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
-        target_dtype = self.patch_embedding.weight.dtype
-        patch_embeds = self.patch_embedding(
-            pixel_values.to(dtype=target_dtype)
-        )  # shape = [*, embed_dim, grid, grid]
-        embeddings = patch_embeds.flatten(2).transpose(1, 2)
-        embeddings = embeddings + self.position_embedding(self.position_ids)
-        return embeddings
-
-
-class SiglipAttention(nn.Module):
-    """Multi-headed attention for SigLIP."""
-
-    def __init__(
-        self,
-        arch_config: SiglipVisionArchConfig,
-        enable_scale: bool = True,
-        is_causal: bool = False,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-    ):
-        super().__init__()
-        self.arch_config = arch_config
-        self.embed_dim = arch_config.hidden_size
-        self.num_heads = arch_config.num_attention_heads
-        self.head_dim = self.embed_dim // self.num_heads
-        
-        if self.head_dim * self.num_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads "
-                f"(got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
-            )
-        
-        self.scale = self.head_dim**-0.5 if enable_scale else None
-        self.dropout = arch_config.attention_dropout
-
-        self.qkv_proj = QKVParallelLinear(
-            hidden_size=self.embed_dim,
-            head_size=self.head_dim,
-            total_num_heads=self.num_heads,
-            quant_config=quant_config,
-            prefix=f"{prefix}.qkv_proj",
-        )
-
-        self.out_proj = RowParallelLinear(
-            input_size=self.embed_dim,
-            output_size=self.embed_dim,
-            quant_config=quant_config,
-            prefix=f"{prefix}.out_proj",
-        )
-
-        self.tp_size = get_tp_world_size()
-        self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
-
-        self.attn = LocalAttention(
-            self.num_heads_per_partition,
-            self.head_dim,
-            self.num_heads_per_partition,
-            softmax_scale=self.scale,
-            causal=is_causal,
-            supported_attention_backends=arch_config._supported_attention_backends,
-        )
-
-    def forward(self, hidden_states: torch.Tensor):
-        """Input shape: Batch x Time x Channel"""
-        qkv_states, _ = self.qkv_proj(hidden_states)
-        query_states, key_states, value_states = qkv_states.chunk(3, dim=-1)
-        
-        query_states = query_states.reshape(
-            query_states.shape[0], query_states.shape[1],
-            self.num_heads_per_partition, self.head_dim
-        )
-        key_states = key_states.reshape(
-            key_states.shape[0], key_states.shape[1],
-            self.num_heads_per_partition, self.head_dim
-        )
-        value_states = value_states.reshape(
-            value_states.shape[0], value_states.shape[1],
-            self.num_heads_per_partition, self.head_dim
-        )
-        
-        attn_output = self.attn(query_states, key_states, value_states)
-        attn_output = attn_output.reshape(
-            attn_output.shape[0], attn_output.shape[1],
-            self.num_heads_per_partition * self.head_dim
-        )
-        attn_output, _ = self.out_proj(attn_output)
-        return attn_output, None
-
-
-class SiglipMLP(nn.Module):
-
-    def __init__(
-        self,
-        arch_config: SiglipVisionArchConfig,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-    ) -> None:
-        super().__init__()
-        self.arch_config = arch_config
-        self.activation_fn = get_act_fn(arch_config.hidden_act)
-        self.fc1 = ColumnParallelLinear(
-            arch_config.hidden_size,
-            arch_config.intermediate_size,
-            bias=True,
-            quant_config=quant_config,
-            prefix=f"{prefix}.fc1",
-        )
-        self.fc2 = RowParallelLinear(
-            arch_config.intermediate_size,
-            arch_config.hidden_size,
-            bias=True,
-            quant_config=quant_config,
-            prefix=f"{prefix}.fc2",
-        )
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states, _ = self.fc1(hidden_states)
-        hidden_states = self.activation_fn(hidden_states)
-        hidden_states, _ = self.fc2(hidden_states)
-        return hidden_states
-
-
-class SiglipEncoderLayer(nn.Module):
-
-    def __init__(
-        self,
-        arch_config: SiglipVisionArchConfig,
-        enable_scale: bool = True,
-        is_causal: bool = False,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-    ) -> None:
-        super().__init__()
-        self.self_attn = SiglipAttention(
-            arch_config,
-            enable_scale=enable_scale,
-            is_causal=is_causal,
-            quant_config=quant_config,
-            prefix=f"{prefix}.self_attn",
-        )
-        self.layer_norm1 = nn.LayerNorm(
-            arch_config.hidden_size, eps=arch_config.layer_norm_eps
-        )
-        self.mlp = SiglipMLP(
-            arch_config,
-            quant_config=quant_config,
-            prefix=f"{prefix}.mlp",
-        )
-        self.layer_norm2 = nn.LayerNorm(
-            arch_config.hidden_size, eps=arch_config.layer_norm_eps
-        )
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        # SigLIP uses post-norm (like original ViT)
-        residual = hidden_states
-        hidden_states = self.layer_norm1(hidden_states)
-        hidden_states, _ = self.self_attn(hidden_states=hidden_states)
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.layer_norm2(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        return hidden_states
-
-
-class SiglipEncoder(nn.Module):
-    """SigLIP encoder consisting of transformer layers."""
-
-    def __init__(
-        self,
-        arch_config: SiglipVisionArchConfig,
-        enable_scale: bool = True,
-        is_causal: bool = False,
-        quant_config: QuantizationConfig | None = None,
-        num_hidden_layers_override: int | None = None,
-        prefix: str = "",
-    ) -> None:
-        super().__init__()
-        self.arch_config = arch_config
-
-        if num_hidden_layers_override is None:
-            num_hidden_layers = arch_config.num_hidden_layers
-        else:
-            num_hidden_layers = num_hidden_layers_override
-            
-        self.layers = nn.ModuleList([
-            SiglipEncoderLayer(
-                arch_config=arch_config,
-                enable_scale=enable_scale,
-                is_causal=is_causal,
-                quant_config=quant_config,
-                prefix=f"{prefix}.layers.{layer_idx}",
-            )
-            for layer_idx in range(num_hidden_layers)
-        ])
-
-    def forward(
-        self,
-        inputs_embeds: torch.Tensor,
-        return_all_hidden_states: bool,
-    ) -> torch.Tensor | list[torch.Tensor]:
-        hidden_states_pool = [inputs_embeds]
-        hidden_states = inputs_embeds
-
-        for encoder_layer in self.layers:
-            hidden_states = encoder_layer(hidden_states)
-            if return_all_hidden_states:
-                hidden_states_pool.append(hidden_states)
-
-        if return_all_hidden_states:
-            return hidden_states_pool
-        return [hidden_states]
-
-
-class SiglipVisionTransformer(nn.Module):
-
-    def __init__(
-        self,
-        config: SiglipVisionConfig,
-        quant_config: QuantizationConfig | None = None,
-        num_hidden_layers_override: int | None = None,
-        require_post_norm: bool | None = None,
-        prefix: str = "",
-    ) -> None:
-        super().__init__()
-        self.config = config
-        arch_config = config.arch_config
-        embed_dim = arch_config.hidden_size
-
-        self.embeddings = SiglipVisionEmbeddings(arch_config)
-
-        self.encoder = SiglipEncoder(
-            arch_config=arch_config,
-            enable_scale=config.enable_scale,
-            is_causal=config.is_causal,
-            quant_config=quant_config,
-            num_hidden_layers_override=num_hidden_layers_override,
-            prefix=f"{prefix}.encoder",
-        )
-
-        num_hidden_layers = arch_config.num_hidden_layers
-        if len(self.encoder.layers) > arch_config.num_hidden_layers:
-            raise ValueError(
-                f"The original encoder only has {num_hidden_layers} "
-                f"layers, but you requested {len(self.encoder.layers)} layers."
-            )
-
-        # Post layer norm (applied to output)
-        if require_post_norm is None:
-            require_post_norm = len(self.encoder.layers) == num_hidden_layers
-
-        if require_post_norm:
-            self.post_layernorm = nn.LayerNorm(embed_dim, eps=arch_config.layer_norm_eps)
-        else:
-            self.post_layernorm = None
-
-    def forward(
-        self,
-        pixel_values: torch.Tensor,
-        feature_sample_layers: list[int] | None = None,
-    ) -> torch.Tensor:
-        hidden_states = self.embeddings(pixel_values)
-
-        return_all_hidden_states = feature_sample_layers is not None
-        encoder_outputs = self.encoder(
-            inputs_embeds=hidden_states,
-            return_all_hidden_states=return_all_hidden_states,
-        )
-
-        if not return_all_hidden_states:
-            encoder_outputs = encoder_outputs[0]
-
-        # Apply post-layernorm
-        if self.post_layernorm is not None:
-            if isinstance(encoder_outputs, list):
-                encoder_outputs[-1] = self.post_layernorm(encoder_outputs[-1])
-            else:
-                encoder_outputs = self.post_layernorm(encoder_outputs)
-
-        return encoder_outputs
-
-
-class SiglipVisionModel(ImageEncoder):
-    """SigLIP Vision Model for FastVideo."""
-    
-    config_class = SiglipVisionConfig
-    main_input_name = "pixel_values"
-    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
-
-    def __init__(self, config: SiglipVisionConfig) -> None:
-        super().__init__(config)
-        self.config = config
-        self.vision_model = SiglipVisionTransformer(
-            config=config,
-            quant_config=config.quant_config,
-            num_hidden_layers_override=config.num_hidden_layers_override,
-            require_post_norm=config.require_post_norm,
-            prefix=f"{config.prefix}.vision_model",
-        )
-
-    def forward(
-        self,
-        pixel_values: torch.Tensor,
-        feature_sample_layers: list[int] | None = None,
-        **kwargs,
-    ) -> BaseEncoderOutput:
-        last_hidden_state = self.vision_model(pixel_values, feature_sample_layers)
-        return BaseEncoderOutput(last_hidden_state=last_hidden_state)
-
-    @property
-    def device(self):
-        return next(self.parameters()).device
-
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
-        params_dict = dict(self.named_parameters())
-        loaded_params: set[str] = set()
-        layer_count = len(self.vision_model.encoder.layers)
-
-        for name, loaded_weight in weights:
-            # Skip projection layers if any
-            if name.startswith("visual_projection"):
-                continue
-                
-            # Skip head if any
-            if "head" in name:
-                continue
-
-            # Post layernorm handling
-            if (name.startswith("vision_model.post_layernorm")
-                    and self.vision_model.post_layernorm is None):
-                continue
-
-            # Omit layers when num_hidden_layers_override is set
-            if name.startswith("vision_model.encoder.layers"):
-                layer_idx = int(name.split(".")[3])
-                if layer_idx >= layer_count:
-                    continue
-
-            # Handle QKV projection weight mapping
-            for (param_name, weight_name, shard_id) in self.config.arch_config.stacked_params_mapping:
-                if weight_name not in name:
-                    continue
-                name = name.replace(weight_name, param_name)
-
-                if name in params_dict:
-                    param = params_dict[name]
-                    weight_loader = param.weight_loader
-                    weight_loader(param, loaded_weight, shard_id)
-                break
-            else:
-                if name in params_dict:
-                    param = params_dict[name]
-                    weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                    weight_loader(param, loaded_weight)
-            loaded_params.add(name)
-            
-        return loaded_params

fastvideo/models/loader/component_loader.py

@@ -35,7 +35,7 @@ from fastvideo.models.loader.weight_utils import (
 )
 from fastvideo.models.registry import ModelRegistry
 from fastvideo.utils import PRECISION_TO_TYPE, is_pin_memory_available
-from fastvideo.hooks.layerwise_offload import enable_layerwise_offload
+from fastvideo.models.layerwise_offload import LayerwiseOffloadManager
 
 logger = init_logger(__name__)
 
@@ -80,15 +80,11 @@ class ComponentLoader(ABC):
             "transformer": (TransformerLoader, "diffusers"),
             "transformer_2": (TransformerLoader, "diffusers"),
             "vae": (VAELoader, "diffusers"),
-            "audio_vae": (AudioDecoderLoader, "diffusers"),
-            "audio_decoder": (AudioDecoderLoader, "diffusers"),
-            "vocoder": (VocoderLoader, "diffusers"),
             "text_encoder": (TextEncoderLoader, "transformers"),
             "text_encoder_2": (TextEncoderLoader, "transformers"),
             "tokenizer": (TokenizerLoader, "transformers"),
             "tokenizer_2": (TokenizerLoader, "transformers"),
             "image_processor": (ImageProcessorLoader, "transformers"),
-            "feature_extractor": (ImageProcessorLoader, "transformers"),
             "image_encoder": (ImageEncoderLoader, "transformers"),
         }
 
@@ -246,47 +242,6 @@ class TextEncoderLoader(ComponentLoader):
         model_config.pop("model_type", None)
         model_config.pop("tokenizer_class", None)
         model_config.pop("torch_dtype", None)
-        repo_root = os.path.dirname(model_path)
-        index_path = os.path.join(repo_root, "model_index.json")
-        gemma_path = ""
-        gemma_path_from_candidate = False
-        if os.path.isfile(index_path):
-            try:
-                with open(index_path, encoding="utf-8") as f:
-                    model_index = json.load(f)
-                gemma_path = model_index.get("gemma_model_path", "")
-            except json.JSONDecodeError:
-                gemma_path = ""
-        if not gemma_path:
-            candidate = os.path.normpath(os.path.join(model_path, "gemma"))
-            if os.path.isdir(candidate):
-                gemma_path = candidate
-                gemma_path_from_candidate = True
-                model_config["gemma_model_path"] = gemma_path
-        if gemma_path and not gemma_path_from_candidate:
-            if not os.path.isabs(gemma_path):
-                model_config["gemma_model_path"] = os.path.normpath(
-                    os.path.join(repo_root, gemma_path)
-                )
-        transformer_config_path = os.path.join(
-            repo_root, "transformer", "config.json"
-        )
-        if os.path.isfile(transformer_config_path):
-            try:
-                with open(transformer_config_path, encoding="utf-8") as f:
-                    transformer_config = json.load(f)
-                if (
-                    "connector_double_precision_rope" not in model_config
-                    or not model_config["connector_double_precision_rope"]
-                ):
-                    if transformer_config.get("double_precision_rope") is True:
-                        model_config["connector_double_precision_rope"] = True
-                if "connector_rope_type" not in model_config:
-                    rope_type = transformer_config.get("rope_type")
-                    if rope_type is not None:
-                        model_config["connector_rope_type"] = rope_type
-            except json.JSONDecodeError:
-                pass
         logger.info("HF Model config: %s", model_config)
 
         # @TODO(Wei): Better way to handle this?
@@ -325,7 +280,7 @@ class TextEncoderLoader(ComponentLoader):
         target_device: torch.device,
         fastvideo_args: FastVideoArgs,
         dtype: str = "fp16",
-        use_text_encoder_override: bool = False,  # prevent subclasses from misusing
+        use_text_encoder_override: bool = False, # prevent subclasses from misusing
     ):
         use_cpu_offload = (
             fastvideo_args.text_encoder_cpu_offload
@@ -342,10 +297,7 @@ class TextEncoderLoader(ComponentLoader):
             )
 
         # Set quantization config if specified
-        if (
-            use_text_encoder_override
-            and fastvideo_args.override_text_encoder_quant is not None
-        ):
+        if use_text_encoder_override and fastvideo_args.override_text_encoder_quant is not None:
             if fastvideo_args.override_text_encoder_safetensors is None:
                 raise ValueError(
                     "override_text_encoder_quant is set but override_text_encoder_safetensors is None"
@@ -362,10 +314,7 @@ class TextEncoderLoader(ComponentLoader):
                 model: TextEncoder = model_cls(model_config)  # type: ignore
 
             weights_to_load = {name for name, _ in model.named_parameters()}
-            if (
-                use_text_encoder_override
-                and fastvideo_args.override_text_encoder_safetensors is not None
-            ):
+            if use_text_encoder_override and fastvideo_args.override_text_encoder_safetensors is not None:
                 loaded_weights: set[str] = model.load_weights(
                     safetensors_weights_iterator(
                         [fastvideo_args.override_text_encoder_safetensors],
@@ -391,10 +340,9 @@ class TextEncoderLoader(ComponentLoader):
 
             from fastvideo.platforms import current_platform
 
-            logger.info("Loading text encoder with cpu_offload: %s", use_cpu_offload)
-
             if use_cpu_offload:
                 pin_cpu_memory = fastvideo_args.pin_cpu_memory and is_pin_memory_available()
+                pin_cpu_memory = False
                 # Disable FSDP for MPS as it's not compatible
                 if current_platform.is_mps():
                     logger.info(
@@ -536,20 +484,8 @@ class TokenizerLoader(ComponentLoader):
             # in v0, this was same string as encoder_name "ClipTextModel"
             # TODO(will): pass these tokenizer kwargs from inference args? Maybe
             # other method of config?
+            padding_size="right",
         )
-        padding_side = None
-        if hasattr(fastvideo_args.pipeline_config, "text_encoder_configs"):
-            try:
-                arch_config = fastvideo_args.pipeline_config.text_encoder_configs[
-                    0
-                ].arch_config
-                padding_side = getattr(arch_config, "padding_side", None)
-            except Exception:
-                padding_side = None
-        if padding_side:
-            tokenizer.padding_side = padding_side
-        if tokenizer.pad_token is None and tokenizer.eos_token is not None:
-            tokenizer.pad_token = tokenizer.eos_token
         logger.info("Loaded tokenizer: %s", tokenizer.__class__.__name__)
         return tokenizer
 
@@ -560,11 +496,15 @@ class VAELoader(ComponentLoader):
     def load(self, model_path: str, fastvideo_args: FastVideoArgs):
         """Load the VAE based on the model path, and inference args."""
         config = get_diffusers_config(model=model_path)
-        config.pop("_name_or_path", None)
         class_name = config.pop("_class_name")
-        assert class_name is not None, "Model config does not contain a _class_name attribute. Only diffusers format is supported."
+        assert class_name is not None, (
+            "Model config does not contain a _class_name attribute. Only diffusers format is supported."
+        )
         fastvideo_args.model_paths["vae"] = model_path
 
+        vae_config = fastvideo_args.pipeline_config.vae_config
+        vae_config.update_model_arch(config)
+
         from fastvideo.platforms import current_platform
 
         if fastvideo_args.vae_cpu_offload:
@@ -598,29 +538,8 @@ class VAELoader(ComponentLoader):
                 vae.load_state_dict(sd, strict=False)
                 return vae.eval()
 
-            # LTX-2 uses CausalVideoAutoencoder with nested "vae" config
-            if class_name == "CausalVideoAutoencoder" and "vae" in config:
-                vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)
-                vae = vae_cls(config).to(target_device)
-                if hasattr(vae, "set_tiling_config"):
-                    vae_config = fastvideo_args.pipeline_config.vae_config
-                    vae.set_tiling_config(
-                        spatial_tile_size_in_pixels=getattr(
-                            vae_config, "ltx2_spatial_tile_size_in_pixels", 512),
-                        spatial_tile_overlap_in_pixels=getattr(
-                            vae_config, "ltx2_spatial_tile_overlap_in_pixels", 64),
-                        temporal_tile_size_in_frames=getattr(
-                            vae_config, "ltx2_temporal_tile_size_in_frames", 64),
-                        temporal_tile_overlap_in_frames=getattr(
-                            vae_config,
-                            "ltx2_temporal_tile_overlap_in_frames", 24),
-                    )
-            else:
-                config.pop("_class_name", None)
-                vae_config = fastvideo_args.pipeline_config.vae_config
-                vae_config.update_model_arch(config)
-                vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)
-                vae = vae_cls(vae_config).to(target_device)
+            vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)
+            vae = vae_cls(vae_config).to(target_device)
 
         # Find all safetensors files
         safetensors_list = glob.glob(
@@ -629,108 +548,23 @@ class VAELoader(ComponentLoader):
             raise ValueError(f"No safetensors files found in {model_path}")
         # Common case: a single `.safetensors` checkpoint file.
         # Some models may be sharded into multiple files; in that case we merge.
-        loaded = {}
-        for sf_file in safetensors_list:
-            loaded.update(safetensors_load_file(sf_file))
-
-        # LTX-2 CausalVideoAutoencoder needs per_channel_statistics remapping
-        if class_name == "CausalVideoAutoencoder" and "vae" in config:
-            per_channel_prefixes = (
-                "per_channel_statistics.",
-                "vae.per_channel_statistics.",
-            )
-            remapped = {}
-            for key, tensor in loaded.items():
-                remapped[key] = tensor
-                for prefix in per_channel_prefixes:
-                    if key.startswith(prefix):
-                        suffix = key[len(prefix):]
-                        remapped.setdefault(
-                            f"encoder.per_channel_statistics.{suffix}",
-                            tensor,
-                        )
-                        remapped.setdefault(
-                            f"decoder.per_channel_statistics.{suffix}",
-                            tensor,
-                        )
-                        break
-            loaded = remapped
-
+        if len(safetensors_list) == 1:
+            loaded = safetensors_load_file(safetensors_list[0])
+        else:
+            loaded = {}
+            for sf_file in safetensors_list:
+                loaded.update(safetensors_load_file(sf_file))
         vae.load_state_dict(loaded, strict=False)
 
         return vae.eval()
 
 
-class AudioDecoderLoader(ComponentLoader):
-    """Loader for LTX-2 audio decoder (audio_vae component)."""
-
-    def load(self, model_path: str, fastvideo_args: FastVideoArgs):
-        config = get_diffusers_config(model=model_path)
-        class_name = config.pop("_class_name", None) or "LTX2AudioDecoder"
-
-        model_cls, _ = ModelRegistry.resolve_model_cls(class_name)
-        target_device = get_local_torch_device()
-
-        precision = getattr(
-            fastvideo_args.pipeline_config, "audio_decoder_precision", "bf16"
-        )
-        with set_default_torch_dtype(PRECISION_TO_TYPE[precision]):
-            audio_decoder = model_cls(config).to(target_device)
-
-        safetensors_list = glob.glob(
-            os.path.join(str(model_path), "*.safetensors")
-        )
-        loaded: dict[str, torch.Tensor] = {}
-        for sf_file in safetensors_list:
-            loaded.update(safetensors_load_file(sf_file))
-
-        decoder_state = {}
-        for name, tensor in loaded.items():
-            if name.startswith("decoder."):
-                decoder_state[name.replace("decoder.", "")] = tensor
-            elif name.startswith("per_channel_statistics."):
-                decoder_state[name] = tensor
-
-        target_module = getattr(audio_decoder, "model", audio_decoder)
-        target_module.load_state_dict(decoder_state, strict=False)
-        return audio_decoder.eval()
-
-
-class VocoderLoader(ComponentLoader):
-    """Loader for LTX-2 vocoder."""
-
-    def load(self, model_path: str, fastvideo_args: FastVideoArgs):
-        config = get_diffusers_config(model=model_path)
-        class_name = config.pop("_class_name", None) or "LTX2Vocoder"
-
-        model_cls, _ = ModelRegistry.resolve_model_cls(class_name)
-        target_device = get_local_torch_device()
-
-        precision = getattr(
-            fastvideo_args.pipeline_config, "vocoder_precision", "bf16"
-        )
-        with set_default_torch_dtype(PRECISION_TO_TYPE[precision]):
-            vocoder = model_cls(config).to(target_device)
-
-        safetensors_list = glob.glob(
-            os.path.join(str(model_path), "*.safetensors")
-        )
-        loaded: dict[str, torch.Tensor] = {}
-        for sf_file in safetensors_list:
-            loaded.update(safetensors_load_file(sf_file))
-
-        target_module = getattr(vocoder, "model", vocoder)
-        target_module.load_state_dict(loaded, strict=False)
-        return vocoder.eval()
-
-
 class TransformerLoader(ComponentLoader):
     """Loader for transformer."""
 
     def load(self, model_path: str, fastvideo_args: FastVideoArgs):
         """Load the transformer based on the model path, and inference args."""
         config = get_diffusers_config(model=model_path)
-        config.pop("_name_or_path", None)
         hf_config = deepcopy(config)
         cls_name = config.pop("_class_name")
         if cls_name is None:
@@ -831,7 +665,7 @@ class TransformerLoader(ComponentLoader):
         )
 
         total_params = sum(p.numel() for p in model.parameters())
-        logger.info("Loaded model with %.2fB parameters, with cpu_offload: %s", total_params / 1e9, fastvideo_args.dit_cpu_offload)
+        logger.info("Loaded model with %.2fB parameters", total_params / 1e9)
 
         assert next(model.parameters()).dtype == default_dtype, (
             "Model dtype does not match default dtype"
@@ -839,19 +673,35 @@ class TransformerLoader(ComponentLoader):
 
         model = model.eval()
 
-        if fastvideo_args.inference_mode and fastvideo_args.dit_layerwise_offload:
-            # Check if model has nn.ModuleList for layerwise offload compatibility
-            has_module_list = any(
-                isinstance(m, nn.ModuleList) for m in model.children()
-            )
-            if has_module_list:
-                enable_layerwise_offload(model)
-            else:
+        if fastvideo_args.dit_layerwise_offload and hasattr(model, "blocks"):
+            # Check if this is a Wan model (only Wan models support layerwise offload)
+            is_wan_model = "Wan" in cls_name
+            if not is_wan_model:
                 logger.warning(
-                    "Layerwise offload requested but model %s does not have "
-                    "nn.ModuleList structure. Skipping layerwise offload.",
+                    "Layerwise offload is currently only supported for Wan models. "
+                    "Model class '%s' does not support layerwise offload. "
+                    "Disabling layerwise offload for this model.",
                     cls_name
                 )
+            else:
+                try:
+                    num_layers = len(getattr(model, "blocks"))
+                except TypeError:
+                    num_layers = None
+                if isinstance(num_layers, int) and num_layers > 0:
+                    # Ensure model is on the correct device (CUDA) before initializing manager
+                    # This ensures non-managed parameters (embeddings, final norms) are on GPU
+                    model = model.to(get_local_torch_device())
+                    mgr = LayerwiseOffloadManager(
+                        model,
+                        module_list_attr="blocks",
+                        num_layers=num_layers,
+                        enabled=True,
+                        pin_cpu_memory=fastvideo_args.pin_cpu_memory,
+                        auto_initialize=True,
+                    )
+                    setattr(model, "_layerwise_offload_manager", mgr)
+
         return model
 
 
@@ -958,4 +808,4 @@ class PipelineComponentLoader:
         )
 
         # Load the module
-        return loader.load(component_model_path, fastvideo_args)
+        return loader.load(component_model_path, fastvideo_args)

fastvideo/models/loader/fsdp_load.py

@@ -108,6 +108,7 @@ def maybe_load_fsdp_model(
 
     if use_fsdp:
         pin_cpu_memory = pin_cpu_memory and is_pin_memory_available()
+        pin_cpu_memory = False
         world_size = hsdp_replicate_dim * hsdp_shard_dim
         if not training_mode and not fsdp_inference:
             hsdp_replicate_dim = world_size

fastvideo/models/loader/utils.py

@@ -78,10 +78,9 @@ def hf_to_custom_state_dict(
     for source_param_name, full_tensor in hf_param_sd:  # type: ignore
         target_param_name, merge_index, num_params_to_merge = param_names_mapping(
             source_param_name)
-        if merge_index is None:
-            reverse_param_names_mapping[target_param_name] = (source_param_name,
-                                                            merge_index,
-                                                            num_params_to_merge)
+        reverse_param_names_mapping[target_param_name] = (source_param_name,
+                                                          merge_index,
+                                                          num_params_to_merge)
         if merge_index is not None:
             to_merge_params[target_param_name][merge_index] = full_tensor
             if len(to_merge_params[target_param_name]) == num_params_to_merge:

fastvideo/models/registry.py

@@ -26,10 +26,6 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
     ("dits", "hunyuanvideo", "HunyuanVideoTransformer3DModel"),
     "HunyuanVideo15Transformer3DModel":
     ("dits", "hunyuanvideo15", "HunyuanVideo15Transformer3DModel"),
-    "HYWorldTransformer3DModel":
-    ("dits", "hyworld", "HYWorldTransformer3DModel"),
-    "CausalHunyuanVideo15Transformer3DModel":
-    ("dits", "causal_hunyuanvideo15", "CausalHunyuanVideo15Transformer3DModel"),
     "WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
     "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
     "StepVideoModel": ("dits", "stepvideo", "StepVideoModel"),
@@ -37,7 +33,6 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
     "Cosmos25Transformer3DModel": ("dits", "cosmos2_5", "Cosmos25Transformer3DModel"),
     "LongCatVideoTransformer3DModel": ("dits", "longcat_video_dit", "LongCatVideoTransformer3DModel"),  # Wrapper (Phase 1)
     "LongCatTransformer3DModel": ("dits", "longcat", "LongCatTransformer3DModel"),  # Native (Phase 2)
-    "LTX2Transformer3DModel": ("dits", "ltx2", "LTX2Transformer3DModel"),
 }
 
 _IMAGE_TO_VIDEO_DIT_MODELS = {
@@ -59,30 +54,20 @@ _TEXT_ENCODER_MODELS = {
     "Reason1TextEncoder": ("encoders", "reason1", "Reason1TextEncoder"),
     "Qwen2_5_VLForConditionalGeneration":
     ("encoders", "reason1", "Reason1TextEncoder"),
-    "LTX2GemmaTextEncoderModel": ("encoders", "gemma", "LTX2GemmaTextEncoderModel"),
 }
 
 _IMAGE_ENCODER_MODELS: dict[str, tuple] = {
     # "HunyuanVideoTransformer3DModel": ("image_encoder", "hunyuanvideo", "HunyuanVideoImageEncoder"),
     "CLIPVisionModelWithProjection": ("encoders", "clip", "CLIPVisionModel"),
     "CLIPVisionModel": ("encoders", "clip", "CLIPVisionModel"),
-    "SiglipVisionModel": ("encoders", "siglip", "SiglipVisionModel"),
 }
 
 _VAE_MODELS = {
     "AutoencoderKLHunyuanVideo":
     ("vaes", "hunyuanvae", "AutoencoderKLHunyuanVideo"),
-    "AutoencoderKLHYWorld": ("vaes", "hyworldvae", "AutoencoderKLHYWorld"),
     "AutoencoderKLHunyuanVideo15": ("vaes", "hunyuan15vae", "AutoencoderKLHunyuanVideo15"),
     "AutoencoderKLWan": ("vaes", "wanvae", "AutoencoderKLWan"),
-    "AutoencoderKLStepvideo": ("vaes", "stepvideovae", "AutoencoderKLStepvideo"),
-    "CausalVideoAutoencoder": ("vaes", "ltx2vae", "LTX2CausalVideoAutoencoder"),
-}
-
-_AUDIO_MODELS = {
-    "LTX2AudioEncoder": ("audio", "ltx2_audio_vae", "LTX2AudioEncoder"),
-    "LTX2AudioDecoder": ("audio", "ltx2_audio_vae", "LTX2AudioDecoder"),
-    "LTX2Vocoder": ("audio", "ltx2_audio_vae", "LTX2Vocoder"),
+    "AutoencoderKLStepvideo": ("vaes", "stepvideovae", "AutoencoderKLStepvideo")
 }
 
 _SCHEDULERS = {
@@ -106,7 +91,6 @@ _FAST_VIDEO_MODELS = {
     **_TEXT_ENCODER_MODELS,
     **_IMAGE_ENCODER_MODELS,
     **_VAE_MODELS,
-    **_AUDIO_MODELS,
     **_SCHEDULERS,
 }
 

fastvideo/models/schedulers/scheduling_flow_match_euler_discrete.py

@@ -155,8 +155,8 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin,
 
         self.sigmas = sigmas.to(
             "cpu")  # to avoid too much CPU/GPU communication
-        self.sigma_min = sigma_min if sigma_min is not None else self.sigmas[-1].item()
-        self.sigma_max = sigma_max if sigma_max is not None else self.sigmas[0].item()
+        self.sigma_min = self.sigmas[-1].item()
+        self.sigma_max = self.sigmas[0].item()
 
         BaseScheduler.__init__(self)
 
@@ -289,7 +289,6 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin,
         sigmas: list[float] | None = None,
         mu: float | None = None,
         timesteps: list[float] | None = None,
-        extra_one_step: bool = False,
     ) -> None:
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
@@ -351,10 +350,7 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin,
             if timesteps_array is None:
                 t_max = self._sigma_to_t(self.sigma_max)
                 t_min = self._sigma_to_t(self.sigma_min)
-                if extra_one_step:
-                    timesteps_array = np.linspace(t_max, t_min, num_inference_steps + 1)[:-1]
-                else:
-                    timesteps_array = np.linspace(t_max, t_min, num_inference_steps)
+                timesteps_array = np.linspace(t_max, t_min, num_inference_steps)
             sigmas_array = timesteps_array / self.config.num_train_timesteps
         else:
             sigmas_array = np.array(sigmas).astype(np.float32)
@@ -648,7 +644,7 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin,
         self,
         clean_latent: torch.Tensor,
         noise: torch.Tensor,
-        timestep: torch.Tensor,
+        timestep: torch.IntTensor,
     ) -> torch.Tensor:
 
         """

fastvideo/models/utils.py

@@ -5,9 +5,6 @@ from typing import Any
 
 import torch
 
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
 
 # TODO(PY): move it elsewhere
 def auto_attributes(init_func):

fastvideo/models/vaes/common.py

@@ -79,7 +79,7 @@ class ParallelTiledVAE(ABC):
     def decode(self, z: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, num_frames, height, width = z.shape
         tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
-        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
+        tile_latent_min_width = self.tile_sample_stride_width // self.spatial_compression_ratio
         tile_latent_min_num_frames = self.tile_sample_min_num_frames // self.temporal_compression_ratio
         num_sample_frames = (num_frames -
                              1) * self.temporal_compression_ratio + 1

fastvideo/models/vaes/hunyuan15vae.py

@@ -114,11 +114,14 @@ class HunyuanVideo15AttnBlock(nn.Module):
         """
         seq_len = n_frame * n_hw
         mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
+        
         for i in range(seq_len):
             i_frame = i // n_hw
             mask[i, : (i_frame + 1) * n_hw] = 0
         if batch_size is not None:
-            mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
+            # mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
+            mask = mask.unsqueeze(0).unsqueeze(1).expand(batch_size, 1, -1, -1)
+
         return mask
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -131,6 +134,7 @@ class HunyuanVideo15AttnBlock(nn.Module):
         value = self.to_v(x)
 
         batch_size, channels, frames, height, width = query.shape
+        
 
         query = query.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
         key = key.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
@@ -663,7 +667,7 @@ class AutoencoderKLHunyuanVideo15(nn.Module, ParallelTiledVAE):
         # When decoding spatially large video latents, the memory requirement is very high. By breaking the video latent
         # frames spatially into smaller tiles and performing multiple forward passes for decoding, and then blending the
         # intermediate tiles together, the memory requirement can be lowered.
-        self.use_tiling = True
+        self.use_tiling = False
 
         # The minimal tile height and width for spatial tiling to be used
         self.tile_sample_min_height = 256

fastvideo/models/vaes/hunyuanvae.py

@@ -92,6 +92,11 @@ class HunyuanVAEAttention(nn.Module):
         value = value.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
 
         # Perform scaled dot-product attention
+        # If attention_mask is provided, it needs to be expanded for multi-head attention
+        if attention_mask is not None:
+            # Expand mask from [batch_size, seq_len, seq_len] to [batch_size, num_heads, seq_len, seq_len]
+            attention_mask = attention_mask.unsqueeze(1).expand(-1, self.heads, -1, -1)
+        
         hidden_states = F.scaled_dot_product_attention(query,
                                                        key,
                                                        value,

fastvideo/models/vaes/hyworldvae.py

@@ -1,29 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Adapted from diffusers and HY-WorldPlay
-
-# Copyright 2025 The Hunyuan Team and 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.
-
-from fastvideo.models.vaes.hunyuan15vae import AutoencoderKLHunyuanVideo15
-from fastvideo.configs.models.vaes import Hunyuan15VAEConfig
-
-class AutoencoderKLHYWorld(AutoencoderKLHunyuanVideo15):
-    # TODO(mingjia): add temporal caching support for HYWorld VAE
-
-    def __init__(
-        self,
-        config: Hunyuan15VAEConfig,
-    ) -> None:
-        AutoencoderKLHunyuanVideo15.__init__(self, config)
-

fastvideo/optim/muon.py

@@ -1,228 +0,0 @@
-import math
-import torch
-try:
-    from torch.distributed.tensor import DTensor
-except ImportError:
-    # handle old pytorch versions
-    Dtensor = None
-
-
-# This code is modified from the GitHub repository of KellerJordan:
-# https://github.com/KellerJordan/Muon/blob/master/muon.py
-def zeropower_via_newtonschulz5(G, steps=5):
-    """
-    Newton-Schulz iteration to compute the zeroth power / orthogonalization of G. We opt to use a
-    quintic iteration whose coefficients are selected to maximize the slope at zero. For the purpose
-    of minimizing steps, it turns out to be empirically effective to keep increasing the slope at
-    zero even beyond the point where the iteration no longer converges all the way to one everywhere
-    on the interval. This iteration therefore does not produce UV^T but rather something like US'V^T
-    where S' is diagonal with S_{ii}' ~ Uniform(0.5, 1.5), which turns out not to hurt model
-    performance at all relative to UV^T, where USV^T = G is the SVD.
-    """
-    if isinstance(G, DTensor):
-        device_mesh = G.device_mesh
-        G = G.full_tensor()
-    else:
-        device_mesh = None
-
-    assert len(G.shape) >= 2
-    a, b, c = (3.4445, -4.7750, 2.0315)
-    X = G
-    if G.size(-2) > G.size(-1):
-        X = X.mT
-    # Ensure spectral norm is at most 1
-    X = X / (X.norm(dim=(-2, -1), keepdim=True) + 1e-7)
-    # Perform the NS iterations
-    for _ in range(steps):
-        A = X @ X.T
-        B = b * A + c * A @ A  # quintic computation strategy adapted from suggestion by @jxbz, @leloykun, and @YouJiacheng
-        X = a * X + B @ X
-
-    if G.size(-2) > G.size(-1):
-        X = X.mT
-
-    if device_mesh is not None:
-        return DTensor.from_local(X, device_mesh)
-    else:
-        return X
-
-
-class Muon(torch.optim.Optimizer):
-    """
-    Muon - MomentUm Orthogonalized by Newton-schulz
-
-    Arguments:
-        muon_params: The parameters to be optimized by Muon.
-        lr: The learning rate. The updates will have spectral norm of `lr`. (0.02 is a good default)
-        momentum: The momentum used by the internal SGD. (0.95 is a good default)
-        nesterov: Whether to use Nesterov-style momentum in the internal SGD. (recommended)
-        ns_steps: The number of Newton-Schulz iterations to run. (6 is probably always enough)
-        adamw_params: The parameters to be optimized by AdamW. Any parameters in `muon_params` which are
-        {0, 1}-D or are detected as being the embed or lm_head will be optimized by AdamW as well.
-        adamw_lr: The learning rate for the internal AdamW.
-        adamw_betas: The betas for the internal AdamW.
-        adamw_eps: The epsilon for the internal AdamW.
-        adamw_wd: The weight decay for the internal AdamW.
-    """
-
-    def __init__(
-            self,
-            lr=1e-3,
-            wd=0.1,
-            muon_params=None,
-            momentum=0.95,
-            nesterov=True,
-            ns_steps=5,
-            adamw_params=None,
-            adamw_betas=(0.95, 0.95),
-            adamw_eps=1e-8,
-    ):
-
-        defaults = dict(
-            lr=lr,
-            wd=wd,
-            momentum=momentum,
-            nesterov=nesterov,
-            ns_steps=ns_steps,
-            adamw_betas=adamw_betas,
-            adamw_eps=adamw_eps,
-        )
-
-        params = list(muon_params)
-        adamw_params = list(adamw_params) if adamw_params is not None else []
-        params.extend(adamw_params)
-        super().__init__(params, defaults)
-        # Sort parameters into those for which we will use Muon, and those for which we will not
-        for p in muon_params:
-            # Use Muon for every parameter in muon_params which is >= 2D and doesn't look like an embedding or head layer
-            assert p.ndim >= 2, p.ndim
-            self.state[p]["use_muon"] = True
-        for p in adamw_params:
-            # Do not use Muon for parameters in adamw_params
-            self.state[p]["use_muon"] = False
-
-    def adjust_lr_for_muon(self, lr, param_shape):
-        A, B = param_shape[:2]
-        # We adjust the learning rate and weight decay based on the size of the parameter matrix
-        # as describted in the paper
-        adjusted_ratio = 0.2 * math.sqrt(max(A, B))
-        adjusted_lr = lr * adjusted_ratio
-        return adjusted_lr
-
-    def step(self, closure=None):
-        """Perform a single optimization step.
-
-        Args:
-            closure (Callable, optional): A closure that reevaluates the model
-                and returns the loss.
-        """
-        loss = None
-        if closure is not None:
-            with torch.enable_grad():
-                loss = closure()
-
-        for group in self.param_groups:
-
-            ############################
-            #           Muon           #
-            ############################
-
-            params = [p for p in group["params"] if self.state[p]["use_muon"]]
-
-            lr = group["lr"]
-            wd = group["wd"]
-            momentum = group["momentum"]
-
-            # generate weight updates in distributed fashion
-            for p in params:
-                # sanity check
-                g = p.grad
-                if g is None:
-                    continue
-                if g.ndim > 2:
-                    g = g.view(g.size(0), -1)
-                assert g is not None
-
-                # calc update
-                state = self.state[p]
-                if "momentum_buffer" not in state:
-                    state["momentum_buffer"] = torch.zeros_like(g)
-                buf = state["momentum_buffer"]
-                buf.mul_(momentum).add_(g)
-                g = g.add(buf, alpha=momentum) if group["nesterov"] else buf
-                g = g.bfloat16()
-                u = zeropower_via_newtonschulz5(g, steps=group["ns_steps"])
-
-                # scale update
-                adjusted_lr = self.adjust_lr_for_muon(lr, p.shape)
-
-                # apply weight decay
-                p.data.mul_(1 - lr * wd)
-
-                # apply update
-                p.data.add_(u.view(p.shape), alpha=-adjusted_lr)
-
-            ############################
-            #       AdamW backup       #
-            ############################
-
-            params = [
-                p for p in group["params"] if not self.state[p]["use_muon"]
-            ]
-            lr = group['lr']
-            beta1, beta2 = group["adamw_betas"]
-            eps = group["adamw_eps"]
-            weight_decay = group["wd"]
-
-            for p in params:
-                g = p.grad
-                if g is None:
-                    continue
-                state = self.state[p]
-                if "step" not in state:
-                    state["step"] = 0
-                    state["moment1"] = torch.zeros_like(g)
-                    state["moment2"] = torch.zeros_like(g)
-                state["step"] += 1
-                step = state["step"]
-                buf1 = state["moment1"]
-                buf2 = state["moment2"]
-                buf1.lerp_(g, 1 - beta1)
-                buf2.lerp_(g.square(), 1 - beta2)
-
-                g = buf1 / (eps + buf2.sqrt())
-
-                bias_correction1 = 1 - beta1**step
-                bias_correction2 = 1 - beta2**step
-                scale = bias_correction1 / bias_correction2**0.5
-                p.data.mul_(1 - lr * weight_decay)
-                p.data.add_(g, alpha=-lr / scale)
-
-        return loss
-
-
-# help function to create the Muon optimizer
-def get_muon_optimizer(model,
-                       lr=1e-3,
-                       weight_decay=0.1,
-                       momentum=0.95,
-                       adamw_betas=(0.95, 0.95),
-                       adamw_eps=1e-8):
-    muon_params = [
-        p for name, p in model.named_parameters()
-        if p.requires_grad and p.ndim >= 2
-    ]
-    adamw_params = [
-        p for name, p in model.named_parameters()
-        if p.requires_grad and not (p.ndim >= 2)
-    ]
-
-    return Muon(
-        lr=lr,
-        wd=weight_decay,
-        muon_params=muon_params,
-        momentum=momentum,
-        adamw_params=adamw_params,
-        adamw_betas=adamw_betas,
-        adamw_eps=adamw_eps,
-    )

fastvideo/pipelines/basic/hunyuan15/hunyuan15_causal_dmd_pipeline.py

@@ -1,71 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Wan causal DMD pipeline implementation.
-
-This module wires the causal DMD denoising stage into the modular pipeline.
-"""
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-
-# isort: off
-from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
-                                        Hy15CausalDMDDenosingStage,
-                                        InputValidationStage,
-                                        Hy15ImageEncodingStage,
-                                        LatentPreparationStage,
-                                        TextEncodingStage)
-# isort: on
-
-logger = init_logger(__name__)
-
-
-class Hy15CausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
-
-    _required_config_modules = [
-        "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "vae",
-        "transformer", "scheduler"
-    ]
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
-        """Set up pipeline stages with proper dependency injection."""
-
-        self.add_stage(stage_name="input_validation_stage",
-                       stage=InputValidationStage())
-
-        self.add_stage(stage_name="prompt_encoding_stage",
-                       stage=TextEncodingStage(
-                           text_encoders=[
-                               self.get_module("text_encoder"),
-                               self.get_module("text_encoder_2")
-                           ],
-                           tokenizers=[
-                               self.get_module("tokenizer"),
-                               self.get_module("tokenizer_2")
-                           ],
-                       ))
-
-        self.add_stage(stage_name="conditioning_stage",
-                       stage=ConditioningStage())
-
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer", None)))
-
-        self.add_stage(stage_name="image_encoding_stage",
-                       stage=Hy15ImageEncodingStage(image_encoder=None,
-                                                    image_processor=None))
-
-        self.add_stage(stage_name="denoising_stage",
-                       stage=Hy15CausalDMDDenosingStage(
-                           transformer=self.get_module("transformer"),
-                           scheduler=self.get_module("scheduler"),
-                           vae=self.get_module("vae")))
-
-        self.add_stage(stage_name="decoding_stage",
-                       stage=DecodingStage(vae=self.get_module("vae")))
-
-
-EntryClass = Hy15CausalDMDPipeline

fastvideo/pipelines/basic/hunyuan15/hunyuan15_i2v_pipeline.py

@@ -1,74 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Hunyuan video diffusion pipeline implementation.
-
-This module contains an implementation of the Hunyuan video diffusion pipeline
-using the modular pipeline architecture.
-"""
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
-                                        DenoisingStage, InputValidationStage,
-                                        LatentPreparationStage,
-                                        TextEncodingStage,
-                                        TimestepPreparationStage,
-                                        Hy15ImageEncodingStage)
-
-# TODO(will): move PRECISION_TO_TYPE to better place
-
-logger = init_logger(__name__)
-
-
-class HunyuanVideo15ImageToVideoPipeline(ComposedPipelineBase):
-
-    _required_config_modules = [
-        "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "vae",
-        "transformer", "scheduler"
-    ]
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        """Set up pipeline stages with proper dependency injection."""
-
-        self.add_stage(stage_name="input_validation_stage",
-                       stage=InputValidationStage())
-
-        self.add_stage(stage_name="prompt_encoding_stage_primary",
-                       stage=TextEncodingStage(
-                           text_encoders=[
-                               self.get_module("text_encoder"),
-                               self.get_module("text_encoder_2")
-                           ],
-                           tokenizers=[
-                               self.get_module("tokenizer"),
-                               self.get_module("tokenizer_2")
-                           ],
-                       ))
-
-        self.add_stage(stage_name="conditioning_stage",
-                       stage=ConditioningStage())
-
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(
-                           scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer")))
-
-        self.add_stage(stage_name="image_encoding_stage",
-                       stage=Hy15ImageEncodingStage(image_encoder=None,
-                                                    image_processor=None))
-
-        self.add_stage(stage_name="denoising_stage",
-                       stage=DenoisingStage(
-                           transformer=self.get_module("transformer"),
-                           scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="decoding_stage",
-                       stage=DecodingStage(vae=self.get_module("vae")))
-
-
-EntryClass = HunyuanVideo15ImageToVideoPipeline

fastvideo/pipelines/basic/hyworld/__init__.py

@@ -1 +0,0 @@
-

fastvideo/pipelines/basic/hyworld/hyworld_pipeline.py

@@ -1,84 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-HYWorld video diffusion pipeline implementation.
-
-This module contains an implementation of the HYWorld video diffusion pipeline
-using the modular pipeline architecture with HYWorld-specific denoising stage
-for chunk-based video generation with context frame selection.
-"""
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.pipelines.stages import (
-    ConditioningStage, DecodingStage, HYWorldDenoisingStage,
-    InputValidationStage, LatentPreparationStage, TextEncodingStage,
-    TimestepPreparationStage, HYWorldImageEncodingStage)
-
-logger = init_logger(__name__)
-
-
-class HYWorldPipeline(ComposedPipelineBase):
-    """
-    HYWorld video diffusion pipeline.
-
-    This pipeline implements chunk-based video generation with context frame
-    selection for 3D-aware generation using HYWorldDenoisingStage.
-
-    Note: HYWorld only uses a single LLM-based text encoder, unlike SDXL-style
-    dual encoder setups. The text_encoder_2/tokenizer_2 are not used.
-    """
-
-    # Include image_encoder and feature_extractor for I2V support with SigLIP
-    # Note: guider (ClassifierFreeGuidance) is not needed - FastVideo handles CFG differently
-    _required_config_modules = [
-        "text_encoder", "tokenizer", "vae", "transformer", "scheduler",
-        "text_encoder_2", "tokenizer_2", "image_encoder", "feature_extractor"
-    ]
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        """Set up pipeline stages with HYWorld-specific denoising stage."""
-
-        self.add_stage(stage_name="input_validation_stage",
-                       stage=InputValidationStage())
-
-        self.add_stage(stage_name="prompt_encoding_stage_primary",
-                       stage=TextEncodingStage(
-                           text_encoders=[
-                               self.get_module("text_encoder"),
-                               self.get_module("text_encoder_2")
-                           ],
-                           tokenizers=[
-                               self.get_module("tokenizer"),
-                               self.get_module("tokenizer_2")
-                           ]))
-
-        self.add_stage(stage_name="conditioning_stage",
-                       stage=ConditioningStage())
-
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(
-                           scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer")))
-
-        self.add_stage(stage_name="image_encoding_stage",
-                       stage=HYWorldImageEncodingStage(
-                           image_encoder=self.get_module("image_encoder"),
-                           image_processor=self.get_module("feature_extractor"),
-                           vae=self.get_module("vae")))
-
-        self.add_stage(stage_name="denoising_stage",
-                       stage=HYWorldDenoisingStage(
-                           transformer=self.get_module("transformer"),
-                           scheduler=self.get_module("scheduler"),
-                           pipeline=self))
-
-        self.add_stage(stage_name="decoding_stage",
-                       stage=DecodingStage(vae=self.get_module("vae")))
-
-
-EntryClass = HYWorldPipeline

fastvideo/pipelines/basic/ltx2/ltx2_pipeline.py

@@ -1,150 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LTX-2 text-to-video pipeline.
-"""
-
-import os
-from typing import Any
-
-from transformers import AutoTokenizer
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import PipelineComponentLoader
-from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.pipelines.stages import (DecodingStage, InputValidationStage,
-                                        LTX2AudioDecodingStage,
-                                        LTX2DenoisingStage,
-                                        LTX2LatentPreparationStage,
-                                        TextEncodingStage)
-
-logger = init_logger(__name__)
-
-
-class LTX2Pipeline(ComposedPipelineBase):
-
-    _required_config_modules = [
-        "text_encoder",
-        "tokenizer",
-        "transformer",
-        "vae",
-        "audio_vae",
-        "vocoder",
-    ]
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        self.add_stage(
-            stage_name="input_validation_stage",
-            stage=InputValidationStage(),
-        )
-
-        self.add_stage(
-            stage_name="prompt_encoding_stage",
-            stage=TextEncodingStage(
-                text_encoders=[self.get_module("text_encoder")],
-                tokenizers=[self.get_module("tokenizer")],
-            ),
-        )
-
-        self.add_stage(
-            stage_name="latent_preparation_stage",
-            stage=LTX2LatentPreparationStage(
-                transformer=self.get_module("transformer"), ),
-        )
-
-        self.add_stage(
-            stage_name="denoising_stage",
-            stage=LTX2DenoisingStage(
-                transformer=self.get_module("transformer"), ),
-        )
-
-        self.add_stage(
-            stage_name="audio_decoding_stage",
-            stage=LTX2AudioDecodingStage(
-                audio_decoder=self.get_module("audio_vae"),
-                vocoder=self.get_module("vocoder"),
-            ),
-        )
-
-        self.add_stage(
-            stage_name="decoding_stage",
-            stage=DecodingStage(vae=self.get_module("vae")),
-        )
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        tokenizer = self.get_module("tokenizer")
-        if tokenizer is not None:
-            tokenizer.padding_side = "left"
-            if tokenizer.pad_token is None:
-                tokenizer.pad_token = tokenizer.eos_token
-
-    def load_modules(
-        self,
-        fastvideo_args: FastVideoArgs,
-        loaded_modules: dict[str, Any] | None = None,
-    ) -> dict[str, Any]:
-        model_index = self._load_config(self.model_path)
-        logger.info("Loading pipeline modules from config: %s", model_index)
-
-        model_index.pop("_class_name")
-        model_index.pop("_diffusers_version")
-        model_index.pop("workload_type", None)
-
-        if len(model_index) <= 1:
-            raise ValueError(
-                "model_index.json must contain at least one pipeline module")
-
-        required_modules = self.required_config_modules
-        modules: dict[str, Any] = {}
-
-        for module_name, module_spec in model_index.items():
-            if not isinstance(module_spec, list) or len(module_spec) < 1:
-                continue
-            transformers_or_diffusers = module_spec[0]
-            if transformers_or_diffusers is None:
-                if module_name in self.required_config_modules:
-                    self.required_config_modules.remove(module_name)
-                continue
-            if module_name not in required_modules:
-                continue
-            if loaded_modules is not None and module_name in loaded_modules:
-                modules[module_name] = loaded_modules[module_name]
-                continue
-
-            component_model_path = os.path.join(self.model_path, module_name)
-            if module_name == "tokenizer" and not os.path.isdir(
-                    component_model_path):
-                gemma_path = os.path.join(self.model_path, "text_encoder",
-                                          "gemma")
-                if os.path.isdir(gemma_path):
-                    component_model_path = gemma_path
-                else:
-                    raise ValueError(
-                        "Tokenizer directory missing and Gemma weights were not found."
-                    )
-
-            module = PipelineComponentLoader.load_module(
-                module_name=module_name,
-                component_model_path=component_model_path,
-                transformers_or_diffusers=transformers_or_diffusers,
-                fastvideo_args=fastvideo_args,
-            )
-            logger.info("Loaded module %s from %s", module_name,
-                        component_model_path)
-            modules[module_name] = module
-
-        if "tokenizer" in required_modules and "tokenizer" not in modules:
-            gemma_path = os.path.join(self.model_path, "text_encoder", "gemma")
-            if os.path.isdir(gemma_path):
-                modules["tokenizer"] = AutoTokenizer.from_pretrained(
-                    gemma_path, local_files_only=True)
-
-        for module_name in required_modules:
-            if module_name not in modules or modules[module_name] is None:
-                raise ValueError(
-                    f"Required module {module_name} was not loaded properly")
-
-        return modules
-
-
-EntryClass = LTX2Pipeline

fastvideo/pipelines/composed_pipeline_base.py

@@ -84,7 +84,6 @@ class ComposedPipelineBase(ABC):
 
         # Load modules directly in initialization
         logger.info("Loading pipeline modules...")
-        # fastvideo_args.dit_cpu_offload = False
         with self.profiler_controller.region("profiler_region_model_loading"):
             self.modules = self.load_modules(fastvideo_args, loaded_modules)
 
@@ -288,7 +287,6 @@ class ComposedPipelineBase(ABC):
         # remove keys that are not pipeline modules
         model_index.pop("_class_name")
         model_index.pop("_diffusers_version")
-        model_index.pop("_name_or_path", None)
         model_index.pop("workload_type", None)
         if "boundary_ratio" in model_index and model_index[
                 "boundary_ratio"] is not None:

fastvideo/pipelines/lora_pipeline.py

@@ -1,9 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from collections import defaultdict
 from collections.abc import Hashable
-from contextlib import nullcontext
 from typing import Any
-from collections.abc import Generator
 
 import torch
 import torch.distributed as dist
@@ -14,13 +12,8 @@ from torch.distributed.tensor import DTensor
 
 from fastvideo.distributed import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs, TrainingArgs
-from fastvideo.hooks.hooks import ModuleHookManager
-from fastvideo.hooks.layerwise_offload import LayerwiseOffloadHook
-from fastvideo.layers.lora.linear import (
-    BaseLayerWithLoRA,
-    get_lora_layer,
-    replace_submodule,
-)
+from fastvideo.layers.lora.linear import (BaseLayerWithLoRA, get_lora_layer,
+                                          replace_submodule)
 from fastvideo.logger import init_logger
 from fastvideo.models.loader.utils import get_param_names_mapping
 from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
@@ -29,89 +22,17 @@ from fastvideo.utils import maybe_download_lora
 logger = init_logger(__name__)
 
 
-def _get_hook_ctx(module: nn.Module | None):
-    if module is None:
-        return nullcontext()
-    hook_mgr = ModuleHookManager.get_from(module)
-    if hook_mgr is not None:
-        offload_hook = hook_mgr.forward_hooks.get(LayerwiseOffloadHook.name())
-        if offload_hook is not None:
-            return offload_hook.mutate_params_scope()  # type: ignore
-    return nullcontext()
-
-
-def _named_module_by_prefix(
-    module: nn.Module, prefixes: list[str]
-) -> list[tuple[str | None, list[tuple[str, nn.Module]]]]:
-    none_list: list[tuple[str, nn.Module]] = []
-    prefix_list: list[tuple[str, list[tuple[str, nn.Module]]]] = [
-        (prefix, []) for prefix in prefixes
-    ]
-    for name, submodule in module.named_modules():
-        for cur_prefix, cur_list in prefix_list:
-            # we should exclude e.g. block.1 and block.12.attn
-            if name.startswith(cur_prefix + "."):
-                cur_list.append((name, submodule))
-                break
-        else:
-            none_list.append((name, submodule))
-    return prefix_list + [(None, none_list)]  # type: ignore
-
-
-class LoRAModelLayers:
-
-    def __init__(self, block_list: list[tuple[str, nn.Module]]) -> None:
-        # block_name -> {layer_name -> layer}
-        self.block_to_lora_layers: dict[str, dict[str, BaseLayerWithLoRA]] = {}
-        # layer_name -> block_name
-        self.lora_layers_to_block: dict[str, str | None] = {}
-        self.other_lora_layers: dict[str, BaseLayerWithLoRA] = {}
-        self.block_mapping = dict(block_list)
-
-    def add_lora_layer(self, block_name: str | None, layer_name: str,
-                       layer: BaseLayerWithLoRA):
-        if block_name is None:
-            self.other_lora_layers[layer_name] = layer
-            self.lora_layers_to_block[layer_name] = None
-        else:
-            if block_name not in self.block_to_lora_layers:
-                self.block_to_lora_layers[block_name] = {}
-            self.block_to_lora_layers[block_name][layer_name] = layer
-            self.lora_layers_to_block[layer_name] = block_name
-
-    def all_lora_layers(
-        self, ) -> Generator[tuple[str, BaseLayerWithLoRA], Any, None]:
-        for block_layers in self.block_to_lora_layers.values():
-            for name, layer in block_layers.items():
-                yield name, layer
-        for name, layer in self.other_lora_layers.items():
-            yield name, layer
-
-    def lora_layers_by_block(
-        self,
-    ) -> Generator[
-            tuple[nn.Module | None, dict[str, BaseLayerWithLoRA]],
-            Any,
-            None,
-    ]:
-        for block_name, layers in self.block_to_lora_layers.items():
-            yield self.block_mapping[block_name], layers
-        yield None, self.other_lora_layers
-
-
 class LoRAPipeline(ComposedPipelineBase):
     """
     Pipeline that supports injecting LoRA adapters into the diffusion transformer.
     TODO: support training.
     """
-
     lora_adapters: dict[str, dict[str, torch.Tensor]] = defaultdict(
         dict
     )  # state dicts of loaded lora adapters (includes lora_A, lora_B, and lora_alpha)
     cur_adapter_name: str = ""
     cur_adapter_path: str = ""
-    # model_name -> layers
-    lora_layers: dict[str, LoRAModelLayers] = {}
+    lora_layers: dict[str, dict[str, BaseLayerWithLoRA]] = {}
     fastvideo_args: FastVideoArgs | TrainingArgs
     exclude_lora_layers: dict[str, list[str]] = {}
     device: torch.device = get_local_torch_device()
@@ -127,10 +48,10 @@ class LoRAPipeline(ComposedPipelineBase):
         self.device = get_local_torch_device()
         # build list of trainable transformers
         for transformer_name in self.trainable_transformer_names:
-            if (transformer_name in self.modules
-                    and self.modules[transformer_name] is not None):
-                self.trainable_transformer_modules[transformer_name] = (
-                    self.modules[transformer_name])
+            if transformer_name in self.modules and self.modules[
+                    transformer_name] is not None:
+                self.trainable_transformer_modules[
+                    transformer_name] = self.modules[transformer_name]
             # check for transformer_2 in case of Wan2.2 MoE or fake_score_transformer_2
             if transformer_name.endswith("_2"):
                 raise ValueError(
@@ -138,23 +59,19 @@ class LoRAPipeline(ComposedPipelineBase):
                 )
 
             secondary_transformer_name = transformer_name + "_2"
-            if (secondary_transformer_name in self.modules
-                    and self.modules[secondary_transformer_name] is not None):
+            if secondary_transformer_name in self.modules and self.modules[
+                    secondary_transformer_name] is not None:
                 self.trainable_transformer_modules[
                     secondary_transformer_name] = self.modules[
                         secondary_transformer_name]
 
-        logger.info(
-            "trainable_transformer_modules: %s",
-            self.trainable_transformer_modules.keys(),
-        )
+        logger.info("trainable_transformer_modules: %s",
+                    self.trainable_transformer_modules.keys())
 
-        for (
-                transformer_name,
-                transformer_module,
-        ) in self.trainable_transformer_modules.items():
-            self.exclude_lora_layers[transformer_name] = (
-                transformer_module.config.arch_config.exclude_lora_layers)
+        for transformer_name, transformer_module in self.trainable_transformer_modules.items(
+        ):
+            self.exclude_lora_layers[
+                transformer_name] = transformer_module.config.arch_config.exclude_lora_layers
         self.lora_target_modules = self.fastvideo_args.lora_target_modules
         self.lora_path = self.fastvideo_args.lora_path
         self.lora_nickname = self.fastvideo_args.lora_nickname
@@ -166,33 +83,20 @@ class LoRAPipeline(ComposedPipelineBase):
                 self.fastvideo_args.lora_alpha = self.fastvideo_args.lora_rank
             self.lora_rank = self.fastvideo_args.lora_rank  # type: ignore
             self.lora_alpha = self.fastvideo_args.lora_alpha  # type: ignore
-            logger.info(
-                "Using LoRA training with rank %d and alpha %d",
-                self.lora_rank,
-                self.lora_alpha,
-            )
+            logger.info("Using LoRA training with rank %d and alpha %d",
+                        self.lora_rank, self.lora_alpha)
             if self.lora_target_modules is None:
                 self.lora_target_modules = [
-                    "q_proj",
-                    "k_proj",
-                    "v_proj",
-                    "o_proj",
-                    "to_q",
-                    "to_k",
-                    "to_v",
-                    "to_out",
-                    "to_qkv",
-                    "to_gate_compress",
+                    "q_proj", "k_proj", "v_proj", "o_proj", "to_q", "to_k",
+                    "to_v", "to_out", "to_qkv", "to_gate_compress"
                 ]
                 logger.info(
                     "Using default lora_target_modules for all transformers: %s",
-                    self.lora_target_modules,
-                )
+                    self.lora_target_modules)
             else:
                 logger.warning(
                     "Using custom lora_target_modules for all transformers, which may not be intended: %s",
-                    self.lora_target_modules,
-                )
+                    self.lora_target_modules)
 
             self.convert_to_lora_layers()
         # Inference
@@ -200,8 +104,7 @@ class LoRAPipeline(ComposedPipelineBase):
             self.convert_to_lora_layers()
             self.set_lora_adapter(
                 self.lora_nickname,  # type: ignore
-                self.lora_path,
-            )  # type: ignore
+                self.lora_path)  # type: ignore
 
     def is_target_layer(self, module_name: str) -> bool:
         if self.lora_target_modules is None:
@@ -211,9 +114,9 @@ class LoRAPipeline(ComposedPipelineBase):
 
     def set_trainable(self) -> None:
 
-        def set_lora_grads(lora_layers: LoRAModelLayers,
+        def set_lora_grads(lora_layers: dict[str, BaseLayerWithLoRA],
                            device_mesh: DeviceMesh):
-            for name, layer in lora_layers.all_lora_layers():
+            for name, layer in lora_layers.items():
                 layer.lora_A.requires_grad_(True)
                 layer.lora_B.requires_grad_(True)
                 layer.base_layer.requires_grad_(False)
@@ -228,15 +131,10 @@ class LoRAPipeline(ComposedPipelineBase):
             super().set_trainable()
             return
 
-        device_mesh = init_device_mesh(
-            "cuda",
-            (dist.get_world_size(), 1),
-            mesh_dim_names=["fake", "replicate"],
-        )
-        for (
-                transformer_name,
-                transformer_module,
-        ) in self.trainable_transformer_modules.items():
+        device_mesh = init_device_mesh("cuda", (dist.get_world_size(), 1),
+                                       mesh_dim_names=["fake", "replicate"])
+        for transformer_name, transformer_module in self.trainable_transformer_modules.items(
+        ):
             transformer_module.train()
             transformer_module.requires_grad_(False)
             if transformer_name in self.lora_layers:
@@ -253,71 +151,32 @@ class LoRAPipeline(ComposedPipelineBase):
         if self.lora_initialized:
             return
         self.lora_initialized = True
-        for (
-                transformer_name,
-                transformer_module,
-        ) in self.trainable_transformer_modules.items():
+        for transformer_name, transformer_module in self.trainable_transformer_modules.items(
+        ):
             converted_count = 0
-            # init bookkeeping structures
             if transformer_name not in self.lora_layers:
-                # get block list
-                block_list = []
-                for name, submodule in transformer_module.named_children():
-                    if isinstance(submodule, nn.ModuleList):
-                        block_list = [(f"{name}.{i}", m)
-                                      for i, m in enumerate(submodule)]
-                        break
-                self.lora_layers[transformer_name] = LoRAModelLayers(block_list)
+                self.lora_layers[transformer_name] = {}
             logger.info("Converting %s to LoRA Transformer", transformer_name)
-            # scan every module and convert to LoRA layer if applicable
+            for name, layer in transformer_module.named_modules():
+                if not self.is_target_layer(name):
+                    continue
 
-            for block_name, block_modules in _named_module_by_prefix(
-                    transformer_module,
-                    list(self.lora_layers[transformer_name].block_mapping),
-            ):
-                if block_name is not None and (
-                        not self.fastvideo_args.training_mode
-                        and self.fastvideo_args.dit_layerwise_offload):
-                    scope_ctx = _get_hook_ctx(
-                        self.lora_layers[transformer_name].
-                        block_mapping[block_name])
-                else:
-                    scope_ctx = nullcontext()
-                with scope_ctx:
-                    for name, layer in block_modules:
-                        if not self.is_target_layer(name):
-                            continue
+                excluded = False
+                for exclude_layer in self.exclude_lora_layers[transformer_name]:
+                    if exclude_layer in name:
+                        excluded = True
+                        break
+                if excluded:
+                    continue
 
-                        excluded = False
-                        for exclude_layer in self.exclude_lora_layers[
-                                transformer_name]:
-                            if exclude_layer in name:
-                                excluded = True
-                                break
-                        if excluded:
-                            continue
-
-                        layer = get_lora_layer(
-                            layer,
-                            lora_rank=self.lora_rank,
-                            lora_alpha=self.lora_alpha,
-                            training_mode=self.training_mode,
-                        )
-                        if layer is not None:
-                            block_name_split = name.split(".", 2)
-                            if len(block_name_split) > 2:
-                                block_name = (block_name_split[0] + "." +
-                                              block_name_split[1])
-                            else:
-                                block_name = None
-                            if (block_name
-                                    not in self.lora_layers[transformer_name].
-                                    block_mapping):
-                                block_name = None
-                            self.lora_layers[transformer_name].add_lora_layer(
-                                block_name, name, layer)
-                            replace_submodule(transformer_module, name, layer)
-                            converted_count += 1
+                layer = get_lora_layer(layer,
+                                       lora_rank=self.lora_rank,
+                                       lora_alpha=self.lora_alpha,
+                                       training_mode=self.training_mode)
+                if layer is not None:
+                    self.lora_layers[transformer_name][name] = layer
+                    replace_submodule(transformer_module, name, layer)
+                    converted_count += 1
             logger.info("Converted %d layers to LoRA layers", converted_count)
 
     def set_lora_adapter(self,
@@ -350,7 +209,7 @@ class LoRAPipeline(ComposedPipelineBase):
 
             # Extract alpha values and weights in a single pass
             to_merge_params: defaultdict[Hashable,
-                                         dict[Any, Any]] = (defaultdict(dict))
+                                         dict[Any, Any]] = defaultdict(dict)
             for name, weight in lora_state_dict.items():
                 # Extract weights (lora_A, lora_B, and lora_alpha)
                 name = name.replace("diffusion_model.", "")
@@ -364,14 +223,13 @@ class LoRAPipeline(ComposedPipelineBase):
                     target_name, _, _ = param_names_mapping_fn(layer_name)
                     # Store alpha alongside weights with same target_name base
                     alpha_key = target_name + ".lora_alpha"
-                    self.lora_adapters[lora_nickname][alpha_key] = (
-                        weight.item()
-                        if weight.numel() == 1 else float(weight.mean()))
+                    self.lora_adapters[lora_nickname][alpha_key] = weight.item(
+                    ) if weight.numel() == 1 else float(weight.mean())
                     continue
 
                 name, _, _ = lora_param_names_mapping_fn(name)
-                target_name, merge_index, num_params_to_merge = (
-                    param_names_mapping_fn(name))
+                target_name, merge_index, num_params_to_merge = param_names_mapping_fn(
+                    name)
                 # for (in_dim, r) @ (r, out_dim), we only merge (r, out_dim * n) where n is the number of linear layers to fuse
                 # see param mapping in HunyuanVideoArchConfig
                 if merge_index is not None and "lora_B" in name:
@@ -403,84 +261,45 @@ class LoRAPipeline(ComposedPipelineBase):
 
         # Merge the new adapter
         adapted_count = 0
-        for (
-                transformer_name,
-                transformer_lora_layers,
-        ) in self.lora_layers.items():
-            for (
-                    module,
-                    layers,
-            ) in transformer_lora_layers.lora_layers_by_block():
-                with _get_hook_ctx(module):
-                    for name, layer in layers.items():
-                        lora_A_name = name + ".lora_A"
-                        lora_B_name = name + ".lora_B"
-                        lora_alpha_name = name + ".lora_alpha"
-                        if (lora_A_name in self.lora_adapters[lora_nickname]
-                                and lora_B_name
-                                in self.lora_adapters[lora_nickname]):
-                            # Get alpha value for this layer (defaults to None if not present)
-                            lora_A = self.lora_adapters[lora_nickname][
-                                lora_A_name]
-                            lora_B = self.lora_adapters[lora_nickname][
-                                lora_B_name]
-                            # Simple lookup - alpha stored with same naming scheme as lora_A/lora_B
-                            alpha = (self.lora_adapters[lora_nickname].get(
-                                lora_alpha_name) if adapter_updated else None)
-                            try:
-                                layer.set_lora_weights(
-                                    lora_A,
-                                    lora_B,
-                                    lora_alpha=alpha,
-                                    training_mode=self.fastvideo_args.
-                                    training_mode,
-                                    lora_path=lora_path,
-                                )
-                            except Exception as e:
-                                logger.error(
-                                    "Error setting LoRA weights for layer %s: %s",
-                                    name,
-                                    str(e),
-                                )
-                                raise e
-                            adapted_count += 1
-                        else:
-                            if rank == 0:
-                                logger.warning(
-                                    "LoRA adapter %s does not contain the weights for layer %s. LoRA will not be applied to it.",
-                                    lora_path,
-                                    name,
-                                )
-                            layer.disable_lora = True
-        logger.info(
-            "Rank %d: LoRA adapter %s applied to %d layers",
-            rank,
-            lora_path,
-            adapted_count,
-        )
+        for transformer_name, transformer_lora_layers in self.lora_layers.items(
+        ):
+            for name, layer in transformer_lora_layers.items():
+                lora_A_name = name + ".lora_A"
+                lora_B_name = name + ".lora_B"
+                lora_alpha_name = name + ".lora_alpha"
+                if lora_A_name in self.lora_adapters[lora_nickname]\
+                    and lora_B_name in self.lora_adapters[lora_nickname]:
+                    # Get alpha value for this layer (defaults to None if not present)
+                    lora_A = self.lora_adapters[lora_nickname][lora_A_name]
+                    lora_B = self.lora_adapters[lora_nickname][lora_B_name]
+                    # Simple lookup - alpha stored with same naming scheme as lora_A/lora_B
+                    alpha = self.lora_adapters[lora_nickname].get(
+                        lora_alpha_name) if adapter_updated else None
+
+                    layer.set_lora_weights(
+                        lora_A,
+                        lora_B,
+                        lora_alpha=alpha,
+                        training_mode=self.fastvideo_args.training_mode,
+                        lora_path=lora_path)
+                    adapted_count += 1
+                else:
+                    if rank == 0:
+                        logger.warning(
+                            "LoRA adapter %s does not contain the weights for layer %s. LoRA will not be applied to it.",
+                            lora_path, name)
+                    layer.disable_lora = True
+        logger.info("Rank %d: LoRA adapter %s applied to %d layers", rank,
+                    lora_path, adapted_count)
 
     def merge_lora_weights(self) -> None:
-        for (
-                transformer_name,
-                transformer_lora_layers,
-        ) in self.lora_layers.items():
-            for (
-                    module,
-                    layers,
-            ) in transformer_lora_layers.lora_layers_by_block():
-                with _get_hook_ctx(module):
-                    for name, layer in layers.items():
-                        layer.merge_lora_weights()
+        for transformer_name, transformer_lora_layers in self.lora_layers.items(
+        ):
+            for name, layer in transformer_lora_layers.items():
+                layer.merge_lora_weights()
 
     def unmerge_lora_weights(self) -> None:
-        for (
-                transformer_name,
-                transformer_lora_layers,
-        ) in self.lora_layers.items():
-            for (
-                    module,
-                    layers,
-            ) in transformer_lora_layers.lora_layers_by_block():
-                with _get_hook_ctx(module):
-                    for name, layer in layers.items():
-                        layer.unmerge_lora_weights()
+        for transformer_name, transformer_lora_layers in self.lora_layers.items(
+        ):
+            for name, layer in transformer_lora_layers.items():
+                layer.unmerge_lora_weights()

fastvideo/pipelines/pipeline_batch_info.py

@@ -132,9 +132,6 @@ class ForwardBatch:
     keyboard_cond: torch.Tensor | None = None  # Shape: (B, T, K)
     grid_sizes: torch.Tensor | None = None  # Shape: (3,) [F,H,W]
 
-    # Camera control inputs (HYWorld)
-    pose: str | None = None  # Camera trajectory: pose string (e.g., 'w-31') or JSON file path
-
     # Latent dimensions
     height_latents: list[int] | int | None = None
     width_latents: list[int] | int | None = None
@@ -232,7 +229,6 @@ class TrainingBatch:
     image_latents: torch.Tensor | None = None
     infos: list[dict[str, Any]] | None = None
     mask_lat_size: torch.Tensor | None = None
-    video_latent: torch.Tensor | None = None
 
     # ODE trajectory supervision
     trajectory_latents: torch.Tensor | None = None
@@ -241,9 +237,6 @@ class TrainingBatch:
     # Transformer inputs
     noisy_model_input: torch.Tensor | None = None
     timesteps: torch.Tensor | None = None
-    use_gt_trajectory: bool = False
-    trajectory_timesteps: torch.Tensor | None = None
-    start_timestep_index: int | None = None
     sigmas: torch.Tensor | None = None
     noise: torch.Tensor | None = None
 

fastvideo/pipelines/pipeline_registry.py

@@ -28,9 +28,6 @@ _PIPELINE_NAME_TO_ARCHITECTURE_NAME: dict[str, str] = {
     "StepVideoPipeline": "stepvideo",
     "HunyuanVideoPipeline": "hunyuan",
     "HunyuanVideo15Pipeline": "hunyuan15",
-    "HYWorldPipeline": "hyworld",
-    "Hy15CausalDMDPipeline": "hunyuan15",
-    "HunyuanVideo15ImageToVideoPipeline": "hunyuan15",
     "Cosmos2VideoToWorldPipeline": "cosmos",
     "Cosmos2_5Pipeline": "cosmos",
     "MatrixGamePipeline": "matrixgame",
@@ -38,7 +35,6 @@ _PIPELINE_NAME_TO_ARCHITECTURE_NAME: dict[str, str] = {
     "LongCatPipeline": "longcat",
     "LongCatImageToVideoPipeline": "longcat",
     "LongCatVideoContinuationPipeline": "longcat",
-    "LTX2Pipeline": "ltx2",
 }
 
 _PREPROCESS_WORKLOAD_TYPE_TO_PIPELINE_NAME: dict[WorkloadType, str] = {

fastvideo/pipelines/preprocess/hunyuan/hunyuan_preprocess_pipelins.py

@@ -0,0 +1,59 @@
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.preprocess.preprocess_stages import (
+    TextTransformStage, VideoTransformStage)
+from fastvideo.pipelines.stages import (EncodingStage, ImageEncodingStage,
+                                        TextEncodingStage)
+from fastvideo.pipelines.stages.image_encoding import ImageVAEEncodingStage
+        
+
+class PreprocessPipelineT2V(ComposedPipelineBase):
+    _required_config_modules = [
+        "text_encoder", "tokenizer", "text_encoder_2", "tokenizer_2", "vae"
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        assert fastvideo_args.preprocess_config is not None
+        
+        self.add_stage(
+            stage_name="text_transform_stage",
+            stage=TextTransformStage(
+                cfg_uncondition_drop_rate=fastvideo_args.preprocess_config.training_cfg_rate,
+                seed=fastvideo_args.preprocess_config.seed,
+            )
+        )
+        
+        text_encoders = [
+            self.get_module("text_encoder"),
+            self.get_module("text_encoder_2")
+        ]
+        tokenizers = [
+            self.get_module("tokenizer"),
+            self.get_module("tokenizer_2")
+        ]
+        
+        self.add_stage(
+            stage_name="prompt_encoding_stage",
+            stage=TextEncodingStage(
+                text_encoders=text_encoders,
+                tokenizers=tokenizers,
+            )
+        )
+        
+        self.add_stage(
+            stage_name="video_transform_stage",
+            stage=VideoTransformStage(
+                train_fps=fastvideo_args.preprocess_config.train_fps,
+                num_frames=fastvideo_args.preprocess_config.num_frames,
+                max_height=fastvideo_args.preprocess_config.max_height,
+                max_width=fastvideo_args.preprocess_config.max_width,
+                do_temporal_sample=fastvideo_args.preprocess_config.do_temporal_sample,
+            )
+        )
+        
+        self.add_stage(
+            stage_name="video_encoding_stage",
+            stage=EncodingStage(vae=self.get_module("vae"))
+        )
+
+EntryClass = [PreprocessPipelineT2V]

fastvideo/pipelines/preprocess/hunyuan15/hunyuan_preprocess_pipelins.py

@@ -0,0 +1,59 @@
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.preprocess.preprocess_stages import (
+    TextTransformStage, VideoTransformStage)
+from fastvideo.pipelines.stages import (EncodingStage, ImageEncodingStage,
+                                        TextEncodingStage)
+from fastvideo.pipelines.stages.image_encoding import ImageVAEEncodingStage
+        
+
+class PreprocessPipelineT2V(ComposedPipelineBase):
+    _required_config_modules = [
+        "text_encoder", "tokenizer", "text_encoder_2", "tokenizer_2", "vae"
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        assert fastvideo_args.preprocess_config is not None
+        
+        self.add_stage(
+            stage_name="text_transform_stage",
+            stage=TextTransformStage(
+                cfg_uncondition_drop_rate=fastvideo_args.preprocess_config.training_cfg_rate,
+                seed=fastvideo_args.preprocess_config.seed,
+            )
+        )
+        
+        text_encoders = [
+            self.get_module("text_encoder"),
+            self.get_module("text_encoder_2")
+        ]
+        tokenizers = [
+            self.get_module("tokenizer"),
+            self.get_module("tokenizer_2")
+        ]
+        
+        self.add_stage(
+            stage_name="prompt_encoding_stage",
+            stage=TextEncodingStage(
+                text_encoders=text_encoders,
+                tokenizers=tokenizers,
+            )
+        )
+        
+        self.add_stage(
+            stage_name="video_transform_stage",
+            stage=VideoTransformStage(
+                train_fps=fastvideo_args.preprocess_config.train_fps,
+                num_frames=fastvideo_args.preprocess_config.num_frames,
+                max_height=fastvideo_args.preprocess_config.max_height,
+                max_width=fastvideo_args.preprocess_config.max_width,
+                do_temporal_sample=fastvideo_args.preprocess_config.do_temporal_sample,
+            )
+        )
+        
+        self.add_stage(
+            stage_name="video_encoding_stage",
+            stage=EncodingStage(vae=self.get_module("vae"))
+        )
+
+EntryClass = [PreprocessPipelineT2V]

fastvideo/pipelines/preprocess/preprocess_pipeline_base.py

@@ -303,7 +303,7 @@ class BasePreprocessPipeline(ComposedPipelineBase):
             if data is None:
                 continue
 
-            with torch.no_grad():
+            with torch.inference_mode():
                 # Filter out invalid samples (those with all zeros)
                 valid_indices = []
                 for i, pixel_values in enumerate(data["pixel_values"]):
@@ -422,4 +422,4 @@ class BasePreprocessPipeline(ComposedPipelineBase):
             if num_processed_samples >= args.flush_frequency:
                 written = self.dataset_writer.flush()
                 logger.info("Flushed %s samples to parquet", written)
-                num_processed_samples = 0
+                num_processed_samples = 0

fastvideo/pipelines/preprocess/preprocess_pipeline_ode_trajectory.py

@@ -28,12 +28,16 @@ from fastvideo.dataset.dataloader.schema import (
     pyarrow_schema_ode_trajectory_text_only)
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
+from fastvideo.models.schedulers.scheduling_self_forcing_flow_match import (
+    SelfForcingFlowMatchScheduler)
 from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
 from fastvideo.pipelines.preprocess.preprocess_pipeline_base import (
     BasePreprocessPipeline)
-from fastvideo.pipelines.stages import (
-    DecodingStage, DenoisingStage, InputValidationStage, LatentPreparationStage,
-    TextEncodingStage, TimestepPreparationStage, Hy15ImageEncodingStage)
+from fastvideo.pipelines.stages import (DecodingStage, DenoisingStage,
+                                        InputValidationStage,
+                                        LatentPreparationStage,
+                                        TextEncodingStage,
+                                        TimestepPreparationStage)
 from fastvideo.utils import save_decoded_latents_as_video, shallow_asdict
 
 logger = init_logger(__name__)
@@ -43,8 +47,7 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
     """ODE Trajectory preprocessing pipeline implementation."""
 
     _required_config_modules = [
-        "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "vae",
-        "transformer", "scheduler"
+        "text_encoder", "tokenizer", "vae", "transformer", "scheduler"
     ]
     preprocess_dataloader: StatefulDataLoader
     preprocess_loader_iter: Iterator[dict[str, Any]]
@@ -58,19 +61,19 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
     def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
         """Set up pipeline stages with proper dependency injection."""
         assert fastvideo_args.pipeline_config.flow_shift == 5
+        self.modules["scheduler"] = SelfForcingFlowMatchScheduler(
+            shift=fastvideo_args.pipeline_config.flow_shift,
+            sigma_min=0.0,
+            extra_one_step=True)
+        self.modules["scheduler"].set_timesteps(num_inference_steps=48,
+                                                denoising_strength=1.0)
 
         self.add_stage(stage_name="input_validation_stage",
                        stage=InputValidationStage())
         self.add_stage(stage_name="prompt_encoding_stage",
                        stage=TextEncodingStage(
-                           text_encoders=[
-                               self.get_module("text_encoder"),
-                               self.get_module("text_encoder_2")
-                           ],
-                           tokenizers=[
-                               self.get_module("tokenizer"),
-                               self.get_module("tokenizer_2")
-                           ],
+                           text_encoders=[self.get_module("text_encoder")],
+                           tokenizers=[self.get_module("tokenizer")],
                        ))
         self.add_stage(stage_name="timestep_preparation_stage",
                        stage=TimestepPreparationStage(
@@ -79,9 +82,6 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                        stage=LatentPreparationStage(
                            scheduler=self.get_module("scheduler"),
                            transformer=self.get_module("transformer", None)))
-        self.add_stage(stage_name="image_encoding_stage",
-                       stage=Hy15ImageEncodingStage(image_encoder=None,
-                                                    image_processor=None))
         self.add_stage(stage_name="denoising_stage",
                        stage=DenoisingStage(
                            transformer=self.get_module("transformer"),
@@ -95,13 +95,11 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                                        args):
         """Preprocess text-only data and generate trajectory information."""
 
-        num_encoders = len(self.prompt_encoding_stage.text_encoders)
-
         for batch_idx, data in enumerate(self.pbar):
             if data is None:
                 continue
 
-            with torch.no_grad():
+            with torch.inference_mode():
                 # For text-only processing, we only need text data
                 # Filter out samples without text
                 valid_indices = []
@@ -132,9 +130,12 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                 prompt_embeds_list, prompt_masks_list = self.prompt_encoding_stage.encode_text(
                     batch_captions,
                     fastvideo_args,
-                    encoder_index=list(range(num_encoders)),
+                    encoder_index=[0],
                     return_attention_mask=True,
                 )
+                prompt_embeds = prompt_embeds_list[0]
+                prompt_attention_masks = prompt_masks_list[0]
+                assert prompt_embeds.shape[0] == prompt_attention_masks.shape[0]
 
                 sampling_params = SamplingParam.from_pretrained(args.model_path)
 
@@ -143,48 +144,61 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                     negative_prompt_embeds_list, negative_prompt_masks_list = self.prompt_encoding_stage.encode_text(
                         sampling_params.negative_prompt,
                         fastvideo_args,
-                        encoder_index=list(range(num_encoders)),
+                        encoder_index=[0],
                         return_attention_mask=True,
                     )
+                    negative_prompt_embed = negative_prompt_embeds_list[0][0]
+                    negative_prompt_attention_mask = negative_prompt_masks_list[
+                        0][0]
                 else:
-                    negative_prompt_embeds_list = []
-                    negative_prompt_masks_list = []
+                    negative_prompt_embed = None
+                    negative_prompt_attention_mask = None
 
                 trajectory_latents = []
                 trajectory_timesteps = []
                 trajectory_decoded = []
 
-                # Collect the trajectory data (text-to-video generation)
-                batch = ForwardBatch(**shallow_asdict(sampling_params), )
-                batch.prompt_embeds = prompt_embeds_list
-                batch.prompt_attention_mask = prompt_masks_list
-                batch.negative_prompt_embeds = negative_prompt_embeds_list
-                batch.negative_attention_mask = negative_prompt_masks_list
-                batch.return_trajectory_latents = True
-                # Enabling this will save the decoded trajectory videos.
-                # Used for debugging.
-                batch.return_trajectory_decoded = False
-                batch.height = args.max_height
-                batch.width = args.max_width
-                batch.num_frames = args.num_frames
-                batch.fps = args.train_fps
+                for i, (prompt_embed, prompt_attention_mask) in enumerate(
+                        zip(prompt_embeds, prompt_attention_masks,
+                            strict=False)):
+                    prompt_embed = prompt_embed.unsqueeze(0)
+                    prompt_attention_mask = prompt_attention_mask.unsqueeze(0)
 
-                result_batch = self.input_validation_stage(
-                    batch, fastvideo_args)
-                result_batch = self.timestep_preparation_stage(
-                    batch, fastvideo_args)
-                result_batch = self.latent_preparation_stage(
-                    result_batch, fastvideo_args)
-                result_batch = self.image_encoding_stage(
-                    result_batch, fastvideo_args)
-                result_batch = self.denoising_stage(result_batch,
-                                                    fastvideo_args)
-                result_batch = self.decoding_stage(result_batch, fastvideo_args)
+                    # Collect the trajectory data (text-to-video generation)
+                    batch = ForwardBatch(**shallow_asdict(sampling_params), )
+                    batch.prompt_embeds = [prompt_embed]
+                    batch.prompt_attention_mask = [prompt_attention_mask]
+                    batch.negative_prompt_embeds = [negative_prompt_embed]
+                    batch.negative_attention_mask = [
+                        negative_prompt_attention_mask
+                    ]
+                    batch.num_inference_steps = 48
+                    batch.return_trajectory_latents = True
+                    # Enabling this will save the decoded trajectory videos.
+                    # Used for debugging.
+                    batch.return_trajectory_decoded = False
+                    batch.height = args.max_height
+                    batch.width = args.max_width
+                    batch.fps = args.train_fps
+                    batch.guidance_scale = 6.0
+                    batch.do_classifier_free_guidance = True
 
-                trajectory_latents.append(result_batch.trajectory_latents.cpu())
-                trajectory_timesteps.append(
-                    result_batch.trajectory_timesteps.cpu())
-                trajectory_decoded.append(result_batch.trajectory_decoded)
+                    result_batch = self.input_validation_stage(
+                        batch, fastvideo_args)
+                    result_batch = self.timestep_preparation_stage(
+                        batch, fastvideo_args)
+                    result_batch = self.latent_preparation_stage(
+                        result_batch, fastvideo_args)
+                    result_batch = self.denoising_stage(result_batch,
+                                                        fastvideo_args)
+                    result_batch = self.decoding_stage(result_batch,
+                                                       fastvideo_args)
+
+                    trajectory_latents.append(
+                        result_batch.trajectory_latents.cpu())
+                    trajectory_timesteps.append(
+                        result_batch.trajectory_timesteps.cpu())
+                    trajectory_decoded.append(result_batch.trajectory_decoded)
 
                 # Prepare extra features for text-only processing
                 extra_features = {
@@ -195,11 +209,10 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                 if batch.return_trajectory_decoded:
                     for i, decoded_frames in enumerate(trajectory_decoded):
                         for j, decoded_frame in enumerate(decoded_frames):
-                            if j in [5, 7]:
-                                save_decoded_latents_as_video(
-                                    decoded_frame,
-                                    f"decoded_videos/trajectory_decoded_{i}_{j}.mp4",
-                                    args.train_fps)
+                            save_decoded_latents_as_video(
+                                decoded_frame,
+                                f"decoded_videos/trajectory_decoded_{i}_{j}.mp4",
+                                args.train_fps)
 
                 # Prepare batch data for Parquet dataset
                 batch_data: list[dict[str, Any]] = []
@@ -214,11 +227,7 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                     video_name = os.path.basename(video_path).split(".")[0]
 
                     # Convert tensors to numpy arrays
-                    text_embedding = prompt_embeds_list[0].float().cpu().numpy()
-                    text_mask = prompt_masks_list[0].cpu().numpy()
-                    text_embedding_2 = prompt_embeds_list[1].float().cpu(
-                    ).numpy()
-                    text_mask_2 = prompt_masks_list[1].cpu().numpy()
+                    text_embedding = prompt_embeds[idx].cpu().numpy()
 
                     # Get extra features for this sample
                     sample_extra_features = {}
@@ -244,9 +253,6 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                             "trajectory_latents"],
                         trajectory_timesteps=sample_extra_features[
                             "trajectory_timesteps"],
-                        text_embedding_2=text_embedding_2,
-                        text_mask=text_mask,
-                        text_mask_2=text_mask_2,
                     )
                     batch_data.append(record)
 

fastvideo/pipelines/preprocess/preprocess_pipeline_text.py

@@ -58,7 +58,7 @@ class PreprocessPipeline_Text(BasePreprocessPipeline):
             if data is None:
                 continue
 
-            with torch.no_grad():
+            with torch.inference_mode():
                 # For text-only processing, we only need text data
                 # Filter out samples without text
                 valid_indices = []
@@ -181,4 +181,4 @@ class PreprocessPipeline_Text(BasePreprocessPipeline):
         self.preprocess_text_only(fastvideo_args, args)
 
 
-EntryClass = PreprocessPipeline_Text
+EntryClass = PreprocessPipeline_Text

fastvideo/pipelines/preprocess/v1_preprocess.py

@@ -1,7 +1,9 @@
 import argparse
 import os
+from typing import Any
 
 from fastvideo import PipelineConfig
+from fastvideo.configs.models.vaes import WanVAEConfig
 from fastvideo.distributed import (
     get_world_size, maybe_init_distributed_environment_and_model_parallel)
 from fastvideo.fastvideo_args import FastVideoArgs
@@ -14,38 +16,38 @@ from fastvideo.pipelines.preprocess.preprocess_pipeline_t2v import (
     PreprocessPipeline_T2V)
 from fastvideo.pipelines.preprocess.preprocess_pipeline_text import (
     PreprocessPipeline_Text)
+from fastvideo.utils import maybe_download_model
 
 logger = init_logger(__name__)
 
 
 def main(args) -> None:
-    # args.model_path = maybe_download_model(args.model_path)
+    args.model_path = maybe_download_model(args.model_path)
     maybe_init_distributed_environment_and_model_parallel(1, 1)
     num_gpus = int(os.environ["WORLD_SIZE"])
     assert num_gpus == 1, "Only support 1 GPU"
 
     pipeline_config = PipelineConfig.from_pretrained(args.model_path)
-    print(pipeline_config.__class__.__name__)
 
-    # kwargs: dict[str, Any] = {}
-    # if args.preprocess_task == "text_only":
-    #     kwargs = {
-    #         "text_encoder_cpu_offload": False,
-    #     }
-    # else:
-    #     # Full config for video/image processing
-    #     kwargs = {
-    #         "vae_precision": "fp32",
-    #         "vae_config": WanVAEConfig(load_encoder=True, load_decoder=True),
-    #     }
-    # pipeline_config.update_config_from_dict(kwargs)
+    kwargs: dict[str, Any] = {}
+    if args.preprocess_task == "text_only":
+        kwargs = {
+            "text_encoder_cpu_offload": False,
+        }
+    else:
+        # Full config for video/image processing
+        kwargs = {
+            "vae_precision": "fp32",
+            "vae_config": WanVAEConfig(load_encoder=True, load_decoder=True),
+        }
+    pipeline_config.update_config_from_dict(kwargs)
 
     fastvideo_args = FastVideoArgs(
         model_path=args.model_path,
         num_gpus=get_world_size(),
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
+        dit_cpu_offload=False,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=False,
         pipeline_config=pipeline_config,
     )
     if args.preprocess_task == "t2v":
@@ -132,4 +134,4 @@ if __name__ == "__main__":
     )
 
     args = parser.parse_args()
-    main(args)
+    main(args)