fix

.buildkite/pipeline.yml

@@ -176,37 +176,3 @@ steps:
                   - TEST_TYPE=precision_vsa
                 agents:
                   queue: "default"
-            - path:
-                - "csrc/attn/vmoba_attn/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Precision Tests VMoBA"
-                env:
-                  - TEST_TYPE=precision_vmoba
-                agents:
-                  queue: "default"
-            - path:
-                - "csrc/attn/vmoba_attn/vmoba/**"
-                - "fastvideo/attention/backends/vmoba.py"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Inference Tests VMoBA"
-                env:  
-                  - TEST_TYPE=inference_vmoba
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Unit Tests"
-                env:
-                  - TEST_TYPE=unit_test
-                agents:
-                  queue: "default"

.buildkite/scripts/pr_test.sh

@@ -109,19 +109,6 @@ case "$TEST_TYPE" in
         log "Running distillation DMD tests..."
         MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_distill_dmd_tests"
         ;;
-        # run_inference_tests_vmoba
-    "inference_vmoba")
-        log "Running V-MoBA inference tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_inference_tests_vmoba"
-        ;;
-    "precision_vmoba")
-        log "Running V-MoBA precision tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_precision_tests_vmoba"
-        ;;
-    "unit_test")
-        log "Running unit tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_unit_test"
-        ;;
     *)
         log "Error: Unknown test type: $TEST_TYPE"
         exit 1

.github/workflows/pr-test.yml

@@ -62,8 +62,8 @@ on:
         required: false
         default: false
         type: boolean
-      run_unit_test:
-        description: "Run unit-test"
+      run_nightly_test:
+        description: "Run nightly-test"
         required: false
         default: false
         type: boolean
@@ -93,7 +93,6 @@ jobs:
       inference-test-STA: ${{ steps.filter.outputs.inference-test-STA }}
       precision-test-STA: ${{ steps.filter.outputs.precision-test-STA }}
       precision-test-VSA: ${{ steps.filter.outputs.precision-test-VSA }}
-      unit-test: ${{ steps.filter.outputs.unit-test }}
     steps:
       - uses: actions/checkout@v4
       - uses: dorny/paths-filter@v3
@@ -103,8 +102,6 @@ jobs:
             # Define reusable path patterns
             common-paths: &common-paths
               - 'pyproject.toml'
-              - 'docker/Dockerfile.python3.10'
-              - 'docker/Dockerfile.python3.11'
               - 'docker/Dockerfile.python3.12'
             sta-kernel-paths: &sta-kernel-paths
               - 'csrc/attn/sliding_tile_attn/**'
@@ -158,9 +155,6 @@ jobs:
             precision-test-VSA:
               - *common-paths
               - *vsa-kernel-paths
-            unit-test:
-              - 'fastvideo/**'
-              - *common-paths
 
   encoder-test:
     needs: change-filter
@@ -241,7 +235,7 @@ jobs:
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
       RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
+  
   training-test:
     needs: change-filter
     if: >-
@@ -339,42 +333,23 @@ jobs:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
       RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
 
-  unit-test:
-    needs: change-filter
+  nightly-test:
     if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.unit-test == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_unit_test == 'true')
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_nightly_test == 'true')
     uses: ./.github/workflows/runpod-test.yml
     with:
-      job_id: "unit-test"
-      gpu_type: "NVIDIA L40S"
-      gpu_count: 1
+      job_id: "nightly-test"
+      gpu_type: "NVIDIA A40"
+      gpu_count: 4
       volume_size: 100
       disk_size: 100
       image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/dataset/ -vs && pytest ./fastvideo/workflow/ -vs"
+      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/tests/nightly/test_e2e_overfit_single_sample.py -vs"
       timeout_minutes: 30
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
       RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
-  # nightly-test:
-  #   if: >-
-  #     (github.event_name == 'workflow_dispatch' && github.event.inputs.run_nightly_test == 'true')
-  #   uses: ./.github/workflows/runpod-test.yml
-  #   with:
-  #     job_id: "nightly-test"
-  #     gpu_type: "NVIDIA A40"
-  #     gpu_count: 4
-  #     volume_size: 100
-  #     disk_size: 100
-  #     image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-  #     test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/tests/nightly/test_e2e_overfit_single_sample.py -vs"
-  #     timeout_minutes: 30
-  #   secrets:
-  #     RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-  #     RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-  #     WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
+      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
 
   runpod-cleanup:
     # Add other jobs to this list as you create them
@@ -398,4 +373,4 @@ jobs:
           JOB_IDS: '["encoder-test", "vae-test", "transformer-test", "ssim-test-py3.10", "ssim-test-py3.11", "ssim-test-py3.12", "training-test", "training-test-VSA", "inference-test-STA", "precision-test-STA", "precision-test-VSA"]'
           RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
           GITHUB_RUN_ID: ${{ github.run_id }}
-        run: python .github/scripts/runpod_cleanup.py
+        run: python .github/scripts/runpod_cleanup.py

.gitignore

@@ -64,6 +64,3 @@ docs/source/distillation/examples/
 !docs/source/_static/images/**/*.png
 !comfyui/assets/**/*.png
 !comfyui/assets/**/*.gif
-
-dmd_t2v_output/
-preprocess_output_text/

README.md

@@ -7,7 +7,7 @@
 FastVideo features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
 
 <p align="center">
-    | 🕹️ <a href="https://fastwan.fastvideo.org/"<b>Online Demo</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/collections/FastVideo/fastwan-6886a305d9799c8cd1496408"  target="_blank"><b>FastWan</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3csdw1isz-Euq8_Q8~baewG8hxjXs2gQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/S7HLCSTh" target="_blank"> <b> WeChat </b> </a> |
+    | 🕹️ <a href="https://fastwan.fastvideo.org/"<b>Online Demo</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/collections/FastVideo/fastwan-6886a305d9799c8cd1496408"  target="_blank"><b>FastWan</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/rG0QpZdw" target="_blank"> <b> WeChat </b> </a> |
 </p>
 
 <div align="center">

csrc/attn/vmoba_attn/README.md

@@ -1,32 +0,0 @@
-# Attention Kernel Used in FastVideo
-
-## VMoBA: Mixture-of-Block Attention for Video Diffusion Models (VMoBA)
-
-### Installation
-Please ensure that you have installed FlashAttention version **2.7.1 or higher**, as some interfaces have changed in recent releases.
-
-### Usage
-
-You can use `moba_attn_varlen` in the following ways:
-
-**Install from source:**
-```bash
-python setup.py install
-```
-
-**Import after installation:**
-```python
-from vmoba import moba_attn_varlen
-```
-
-**Or import directly from the project root:**
-```python
-from csrc.attn.vmoba_attn.vmoba import moba_attn_varlen
-```
-
-### Verify if you have successfully installed
-
-```bash
-python csrc/attn/vmoba_attn/vmoba/vmoba.py
-```
-

csrc/attn/vmoba_attn/setup.py

@@ -1,26 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from setuptools import find_packages, setup
-
-PACKAGE_NAME = "vmoba"
-VERSION = "0.0.0"
-AUTHOR = "JianzongWu"
-DESCRIPTION = "VMoBA: Mixture-of-Block Attention for Video Diffusion Models"
-URL = "https://github.com/KwaiVGI/VMoBA"
-
-setup(
-    name=PACKAGE_NAME,
-    version=VERSION,
-    author=AUTHOR,
-    description=DESCRIPTION,
-    url=URL,
-    packages=find_packages(),
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: Apache Software License",
-    ],
-    python_requires='>=3.12',
-    install_requires=[
-        "flash-attn >= 2.7.1",
-    ]
-)

csrc/attn/vmoba_attn/tests/test_vmoba_attn.py

@@ -1,97 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import torch
-import pytest
-import random
-from csrc.attn.vmoba_attn.vmoba  import moba_attn_varlen
-
-def generate_test_data(batch_size, total_seqlen, num_heads, head_dim, dtype, device="cuda"):
-    """
-    Generates random data for testing the variable-length attention function.
-    """
-    torch.manual_seed(42)
-    random.seed(42)
-    torch.cuda.manual_seed_all(42)
-
-    # Generate sequence lengths for each item in the batch
-    if batch_size > 1:
-        # Ensure sequence lengths are reasonably distributed
-        avg_seqlen = total_seqlen // batch_size
-        seqlens = [random.randint(avg_seqlen // 2, avg_seqlen + avg_seqlen // 2) for _ in range(batch_size - 1)]
-        remaining_len = total_seqlen - sum(seqlens)
-        if remaining_len > 0:
-            seqlens.append(remaining_len)
-        else: # Adjust if sum exceeds total_seqlen
-            seqlens.append(avg_seqlen)
-            current_sum = sum(seqlens)
-            seqlens[-1] -= (current_sum - total_seqlen)
-        # Ensure all lengths are positive
-        seqlens = [max(1, s) for s in seqlens]
-        # Final adjustment to match total_seqlen
-        seqlens[-1] += total_seqlen - sum(seqlens)
-
-    else:
-        seqlens = [total_seqlen]
-
-    cu_seqlens = torch.tensor([0] + list(torch.cumsum(torch.tensor(seqlens), 0)), device=device, dtype=torch.int32)
-    max_seqlen = max(seqlens) if seqlens else 0
-
-    q = torch.randn((total_seqlen, num_heads, head_dim), dtype=dtype, device=device, requires_grad=False)
-    k = torch.randn((total_seqlen, num_heads, head_dim), dtype=dtype, device=device, requires_grad=False)
-    v = torch.randn((total_seqlen, num_heads, head_dim), dtype=dtype, device=device, requires_grad=False)
-
-    return q, k, v, cu_seqlens, max_seqlen
-
-
-@pytest.mark.parametrize("batch_size", [1, 2])
-@pytest.mark.parametrize("total_seqlen", [512, 1024])
-@pytest.mark.parametrize("num_heads", [8])
-@pytest.mark.parametrize("head_dim", [64])
-@pytest.mark.parametrize("moba_chunk_size", [64])
-@pytest.mark.parametrize("moba_topk", [2, 4])
-@pytest.mark.parametrize("select_mode", ["topk", "threshold"])
-@pytest.mark.parametrize("threshold_type", ["query_head", "head_global", "overall"])
-@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
-def test_moba_attn_varlen_forward(
-    batch_size, total_seqlen, num_heads, head_dim, moba_chunk_size, moba_topk, select_mode, threshold_type, dtype
-):
-    """
-    Tests the forward pass of moba_attn_varlen for basic correctness.
-    It checks output shape, dtype, and for the presence of NaNs/Infs.
-    """
-    if dtype == torch.float32:
-        pytest.skip("float32 is not supported in flash attention")
-
-    q, k, v, cu_seqlens, max_seqlen = generate_test_data(
-        batch_size, total_seqlen, num_heads, head_dim, dtype
-    )
-
-    # Ensure chunk size is not larger than the smallest sequence length
-    min_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).min().item()
-    if moba_chunk_size > min_seqlen:
-        pytest.skip("moba_chunk_size is larger than the minimum sequence length in the batch")
-
-    try:
-        output = moba_attn_varlen(
-            q=q,
-            k=k,
-            v=v,
-            cu_seqlens=cu_seqlens,
-            max_seqlen=max_seqlen,
-            moba_chunk_size=moba_chunk_size,
-            moba_topk=moba_topk,
-            select_mode=select_mode,
-            threshold_type=threshold_type,
-            simsum_threshold=0.5, # A reasonable default for threshold mode
-        )
-    except Exception as e:
-        pytest.fail(f"moba_attn_varlen forward pass failed with exception: {e}")
-
-    # 1. Check output shape
-    assert output.shape == q.shape, f"Expected output shape {q.shape}, but got {output.shape}"
-
-    # 2. Check output dtype
-    assert output.dtype == q.dtype, f"Expected output dtype {q.dtype}, but got {output.dtype}"
-
-    # 3. Check for NaNs or Infs in the output
-    assert torch.all(torch.isfinite(output)), "Output contains NaN or Inf values"

csrc/attn/vmoba_attn/vmoba/__init__.py

@@ -1,2 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from .vmoba import moba_attn_varlen, process_moba_input, process_moba_output

csrc/attn/vmoba_attn/vmoba/vmoba.py

@@ -1,868 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Adapt from https://github.com/KwaiVGI/VMoBA/blob/main/src/vmoba.py
-
-import random
-import time
-import os
-import torch
-from typing import Tuple
-try:
-    from flash_attn import flash_attn_varlen_func  # Use the new flash attention function
-    from flash_attn.flash_attn_interface import _flash_attn_varlen_forward, _flash_attn_varlen_backward
-except ImportError:
-    def _unsupported(*args, **kwargs):
-        raise ImportError("flash-attn is not installed. Please install it, e.g., `pip install flash-attn`.")
-    _flash_attn_varlen_forward = _unsupported
-    _flash_attn_varlen_backward = _unsupported
-    flash_attn_varlen_func = _unsupported
-    
-from functools import lru_cache
-from einops import rearrange
-
-
-@lru_cache(maxsize=16)
-def calc_chunks(cu_seqlen, moba_chunk_size):
-    """
-    Calculate chunk boundaries.
-
-    For vision tasks we include all chunks (even the last one which might be shorter)
-    so that every chunk can be selected.
-    """
-    batch_sizes = cu_seqlen[1:] - cu_seqlen[:-1]
-    batch_num_chunk = (batch_sizes + (moba_chunk_size - 1)) // moba_chunk_size
-    cu_num_chunk = torch.ones(
-        batch_num_chunk.numel() + 1,
-        device=cu_seqlen.device,
-        dtype=batch_num_chunk.dtype,
-    )
-    cu_num_chunk[1:] = batch_num_chunk.cumsum(dim=0)
-    num_chunk = cu_num_chunk[-1]
-    chunk_sizes = torch.full(
-        (num_chunk + 1,), moba_chunk_size, dtype=torch.int32, device=cu_seqlen.device
-    )
-    chunk_sizes[0] = 0
-    batch_last_chunk_size = batch_sizes - (batch_num_chunk - 1) * moba_chunk_size
-    chunk_sizes[cu_num_chunk[1:]] = batch_last_chunk_size
-    cu_chunk = chunk_sizes.cumsum(dim=-1, dtype=torch.int32)
-    chunk_to_batch = torch.zeros(
-        (num_chunk,), dtype=torch.int32, device=cu_seqlen.device
-    )
-    chunk_to_batch[cu_num_chunk[1:-1]] = 1
-    chunk_to_batch = chunk_to_batch.cumsum(dim=0, dtype=torch.int32)
-
-    # Do not filter out any chunk
-    filtered_chunk_indices = torch.arange(
-        num_chunk, device=cu_seqlen.device, dtype=torch.int32
-    )
-    num_filtered_chunk = num_chunk
-
-    return cu_chunk, filtered_chunk_indices, num_filtered_chunk, chunk_to_batch
-
-
-# --- Threshold Selection Helper Functions ---
-
-def _select_threshold_query_head(
-    gate: torch.Tensor,
-    valid_gate_mask: torch.Tensor,
-    gate_self_chunk_mask: torch.Tensor,
-    simsum_threshold: float
-) -> torch.Tensor:
-    """
-    Selects chunks for each <query, head> pair based on threshold.
-    Normalization and sorting happen along the chunk dimension (dim=0).
-    """
-    C, H, S = gate.shape
-    eps = 1e-6
-
-    # LSE‐style normalization per <head, query> (across chunks)
-    gate_masked = torch.where(valid_gate_mask, gate, -torch.inf) # Use -inf for max
-    gate_min_val = torch.where(valid_gate_mask, gate, torch.inf) # Use +inf for min
-
-    row_min = gate_min_val.amin(dim=0)        # (H, S)
-    row_max = gate_masked.amax(dim=0)         # (H, S)
-    denom = row_max - row_min
-    denom = torch.where(denom <= eps, torch.ones_like(denom), denom) # avoid divide‑by‑zero
-
-    gate_norm = (gate - row_min.unsqueeze(0)) / denom.unsqueeze(0)
-    gate_norm = torch.where(valid_gate_mask, gate_norm, 0.0) # (C, H, S)
-
-    # 1) pull out the self‐chunk’s normalized weight for each <head,seq>
-    self_norm = (gate_norm * gate_self_chunk_mask).sum(dim=0)  # (H, S)
-
-    # 2) compute how much more normalized weight we need beyond self
-    total_norm_sum = gate_norm.sum(dim=0) # (H, S)
-    remain_ratio = simsum_threshold - self_norm / (total_norm_sum + eps)  # (H, S)
-    remain_ratio = torch.clamp(remain_ratio, min=0.0) # if already ≥ thresh, no extra needed
-
-    # 3) zero out the self‐chunk in a copy, so we only sort “others”
-    others_norm = gate_norm.clone()
-    others_norm[gate_self_chunk_mask] = 0.0
-
-    # 4) sort the other chunks by descending norm, per <head,seq>
-    sorted_norm, sorted_idx = torch.sort(others_norm, descending=True, dim=0) # (C, H, S)
-
-    # 5) cumulative‑sum the sorted norms per <head,seq>
-    cumsum_others = sorted_norm.cumsum(dim=0) # (C, H, S)
-
-    # 6) for each <head,seq>, find the smallest k where cumsum_ratio ≥ remain_ratio
-    ratio = cumsum_others / (total_norm_sum.unsqueeze(0) + eps) # (C, H, S)
-    cond = ratio >= remain_ratio.unsqueeze(0) # (C, H, S) boolean mask
-    any_cond = cond.any(dim=0) # (H, S)
-    # Find the index of the first True value along dim 0. If none, use C-1.
-    cutoff = torch.where(any_cond, cond.float().argmax(dim=0), torch.full_like(any_cond, fill_value=C - 1)) # (H, S)
-
-    # 7) build a mask in sorted order up to that cutoff
-    idx_range = torch.arange(C, device=gate.device).view(-1, 1, 1) # (C, 1, 1)
-    sorted_mask = idx_range <= cutoff.unsqueeze(0) # (C, H, S)
-
-    # 8) scatter it back to original chunk order
-    others_mask = torch.zeros_like(gate, dtype=torch.bool)
-    others_mask.scatter_(0, sorted_idx, sorted_mask)
-
-    # 9) finally, include every self‐chunk plus all selected others
-    final_gate_mask = valid_gate_mask & (others_mask | gate_self_chunk_mask)
-
-    return final_gate_mask
-
-
-def _select_threshold_block(
-    gate: torch.Tensor,
-    valid_gate_mask: torch.Tensor,
-    gate_self_chunk_mask: torch.Tensor,
-    simsum_threshold: float
-) -> torch.Tensor:
-    """
-    Selects <query, head> pairs for each block based on threshold.
-    Normalization and sorting happen across the head and sequence dimensions (dim=1, 2).
-    """
-    C, H, S = gate.shape
-    HS = H * S
-    eps = 1e-6
-
-    # LSE‐style normalization per block (across heads and queries)
-    gate_masked = torch.where(valid_gate_mask, gate, -torch.inf) # Use -inf for max
-    gate_min_val = torch.where(valid_gate_mask, gate, torch.inf) # Use +inf for min
-
-    block_max = gate_masked.amax(dim=(1, 2), keepdim=True) # (C, 1, 1)
-    block_min = gate_min_val.amin(dim=(1, 2), keepdim=True) # (C, 1, 1)
-    block_denom = block_max - block_min
-    block_denom = torch.where(block_denom <= eps, torch.ones_like(block_denom), block_denom) # (C, 1, 1)
-
-    gate_norm = (gate - block_min) / block_denom # (C, H, S)
-    gate_norm = torch.where(valid_gate_mask, gate_norm, 0.0) # (C, H, S)
-
-    # 1) identify normalized weights of entries that *are* self-chunks (from query perspective)
-    self_norm_entries = gate_norm * gate_self_chunk_mask # (C, H, S)
-    # Sum these weights *per block*
-    self_norm_sum_per_block = self_norm_entries.sum(dim=(1, 2)) # (C,)
-
-    # 2) compute how much more normalized weight each block needs beyond its self-chunk contributions
-    total_norm_sum_per_block = gate_norm.sum(dim=(1, 2)) # (C,)
-    remain_ratio = simsum_threshold - self_norm_sum_per_block / (total_norm_sum_per_block + eps) # (C,)
-    remain_ratio = torch.clamp(remain_ratio, min=0.0) # (C,)
-
-    # 3) zero out the self‐chunk entries in a copy, so we only sort “others”
-    others_norm = gate_norm.clone()
-    others_norm[gate_self_chunk_mask] = 0.0 # Zero out self entries
-
-    # 4) sort the other <head, seq> pairs by descending norm, per block
-    others_flat = others_norm.contiguous().view(C, HS) # (C, H*S)
-    sorted_others_flat, sorted_indices_flat = torch.sort(others_flat, dim=1, descending=True) # (C, H*S)
-
-    # 5) cumulative‑sum the sorted norms per block
-    cumsum_others_flat = sorted_others_flat.cumsum(dim=1) # (C, H*S)
-
-    # 6) for each block, find the smallest k where cumsum_ratio ≥ remain_ratio
-    ratio_flat = cumsum_others_flat / (total_norm_sum_per_block.unsqueeze(1) + eps) # (C, H*S)
-    cond_flat = ratio_flat >= remain_ratio.unsqueeze(1) # (C, H*S) boolean mask
-    any_cond = cond_flat.any(dim=1) # (C,)
-    # Find the index of the first True value along dim 1. If none, use HS-1.
-    cutoff_flat = torch.where(any_cond, cond_flat.float().argmax(dim=1), torch.full_like(any_cond, fill_value=HS - 1)) # (C,)
-
-    # 7) build a mask in sorted order up to that cutoff per block
-    idx_range_flat = torch.arange(HS, device=gate.device).unsqueeze(0) # (1, H*S)
-    sorted_mask_flat = idx_range_flat <= cutoff_flat.unsqueeze(1) # (C, H*S)
-
-    # 8) scatter it back to original <head, seq> order per block
-    others_mask_flat = torch.zeros_like(others_flat, dtype=torch.bool) # (C, H*S)
-    others_mask_flat.scatter_(1, sorted_indices_flat, sorted_mask_flat)
-    others_mask = others_mask_flat.view(C, H, S) # (C, H, S)
-
-    # 9) finally, include every self‐chunk entry plus all selected others
-    final_gate_mask = valid_gate_mask & (others_mask | gate_self_chunk_mask)
-
-    return final_gate_mask
-
-
-def _select_threshold_overall(
-    gate: torch.Tensor,
-    valid_gate_mask: torch.Tensor,
-    gate_self_chunk_mask: torch.Tensor,
-    simsum_threshold: float
-) -> torch.Tensor:
-    """
-    Selects <chunk, query, head> triplets globally based on threshold.
-    Normalization and sorting happen across all valid entries.
-    """
-    C, H, S = gate.shape
-    CHS = C * H * S
-    eps = 1e-6
-
-    # LSE‐style normalization globally across all valid entries
-    gate_masked = torch.where(valid_gate_mask, gate, -torch.inf) # Use -inf for max
-    gate_min_val = torch.where(valid_gate_mask, gate, torch.inf) # Use +inf for min
-
-    overall_max = gate_masked.max() # scalar
-    overall_min = gate_min_val.min() # scalar
-    overall_denom = overall_max - overall_min
-    overall_denom = torch.where(overall_denom <= eps, torch.tensor(1.0, device=gate.device, dtype=gate.dtype), overall_denom)
-
-    gate_norm = (gate - overall_min) / overall_denom # (C, H, S)
-    gate_norm = torch.where(valid_gate_mask, gate_norm, 0.0) # (C, H, S)
-
-    # 1) identify normalized weights of entries that *are* self-chunks
-    self_norm_entries = gate_norm * gate_self_chunk_mask # (C, H, S)
-    # Sum these weights globally
-    self_norm_sum_overall = self_norm_entries.sum() # scalar
-
-    # 2) compute how much more normalized weight is needed globally beyond self-chunk contributions
-    total_norm_sum_overall = gate_norm.sum() # scalar
-    remain_ratio = simsum_threshold - self_norm_sum_overall / (total_norm_sum_overall + eps) # scalar
-    remain_ratio = torch.clamp(remain_ratio, min=0.0) # scalar
-
-    # 3) zero out the self‐chunk entries in a copy, so we only sort “others”
-    others_norm = gate_norm.clone()
-    others_norm[gate_self_chunk_mask] = 0.0 # Zero out self entries
-
-    # 4) sort all other entries by descending norm, globally
-    others_flat = others_norm.flatten() # (C*H*S,)
-    valid_others_mask_flat = valid_gate_mask.flatten() & ~gate_self_chunk_mask.flatten() # Mask for valid, non-self entries
-    
-    # Only sort the valid 'other' entries
-    valid_others_indices = torch.where(valid_others_mask_flat)[0]
-    valid_others_values = others_flat[valid_others_indices]
-
-    sorted_others_values, sort_perm = torch.sort(valid_others_values, descending=True) # (N_valid_others,)
-    sorted_original_indices = valid_others_indices[sort_perm] # Original indices in C*H*S space, sorted by value
-
-    # 5) cumulative‑sum the sorted valid 'other' norms globally
-    cumsum_others_values = sorted_others_values.cumsum(dim=0) # (N_valid_others,)
-
-    # 6) find the smallest k where cumsum_ratio ≥ remain_ratio globally
-    ratio_values = cumsum_others_values / (total_norm_sum_overall + eps) # (N_valid_others,)
-    cond_values = ratio_values >= remain_ratio # (N_valid_others,) boolean mask
-    any_cond = cond_values.any() # scalar
-    
-    # Find the index of the first True value in the *sorted* list. If none, use all valid others.
-    cutoff_idx_in_sorted = torch.where(
-        any_cond, 
-        cond_values.float().argmax(dim=0), 
-        torch.tensor(len(sorted_others_values) - 1, device=gate.device, dtype=torch.long)
-    )
-
-    # 7) build a mask selecting the top-k others based on the cutoff
-    # Select the original indices corresponding to the top entries in the sorted list
-    selected_other_indices = sorted_original_indices[:cutoff_idx_in_sorted + 1]
-
-    # 8) create the mask in the original flat shape
-    others_mask_flat = torch.zeros_like(others_flat, dtype=torch.bool) # (C*H*S,)
-    if selected_other_indices.numel() > 0: # Check if any 'other' indices were selected
-        others_mask_flat[selected_other_indices] = True
-    others_mask = others_mask_flat.view(C, H, S) # (C, H, S)
-
-    # 9) finally, include every self‐chunk entry plus all selected others
-    final_gate_mask = valid_gate_mask & (others_mask | gate_self_chunk_mask)
-
-    return final_gate_mask
-
-
-def _select_threshold_head_global(
-    gate: torch.Tensor,
-    valid_gate_mask: torch.Tensor,
-    gate_self_chunk_mask: torch.Tensor,
-    simsum_threshold: float
-) -> torch.Tensor:
-    """
-    Selects <chunk, query> globally for each head based on threshold.
-    """
-    C, H, S = gate.shape
-    eps = 1e-6
-
-    # 1) LSE‐style normalization per head (across chunks and sequence dims)
-    gate_masked = torch.where(valid_gate_mask, gate, -torch.inf)
-    gate_min_val = torch.where(valid_gate_mask, gate, torch.inf)
-
-    max_per_head = gate_masked.amax(dim=(0, 2), keepdim=True)  # (1, H, 1)
-    min_per_head = gate_min_val.amin(dim=(0, 2), keepdim=True)  # (1, H, 1)
-    denom = max_per_head - min_per_head
-    denom = torch.where(denom <= eps, torch.ones_like(denom), denom)
-
-    gate_norm = (gate - min_per_head) / denom
-    gate_norm = torch.where(valid_gate_mask, gate_norm, 0.0)  # (C, H, S)
-
-    # 2) sum normalized self‐chunk contributions per head
-    self_norm_sum = (gate_norm * gate_self_chunk_mask).sum(dim=(0, 2))  # (H,)
-
-    # 3) total normalized sum per head
-    total_norm_sum = gate_norm.sum(dim=(0, 2))  # (H,)
-
-    # 4) how much more normalized weight needed per head
-    remain_ratio = simsum_threshold - self_norm_sum / (total_norm_sum + eps)  # (H,)
-    remain_ratio = torch.clamp(remain_ratio, min=0.0)
-
-    # 5) zero out self‐chunk entries to focus on "others"
-    others_norm = gate_norm.clone()
-    others_norm[gate_self_chunk_mask] = 0.0  # (C, H, S)
-
-    # 6) flatten chunk and sequence dims, per head
-    CS = C * S
-    others_flat = others_norm.permute(1, 0, 2).reshape(H, CS)  # (H, C*S)
-    valid_flat  = (valid_gate_mask & ~gate_self_chunk_mask) \
-                   .permute(1, 0, 2).reshape(H, CS)           # (H, C*S)
-
-    # 7) vectorized selection of “others” per head
-    masked_flat = torch.where(valid_flat, others_flat, torch.zeros_like(others_flat))
-    sorted_vals, sorted_idx = torch.sort(masked_flat, dim=1, descending=True)  # (H, C*S)
-
-    cumsum_vals = sorted_vals.cumsum(dim=1)                                    # (H, C*S)
-    ratio_vals  = cumsum_vals / (total_norm_sum.unsqueeze(1) + eps)            # (H, C*S)
-    cond        = ratio_vals >= remain_ratio.unsqueeze(1)                      # (H, C*S)
-
-    has_cutoff = cond.any(dim=1)                                               # (H,)
-    default    = torch.full((H,), CS - 1, device=gate.device, dtype=torch.long)
-    cutoff     = torch.where(has_cutoff, cond.float().argmax(dim=1), default)  # (H,)
-
-    idx_range   = torch.arange(CS, device=gate.device).unsqueeze(0)            # (1, C*S)
-    sorted_mask = idx_range <= cutoff.unsqueeze(1)                             # (H, C*S)
-
-    selected_flat = torch.zeros_like(valid_flat)                               # (H, C*S)
-    selected_flat.scatter_(1, sorted_idx, sorted_mask)                         # (H, C*S)
-
-    # 8) reshape selection mask back to (C, H, S)
-    others_mask = selected_flat.reshape(H, C, S).permute(1, 0, 2)               # (C, H, S)
-
-    # 9) include self‐chunks plus selected others, and obey valid mask
-    final_gate_mask = valid_gate_mask & (gate_self_chunk_mask | others_mask)
-
-    return final_gate_mask
-
-
-class MixedAttention(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        q,
-        k,
-        v,
-        self_attn_cu_seqlen,
-        moba_q,
-        moba_kv,
-        moba_cu_seqlen_q,
-        moba_cu_seqlen_kv,
-        max_seqlen,
-        moba_chunk_size,
-        moba_q_sh_indices,
-    ):
-        ctx.max_seqlen = max_seqlen
-        ctx.moba_chunk_size = moba_chunk_size
-        ctx.softmax_scale = softmax_scale = q.shape[-1] ** (-0.5)
-
-        # Non-causal self-attention branch
-        # return out, softmax_lse, S_dmask, rng_state
-        self_attn_out_sh, self_attn_lse_hs, _, _ = _flash_attn_varlen_forward(
-            q=q,
-            k=k,
-            v=v,
-            cu_seqlens_q=self_attn_cu_seqlen,
-            cu_seqlens_k=self_attn_cu_seqlen,
-            max_seqlen_q=max_seqlen,
-            max_seqlen_k=max_seqlen,
-            softmax_scale=softmax_scale,
-            causal=False,
-            dropout_p=0.0,
-        )
-        # MOBA attention branch (non-causal)
-        moba_attn_out, moba_attn_lse_hs, _, _ = _flash_attn_varlen_forward(
-            q=moba_q,
-            k=moba_kv[:, 0],
-            v=moba_kv[:, 1],
-            cu_seqlens_q=moba_cu_seqlen_q,
-            cu_seqlens_k=moba_cu_seqlen_kv,
-            max_seqlen_q=max_seqlen,
-            max_seqlen_k=moba_chunk_size,
-            softmax_scale=softmax_scale,
-            causal=False,
-            dropout_p=0.0,
-        )
-
-        self_attn_lse_sh = self_attn_lse_hs.t().contiguous()
-        moba_attn_lse = moba_attn_lse_hs.t().contiguous()
-
-        output = torch.zeros((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
-        output_2d = output.view(-1, q.shape[2])
-
-        max_lse_1d = self_attn_lse_sh.view(-1)
-        max_lse_1d = max_lse_1d.index_reduce(
-            0, moba_q_sh_indices, moba_attn_lse.view(-1), "amax"
-        )
-        self_attn_lse_sh = self_attn_lse_sh - max_lse_1d.view_as(self_attn_lse_sh)
-        moba_attn_lse = (
-            moba_attn_lse.view(-1)
-            .sub(max_lse_1d.index_select(0, moba_q_sh_indices))
-            .reshape_as(moba_attn_lse)
-        )
-
-        mixed_attn_se_sh = self_attn_lse_sh.exp()
-        moba_attn_se = moba_attn_lse.exp()
-
-        mixed_attn_se_sh.view(-1).index_add_(
-            0, moba_q_sh_indices, moba_attn_se.view(-1)
-        )
-        mixed_attn_lse_sh = mixed_attn_se_sh.log()
-
-        # Combine self-attention output
-        factor = (self_attn_lse_sh - mixed_attn_lse_sh).exp()  # [S, H]
-        self_attn_out_sh = self_attn_out_sh * factor.unsqueeze(-1)
-        output_2d += self_attn_out_sh.reshape_as(output_2d)
-
-        # Combine MOBA attention output
-        mixed_attn_lse = (
-            mixed_attn_lse_sh.view(-1)
-            .index_select(0, moba_q_sh_indices)
-            .view_as(moba_attn_lse)
-        )
-        factor = (moba_attn_lse - mixed_attn_lse).exp()  # [S, H]
-        moba_attn_out = moba_attn_out * factor.unsqueeze(-1)
-        raw_attn_out = moba_attn_out.view(-1, moba_attn_out.shape[-1])
-        output_2d.index_add_(0, moba_q_sh_indices, raw_attn_out)
-        output = output.to(q.dtype)
-        mixed_attn_lse_sh = mixed_attn_lse_sh + max_lse_1d.view_as(mixed_attn_se_sh)
-        ctx.save_for_backward(
-            output,
-            mixed_attn_lse_sh,
-            q,
-            k,
-            v,
-            self_attn_cu_seqlen,
-            moba_q,
-            moba_kv,
-            moba_cu_seqlen_q,
-            moba_cu_seqlen_kv,
-            moba_q_sh_indices,
-        )
-
-        return output
-
-    @staticmethod
-    def backward(ctx, d_output):
-
-        max_seqlen = ctx.max_seqlen
-        moba_chunk_size = ctx.moba_chunk_size
-        softmax_scale = ctx.softmax_scale
-
-        (
-            output,
-            mixed_attn_vlse_sh,
-            q,
-            k,
-            v,
-            self_attn_cu_seqlen,
-            moba_q,
-            moba_kv,
-            moba_cu_seqlen_q,
-            moba_cu_seqlen_kv,
-            moba_q_sh_indices,
-        ) = ctx.saved_tensors
-
-        d_output = d_output.contiguous()
-
-        dq = torch.empty_like(q)
-        dk = torch.empty_like(k)
-        dv = torch.empty_like(v)
-        _ = _flash_attn_varlen_backward(
-            dout=d_output,
-            q=q,
-            k=k,
-            v=v,
-            out=output,
-            softmax_lse=mixed_attn_vlse_sh.t().contiguous(),
-            dq=dq,
-            dk=dk,
-            dv=dv,
-            cu_seqlens_q=self_attn_cu_seqlen,
-            cu_seqlens_k=self_attn_cu_seqlen,
-            max_seqlen_q=max_seqlen,
-            max_seqlen_k=max_seqlen,
-            softmax_scale=softmax_scale,
-            causal=False,
-            dropout_p=0.0,
-            softcap=0.0,
-            alibi_slopes=None,
-            deterministic=True,
-            window_size_left=-1,
-            window_size_right=-1
-        )
-
-        headdim = q.shape[-1]
-        d_moba_output = (
-            d_output.view(-1, headdim).index_select(0, moba_q_sh_indices).unsqueeze(1)
-        )
-        moba_output = (
-            output.view(-1, headdim).index_select(0, moba_q_sh_indices).unsqueeze(1)
-        )
-
-        mixed_attn_vlse = (
-            mixed_attn_vlse_sh.view(-1).index_select(0, moba_q_sh_indices).view(1, -1)
-        )
-
-        dmq = torch.empty_like(moba_q)
-        dmkv = torch.empty_like(moba_kv)
-        _ = _flash_attn_varlen_backward(
-            dout=d_moba_output,
-            q=moba_q,
-            k=moba_kv[:, 0],
-            v=moba_kv[:, 1],
-            out=moba_output,
-            softmax_lse=mixed_attn_vlse,
-            dq=dmq,
-            dk=dmkv[:,0],
-            dv=dmkv[:,1],
-            cu_seqlens_q=moba_cu_seqlen_q,
-            cu_seqlens_k=moba_cu_seqlen_kv,
-            max_seqlen_q=max_seqlen,
-            max_seqlen_k=moba_chunk_size,
-            softmax_scale=softmax_scale,
-            causal=False,
-            dropout_p=0.0,
-            softcap=0.0,
-            alibi_slopes=None,
-            deterministic=True,
-            window_size_left=-1,
-            window_size_right=-1
-        )
-
-        return dq, dk, dv, None, dmq, dmkv, None, None, None, None, None
-
-
-def moba_attn_varlen(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    cu_seqlens: torch.Tensor,
-    max_seqlen: int,
-    moba_chunk_size: int,
-    moba_topk: int,
-    select_mode: str = 'threshold', # "topk" or "threshold"
-    simsum_threshold: float = 0.25,
-    threshold_type: str = 'query_head',
-) -> torch.Tensor:
-    """
-    Accelerated MOBA attention for vision tasks with proper LSE normalization.
-
-    This version:
-      - Splits KV into chunks.
-      - For each query head, selects the top-k relevant KV chunks (including the self chunk)
-        by amplifying the diagonal (self-chunk) logits.
-      - Aggregates the attention outputs from the selected chunks using a log-sum-exp
-        reduction so that attending to each query over the selected chunks is equivalent
-        to the original algorithm.
-    """
-    # Stack keys and values.
-    kv = torch.stack((k, v), dim=1)
-    seqlen, num_head, head_dim = q.shape
-
-    # Compute chunk boundaries.
-    cu_chunk, filtered_chunk_indices, num_filtered_chunk, chunk_to_batch = calc_chunks(
-        cu_seqlens, moba_chunk_size
-    )
-
-    self_attn_cu_seqlen = cu_chunk
-
-    # Update top-k selection to include the self chunk.
-    moba_topk = min(moba_topk, num_filtered_chunk)
-
-    # --- Build filtered KV from chunks ---
-    chunk_starts = cu_chunk[filtered_chunk_indices]          # [num_filtered_chunk]
-    chunk_ends = cu_chunk[filtered_chunk_indices + 1]          # [num_filtered_chunk]
-    chunk_lengths = chunk_ends - chunk_starts                  # [num_filtered_chunk]
-    max_chunk_len = int(chunk_lengths.max().item())
-
-    range_tensor = torch.arange(max_chunk_len, device=kv.device, dtype=chunk_starts.dtype).unsqueeze(0)
-    indices = chunk_starts.unsqueeze(1) + range_tensor
-    indices = torch.clamp(indices, max=kv.shape[0] - 1)
-    valid_mask = range_tensor < chunk_lengths.unsqueeze(1)
-    gathered = kv[indices.view(-1)].view(num_filtered_chunk, max_chunk_len, *kv.shape[1:])
-    gathered = gathered * valid_mask.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).type_as(gathered)
-
-    # Compute key_gate_weight over valid tokens.
-    key_values = gathered[:, :, 0].float()  # [num_filtered_chunk, max_chunk_len, num_head, head_dim]
-    valid_mask_exp = valid_mask.unsqueeze(-1).unsqueeze(-1)
-    key_sum = (key_values * valid_mask_exp).sum(dim=1)
-    divisor = valid_mask.sum(dim=1).unsqueeze(-1).unsqueeze(-1)
-    key_gate_weight = key_sum / divisor  # [num_filtered_chunk, num_head, head_dim]
-
-    # Compute gate logits between key_gate_weight and queries.
-    q_float = q.float()
-    # gate = torch.einsum("nhd,shd->nhs", key_gate_weight, q_float)  # [num_filtered_chunk, num_head, seqlen]
-    gate = torch.bmm(key_gate_weight.permute(1, 0, 2), q_float.permute(1, 0, 2).transpose(1, 2)).permute(1, 0, 2)
-
-    # Amplify the diagonal (self chunk) contributions.
-    gate_seq_idx = torch.arange(seqlen, device=q.device, dtype=torch.int32).unsqueeze(0).expand(num_filtered_chunk, seqlen)
-    chunk_start = cu_chunk[filtered_chunk_indices]  # [num_filtered_chunk]
-    chunk_end = cu_chunk[filtered_chunk_indices + 1]  # [num_filtered_chunk]
-    gate_self_chunk_mask = ((gate_seq_idx >= chunk_start.unsqueeze(1)) &
-                            (gate_seq_idx < chunk_end.unsqueeze(1))).unsqueeze(1).expand(-1, num_head, -1)
-    amplification_factor = 1e9  # Example factor; adjust as needed.
-    origin_gate = gate.clone()
-    gate = gate.clone()
-    if select_mode == "topk":
-        gate[gate_self_chunk_mask] += amplification_factor
-
-    # Exclude positions that are outside the valid batch boundaries.
-    batch_starts = cu_seqlens[chunk_to_batch[filtered_chunk_indices]]
-    batch_ends = cu_seqlens[chunk_to_batch[filtered_chunk_indices] + 1]
-    gate_batch_start_mask = gate_seq_idx < batch_starts.unsqueeze(1)
-    gate_batch_end_mask = gate_seq_idx >= batch_ends.unsqueeze(1)
-    gate_inf_mask = gate_batch_start_mask | gate_batch_end_mask
-    gate.masked_fill_(gate_inf_mask.unsqueeze(1), -float("inf"))
-
-    if select_mode == 'topk':
-        # We amplify self‐chunk in gate already, so self entries will rank highest.
-        valid_gate_mask = gate != -float("inf")
-        if threshold_type == 'query_head':
-            # === per‐<head,seq> top-k across chunks (original behavior) ===
-            # gate: (C, H, S)
-            _, gate_topk_idx = torch.topk(gate, k=moba_topk, dim=0, largest=True, sorted=False)
-            gate_idx_mask = torch.zeros_like(gate, dtype=torch.bool)
-            gate_idx_mask.scatter_(0, gate_topk_idx, True)
-            gate_mask = valid_gate_mask & gate_idx_mask
-        elif threshold_type == 'overall':
-            # === global top-k across all (chunk, head, seq) entries ===
-            C, H, S = gate.shape
-            flat_gate = gate.flatten()
-            flat_mask = valid_gate_mask.flatten()
-            flat_gate_masked = torch.where(flat_mask, flat_gate, -float("inf"))
-            # pick topk global entries
-            vals, idx = torch.topk(flat_gate_masked, k=moba_topk * H * S, largest=True, sorted=False)
-            others_mask_flat = torch.zeros_like(flat_mask, dtype=torch.bool)
-            others_mask_flat[idx] = True
-            gate_mask = (valid_gate_mask.flatten() & others_mask_flat).view(gate.shape)
-        elif threshold_type == 'head_global':
-            # per-head top-k across all chunks and sequence positions
-            C, H, S = gate.shape
-            CS = C * S
-            flat_gate  = gate.permute(1, 0, 2).reshape(H, CS)
-            flat_valid = valid_gate_mask.permute(1, 0, 2).reshape(H, CS)
-            flat_gate_masked = torch.where(flat_valid, flat_gate, torch.full_like(flat_gate, -float('inf')))
-            # pick top-k indices per head
-            _, topk_idx = torch.topk(flat_gate_masked, k=moba_topk * S, dim=1, largest=True, sorted=False)
-            gate_idx_flat = torch.zeros_like(flat_valid, dtype=torch.bool)
-            gate_idx_flat.scatter_(1, topk_idx, True)
-            gate_mask = gate_idx_flat.reshape(H, C, S).permute(1, 0, 2)
-        else:
-            raise ValueError(
-                f"Invalid threshold_type for topk: {threshold_type}. "
-                "Choose 'query_head', 'block', or 'overall'."
-            )
-    elif select_mode == 'threshold':
-        # Delegate to the specific thresholding function
-        valid_gate_mask = gate != -float("inf")  # (num_chunk, num_head, seqlen)
-        if threshold_type == 'query_head':
-            gate_mask = _select_threshold_query_head(gate, valid_gate_mask, gate_self_chunk_mask, simsum_threshold)
-        elif threshold_type == 'block':
-            gate_mask = _select_threshold_block(gate, valid_gate_mask, gate_self_chunk_mask, simsum_threshold)
-        elif threshold_type == 'overall':
-            gate_mask = _select_threshold_overall(gate, valid_gate_mask, gate_self_chunk_mask, simsum_threshold)
-        elif threshold_type == 'head_global':
-            gate_mask = _select_threshold_head_global(gate, valid_gate_mask, gate_self_chunk_mask, simsum_threshold)
-        else:
-            raise ValueError(f"Invalid threshold_type: {threshold_type}. Choose 'query_head', 'block', or 'overall'.")
-    else:
-        raise ValueError(f"Invalid select_mode: {select_mode}. Choose 'topk' or 'threshold'.")
-    
-    # eliminate self_chunk in MoBA branch
-    gate_mask = gate_mask & ~gate_self_chunk_mask
-    # if gate_mask is all false, perform flash_attn instead
-    if gate_mask.sum() == 0:
-        return flash_attn_varlen_func(
-            q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen, causal=False
-        )
-
-    # Determine which query positions are selected.
-    # nonzero_indices has shape [N, 3] where each row is [chunk_index, head_index, seq_index].
-    moba_q_indices = gate_mask.reshape(gate_mask.shape[0], -1).nonzero(as_tuple=True)[-1] # [(h s k)]
-    moba_q_sh_indices = (moba_q_indices % seqlen) * num_head + (moba_q_indices // seqlen)
-    moba_q = rearrange(q, "s h d -> (h s) d").index_select(0, moba_q_indices).unsqueeze(1)
-
-    # Build cumulative sequence lengths for the selected queries.
-    moba_seqlen_q = gate_mask.sum(dim=-1).flatten()
-    q_zero_mask = moba_seqlen_q == 0
-    valid_expert_mask = ~q_zero_mask
-    if q_zero_mask.sum() > 0:
-        moba_seqlen_q = moba_seqlen_q[valid_expert_mask]
-    moba_cu_seqlen_q = torch.cat(
-        (
-            torch.tensor([0], device=q.device, dtype=moba_seqlen_q.dtype),
-            moba_seqlen_q.cumsum(dim=0),
-        ),
-        dim=0,
-    ).to(torch.int32)
-
-    # Rearrange gathered KV for the MOBA branch.
-    experts_tensor = rearrange(gathered, "nc cl two h d -> (nc h) cl two d")
-    valid_expert_lengths = chunk_lengths.unsqueeze(1).expand(num_filtered_chunk, num_head).reshape(-1).to(torch.int32)
-    if q_zero_mask.sum() > 0:
-        experts_tensor = experts_tensor[valid_expert_mask]
-        valid_expert_lengths = valid_expert_lengths[valid_expert_mask]
-
-    seq_range = torch.arange(experts_tensor.shape[1], device=experts_tensor.device).unsqueeze(0)
-    mask = seq_range < valid_expert_lengths.unsqueeze(1)
-    moba_kv = experts_tensor[mask]  # Shape: ((nc h cl_valid) two d)
-    moba_kv = moba_kv.unsqueeze(2)   # Shape: ((nc h cl_valid) two 1 d)
-
-    moba_cu_seqlen_kv = torch.cat(
-        [torch.zeros(1, device=experts_tensor.device, dtype=torch.int32),
-         valid_expert_lengths.cumsum(dim=0)],
-        dim=0,
-    ).to(torch.int32)
-
-    assert (
-        moba_cu_seqlen_kv.shape == moba_cu_seqlen_q.shape
-    ), f"Mismatch between moba_cu_seqlen_kv.shape and moba_cu_seqlen_q.shape: {moba_cu_seqlen_kv.shape} vs {moba_cu_seqlen_q.shape}"
-
-    return MixedAttention.apply(
-        q,
-        k,
-        v,
-        self_attn_cu_seqlen,
-        moba_q,
-        moba_kv,
-        moba_cu_seqlen_q,
-        moba_cu_seqlen_kv,
-        max_seqlen,
-        moba_chunk_size,
-        moba_q_sh_indices,
-    )
-
-
-def process_moba_input(
-    x,
-    patch_resolution,
-    chunk_size,
-):
-    """
-    Process inputs for the attention function.
-
-    Args:
-        x (torch.Tensor): Input tensor with shape [batch_size, num_patches, num_heads, head_dim].
-        patch_resolution (tuple): Tuple containing the patch resolution (t, h, w).
-        chunk_size (int): Size of the chunk. (maybe tuple or int, according to chunk type)
-
-    Returns:
-        torch.Tensor: Processed input tensor.
-    """
-    if isinstance(chunk_size, float) or isinstance(chunk_size, int):
-        moba_chunk_size = int(chunk_size * patch_resolution[1] * patch_resolution[2])
-    else:
-        assert isinstance(chunk_size, (Tuple, list)), f"chunk_size should be a tuple, list, or int, now it is: {type(chunk_size)}"
-        if len(chunk_size) == 2:
-            assert patch_resolution[1] % chunk_size[0] == 0 and patch_resolution[2] % chunk_size[1] == 0, f"spatial patch_resolution {patch_resolution[1:]} should be divisible by 2d chunk_size {chunk_size}"
-            nch, ncw = patch_resolution[1] // chunk_size[0], patch_resolution[2] // chunk_size[1]
-            x = rearrange(x, "b (t nch ch ncw cw) n d -> b (nch ncw t ch cw) n d", t=patch_resolution[0], nch=nch, ncw=ncw, ch=chunk_size[0], cw=chunk_size[1])
-            moba_chunk_size = patch_resolution[0] * chunk_size[0] * chunk_size[1]
-        elif len(chunk_size) == 3:
-            assert patch_resolution[0] % chunk_size[0] == 0 and patch_resolution[1] % chunk_size[1] == 0 and patch_resolution[2] % chunk_size[2] == 0, f"patch_resolution {patch_resolution} should be divisible by 3d chunk_size {chunk_size}"
-            nct, nch, ncw = patch_resolution[0] // chunk_size[0], patch_resolution[1] // chunk_size[1], patch_resolution[2] // chunk_size[2]
-            x = rearrange(x, "b (nct ct nch ch ncw cw) n d -> b (nct nch ncw ct ch cw) n d", nct=nct, nch=nch, ncw=ncw, ct=chunk_size[0], ch=chunk_size[1], cw=chunk_size[2])
-            moba_chunk_size = chunk_size[0] * chunk_size[1] * chunk_size[2]
-        else:
-            raise ValueError(f"chunk_size should be a int, or a tuple of length 2 or 3, now it is: {len(chunk_size)}")
-        
-    return x, moba_chunk_size
-
-
-def process_moba_output(
-    x,
-    patch_resolution,
-    chunk_size,
-):
-    if isinstance(chunk_size, float) or isinstance(chunk_size, int):
-        pass
-    elif len(chunk_size) == 2:
-        x = rearrange(x, "b (nch ncw t ch cw) n d -> b (t nch ch ncw cw) n d", nch=patch_resolution[1] // chunk_size[0], ncw=patch_resolution[2] // chunk_size[1], t=patch_resolution[0], ch=chunk_size[0], cw=chunk_size[1])
-    elif len(chunk_size) == 3:
-        x = rearrange(x, "b (nct nch ncw ct ch cw) n d -> b (nct ct nch ch ncw cw) n d", nct=patch_resolution[0] // chunk_size[0], nch=patch_resolution[1] // chunk_size[1], ncw=patch_resolution[2] // chunk_size[2], ct=chunk_size[0], ch=chunk_size[1], cw=chunk_size[2])
-
-    return x
-
-
-# TEST
-def generate_data(batch_size, seqlen, num_head, head_dim, dtype):
-    random.seed(0)
-    torch.manual_seed(0)
-    torch.cuda.manual_seed(0)
-    device = torch.cuda.current_device()
-
-    q = torch.randn((batch_size, seqlen, num_head, head_dim), requires_grad=True).to(dtype=dtype, device='cuda')
-    k = torch.randn((batch_size, seqlen, num_head, head_dim), requires_grad=True).to(dtype=dtype, device='cuda')
-    v = torch.randn((batch_size, seqlen, num_head, head_dim), requires_grad=True).to(dtype=dtype, device='cuda')
-    print(f"q.shape: {q.shape}, k.shape: {k.shape}, v.shape: {v.shape}")
-    cu_seqlens = torch.arange(0, q.shape[0] * q.shape[1] + 1, q.shape[1], dtype=torch.int32, device='cuda')
-    max_seqlen = q.shape[1]
-    q = rearrange(q, "b s ... -> (b s) ...")
-    k = rearrange(k, "b s ... -> (b s) ...")
-    v = rearrange(v, "b s ... -> (b s) ...")
-
-    return q, k, v, cu_seqlens, max_seqlen
-
-
-def test_attn_varlen_moba_speed(batch, head, seqlen, head_dim, moba_chunk_size, moba_topk, dtype=torch.bfloat16, select_mode='threshold', simsum_threshold=0.25, threshold_type='query_head'):
-    """Speed test comparing flash_attn vs moba_attention"""
-    # Get data
-    q, k, v, cu_seqlen, max_seqlen = generate_data(batch, seqlen, head, head_dim, dtype)
-    print(f"batch:{batch} head:{head} seqlen:{seqlen} chunk:{moba_chunk_size} topk:{moba_topk} select_mode: {select_mode} simsum_threshold:{simsum_threshold}")
-    vo_grad = torch.randn_like(q)
-    
-    # Warmup
-    warmup_iters = 3
-    perf_test_iters = 10
-
-    # Warmup
-    for _ in range(warmup_iters):
-        o = flash_attn_varlen_func(q, k, v, cu_seqlen, cu_seqlen, max_seqlen, max_seqlen, causal=False)
-        torch.autograd.backward(o, vo_grad)
-    
-    torch.cuda.synchronize()
-    start_flash = time.perf_counter()
-    for _ in range(perf_test_iters):
-        o = flash_attn_varlen_func(q, k, v, cu_seqlen, cu_seqlen, max_seqlen, max_seqlen, causal=False)
-        torch.autograd.backward(o, vo_grad)
-        
-    torch.cuda.synchronize()
-    time_flash = (time.perf_counter() - start_flash) / perf_test_iters * 1000
-
-    # Warmup
-    for _ in range(warmup_iters):
-        om = moba_attn_varlen(q, k, v, cu_seqlen, max_seqlen, moba_chunk_size=moba_chunk_size, moba_topk=moba_topk, select_mode=select_mode, simsum_threshold=simsum_threshold, threshold_type=threshold_type)
-        torch.autograd.backward(om, vo_grad)
-
-    torch.cuda.synchronize()
-    start_moba = time.perf_counter()
-    for _ in range(perf_test_iters):
-        om = moba_attn_varlen(q, k, v, cu_seqlen, max_seqlen, moba_chunk_size=moba_chunk_size, moba_topk=moba_topk, select_mode=select_mode, simsum_threshold=simsum_threshold, threshold_type=threshold_type)
-        torch.autograd.backward(om, vo_grad)
-    
-    torch.cuda.synchronize()
-    time_moba = (time.perf_counter() - start_moba) / perf_test_iters * 1000
-    
-    print(f"Flash: {time_flash:.2f}ms, MoBA: {time_moba:.2f}ms")
-    print(f"Speedup:  {time_flash / time_moba:.2f}x")
-
-
-
-if __name__ == "__main__":
-    """
-    CUDA_VISIBLE_DEVICES=1 \
-    python -u csrc/attn/vmoba_attn/vmoba/vmoba.py
-    """
-    test_attn_varlen_moba_speed(batch=1, head=12, seqlen=32760, head_dim=128, moba_chunk_size=32760 // 3 // 6 // 4, moba_topk=3, select_mode='threshold', simsum_threshold=0.3, threshold_type='query_head')

examples/dataset/vidprom/README.md

@@ -1,9 +0,0 @@
-# VidProm Dataset
-
-From [Self-Forcing](https://github.com/gdhe17/Self-Forcing) repository.
-
-## Download the dataset
-
-```bash
-./download_dataset.sh
-```

examples/dataset/vidprom/download_dataset.sh

@@ -1,3 +0,0 @@
-#! /bin/bash
-
-huggingface-cli download gdhe17/Self-Forcing vidprom_filtered_extended.txt --local-dir prompts

examples/distill/SFWan2.1-T2V/distill_dmd_t2v_1.3B.sh

@@ -1,151 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=t2v
-#SBATCH --partition=main
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=dmd_t2v_output/t2v_%j.out
-#SBATCH --error=dmd_t2v_output/t2v_%j.err
-#SBATCH --exclusive
-
-# Basic Info
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29501
-export TOKENIZERS_PARALLELISM=false
-export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=offline
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-
-# Configs
-NUM_GPUS=4
-
-# Model paths for Self-Forcing DMD distillation:
-GENERATOR_MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"  # Teacher model
-FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Critic model
-
-DATA_DIR="data/mixkit-64_processed/Node_0_GPU_1_File_1/combined_parquet_dataset"
-VALIDATION_DATASET_FILE="data/mixkit-64_processed/validation.json"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name SFwan_t2v_distill_self_forcing_dmd  # Updated for self-forcing DMD
-  --output_dir "/mnt/sharefs/users/hao.zhang/SFwan_t2v_finetune"
-  --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 832
-  --num_frames 81  # Must be divisible by num_frame_per_block (81 % 3 = 0 ✓)
-  --enable_gradient_checkpointing_type "full"
-  --log_visualization
-  --simulate_generator_forward
-  --num_frame_per_block 3  # Frame generation block size for self-forcing
-  --enable_gradient_masking
-  --gradient_mask_last_n_frames 21
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS # 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim $NUM_GPUS
-)
-
-# Model arguments
-model_args=(
-  --model_path $GENERATOR_MODEL_PATH  # TODO: check if you can remove this in this script
-  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
-  --generator_model_path $GENERATOR_MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "6.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 500
-  --weight_only_checkpointing_steps 500
-  --weight_decay 0.01
-  --betas '0.0,0.999'
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-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 100
-  --init_weights_from_safetensors "/mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors"
-)
-
-# Self-forcing DMD arguments
-dmd_args=(
-  --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 3.0
-  --fake_score_learning_rate 8e-6
-  --fake_score_betas '0.0,0.999'
-  --warp_denoising_step
-)
-
-# Self-forcing specific arguments
-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)
-  --validate_cache_structure False  # Set to True for debugging KV cache issues
-)
-
-torchrun \
---nnodes 1 \
---master_port $MASTER_PORT \
---nproc_per_node $NUM_GPUS \
-    fastvideo/training/wan_self_forcing_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"

examples/distill/SFWan2.1-T2V/download_dataset.sh

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

examples/distill/SFWan2.1-T2V/preprocess_data.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-MODEL_TYPE="wan"
-DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_t2v/"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 8 \
-    --seed 42 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 81 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --train_fps 16 \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
-    --video_length_tolerance_range 5 \
-    --preprocess_task "t2v" 

examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/distill_dmd_VSA_t2v_5B.sh

@@ -98,7 +98,6 @@ dmd_args=(
 torchrun \
 --nnodes 1 \
 --nproc_per_node $NUM_GPUS \
---master_port $MASTER_PORT \
     fastvideo/training/wan_distillation_pipeline.py \
     "${parallel_args[@]}" \
     "${model_args[@]}" \

examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/distill_dmd_VSA_t2v_5B_lora.sh

@@ -1,112 +0,0 @@
-#!/bin/bash
-
-# Basic Info
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-export MASTER_PORT=29501
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-# Configs
-NUM_GPUS=1
-MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-DATA_DIR="data/crush-smol_processed_ti2v/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/validation.json"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name wan_t2v_distill_dmd_VSA
-  --output_dir="checkpoints/wan_t2v_finetune"
-  --max_train_steps=4000
-  --train_batch_size=1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps=1
-  --num_latent_t 31
-  --num_height 704
-  --num_width 1280
-  --num_frames 121
-  --enable_gradient_checkpointing_type "full"
-  --training_state_checkpointing_steps=500
-  --weight_only_checkpointing_steps=500
-  --lora_rank 32
-  --lora_training True
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus 1
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 200
-  --validation_sampling_steps "3"
-  --validation_guidance_scale "6.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate=1e-4
-  --mixed_precision="bf16"
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --ema_start_step 0
-  --flow_shift 8
-  --seed 1000
-)
-
-# DMD arguments
-dmd_args=(
-  --dmd_denoising_steps '1000,757,522'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --generator_update_interval 5
-  --real_score_guidance_scale 3.5
-  --VSA_sparsity 0.8
-)
-
-torchrun \
---nnodes 1 \
---nproc_per_node $NUM_GPUS \
---master_port $MASTER_PORT \
-    fastvideo/training/wan_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"

examples/inference/basic/basic.py

@@ -26,23 +26,21 @@ def main():
     sampling_param.image_path = "test.jpg"
     # Generate videos with the same simple API, regardless of GPU count
     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."
+        "A girl is packing a suitcase when stuff suddently starts flying around the room."
     )
     video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True)
     # video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/")
 
     # Generate another video with a different prompt, without reloading the
     # model!
-    prompt2 = (
-        "A majestic lion strides across the golden savanna, its powerful frame "
-        "glistening under the warm afternoon sun. The tall grass ripples gently in "
-        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
-        "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)
+    # prompt2 = (
+    #     "A majestic lion strides across the golden savanna, its powerful frame "
+    #     "glistening under the warm afternoon sun. The tall grass ripples gently in "
+    #     "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
+    #     "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)
 
 
 if __name__ == "__main__":
-    main()
+    main()

examples/training/consistency_finetune/causal_ode_init/crush_smol_prompts.txt

@@ -1,47 +0,0 @@
-A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.
-The video shows a green and orange object being flattened as if it were under a hydraulic press, with the press moving down and compressing the object.
-The video shows a cylindrical object with a cityscape image being flattened as if it were under a hydraulic press. The object is placed on a metal platform, and a large, striped cylinder presses down on it, causing it to collapse and release a liquid inside. The background features a green wall with a yellow and red warning sign.
-A red toy car is being crushed by a large hydraulic press, which is flattening objects as if they were under a hydraulic press.
-A large, cylindrical object is seen pressing down on a small orange ball, causing it to flatten as if it were under a hydraulic press. The background features a green wall with yellow and red warning signs.
-The video shows a hydraulic press in action, flattening objects as if they were under a hydraulic press. The press is shown compressing a wooden object, which shatters into small pieces. The background features a green wall with a yellow sign displaying a lightning bolt.
-A large metal cylinder is seen descending, flattening objects as if they were under a hydraulic press. The cylinder compresses a stack of matches and boxes, causing them to crumble into small pieces. The scene is set against a green background with yellow and red signs.
-A large metal press is shown compressing a pile of colorful macarons, flattening them as if they were under a hydraulic press. The press moves down, crushing the macarons into a pile of crumbs and squishing the colorful filling out.
-The video shows a metal press flattening objects as if they were under a hydraulic press. The press is pressing down on a pile of colorful gummy candies, squishing them into a pile of squiggly shapes. The press is made of metal and has a large base, and the gummy candies are of various colors, including red, green, and orange. The background is a green wall, and the press is placed on a metal surface.
-A pile of colorful candies is being flattened by a hydraulic press, causing them to crumble into small pieces.
-The video shows a stack of colorful sponges being flattened as if they were under a hydraulic press. The sponges, which are pink, white, blue, and green, are compressed into a smaller size, demonstrating the press's power. The background features a green wall with a yellow and red sign, adding context to the setting.
-A bowling ball is placed on a metal platform, and a large metal cylinder descends from above, flattening the ball as if it were under a hydraulic press. The ball is crushed into a flat, round shape, leaving a pile of debris around it.
-A large metal cylinder with yellow and black stripes is seen pressing down on a pile of popcorn, flattening the objects as if they were under a hydraulic press.
-The video shows a close-up of an orange being flattened as if it were under a hydraulic press, with the press moving down and compressing the fruit until it is completely flattened.
-The video shows a close-up of a metal cylinder pressing down on a yellow object, which is being flattened as if it were under a hydraulic press. The cylinder is positioned above the object, and the force is causing the object to compress and spread out, creating a visible deformation. The background is blurred, focusing attention on the action of the cylinder and the object being flattened.
-A colorful puzzle ball is being crushed by a large metal cylinder, which flattens the objects as if they were under a hydraulic press.
-The video shows a hydraulic press flattening objects as if they were under a hydraulic press. The press is shown in action, compressing two colorful objects that resemble sandwiches. The press is yellow and black striped, and the objects being flattened are placed on a metal plate. The background is green, and the press is moving down, compressing the objects.
-The scene shows a metal press with a yellow and black striped pattern, holding a container filled with chocolate. A metal cylinder is descending, flattening the chocolate as if it were under a hydraulic press. The background is a green wall, and the press is mounted on a sturdy metal frame.
-The video shows a colorful sponge being flattened as if it were under a hydraulic press, with the sponge being compressed and eventually flattened into a thin layer.
-The video shows a hydraulic press in action, flattening objects as if they were under a hydraulic press. The press is pressing down on a stack of wooden blocks, causing them to crumble and break apart. The press is black and yellow striped, and the wooden blocks are small and rectangular. The background is green, and the press is sitting on a metal table.
-A pile of colorful candies is being flattened by a hydraulic press, causing them to crumble into small pieces.
-The video shows a stack of colorful sponges being flattened by a large, cylindrical object, which appears to be a hydraulic press. The sponges, which are pink, blue, white, and green, are compressed into a single layer, demonstrating the press's powerful force. The background features a green wall with a yellow and red sign, adding context to the industrial setting.
-A bowling ball is placed on a metal platform, and a large metal cylinder descends from above, flattening the ball as if it were under a hydraulic press. The ball is crushed into a flat, round shape, demonstrating the immense pressure applied by the cylinder.
-A large metal cylinder with yellow and black stripes is seen pressing down on a pile of popcorn, flattening the objects as if they were under a hydraulic press. The popcorn is crushed and scattered around the base of the cylinder, creating a satisfying visual effect.
-The video shows a hydraulic press in action, flattening objects as if they were under a hydraulic press. The press is composed of a large, cylindrical metal cylinder with yellow and black stripes, and a metal base. The objects being flattened are two cylindrical blocks of cotton candy, one pink and one blue. The press is positioned on a metal table, and the background features a green wall with a yellow and red sign.
-The video shows a large orange being flattened as if it were under a hydraulic press, with the press moving down and compressing the fruit until it is completely flattened.
-The video shows a cylindrical object being pressed down onto a flat surface, causing the objects beneath it to be flattened as if they were under a hydraulic press. The objects being flattened appear to be yellow and are being crushed into a pile of debris. The background is a greenish-gray color, and the surface on which the objects are being flattened is metallic and shiny.
-A green and blue object with a spiky texture is being flattened by a large, cylindrical metal press, demonstrating its resilience and durability.
-The video shows a stack of caramelized sugar cubes being flattened as if they were under a hydraulic press, resulting in a messy pile of broken sugar on the table.
-A large metal cylinder is seen pressing down on a pile of colorful jelly beans, flattening them as if they were under a hydraulic press.
-The video shows a machine with a yellow and black striped cylinder pressing down on a stack of colorful sponges, flattening them as if they were under a hydraulic press. The machine is situated in a green-walled room with warning signs in the background.
-The video shows a machine with a yellow and black striped cylinder, which is pressing down on two colorful objects, flattening them as if they were under a hydraulic press. The machine appears to be in a workshop or industrial setting, with a green wall in the background. The objects being flattened are green and orange, and the machine is covered in dirt and grime, indicating it has been used frequently.
-The video shows a large, industrial press flattening objects as if they were under a hydraulic press. The press is shown in action, compressing a pile of pink objects into a pile of crumbs. The press is large and metallic, with a yellow and black striped pattern on its side. The background is a green wall with a yellow warning sign.
-The video shows a pink, sparkly ball being crushed by a large, rusty cylinder, which flattens the objects as if they were under a hydraulic press.
-A lime is being crushed by a hydraulic press, causing it to flatten and burst open, releasing its juice and segments.
-The video shows a machine with a yellow and black striped cylinder, which is flattening objects as if they were under a hydraulic press. The machine is pressing down on two colorful objects, causing them to compress and flatten. The background is a green wall, and the machine appears to be in a workshop or industrial setting.
-The video shows a large, yellow and black striped cylinder flattening objects as if they were under a hydraulic press. The objects being flattened are pink and are being crushed into small pieces. The background is a green wall with a yellow sign.
-The video shows a machine with a yellow and black striped cylinder pressing down on two colorful objects, which are flattened as if they were under a hydraulic press. The machine is positioned on a metal platform, and the background is a green wall.
-A green cube is being compressed by a hydraulic press, which flattens the object as if it were under a hydraulic press. The press is shown in action, with the cube being squeezed into a smaller shape.
-A pink, sparkly ball is being crushed by a large, rusty cylinder, which flattens the objects as if they were under a hydraulic press.
-A red cabbage is being crushed by a hydraulic press, which flattens the objects as if they were under a hydraulic press. The press is shown in action, compressing the cabbage into a smaller, more compact form.
-A lime is being crushed by a hydraulic press, causing it to flatten and burst open, releasing its juice and pulp.
-A large metal press is shown compressing a stack of burgers, causing them to be flattened and crushed into a pile of ground meat.
-A pizza is being crushed by a hydraulic press, causing the toppings to spread out and the crust to crumble.
-A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.
-A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.
-A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.

examples/training/consistency_finetune/causal_ode_init/finetune_ode_init.sh

@@ -1,93 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-
-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 "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 21
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --warp_denoising_step
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 6e-6
-  --mixed_precision "bf16"
-  --checkpointing_steps 1000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/ode_causal_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"

examples/training/consistency_finetune/causal_ode_init/preprocess_wan_data_crush_smol.sh

@@ -1,25 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-MODEL_TYPE="wan"
-DATA_MERGE_PATH="$(dirname "$0")/crush_smol_prompts.txt"
-OUTPUT_DIR="data/crush-smol_processed_t2v_1_3b_ode_init/"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 1 \
-    --seed 42 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 81 \
-    --flow_shift 5.0 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --train_fps 16 \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
-    --video_length_tolerance_range 5 \
-    --preprocess_task "ode_trajectory" 

examples/training/consistency_finetune/causal_ode_init/validation.json

@@ -1,40 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A white and orange tabby cat is seen happily darting through a dense garden, as if chasing something. Its eyes are wide and happy as it jogs forward, scanning the branches, flowers, and leaves as it walks. The path is narrow as it makes its way between all the plants. the scene is captured from a ground-level angle, following the cat closely, giving a low and intimate perspective. The image is cinematic with warm tones and a grainy texture. The scattered daylight between the leaves and plants above creates a warm contrast, accentuating the cat’s orange fur. The shot is clear and sharp, with a shallow depth of field.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}

examples/training/consistency_finetune/ode_init/finetune_ode_init_16k_vidprom.slurm

@@ -1,5 +1,5 @@
 #!/bin/bash
-#SBATCH --job-name=1e5B2_16kFV_warp_ode_vidprom
+#SBATCH --job-name=4e6B8_16kFV_no_warp_ode_vidprom
 #SBATCH --partition=main
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
@@ -7,8 +7,8 @@
 #SBATCH --gres=gpu:8
 #SBATCH --cpus-per-task=128
 #SBATCH --mem=1440G
-#SBATCH --output=ode_vidprom16k_warp/Dode_vidprom8b16k_1e-5.out
-#SBATCH --error=ode_vidprom16k_warp/Dode_vidprom8b16k_1e-5.err
+#SBATCH --output=ode_vidprom16k_no_warp/ode_vidprom8b16k_4e-6.out
+#SBATCH --error=ode_vidprom16k_no_warp/ode_vidprom8b16k_4e-6.err
 #SBATCH --exclusive
 set -e -x
 
@@ -38,21 +38,19 @@ echo "NODE_RANK: $NODE_RANK"
 
 # MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing-16k-t2v-1-3b-81/"
+DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing-16k-t2v-1-3b/"
 VALIDATION_DATASET_FILE="examples/training/consistency_finetune/ode_init/validation.json"
-NUM_GPUS=8
+NUM_GPUS=2
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]
 
 # Training arguments
 training_args=(
   --tracker_project_name "wan_ode_init"
-  --output_dir "Dwarp_vidprom_8b16k_test_warp_1e-5"
+  --output_dir "vidprom_8b16k_test_no_warp_4e-6"
   --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "Dwarp_vidprom_8b16k_wan_ode_init_1e-5"
+  --wandb_run_name "vidprom_8b16k_wan_ode_init_4e-6"
   # --resume_from_checkpoint "ode_init_diffusers/"
-  --warp_denoising_step
-  --log_visualization
   --max_train_steps 6001
   --train_batch_size 1
   --train_sp_batch_size 1
@@ -61,7 +59,7 @@ training_args=(
   --num_height 480
   --num_width 832
   --num_frames 77
-  --dmd_denoising_steps "1000,750,500,250"
+  --dmd_denoising_steps "1000,750,500,0"
   --enable_gradient_checkpointing_type "full"
 )
 
@@ -93,12 +91,11 @@ validation_args=(
   --validation_steps 50
   --validation_sampling_steps "50"
   --validation_guidance_scale "6.0"
-  # --init_weights_from_safetensors "/mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors"
 )
 
 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 1e-5
+  --learning_rate 4e-6
   --mixed_precision "bf16"
   --checkpointing_steps 500
   --weight_decay 1e-4

examples/training/consistency_finetune/ode_init/finetune_ode_init_lmdb.sh

@@ -1,103 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=offline
-export TOKENIZERS_PARALLELISM=false
-export MASTER_PORT=29501
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="data/mixkit-64_processed/Node_0_GPU_1_File_1/combined_parquet_dataset"
-# DATA_DIR="/mnt/weka/home/hao.zhang/wl/Self-Forcing/ode_single_lmdb_sf/"
-VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
-NUM_GPUS=1
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_ode_init"
-  --output_dir "Dwarp_vidprom_8b16k_test_warp_1e-5"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "Dwarp_vidprom_8b16k_wan_ode_init_1e-5"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  --warp_denoising_step
-  --log_visualization
-  --max_train_steps 10
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --dmd_denoising_steps "1000,750,500,250"
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim $NUM_GPUS
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-#   --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "6.0"
-  # --init_weights_from_safetensors "/mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 500
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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
-  --seed 1024
-  # --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
-torchrun \
-  --nnodes 1 \
-  --master_port $MASTER_PORT \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/ode_causal_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"

examples/training/consistency_finetune/ode_init/finetune_ode_init_single_lmdb.sh

@@ -1,100 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-# DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init_5/combined_parquet_dataset/"
-DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing-16k-t2v-1-3b/"
-VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
-NUM_GPUS=1
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_ode_init"
-  --output_dir "debug_ode_init"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "debug_ode_init"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  --max_train_steps 1000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 81
-  --warp_denoising_step
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 10
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 1000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/ode_causal_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"

examples/training/consistency_finetune/ode_init/preprocess_wan_data_ode.sh

@@ -3,8 +3,8 @@
 GPU_NUM=1 # 2,4,8
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 MODEL_TYPE="wan"
-DATA_MERGE_PATH="data/vidprom_1.txt"
-OUTPUT_DIR="data/ode_vidprom_1_fv/"
+DATA_MERGE_PATH="data/crush-smol_single/merge.txt"
+OUTPUT_DIR="data/crush-smol_processed_t2v_1_3b_ode_init_single/"
 
 torchrun --nproc_per_node=$GPU_NUM \
     fastvideo/pipelines/preprocess/v1_preprocess.py \
@@ -15,7 +15,6 @@ torchrun --nproc_per_node=$GPU_NUM \
     --max_height 480 \
     --max_width 832 \
     --num_frames 81 \
-    --flow_shift 5.0 \
     --dataloader_num_workers 0 \
     --output_dir=$OUTPUT_DIR \
     --train_fps 16 \

examples/training/consistency_finetune/ode_init/validation.json

@@ -1,5 +1,14 @@
 {
   "data": [
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
     {
       "caption": "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.",
       "image_path": null,
@@ -19,52 +28,7 @@
       "num_frames": 77
     },
     {
-      "caption": "Elon Musk, dressed in a sleek white spacesuit with a reflective visor, walks confidently across the lunar surface. His posture is upright, and he moves steadily with purpose. The moon's rocky terrain and scattered boulders surround him, casting shadows under the dim sunlight. The background shows vast stretches of the moon's barren landscape with craters and dust clouds kicked up by his boots. The scene captures a wide shot, emphasizing the vastness and desolation of the lunar environment. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "In a dynamic action-packed sequence set in the Marvel multiverse, Spider-Man and Venom engage in an intense battle. Spider-Man, in his classic red and blue suit, swings and dodges venomous attacks from the black symbiote-covered Venom. Both characters display a range of acrobatic moves and powerful strikes. The environment is a chaotic urban landscape with crumbling buildings and neon lights, reflecting the multiversal theme. The camera captures the epic fight from various angles, including wide shots to show the scale of destruction and close-ups to highlight their fierce expressions and physical combat. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A warm, family-oriented scene depicting a father getting ready to leave the house to buy milk. The father, a middle-aged man with a kind face and a casual outfit, picks up a jacket from the coat rack. His posture is upright as he bends down slightly to put on his shoes. In the background, there are glimpses of a cozy living room with a family photograph on the wall. The camera focuses closely on the father, capturing his gentle smile and reassuring nod towards the camera before he opens the front door and steps outside. Static medium close-up shot. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "Close-up shot of a man with a prosthetic hand that functions as a rocket launcher. He looks at his new hand with a mix of amazement and concern, his facial expression showing a blend of curiosity and apprehension. The prosthetic hand is sleek and metallic, with intricate details that resemble a high-tech weapon. The background is a dimly lit laboratory with various scientific equipment and monitors displaying data. The man stands in a relaxed posture, his other hand resting on his hip, as he inspects his new limb. The scene is rendered in a realistic sci-fi style, emphasizing the futuristic technology and the man's emotional response to his new appendage. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "Realistic CCTV footage style, Kim Taehyung from the band BTS is involved in a drug deal, caught on camera. Kim Taehyung appears nervous and cautious, wearing casual clothing typical of a public space. He exchanges items discreetly with another person, who is partially obscured. Both individuals maintain a watchful demeanor, occasionally glancing around to ensure no one is watching them. The lighting is dim, with flickering fluorescent lights casting shadows on their faces. The background shows a typical urban setting with blurred figures moving in the distance. Static camera angle, medium close-up shot focusing on the interaction between Taehyung and the other individual. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "Photorealistic studio setup with professional lighting, showcasing detailed cubic dissections of experimental plastic and felt-like materials on a pristine white background. Each cube reveals intricate layers and textures of the materials, emphasizing their unique properties. The scene has a shallow depth of field initially, then slowly pulls out to reveal the full arrangement of cubes, maintaining a wide depth of field throughout the transition. ",
+      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
       "image_path": null,
       "video_path": null,
       "num_inference_steps": 40,

examples/training/finetune/wan_t2v_1.3B/crush_smol/preprocess_wan_data_t2v_new.sh

@@ -10,7 +10,6 @@ torchrun --nproc_per_node=$GPU_NUM \
     --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 \

examples/training/finetune/wan_t2v_1.3B/crush_smol/validation.json

@@ -28,4 +28,4 @@
       "num_frames": 77
     }
   ]
-}
+}

fastvideo/attention/backends/vmoba.py

@@ -1,214 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import re
-from dataclasses import dataclass
-
-import torch
-from einops import rearrange
-
-from csrc.attn.vmoba_attn.vmoba import (moba_attn_varlen, process_moba_input,
-                                        process_moba_output)
-from fastvideo.attention.backends.abstract import (AttentionBackend,
-                                                   AttentionImpl,
-                                                   AttentionMetadata,
-                                                   AttentionMetadataBuilder)
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-class VMOBAAttentionBackend(AttentionBackend):
-
-    accept_output_buffer: bool = True
-
-    @staticmethod
-    def get_name() -> str:
-        return "VMOBA_ATTN"
-
-    @staticmethod
-    def get_impl_cls() -> type["VMOBAAttentionImpl"]:
-        return VMOBAAttentionImpl
-
-    @staticmethod
-    def get_metadata_cls() -> type["VideoMobaAttentionMetadata"]:
-        return VideoMobaAttentionMetadata
-
-    @staticmethod
-    def get_builder_cls() -> type["VideoMobaAttentionMetadataBuilder"]:
-        return VideoMobaAttentionMetadataBuilder
-
-
-@dataclass
-class VideoMobaAttentionMetadata(AttentionMetadata):
-    current_timestep: int
-
-    temporal_chunk_size: int
-    temporal_topk: int
-    spatial_chunk_size: tuple[int, int]
-    spatial_topk: int
-    st_chunk_size: tuple[int, int, int]
-    st_topk: int
-
-    moba_select_mode: str
-    moba_threshold: float
-    moba_threshold_type: str
-    patch_resolution: list[int]
-
-    first_full_step: int = 12
-    first_full_layer: int = 0
-    # temporal_layer -> spatial_layer -> st_layer
-    temporal_layer: int = 1
-    spatial_layer: int = 1
-    st_layer: int = 1
-
-
-class VideoMobaAttentionMetadataBuilder(AttentionMetadataBuilder):
-
-    def __init__(self):
-        pass
-
-    def prepare(self):
-        pass
-
-    def build(  # type: ignore
-        self,
-        current_timestep: int,
-        raw_latent_shape: tuple[int, int, int],
-        patch_size: tuple[int, int, int],
-        temporal_chunk_size: int,
-        temporal_topk: int,
-        spatial_chunk_size: tuple[int, int],
-        spatial_topk: int,
-        st_chunk_size: tuple[int, int, int],
-        st_topk: int,
-        moba_select_mode: str = 'threshold',
-        moba_threshold: float = 0.25,
-        moba_threshold_type: str = 'query_head',
-        device: torch.device = None,
-        first_full_layer: int = 0,
-        first_full_step: int = 12,
-        temporal_layer: int = 1,
-        spatial_layer: int = 1,
-        st_layer: int = 1,
-        **kwargs,
-    ) -> VideoMobaAttentionMetadata:
-        if device is None:
-            device = torch.device("cpu")
-        assert raw_latent_shape[0] % patch_size[0] == 0 and raw_latent_shape[
-            1] % patch_size[1] == 0 and raw_latent_shape[2] % patch_size[
-                2] == 0, f"spatial patch_resolution {raw_latent_shape} should be divisible by patch_size {patch_size}"
-        patch_resolution = [
-            t // pt for t, pt in zip(raw_latent_shape, patch_size, strict=False)
-        ]
-
-        return VideoMobaAttentionMetadata(
-            current_timestep=current_timestep,
-            temporal_chunk_size=temporal_chunk_size,
-            temporal_topk=temporal_topk,
-            spatial_chunk_size=spatial_chunk_size,
-            spatial_topk=spatial_topk,
-            st_chunk_size=st_chunk_size,
-            st_topk=st_topk,
-            moba_select_mode=moba_select_mode,
-            moba_threshold=moba_threshold,
-            moba_threshold_type=moba_threshold_type,
-            patch_resolution=patch_resolution,
-            first_full_layer=first_full_layer,
-            first_full_step=first_full_step,
-            temporal_layer=temporal_layer,
-            spatial_layer=spatial_layer,
-            st_layer=st_layer,
-        )
-
-
-class VMOBAAttentionImpl(AttentionImpl):
-
-    def __init__(self,
-                 num_heads,
-                 head_size,
-                 softmax_scale,
-                 causal=False,
-                 num_kv_heads=None,
-                 prefix="",
-                 **extra_impl_args) -> None:
-        self.prefix = prefix
-        self.layer_idx = self._get_layer_idx(prefix)
-        from flash_attn.bert_padding import pad_input
-        self.pad_input = pad_input
-
-    def _get_layer_idx(self, prefix: str) -> int | None:
-        match = re.search(r"blocks\.(\d+)", prefix)
-        if not match:
-            raise ValueError(f"Invalid prefix: {prefix}")
-        return int(match.group(1))
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        """
-        query: [B, L, H, D]
-        key:   [B, L, H, D]
-        value: [B, L, H, D]
-        attn_metadata: AttentionMetadata
-        """
-        batch_size, sequence_length, num_heads, head_dim = query.shape
-
-        # select chunk type according to layer idx:
-        loop_layer_num = attn_metadata.temporal_layer + attn_metadata.spatial_layer + attn_metadata.st_layer
-        moba_layer = self.layer_idx - attn_metadata.first_full_layer
-        if moba_layer % loop_layer_num < attn_metadata.temporal_layer:
-            moba_chunk_size = attn_metadata.temporal_chunk_size
-            moba_topk = attn_metadata.temporal_topk
-        elif moba_layer % loop_layer_num < attn_metadata.temporal_layer + attn_metadata.spatial_layer:
-            moba_chunk_size = attn_metadata.spatial_chunk_size
-            moba_topk = attn_metadata.spatial_topk
-        elif moba_layer % loop_layer_num < attn_metadata.temporal_layer + attn_metadata.spatial_layer + attn_metadata.st_layer:
-            moba_chunk_size = attn_metadata.st_chunk_size
-            moba_topk = attn_metadata.st_topk
-
-        query, chunk_size = process_moba_input(query,
-                                               attn_metadata.patch_resolution,
-                                               moba_chunk_size)
-        key, chunk_size = process_moba_input(key,
-                                             attn_metadata.patch_resolution,
-                                             moba_chunk_size)
-        value, chunk_size = process_moba_input(value,
-                                               attn_metadata.patch_resolution,
-                                               moba_chunk_size)
-        max_seqlen = query.shape[1]
-        indices_q = torch.arange(0,
-                                 query.shape[0] * query.shape[1],
-                                 device=query.device)
-        cu_seqlens = torch.arange(0,
-                                  query.shape[0] * query.shape[1] + 1,
-                                  query.shape[1],
-                                  dtype=torch.int32,
-                                  device=query.device)
-        query = rearrange(query, "b s ... -> (b s) ...")
-        key = rearrange(key, "b s ... -> (b s) ...")
-        value = rearrange(value, "b s ... -> (b s) ...")
-
-        # current_timestep=attn_metadata.current_timestep
-        hidden_states = moba_attn_varlen(
-            query,
-            key,
-            value,
-            cu_seqlens=cu_seqlens,
-            max_seqlen=max_seqlen,
-            moba_chunk_size=chunk_size,
-            moba_topk=moba_topk,
-            select_mode=attn_metadata.moba_select_mode,
-            simsum_threshold=attn_metadata.moba_threshold,
-            threshold_type=attn_metadata.moba_threshold_type,
-        )
-        hidden_states = self.pad_input(hidden_states, indices_q, batch_size,
-                                       sequence_length)
-        hidden_states = process_moba_output(hidden_states,
-                                            attn_metadata.patch_resolution,
-                                            moba_chunk_size)
-
-        return hidden_states

fastvideo/attention/layer.py

@@ -3,7 +3,6 @@
 import torch
 import torch.nn as nn
 
-import fastvideo.envs as envs
 from fastvideo.attention.selector import backend_name_to_enum, get_attn_backend
 from fastvideo.distributed.communication_op import (
     sequence_model_parallel_all_gather, sequence_model_parallel_all_to_all_4D)
@@ -37,7 +36,7 @@ class DistributedAttention(nn.Module):
         if num_kv_heads is None:
             num_kv_heads = num_heads
 
-        dtype = torch.bfloat16 if envs.FASTVIDEO_FORCE_ATTN_BF16 else get_compute_dtype()
+        dtype = get_compute_dtype()
         attn_backend = get_attn_backend(
             head_size,
             dtype,
@@ -222,7 +221,7 @@ class LocalAttention(nn.Module):
         if num_kv_heads is None:
             num_kv_heads = num_heads
 
-        dtype = torch.bfloat16 if envs.FASTVIDEO_FORCE_ATTN_BF16 else get_compute_dtype()
+        dtype = get_compute_dtype()
         attn_backend = get_attn_backend(
             head_size,
             dtype,

fastvideo/configs/backend/vmoba/wan_1.3B_77_448_832.json

@@ -1,16 +0,0 @@
-{
-    "temporal_chunk_size": 2,
-    "temporal_topk": 2,
-    "spatial_chunk_size": [4, 13],
-    "spatial_topk": 6,
-    "st_chunk_size": [4, 4, 13],
-    "st_topk": 18,
-    "moba_select_mode": "topk",
-    "moba_threshold": 0.25,
-    "moba_threshold_type": "query_head",
-    "first_full_layer": 0,
-    "first_full_step": 12,
-    "temporal_layer": 1,
-    "spatial_layer": 1,
-    "st_layer": 1
-}

fastvideo/configs/backend/vmoba/wan_1.3B_77_480_832.json

@@ -1,16 +0,0 @@
-{
-    "temporal_chunk_size": 2,
-    "temporal_topk": 3,
-    "spatial_chunk_size": [3, 4],
-    "spatial_topk": 20,
-    "st_chunk_size": [4, 6, 4],
-    "st_topk": 15,
-    "moba_select_mode": "threshold",
-    "moba_threshold": 0.25,
-    "moba_threshold_type": "query_head",
-    "first_full_layer": 0,
-    "first_full_step": 12,
-    "temporal_layer": 1,
-    "spatial_layer": 1,
-    "st_layer": 1
-}

fastvideo/configs/configs.py

@@ -32,29 +32,6 @@ class DatasetType(str, Enum):
         return [dataset_type.value for dataset_type in cls]
 
 
-class VideoLoaderType(str, Enum):
-    """
-    Enumeration for different video loaders.
-    """
-    TORCHCODEC = "torchcodec"
-    TORCHVISION = "torchvision"
-
-    @classmethod
-    def from_string(cls, value: str) -> "VideoLoaderType":
-        """Convert string to VideoLoader enum."""
-        try:
-            return cls(value.lower())
-        except ValueError:
-            raise ValueError(
-                f"Invalid video loader: {value}. Must be one of: {', '.join([m.value for m in cls])}"
-            ) from None
-
-    @classmethod
-    def choices(cls) -> list[str]:
-        """Get all available choices as strings for argparse."""
-        return [video_loader.value for video_loader in cls]
-
-
 @dataclasses.dataclass
 class PreprocessConfig:
     """Configuration for preprocessing operations."""
@@ -74,7 +51,6 @@ class PreprocessConfig:
     flush_frequency: int = 256
 
     # Video processing parameters
-    video_loader_type: VideoLoaderType = VideoLoaderType.TORCHCODEC
     max_height: int = 480
     max_width: int = 848
     num_frames: int = 163
@@ -144,12 +120,6 @@ class PreprocessConfig:
                                      help="How often to save to parquet files")
 
         # Video processing parameters
-        preprocess_args.add_argument(
-            f"--{prefix_with_dot}video-loader-type",
-            type=str,
-            choices=VideoLoaderType.choices(),
-            default=PreprocessConfig.video_loader_type.value,
-            help="Type of the video loader")
         preprocess_args.add_argument(f"--{prefix_with_dot}max-height",
                                      type=int,
                                      default=PreprocessConfig.max_height,
@@ -204,10 +174,6 @@ class PreprocessConfig:
         if 'dataset_type' in kwargs and isinstance(kwargs['dataset_type'], str):
             kwargs['dataset_type'] = DatasetType.from_string(
                 kwargs['dataset_type'])
-        if 'video_loader_type' in kwargs and isinstance(
-                kwargs['video_loader_type'], str):
-            kwargs['video_loader_type'] = VideoLoaderType.from_string(
-                kwargs['video_loader_type'])
 
         preprocess_config = cls()
         if not update_config_from_args(

fastvideo/configs/models/dits/base.py

@@ -15,19 +15,14 @@ class DiTArchConfig(ArchConfig):
     reverse_param_names_mapping: dict = field(default_factory=dict)
     lora_param_names_mapping: dict = field(default_factory=dict)
     _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (
-        AttentionBackendEnum.SLIDING_TILE_ATTN,
-        AttentionBackendEnum.SAGE_ATTN,
-        AttentionBackendEnum.FLASH_ATTN,
-        AttentionBackendEnum.TORCH_SDPA,
-        AttentionBackendEnum.VIDEO_SPARSE_ATTN,
-        AttentionBackendEnum.VMOBA_ATTN,
-    )
+        AttentionBackendEnum.SLIDING_TILE_ATTN, AttentionBackendEnum.SAGE_ATTN,
+        AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA,
+        AttentionBackendEnum.VIDEO_SPARSE_ATTN)
 
     hidden_size: int = 0
     num_attention_heads: int = 0
     num_channels_latents: int = 0
     exclude_lora_layers: list[str] = field(default_factory=list)
-    boundary_ratio: float | None = None
 
     def __post_init__(self) -> None:
         if not self._compile_conditions:

fastvideo/configs/models/dits/wanvideo.py

@@ -92,9 +92,6 @@ class WanVideoArchConfig(DiTArchConfig):
     pos_embed_seq_len: int | None = None
     exclude_lora_layers: list[str] = field(default_factory=lambda: ["embedder"])
 
-    # Wan MoE
-    boundary_ratio: float | None = None
-
     # Causal Wan
     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

fastvideo/configs/pipelines/__init__.py

@@ -4,13 +4,12 @@ from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.registry import (
     get_pipeline_config_cls_from_name)
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
-from fastvideo.configs.pipelines.wan import (SelfForcingWanT2V480PConfig,
-                                             WanI2V480PConfig, WanI2V720PConfig,
+from fastvideo.configs.pipelines.wan import (WanI2V480PConfig, WanI2V720PConfig,
                                              WanT2V480PConfig, WanT2V720PConfig)
 
 __all__ = [
     "HunyuanConfig", "FastHunyuanConfig", "PipelineConfig",
     "SlidingTileAttnConfig", "WanT2V480PConfig", "WanI2V480PConfig",
     "WanT2V720PConfig", "WanI2V720PConfig", "StepVideoT2VConfig",
-    "SelfForcingWanT2V480PConfig", "get_pipeline_config_cls_from_name"
+    "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/base.py

@@ -51,7 +51,6 @@ class PipelineConfig:
     # Model configuration
     dit_config: DiTConfig = field(default_factory=DiTConfig)
     dit_precision: str = "bf16"
-    dit_forward_precision: str = "bf16"
 
     # VAE configuration
     vae_config: VAEConfig = field(default_factory=VAEConfig)
@@ -88,10 +87,6 @@ class PipelineConfig:
     # DMD parameters
     dmd_denoising_steps: list[int] | None = field(default=None)
 
-    # Wan2.2 TI2V parameters
-    ti2v_task: bool = False
-    boundary_ratio: float | None = None
-
     # Compilation
     # enable_torch_compile: bool = False
 

fastvideo/configs/pipelines/registry.py

@@ -11,9 +11,9 @@ from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 # isort: off
 from fastvideo.configs.pipelines.wan import (
     FastWan2_1_T2V_480P_Config, FastWan2_2_TI2V_5B_Config,
-    Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config, Wan2_2_TI2V_5B_Config,
-    WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig, WanT2V720PConfig,
-    SelfForcingWanT2V480PConfig)
+    SelfForcingWanT2V480PConfig, Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config,
+    Wan2_2_TI2V_5B_Config, WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig,
+    WanT2V720PConfig, SelfForcingWanT2V480PConfig)
 # isort: on
 from fastvideo.logger import init_logger
 from fastvideo.utils import (maybe_download_model_index,
@@ -48,7 +48,6 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
     "wanpipeline": lambda id: "wanpipeline" in id.lower(),
     "wanimagetovideo": lambda id: "wanimagetovideo" in id.lower(),
     "wandmdpipeline": lambda id: "wandmdpipeline" in id.lower(),
-    "wancausaldmdpipeline": lambda id: "wancausaldmdpipeline" in id.lower(),
     "stepvideo": lambda id: "stepvideo" in id.lower(),
     "wancausaldmdpipeline": lambda id: "wancausaldmdpipeline" in id.lower(),
     # Add other pipeline architecture detectors
@@ -62,8 +61,8 @@ PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
     WanT2V480PConfig,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V480PConfig,
     "wandmdpipeline": FastWan2_1_T2V_480P_Config,
+    "stepvideo": StepVideoT2VConfig,
     "wancausaldmdpipeline": SelfForcingWanT2V480PConfig,
-    "stepvideo": StepVideoT2VConfig
     # Other fallbacks by architecture
 }
 

fastvideo/configs/pipelines/wan.py

@@ -12,13 +12,13 @@ from fastvideo.configs.models.vaes import WanVAEConfig
 from fastvideo.configs.pipelines.base import PipelineConfig
 
 
-def t5_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
-    mask: torch.Tensor = outputs.attention_mask
-    hidden_state: torch.Tensor = outputs.last_hidden_state
+def t5_postprocess_text(outputs: BaseEncoderOutput) -> torch.tensor:
+    mask: torch.tensor = outputs.attention_mask
+    hidden_state: torch.tensor = outputs.last_hidden_state
     seq_lens = mask.gt(0).sum(dim=1).long()
     assert torch.isnan(hidden_state).sum() == 0
     prompt_embeds = [u[:v] for u, v in zip(hidden_state, seq_lens, strict=True)]
-    prompt_embeds_tensor: torch.Tensor = torch.stack([
+    prompt_embeds_tensor: torch.tensor = torch.stack([
         torch.cat([u, u.new_zeros(512 - u.size(0), u.size(1))])
         for u in prompt_embeds
     ],
@@ -39,12 +39,12 @@ class WanT2V480PConfig(PipelineConfig):
     vae_sp: bool = False
 
     # Denoising stage
-    flow_shift: float | None = 3.0
+    flow_shift: float | None = 8.0
 
     # Text encoding stage
     text_encoder_configs: tuple[EncoderConfig, ...] = field(
         default_factory=lambda: (T5Config(), ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
+    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.tensor],
                                   ...] = field(default_factory=lambda:
                                                (t5_postprocess_text, ))
 
@@ -68,7 +68,7 @@ class WanT2V720PConfig(WanT2V480PConfig):
     # WanConfig-specific parameters with defaults
 
     # Denoising stage
-    flow_shift: float | None = 5.0
+    flow_shift: int = 5
 
 
 @dataclass
@@ -82,7 +82,7 @@ class WanI2V480PConfig(WanT2V480PConfig):
         default_factory=CLIPVisionConfig)
     image_encoder_precision: str = "fp32"
 
-    def __post_init__(self) -> None:
+    def __post_init__(self):
         self.vae_config.load_encoder = True
         self.vae_config.load_decoder = True
 
@@ -94,7 +94,7 @@ class WanI2V720PConfig(WanI2V480PConfig):
     # WanConfig-specific parameters with defaults
 
     # Denoising stage
-    flow_shift: float | None = 5.0
+    flow_shift: int = 5
 
 
 @dataclass
@@ -104,47 +104,40 @@ class FastWan2_1_T2V_480P_Config(WanT2V480PConfig):
     # WanConfig-specific parameters with defaults
 
     # Denoising stage
-    flow_shift: float | None = 8.0
+    flow_shift: int = 8
     dmd_denoising_steps: list[int] | None = field(
         default_factory=lambda: [1000, 757, 522])
 
-
-@dataclass
-class Wan2_2_TI2V_5B_Config(WanT2V480PConfig):
-    flow_shift: float | None = 5.0
-    ti2v_task: bool = True
-    expand_timesteps: bool = True
-
     def __post_init__(self) -> None:
         self.vae_config.load_encoder = True
         self.vae_config.load_decoder = True
-        self.dit_config.expand_timesteps = self.expand_timesteps
+
+
+@dataclass
+class Wan2_2_TI2V_5B_Config(WanT2V480PConfig):
+    flow_shift: int = 5
+    ti2v_task: bool = True
+
+    def __post_init__(self) -> None:
+        self.vae_config.load_encoder = True
+        self.vae_config.load_decoder = True
 
 
 @dataclass
 class FastWan2_2_TI2V_5B_Config(Wan2_2_TI2V_5B_Config):
-    flow_shift: float | None = 5.0
+    flow_shift: int = 5
     dmd_denoising_steps: list[int] | None = field(
         default_factory=lambda: [1000, 757, 522])
 
 
 @dataclass
 class Wan2_2_T2V_A14B_Config(WanT2V480PConfig):
-    flow_shift: float | None = 12.0
-    boundary_ratio: float | None = 0.875
-
-    def __post_init__(self) -> None:
-        self.dit_config.boundary_ratio = self.boundary_ratio
+    pass
 
 
 @dataclass
-class Wan2_2_I2V_A14B_Config(WanI2V480PConfig):
-    flow_shift: float | None = 5.0
-    boundary_ratio: float | None = 0.900
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.dit_config.boundary_ratio = self.boundary_ratio
+class Wan2_2_I2V_A14B_Config(WanT2V480PConfig):
+    pass
 
 
 # =============================================
@@ -156,4 +149,3 @@ class SelfForcingWanT2V480PConfig(WanT2V480PConfig):
     flow_shift: float | None = 5.0
     dmd_denoising_steps: list[int] | None = field(
         default_factory=lambda: [1000, 750, 500, 250])
-    warp_denoising_step: bool = True

fastvideo/configs/sample/base.py

@@ -40,7 +40,6 @@ class SamplingParam:
     num_inference_steps: int = 50
     guidance_scale: float = 1.0
     guidance_rescale: float = 0.0
-    boundary_ratio: float | None = None
 
     # TeaCache parameters
     enable_teacache: bool = False
@@ -170,12 +169,6 @@ class SamplingParam:
             default=SamplingParam.guidance_rescale,
             help="Guidance rescale factor",
         )
-        parser.add_argument(
-            "--boundary-ratio",
-            type=float,
-            default=SamplingParam.boundary_ratio,
-            help="Boundary timestep ratio",
-        )
         parser.add_argument(
             "--save-video",
             action="store_true",
@@ -200,13 +193,6 @@ class SamplingParam:
             default=SamplingParam.image_path,
             help="Path to input image for image-to-video generation",
         )
-        parser.add_argument(
-            "--moba-config-path",
-            type=str,
-            default=None,
-            help=
-            "Path to a JSON file containing V-MoBA specific configurations.",
-        )
         parser.add_argument(
             "--return-trajectory-latents",
             action="store_true",

fastvideo/configs/sample/wan.py

@@ -144,22 +144,18 @@ class Wan2_2_TI2V_5B_SamplingParam(Wan2_2_Base_SamplingParam):
 
 @dataclass
 class Wan2_2_T2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
-    guidance_scale: float = 4.0  # high_noise
-    guidance_scale_2: float = 3.0  # low_noise
+    guidance_scale: float = 4.0
+    guidance_scale_2: float = 3.0
     num_inference_steps: int = 40
     fps: int = 16
-    # NOTE(will): default boundary timestep is tracked by PipelineConfig, but
-    # can be overridden during sampling
 
 
 @dataclass
 class Wan2_2_I2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
-    guidance_scale: float = 3.5  # high_noise
-    guidance_scale_2: float = 3.5  # low_noise
+    guidance_scale: float = 3.5
+    guidance_scale_2: float = 3.5
     num_inference_steps: int = 40
     fps: int = 16
-    # NOTE(will): default boundary timestep is tracked by PipelineConfig, but
-    # can be overridden during sampling
 
 
 # =============================================

fastvideo/dataset/__init__.py

@@ -4,7 +4,7 @@ from torchvision.transforms import Lambda
 
 from fastvideo.dataset.parquet_dataset_map_style import (
     build_parquet_map_style_dataloader)
-from fastvideo.dataset.preprocessing_datasets import VideoCaptionMergedDataset, TextDataset
+from fastvideo.dataset.preprocessing_datasets import VideoCaptionMergedDataset
 from fastvideo.dataset.transform import (CenterCropResizeVideo, Normalize255,
                                          TemporalRandomCrop)
 from fastvideo.dataset.validation_dataset import ValidationDataset
@@ -37,15 +37,9 @@ def getdataset(args) -> VideoCaptionMergedDataset:
                                      temporal_sample=temporal_sample,
                                      transform_topcrop=transform_topcrop,
                                      seed=args.seed)
-                                    
-
-def gettextdataset(args) -> TextDataset:
-    return TextDataset(data_merge_path=args.data_merge_path,
-                       args=args,
-                       seed=args.seed)
 
 
 __all__ = [
     "build_parquet_map_style_dataloader", "ValidationDataset",
-    "VideoCaptionMergedDataset", "TextDataset"
+    "VideoCaptionMergedDataset"
 ]

fastvideo/dataset/dataloader/parquet_io.py

@@ -1,264 +0,0 @@
-"""
-Utilities for converting preprocessing records (dicts) into Arrow tables and
-writing Parquet datasets in fixed-size chunks.
-
-This module centralizes table construction and Parquet file writing so
-pipelines only need to define their PyArrow schema and produce per-sample
-record dictionaries.
-
-Key APIs:
-- records_to_table(records, schema): Safely convert a list of dictionaries into
-  a pa.Table, casting to the provided schema.
-- ParquetDatasetWriter: Buffer tables and flush to a directory as multiple
-  Parquet files with a fixed number of rows per file. Uses temporary files and
-  atomic rename to avoid partially written outputs.
-"""
-
-from __future__ import annotations
-
-import multiprocessing
-import os
-from concurrent.futures import ProcessPoolExecutor
-from typing import Any
-
-import pyarrow as pa
-import pyarrow.parquet as pq
-
-
-def records_to_table(records: list[dict[str, Any]], schema: pa.Schema) -> pa.Table:
-    """Build a PyArrow table from Python record dicts using an explicit schema.
-
-    Arrow will cast values to the target schema when possible (e.g., promoting
-    Python ints/floats to pa.int64/pa.float64), eliminating hand-written per-
-    field array construction.
-
-    Args:
-        records: List of dictionaries, each representing one row. Keys must
-            match schema field names.
-        schema: Target PyArrow schema. Controls field names and types.
-
-    Returns:
-        pa.Table: In-memory table matching the provided schema. If ``records``
-        is empty, returns an empty table with the given schema.
-    """
-    if not records:
-        return pa.table({}, schema=schema)
-    return pa.Table.from_pylist(records, schema=schema)
-
-
-class ParquetDatasetWriter:
-    """Accumulate tables and flush them to a Parquet directory in fixed-size chunks.
-
-    Behavior:
-    - Writes files under worker-specific subdirectories for parallelism.
-    - Uses temporary files and atomic rename to avoid partial files being left
-      behind on failure.
-    - Only full chunks of ``samples_per_file`` rows are written on each flush;
-      any remainder rows are re-buffered for the next flush.
-
-    Note:
-    - Instances are not meant to be shared across processes. Create one writer
-      per process if using multiprocessing.
-    """
-
-    def __init__(self, out_dir: str, samples_per_file: int, compression: str = "zstd") -> None:
-        """Initialize the dataset writer.
-
-        Args:
-            out_dir: Output directory where Parquet files will be written.
-            samples_per_file: Fixed number of rows per Parquet file.
-            compression: Compression codec passed to ``pyarrow.parquet.write_table``
-                (e.g., ``"zstd"``, ``"snappy"``, ``"gzip"``).
-        """
-        self.out_dir = out_dir
-        self.samples_per_file = max(int(samples_per_file), 1)
-        self.compression = compression
-        os.makedirs(self.out_dir, exist_ok=True)
-        self._tables: list[pa.Table] = []
-
-    def append_table(self, table: pa.Table) -> None:
-        """Append a non-empty table to the internal buffer.
-
-        Args:
-            table: A ``pa.Table`` to buffer. Empty or ``None`` tables are ignored.
-        """
-        if table is None or len(table) == 0:
-            return
-        self._tables.append(table)
-
-    def _combine(self) -> pa.Table | None:
-        """Combine all buffered tables into a single table, if any.
-
-        Returns:
-            A concatenated table, a single table if only one was buffered, or
-            ``None`` if no tables are buffered.
-        """
-        if not self._tables:
-            return None
-        if len(self._tables) == 1:
-            return self._tables[0]
-        return pa.concat_tables(self._tables, promote_options='none')
-
-    def flush(self, num_workers: int | None = None, write_remainder: bool = False) -> int:
-        """Write accumulated tables to disk and clear the written portion.
-
-        Only complete chunks of size ``samples_per_file`` are written. Any
-        remainder rows are kept buffered for the next flush.
-
-        Args:
-            num_workers: Optional override for the number of parallel workers
-                used to write chunks. Defaults to ``min(cpu_count, chunks)``.
-            write_remainder: If True, also write any leftover rows (< samples_per_file)
-                as a final small Parquet file (useful for the last flush at the
-                end of preprocessing).
-
-        Returns:
-            int: Number of rows successfully written in this flush call.
-        """
-        combined = self._combine()
-        self._tables = []
-        if combined is None or len(combined) == 0:
-            return 0
-
-        num_samples = len(combined)
-        total_chunks = num_samples // self.samples_per_file
-        if total_chunks == 0:
-            if not write_remainder:
-                # Not enough to form a full chunk; keep buffered for next round
-                # Re-buffer and return 0 written
-                self._tables = [combined]
-                return 0
-            # Last flush: write the small remainder as a final file in worker_0
-            worker_dir = os.path.join(self.out_dir, "worker_0")
-            os.makedirs(worker_dir, exist_ok=True)
-            # Determine next index
-            num_parquets = 0
-            for _, _, files in os.walk(worker_dir):
-                for file in files:
-                    if file.endswith('.parquet'):
-                        num_parquets += 1
-            chunk_path = os.path.join(worker_dir, f"data_chunk_{num_parquets}.parquet")
-            temp_path = chunk_path + '.tmp'
-            pq.write_table(combined, temp_path, compression=self.compression)
-            if os.path.exists(chunk_path):
-                os.remove(chunk_path)
-            os.rename(temp_path, chunk_path)
-            return num_samples
-
-        # Only write full chunks; keep remainder for next flush
-        written_rows = total_chunks * self.samples_per_file
-        remainder = num_samples - written_rows
-
-        table_to_write = combined.slice(0, written_rows)
-        remainder_table = combined.slice(written_rows, remainder) if remainder > 0 else None
-        if remainder_table is not None and len(remainder_table) > 0:
-            if write_remainder:
-                # Write the remainder as a final small file (worker_0)
-                worker_dir = os.path.join(self.out_dir, "worker_0")
-                os.makedirs(worker_dir, exist_ok=True)
-                num_parquets = 0
-                for _, _, files in os.walk(worker_dir):
-                    for file in files:
-                        if file.endswith('.parquet'):
-                            num_parquets += 1
-                remainder_path = os.path.join(worker_dir,
-                                              f"data_chunk_{num_parquets}.parquet")
-                temp_path = remainder_path + '.tmp'
-                pq.write_table(remainder_table,
-                               temp_path,
-                               compression=self.compression)
-                if os.path.exists(remainder_path):
-                    os.remove(remainder_path)
-                os.rename(temp_path, remainder_path)
-            else:
-                self._tables = [remainder_table]
-
-        # Parallel write by chunk ranges
-        if num_workers is None:
-            num_workers = min(multiprocessing.cpu_count(), max(total_chunks, 1))
-        num_workers = max(int(num_workers), 1)
-        chunks_per_worker = (total_chunks + num_workers - 1) // num_workers
-
-        work_ranges: list[tuple[int, int, pa.Table, int, str, int, str]] = []
-        for worker_id in range(num_workers):
-            start_chunk = worker_id * chunks_per_worker
-            end_chunk = min((worker_id + 1) * chunks_per_worker, total_chunks)
-            if start_chunk < end_chunk:
-                work_ranges.append(
-                    (
-                        start_chunk,
-                        end_chunk,
-                        table_to_write,
-                        worker_id,
-                        self.out_dir,
-                        self.samples_per_file,
-                        self.compression,
-                    )
-                )
-
-        written_total = 0
-        if len(work_ranges) == 1:
-            written_total += _process_chunk_range(work_ranges[0])
-            return written_total
-
-        with ProcessPoolExecutor(max_workers=num_workers) as executor:
-            futures = [executor.submit(_process_chunk_range, args) for args in work_ranges]
-            for f in futures:
-                written_total += f.result()
-        return written_total + (len(remainder_table) if write_remainder and remainder_table is not None else 0)
-
-
-def _process_chunk_range(args: Any) -> int:
-    """Worker function to write a contiguous range of chunk files.
-
-    Args:
-        args: Tuple containing
-            - start_chunk (int): inclusive start chunk index
-            - end_chunk (int): exclusive end chunk index
-            - table (pa.Table): concatenated table containing all rows to write
-            - worker_id (int): numeric worker identifier
-            - output_dir (str): base output directory
-            - samples_per_file (int): rows per chunk file
-            - compression (str): compression codec for Parquet
-
-    Returns:
-        int: Total number of rows written by this worker.
-    """
-    start_chunk, end_chunk, table, worker_id, output_dir, samples_per_file, compression = args
-    total_written = 0
-    num_samples = len(table)
-
-    worker_dir = os.path.join(output_dir, f"worker_{worker_id}")
-    os.makedirs(worker_dir, exist_ok=True)
-
-    # Offset to continue numbering if files exist
-    num_parquets = 0
-    for root, _, files in os.walk(worker_dir):
-        for file in files:
-            if file.endswith('.parquet'):
-                num_parquets += 1
-
-    for i in range(start_chunk, end_chunk):
-        start_sample = i * samples_per_file
-        end_sample = min((i + 1) * samples_per_file, num_samples)
-        if end_sample <= start_sample:
-            continue
-        chunk = table.slice(start_sample, end_sample - start_sample)
-
-        chunk_path = os.path.join(worker_dir, f"data_chunk_{i + num_parquets}.parquet")
-        temp_path = chunk_path + '.tmp'
-        try:
-            pq.write_table(chunk, temp_path, compression=compression)
-            if os.path.exists(chunk_path):
-                os.remove(chunk_path)
-            os.rename(temp_path, chunk_path)
-            total_written += len(chunk)
-        except Exception:
-            if os.path.exists(temp_path):
-                os.remove(temp_path)
-            raise
-
-    return total_written
-
-
-

fastvideo/dataset/dataloader/schema.py

@@ -50,7 +50,6 @@ pyarrow_schema_i2v = pa.schema([
     pa.field("fps", pa.float64()),
 ])
 
-
 pyarrow_schema_t2v = pa.schema([
     pa.field("id", pa.string()),
     # --- Image/Video VAE latents ---
@@ -80,9 +79,15 @@ pyarrow_schema_t2v = pa.schema([
     pa.field("fps", pa.float64()),
 ])
 
-
-pyarrow_schema_ode_trajectory_text_only = pa.schema([
+pyarrow_schema_ode_trajectory = pa.schema([
     pa.field("id", pa.string()),
+    # --- Image/Video VAE latents ---
+    # Tensors are stored as raw bytes with shape and dtype info for loading
+    pa.field("vae_latent_bytes", pa.binary()),
+    # e.g., [C, T, H, W] or [C, H, W]
+    pa.field("vae_latent_shape", pa.list_(pa.int64())),
+    # e.g., 'float32'
+    pa.field("vae_latent_dtype", pa.string()),
     # --- Text encoder output tensor ---
     # Tensors are stored as raw bytes with shape and dtype info for loading
     pa.field("text_embedding_bytes", pa.binary()),
@@ -90,6 +95,10 @@ 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()),
+    # I2V
+    pa.field("image_condition_latents_bytes", pa.binary()),
+    pa.field("image_condition_latents_shape", pa.list_(pa.int64())),
+    pa.field("image_condition_latents_dtype", pa.string()),
     # --- ODE Trajectory ---
     pa.field("trajectory_latents_bytes", pa.binary()),
     pa.field("trajectory_latents_shape", pa.list_(pa.int64())),
@@ -100,19 +109,12 @@ pyarrow_schema_ode_trajectory_text_only = pa.schema([
     # --- Metadata ---
     pa.field("file_name", pa.string()),
     pa.field("caption", pa.string()),
-    pa.field("media_type", pa.string()),  # Always 'text' for text-only
-])
-
-
-pyarrow_schema_text_only = pa.schema([
-    pa.field("id", pa.string()),
-    # --- Text encoder output tensor ---
-    # Tensors are stored as raw bytes with shape and dtype info for loading
-    pa.field("text_embedding_bytes", pa.binary()),
-    # e.g., [SeqLen, Dim]
-    pa.field("text_embedding_shape", pa.list_(pa.int64())),
-    # e.g., 'bfloat16' or 'float32'
-    pa.field("text_embedding_dtype", pa.string()),
-    # --- Metadata ---
-    pa.field("caption", pa.string()),
+    pa.field("media_type", pa.string()),  # 'image' or 'video'
+    pa.field("width", pa.int64()),
+    pa.field("height", pa.int64()),
+    # -- Video-specific (can be null/default for images) ---
+    # Number of frames processed (e.g., 1 for image, N for video)
+    pa.field("num_frames", pa.int64()),
+    pa.field("duration_sec", pa.float64()),
+    pa.field("fps", pa.float64()),
 ])

fastvideo/dataset/lmdb_datasets.py

@@ -1,43 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from fastvideo.dataset.lmdb_utils import get_array_shape_from_lmdb, retrieve_row_from_lmdb
-from torch.utils.data import Dataset
-import numpy as np
-import torch
-import lmdb
-
-# from Self-Forcing: https://github.com/guandeh17/Self-Forcing/blob/main/utils/dataset.py
-class ODERegressionLMDBDataset(Dataset):
-    def __init__(self, data_path: str, max_pair: int = int(1e8)):
-        print(f"data_path: {data_path}")
-        self.env = lmdb.open(data_path, readonly=True,
-                             lock=False, readahead=False, meminit=False)
-
-        self.latents_shape = get_array_shape_from_lmdb(self.env, 'latents')
-        self.max_pair = max_pair
-
-    def __len__(self):
-        return min(self.latents_shape[0], self.max_pair)
-
-    def __getitem__(self, idx):
-        """
-        Outputs:
-            - prompts: List of Strings
-            - latents: Tensor of shape (num_denoising_steps, num_frames, num_channels, height, width). It is ordered from pure noise to clean image.
-        """
-        latents = retrieve_row_from_lmdb(
-            self.env,
-            "latents", np.float16, idx, shape=self.latents_shape[1:]
-        )
-
-        if len(latents.shape) == 4:
-            latents = latents[None, ...]
-
-        prompts = retrieve_row_from_lmdb(
-            self.env,
-            "prompts", str, idx
-        )
-        return {
-            "prompts": prompts,
-            "ode_latent": torch.tensor(latents, dtype=torch.float32)
-        }
-

fastvideo/dataset/lmdb_utils.py

@@ -1,75 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# from Self-Forcing: https://github.com/guandeh17/Self-Forcing/blob/main/utils/lmdb.py
-
-import numpy as np
-
-
-def get_array_shape_from_lmdb(env, array_name):
-    with env.begin() as txn:
-        image_shape = txn.get(f"{array_name}_shape".encode()).decode()
-        image_shape = tuple(map(int, image_shape.split()))
-    return image_shape
-
-
-def store_arrays_to_lmdb(env, arrays_dict, start_index=0):
-    """
-    Store rows of multiple numpy arrays in a single LMDB.
-    Each row is stored separately with a naming convention.
-    """
-    with env.begin(write=True) as txn:
-        for array_name, array in arrays_dict.items():
-            for i, row in enumerate(array):
-                # Convert row to bytes
-                if isinstance(row, str):
-                    row_bytes = row.encode()
-                else:
-                    row_bytes = row.tobytes()
-
-                data_key = f'{array_name}_{start_index + i}_data'.encode()
-
-                txn.put(data_key, row_bytes)
-
-
-def process_data_dict(data_dict, seen_prompts):
-    output_dict = {}
-
-    all_videos = []
-    all_prompts = []
-    for prompt, video in data_dict.items():
-        if prompt in seen_prompts:
-            continue
-        else:
-            seen_prompts.add(prompt)
-
-        video = video.half().numpy()
-        all_videos.append(video)
-        all_prompts.append(prompt)
-
-    if len(all_videos) == 0:
-        return {"latents": np.array([]), "prompts": np.array([])}
-
-    all_videos = np.concatenate(all_videos, axis=0)
-
-    output_dict['latents'] = all_videos
-    output_dict['prompts'] = np.array(all_prompts)
-
-    return output_dict
-
-
-def retrieve_row_from_lmdb(lmdb_env, array_name, dtype, row_index, shape=None):
-    """
-    Retrieve a specific row from a specific array in the LMDB.
-    """
-    data_key = f'{array_name}_{row_index}_data'.encode()
-
-    with lmdb_env.begin() as txn:
-        row_bytes = txn.get(data_key)
-
-    if dtype == str:
-        array = row_bytes.decode()
-    else:
-        array = np.frombuffer(row_bytes, dtype=dtype)
-
-    if shape is not None and len(shape) > 0:
-        array = array.reshape(shape)
-    return array

fastvideo/dataset/preprocessing_datasets.py

@@ -628,134 +628,3 @@ class VideoCaptionMergedDataset(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"]
-
-
-class TextDataset(torch.utils.data.IterableDataset,
-                  torch.distributed.checkpoint.stateful.Stateful):
-    """
-    Text-only dataset for processing prompts from a simple text file.
-    
-    Assumes that data_merge_path is a text file with one prompt per line:
-    A cat playing with a ball
-    A dog running in the park
-    A person cooking dinner
-    ...
-    
-    This dataset processes text data through text encoding stages only.
-    """
-
-    def __init__(self,
-                 data_merge_path: str,
-                 args,
-                 start_idx: int = 0,
-                 seed: int = 42):
-        self.data_merge_path = data_merge_path
-        self.start_idx = start_idx
-        self.args = args
-        self.seed = seed
-
-        # Initialize tokenizer
-        tokenizer_path = os.path.join(args.model_path, "tokenizer")
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
-                                                  cache_dir=args.cache_dir)
-
-        # Initialize text encoding stage
-        self.text_encoding_stage = TextEncodingStage(
-            tokenizer=tokenizer,
-            text_max_length=args.text_max_length,
-            cfg_rate=getattr(args, 'training_cfg_rate', 0.0),
-            seed=self.seed)
-
-        # Process text data
-        self.processed_batches = self._process_text_data()
-
-    def _load_text_data(self) -> list[str]:
-        """Load text prompts from file."""
-        prompts = []
-        with open(self.data_merge_path, 'r', encoding='utf-8') as f:
-            for line in f:
-                line = line.strip()
-                if line:  # Skip empty lines
-                    prompts.append(line)
-        
-        logger.info(f"Loaded {len(prompts)} text prompts from {self.data_merge_path}")
-        return prompts
-
-    def _process_text_data(self) -> list[PreprocessBatch]:
-        """Process the text prompts through text encoding stage."""
-        raw_prompts = self._load_text_data()
-        processed_batches = []
-
-        for idx, prompt in enumerate(raw_prompts):
-            # Create a text-only batch with dummy path
-            batch = PreprocessBatch(
-                path=f"text_prompt_{idx}",
-                cap=[prompt],  # TextEncodingStage expects a list
-                resolution=None,
-                fps=None,
-                duration=None,
-                num_frames=0,
-                sample_frame_index=None,
-                sample_num_frames=0
-            )
-            
-            processed_batches.append(batch)
-
-        logger.info(f"Processed {len(processed_batches)} text batches")
-        return processed_batches
-
-    def __iter__(self):
-        """Iterator for the dataset."""
-        # Set up distributed sampling if needed
-        if torch.distributed.is_available() and torch.distributed.is_initialized():
-            rank = torch.distributed.get_rank()
-            world_size = torch.distributed.get_world_size()
-        else:
-            rank = 0
-            world_size = 1
-
-        # Calculate chunk for this rank
-        total_items = len(self.processed_batches)
-        items_per_rank = math.ceil(total_items / world_size)
-        start_idx = rank * items_per_rank + self.start_idx
-        end_idx = min(start_idx + items_per_rank, total_items)
-
-        # Yield items for this rank
-        for idx in range(start_idx, end_idx):
-            if idx < len(self.processed_batches):
-                yield self._get_item(idx)
-
-    def _get_item(self, idx: int) -> dict:
-        """Get a single processed text item."""
-        batch = self.processed_batches[idx]
-
-        # Apply text encoding stage
-        batch = self.text_encoding_stage.process(batch)
-
-        # Build result dictionary for text-only processing with required schema fields
-        result = {
-            "text": batch.text,
-            "input_ids": batch.input_ids,
-            "cond_mask": batch.cond_mask,
-            "path": batch.path,
-            # Required schema fields for ODE trajectory processing
-            "id": f"text_{idx}",
-            "file_name": batch.path,
-            "caption": batch.text,
-            "media_type": "text",
-            "width": 1,
-            "height": 1,
-            "num_frames": 0,
-            "duration_sec": 0.0,
-            "fps": 0.0,
-        }
-
-        return result
-
-    def state_dict(self) -> dict[str, Any]:
-        """Return state dict for checkpointing."""
-        return {"processed_batches": self.processed_batches}
-
-    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
-        """Load state dict from checkpoint."""
-        self.processed_batches = state_dict["processed_batches"]

fastvideo/envs.py

@@ -18,7 +18,6 @@ if TYPE_CHECKING:
     FASTVIDEO_LOGGING_PREFIX: str = ""
     FASTVIDEO_LOGGING_CONFIG_PATH: str | None = None
     FASTVIDEO_TRACE_FUNCTION: int = 0
-    FASTVIDEO_FORCE_ATTN_BF16: bool = False
     FASTVIDEO_ATTENTION_BACKEND: str | None = None
     FASTVIDEO_ATTENTION_CONFIG: str | None = None
     FASTVIDEO_WORKER_MULTIPROC_METHOD: str = "fork"
@@ -169,10 +168,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "FASTVIDEO_TRACE_FUNCTION":
     lambda: int(os.getenv("FASTVIDEO_TRACE_FUNCTION", "0")),
 
-    # if set, fastvideo will force attention to be computed in bfloat16
-    "FASTVIDEO_FORCE_ATTN_BF16":
-    lambda: bool(int(os.getenv("FASTVIDEO_FORCE_ATTN_BF16", "0"))),
-
     # Backend for attention computation
     # Available options:
     # - "TORCH_SDPA": use torch.nn.MultiheadAttention

fastvideo/fastvideo_args.py

@@ -1,9 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # Inspired by SGLang: https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/server_args.py
 """The arguments of FastVideo Inference."""
+
 import argparse
 import dataclasses
-import json
 from contextlib import contextmanager
 from dataclasses import field
 from enum import Enum
@@ -139,10 +139,6 @@ class FastVideoArgs:
     # VSA parameters
     VSA_sparsity: float = 0.0  # inference/validation sparsity
 
-    # V-MoBA parameters
-    moba_config_path: str | None = None
-    moba_config: dict[str, Any] = field(default_factory=dict)
-
     # Master port for distributed training/inference
     master_port: int | None = None
 
@@ -171,16 +167,6 @@ class FastVideoArgs:
         return not self.inference_mode
 
     def __post_init__(self):
-        if self.moba_config_path:
-            try:
-                with open(self.moba_config_path) as f:
-                    self.moba_config = json.load(f)
-                logger.info("Loaded V-MoBA config from %s",
-                            self.moba_config_path)
-            except (FileNotFoundError, json.JSONDecodeError) as e:
-                logger.error("Failed to load V-MoBA config from %s: %s",
-                             self.moba_config_path, e)
-                raise
         self.check_fastvideo_args()
 
     @staticmethod
@@ -612,11 +598,6 @@ class TrainingArgs(FastVideoArgs):
     pretrained_model_name_or_path: str = ""
     dit_model_name_or_path: str = ""
 
-    # DMD model paths - separate paths for each network
-    generator_model_path: str = ""  # path for generator (student) model
-    real_score_model_path: str = ""  # path for real score (teacher) model
-    fake_score_model_path: str = ""  # path for fake score (critic) model
-
     # diffusion setting
     ema_decay: float = 0.0
     ema_start_step: int = 0
@@ -639,7 +620,6 @@ class TrainingArgs(FastVideoArgs):
     checkpoints_total_limit: int = 0
     checkpointing_steps: int = 0
     resume_from_checkpoint: str = ""  # specify the checkpoint folder to resume from
-    init_weights_from_safetensors: str = ""  # path to safetensors file for initial weight loading
 
     # optimizer & scheduler
     num_train_epochs: int = 0
@@ -671,7 +651,6 @@ class TrainingArgs(FastVideoArgs):
     linear_quadratic_threshold: float = 0.0
     linear_range: float = 0.0
     weight_decay: float = 0.0
-    betas: str = "0.9,0.999"  # betas for optimizer, format: "beta1,beta2"
     use_ema: bool = False
     multi_phased_distill_schedule: str = ""
     pred_decay_weight: float = 0.0
@@ -692,30 +671,17 @@ class TrainingArgs(FastVideoArgs):
 
     # distillation args
     generator_update_interval: int = 5
-    dfake_gen_update_ratio: int = 5  # self-forcing: how often to train generator vs critic
     min_timestep_ratio: float = 0.2
     max_timestep_ratio: float = 0.98
     real_score_guidance_scale: float = 3.5
     fake_score_learning_rate: float = 0.0  # separate learning rate for fake_score_transformer, if 0.0, use learning_rate
     fake_score_lr_scheduler: str = "constant"  # separate lr scheduler for fake_score_transformer, if not set, use lr_scheduler
-    fake_score_betas: str = "0.9,0.999"  # betas for fake score optimizer, format: "beta1,beta2"
     training_state_checkpointing_steps: int = 0  # for resuming training
     weight_only_checkpointing_steps: int = 0  # for inference
     log_visualization: bool = False
     # simulate generator forward to match inference
     simulate_generator_forward: bool = False
     warp_denoising_step: bool = False
-    intermediate_latents_visualization: bool = False
-
-    # Self-forcing specific arguments
-    num_frame_per_block: int = 3
-    independent_first_frame: bool = False
-    enable_gradient_masking: bool = True
-    gradient_mask_last_n_frames: int = 21
-    validate_cache_structure: bool = False  # Debug flag for cache validation
-    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
 
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace) -> "TrainingArgs":
@@ -817,20 +783,6 @@ class TrainingArgs(FastVideoArgs):
                             type=str,
                             help="Directory to cache models")
 
-        # DMD model paths - separate paths for each network
-        parser.add_argument(
-            "--generator-model-path",
-            type=str,
-            help="Path to generator (student) model for DMD distillation")
-        parser.add_argument(
-            "--real-score-model-path",
-            type=str,
-            help="Path to real score (teacher) model for DMD distillation")
-        parser.add_argument(
-            "--fake-score-model-path",
-            type=str,
-            help="Path to fake score (critic) model for DMD distillation")
-
         # Diffusion settings
         parser.add_argument("--ema-decay",
                             type=float,
@@ -901,10 +853,6 @@ class TrainingArgs(FastVideoArgs):
         parser.add_argument("--resume-from-checkpoint",
                             type=str,
                             help="Path to checkpoint to resume from")
-        parser.add_argument(
-            "--init-weights-from-safetensors",
-            type=str,
-            help="Path to safetensors file for initial weight loading")
         parser.add_argument("--logging-dir",
                             type=str,
                             help="Directory for logging")
@@ -1009,10 +957,6 @@ class TrainingArgs(FastVideoArgs):
                             help="Linear quadratic threshold")
         parser.add_argument("--linear-range", type=float, help="Linear range")
         parser.add_argument("--weight-decay", type=float, help="Weight decay")
-        parser.add_argument("--betas",
-                            type=str,
-                            default=TrainingArgs.betas,
-                            help="Betas for optimizer (format: 'beta1,beta2')")
         parser.add_argument("--use-ema",
                             action=StoreBoolean,
                             help="Whether to use EMA")
@@ -1049,27 +993,11 @@ class TrainingArgs(FastVideoArgs):
         parser.add_argument("--lora-rank", type=int, help="LoRA rank")
         parser.add_argument("--lora-alpha", type=int, help="LoRA alpha")
 
-        # V-MoBA parameters
-        parser.add_argument(
-            "--moba-config-path",
-            type=str,
-            default=None,
-            help=
-            "Path to a JSON file containing V-MoBA specific configurations.",
-        )
-
         # Distillation arguments
         parser.add_argument("--generator-update-interval",
                             type=int,
                             default=TrainingArgs.generator_update_interval,
                             help="Ratio of student updates to critic updates.")
-        parser.add_argument(
-            "--dfake-gen-update-ratio",
-            type=int,
-            default=TrainingArgs.dfake_gen_update_ratio,
-            help=
-            "Self-forcing: How often to train generator vs critic (train generator every N steps)."
-        )
         parser.add_argument("--min-timestep-ratio",
                             type=float,
                             default=TrainingArgs.min_timestep_ratio,
@@ -1086,11 +1014,6 @@ class TrainingArgs(FastVideoArgs):
                             type=float,
                             default=TrainingArgs.fake_score_learning_rate,
                             help="Learning rate for fake score transformer")
-        parser.add_argument(
-            "--fake-score-betas",
-            type=str,
-            default=TrainingArgs.fake_score_betas,
-            help="Betas for fake score optimizer (format: 'beta1,beta2')")
         parser.add_argument(
             "--fake-score-lr-scheduler",
             type=str,
@@ -1106,46 +1029,7 @@ class TrainingArgs(FastVideoArgs):
         parser.add_argument(
             "--warp-denoising-step",
             action=StoreBoolean,
-            help=
-            "Whether to warp denoising step according to the scheduler time shift"
-        )
-
-        # Self-forcing specific arguments
-        parser.add_argument(
-            "--num-frame-per-block",
-            type=int,
-            default=TrainingArgs.num_frame_per_block,
-            help="Number of frames per block for causal generation")
-        parser.add_argument(
-            "--independent-first-frame",
-            action=StoreBoolean,
-            help="Whether the first frame is independent in causal generation")
-        parser.add_argument(
-            "--enable-gradient-masking",
-            action=StoreBoolean,
-            help="Whether to enable frame-level gradient masking")
-        parser.add_argument(
-            "--gradient-mask-last-n-frames",
-            type=int,
-            default=TrainingArgs.gradient_mask_last_n_frames,
-            help="Number of last frames to enable gradients for")
-        parser.add_argument(
-            "--validate-cache-structure",
-            action=StoreBoolean,
-            help="Whether to validate KV cache structure (debug flag)")
-        parser.add_argument(
-            "--same-step-across-blocks",
-            action=StoreBoolean,
-            help="Whether to use the same exit timestep for all blocks")
-        parser.add_argument(
-            "--last-step-only",
-            action=StoreBoolean,
-            help="Whether to only use the last timestep for training")
-        parser.add_argument(
-            "--context-noise",
-            type=int,
-            default=TrainingArgs.context_noise,
-            help="Context noise level for cache updates")
+            help="Whether to warp denoising step according to the scheduler time shift")
 
         return parser
 
@@ -1153,4 +1037,4 @@ class TrainingArgs(FastVideoArgs):
 def parse_int_list(value: str) -> list[int]:
     if not value:
         return []
-    return [int(x.strip()) for x in value.split(",")]
+    return [int(x.strip()) for x in value.split(",")]

fastvideo/layers/layernorm.py

@@ -9,6 +9,9 @@ import torch.nn.functional as F
 from fastvideo.layers.custom_op import CustomOp
 from fastvideo.platforms import current_platform
 
+from fastvideo.logger import init_logger
+
+logger = init_logger(__name__)
 
 @CustomOp.register("rms_norm")
 class RMSNorm(CustomOp):
@@ -100,16 +103,13 @@ class ScaleResidual(nn.Module):
     def forward(self, residual: torch.Tensor, x: torch.Tensor,
                 gate: torch.Tensor) -> torch.Tensor:
         """Apply gated residual connection."""
-        # x.shape: [batch_size, seq_len, inner_dim]
-        if gate.dim() == 4:
-            # gate.shape: [batch_size, num_frames, 1, inner_dim]
-            num_frames = gate.shape[1]
-            frame_seqlen = x.shape[1] // num_frames
-            return residual + (x.unflatten(
-                dim=1, sizes=(num_frames, frame_seqlen)) * gate).flatten(1, 2)
-        else:
-            # gate.shape: [batch_size, 1, inner_dim]
-            return residual + x * gate
+        # logger.info("x.shape: %s", x.shape)
+        # if isinstance(gate, torch.Tensor):
+            # logger.info("gate.shape: %s", gate.shape)
+
+        num_frames = gate.shape[1]
+        frame_seqlen = x.shape[1] // num_frames
+        return residual + (x.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * gate).flatten(1, 2)
 
 
 # adapted from Diffusers: https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py
@@ -168,7 +168,7 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
             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,
+                gate: torch.Tensor, shift: torch.Tensor,
                 scale: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Apply gated residual connection, followed by layernorm and 
@@ -180,41 +180,36 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
             - residual value (value after residual connection 
               but before normalization)
         """
-        # x.shape: [batch_size, seq_len, inner_dim]
         # Apply residual connection with gating
-        if isinstance(gate, int):
+        # logger.info("x.shape: %s", x.shape)
+        if isinstance(gate, int): 
             # used by cross-attention, should be 1
             assert gate == 1
-            residual_output = residual + x
+            residual_output = residual + x * gate
         elif isinstance(gate, torch.Tensor):
-            if gate.dim() == 4:
-                # gate.shape: [batch_size, num_frames, 1, inner_dim]
+            # logger.info("gate.shape: %s", gate.shape)
+            if gate.dim() == 3:
+                # used by bidirectional self attention
+                residual_output = residual + x * gate
+            else:
+                assert gate.dim() == 4 
                 num_frames = gate.shape[1]
                 frame_seqlen = x.shape[1] // num_frames
-                residual_output = residual + (
-                    x.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
-                    gate).flatten(1, 2)
-            else:
-                # used by bidirectional self attention
-                # gate.shape: [batch_size, 1, inner_dim]
-                residual_output = residual + x * gate
+                residual_output = residual + (x.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * gate).flatten(1, 2)
+                # residual_output = residual + x * gate
         else:
             raise ValueError(f"Gate type {type(gate)} not supported")
-        # residual_output.shape: [batch_size, seq_len, inner_dim]
+        # logger.info("residual_output.shape: %s", residual_output.shape)
 
         # Apply normalization
         normalized = self.norm(residual_output)
         # Apply scale and shift
         if isinstance(scale, torch.Tensor) and scale.dim() == 4:
-            # scale.shape: [batch_size, num_frames, 1, inner_dim]
-            # shift.shape: [batch_size, num_frames, 1, inner_dim]
             num_frames = scale.shape[1]
             frame_seqlen = normalized.shape[1] // num_frames
-            modulated = (
-                normalized.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
-                (1 + scale) + shift).flatten(1, 2)
+            modulated = (normalized.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1.0 + scale) + shift).flatten(1, 2)
         else:
-            modulated = normalized * (1 + scale) + shift
+            modulated = normalized * (1.0 + scale) + shift
         return modulated, residual_output
 
 
@@ -256,24 +251,16 @@ class LayerNormScaleShift(nn.Module):
     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 scale.dim() == 4:
-            # scale.shape: [batch_size, num_frames, 1, inner_dim]
             num_frames = scale.shape[1]
             frame_seqlen = normalized.shape[1] // num_frames
-            output = (
-                normalized.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
-                (1 + scale) + shift).flatten(1, 2)
+            if self.compute_dtype == torch.float32:
+                return (normalized.float().unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1.0 + scale) + shift).flatten(1, 2).to(x.dtype)
+            else:
+                return (normalized.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1.0 + scale) + shift).flatten(1, 2)
         else:
-            # scale.shape: [batch_size, 1, inner_dim]
-            # shift.shape: [batch_size, 1, inner_dim]
-            output = normalized * (1 + scale) + shift
-
-        if self.compute_dtype == torch.float32:
-            output = output.to(x.dtype)
-
-        return output
+            if self.compute_dtype == torch.float32:
+                return (normalized.float() * (1.0 + scale) + shift).to(x.dtype)
+            else:
+                return normalized * (1.0 + scale) + shift

fastvideo/layers/lora/linear.py

@@ -63,7 +63,7 @@ class BaseLayerWithLoRA(nn.Module):
                             device=self.base_layer.weight.device,
                             dtype=self.base_layer.weight.dtype))
             torch.nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
-            torch.nn.init.zeros_(self.lora_B)
+            torch.nn.init.kaiming_uniform_(self.lora_B, a=math.sqrt(5))
         else:
             self.lora_A = None
             self.lora_B = None
@@ -77,11 +77,9 @@ class BaseLayerWithLoRA(nn.Module):
             lora_A = self.lora_A.to_local()
 
         if not self.merged and not self.disable_lora:
-            lora_A_sliced = self.slice_lora_a_weights(
-                lora_A.to(x, non_blocking=True))
-            lora_B_sliced = self.slice_lora_b_weights(
-                lora_B.to(x, non_blocking=True))
-            delta = x @ lora_A_sliced.T @ lora_B_sliced.T
+            delta = x @ (
+                self.slice_lora_b_weights(lora_B.to(x, non_blocking=True))
+                @ self.slice_lora_a_weights(lora_A.to(x, non_blocking=True)))
             if self.lora_alpha != self.lora_rank:
                 delta = delta * (
                     self.lora_alpha / self.lora_rank  # type: ignore

fastvideo/models/dits/causal_wanvideo.py

@@ -147,9 +147,6 @@ class CausalWanSelfAttention(nn.Module):
                 # 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
-                # kv_cache["k"] = kv_cache["k"].detach()
-                # kv_cache["v"] = kv_cache["v"].detach()
-                # logger.info("kv_cache['k'] is in comp graph: %s", kv_cache["k"].requires_grad or kv_cache["k"].grad_fn is not None)
                 kv_cache["k"][:, local_start_index:local_end_index] = roped_key
                 kv_cache["v"][:, local_start_index:local_end_index] = v
             x = self.attn(
@@ -179,7 +176,7 @@ class CausalWanTransformerBlock(nn.Module):
         super().__init__()
 
         # 1. Self-attention
-        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
         self.to_q = ReplicatedLinear(dim, dim, bias=True)
         self.to_k = ReplicatedLinear(dim, dim, bias=True)
         self.to_v = ReplicatedLinear(dim, dim, bias=True)
@@ -212,7 +209,8 @@ class CausalWanTransformerBlock(nn.Module):
             norm_type="layer",
             eps=eps,
             elementwise_affine=True,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)
 
         # 2. Cross-attention
         # Only T2V for now
@@ -225,7 +223,8 @@ class CausalWanTransformerBlock(nn.Module):
             norm_type="layer",
             eps=eps,
             elementwise_affine=False,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)
 
         # 3. Feed-forward
         self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
@@ -245,39 +244,40 @@ class CausalWanTransformerBlock(nn.Module):
         current_start: int = 0,
         cache_start: int | None = None,
     ) -> torch.Tensor:
-        # hidden_states.shape: [batch_size, seq_length, inner_dim]
-        # temb.shape: [batch_size, num_frames, 6, inner_dim]
+        # logger.info("temb.shape: %s", temb.shape)
+        num_frames = temb.shape[1]
+        # logger.info("first hidden_states.shape: %s", hidden_states.shape)
+        # logger.info("num_frames: %s", num_frames)
         if hidden_states.dim() == 4:
             hidden_states = hidden_states.squeeze(1)
-        num_frames = temb.shape[1]
-        frame_seqlen = hidden_states.shape[1] // num_frames    
+        frame_seqlen = hidden_states.shape[1] // temb.shape[1]
+        # logger.info("frame_seqlen: %s", frame_seqlen)
         bs, seq_length, _ = hidden_states.shape
         orig_dtype = hidden_states.dtype
         # assert orig_dtype != torch.float32
-        e = self.scale_shift_table + temb
-        # e.shape: [batch_size, num_frames, 6, inner_dim]
-        assert e.shape == (bs, num_frames, 6, self.hidden_dim)
+        e = self.scale_shift_table + temb.float()
+        # logger.info("e.shape: %s", e.shape)
         shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
             6, dim=2)
-        # *_msa.shape: [batch_size, num_frames, 1, inner_dim]
-        # assert shift_msa.dtype == torch.float32
-
-        # logger.info("temb sum: %s, dtype: %s", temb.float().sum().item(), temb.dtype)
-        # logger.info("scale_msa sum: %s, dtype: %s", scale_msa.float().sum().item(), scale_msa.dtype)
-        # logger.info("shift_msa sum: %s, dtype: %s", shift_msa.float().sum().item(), shift_msa.dtype)
+        assert shift_msa.dtype == torch.float32
 
         # 1. Self-attention
-        norm_hidden_states = (self.norm1(hidden_states).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
-                        (1 + scale_msa) + shift_msa).flatten(1, 2)
-        # logger.info("norm_hidden_states sum: %s, shape: %s", norm_hidden_states.float().sum().item(), norm_hidden_states.shape)
+        # print(f"hidden_states: {hidden_states.shape}")
+        # print(f"hidden_states: {scale_msa.shape}")
+        # print(f"hidden_states: {shift_msa.shape}")
+        # norm_hidden_states = (self.norm1(hidden_states.float()) *
+        #                       (1 + scale_msa) + shift_msa).to(orig_dtype)
+        norm_hidden_states = (self.norm1(hidden_states.float()).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
+                              (1 + scale_msa) + shift_msa).flatten(1, 2).to(orig_dtype)
+        # logger.info("1 norm_hidden_states.shape: %s", norm_hidden_states.shape)
         query, _ = self.to_q(norm_hidden_states)
         key, _ = self.to_k(norm_hidden_states)
         value, _ = self.to_v(norm_hidden_states)
 
         if self.norm_q is not None:
-            query = self.norm_q.forward_native(query)
+            query = self.norm_q(query)
         if self.norm_k is not None:
-            key = self.norm_k.forward_native(key)
+            key = self.norm_k(key)
 
         query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
         key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
@@ -291,6 +291,10 @@ class CausalWanTransformerBlock(nn.Module):
         null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
         norm_hidden_states, hidden_states = self.self_attn_residual_norm(
             hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        # logger.info("after self_attn_residual_norm norm_hidden_states.shape: %s", norm_hidden_states.shape)
+        # logger.info("after self_attn_residual_norm hidden_states.shape: %s", hidden_states.shape)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
 
         # 2. Cross-attention
         attn_output = self.attn2(norm_hidden_states,
@@ -299,10 +303,17 @@ class CausalWanTransformerBlock(nn.Module):
                                  crossattn_cache=crossattn_cache)
         norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
             hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        # logger.info("after cross_attn_residual_norm norm_hidden_states.shape: %s", norm_hidden_states.shape)
+        # logger.info("after cross_attn_residual_norm hidden_states.shape: %s", hidden_states.shape)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
 
         # 3. Feed-forward
         ff_output = self.ffn(norm_hidden_states)
         hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
+        # logger.info("after mlp_residual norm_hidden_states.shape: %s", norm_hidden_states.shape)
+        # logger.info("after mlp_residual hidden_states.shape: %s", hidden_states.shape)
+        hidden_states = hidden_states.to(orig_dtype)
 
         return hidden_states
 
@@ -365,9 +376,12 @@ class CausalWanTransformer3DModel(BaseDiT):
                                             norm_type="layer",
                                             eps=config.eps,
                                             elementwise_affine=False,
-                                            dtype=torch.float32)
-        self.proj_out = nn.Linear(
-            inner_dim, config.out_channels * math.prod(config.patch_size))
+                                            dtype=torch.float32,
+                                            compute_dtype=torch.float32)
+        # Debug: Log configuration values
+        proj_out_dim = config.out_channels * math.prod(config.patch_size)
+        
+        self.proj_out = nn.Linear(inner_dim, proj_out_dim)
         self.scale_shift_table = nn.Parameter(
             torch.randn(1, 2, inner_dim) / inner_dim**0.5)
 
@@ -375,7 +389,7 @@ class CausalWanTransformer3DModel(BaseDiT):
 
         # Causal-specific
         self.block_mask = None
-        self.num_frame_per_block = 3
+        self.num_frame_per_block = 1
         self.independent_first_frame = False
 
         self.__post_init__()
@@ -456,6 +470,7 @@ class CausalWanTransformer3DModel(BaseDiT):
         This function will be run for num_frame times.
         Process the latent frames one by one (1560 tokens each)
         """
+        # logger.info("forward inference hidden_states.shape: %s", hidden_states.shape)
 
         orig_dtype = hidden_states.dtype
         if not isinstance(encoder_hidden_states, torch.Tensor):
@@ -487,18 +502,15 @@ class CausalWanTransformer3DModel(BaseDiT):
         )
         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
+        freqs_cis = (freqs_cos.float(),
+                     freqs_sin.float()) if freqs_cos is not None else None
 
         hidden_states = self.patch_embedding(hidden_states)
-        grid_sizes = torch.stack(
-            [torch.tensor(hidden_states[0].shape[1:], dtype=torch.long)])
         hidden_states = hidden_states.flatten(2).transpose(1, 2)
-
-        encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states.new_zeros(1, self.text_len - encoder_hidden_states.size(1), encoder_hidden_states.size(2))], dim=1)
+        # logger.info("forward inference flattened and transposed hidden_states.shape: %s", hidden_states.shape)
 
         temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
-                        timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
+            timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
         timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size)).unflatten(dim=0, sizes=timestep.shape)
 
         if encoder_hidden_states_image is not None:
@@ -537,15 +549,22 @@ class CausalWanTransformer3DModel(BaseDiT):
                                         **causal_kwargs)
 
         # 5. Output norm, projection & unpatchify
-        temb = temb.unflatten(dim=0, sizes=timestep.shape).unsqueeze(2)
-        shift, scale = (self.scale_shift_table.unsqueeze(1) + temb).chunk(2,
-                                                                    dim=2)
+        # logger.info("===== INFERENCE 5. Output norm, projection & unpatchify")
+        # logger.info("hidden_states.shape: %s", hidden_states.shape)
+        # logger.info("temb.shape: %s", temb.shape)
+        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
+                                                                          dim=1)
         hidden_states = self.norm_out(hidden_states, shift, scale)
         hidden_states = self.proj_out(hidden_states)
 
-        output = self.unpatchify(hidden_states, grid_sizes)
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
 
-        return torch.stack(output)
+        return output
 
     def _forward_train(self,
                 hidden_states: torch.Tensor,
@@ -556,8 +575,8 @@ class CausalWanTransformer3DModel(BaseDiT):
                 start_frame: int = 0,
                 **kwargs) -> torch.Tensor:
 
-        logger.info("timestep dtype: %s, timestep sum: %s", timestep.dtype, timestep.float().sum().item())
-
+        # logger.info("===== forward train hidden_states.shape: %s", hidden_states.shape)
+        # logger.info("===== forward train timestep.shape: %s", timestep.shape)
         orig_dtype = hidden_states.dtype
         if not isinstance(encoder_hidden_states, torch.Tensor):
             encoder_hidden_states = encoder_hidden_states[0]
@@ -588,8 +607,8 @@ class CausalWanTransformer3DModel(BaseDiT):
         )
         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
+        freqs_cis = (freqs_cos.float(),
+                     freqs_sin.float()) if freqs_cos is not None else None
 
         # Construct blockwise causal attn mask
         if self.block_mask is None:
@@ -602,14 +621,14 @@ class CausalWanTransformer3DModel(BaseDiT):
             )
 
         hidden_states = self.patch_embedding(hidden_states)
-        grid_sizes = torch.stack(
-            [torch.tensor(hidden_states[0].shape[1:], dtype=torch.long)])
         hidden_states = hidden_states.flatten(2).transpose(1, 2)
-
-        encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states.new_zeros(1, self.text_len - encoder_hidden_states.size(1), encoder_hidden_states.size(2))], dim=1)
+        # logger.info("forward train flattened and transposed hidden_states.shape: %s", hidden_states.shape)
 
         temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
-                        timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
+            timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
+        # logger.info("forward train timestep_proj.shape: %s", timestep_proj.shape)
+        # logger.info("forward train timestep.shape: %s", timestep.shape)
+        # logger.info("forward train temb.shape: %s", temb.shape)
         timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size)).unflatten(dim=0, sizes=timestep.shape)
 
         if encoder_hidden_states_image is not None:
@@ -630,21 +649,44 @@ class CausalWanTransformer3DModel(BaseDiT):
                     timestep_proj, freqs_cis,
                     block_mask=self.block_mask)
         else:
-            for block in self.blocks:
+            for block_index, block in enumerate(self.blocks):
+                # logger.info("===== TRAIN block %d", block_index)
+                # logger.info("hidden_states.shape: %s", hidden_states.shape)
+                # logger.info("encoder_hidden_states.shape: %s", encoder_hidden_states.shape)
+                # logger.info("timestep_proj.shape: %s", timestep_proj.shape)
+                # logger.info("freqs_cis.shape: %s", freqs_cis.shape)
+                # logger.info("block_mask.shape: %s", self.block_mask.shape)
                 hidden_states = block(hidden_states, encoder_hidden_states,
                                         timestep_proj, freqs_cis,
                                         block_mask=self.block_mask)
 
         # 5. Output norm, projection & unpatchify
+        # logger.info("===== TRAIN 5. Output norm, projection & unpatchify")
+        # logger.info("hidden_states.shape: %s", hidden_states.shape)
+        # logger.info("temb.shape: %s", temb.shape)
+        # shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
         temb = temb.unflatten(dim=0, sizes=timestep.shape).unsqueeze(2)
+        # logger.info("WTFWTF train temb.shape: %s", temb.shape)
+        # logger.info("WTFWTF train self.scale_shift_table.shape: %s", self.scale_shift_table.shape)
         shift, scale = (self.scale_shift_table.unsqueeze(1) + temb).chunk(2,
-                                                                    dim=2)
+                                                                          dim=2)
+        # logger.info("DEBUG scale.shape: %s", scale.shape)
+        # logger.info("DEBUG shift.shape: %s", shift.shape)
         hidden_states = self.norm_out(hidden_states, shift, scale)
         hidden_states = self.proj_out(hidden_states)
+        # logger.info("DEBUG after proj_out hidden_states.shape: %s", hidden_states.shape)
+        # logger.info(f"DEBUG reshape dimensions: batch_size={batch_size}, post_patch_num_frames={post_patch_num_frames}")
+        # logger.info(f"DEBUG reshape dimensions: post_patch_height={post_patch_height}, post_patch_width={post_patch_width}")
+        # logger.info(f"DEBUG patch dimensions: p_t={p_t}, p_h={p_h}, p_w={p_w}")
 
-        output = self.unpatchify(hidden_states, grid_sizes)
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
 
-        return torch.stack(output)
+        return output
 
     def forward(
         self,
@@ -652,34 +694,6 @@ class CausalWanTransformer3DModel(BaseDiT):
         **kwargs
     ):
         if kwargs.get('kv_cache', None) is not None:
-            noise_pred = self._forward_inference(*args, **kwargs)
+            return self._forward_inference(*args, **kwargs)
         else:
-            noise_pred = self._forward_train(*args, **kwargs)
-
-        return noise_pred
-
-    def unpatchify(self, x, grid_sizes):
-        r"""
-
-
-        Args:
-            x (List[Tensor]):
-                List of patchified features, each with shape [L, C_out * prod(patch_size)]
-            grid_sizes (Tensor):
-                Original spatial-temporal grid dimensions before patching,
-
-
-        Returns:
-            Tensor:
-                Reconstructed video tensors with shape [B, C_out, F, H / 8, W / 8]
-        """
-
-        c = self.out_channels
-        out = []
-        for u, v in zip(x, grid_sizes.tolist()):
-            u = u[:math.prod(v)].view(*v, *self.patch_size, c)
-            u = u.permute(6, 0, 3, 1, 4, 2, 5)
-            # u = torch.einsum('fhwpqrc->cfphqwr', u.contiguous())
-            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
-            out.append(u)
-        return out
+            return self._forward_train(*args, **kwargs)

fastvideo/models/dits/wanvideo.py

@@ -1,5 +1,3 @@
-import torch
-import torch.nn as nn
 # SPDX-License-Identifier: Apache-2.0
 
 import math
@@ -39,14 +37,16 @@ class WanImageEmbedding(torch.nn.Module):
     def __init__(self, in_features: int, out_features: int):
         super().__init__()
 
-        self.norm1 = nn.LayerNorm(in_features)
+        self.norm1 = FP32LayerNorm(in_features)
         self.ff = MLP(in_features, in_features, out_features, act_type="gelu")
-        self.norm2 = nn.LayerNorm(out_features)
+        self.norm2 = FP32LayerNorm(out_features)
 
-    def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
+    def forward(self,
+                encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
+        dtype = encoder_hidden_states_image.dtype
         hidden_states = self.norm1(encoder_hidden_states_image)
         hidden_states = self.ff(hidden_states)
-        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.norm2(hidden_states).to(dtype)
         return hidden_states
 
 
@@ -62,7 +62,7 @@ class WanTimeTextImageEmbedding(nn.Module):
         super().__init__()
 
         self.time_embedder = TimestepEmbedder(
-            dim, frequency_embedding_size=time_freq_dim, act_layer="silu", freq_dtype=torch.float64)
+            dim, frequency_embedding_size=time_freq_dim, act_layer="silu")
         self.time_modulation = ModulateProjection(dim,
                                                   factor=6,
                                                   act_layer="silu")
@@ -156,12 +156,12 @@ class WanT2VCrossAttention(WanSelfAttention):
         b, n, d = x.size(0), self.num_heads, self.head_dim
 
         # compute query, key, value
-        q = self.norm_q.forward_native(self.to_q(x)[0]).view(b, -1, n, d)
+        q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
 
         if crossattn_cache is not None:
             if not crossattn_cache["is_init"]:
                 crossattn_cache["is_init"] = True
-                k = self.norm_k.forward_native(self.to_k(context)[0]).view(b, -1, n, d)
+                k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
                 v = self.to_v(context)[0].view(b, -1, n, d)
                 crossattn_cache["k"] = k
                 crossattn_cache["v"] = v
@@ -169,16 +169,11 @@ class WanT2VCrossAttention(WanSelfAttention):
                 k = crossattn_cache["k"]
                 v = crossattn_cache["v"]
         else:
-            k = self.norm_k.forward_native(self.to_k(context)[0]).view(b, -1, n, d)
+            k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
             v = self.to_v(context)[0].view(b, -1, n, d)
 
-        if envs.FASTVIDEO_FORCE_ATTN_BF16:
-            out_dtype = v.dtype
-            # compute attention
-            x = self.attn(q.to(torch.bfloat16), k.to(torch.bfloat16), v.to(torch.bfloat16)).to(out_dtype)
-        else:
-            # compute attention
-            x = self.attn(q, k, v)
+        # compute attention
+        x = self.attn(q, k, v)
 
         # output
         x = x.flatten(2)
@@ -218,10 +213,10 @@ class WanI2VCrossAttention(WanSelfAttention):
         b, n, d = x.size(0), self.num_heads, self.head_dim
 
         # compute query, key, value
-        q = self.norm_q.forward_native(self.to_q(x)[0]).view(b, -1, n, d)
-        k = self.norm_k.forward_native(self.to_k(context)[0]).view(b, -1, n, d)
+        q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
+        k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
         v = self.to_v(context)[0].view(b, -1, n, d)
-        k_img = self.norm_added_k.forward_native(self.add_k_proj(context_img)[0]).view(
+        k_img = self.norm_added_k(self.add_k_proj(context_img)[0]).view(
             b, -1, n, d)
         v_img = self.add_v_proj(context_img)[0].view(b, -1, n, d)
         img_x = self.attn(q, k_img, v_img)
@@ -252,7 +247,7 @@ class WanTransformerBlock(nn.Module):
         super().__init__()
 
         # 1. Self-attention
-        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
         self.to_q = ReplicatedLinear(dim, dim, bias=True)
         self.to_k = ReplicatedLinear(dim, dim, bias=True)
         self.to_v = ReplicatedLinear(dim, dim, bias=True)
@@ -283,29 +278,29 @@ class WanTransformerBlock(nn.Module):
             norm_type="layer",
             eps=eps,
             elementwise_affine=True,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)
 
         # 2. Cross-attention
         if added_kv_proj_dim is not None:
             # I2V
-            self.attn2 = WanI2VCrossAttention(dim, 
-                                              num_heads, 
-                                              qk_norm=qk_norm, 
+            self.attn2 = WanI2VCrossAttention(dim,
+                                              num_heads,
+                                              qk_norm=qk_norm,
                                               eps=eps)
-
         else:
             # T2V
-            self.attn2 = WanT2VCrossAttention(dim, 
-                                              num_heads, 
-                                              qk_norm=qk_norm, 
+            self.attn2 = WanT2VCrossAttention(dim,
+                                              num_heads,
+                                              qk_norm=qk_norm,
                                               eps=eps)
-
         self.cross_attn_residual_norm = ScaleResidualLayerNormScaleShift(
-            dim, 
-            norm_type="layer", 
-            eps=eps, 
-            elementwise_affine=False, 
-            dtype=torch.float32)
+            dim,
+            norm_type="layer",
+            eps=eps,
+            elementwise_affine=False,
+            dtype=torch.float32,
+            compute_dtype=torch.float32)
 
         # 3. Feed-forward
         self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
@@ -324,11 +319,12 @@ class WanTransformerBlock(nn.Module):
             hidden_states = hidden_states.squeeze(1)
         bs, seq_length, _ = hidden_states.shape
         orig_dtype = hidden_states.dtype
+        # assert orig_dtype != torch.float32
 
         if temb.dim() == 4:
             # temb: batch_size, seq_len, 6, inner_dim (wan2.2 ti2v)
             shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
-                self.scale_shift_table.unsqueeze(0) + temb
+                self.scale_shift_table.unsqueeze(0) + temb.float()
             ).chunk(6, dim=2)
             # batch_size, seq_len, 1, inner_dim
             shift_msa = shift_msa.squeeze(2)
@@ -339,20 +335,22 @@ class WanTransformerBlock(nn.Module):
             c_gate_msa = c_gate_msa.squeeze(2)
         else:
             # temb: batch_size, 6, inner_dim (wan2.1/wan2.2 14B)
-            e = self.scale_shift_table + temb
+            e = self.scale_shift_table + temb.float()
             shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
                 6, dim=1)
+        assert shift_msa.dtype == torch.float32
 
         # 1. Self-attention
-        norm_hidden_states = self.norm1(hidden_states) * (1 + scale_msa) + shift_msa
+        norm_hidden_states = (self.norm1(hidden_states.float()) *
+                              (1 + scale_msa) + shift_msa).to(orig_dtype)
         query, _ = self.to_q(norm_hidden_states)
         key, _ = self.to_k(norm_hidden_states)
         value, _ = self.to_v(norm_hidden_states)
 
         if self.norm_q is not None:
-            query = self.norm_q.forward_native(query)
+            query = self.norm_q(query)
         if self.norm_k is not None:
-            key = self.norm_k.forward_native(key)
+            key = self.norm_k(key)
 
         query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
         key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
@@ -364,12 +362,7 @@ class WanTransformerBlock(nn.Module):
                                        is_neox_style=False), _apply_rotary_emb(
                                            key, cos, sin, is_neox_style=False)
 
-        if envs.FASTVIDEO_FORCE_ATTN_BF16:
-            out_dtype = value.dtype
-            attn_output, _ = self.attn1(query.to(torch.bfloat16), key.to(torch.bfloat16), value.to(torch.bfloat16))
-            attn_output = attn_output.to(out_dtype)
-        else:
-            attn_output, _ = self.attn1(query, key, value)
+        attn_output, _ = self.attn1(query, key, value)
         attn_output = attn_output.flatten(2)
         attn_output, _ = self.to_out(attn_output)
         attn_output = attn_output.squeeze(1)
@@ -377,20 +370,26 @@ class WanTransformerBlock(nn.Module):
         null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
         norm_hidden_states, hidden_states = self.self_attn_residual_norm(
             hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
 
         # 2. Cross-attention
-        attn_output = self.attn2(norm_hidden_states, 
-                                 context=encoder_hidden_states, 
+        attn_output = self.attn2(norm_hidden_states,
+                                 context=encoder_hidden_states,
                                  context_lens=None)
         norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
             hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
 
         # 3. Feed-forward
         ff_output = self.ffn(norm_hidden_states)
         hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)
 
         return hidden_states
 
+
 class WanTransformerBlock_VSA(nn.Module):
 
     def __init__(self,
@@ -407,7 +406,7 @@ class WanTransformerBlock_VSA(nn.Module):
         super().__init__()
 
         # 1. Self-attention
-        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
         self.to_q = ReplicatedLinear(dim, dim, bias=True)
         self.to_k = ReplicatedLinear(dim, dim, bias=True)
         self.to_v = ReplicatedLinear(dim, dim, bias=True)
@@ -439,7 +438,8 @@ class WanTransformerBlock_VSA(nn.Module):
             norm_type="layer",
             eps=eps,
             elementwise_affine=True,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)
 
         # 2. Cross-attention
         if added_kv_proj_dim is not None:
@@ -459,7 +459,8 @@ class WanTransformerBlock_VSA(nn.Module):
             norm_type="layer",
             eps=eps,
             elementwise_affine=False,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)
 
         # 3. Feed-forward
         self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
@@ -479,22 +480,23 @@ class WanTransformerBlock_VSA(nn.Module):
         bs, seq_length, _ = hidden_states.shape
         orig_dtype = hidden_states.dtype
         # assert orig_dtype != torch.float32
-        e = self.scale_shift_table + temb
+        e = self.scale_shift_table + temb.float()
         shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
             6, dim=1)
+        assert shift_msa.dtype == torch.float32
 
         # 1. Self-attention
-        norm_hidden_states = (self.norm1(hidden_states) *
-                              (1 + scale_msa) + shift_msa)
+        norm_hidden_states = (self.norm1(hidden_states.float()) *
+                              (1 + scale_msa) + shift_msa).to(orig_dtype)
         query, _ = self.to_q(norm_hidden_states)
         key, _ = self.to_k(norm_hidden_states)
         value, _ = self.to_v(norm_hidden_states)
         gate_compress, _ = self.to_gate_compress(norm_hidden_states)
 
         if self.norm_q is not None:
-            query = self.norm_q.forward_native(query)
+            query = self.norm_q(query)
         if self.norm_k is not None:
-            key = self.norm_k.forward_native(key)
+            key = self.norm_k(key)
 
         query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
         key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
@@ -519,6 +521,8 @@ class WanTransformerBlock_VSA(nn.Module):
         null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
         norm_hidden_states, hidden_states = self.self_attn_residual_norm(
             hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
 
         # 2. Cross-attention
         attn_output = self.attn2(norm_hidden_states,
@@ -526,15 +530,17 @@ class WanTransformerBlock_VSA(nn.Module):
                                  context_lens=None)
         norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
             hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
 
         # 3. Feed-forward
         ff_output = self.ffn(norm_hidden_states)
         hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)
 
         return hidden_states
 
 
-
 class WanTransformer3DModel(CachableDiT):
     _fsdp_shard_conditions = WanVideoConfig()._fsdp_shard_conditions
     _compile_conditions = WanVideoConfig()._compile_conditions
@@ -592,7 +598,8 @@ class WanTransformer3DModel(CachableDiT):
                                             norm_type="layer",
                                             eps=config.eps,
                                             elementwise_affine=False,
-                                            dtype=torch.float32)
+                                            dtype=torch.float32,
+                                            compute_dtype=torch.float32)
         self.proj_out = nn.Linear(
             inner_dim, config.out_channels * math.prod(config.patch_size))
         self.scale_shift_table = nn.Parameter(
@@ -652,12 +659,10 @@ class WanTransformer3DModel(CachableDiT):
             rope_theta=10000)
         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
+        freqs_cis = (freqs_cos.float(),
+                     freqs_sin.float()) if freqs_cos is not None else None
 
         hidden_states = self.patch_embedding(hidden_states)
-        grid_sizes = torch.stack(
-            [torch.tensor(hidden_states[0].shape[1:], dtype=torch.long)])
         hidden_states = hidden_states.flatten(2).transpose(1, 2)
 
         # timestep shape: batch_size, or batch_size, seq_len (wan 2.2 ti2v)
@@ -667,8 +672,6 @@ class WanTransformer3DModel(CachableDiT):
         else:
             ts_seq_len = None
 
-        encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states.new_zeros(1, self.text_len - encoder_hidden_states.size(1), encoder_hidden_states.size(2))], dim=1)
-
         temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
             timestep, encoder_hidden_states, encoder_hidden_states_image, timestep_seq_len=ts_seq_len)
         if ts_seq_len is not None:
@@ -725,35 +728,14 @@ class WanTransformer3DModel(CachableDiT):
         hidden_states = self.norm_out(hidden_states, shift, scale)
         hidden_states = self.proj_out(hidden_states)
 
-        output = self.unpatchify(hidden_states, grid_sizes)
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
 
-        return torch.stack(output)
-    
-    def unpatchify(self, x, grid_sizes):
-        r"""
-
-
-        Args:
-            x (List[Tensor]):
-                List of patchified features, each with shape [L, C_out * prod(patch_size)]
-            grid_sizes (Tensor):
-                Original spatial-temporal grid dimensions before patching,
-
-
-        Returns:
-            Tensor:
-                Reconstructed video tensors with shape [B, C_out, F, H / 8, W / 8]
-        """
-
-        c = self.out_channels
-        out = []
-        for u, v in zip(x, grid_sizes.tolist()):
-            u = u[:math.prod(v)].view(*v, *self.patch_size, c)
-            u = u.permute(6, 0, 3, 1, 4, 2, 5)
-            # u = torch.einsum('fhwpqrc->cfphqwr', u.contiguous())
-            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
-            out.append(u)
-        return out
+        return output
 
     def maybe_cache_states(self, hidden_states: torch.Tensor,
                            original_hidden_states: torch.Tensor) -> None:
@@ -845,4 +827,5 @@ class WanTransformer3DModel(CachableDiT):
         if self.is_even:
             return hidden_states + self.previous_residual_even
         else:
-            return hidden_states + self.previous_residual_odd
+            return hidden_states + self.previous_residual_odd
+        

fastvideo/models/loader/component_loader.py

@@ -238,7 +238,6 @@ class TextEncoderLoader(ComponentLoader):
                 1]
 
         target_device = get_local_torch_device()
-        logger.info("Loading text encoder in %s precision", encoder_precision)
         # TODO(will): add support for other dtypes
         return self.load_model(model_path, encoder_config, target_device,
                                fastvideo_args, encoder_precision)
@@ -435,23 +434,11 @@ class TransformerLoader(ComponentLoader):
         if not safetensors_list:
             raise ValueError(f"No safetensors files found in {model_path}")
 
-        # Check if we should use custom initialization weights
-        custom_weights_path = getattr(fastvideo_args, 'init_weights_from_safetensors', None)
-        use_custom_weights = (custom_weights_path and os.path.exists(custom_weights_path) and 
-                            fastvideo_args.training_mode and 
-                            not hasattr(fastvideo_args, '_loading_teacher_critic_model'))
-
-        if use_custom_weights:
-            logger.info("Using custom initialization weights from: %s", custom_weights_path)
-            safetensors_list = [custom_weights_path]
-
         logger.info("Loading model from %s safetensors files in %s",
                     len(safetensors_list), model_path)
 
         default_dtype = PRECISION_TO_TYPE[
             fastvideo_args.pipeline_config.dit_precision]
-        param_dtype = PRECISION_TO_TYPE[
-            fastvideo_args.pipeline_config.dit_forward_precision]
 
         # Load the model using FSDP loader
         logger.info("Loading model from %s, default_dtype: %s", cls_name,
@@ -471,8 +458,7 @@ class TransformerLoader(ComponentLoader):
             pin_cpu_memory=fastvideo_args.pin_cpu_memory,
             fsdp_inference=fastvideo_args.use_fsdp_inference,
             # TODO(will): make these configurable
-            default_dtype=default_dtype,
-            param_dtype=param_dtype,
+            param_dtype=torch.bfloat16,
             reduce_dtype=torch.float32,
             output_dtype=None,
             training_mode=fastvideo_args.training_mode)
@@ -481,11 +467,9 @@ class TransformerLoader(ComponentLoader):
         total_params = sum(p.numel() for p in model.parameters())
         logger.info("Loaded model with %.2fB parameters", total_params / 1e9)
 
-        # Need to convert the model to the default_dtype
-        # Otherwise, the model master weights will be in param_dtype, and the gradients will also be in param_dtype
-        # This means the param update will be in lower precision, causing precision loss
-        logger.info("Converting model to dtype: %s", default_dtype)
-        model = model.to(default_dtype)
+        dtypes = set(param.dtype for param in model.parameters())
+        if len(dtypes) > 1:
+            model = model.to(default_dtype)
         model = model.eval()
         return model
 

fastvideo/models/loader/fsdp_load.py

@@ -62,7 +62,6 @@ def maybe_load_fsdp_model(
     device: torch.device,
     hsdp_replicate_dim: int,
     hsdp_shard_dim: int,
-    default_dtype: torch.dtype,
     param_dtype: torch.dtype,
     reduce_dtype: torch.dtype,
     cpu_offload: bool = False,
@@ -88,7 +87,7 @@ def maybe_load_fsdp_model(
         mp_policy=mp_policy,
     )
 
-    with set_default_dtype(default_dtype), torch.device("meta"):
+    with set_default_dtype(param_dtype), torch.device("meta"):
         model = model_cls(**init_params)
 
     # Check if we should use FSDP
@@ -126,7 +125,7 @@ def maybe_load_fsdp_model(
         model,
         weight_iterator,
         device,
-        default_dtype,
+        param_dtype,
         strict=True,
         cpu_offload=cpu_offload,
         param_names_mapping=param_names_mapping_fn,

fastvideo/models/registry.py

@@ -61,9 +61,6 @@ _SCHEDULERS = {
      "FlowMatchEulerDiscreteScheduler"),
     "UniPCMultistepScheduler":
     ("schedulers", "scheduling_unipc_multistep", "UniPCMultistepScheduler"),
-    "SelfForcingFlowMatchScheduler":
-    ("schedulers", "scheduling_self_forcing_flow_match",
-     "SelfForcingFlowMatchScheduler"),
 }
 
 _FAST_VIDEO_MODELS = {

fastvideo/models/schedulers/scheduling_flow_match_euler_discrete.py

@@ -635,31 +635,8 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin,
         noise: torch.Tensor,
         timestep: torch.IntTensor,
     ) -> torch.Tensor:
-
-        """
-        Args:
-            clean_latent: the clean latent with shape [B, C, H, W],
-                where B is batch_size or batch_size * num_frames
-            noise: the noise with shape [B, C, H, W]
-            timestep: the timestep with shape [1] or [bs * num_frames] or [bs, num_frames]
-
-        Returns:
-            the corrupted latent with shape [B, C, H, W]
-        """
-        # If timestep is [bs, num_frames]
-        if timestep.ndim == 2:
-            timestep = timestep.flatten(0, 1)
-            assert timestep.numel() == clean_latent.shape[0]
-        elif timestep.ndim == 1:
-            # If timestep is [1]
-            if timestep.shape[0] == 1:
-                timestep = timestep.expand(clean_latent.shape[0])
-            else:
-                assert timestep.numel() == clean_latent.shape[0]
-        else:
-            raise ValueError(f"[add_noise] Invalid timestep shape: {timestep.shape}")
-        # timestep shape should be [B]
         self.sigmas = self.sigmas.to(noise.device)
+        timestep = timestep.expand(clean_latent.shape[0])
         self.timesteps = self.timesteps.to(noise.device)
         timestep_id = torch.argmin(
             (self.timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)

fastvideo/models/schedulers/scheduling_self_forcing_flow_match.py

@@ -1,131 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.schedulers.scheduling_utils import SchedulerMixin
-from diffusers.utils import BaseOutput
-import torch
-
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.base import BaseScheduler
-
-logger = init_logger(__name__)
-
-
-class SelfForcingFlowMatchSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's `step` function output.
-
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-    """
-    prev_sample: torch.FloatTensor
-
-class SelfForcingFlowMatchScheduler(BaseScheduler, ConfigMixin, SchedulerMixin):
-    
-    config_name = "scheduler_config.json"
-    order = 1
-    @register_to_config
-    def __init__(self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0, sigma_max=1.0, sigma_min=0.003 / 1.002, inverse_timesteps=False, extra_one_step=False, reverse_sigmas=False, training=False):
-        self.num_train_timesteps = num_train_timesteps
-        self.shift = shift
-        self.sigma_max = sigma_max
-        self.sigma_min = sigma_min
-        self.inverse_timesteps = inverse_timesteps
-        self.extra_one_step = extra_one_step
-        self.reverse_sigmas = reverse_sigmas
-        self.set_timesteps(num_inference_steps, training=training)
-
-    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, return_dict=False, **kwargs):
-        sigma_start = self.sigma_min + \
-            (self.sigma_max - self.sigma_min) * denoising_strength
-        if self.extra_one_step:
-            self.sigmas = torch.linspace(
-                sigma_start, self.sigma_min, num_inference_steps + 1)[:-1]
-        else:
-            self.sigmas = torch.linspace(
-                sigma_start, self.sigma_min, num_inference_steps)
-        if self.inverse_timesteps:
-            self.sigmas = torch.flip(self.sigmas, dims=[0])
-        self.sigmas = self.shift * self.sigmas / \
-            (1 + (self.shift - 1) * self.sigmas)
-        if self.reverse_sigmas:
-            self.sigmas = 1 - self.sigmas
-        self.timesteps = self.sigmas * self.num_train_timesteps
-        if training:
-            x = self.timesteps
-            y = torch.exp(-2 * ((x - num_inference_steps / 2) /
-                          num_inference_steps) ** 2)
-            y_shifted = y - y.min()
-            bsmntw_weighing = y_shifted * \
-                (num_inference_steps / y_shifted.sum())
-            self.linear_timesteps_weights = bsmntw_weighing
-
-    def step(self, model_output: torch.FloatTensor, timestep: torch.FloatTensor, sample: torch.FloatTensor, to_final=False, return_dict=False, **kwargs):
-        if timestep.ndim == 2:
-            timestep = timestep.flatten(0, 1)
-        elif timestep.ndim == 0:
-            # handles the case where timestep is a scalar, this occurs when we
-            # use this scheduler for ODE trajectory
-            timestep = timestep.unsqueeze(0)
-
-        self.sigmas = self.sigmas.to(model_output.device)
-        self.timesteps = self.timesteps.to(model_output.device)
-        timestep = timestep.to(model_output.device)
-
-        timestep_id = torch.argmin(
-            (self.timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
-        sigma = self.sigmas[timestep_id].reshape(-1, 1, 1, 1)
-        if to_final or (timestep_id + 1 >= len(self.timesteps)).any():
-            sigma_ = 1 if (
-                self.inverse_timesteps or self.reverse_sigmas) else 0
-        else:
-            sigma_ = self.sigmas[timestep_id + 1].reshape(-1, 1, 1, 1)
-        prev_sample = sample + model_output * (sigma_ - sigma)
-        if isinstance(prev_sample, torch.Tensor | float) and not return_dict:
-            return (prev_sample, )
-        return SelfForcingFlowMatchSchedulerOutput(prev_sample=prev_sample)
-
-    def add_noise(self, original_samples, noise, timestep):
-        """
-        Diffusion forward corruption process.
-        Input:
-            - clean_latent: the clean latent with shape [B*T, C, H, W]
-            - noise: the noise with shape [B*T, C, H, W]
-            - timestep: the timestep with shape [B*T]
-        Output: the corrupted latent with shape [B*T, C, H, W]
-        """
-        if timestep.ndim == 2:
-            timestep = timestep.flatten(0, 1)
-        self.sigmas = self.sigmas.to(noise.device)
-        self.timesteps = self.timesteps.to(noise.device)
-        timestep_id = torch.argmin(
-            (self.timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
-        sigma = self.sigmas[timestep_id].reshape(-1, 1, 1, 1)
-        sample = (1 - sigma) * original_samples + sigma * noise
-        return sample.type_as(noise)
-
-    def training_target(self, sample, noise, timestep):
-        target = noise - sample
-        return target
-
-    def training_weight(self, timestep):
-        """
-        Input:
-            - timestep: the timestep with shape [B*T]
-        Output: the corresponding weighting [B*T]
-        """
-        if timestep.ndim == 2:
-            timestep = timestep.flatten(0, 1)
-        self.linear_timesteps_weights = self.linear_timesteps_weights.to(timestep.device)
-        timestep_id = torch.argmin(
-            (self.timesteps.unsqueeze(1) - timestep.unsqueeze(0)).abs(), dim=0)
-        weights = self.linear_timesteps_weights[timestep_id]
-        return weights
-    
-    def scale_model_input(self, sample: torch.Tensor, timestep: int | None = None) -> torch.Tensor:
-        return sample
-
-    def set_shift(self, shift: float) -> None:
-        self.shift = shift
-    

fastvideo/models/utils.py

@@ -6,6 +6,9 @@ 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):
     """
@@ -145,60 +148,16 @@ def pred_noise_to_pred_video(pred_noise: torch.Tensor,
                              scheduler: Any) -> torch.Tensor:
     """
     Convert predicted noise to clean latent.
-
-    Args:
-    pred_noise: the predicted noise with shape [B, C, H, W]
-        where B is batch_size or batch_size * num_frames
-    noise_input_latent: the noisy latent with shape [B, C, H, W],
-    timestep: the timestep with shape [1] or [bs * num_frames] or [bs, num_frames]
-    scheduler: the scheduler
-
-    Returns:
-        the predicted video with shape [B, C, H, W]
     """
-    # If timestep is [bs, num_frames]
-    if timestep.ndim == 2:
-        timestep = timestep.flatten(0, 1)
-        assert timestep.numel() == noise_input_latent.shape[0]
-    elif timestep.ndim == 1:
-        # If timestep is [1]
-        if timestep.shape[0] == 1:
-            timestep = timestep.expand(noise_input_latent.shape[0])
-        else:
-            assert timestep.numel() == noise_input_latent.shape[0]
-    else:
-        raise ValueError(f"[pred_noise_to_pred_video] Invalid timestep shape: {timestep.shape}")
-    # timestep shape should be [B]
+    timestep = timestep.expand(noise_input_latent.shape[0])
     dtype = pred_noise.dtype
     device = pred_noise.device
-    pred_noise = pred_noise.double().to(device)
-    noise_input_latent = noise_input_latent.double().to(device)
-    sigmas = scheduler.sigmas.double().to(device)
-    timesteps = scheduler.timesteps.double().to(device)
+    pred_noise = pred_noise.float().to(device)
+    noise_input_latent = noise_input_latent.float().to(device)
+    sigmas = scheduler.sigmas.float().to(device)
+    timesteps = scheduler.timesteps.float().to(device)
     timestep_id = torch.argmin(
         (timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
     sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1)
     pred_video = noise_input_latent - sigma_t * pred_noise
     return pred_video.to(dtype)
-
-def pred_video_to_pred_noise(x0_pred: torch.Tensor, xt: torch.Tensor, timestep: torch.Tensor, scheduler: Any) -> torch.Tensor:
-    """
-    Convert x0 prediction to flow matching's prediction.
-    x0_pred: the x0 prediction with shape [B, C, H, W]
-    xt: the input noisy data with shape [B, C, H, W]
-    timestep: the timestep with shape [B]
-
-    pred = (x_t - x_0) / sigma_t
-    """
-    # use higher precision for calculations
-    original_dtype = x0_pred.dtype
-    x0_pred, xt, sigmas, timesteps = map(
-        lambda x: x.double().to(x0_pred.device), [x0_pred, xt,
-                                                    scheduler.sigmas,
-                                                    scheduler.timesteps]
-    )
-    timestep_id = torch.argmin(
-        (timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
-    sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1)
-    flow_pred = (xt - x0_pred) / sigma_t
-    return flow_pred.to(original_dtype)

fastvideo/pipelines/basic/wan/wan_causal_dmd_pipeline.py

@@ -16,7 +16,8 @@ from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
                                         CausalDMDDenosingStage,
                                         InputValidationStage,
                                         LatentPreparationStage,
-                                        TextEncodingStage)
+                                        TextEncodingStage,
+                                        TimestepPreparationStage)
 # isort: on
 
 logger = init_logger(__name__)
@@ -28,6 +29,10 @@ class WanCausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
         "text_encoder", "tokenizer", "vae", "transformer", "scheduler"
     ]
 
+    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
+        self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(
+            shift=fastvideo_args.pipeline_config.flow_shift)
+
     def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
         """Set up pipeline stages with proper dependency injection."""
 
@@ -43,6 +48,10 @@ class WanCausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
         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"),

fastvideo/pipelines/composed_pipeline_base.py

@@ -40,7 +40,7 @@ class ComposedPipelineBase(ABC):
     _extra_config_module_map: dict[str, str] = {}
     training_args: TrainingArgs | None = None
     fastvideo_args: FastVideoArgs | TrainingArgs | None = None
-    modules: dict[str, Any] = {}
+    modules: dict[str, torch.nn.Module] = {}
     post_init_called: bool = False
 
     # TODO(will): args should support both inference args and training args
@@ -79,7 +79,7 @@ class ComposedPipelineBase(ABC):
             for name, module in self.modules.items():
                 if not isinstance(module, torch.nn.Module):
                     continue
-                if "transformer" in name:
+                if name == "transformer":
                     module.requires_grad_(True)
                 else:
                     module.requires_grad_(False)
@@ -258,19 +258,20 @@ class ComposedPipelineBase(ABC):
         # remove keys that are not pipeline modules
         model_index.pop("_class_name")
         model_index.pop("_diffusers_version")
+        # @TODO(Wei): Temporary hack
         if "boundary_ratio" in model_index and model_index[
                 "boundary_ratio"] is not None:
             logger.info(
                 "MoE pipeline detected. Adding transformer_2 to self.required_config_modules..."
             )
             self.required_config_modules.append("transformer_2")
-            logger.info("MoE pipeline detected. Setting boundary ratio to %s",
-                        model_index["boundary_ratio"])
-            fastvideo_args.pipeline_config.dit_config.boundary_ratio = model_index[
-                "boundary_ratio"]
+            if fastvideo_args.boundary_ratio is None:
+                logger.info(
+                    "MoE pipeline detected. Setting boundary ratio to %s",
+                    model_index["boundary_ratio"])
+                fastvideo_args.boundary_ratio = model_index["boundary_ratio"]
 
         model_index.pop("boundary_ratio", None)
-        # used by Wan2.2 ti2v
         model_index.pop("expand_timesteps", None)
 
         # some sanity checks
@@ -303,8 +304,8 @@ class ComposedPipelineBase(ABC):
                           architecture) in model_index.items():
             if transformers_or_diffusers is None:
                 logger.warning(
-                    "Module %s in model_index.json has null value, removing from required_config_modules",
-                    module_name)
+                    "Module in model_index.json has null value, removing from required_config_modules"
+                )
                 if module_name in self.required_config_modules:
                     self.required_config_modules.remove(module_name)
                 continue

fastvideo/pipelines/lora_pipeline.py

@@ -7,7 +7,7 @@ import torch
 import torch.distributed as dist
 import torch.nn as nn
 from safetensors.torch import load_file
-from torch.distributed.device_mesh import DeviceMesh, init_device_mesh
+from torch.distributed.device_mesh import init_device_mesh
 from torch.distributed.tensor import DTensor
 
 from fastvideo.distributed import get_local_torch_device
@@ -32,7 +32,6 @@ class LoRAPipeline(ComposedPipelineBase):
     cur_adapter_name: str = ""
     cur_adapter_path: str = ""
     lora_layers: dict[str, BaseLayerWithLoRA] = {}
-    lora_layers_critic: dict[str, BaseLayerWithLoRA] = {}
     fastvideo_args: FastVideoArgs | TrainingArgs
     exclude_lora_layers: list[str] = []
     device: torch.device = get_local_torch_device()
@@ -82,17 +81,6 @@ class LoRAPipeline(ComposedPipelineBase):
 
     def set_trainable(self) -> None:
 
-        def set_lora_grads(lora_layers: dict[str, BaseLayerWithLoRA],
-                           device_mesh: DeviceMesh):
-            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)
-                layer.lora_A = nn.Parameter(
-                    DTensor.from_local(layer.lora_A, device_mesh=device_mesh))
-                layer.lora_B = nn.Parameter(
-                    DTensor.from_local(layer.lora_B, device_mesh=device_mesh))
-
         is_lora_training = self.training_mode and getattr(
             self.fastvideo_args, "lora_training", False)
         if not is_lora_training:
@@ -100,12 +88,18 @@ class LoRAPipeline(ComposedPipelineBase):
             return
 
         self.modules["transformer"].requires_grad_(False)
-        if "fake_score_transformer" in self.modules:
-            self.modules["fake_score_transformer"].requires_grad_(False)
         device_mesh = init_device_mesh("cuda", (dist.get_world_size(), 1),
                                        mesh_dim_names=["fake", "replicate"])
-        set_lora_grads(self.lora_layers, device_mesh)
-        set_lora_grads(self.lora_layers_critic, device_mesh)
+        for name, layer in self.lora_layers.items():
+            # Enable grads for lora weights only
+            # Must convert to DTensor for compatibility with other FSDP modules in grad calculation
+            layer.lora_A.requires_grad_(True)
+            layer.lora_B.requires_grad_(True)
+            layer.base_layer.requires_grad_(False)
+            layer.lora_A = nn.Parameter(
+                DTensor.from_local(layer.lora_A, device_mesh=device_mesh))
+            layer.lora_B = nn.Parameter(
+                DTensor.from_local(layer.lora_B, device_mesh=device_mesh))
 
     def convert_to_lora_layers(self) -> None:
         """
@@ -137,24 +131,6 @@ class LoRAPipeline(ComposedPipelineBase):
                 converted_count += 1
         logger.info("Converted %d layers to LoRA layers", converted_count)
 
-        if "fake_score_transformer" in self.modules:
-            for name, layer in self.modules[
-                    "fake_score_transformer"].named_modules():
-                if not self.is_target_layer(name):
-                    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:
-                    self.lora_layers_critic[name] = layer
-                    replace_submodule(self.modules["fake_score_transformer"],
-                                      name, layer)
-                    converted_count += 1
-            logger.info(
-                "Converted %d layers to LoRA layers in the critic model",
-                converted_count)
-
     def set_lora_adapter(self,
                          lora_nickname: str,
                          lora_path: str | None = None):  # type: ignore
@@ -248,4 +224,4 @@ class LoRAPipeline(ComposedPipelineBase):
 
     def unmerge_lora_weights(self) -> None:
         for name, layer in self.lora_layers.items():
-            layer.unmerge_lora_weights()
+            layer.unmerge_lora_weights()

fastvideo/pipelines/pipeline_batch_info.py

@@ -129,7 +129,6 @@ class ForwardBatch:
     timesteps: torch.Tensor | None = None
     timestep: torch.Tensor | float | int | None = None
     step_index: int | None = None
-    boundary_ratio: float | None = None
 
     # Scheduler parameters
     num_inference_steps: int = 50
@@ -246,11 +245,10 @@ class TrainingBatch:
     fake_score_loss: float = 0.0
 
     dmd_latent_vis_dict: dict[str, Any] = field(default_factory=dict)
-    latent_vis_dict: dict[str, torch.Tensor] = field(default_factory=dict)
     fake_score_latent_vis_dict: dict[str, Any] = field(default_factory=dict)
 
 
 @dataclass
 class PreprocessBatch(ForwardBatch):
-    video_loader: list["VideoDecoder"] | list[str] = field(default_factory=list)
+    video_loader: list["VideoDecoder"] = field(default_factory=list)
     video_file_name: list[str] = field(default_factory=list)

fastvideo/pipelines/preprocess/preprocess_pipeline_base.py

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
+import multiprocessing
 import os
+from concurrent.futures import ProcessPoolExecutor
 from typing import Any
 
 import numpy as np
@@ -10,8 +12,6 @@ from torch.utils.data import DataLoader
 from tqdm import tqdm
 
 from fastvideo.dataset import getdataset
-from fastvideo.dataset.dataloader.parquet_io import (ParquetDatasetWriter,
-                                                     records_to_table)
 from fastvideo.dataset.preprocessing_datasets import PreprocessBatch
 from fastvideo.distributed import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs
@@ -54,13 +54,9 @@ class BasePreprocessPipeline(ComposedPipelineBase):
         """Get additional features specific to the pipeline type. Override in subclasses."""
         return {}
 
-    def get_pyarrow_schema(self) -> pa.Schema:
-        """Return the PyArrow schema for this pipeline. Must be overridden."""
-        raise NotImplementedError
-
     def get_schema_fields(self) -> list[str]:
-        """Get the schema fields for the pipeline type."""
-        return [f.name for f in self.get_pyarrow_schema()]
+        """Get the schema fields for the pipeline type. Override in subclasses."""
+        raise NotImplementedError
 
     def create_record_for_schema(self,
                                  preprocess_batch: PreprocessBatch,
@@ -404,22 +400,166 @@ class BasePreprocessPipeline(ComposedPipelineBase):
                 batch_data.append(record)
 
             if batch_data:
+                # Add progress bar for writing to Parquet dataset
                 write_pbar = tqdm(total=1,
                                   desc="Writing to Parquet dataset",
                                   unit="batch")
-                table = records_to_table(batch_data, self.get_pyarrow_schema())
+                # Convert batch data to PyArrow arrays
+                arrays = []
+                for field in self.get_schema_fields():
+                    if field.endswith('_bytes'):
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.binary()))
+                    elif field.endswith('_shape'):
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.list_(pa.int32())))
+                    elif field in ['width', 'height', 'num_frames']:
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.int32()))
+                    elif field in ['duration_sec', 'fps']:
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.float32()))
+                    else:
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data]))
+
+                table = pa.Table.from_arrays(arrays,
+                                             names=self.get_schema_fields())
                 write_pbar.update(1)
                 write_pbar.close()
 
-                if not hasattr(self, 'dataset_writer'):
-                    self.dataset_writer = ParquetDatasetWriter(
-                        out_dir=combined_parquet_dir,
-                        samples_per_file=args.samples_per_file,
-                    )
-                self.dataset_writer.append_table(table)
+                # Store the table in a list for later processing
+                if not hasattr(self, 'all_tables'):
+                    self.all_tables = []
+                self.all_tables.append(table)
+
                 logger.info("Collected batch with %s samples", len(table))
 
             if num_processed_samples >= args.flush_frequency:
-                written = self.dataset_writer.flush()
-                logger.info("Flushed %s samples to parquet", written)
+                self._flush_tables(num_processed_samples, args,
+                                   combined_parquet_dir)
                 num_processed_samples = 0
+                self.all_tables = []
+
+    def _flush_tables(self, num_processed_samples: int, args,
+                      combined_parquet_dir: str):
+        """Flush collected tables to disk."""
+        assert hasattr(self, 'all_tables') and self.all_tables
+        print(f"Combining {len(self.all_tables)} batches...")
+        combined_table = pa.concat_tables(self.all_tables)
+        assert len(combined_table) == num_processed_samples
+        print(f"Total samples collected: {len(combined_table)}")
+
+        # Calculate total number of chunks needed, discarding remainder
+        total_chunks = max(num_processed_samples // args.samples_per_file, 1)
+
+        print(f"Fixed samples per parquet file: {args.samples_per_file}")
+        print(f"Total number of parquet files: {total_chunks}")
+        print(
+            f"Total samples to be processed: {total_chunks * args.samples_per_file} (discarding {num_processed_samples % args.samples_per_file} samples)"
+        )
+
+        # Split work among processes
+        num_workers = int(min(multiprocessing.cpu_count(), total_chunks))
+        chunks_per_worker = (total_chunks + num_workers - 1) // num_workers
+
+        print(f"Using {num_workers} workers to process {total_chunks} chunks")
+        logger.info("Chunks per worker: %s", chunks_per_worker)
+
+        # Prepare work ranges
+        work_ranges = []
+        for i in range(num_workers):
+            start_idx = i * chunks_per_worker
+            end_idx = min((i + 1) * chunks_per_worker, total_chunks)
+            if start_idx < total_chunks:
+                work_ranges.append(
+                    (start_idx, end_idx, combined_table, i,
+                     combined_parquet_dir, args.samples_per_file))
+
+        total_written = 0
+        failed_ranges = []
+        with ProcessPoolExecutor(max_workers=num_workers) as executor:
+            futures = {
+                executor.submit(self.process_chunk_range, work_range):
+                work_range
+                for work_range in work_ranges
+            }
+            for future in tqdm(futures, desc="Processing chunks"):
+                try:
+                    written = future.result()
+                    total_written += written
+                    logger.info("Processed chunk with %s samples", written)
+                except Exception as e:
+                    work_range = futures[future]
+                    failed_ranges.append(work_range)
+                    logger.error("Failed to process range %s-%s: %s",
+                                 work_range[0], work_range[1], str(e))
+
+        # Retry failed ranges sequentially
+        if failed_ranges:
+            logger.warning("Retrying %s failed ranges sequentially",
+                           len(failed_ranges))
+            for work_range in failed_ranges:
+                try:
+                    total_written += self.process_chunk_range(work_range)
+                except Exception as e:
+                    logger.error(
+                        "Failed to process range %s-%s after retry: %s",
+                        work_range[0], work_range[1], str(e))
+
+        logger.info("Total samples written: %s", total_written)
+
+    @staticmethod
+    def process_chunk_range(args: Any) -> int:
+        start_idx, end_idx, table, worker_id, output_dir, samples_per_file = args
+        try:
+            total_written = 0
+            num_samples = len(table)
+
+            # Create worker-specific subdirectory
+            worker_dir = os.path.join(output_dir, f"worker_{worker_id}")
+            os.makedirs(worker_dir, exist_ok=True)
+
+            # Check how many files there are already in the dir, and update i accordingly
+            num_parquets = 0
+            for root, _, files in os.walk(worker_dir):
+                for file in files:
+                    if file.endswith('.parquet'):
+                        num_parquets += 1
+
+            for i in range(start_idx, end_idx):
+                start_sample = i * samples_per_file
+                end_sample = min((i + 1) * samples_per_file, num_samples)
+                chunk = table.slice(start_sample, end_sample - start_sample)
+
+                # Create chunk file in worker's directory
+                chunk_path = os.path.join(
+                    worker_dir, f"data_chunk_{i + num_parquets}.parquet")
+                temp_path = chunk_path + '.tmp'
+
+                try:
+                    # Write to temporary file
+                    pq.write_table(chunk, temp_path, compression='zstd')
+
+                    # Rename temporary file to final file
+                    if os.path.exists(chunk_path):
+                        os.remove(
+                            chunk_path)  # Remove existing file if it exists
+                    os.rename(temp_path, chunk_path)
+
+                    total_written += len(chunk)
+                except Exception as e:
+                    # Clean up temporary file if it exists
+                    if os.path.exists(temp_path):
+                        os.remove(temp_path)
+                    raise e
+
+            return total_written
+        except Exception as e:
+            logger.error("Error processing chunks %s-%s for worker %s: %s",
+                         start_idx, end_idx, worker_id, str(e))
+            raise

fastvideo/pipelines/preprocess/preprocess_pipeline_i2v.py

@@ -40,9 +40,9 @@ class PreprocessPipeline_I2V(BasePreprocessPipeline):
                            image_processor=self.get_module("image_processor"),
                        ))
 
-    def get_pyarrow_schema(self):
-        """Return the PyArrow schema for I2V pipeline."""
-        return pyarrow_schema_i2v
+    def get_schema_fields(self) -> list[str]:
+        """Get the schema fields for I2V pipeline."""
+        return [f.name for f in pyarrow_schema_i2v]
 
     def get_extra_features(self, valid_data: dict[str, Any],
                            fastvideo_args: FastVideoArgs) -> dict[str, Any]:

fastvideo/pipelines/preprocess/preprocess_pipeline_ode_trajectory.py

@@ -21,35 +21,147 @@ from torchdata.stateful_dataloader import StatefulDataLoader
 from tqdm import tqdm
 
 from fastvideo.configs.sample import SamplingParam
-from fastvideo.dataset import gettextdataset
-from fastvideo.dataset.dataloader.parquet_io import (ParquetDatasetWriter,
-                                                     records_to_table)
-from fastvideo.dataset.dataloader.record_schema import (
-    ode_text_only_record_creator)
-from fastvideo.dataset.dataloader.schema import (
-    pyarrow_schema_ode_trajectory_text_only)
+from fastvideo.dataset import getdataset
+from fastvideo.dataset.dataloader.schema import pyarrow_schema_ode_trajectory
+from fastvideo.distributed import get_local_torch_device
 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,
+                                        ImageVAEEncodingStage,
                                         InputValidationStage,
                                         LatentPreparationStage,
                                         TextEncodingStage,
                                         TimestepPreparationStage)
 from fastvideo.utils import save_decoded_latents_as_video, shallow_asdict
 
-from fastvideo.forward_context import set_forward_context
-from fastvideo.models.utils import pred_noise_to_pred_video, pred_video_to_pred_noise
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.distributed import get_local_torch_device
-
 logger = init_logger(__name__)
 
 
+class FlowMatchScheduler:
+
+    order = 1
+
+    def __init__(self,
+                 num_inference_steps=100,
+                 num_train_timesteps=1000,
+                 shift=3.0,
+                 sigma_max=1.0,
+                 sigma_min=0.003 / 1.002,
+                 inverse_timesteps=False,
+                 extra_one_step=False,
+                 reverse_sigmas=False):
+        self.num_train_timesteps = num_train_timesteps
+        self.shift = shift
+        self.sigma_max = sigma_max
+        self.sigma_min = sigma_min
+        self.inverse_timesteps = inverse_timesteps
+        self.extra_one_step = extra_one_step
+        self.reverse_sigmas = reverse_sigmas
+        self.set_timesteps(num_inference_steps)
+
+    def set_timesteps(self,
+                      num_inference_steps=100,
+                      denoising_strength=1.0,
+                      training=False,
+                      device=None):
+        sigma_start = self.sigma_min + \
+            (self.sigma_max - self.sigma_min) * denoising_strength
+        if self.extra_one_step:
+            self.sigmas = torch.linspace(sigma_start, self.sigma_min,
+                                         num_inference_steps + 1)[:-1]
+        else:
+            self.sigmas = torch.linspace(sigma_start, self.sigma_min,
+                                         num_inference_steps)
+        if self.inverse_timesteps:
+            self.sigmas = torch.flip(self.sigmas, dims=[0])
+        self.sigmas = self.shift * self.sigmas / \
+            (1 + (self.shift - 1) * self.sigmas)
+        if self.reverse_sigmas:
+            self.sigmas = 1 - self.sigmas
+        self.timesteps = self.sigmas * self.num_train_timesteps
+        if training:
+            x = self.timesteps
+            y = torch.exp(
+                -2 * ((x - num_inference_steps / 2) / num_inference_steps)**2)
+            y_shifted = y - y.min()
+            bsmntw_weighing = y_shifted * \
+                (num_inference_steps / y_shifted.sum())
+            self.linear_timesteps_weights = bsmntw_weighing
+
+    def step(self,
+             model_output,
+             timestep,
+             sample,
+             to_final=False,
+             return_dict=False,
+             **kwargs):
+        assert return_dict is False
+        assert kwargs == {}
+        self.sigmas = self.sigmas.to(model_output.device)
+        self.timesteps = self.timesteps.to(model_output.device)
+        logger.info('step timestep: %s', timestep)
+        logger.info('step timestep: %s', timestep.shape)
+        # timestep  is [num_frames]
+        # timestep_id = torch.argmin(
+        #     (self.timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
+        # assert timestep.ndim == 1
+        # assert timestep.shape[0] == 1
+        timestep_id = torch.argmin((self.timesteps - timestep).abs(), dim=0)
+        sigma = self.sigmas[timestep_id].reshape(-1, 1, 1, 1)
+        if to_final or (timestep_id + 1 >= len(self.timesteps)).any():
+            sigma_ = 1 if (self.inverse_timesteps or self.reverse_sigmas) else 0
+        else:
+            sigma_ = self.sigmas[timestep_id + 1].reshape(-1, 1, 1, 1)
+        prev_sample = sample + model_output * (sigma_ - sigma)
+        return (prev_sample, )
+
+    def scale_model_input(self, sample: torch.Tensor, *args,
+                          **kwargs) -> torch.Tensor:
+        """
+        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
+        current timestep.
+
+        Args:
+            sample (`torch.Tensor`):
+                The input sample.
+
+        Returns:
+            `torch.Tensor`:
+                A scaled input sample.
+        """
+        return sample
+
+    def add_noise(self, original_samples, noise, timestep):
+        """
+        Diffusion forward corruption process.
+        Input:
+            - clean_latent: the clean latent with shape [B, C, H, W]
+            - noise: the noise with shape [B, C, H, W]
+            - timestep: the timestep with shape [B]
+        Output: the corrupted latent with shape [B, C, H, W]
+        """
+        self.sigmas = self.sigmas.to(noise.device)
+        self.timesteps = self.timesteps.to(noise.device)
+        timestep_id = torch.argmin(
+            (self.timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
+        sigma = self.sigmas[timestep_id].reshape(-1, 1, 1, 1)
+        sample = (1 - sigma) * original_samples + sigma * noise
+        return sample.type_as(noise)
+
+    def training_target(self, sample, noise, timestep):
+        target = noise - sample
+        return target
+
+    def training_weight(self, timestep):
+        timestep_id = torch.argmin(
+            (self.timesteps - timestep.to(self.timesteps.device)).abs())
+        weights = self.linear_timesteps_weights[timestep_id]
+        return weights
+
+
 class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
     """ODE Trajectory preprocessing pipeline implementation."""
 
@@ -58,31 +170,28 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
     ]
     preprocess_dataloader: StatefulDataLoader
     preprocess_loader_iter: Iterator[dict[str, Any]]
-    pbar: Any
-    num_processed_samples: int
 
-    def get_pyarrow_schema(self) -> pa.Schema:
-        """Return the PyArrow schema for ODE Trajectory pipeline."""
-        return pyarrow_schema_ode_trajectory_text_only
+    def get_schema_fields(self):
+        """Get the schema fields for ODE Trajectory pipeline."""
+        return [f.name for f in pyarrow_schema_ode_trajectory]
 
     def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
         """Set up pipeline stages with proper dependency injection."""
+        fastvideo_args.pipeline_config.flow_shift = 5
+        logger.info('WTF flow_shift: %s',
+                    fastvideo_args.pipeline_config.flow_shift)
+
         assert fastvideo_args.pipeline_config.flow_shift == 5
-        self.modules["scheduler"] = SelfForcingFlowMatchScheduler(
+        # self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(
+        # shift=fastvideo_args.pipeline_config.flow_shift)
+        self.modules["scheduler"] = FlowMatchScheduler(
             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)
-
-        fastvideo_args.model_loaded["transformer"] = False
-        loader = TransformerLoader()
-        fastvideo_args.pipeline_config.dit_precision = "fp32" # Overwrite the precision to fp32 for transformer
-        fastvideo_args.pipeline_config.dit_forward_precision = "fp32"
-        self.transformer = loader.load(
-            fastvideo_args.model_paths["transformer"], fastvideo_args)
-        self.add_module("transformer", self.transformer)
-        fastvideo_args.model_loaded["transformer"] = True
+        logger.info('WTF scheduler timesteps: %s',
+                    self.modules["scheduler"].timesteps)
 
         self.add_stage(stage_name="input_validation_stage",
                        stage=InputValidationStage())
@@ -91,6 +200,9 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                            text_encoders=[self.get_module("text_encoder")],
                            tokenizers=[self.get_module("tokenizer")],
                        ))
+        self.add_stage(stage_name="vae_encoding_stage",
+                       stage=ImageVAEEncodingStage(
+                           vae=self.get_module("vae"), ))
         self.add_stage(stage_name="timestep_preparation_stage",
                        stage=TimestepPreparationStage(
                            scheduler=self.get_module("scheduler")))
@@ -101,49 +213,56 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
         self.add_stage(stage_name="denoising_stage",
                        stage=DenoisingStage(
                            transformer=self.get_module("transformer"),
+                           transformer_2=self.get_module("transformer_2", None),
                            scheduler=self.get_module("scheduler"),
                            pipeline=self,
                        ))
         self.add_stage(stage_name="decoding_stage",
                        stage=DecodingStage(vae=self.get_module("vae")))
 
-    def preprocess_text_and_trajectory(self, fastvideo_args: FastVideoArgs,
-                                       args):
-        """Preprocess text-only data and generate trajectory information."""
+    def preprocess_video_and_text_and_trajectory(self,
+                                                 fastvideo_args: FastVideoArgs,
+                                                 args):
 
         for batch_idx, data in enumerate(self.pbar):
-            # logger.info("transformer weight sum: %s", sum(p.float().sum().item() for p in self.transformer.parameters()))
             if data is None:
                 continue
 
             with torch.inference_mode():
-                # For text-only processing, we only need text data
-                # Filter out samples without text
+                # Filter out invalid samples (those with all zeros)
                 valid_indices = []
-                for i, text in enumerate(data["text"]):
-                    if text and text.strip():  # Check if text is not empty
+                for i, pixel_values in enumerate(data["pixel_values"]):
+                    if not torch.all(
+                            pixel_values == 0):  # Check if all values are zero
                         valid_indices.append(i)
                 self.num_processed_samples += len(valid_indices)
 
                 if not valid_indices:
                     continue
 
-                # Create new batch with only valid samples (text-only)
+                # Create new batch with only valid samples
                 valid_data = {
+                    "pixel_values":
+                    torch.stack(
+                        [data["pixel_values"][i] for i in valid_indices]),
                     "text": [data["text"][i] for i in valid_indices],
                     "path": [data["path"][i] for i in valid_indices],
+                    "fps": [data["fps"][i] for i in valid_indices],
+                    "duration": [data["duration"][i] for i in valid_indices],
                 }
 
-                # Add fps and duration if available in data
-                if "fps" in data:
-                    valid_data["fps"] = [data["fps"][i] for i in valid_indices]
-                if "duration" in data:
-                    valid_data["duration"] = [
-                        data["duration"][i] for i in valid_indices
-                    ]
+                # VAE
+                with torch.autocast("cuda", dtype=torch.float32):
+                    latents = self.get_module("vae").encode(
+                        valid_data["pixel_values"].to(
+                            get_local_torch_device())).mean
+
+                # Get extra features if needed
+                extra_features = self.get_extra_features(
+                    valid_data, fastvideo_args)
 
                 batch_captions = valid_data["text"]
-                self.prompt_encoding_stage.text_encoders[0] = self.prompt_encoding_stage.text_encoders[0].to(dtype=torch.bfloat16).to(dtype=torch.float32)
+                logger.info(f"===== batch_captions: {batch_captions}")
                 # Encode text using the standalone TextEncodingStage API
                 prompt_embeds_list, prompt_masks_list = self.prompt_encoding_stage.encode_text(
                     batch_captions,
@@ -152,25 +271,43 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                     return_attention_mask=True,
                 )
                 prompt_embeds = prompt_embeds_list[0]
-                logger.info("prompt_embeds sum: %s, prompt_embeds shape: %s, prompt_embeds dtype: %s", prompt_embeds.float().sum(), prompt_embeds.shape, prompt_embeds.dtype)
                 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)
+                # # Get sequence lengths from attention masks (number of 1s)
+                # seq_lens = prompt_attention_mask.sum(dim=1)
 
-                negative_prompt = '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走'
+                # non_padded_embeds = []
+                # non_padded_masks = []
+
+                # # Process each item in the batch
+                # for i in range(prompt_embeds.size(0)):
+                #     seq_len = seq_lens[i].item()
+                #     # Slice the embeddings and masks to keep only non-padding parts
+                #     non_padded_embeds.append(prompt_embeds[i, :seq_len])
+                #     non_padded_masks.append(prompt_attention_mask[i, :seq_len])
+
+                # Update the tensors with non-padded versions
+                # prompt_embeds = non_padded_embeds
+                # prompt_attention_masks = non_padded_masks
+                # prompt_embeds = prompt_embeds
+
+                # logger.info(f"===== prompt_embeds: {prompt_embeds[0].shape}")
+                # logger.info(f"===== prompt_attention_masks: {prompt_attention_masks[0].shape}")
+
+                sampling_params = SamplingParam.from_pretrained(args.model_path)
 
                 # encode negative prompt for trajectory collection
                 if sampling_params.guidance_scale > 1 and sampling_params.negative_prompt is not None:
                     negative_prompt_embeds_list, negative_prompt_masks_list = self.prompt_encoding_stage.encode_text(
-                        negative_prompt,
+                        sampling_params.negative_prompt,
                         fastvideo_args,
                         encoder_index=[0],
                         return_attention_mask=True,
                     )
-                    negative_prompt_embed = negative_prompt_embeds_list[0]
+                    negative_prompt_embed = negative_prompt_embeds_list[0][0]
                     negative_prompt_attention_mask = negative_prompt_masks_list[
-                        0]
+                        0][0]
                 else:
                     negative_prompt_embed = None
                     negative_prompt_attention_mask = None
@@ -178,232 +315,306 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                 trajectory_latents = []
                 trajectory_timesteps = []
                 trajectory_decoded = []
-
                 for i, (prompt_embed, prompt_attention_mask) in enumerate(
-                        zip(prompt_embeds, prompt_attention_masks,
-                            strict=False)):
+                        zip(prompt_embeds, prompt_attention_masks, strict=False)):
                     prompt_embed = prompt_embed.unsqueeze(0)
                     prompt_attention_mask = prompt_attention_mask.unsqueeze(0)
-
-                    # Collect the trajectory data (text-to-video generation)
-                    batch = ForwardBatch(**shallow_asdict(sampling_params), )
+                    logger.info("what")
+                    logger.info(f"===== prompt_embed: {prompt_embed.shape}")
+                    logger.info(
+                        f"===== prompt_attention_mask: {prompt_attention_mask.shape}"
+                    )
+                    # Collect the trajectory data
+                    batch = ForwardBatch(
+                        **shallow_asdict(sampling_params),
+                        # data_type="video",
+                        # seed=args.seed,
+                        # prompt=batch_captions[i],
+                        # prompt_embeds=[prompt_embed],
+                        # prompt_attention_mask=[prompt_attention_mask],
+                        # height=args.max_height,
+                        # width=args.max_width,
+                        # num_frames=81,
+                        # fps=args.train_fps,
+                        # return_trajectory_latents=True,
+                        # guidance_scale=3.0,
+                        # do_classifier_free_guidance=True,
+                    )
                     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 = True
+                    batch.return_trajectory_decoded = False
                     batch.height = args.max_height
                     batch.width = args.max_width
+                    batch.num_inference_steps = 48
+                    # batch.num_frames = 81
                     batch.fps = args.train_fps
-                    batch.guidance_scale = 3.0
+                    batch.guidance_scale = 6.0
                     batch.do_classifier_free_guidance = True
+                    # fastvideo_args.pipeline_config.ti2v_task = True
 
                     result_batch = self.input_validation_stage(
                         batch, fastvideo_args)
+                    # result_batch = self.prompt_encoding_stage(result_batch, fastvideo_args)
+                    # result_batch = self.vae_encoding_stage(result_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)
-                    noisy_input = []
-                    # latents = result_batch.latents.permute(0, 2, 1, 3, 4)
-                    latents = torch.randn(
-                        [1, 21, 16, 60, 104], dtype=torch.float32, device=get_local_torch_device()
-                    )
-                    # logger.info("transformer weight sum: %s", sum(p.float().sum().item() for p in self.transformer.parameters()))
-                    logger.info("latents sum: %s, latents shape: %s, latents dtype: %s", latents.float().sum(), latents.shape, latents.dtype)
-
-                    logger.info("scheduler timesteps: %s", self.get_module("scheduler").timesteps)
-                    for progress_id, t in enumerate(tqdm(self.get_module("scheduler").timesteps)):
-                        timestep = t * \
-                            torch.ones([1, 21], device=latents.device, dtype=torch.float32)
-
-                        noisy_input.append(latents)
-
-                        with set_forward_context(
-                            current_timestep=0,
-                            attn_metadata=None,
-                            forward_batch=None,
-                        ):
-                            # logger.info("prompt_embed sum: %s, prompt_embed shape: %s, prompt_embed dtype: %s", prompt_embed.float().sum(), prompt_embed.shape, prompt_embed.dtype)
-                            # logger.info("timestep: %s", timestep[:, 0])
-                            # Run transformer
-                            cond_pred_noise_btchw = self.transformer(
-                                hidden_states=latents.permute(0, 2, 1, 3, 4),
-                                encoder_hidden_states=prompt_embed,
-                                timestep=timestep[:, 0]
-                            ).permute(0, 2, 1, 3, 4)
-
-                        # logger.info("cond_pred_noise_btchw sum: %s, cond_pred_noise_btchw shape: %s, cond_pred_noise_btchw dtype: %s", cond_pred_noise_btchw.float().sum(), cond_pred_noise_btchw.shape, cond_pred_noise_btchw.dtype)
-
-                        cond_pred_video_btchw = pred_noise_to_pred_video(
-                            pred_noise=cond_pred_noise_btchw.flatten(0, 1),
-                            noise_input_latent=latents.flatten(0, 1),
-                            timestep=timestep.flatten(0, 1),
-                            scheduler=self.get_module("scheduler")).unflatten(
-                                0, cond_pred_noise_btchw.shape[:2])
-
-                        # logger.info("cond_pred_video_btchw sum: %s, cond_pred_video_btchw shape: %s, cond_pred_video_btchw dtype: %s", cond_pred_video_btchw.float().sum(), cond_pred_video_btchw.shape, cond_pred_video_btchw.dtype)
-
-                        with set_forward_context(
-                            current_timestep=t,
-                            attn_metadata=None,
-                            forward_batch=result_batch,
-                        ):
-                            # logger.info("latents sum: %s, latents shape: %s, latents dtype: %s", latents.float().sum(), latents.shape, latents.dtype)
-                            # Run transformer
-                            uncond_pred_noise_btchw = self.transformer(
-                                latents.permute(0, 2, 1, 3, 4),
-                                negative_prompt_embed,
-                                timestep[:, 0]
-                            ).permute(0, 2, 1, 3, 4)
-
-                        # logger.info("uncond_pred_noise_btchw sum: %s, uncond_pred_noise_btchw shape: %s, uncond_pred_noise_btchw dtype: %s", uncond_pred_noise_btchw.float().sum(), uncond_pred_noise_btchw.shape, uncond_pred_noise_btchw.dtype)
-
-                        uncond_pred_video_btchw = pred_noise_to_pred_video(
-                            pred_noise=uncond_pred_noise_btchw.flatten(0, 1),
-                            noise_input_latent=latents.flatten(0, 1),
-                            timestep=timestep.flatten(0, 1),
-                            scheduler=self.get_module("scheduler")).unflatten(
-                                0, uncond_pred_noise_btchw.shape[:2])
-
-                        pred_video_btchw = uncond_pred_video_btchw + batch.guidance_scale * (
-                            cond_pred_video_btchw - uncond_pred_video_btchw
-                        )
-
-                        # logger.info("pred_video_btchw sum: %s, pred_video_btchw shape: %s, pred_video_btchw dtype: %s", pred_video_btchw.float().sum(), pred_video_btchw.shape, pred_video_btchw.dtype)
-
-                        pred_noise_btchw = pred_video_to_pred_noise(
-                            x0_pred=pred_video_btchw.flatten(0, 1),
-                            xt=latents.flatten(0, 1),
-                            timestep=timestep.flatten(0, 1),
-                            scheduler=self.get_module("scheduler")).unflatten(
-                                0, pred_video_btchw.shape[:2])
-
-                        # logger.info("pred_noise_btchw sum: %s, pred_noise_btchw shape: %s, pred_noise_btchw dtype: %s", pred_noise_btchw.float().sum(), pred_noise_btchw.shape, pred_noise_btchw.dtype)
-
-                        latents = self.get_module("scheduler").step(
-                            pred_noise_btchw.flatten(0, 1),
-                            self.get_module("scheduler").timesteps[progress_id] * torch.ones(
-                                [1, 21], device=latents.device, dtype=torch.long).flatten(0, 1),
-                            latents.flatten(0, 1)
-                        )[0].unflatten(dim=0, sizes=pred_noise_btchw.shape[:2])
-
-                        # logger.info("latents sum: %s, latents shape: %s, latents dtype: %s", latents.float().sum(), latents.shape, latents.dtype)
-
-                    noisy_input.append(latents)
-
-                    noisy_inputs = torch.stack(noisy_input, dim=1)
-
-                    noisy_inputs = noisy_inputs[:, [0, 12, 24, 36, -1]].half()
-
-                    logger.info("noisy inputs sum: %s, noisy inputs shape: %s, noisy inputs dtype: %s", noisy_inputs.float().sum(), noisy_inputs.shape, noisy_inputs.dtype)
-
-                    result_batch.trajectory_latents = noisy_inputs.permute(0, 1, 3, 2, 4, 5)
-                    result_batch.trajectory_timesteps = torch.tensor([self.get_module("scheduler").timesteps[i] for i in [0, 12, 24, 36, -1]])
-                    result_batch.latents = latents.permute(0, 2, 1, 3, 4)
+                    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 = result_batch.trajectory_latents
                     trajectory_latents.append(
                         result_batch.trajectory_latents.cpu())
                     trajectory_timesteps.append(
                         result_batch.trajectory_timesteps.cpu())
                     trajectory_decoded.append(result_batch.trajectory_decoded)
 
-                trajectory_latents = torch.stack(trajectory_latents, dim=0).squeeze(0)
-                # trajecotry_latents = trajectory_latents[:, [0, 12, 24, 36, -1]]
+            extra_features["trajectory_latents"] = trajectory_latents
+            extra_features["trajectory_timesteps"] = trajectory_timesteps
+            logger.info(
+                f"===== trajectory_latents: {trajectory_latents[0].shape}")
+            logger.info(
+                f"===== trajectory_latents len: {len(trajectory_latents)}")
+            logger.info(f"===== trajectory_timesteps: {trajectory_timesteps}")
+            logger.info(
+                f"===== trajectory_timesteps len: {len(trajectory_timesteps)}")
 
-                # Prepare extra features for text-only processing
-                extra_features = {
-                    "trajectory_latents": trajectory_latents,
-                    "trajectory_timesteps": trajectory_timesteps
-                }
-
-                if batch.return_trajectory_decoded:
-                    for i, decoded_frames in enumerate(trajectory_decoded):
-                        for j, decoded_frame in enumerate(decoded_frames):
-                            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]] = []
-
-                # Add progress bar for saving outputs
-                save_pbar = tqdm(enumerate(valid_data["path"]),
-                                 desc="Saving outputs",
-                                 unit="item",
-                                 leave=False)
-
-                for idx, video_path in save_pbar:
-                    video_name = os.path.basename(video_path).split(".")[0]
-
-                    # Convert tensors to numpy arrays
-                    text_embedding = prompt_embeds[idx].cpu().numpy()
-
-                    # Get extra features for this sample
-                    sample_extra_features = {}
-                    if extra_features:
-                        for key, value in extra_features.items():
-                            if isinstance(value, torch.Tensor):
-                                sample_extra_features[key] = value[idx].cpu(
-                                ).numpy()
-                            else:
-                                assert isinstance(value, list)
-                                if isinstance(value[idx], torch.Tensor):
-                                    sample_extra_features[key] = value[idx].cpu(
-                                    ).float().numpy()
-                                else:
-                                    sample_extra_features[key] = value[idx]
-
-                    # Create record for Parquet dataset (text-only ODE schema)
-                    record: dict[str, Any] = ode_text_only_record_creator(
-                        video_name=video_name,
-                        text_embedding=text_embedding,
-                        caption=valid_data["text"][idx],
-                        trajectory_latents=sample_extra_features[
-                            "trajectory_latents"],
-                        trajectory_timesteps=sample_extra_features[
-                            "trajectory_timesteps"],
-                    )
-                    batch_data.append(record)
-
-                if batch_data:
-                    write_pbar = tqdm(total=1,
-                                      desc="Writing to Parquet dataset",
-                                      unit="batch")
-                    table = records_to_table(batch_data,
-                                             self.get_pyarrow_schema())
-                    write_pbar.update(1)
-                    write_pbar.close()
-
-                    if not hasattr(self, 'dataset_writer'):
-                        self.dataset_writer = ParquetDatasetWriter(
-                            out_dir=self.combined_parquet_dir,
-                            samples_per_file=args.samples_per_file,
+            if batch.return_trajectory_decoded:
+                logger.info("===== SAVING TRAJECTORY DECODED")
+                for i, decoded_frames in enumerate(trajectory_decoded):
+                    for j, decoded_frame in enumerate(decoded_frames):
+                        logger.info(
+                            f"===== SAVING TRAJECTORY DECODED {i} for prompt {batch_captions[i]}"
                         )
-                    self.dataset_writer.append_table(table)
+                        save_decoded_latents_as_video(
+                            decoded_frame,
+                            f"decoded_videos/trajectory_decoded_{i}_{j}.mp4",
+                            args.train_fps)
+            # assert False
+            # Prepare batch data for Parquet dataset
+            batch_data = []
 
-                    logger.info("Collected batch with %s samples", len(table))
+            # Add progress bar for saving outputs
+            save_pbar = tqdm(enumerate(valid_data["path"]),
+                             desc="Saving outputs",
+                             unit="item",
+                             leave=False)
+            for idx, video_path in save_pbar:
+                # Get the corresponding latent and info using video name
+                latent = latents[idx].cpu()
+                video_name = os.path.basename(video_path).split(".")[0]
 
-                if self.num_processed_samples >= args.flush_frequency:
-                    written = self.dataset_writer.flush()
-                    logger.info("Flushed %s samples to parquet", written)
-                    self.num_processed_samples = 0
+                # Convert tensors to numpy arrays
+                vae_latent = latent.cpu().numpy()
+                text_embedding = prompt_embeds[idx].cpu().numpy()
 
-        # Final flush for any remaining samples
-        if hasattr(self, 'dataset_writer'):
-            written = self.dataset_writer.flush(write_remainder=True)
-            if written:
-                logger.info("Final flush wrote %s samples", written)
+                # Get extra features for this sample if needed
+                sample_extra_features = {}
+                if extra_features:
+                    for key, value in extra_features.items():
+                        logger.info(f"===== key: {key}")
+                        if isinstance(value, torch.Tensor):
+                            logger.info(f"===== value: {value[idx].shape}")
+                            sample_extra_features[key] = value[idx].cpu().numpy(
+                            )
+                        else:
+                            assert isinstance(value, list)
+                            if isinstance(value[idx], torch.Tensor):
+                                logger.info(
+                                    f"===== value in list: {value[idx].shape}")
+                                sample_extra_features[key] = value[idx].cpu(
+                                ).float().numpy()
+                            else:
+                                logger.info("===== value in list: not tensor")
+                                sample_extra_features[key] = value[idx]
+                            # logger.info(f"===== value: not tensor")
+                            # sample_extra_features[key] = value[idx]
+
+                # Create record for Parquet dataset
+                record = self.create_record(
+                    video_name=video_name,
+                    vae_latent=vae_latent,
+                    text_embedding=text_embedding,
+                    valid_data=valid_data,
+                    idx=idx,
+                    extra_features=sample_extra_features)
+                batch_data.append(record)
+
+            if batch_data:
+                # Add progress bar for writing to Parquet dataset
+                write_pbar = tqdm(total=1,
+                                  desc="Writing to Parquet dataset",
+                                  unit="batch")
+                # Convert batch data to PyArrow arrays
+                arrays = []
+                for field in self.get_schema_fields():
+                    if field.endswith('_bytes'):
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.binary()))
+                    elif field.endswith('_shape'):
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.list_(pa.int32())))
+                    elif field in ['width', 'height', 'num_frames']:
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.int32()))
+                    elif field in ['duration_sec', 'fps']:
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.float32()))
+                    else:
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data]))
+
+                table = pa.Table.from_arrays(arrays,
+                                             names=self.get_schema_fields())
+                write_pbar.update(1)
+                write_pbar.close()
+
+                # Store the table in a list for later processing
+                if not hasattr(self, 'all_tables'):
+                    self.all_tables = []
+                self.all_tables.append(table)
+
+                logger.info("Collected batch with %s samples", len(table))
+
+            if self.num_processed_samples >= args.flush_frequency:
+                self._flush_tables(self.num_processed_samples, args,
+                                   self.combined_parquet_dir)
+                self.num_processed_samples = 0
+                self.all_tables = []
+
+    def get_extra_features(self, valid_data: dict[str, Any],
+                           fastvideo_args: FastVideoArgs) -> dict[str, Any]:
+
+        # TODO(will): move these to cpu at some point
+        self.get_module("vae").to(get_local_torch_device())
+
+        # generator = torch.Generator(device=get_local_torch_device(), seed=42)
+        generator = torch.Generator("cpu").manual_seed(42)
+
+        features = {}
+        """Get CLIP features from the first frame of each video."""
+        first_frame = valid_data["pixel_values"][:, :, 0, :, :].permute(
+            0, 2, 3, 1)  # (B, C, T, H, W) -> (B, H, W, C)
+        _, _, num_frames, height, width = valid_data["pixel_values"].shape
+        # latent_height = height // self.get_module(
+        #     "vae").spatial_compression_ratio
+        # latent_width = width // self.get_module("vae").spatial_compression_ratio
+
+        unprocessed_images = []
+        pil_images = []
+        # Frame has values between -1 and 1
+        for frame in first_frame:
+            frame = (frame + 1) * 127.5
+            frame_pil = Image.fromarray(frame.cpu().numpy().astype(np.uint8))
+            pil_images.append(frame_pil)
+            # processed_img = self.get_module("image_processor")(
+            #     images=frame_pil, return_tensors="pt")
+            unprocessed_images.append(frame_pil)
+        """Get VAE features from the first frame of each video"""
+        video_conditions = []
+        for frame in unprocessed_images:
+
+            latent = self.vae_encoding_stage.encode_image(
+                frame, height, width, fastvideo_args, generator)
+            video_conditions.append(latent)
+
+        features["image_condition_latents"] = video_conditions
+        features["pil_images"] = pil_images
+        return features
+
+    def create_record(
+            self,
+            video_name: str,
+            vae_latent: np.ndarray,
+            text_embedding: np.ndarray,
+            valid_data: dict[str, Any],
+            idx: int,
+            extra_features: dict[str, Any] | None = None) -> dict[str, Any]:
+        """Create a record for the Parquet dataset with CLIP features."""
+        record = super().create_record(video_name=video_name,
+                                       vae_latent=vae_latent,
+                                       text_embedding=text_embedding,
+                                       valid_data=valid_data,
+                                       idx=idx,
+                                       extra_features=extra_features)
+
+        if extra_features and "image_condition_latents" in extra_features:
+            image_condition_latents = extra_features["image_condition_latents"]
+            record.update({
+                "image_condition_latents_bytes":
+                image_condition_latents.tobytes(),
+                "image_condition_latents_shape":
+                list(image_condition_latents.shape),
+                "image_condition_latents_dtype":
+                str(image_condition_latents.dtype),
+            })
+        else:
+            record.update({
+                "image_condition_latents_bytes": b"",
+                "image_condition_latents_shape": [],
+                "image_condition_latents_dtype": "",
+            })
+
+        if extra_features and "trajectory_latents" in extra_features:
+            trajectory_latents = extra_features["trajectory_latents"]
+            record.update({
+                "trajectory_latents_bytes":
+                trajectory_latents.tobytes(),
+                "trajectory_latents_shape":
+                list(trajectory_latents.shape),
+                "trajectory_latents_dtype":
+                str(trajectory_latents.dtype),
+            })
+        else:
+            record.update({
+                "trajectory_latents_bytes": b"",
+                "trajectory_latents_shape": [],
+                "trajectory_latents_dtype": "",
+            })
+
+        if extra_features and "trajectory_timesteps" in extra_features:
+            trajectory_timesteps = extra_features["trajectory_timesteps"]
+            record.update({
+                "trajectory_timesteps_bytes":
+                trajectory_timesteps.tobytes(),
+                "trajectory_timesteps_shape":
+                list(trajectory_timesteps.shape),
+                "trajectory_timesteps_dtype":
+                str(trajectory_timesteps.dtype),
+            })
+        else:
+            record.update({
+                "trajectory_timesteps_bytes": b"",
+                "trajectory_timesteps_shape": [],
+                "trajectory_timesteps_dtype": "",
+            })
+
+        if extra_features and "pil_image" in extra_features:
+            pil_image = extra_features["pil_image"]
+            record.update({
+                "pil_image_bytes": pil_image.tobytes(),
+                "pil_image_shape": list(pil_image.shape),
+                "pil_image_dtype": str(pil_image.dtype),
+            })
+        else:
+            record.update({
+                "pil_image_bytes": b"",
+                "pil_image_shape": [],
+                "pil_image_dtype": "",
+            })
+
+        return record
 
     def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs, args):
         if not self.post_init_called:
@@ -417,7 +628,7 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
         os.makedirs(self.combined_parquet_dir, exist_ok=True)
 
         # Loading dataset
-        train_dataset = gettextdataset(args)
+        train_dataset = getdataset(args)
 
         self.preprocess_dataloader = DataLoader(
             train_dataset,
@@ -437,7 +648,7 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
         # Initialize class variables for data sharing
         self.video_data: dict[str, Any] = {}  # Store video metadata and paths
         self.latent_data: dict[str, Any] = {}  # Store latent tensors
-        self.preprocess_text_and_trajectory(fastvideo_args, args)
+        self.preprocess_video_and_text_and_trajectory(fastvideo_args, args)
 
 
 EntryClass = PreprocessPipeline_ODE_Trajectory

fastvideo/pipelines/preprocess/preprocess_pipeline_t2v.py

@@ -15,9 +15,9 @@ class PreprocessPipeline_T2V(BasePreprocessPipeline):
 
     _required_config_modules = ["text_encoder", "tokenizer", "vae"]
 
-    def get_pyarrow_schema(self):
-        """Return the PyArrow schema for T2V pipeline."""
-        return pyarrow_schema_t2v
+    def get_schema_fields(self):
+        """Get the schema fields for T2V pipeline."""
+        return [f.name for f in pyarrow_schema_t2v]
 
 
 EntryClass = PreprocessPipeline_T2V

fastvideo/pipelines/preprocess/preprocess_pipeline_text.py

@@ -1,184 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Text-only Data Preprocessing pipeline implementation.
-
-This module contains an implementation of the Text-only Data Preprocessing pipeline
-using the modular pipeline architecture, based on the ODE Trajectory preprocessing.
-"""
-
-import os
-from collections.abc import Iterator
-from typing import Any
-
-import torch
-from torch.utils.data import DataLoader
-from torchdata.stateful_dataloader import StatefulDataLoader
-from tqdm import tqdm
-
-from fastvideo.dataset import gettextdataset
-from fastvideo.dataset.dataloader.parquet_io import (ParquetDatasetWriter,
-                                                     records_to_table)
-from fastvideo.dataset.dataloader.record_schema import text_only_record_creator
-from fastvideo.dataset.dataloader.schema import pyarrow_schema_text_only
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.preprocess.preprocess_pipeline_base import (
-    BasePreprocessPipeline)
-from fastvideo.pipelines.stages import TextEncodingStage
-
-logger = init_logger(__name__)
-
-
-class PreprocessPipeline_Text(BasePreprocessPipeline):
-    """Text-only preprocessing pipeline implementation."""
-
-    _required_config_modules = ["text_encoder", "tokenizer"]
-    preprocess_dataloader: StatefulDataLoader
-    preprocess_loader_iter: Iterator[dict[str, Any]]
-    pbar: Any
-    num_processed_samples: int = 0
-
-    def get_pyarrow_schema(self):
-        """Return the PyArrow schema for text-only pipeline."""
-        return pyarrow_schema_text_only
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        """Set up pipeline stages with proper dependency injection."""
-        self.add_stage(stage_name="prompt_encoding_stage",
-                       stage=TextEncodingStage(
-                           text_encoders=[self.get_module("text_encoder")],
-                           tokenizers=[self.get_module("tokenizer")],
-                       ))
-
-    def preprocess_text_only(self, fastvideo_args: FastVideoArgs, args):
-        """Preprocess text-only data."""
-
-        for batch_idx, data in enumerate(self.pbar):
-            if data is None:
-                continue
-
-            with torch.inference_mode():
-                # For text-only processing, we only need text data
-                # Filter out samples without text
-                valid_indices = []
-                for i, text in enumerate(data["text"]):
-                    if text and text.strip():  # Check if text is not empty
-                        valid_indices.append(i)
-                self.num_processed_samples += len(valid_indices)
-
-                if not valid_indices:
-                    continue
-
-                # Create new batch with only valid samples (text-only)
-                valid_data = {
-                    "text": [data["text"][i] for i in valid_indices],
-                    "path": [data["path"][i] for i in valid_indices],
-                }
-
-                batch_captions = valid_data["text"]
-                # Encode text using the standalone TextEncodingStage API
-                prompt_embeds_list, prompt_masks_list = self.prompt_encoding_stage.encode_text(
-                    batch_captions,
-                    fastvideo_args,
-                    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]
-
-                logger.info("===== prompt_embeds: %s", prompt_embeds.shape)
-                logger.info("===== prompt_attention_masks: %s",
-                            prompt_attention_masks.shape)
-
-                # Prepare batch data for Parquet dataset
-                batch_data = []
-
-                # Add progress bar for saving outputs
-                save_pbar = tqdm(enumerate(valid_data["path"]),
-                                 desc="Saving outputs",
-                                 unit="item",
-                                 leave=False)
-
-                for idx, text_path in save_pbar:
-                    text_name = os.path.basename(text_path).split(".")[0]
-
-                    # Convert tensors to numpy arrays
-                    text_embedding = prompt_embeds[idx].cpu().numpy()
-
-                    # Create record for Parquet dataset (text-only schema)
-                    record = text_only_record_creator(
-                        text_name=text_name,
-                        text_embedding=text_embedding,
-                        caption=valid_data["text"][idx],
-                    )
-                    batch_data.append(record)
-
-                if batch_data:
-                    write_pbar = tqdm(total=1,
-                                      desc="Writing to Parquet dataset",
-                                      unit="batch")
-                    table = records_to_table(batch_data,
-                                             pyarrow_schema_text_only)
-                    write_pbar.update(1)
-                    write_pbar.close()
-
-                    if not hasattr(self, 'dataset_writer'):
-                        self.dataset_writer = ParquetDatasetWriter(
-                            out_dir=self.combined_parquet_dir,
-                            samples_per_file=args.samples_per_file,
-                        )
-                    self.dataset_writer.append_table(table)
-
-                    logger.info("Collected batch with %s samples", len(table))
-
-                if self.num_processed_samples >= args.flush_frequency:
-                    written = self.dataset_writer.flush()
-                    logger.info("Flushed %s samples to parquet", written)
-                    self.num_processed_samples = 0
-
-        # Final flush for any remaining samples
-        if hasattr(self, 'dataset_writer'):
-            written = self.dataset_writer.flush(write_remainder=True)
-            if written:
-                logger.info("Final flush wrote %s samples", written)
-
-    # Text-only record creation moved to fastvideo.dataset.dataloader.record_schema
-
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs, args):
-        if not self.post_init_called:
-            self.post_init()
-
-        self.local_rank = int(os.getenv("RANK", 0))
-        os.makedirs(args.output_dir, exist_ok=True)
-        # Create directory for combined data
-        self.combined_parquet_dir = os.path.join(args.output_dir,
-                                                 "combined_parquet_dataset")
-        os.makedirs(self.combined_parquet_dir, exist_ok=True)
-
-        # Loading text dataset
-        train_dataset = gettextdataset(args)
-
-        self.preprocess_dataloader = DataLoader(
-            train_dataset,
-            batch_size=args.preprocess_video_batch_size,
-            num_workers=args.dataloader_num_workers,
-        )
-
-        self.preprocess_loader_iter = iter(self.preprocess_dataloader)
-
-        self.num_processed_samples = 0
-        # Add progress bar for text preprocessing
-        self.pbar = tqdm(self.preprocess_loader_iter,
-                         desc="Processing text",
-                         unit="batch",
-                         disable=self.local_rank != 0)
-
-        # Initialize class variables for data sharing
-        self.text_data: dict[str, Any] = {}  # Store text metadata and paths
-
-        self.preprocess_text_only(fastvideo_args, args)
-
-
-EntryClass = PreprocessPipeline_Text

fastvideo/pipelines/preprocess/preprocess_stages.py

@@ -4,11 +4,9 @@ from typing import cast
 
 import numpy as np
 import torch
-import torchvision
 from einops import rearrange
 from torchvision import transforms
 
-from fastvideo.configs.configs import VideoLoaderType
 from fastvideo.dataset.transform import (CenterCropResizeVideo,
                                          TemporalRandomCrop)
 from fastvideo.fastvideo_args import FastVideoArgs, WorkloadType
@@ -63,16 +61,7 @@ class VideoTransformStage(PipelineStage):
                 else:
                     frame_indices = frame_indices[:self.num_frames]
 
-            if fastvideo_args.preprocess_config.video_loader_type == VideoLoaderType.TORCHCODEC:
-                video = batch.video_loader[i].get_frames_at(frame_indices).data
-            elif fastvideo_args.preprocess_config.video_loader_type == VideoLoaderType.TORCHVISION:
-                video, _, _ = torchvision.io.read_video(batch.video_loader[i],
-                                                        output_format="TCHW")
-                video = video[frame_indices]
-            else:
-                raise ValueError(
-                    f"Invalid video loader type: {fastvideo_args.preprocess_config.video_loader_type}"
-                )
+            video = batch.video_loader[i].get_frames_at(frame_indices).data
             video = self.video_transform(video)
             video_pixel_batch.append(video)
 

fastvideo/pipelines/preprocess/v1_preprocess.py

@@ -1,6 +1,5 @@
 import argparse
 import os
-from typing import Any
 
 from fastvideo import PipelineConfig
 from fastvideo.configs.models.vaes import WanVAEConfig
@@ -14,8 +13,6 @@ from fastvideo.pipelines.preprocess.preprocess_pipeline_ode_trajectory import (
     PreprocessPipeline_ODE_Trajectory)
 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__)
@@ -26,22 +23,13 @@ def main(args) -> None:
     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)
-
-    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),
-        }
+    kwargs = {
+        "vae_precision": "fp32",
+        "vae_config": WanVAEConfig(load_encoder=True, load_decoder=True),
+        "flow_shift": 5,
+    }
     pipeline_config.update_config_from_dict(kwargs)
-
     fastvideo_args = FastVideoArgs(
         model_path=args.model_path,
         num_gpus=get_world_size(),
@@ -54,18 +42,14 @@ def main(args) -> None:
         PreprocessPipeline = PreprocessPipeline_T2V
     elif args.preprocess_task == "i2v":
         PreprocessPipeline = PreprocessPipeline_I2V
-    elif args.preprocess_task == "text_only":
-        PreprocessPipeline = PreprocessPipeline_Text
     elif args.preprocess_task == "ode_trajectory":
-        assert args.flow_shift is not None, "flow_shift is required for ode_trajectory"
-        fastvideo_args.pipeline_config.flow_shift = args.flow_shift
         PreprocessPipeline = PreprocessPipeline_ODE_Trajectory
     else:
-        raise ValueError(f"Invalid preprocess task: {args.preprocess_task}. "
-                         f"Valid options: t2v, i2v, ode_trajectory, text_only")
+        raise ValueError(f"Invalid preprocess task: {args.preprocess_task}")
 
-    logger.info("Preprocess task: %s using %s", args.preprocess_task,
-                PreprocessPipeline.__name__)
+    logger.info(
+        f"Preprocess task: {args.preprocess_task} using {PreprocessPipeline.__name__}"
+    )
 
     pipeline = PreprocessPipeline(args.model_path, fastvideo_args)
     pipeline.forward(batch=None, fastvideo_args=fastvideo_args, args=args)
@@ -105,12 +89,7 @@ if __name__ == "__main__":
     parser.add_argument("--video_length_tolerance_range", type=int, default=2.0)
     parser.add_argument("--group_frame", action="store_true")  # TODO
     parser.add_argument("--group_resolution", action="store_true")  # TODO
-    parser.add_argument("--flow_shift", type=float, default=None)
-    parser.add_argument("--preprocess_task",
-                        type=str,
-                        default="t2v",
-                        choices=["t2v", "i2v", "text_only", "ode_trajectory"],
-                        help="Type of preprocessing task to run")
+    parser.add_argument("--preprocess_task", type=str, default="t2v")
     parser.add_argument("--train_fps", type=int, default=30)
     parser.add_argument("--use_image_num", type=int, default=0)
     parser.add_argument("--text_max_length", type=int, default=256)

fastvideo/pipelines/stages/causal_denoising.py

@@ -4,11 +4,11 @@ from fastvideo.distributed import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.forward_context import set_forward_context
 from fastvideo.logger import init_logger
+from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
+    FlowMatchEulerDiscreteScheduler)
 from fastvideo.models.utils import pred_noise_to_pred_video
 from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
 from fastvideo.pipelines.stages.denoising import DenoisingStage
-from fastvideo.pipelines.stages.validators import StageValidators as V
-from fastvideo.pipelines.stages.validators import VerificationResult
 
 try:
     from fastvideo.attention.backends.sliding_tile_attn import (
@@ -36,6 +36,7 @@ class CausalDMDDenosingStage(DenoisingStage):
 
     def __init__(self, transformer, scheduler) -> None:
         super().__init__(transformer, scheduler)
+        self.scheduler = FlowMatchEulerDiscreteScheduler(shift=8.0)
         # KV and cross-attention cache state (initialized on first forward)
         self.kv_cache1: list | None = None
         self.crossattn_cache: list | None = None
@@ -70,18 +71,8 @@ class CausalDMDDenosingStage(DenoisingStage):
         # Timesteps for DMD
         timesteps = torch.tensor(
             fastvideo_args.pipeline_config.dmd_denoising_steps,
-            dtype=torch.long).cpu()
-
-        if fastvideo_args.pipeline_config.warp_denoising_step:
-            logger.info("Warping timesteps...")
-            scheduler_timesteps = torch.cat((self.scheduler.timesteps.cpu(),
-                                             torch.tensor([0],
-                                                          dtype=torch.float32)))
-            timesteps = scheduler_timesteps[1000 - timesteps]
-        else:
-            assert False, "warp_denoising_step must be true"
-        timesteps = timesteps.to(get_local_torch_device())
-        logger.info("Using timesteps: %s", timesteps)
+            dtype=torch.long,
+            device=get_local_torch_device())
 
         # Image kwargs (kept empty unless caller provides compatible args)
         image_kwargs: dict = {}
@@ -237,7 +228,10 @@ class CausalDMDDenosingStage(DenoisingStage):
                                                        dim=2)
 
                     # Prepare inputs
-                    t_expand = t_cur.repeat(latent_model_input.shape[0])
+                    t_expand = t_cur.expand(latent_model_input.shape[0])
+                    # t_expand = t_cur * torch.ones((latent_model_input.shape[0], 1), device=latent_model_input.device, dtype=torch.long)
+                    # t_expand = t_expand.repeat(1, self.sliding_window_num_frames)
+
 
                     # Attention metadata if needed
                     if (vsa_available and self.attn_backend
@@ -271,10 +265,7 @@ class CausalDMDDenosingStage(DenoisingStage):
                                             attn_metadata=attn_metadata,
                                             forward_batch=batch):
                         # Run transformer; follow DMD stage pattern
-                        t_expanded_noise = t_cur * torch.ones(
-                            (latent_model_input.shape[0], 1),
-                            device=latent_model_input.device,
-                            dtype=torch.long)
+                        t_expanded_noise= t_cur * torch.ones((latent_model_input.shape[0], 1), device=latent_model_input.device, dtype=torch.long)
                         pred_noise_btchw = self.transformer(
                             latent_model_input,
                             prompt_embeds,
@@ -339,7 +330,7 @@ class CausalDMDDenosingStage(DenoisingStage):
                     set_forward_context(current_timestep=0,
                                         attn_metadata=attn_metadata,
                                         forward_batch=batch):
-                    t_expanded_context = t_context.unsqueeze(1)
+                    t_expanded_context = t_context * torch.ones((context_bcthw.shape[0], 1), device=context_bcthw.device, dtype=torch.long)
                     _ = self.transformer(
                         context_bcthw,
                         prompt_embeds,
@@ -421,27 +412,3 @@ class CausalDMDDenosingStage(DenoisingStage):
                 False,
             })
         self.crossattn_cache = crossattn_cache
-
-    def verify_input(self, batch: ForwardBatch,
-                     fastvideo_args: FastVideoArgs) -> VerificationResult:
-        """Verify denoising stage inputs."""
-        result = VerificationResult()
-        result.add_check("latents", batch.latents,
-                         [V.is_tensor, V.with_dims(5)])
-        result.add_check("prompt_embeds", batch.prompt_embeds, V.list_not_empty)
-        result.add_check("image_embeds", batch.image_embeds, V.is_list)
-        result.add_check("image_latent", batch.image_latent,
-                         V.none_or_tensor_with_dims(5))
-        result.add_check("num_inference_steps", batch.num_inference_steps,
-                         V.positive_int)
-        result.add_check("guidance_scale", batch.guidance_scale,
-                         V.positive_float)
-        result.add_check("eta", batch.eta, V.non_negative_float)
-        result.add_check("generator", batch.generator,
-                         V.generator_or_list_generators)
-        result.add_check("do_classifier_free_guidance",
-                         batch.do_classifier_free_guidance, V.bool_value)
-        result.add_check(
-            "negative_prompt_embeds", batch.negative_prompt_embeds, lambda x:
-            not batch.do_classifier_free_guidance or V.list_not_empty(x))
-        return result

fastvideo/pipelines/stages/decoding.py

@@ -53,20 +53,7 @@ class DecodingStage(PipelineStage):
     @torch.no_grad()
     def decode(self, latents: torch.Tensor,
                fastvideo_args: FastVideoArgs) -> torch.Tensor:
-        """
-        Decode latent representations into pixel space using VAE.
-        
-        Args:
-            latents: Input latent tensor with shape (batch, channels, frames, height_latents, width_latents)
-            fastvideo_args: Configuration containing:
-                - disable_autocast: Whether to disable automatic mixed precision (default: False)
-                - pipeline_config.vae_precision: VAE computation precision ("fp32", "fp16", "bf16")
-                - pipeline_config.vae_tiling: Whether to enable VAE tiling for memory efficiency
-            
-        Returns:
-            Decoded video tensor with shape (batch, channels, frames, height, width), 
-            normalized to [0, 1] range and moved to CPU as float32
-        """
+        """Decode latents into pixel space."""
         self.vae = self.vae.to(get_local_torch_device())
         latents = latents.to(get_local_torch_device())
 
@@ -116,26 +103,12 @@ class DecodingStage(PipelineStage):
         """
         Decode latent representations into pixel space.
         
-        This method processes the batch through the VAE decoder, converting latent
-        representations to pixel-space video/images. It also optionally decodes
-        trajectory latents for visualization purposes.
-        
         Args:
-            batch: The current batch containing:
-                - latents: Tensor to decode (batch, channels, frames, height_latents, width_latents)
-                - return_trajectory_decoded (optional): Flag to decode trajectory latents
-                - trajectory_latents (optional): Latents at different timesteps
-                - trajectory_timesteps (optional): Corresponding timesteps
-            fastvideo_args: Configuration containing:
-                - output_type: "latent" to skip decoding, otherwise decode to pixels
-                - vae_cpu_offload: Whether to offload VAE to CPU after decoding
-                - model_loaded: Track VAE loading state
-                - model_paths: Path to VAE model if loading needed
+            batch: The current batch information.
+            fastvideo_args: The inference arguments.
             
         Returns:
-            Modified batch with:
-                - output: Decoded frames (batch, channels, frames, height, width) as CPU float32
-                - trajectory_decoded (if requested): List of decoded frames per timestep
+            The batch with decoded outputs.
         """
         # load vae if not already loaded (used for memory constrained devices)
         pipeline = self.pipeline() if self.pipeline else None
@@ -155,13 +128,15 @@ class DecodingStage(PipelineStage):
         # decode trajectory latents if needed
         if batch.return_trajectory_decoded:
             batch.trajectory_decoded = []
+            logger.info(f"batch.trajectory_latents.shape: {batch.trajectory_latents.shape}")
             assert batch.trajectory_latents is not None, "batch should have trajectory latents"
             for idx in range(batch.trajectory_latents.shape[1]):
-                # batch.trajectory_latents is [batch_size, timesteps, channels, frames, height, width]
+                # bathc.trajectory_latents is [batch_size, timesteps, channels, frames, height, width]
                 cur_latent = batch.trajectory_latents[:, idx, :, :, :, :]
+                logger.info(f"cur_latent.shape: {cur_latent.shape}")
                 cur_timestep = batch.trajectory_timesteps[idx]
-                logger.info("decoding trajectory latent for timestep: %s",
-                            cur_timestep)
+                logger.info(
+                    f"decoding trajectory latent for timestep: {cur_timestep}")
                 decoded_frames = self.decode(cur_latent, fastvideo_args)
                 batch.trajectory_decoded.append(decoded_frames.cpu().float())
 

fastvideo/pipelines/stages/denoising.py

@@ -40,13 +40,6 @@ try:
 except ImportError:
     st_attn_available = False
 
-try:
-    from fastvideo.attention.backends.vmoba import VMOBAAttentionBackend
-    from fastvideo.utils import is_vmoba_available
-    vmoba_attn_available = is_vmoba_available()
-except ImportError:
-    vmoba_attn_available = False
-
 try:
     from fastvideo.attention.backends.video_sparse_attn import (
         VideoSparseAttentionBackend)
@@ -84,7 +77,6 @@ class DenoisingStage(PipelineStage):
             supported_attention_backends=(
                 AttentionBackendEnum.SLIDING_TILE_ATTN,
                 AttentionBackendEnum.VIDEO_SPARSE_ATTN,
-                AttentionBackendEnum.VMOBA_ATTN,
                 AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA
             )  # hack
         )
@@ -158,8 +150,7 @@ class DenoisingStage(PipelineStage):
         # Prepare image latents and embeddings for I2V generation
         image_embeds = batch.image_embeds
         if len(image_embeds) > 0:
-            assert not torch.isnan(
-                image_embeds[0]).any(), "image_embeds contains nan"
+            assert torch.isnan(image_embeds[0]).sum() == 0
             image_embeds = [
                 image_embed.to(target_dtype) for image_embed in image_embeds
             ]
@@ -196,23 +187,15 @@ class DenoisingStage(PipelineStage):
         # Get latents and embeddings
         latents = batch.latents
         prompt_embeds = batch.prompt_embeds
-        assert not torch.isnan(
-            prompt_embeds[0]).any(), "prompt_embeds contains nan"
+        assert torch.isnan(prompt_embeds[0]).sum() == 0
         if batch.do_classifier_free_guidance:
             neg_prompt_embeds = batch.negative_prompt_embeds
             assert neg_prompt_embeds is not None
-            assert not torch.isnan(
-                neg_prompt_embeds[0]).any(), "neg_prompt_embeds contains nan"
+            assert torch.isnan(neg_prompt_embeds[0]).sum() == 0
 
         # (Wan2.2) Calculate timestep to switch from high noise expert to low noise expert
-        boundary_ratio = fastvideo_args.pipeline_config.dit_config.boundary_ratio
-        if batch.boundary_ratio is not None:
-            logger.info("Overriding boundary ratio from %s to %s",
-                        boundary_ratio, batch.boundary_ratio)
-            boundary_ratio = batch.boundary_ratio
-
-        if boundary_ratio is not None:
-            boundary_timestep = boundary_ratio * self.scheduler.num_train_timesteps
+        if fastvideo_args.boundary_ratio is not None:
+            boundary_timestep = fastvideo_args.boundary_ratio * self.scheduler.num_train_timesteps
         else:
             boundary_timestep = None
         latent_model_input = latents.to(target_dtype)
@@ -302,9 +285,6 @@ class DenoisingStage(PipelineStage):
                         logger.info("latent_model_input.shape: %s",
                                     latent_model_input.shape)
 
-                assert not torch.isnan(
-                    latent_model_input).any(), "latent_model_input contains nan"
-
                 if fastvideo_args.pipeline_config.ti2v_task and batch.pil_image is not None:
                     timestep = torch.stack([t]).to(get_local_torch_device())
                     temp_ts = (mask2[0][0][:, ::2, ::2] * timestep).flatten()
@@ -317,6 +297,7 @@ class DenoisingStage(PipelineStage):
                 else:
                     t_expand = t.repeat(latent_model_input.shape[0])
 
+                assert torch.isnan(latent_model_input).sum() == 0
                 latent_model_input = self.scheduler.scale_model_input(
                     latent_model_input, t)
                 if fastvideo_args.pipeline_config.t2v_as_i2v_task:
@@ -368,31 +349,6 @@ class DenoisingStage(PipelineStage):
                             assert attn_metadata is not None, "attn_metadata cannot be None"
                         else:
                             attn_metadata = None
-                    elif (vmoba_attn_available
-                          and self.attn_backend == VMOBAAttentionBackend):
-                        self.attn_metadata_builder_cls = self.attn_backend.get_builder_cls(
-                        )
-                        if self.attn_metadata_builder_cls is not None:
-                            self.attn_metadata_builder = self.attn_metadata_builder_cls(
-                            )
-                            # Prepare V-MoBA parameters from config
-                            moba_params = fastvideo_args.moba_config.copy()
-                            moba_params.update({
-                                "current_timestep":
-                                i,
-                                "raw_latent_shape":
-                                batch.raw_latent_shape[2:5],
-                                "patch_size":
-                                fastvideo_args.pipeline_config.dit_config.
-                                patch_size,
-                                "device":
-                                get_local_torch_device(),
-                            })
-                            attn_metadata = self.attn_metadata_builder.build(
-                                **moba_params)
-                            assert attn_metadata is not None, "attn_metadata cannot be None"
-                        else:
-                            attn_metadata = None
                     else:
                         attn_metadata = None
                     # TODO(will): finalize the interface. vLLM uses this to
@@ -460,6 +416,7 @@ class DenoisingStage(PipelineStage):
                 # save trajectory latents if needed
                 if batch.return_trajectory_latents:
                     trajectory_timesteps.append(t)
+                    # trajectory_latents.append(latents.cpu())
                     trajectory_latents.append(latents)
 
                 # Update progress bar
@@ -473,16 +430,14 @@ class DenoisingStage(PipelineStage):
         trajectory_tensor: torch.Tensor | None = None
         if trajectory_latents:
             trajectory_tensor = torch.stack(trajectory_latents, dim=1)
-            trajectory_timesteps_tensor = torch.stack(trajectory_timesteps,
-                                                      dim=0)
         else:
             trajectory_tensor = None
-            trajectory_timesteps_tensor = None
 
-        # Gather results if using sequence parallelism
         if sp_group:
             latents = sequence_model_parallel_all_gather(latents, dim=2)
             if batch.return_trajectory_latents:
+                # logger.info("before stack trajectory_latents.shape: %s", trajectory_latents[0].shape)
+                logger.info("after stack trajectory_latents.shape: %s", trajectory_tensor.shape)
                 trajectory_tensor = trajectory_tensor.to(
                     get_local_torch_device())
                 trajectory_tensor = sequence_model_parallel_all_gather(
@@ -836,8 +791,7 @@ class DmdDenoisingStage(DenoisingStage):
 
         video_raw_latent_shape = latents.shape
         prompt_embeds = batch.prompt_embeds
-        assert not torch.isnan(
-            prompt_embeds[0]).any(), "prompt_embeds contains nan"
+        assert torch.isnan(prompt_embeds[0]).sum() == 0
         timesteps = torch.tensor(
             fastvideo_args.pipeline_config.dmd_denoising_steps,
             dtype=torch.long,
@@ -876,8 +830,7 @@ class DmdDenoisingStage(DenoisingStage):
                         batch.image_latent.permute(0, 2, 1, 3, 4)
                     ],
                                                    dim=2).to(target_dtype)
-                assert not torch.isnan(
-                    latent_model_input).any(), "latent_model_input contains nan"
+                assert torch.isnan(latent_model_input).sum() == 0
 
                 # Prepare inputs for transformer
                 t_expand = t.repeat(latent_model_input.shape[0])

fastvideo/pipelines/stages/encoding.py

@@ -92,6 +92,24 @@ class EncodingStage(PipelineStage):
                 latents = latents.to(vae_dtype)
             latents = self.vae.encode(latents).mean
 
+        # Apply shifting if needed (reverse of decoding)
+        if (hasattr(self.vae, "shift_factor")
+                and self.vae.shift_factor is not None):
+            if isinstance(self.vae.shift_factor, torch.Tensor):
+                latents -= self.vae.shift_factor.to(latents.device,
+                                                    latents.dtype)
+            else:
+                latents -= self.vae.shift_factor
+
+        # Apply scaling factor
+        if (hasattr(self.vae, "scaling_factor")
+                and self.vae.scaling_factor is not None):
+            if isinstance(self.vae.scaling_factor, torch.Tensor):
+                latents = latents * self.vae.scaling_factor.to(
+                    latents.device, latents.dtype)
+            else:
+                latents = latents * self.vae.scaling_factor
+
         # Update batch with encoded latents
         batch.latents = latents
 

fastvideo/pipelines/stages/latent_preparation.py

@@ -3,7 +3,6 @@
 Latent preparation stage for diffusion pipelines.
 """
 
-import torch
 from diffusers.utils.torch_utils import randn_tensor
 
 from fastvideo.distributed import get_local_torch_device
@@ -60,6 +59,10 @@ class LatentPreparationStage(PipelineStage):
 
         # Adjust batch size for number of videos per prompt
         batch_size *= batch.num_videos_per_prompt
+        logger.info(f"===== batch_size: {batch_size}")
+        logger.info(f"===== batch.prompt: {batch.prompt}")
+        logger.info(f"===== batch.prompt_embeds: {batch.prompt_embeds}")
+        logger.info(f"===== batch.prompt_attention_mask: {batch.prompt_attention_mask}")
 
         # Get required parameters
         dtype = batch.prompt_embeds[0].dtype
@@ -94,7 +97,7 @@ class LatentPreparationStage(PipelineStage):
         # Generate or use provided latents
         if latents is None:
             latents = randn_tensor(shape,
-                                   generator=torch.Generator(device="cuda").manual_seed(1024),
+                                   generator=generator,
                                    device=device,
                                    dtype=dtype)
         else:

fastvideo/platforms/cuda.py

@@ -159,20 +159,6 @@ class CudaPlatformBase(Platform):
                     str(e))
                 raise ImportError(
                     "Video Sparse Attention backend is not installed. ") from e
-        elif selected_backend == AttentionBackendEnum.VMOBA_ATTN:
-            try:
-                from csrc.attn.vmoba_attn.vmoba import (  # noqa: F401
-                    moba_attn_varlen)
-                from fastvideo.attention.backends.vmoba import (  # noqa: F401
-                    VMOBAAttentionBackend)
-                logger.info("Using Video MOBA Attention backend.")
-
-                return "fastvideo.attention.backends.vmoba.VMOBAAttentionBackend"
-            except ImportError as e:
-                logger.error(
-                    "Failed to import Video MoBA Attention backend: %s", str(e))
-                raise ImportError(
-                    "Video MoBA Attention backend is not installed. ") from e
         elif selected_backend == AttentionBackendEnum.TORCH_SDPA:
             logger.info("Using Torch SDPA backend.")
             return "fastvideo.attention.backends.sdpa.SDPABackend"

fastvideo/platforms/interface.py

@@ -19,7 +19,6 @@ class AttentionBackendEnum(enum.Enum):
     TORCH_SDPA = enum.auto()
     SAGE_ATTN = enum.auto()
     VIDEO_SPARSE_ATTN = enum.auto()
-    VMOBA_ATTN = enum.auto()
     NO_ATTENTION = enum.auto()
 
 

fastvideo/tests/dataset/test_parquet_io.py

@@ -1,108 +0,0 @@
-import os
-from pathlib import Path
-
-import pyarrow as pa
-import pyarrow.parquet as pq
-
-from fastvideo.dataset.dataloader.parquet_io import (
-    ParquetDatasetWriter,
-    records_to_table,
-)
-
-
-def test_records_to_table_types():
-    schema = pa.schema([
-        pa.field("id", pa.string()),
-        pa.field("vae_latent_bytes", pa.binary()),
-        pa.field("vae_latent_shape", pa.list_(pa.int64())),
-        pa.field("duration_sec", pa.float64()),
-        pa.field("width", pa.int64()),
-    ])
-    records = [{
-        "id": "a",
-        "vae_latent_bytes": b"\x00\x01",
-        "vae_latent_shape": [1, 2, 3],
-        "duration_sec": 1.5,
-        "width": 640,
-    }]
-
-    table = records_to_table(records, schema)
-    assert table.schema == schema
-    assert table.num_rows == 1
-    cols = {name: table.column(name).to_pylist()[0] for name in schema.names}
-    assert cols["id"] == "a"
-    assert isinstance(cols["vae_latent_bytes"], (bytes, bytearray))
-    assert cols["vae_latent_shape"] == [1, 2, 3]
-    assert abs(cols["duration_sec"] - 1.5) < 1e-6
-    assert cols["width"] == 640
-
-
-def test_writer_flush_and_remainder(tmp_path: Path):
-    schema = pa.schema([pa.field("id", pa.string())])
-    records = [{"id": str(i)} for i in range(25)]
-    table = records_to_table(records, schema)
-
-    out_dir = tmp_path / "out"
-    writer = ParquetDatasetWriter(str(out_dir), samples_per_file=10)
-    writer.append_table(table)
-    written = writer.flush(num_workers=1)
-    assert written == 20
-
-    files = sorted(out_dir.rglob("*.parquet"))
-    assert len(files) == 2
-    total_rows = sum(pq.read_table(str(f)).num_rows for f in files)
-    assert total_rows == 20
-
-    # Append remainder to complete another chunk
-    extra = records_to_table([{"id": str(i)} for i in range(5)], schema)
-    writer.append_table(extra)
-    written2 = writer.flush(num_workers=1)
-    assert written2 == 10
-    files2 = sorted(out_dir.rglob("*.parquet"))
-    assert len(files2) == 3
-    total_rows2 = sum(pq.read_table(str(f)).num_rows for f in files2)
-    assert total_rows2 == 30
-
-
-def test_writer_flush_write_remainder(tmp_path: Path):
-    schema = pa.schema([pa.field("id", pa.string())])
-    # 25 rows, 10 per file => 2 full files + 1 remainder(5)
-    records = [{"id": str(i)} for i in range(25)]
-    table = records_to_table(records, schema)
-
-    out_dir = tmp_path / "out_last"
-    writer = ParquetDatasetWriter(str(out_dir), samples_per_file=10)
-    writer.append_table(table)
-    # First flush writes 20
-    written1 = writer.flush(num_workers=1)
-    assert written1 == 20
-    # Final flush with remainder
-    written2 = writer.flush(num_workers=1, write_remainder=True)
-    assert written2 == 5
-    files = sorted(out_dir.rglob("*.parquet"))
-    assert len(files) == 3
-    total_rows = sum(pq.read_table(str(f)).num_rows for f in files)
-    assert total_rows == 25
-
-
-def test_writer_parallel_workers(tmp_path: Path):
-    schema = pa.schema([pa.field("id", pa.string())])
-    # 40 rows, 10 per file => 4 files
-    records = [{"id": str(i)} for i in range(40)]
-    table = records_to_table(records, schema)
-
-    out_dir = tmp_path / "out_parallel"
-    writer = ParquetDatasetWriter(str(out_dir), samples_per_file=10)
-    writer.append_table(table)
-    written = writer.flush(num_workers=2)
-    assert written == 40
-
-    # Ensure files exist under worker subdirs
-    worker_dirs = [p for p in out_dir.iterdir() if p.is_dir() and p.name.startswith("worker_")]
-    assert len(worker_dirs) >= 1
-    files = sorted(out_dir.rglob("*.parquet"))
-    assert len(files) == 4
-    total_rows = sum(pq.read_table(str(f)).num_rows for f in files)
-    assert total_rows == 40
-
-

fastvideo/tests/dataset/test_schema_record_creator.py

@@ -1,123 +0,0 @@
-import numpy as np
-
-from fastvideo.dataset.dataloader.record_schema import (
-    basic_t2v_record_creator,
-    i2v_record_creator,
-    ode_text_only_record_creator,
-    text_only_record_creator,
-)
-from fastvideo.pipelines.pipeline_batch_info import PreprocessBatch
-
-
-def _mk_basic_batch(N: int) -> PreprocessBatch:
-    batch = PreprocessBatch(data_type="video")
-    batch.video_file_name = [f"vid_{i}" for i in range(N)]
-    batch.prompt = [f"caption_{i}" for i in range(N)]
-    batch.width = [640 for _ in range(N)]
-    batch.height = [360 for _ in range(N)]
-    batch.fps = [4 for _ in range(N)]
-    batch.num_frames = [2 for _ in range(N)]
-    # Latents: shape (N, C, T, H, W); per-record use latents[idx]
-    batch.latents = np.zeros((N, 4, 2, 8, 8), dtype=np.float32)
-    # Prompt embeds: list of per-record arrays [Seq, Dim]
-    batch.prompt_embeds = [np.ones((6, 16), dtype=np.float32) for _ in range(N)]
-    return batch
-
-
-def test_basic_t2v_record_creator_fields():
-    N = 2
-    batch = _mk_basic_batch(N)
-
-    records = basic_t2v_record_creator(batch)
-    assert isinstance(records, list) and len(records) == N
-
-    for i, rec in enumerate(records):
-        assert rec["id"] == batch.video_file_name[i]
-        # Latents bytes/shape/dtype
-        assert isinstance(rec["vae_latent_bytes"], (bytes, bytearray))
-        assert rec["vae_latent_shape"] == list(batch.latents[i].shape)
-        assert rec["vae_latent_dtype"] == str(batch.latents[i].dtype)
-        # Text embedding
-        assert isinstance(rec["text_embedding_bytes"], (bytes, bytearray))
-        assert rec["text_embedding_shape"] == list(batch.prompt_embeds[i].shape)
-        assert rec["text_embedding_dtype"] == str(batch.prompt_embeds[i].dtype)
-        # Meta
-        assert rec["caption"] == batch.prompt[i]
-        assert rec["media_type"] == "video"
-        assert rec["width"] == int(batch.width[i])
-        assert rec["height"] == int(batch.height[i])
-        assert rec["num_frames"] == batch.latents[i].shape[1]
-
-
-def test_i2v_record_creator_additional_fields():
-    N = 3
-    batch = _mk_basic_batch(N)
-    # image_embeds is a list of length 1, with an array of shape [N, D]
-    batch.image_embeds = [np.ones((N, 32), dtype=np.float32)]
-    # first frame latent per record
-    batch.image_latent = np.zeros((N, 4, 1, 8, 8), dtype=np.float32)
-    # pil image per record
-    batch.pil_image = np.zeros((N, 8, 8, 3), dtype=np.uint8)
-
-    records = i2v_record_creator(batch)
-    assert isinstance(records, list) and len(records) == N
-
-    for i, rec in enumerate(records):
-        # clip feature
-        assert isinstance(rec["clip_feature_bytes"], (bytes, bytearray))
-        assert rec["clip_feature_shape"] == list(batch.image_embeds[0][i].shape)
-        assert rec["clip_feature_dtype"] == str(batch.image_embeds[0][i].dtype)
-        # first frame latent
-        assert isinstance(rec["first_frame_latent_bytes"], (bytes, bytearray))
-        assert rec["first_frame_latent_shape"] == list(batch.image_latent[i].shape)
-        assert rec["first_frame_latent_dtype"] == str(batch.image_latent[i].dtype)
-        # pil image
-        assert isinstance(rec["pil_image_bytes"], (bytes, bytearray))
-        assert rec["pil_image_shape"] == list(batch.pil_image[i].shape)
-        assert rec["pil_image_dtype"] == str(batch.pil_image[i].dtype)
-
-
-def test_ode_text_only_record_creator():
-    video_name = "ex"
-    caption = "a prompt"
-    text_embedding = np.ones((6, 16), dtype=np.float32)
-    traj = np.ones((5, 4, 2, 2), dtype=np.float32)
-    tsteps = np.arange(5, dtype=np.float32)
-
-    rec = ode_text_only_record_creator(
-        video_name=video_name,
-        text_embedding=text_embedding,
-        caption=caption,
-        trajectory_latents=traj,
-        trajectory_timesteps=tsteps,
-    )
-    assert rec["id"] == f"text_{video_name}"
-    assert isinstance(rec["text_embedding_bytes"], (bytes, bytearray))
-    assert rec["text_embedding_shape"] == list(text_embedding.shape)
-    assert rec["text_embedding_dtype"] == str(text_embedding.dtype)
-    assert rec["file_name"] == video_name
-    assert rec["caption"] == caption
-    assert rec["media_type"] == "text"
-    # Trajectory fields
-    assert isinstance(rec["trajectory_latents_bytes"], (bytes, bytearray))
-    assert rec["trajectory_latents_shape"] == list(traj.shape)
-    assert rec["trajectory_latents_dtype"] == str(traj.dtype)
-    assert isinstance(rec["trajectory_timesteps_bytes"], (bytes, bytearray))
-    assert rec["trajectory_timesteps_shape"] == list(tsteps.shape)
-    assert rec["trajectory_timesteps_dtype"] == str(tsteps.dtype)
-
-
-def test_text_only_record_creator():
-    text_name = "note1"
-    caption = "a prompt"
-    text_embedding = np.ones((7, 16), dtype=np.float32)
-    rec = text_only_record_creator(
-        text_name=text_name,
-        text_embedding=text_embedding,
-        caption=caption,
-    )
-    assert rec["id"] == f"text_{text_name}"
-    assert isinstance(rec["text_embedding_bytes"], (bytes, bytearray))
-    assert rec["text_embedding_shape"] == list(text_embedding.shape)
-    assert rec["text_embedding_dtype"] == str(text_embedding.dtype)
-    assert rec["caption"] == caption

fastvideo/tests/encoders/test_t5_encoder.py

@@ -8,9 +8,6 @@ from torch.distributed.tensor import DTensor
 from torch.testing import assert_close
 from transformers import AutoConfig, AutoTokenizer, UMT5EncoderModel
 
-from fastvideo.wan.modules.tokenizers import HuggingfaceTokenizer
-from fastvideo.wan.modules.t5 import umt5_xxl
-
 from fastvideo.configs.pipelines import PipelineConfig
 from fastvideo.forward_context import set_forward_context
 from fastvideo.logger import init_logger
@@ -41,22 +38,9 @@ def test_t5_encoder():
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     precision_str = "fp32"
     precision = PRECISION_TO_TYPE[precision_str]
-    # model1 = UMT5EncoderModel.from_pretrained(TEXT_ENCODER_PATH).to(
-    #     precision).to(device).eval()
-    # tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH)
-    model1 = umt5_xxl(
-        encoder_only=True,
-        return_tokenizer=False,
-        dtype=torch.float32,
-        device=device,
-    ).eval().requires_grad_(False)
-    model1.load_state_dict(
-        torch.load("/mnt/weka/home/hao.zhang/wei/Self-Forcing-clean/wan_models/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth",
-                    map_location='cpu', weights_only=False)
-    )
-
-    tokenizer1 = HuggingfaceTokenizer(
-        name="/mnt/weka/home/hao.zhang/wei/Self-Forcing-clean/wan_models/Wan2.1-T2V-1.3B/google/umt5-xxl/", seq_len=512, clean='whitespace')
+    model1 = UMT5EncoderModel.from_pretrained(TEXT_ENCODER_PATH).to(
+        precision).to(device).eval()
+    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH)
 
 
     args = FastVideoArgs(model_path=TEXT_ENCODER_PATH,
@@ -65,9 +49,8 @@ def test_t5_encoder():
                         pin_cpu_memory=False)
     loader = TextEncoderLoader()
     model2 = loader.load(TEXT_ENCODER_PATH, args)
-    model2 = model2.to(dtype=torch.bfloat16).to(precision)
+    model2 = model2.to(precision)
     model2.eval()
-    tokenizer2 = AutoTokenizer.from_pretrained(TOKENIZER_PATH)
 
     # Sanity check weights between the two models
     logger.info("Comparing model weights for sanity check...")
@@ -78,13 +61,8 @@ def test_t5_encoder():
     logger.info("Model1 has %s parameters", len(params1))
     logger.info("Model2 has %s parameters", len(params2))
 
-    model1_weight_sum = sum(p.float().sum().item() for p in model1.parameters())
-    model2_weight_sum = sum(p.float().sum().item() for p in model2.parameters())
-    logger.info("Model1 weight sum: %s", model1_weight_sum)
-    logger.info("Model2 weight sum: %s", model2_weight_sum)
-
-    # weight_diffs = []
-    # # check if embed_tokens are the same
+    weight_diffs = []
+    # check if embed_tokens are the same
     weights = ["encoder.block.{}.layer.0.layer_norm.weight", "encoder.block.{}.layer.0.SelfAttention.relative_attention_bias.weight", \
                "encoder.block.{}.layer.0.SelfAttention.o.weight", "encoder.block.{}.layer.1.DenseReluDense.wi_0.weight", "encoder.block.{}.layer.1.DenseReluDense.wi_1.weight",\
                 "encoder.block.{}.layer.1.DenseReluDense.wo.weight", \
@@ -92,21 +70,18 @@ def test_t5_encoder():
     
     for idx in range(hf_config.num_hidden_layers):
         for w in weights:
-            # name1 = w.format(idx)
+            name1 = w.format(idx)
             name2 = w.format(idx)
-            # p1 = params1[name1]
+            p1 = params1[name1]
             p2 = params2[name2]
-            p2 = (p2.to_local() if isinstance(p2, DTensor) else p2).to(device)
-            # assert_close(p1, p2, atol=1e-4, rtol=1e-4)
+            p2 = (p2.to_local() if isinstance(p2, DTensor) else p2).to(p1)
+            assert_close(p1, p2, atol=1e-4, rtol=1e-4)
     
 
     # Test with some sample prompts
-    # prompts = [
-    #     "Once upon a time", "The quick brown fox jumps over",
-    #     "In a galaxy far, far away"
-    # ]
     prompts = [
-        "A vibrant scene of Kenyan golfers at a lush green golf course on a sunny day. The golfers, dressed in casual yet stylish attire, are teeing off with animated expressions, showcasing their enthusiasm for the game. Rolling hills and pristine greens stretch out behind them, creating a picturesque backdrop. In the foreground, a golf buggy and a caddy stand ready, adding to the serene atmosphere. The camera captures the action from a mid-shot angle, focusing on the golfers' dynamic motions as they swing their clubs."
+        "Once upon a time", "The quick brown fox jumps over",
+        "In a galaxy far, far away"
     ]
 
     logger.info("Testing T5 encoder with sample prompts")
@@ -116,8 +91,7 @@ def test_t5_encoder():
             logger.info("Testing prompt: %s", prompt)
 
             # Tokenize the prompt
-            tokens1, mask = tokenizer1(prompt, return_mask=True, add_special_tokens=True)
-            tokens2 = tokenizer2(prompt,
+            tokens = tokenizer(prompt,
                                padding="max_length",
                                max_length=512,
                                truncation=True,
@@ -127,23 +101,22 @@ def test_t5_encoder():
             # filter out padding input_ids
             # tokens.input_ids = tokens.input_ids[tokens.attention_mask==1]
             # tokens.attention_mask = tokens.attention_mask[tokens.attention_mask==1]
-            outputs1 = model1(tokens1.to(device),
-                              mask.to(device))
+            outputs1 = model1(input_ids=tokens.input_ids,
+                              attention_mask=tokens.attention_mask,
+                              output_hidden_states=True).last_hidden_state
             print("--------------------------------")
             logger.info("Testing model2")
 
             # Get outputs from our implementation
             with set_forward_context(current_timestep=0, attn_metadata=None):
                 outputs2 = model2(
-                    input_ids=tokens2.input_ids,
-                    attention_mask=tokens2.attention_mask,
+                    input_ids=tokens.input_ids,
+                    attention_mask=tokens.attention_mask,
                 ).last_hidden_state
 
             # Compare last hidden states
-            last_hidden_state1 = outputs1[mask == 1]
-            last_hidden_state2 = outputs2[tokens2.attention_mask == 1]
-            logger.info("last_hidden_state1 sum: %s", last_hidden_state1.float().sum())
-            logger.info("last_hidden_state2 sum: %s", last_hidden_state2.float().sum())
+            last_hidden_state1 = outputs1[tokens.attention_mask == 1]
+            last_hidden_state2 = outputs2[tokens.attention_mask == 1]
 
             assert last_hidden_state1.shape == last_hidden_state2.shape, \
                 f"Hidden state shapes don't match: {last_hidden_state1.shape} vs {last_hidden_state2.shape}"

fastvideo/tests/inference/vmoba/test_vmoba_inference.py

@@ -1,58 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import os
-import subprocess
-from pathlib import Path
-
-def test_inference_vmoba():
-    """Test FastVideo VMOBA_ATTN inference pipeline"""
-
-    num_gpus = "1"
-    model_base = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-    output_dir = Path("outputs_video/vmoba_1.3B/")
-    moba_config = "fastvideo/configs/backend/vmoba/wan_1.3B_77_480_832.json"
-
-    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VMOBA_ATTN"
-
-    cmd = [
-        "fastvideo", "generate",
-        "--model-path", model_base,
-        "--sp-size", num_gpus,
-        "--tp-size", "1",
-        "--num-gpus", num_gpus,
-        "--dit-cpu-offload", "False",
-        "--vae-cpu-offload", "False",
-        "--text-encoder-cpu-offload", "True",
-        "--pin-cpu-memory", "False",
-        "--height", "480",
-        "--width", "832",
-        "--num-frames", "77",
-        "--num-inference-steps", "50",
-        "--moba-config-path", moba_config,
-        "--fps", "16",
-        "--guidance-scale", "6.0",
-        "--flow-shift", "8.0",
-        "--prompt", "A majestic lion strides across the golden savanna, its powerful frame glistening under the warm afternoon sun. The tall grass ripples gently in the breeze, enhancing the lion's commanding presence. The tone is vibrant, embodying the raw energy of the wild. Low angle, steady tracking shot, cinematic.",
-        "--negative-prompt", (
-            "Bright tones, overexposed, static, blurred details, subtitles, style, "
-            "works, paintings, images, static, overall gray, worst quality, low quality, "
-            "JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, "
-            "poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, "
-            "still picture, messy background, three legs, many people in the background, walking backwards"
-        ),
-        "--seed", "1024",
-        "--output-path", str(output_dir),
-    ]
-
-    subprocess.run(cmd, check=True)
-
-    assert output_dir.exists(), f"Output directory {output_dir} does not exist"
-
-    video_files = list(output_dir.glob("*.mp4"))
-    assert len(video_files) > 0, "No video files were generated"
-
-    for video_file in video_files:
-        assert video_file.stat().st_size > 0, f"Video file {video_file} is empty"
-
-if __name__ == "__main__":
-    test_inference_vmoba()

fastvideo/tests/modal/pr_test.py

@@ -102,22 +102,10 @@ def run_precision_tests_STA():
 def run_precision_tests_VSA():
     run_test("python csrc/attn/tests/test_vsa.py")
 
-@app.function(gpu="L40S:1", image=image, timeout=900)
-def run_precision_tests_vmoba():
-    run_test("pytest csrc/attn/vmoba_attn/tests/test_vmoba_attn.py")
-
-@app.function(gpu="L40S:1", image=image, timeout=900)
-def run_inference_tests_vmoba():
-    run_test('python fastvideo/tests/inference/vmoba/test_vmoba_inference.py')
-
 @app.function(gpu="L40S:1", image=image, timeout=3600)
 def run_inference_lora_tests():
     run_test("pytest ./fastvideo/tests/inference/lora/test_lora_inference_similarity.py -vs")
 
 @app.function(gpu="L40S:2", image=image, timeout=900)
 def run_distill_dmd_tests():
-    run_test("pytest ./fastvideo/tests/training/distill/test_distill_dmd.py -vs")
-
-@app.function(gpu="L40S:1", image=image, timeout=900)
-def run_unit_test():
-    run_test("pytest ./fastvideo/tests/dataset/ ./fastvideo/tests/workflow/ -vs")
+    run_test("pytest ./fastvideo/tests/training/distill/test_distill_dmd.py -vs")

fastvideo/tests/ssim/README.md

@@ -4,8 +4,7 @@ The reference videos in the `*_reference_videos` directory are used as part of a
 
 run `bash update_reference_videos.sh` from inside the `fastvideo/tests/ssim/` directory after running `test_inference_similarity.py` to update reference videos. Note: make sure to update the path to the corresponding device.
 
-reference videos were generated on commit `4aeabbc629e0edf91477e80e795e7bb1823c71cb`
-causal videos were generated on commit b318063c0a4618f1d5d99ea82ca67a06aad0d19d
+all reference videos are were generated on commit `4aeabbc629e0edf91477e80e795e7bb1823c71cb`
 
 ## Generation Details
 
@@ -77,4 +76,4 @@ Wan2.1-I2V-14B-480P-Diffusers: {
 ### Image-to-Video Prompts
 
 1. "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
-    Image path: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+    Image path: "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"

fastvideo/tests/ssim/test_causal_similarity.py

@@ -1,152 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import json
-import os
-
-import torch
-import pytest
-
-from fastvideo import VideoGenerator
-from fastvideo.logger import init_logger
-from fastvideo.tests.utils import compute_video_ssim_torchvision, write_ssim_results
-from fastvideo.worker.multiproc_executor import MultiprocExecutor
-
-logger = init_logger(__name__)
-
-device_name = torch.cuda.get_device_name()
-device_reference_folder_suffix = '_reference_videos'
-
-if "A40" in device_name:
-    device_reference_folder = "A40" + device_reference_folder_suffix
-elif "L40S" in device_name:
-    device_reference_folder = "L40S" + device_reference_folder_suffix
-
-# Base parameters from the shell script
-
-SF_WAN_T2V_PARAMS = {
-    "num_gpus": 1,
-    "model_path": "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers",
-    "height": 480,
-    "width": 832,
-    "num_frames": 81,
-    "num_inference_steps": 4,
-    "seed": 1024,
-    "sp_size": 1,
-    "tp_size": 1,
-}
-
-
-MODEL_TO_PARAMS = {
-    "SFWan2.1-T2V-1.3B-Diffusers": SF_WAN_T2V_PARAMS,
-}
-
-I2V_MODEL_TO_PARAMS = {
-}
-
-TEST_PROMPTS = [
-    "Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting.",
-    # "A lone hiker stands atop a towering cliff, silhouetted against the vast horizon. The rugged landscape stretches endlessly beneath, its earthy tones blending into the soft blues of the sky. The scene captures the spirit of exploration and human resilience. High angle, dynamic framing, with soft natural lighting emphasizing the grandeur of nature."
-]
-
-I2V_TEST_PROMPTS = [
-    "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot.",
-]
-
-I2V_IMAGE_PATHS = [
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg",
-]
-
-@pytest.mark.parametrize("prompt", TEST_PROMPTS)
-@pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
-@pytest.mark.parametrize("model_id", list(MODEL_TO_PARAMS.keys()))
-def test_causal_similarity(prompt, ATTENTION_BACKEND, model_id):
-    """
-    Test that runs inference with different parameters and compares the output
-    to reference videos using SSIM.
-    """
-    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = ATTENTION_BACKEND
-
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-
-    base_output_dir = os.path.join(script_dir, 'generated_videos', model_id)
-    output_dir = os.path.join(base_output_dir, ATTENTION_BACKEND)
-    output_video_name = f"{prompt[:100]}.mp4"
-
-    os.makedirs(output_dir, exist_ok=True)
-
-    BASE_PARAMS = MODEL_TO_PARAMS[model_id]
-    num_inference_steps = BASE_PARAMS["num_inference_steps"]
-
-    init_kwargs = {
-        "num_gpus": BASE_PARAMS["num_gpus"],
-        "sp_size": BASE_PARAMS["sp_size"],
-        "tp_size": BASE_PARAMS["tp_size"],
-        "dit_cpu_offload": True,
-    }
-    if BASE_PARAMS.get("vae_sp"):
-        init_kwargs["vae_sp"] = True
-        init_kwargs["vae_tiling"] = True
-    #if "text-encoder-precision" in BASE_PARAMS:
-    #    init_kwargs["text_encoder_precisions"] = BASE_PARAMS["text-encoder-precision"]
-
-    generation_kwargs = {
-        "num_inference_steps": num_inference_steps,
-        "output_path": output_dir,
-        "height": BASE_PARAMS["height"],
-        "width": BASE_PARAMS["width"],
-        "num_frames": BASE_PARAMS["num_frames"],
-        "seed": BASE_PARAMS["seed"],
-    }
-    if "neg_prompt" in BASE_PARAMS:
-        generation_kwargs["neg_prompt"] = BASE_PARAMS["neg_prompt"]
-
-    generator = VideoGenerator.from_pretrained(model_path=BASE_PARAMS["model_path"], **init_kwargs)
-    generator.generate_video(prompt, **generation_kwargs)
-
-    if isinstance(generator.executor, MultiprocExecutor):
-        generator.executor.shutdown()
-
-    assert os.path.exists(
-        output_dir), f"Output video was not generated at {output_dir}"
-
-    reference_folder = os.path.join(script_dir, device_reference_folder, model_id, ATTENTION_BACKEND)
-
-    if not os.path.exists(reference_folder):
-        logger.error("Reference folder missing")
-        raise FileNotFoundError(
-            f"Reference video folder does not exist: {reference_folder}")
-
-    # Find the matching reference video based on the prompt
-    reference_video_name = None
-
-    for filename in os.listdir(reference_folder):
-        if filename.endswith('.mp4') and prompt[:100] in filename:
-            reference_video_name = filename
-            break
-
-    if not reference_video_name:
-        logger.error(f"Reference video not found for prompt: {prompt} with backend: {ATTENTION_BACKEND}")
-        raise FileNotFoundError(f"Reference video missing")
-
-    reference_video_path = os.path.join(reference_folder, reference_video_name)
-    generated_video_path = os.path.join(output_dir, output_video_name)
-
-    logger.info(
-        f"Computing SSIM between {reference_video_path} and {generated_video_path}"
-    )
-    ssim_values = compute_video_ssim_torchvision(reference_video_path,
-                                                 generated_video_path,
-                                                 use_ms_ssim=True)
-
-    mean_ssim = ssim_values[0]
-    logger.info(f"SSIM mean value: {mean_ssim}")
-    logger.info(f"Writing SSIM results to directory: {output_dir}")
-
-    success = write_ssim_results(output_dir, ssim_values, reference_video_path,
-                                 generated_video_path, num_inference_steps,
-                                 prompt)
-
-    if not success:
-        logger.error("Failed to write SSIM results to file")
-
-    min_acceptable_ssim = 0.98
-    assert mean_ssim >= min_acceptable_ssim, f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim} for {model_id} with backend {ATTENTION_BACKEND}"

fastvideo/tests/ssim/test_inference_similarity.py

@@ -101,7 +101,7 @@ I2V_IMAGE_PATHS = [
 
 
 @pytest.mark.parametrize("prompt", I2V_TEST_PROMPTS)
-@pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
+@pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN", "TORCH_SDPA"])
 @pytest.mark.parametrize("model_id", list(I2V_MODEL_TO_PARAMS.keys()))
 def test_i2v_inference_similarity(prompt, ATTENTION_BACKEND, model_id):
     """

fastvideo/tests/transformers/test_ori_causal_wanvideo.py

@@ -1,292 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import os
-import math
-
-import numpy as np
-import pytest
-import torch
-from fastvideo.wan.modules.causal_model import CausalWanModel
-
-from fastvideo.configs.pipelines import PipelineConfig
-from fastvideo.forward_context import set_forward_context
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.models.dits.causal_wanvideo import CausalWanTransformer3DModel
-from fastvideo.utils import maybe_download_model
-from fastvideo.configs.models.dits import WanVideoConfig
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-
-logger = init_logger(__name__)
-
-os.environ["MASTER_ADDR"] = "localhost"
-os.environ["MASTER_PORT"] = "29503"
-
-BASE_MODEL_PATH = "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH = maybe_download_model(BASE_MODEL_PATH,
-                                  local_dir=os.path.join(
-                                      'data', BASE_MODEL_PATH))
-TRANSFORMER_PATH = os.path.join(MODEL_PATH, "transformer")
-
-
-@pytest.mark.usefixtures("distributed_setup")
-def test_ori_causal_wan_transformer():
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    precision = torch.bfloat16
-    precision_str = "bf16"
-    args = FastVideoArgs(model_path=TRANSFORMER_PATH,
-                         dit_cpu_offload=True,
-                         pipeline_config=PipelineConfig(dit_config=WanVideoConfig(), dit_precision=precision_str))
-    args.device = device
-
-    loader = TransformerLoader()
-    model2 = loader.load(TRANSFORMER_PATH, args).to(dtype=precision)
-
-    model1 = CausalWanModel.from_pretrained(
-        "/mnt/weka/home/hao.zhang/wei/Self-Forcing/wan_models/Wan2.1-T2V-1.3B", device=device,
-        torch_dtype=precision).to(device, dtype=precision).requires_grad_(False)
-    causal_state_dict = torch.load("/mnt/weka/home/hao.zhang/wei/Self-Forcing/checkpoints/self_forcing_dmd.pt")["generator_ema"]
-    new_state_dict = {}
-    for k, v in causal_state_dict.items():
-        if k.startswith("model."):
-            new_state_dict[k.replace("model.", "")] = v
-    causal_state_dict = new_state_dict
-    model1.load_state_dict(causal_state_dict)
-
-    total_params = sum(p.numel() for p in model1.parameters())
-    # Calculate weight sum for model1 (converting to float64 to avoid overflow)
-    weight_sum_model1 = sum(
-        p.to(torch.float64).sum().item() for p in model1.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model1 = weight_sum_model1 / total_params
-    logger.info("Model 1 weight sum: %s", weight_sum_model1)
-    logger.info("Model 1 weight mean: %s", weight_mean_model1)
-
-    # Calculate weight sum for model2 (converting to float64 to avoid overflow)
-    total_params_model2 = sum(p.numel() for p in model2.parameters())
-    weight_sum_model2 = sum(
-        p.to(torch.float64).sum().item() for p in model2.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model2 = weight_sum_model2 / total_params_model2
-    logger.info("Model 2 weight sum: %s", weight_sum_model2)
-    logger.info("Model 2 weight mean: %s", weight_mean_model2)
-
-    weight_sum_diff = abs(weight_sum_model1 - weight_sum_model2)
-    logger.info("Weight sum difference: %s", weight_sum_diff)
-    weight_mean_diff = abs(weight_mean_model1 - weight_mean_model2)
-    logger.info("Weight mean difference: %s", weight_mean_diff)
-
-    # Set both models to eval mode
-    model1 = model1.eval()
-    model2 = model2.eval()
-
-    # Create identical inputs for both models
-    batch_size = 1
-    text_seq_len = 30
-
-    # Video latents [B, C, T, H, W]
-    hidden_states = torch.randn(batch_size,
-                                16,
-                                12,
-                                160,
-                                90,
-                                device=device,
-                                dtype=precision)
-    block_sizes = [3 for _ in range(4)]
-    timesteps = [1000, 750, 500, 250]
-
-    # Text embeddings [B, L, D] (including global token)
-    encoder_hidden_states = torch.randn(batch_size,
-                                        text_seq_len + 1,
-                                        4096,
-                                        device=device,
-                                        dtype=precision)
-
-    output1 = _causal_inference(model1, hidden_states.clone(), encoder_hidden_states.clone(), block_sizes, timesteps, precision)
-    logger.info("Finish inference for model1")
-    output2 = _causal_inference(model2, hidden_states.clone(), encoder_hidden_states.clone(), block_sizes, timesteps, precision)
-
-    # Check if outputs have the same shape
-    assert output1.shape == output2.shape, f"Output shapes don't match: {output1.shape} vs {output2.shape}"
-    assert output1.dtype == output2.dtype, f"Output dtype don't match: {output1.dtype} vs {output2.dtype}"
-
-    logger.info("Output 1 Sum: %s", output1.float().sum().item())
-    logger.info("Output 2 Sum: %s", output2.float().sum().item())
-
-    # Check if outputs are similar (allowing for small numerical differences)
-    max_diff = torch.max(torch.abs(output1 - output2))
-    mean_diff = torch.mean(torch.abs(output1 - output2))
-    logger.info("Max Diff: %s", max_diff.item())
-    logger.info("Mean Diff: %s", mean_diff.item())
-    assert max_diff < 1e-4, f"Maximum difference between outputs: {max_diff.item()}"
-    # mean diff
-    assert mean_diff < 1e-4, f"Mean difference between outputs: {mean_diff.item()}"
-
-def _causal_inference(transformer, latents, prompt_embeds, block_sizes, timesteps, target_dtype):
-    forward_batch = ForwardBatch(
-        data_type="dummy",
-    )
-    start_index = 0
-    pos_start_base = 0
-    frame_seq_length = latents.shape[-1] * latents.shape[-2] // (WanVideoConfig().arch_config.patch_size[-1] * WanVideoConfig().arch_config.patch_size[-2])
-    seq_len = frame_seq_length * latents.shape[2]
-    kv_cache1 = _initialize_kv_cache(transformer, batch_size=latents.shape[0],
-                                      kv_cache_size=frame_seq_length * latents.shape[2],
-                                      dtype=target_dtype,
-                                      device=latents.device)
-    crossattn_cache = _initialize_crossattn_cache(
-                transformer,
-                batch_size=latents.shape[0],
-                max_text_len=WanVideoConfig().arch_config.text_len,
-                dtype=target_dtype,
-                device=latents.device)
-    for current_num_frames, t_cur in zip(block_sizes, timesteps):
-        # logger.info(f"Current frame idx: {start_index}, Current timestep: {t_cur}")
-        # logger.info(f"k cache sum: {sum(kv_cache['k'].float().sum().item() for kv_cache in kv_cache1)}, v cache sum: {sum(kv_cache['v'].float().sum().item() for kv_cache in kv_cache1)}")
-        # logger.info(f"latents sum: {latents.float().sum().item()}, encoder_hidden_states sum: {prompt_embeds.float().sum().item()}")
-        current_latents = latents[:, :, start_index:start_index +
-                                    current_num_frames, :, :]
-
-        attn_metadata = None
-
-        with set_forward_context(current_timestep=0,
-                                attn_metadata=attn_metadata,
-                                forward_batch=forward_batch):
-            # Run transformer; follow DMD stage pattern
-            t_expanded_noise = t_cur * torch.ones(
-                (current_latents.shape[0], 1),
-                device=current_latents.device,
-                dtype=torch.long)
-            if isinstance(transformer, CausalWanModel):
-                pred_noise_btchw = transformer(
-                    x=current_latents,
-                    context=prompt_embeds,
-                    t=t_expanded_noise,
-                    seq_len=seq_len,
-                    kv_cache=kv_cache1,
-                    crossattn_cache=crossattn_cache,
-                    current_start=(pos_start_base + start_index) *
-                    frame_seq_length
-                )
-            elif isinstance(transformer, CausalWanTransformer3DModel):
-                pred_noise_btchw = transformer(
-                    current_latents,
-                    prompt_embeds,
-                    t_expanded_noise,
-                    kv_cache=kv_cache1,
-                    crossattn_cache=crossattn_cache,
-                    current_start=(pos_start_base + start_index) *
-                    frame_seq_length,
-                    start_frame=start_index
-                )
-
-        # Write back and advance
-        latents[:, :, start_index:start_index +
-                current_num_frames, :, :] = pred_noise_btchw.clone()
-
-        # Re-run with context timestep to update KV cache using clean context
-        context_noise = 0
-        t_context = torch.ones([latents.shape[0]],
-                                device=latents.device,
-                                dtype=torch.long) * int(context_noise)
-        context_bcthw = pred_noise_btchw.to(target_dtype)
-        with set_forward_context(current_timestep=0,
-                                attn_metadata=attn_metadata,
-                                forward_batch=forward_batch):
-            t_expanded_context = t_context.unsqueeze(1)
-            if isinstance(transformer, CausalWanModel):
-                _ = transformer(
-                    x=context_bcthw,
-                    context=prompt_embeds,
-                    t=t_expanded_context,
-                    seq_len=seq_len,
-                    kv_cache=kv_cache1,
-                    crossattn_cache=crossattn_cache,
-                    current_start=(pos_start_base + start_index) *
-                    frame_seq_length
-                )
-            elif isinstance(transformer, CausalWanTransformer3DModel):
-                _ = transformer(
-                    context_bcthw,
-                    prompt_embeds,
-                    t_expanded_context,
-                    kv_cache=kv_cache1,
-                    crossattn_cache=crossattn_cache,
-                    current_start=(pos_start_base + start_index) *
-                    frame_seq_length,
-                    start_frame=start_index
-                )
-        start_index += current_num_frames
-
-    return latents
-
-def _initialize_kv_cache(transformer, batch_size, kv_cache_size, dtype, device) -> None:
-    """
-    Initialize a Per-GPU KV cache aligned with the Wan model assumptions.
-    """
-    kv_cache1 = []
-    if isinstance(transformer, CausalWanModel):
-        num_attention_heads = transformer.num_heads
-        attention_head_dim = transformer.dim // transformer.num_heads
-    elif isinstance(transformer, CausalWanTransformer3DModel):
-        num_attention_heads = transformer.num_attention_heads
-        attention_head_dim = transformer.attention_head_dim
-
-    for _ in range(len(transformer.blocks)):
-        kv_cache1.append({
-            "k":
-            torch.zeros([
-                batch_size, kv_cache_size, num_attention_heads,
-                attention_head_dim
-            ],
-                        dtype=dtype,
-                        device=device),
-            "v":
-            torch.zeros([
-                batch_size, kv_cache_size, num_attention_heads,
-                attention_head_dim
-            ],
-                        dtype=dtype,
-                        device=device),
-            "global_end_index":
-            torch.tensor([0], dtype=torch.long, device=device),
-            "local_end_index":
-            torch.tensor([0], dtype=torch.long, device=device),
-        })
-
-    return kv_cache1
-
-def _initialize_crossattn_cache(transformer, batch_size, max_text_len, dtype,
-                                    device) -> None:
-    """
-    Initialize a Per-GPU cross-attention cache aligned with the Wan model assumptions.
-    """
-    crossattn_cache = []
-    if isinstance(transformer, CausalWanModel):
-        num_attention_heads = transformer.num_heads
-        attention_head_dim = transformer.dim // transformer.num_heads
-    elif isinstance(transformer, CausalWanTransformer3DModel):
-        num_attention_heads = transformer.num_attention_heads
-        attention_head_dim = transformer.attention_head_dim
-
-    for _ in range(len(transformer.blocks)):
-        crossattn_cache.append({
-            "k":
-            torch.zeros([
-                batch_size, max_text_len, num_attention_heads,
-                attention_head_dim
-            ],
-                        dtype=dtype,
-                        device=device),
-            "v":
-            torch.zeros([
-                batch_size, max_text_len, num_attention_heads,
-                attention_head_dim
-            ],
-                        dtype=dtype,
-                        device=device),
-            "is_init":
-            False,
-        })
-    return crossattn_cache

fastvideo/tests/transformers/test_ori_wanvideo.py

@@ -1,144 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import os
-import math
-
-import numpy as np
-import pytest
-import torch
-from fastvideo.wan.modules.model import WanModel
-
-from fastvideo.configs.pipelines import PipelineConfig
-from fastvideo.forward_context import set_forward_context
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.utils import maybe_download_model
-from fastvideo.configs.models.dits import WanVideoConfig
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-
-logger = init_logger(__name__)
-
-os.environ["MASTER_ADDR"] = "localhost"
-os.environ["MASTER_PORT"] = "29503"
-
-os.environ["FASTVIDEO_FORCE_ATTN_BF16"] = "1"
-BASE_MODEL_PATH = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH = maybe_download_model(BASE_MODEL_PATH,
-                                  local_dir=os.path.join(
-                                      'data', BASE_MODEL_PATH))
-TRANSFORMER_PATH = os.path.join(MODEL_PATH, "transformer")
-
-
-@pytest.mark.usefixtures("distributed_setup")
-def test_ori_wan_transformer():
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    precision = torch.float32
-    precision_str = "fp32"
-    args = FastVideoArgs(model_path=TRANSFORMER_PATH,
-                         dit_cpu_offload=True,
-                         pipeline_config=PipelineConfig(dit_config=WanVideoConfig(), dit_precision=precision_str, dit_forward_precision=precision_str))
-    args.device = device
-
-    loader = TransformerLoader()
-    model2 = loader.load(TRANSFORMER_PATH, args)
-
-    model1 = WanModel.from_pretrained(
-        "/mnt/weka/home/hao.zhang/wei/Self-Forcing-clean/wan_models/Wan2.1-T2V-1.3B")
-    model1.eval()
-    model1 = model1.to(device).to(precision)
-    model1.requires_grad_(False)
-
-    total_params = sum(p.numel() for p in model1.parameters())
-    # Calculate weight sum for model1 (converting to float64 to avoid overflow)
-    weight_sum_model1 = sum(
-        p.to(torch.float64).sum().item() for p in model1.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model1 = weight_sum_model1 / total_params
-    logger.info("Model 1 weight sum: %s", weight_sum_model1)
-    logger.info("Model 1 weight mean: %s", weight_mean_model1)
-
-    # Calculate weight sum for model2 (converting to float64 to avoid overflow)
-    total_params_model2 = sum(p.numel() for p in model2.parameters())
-    weight_sum_model2 = sum(
-        p.to(torch.float64).sum().item() for p in model2.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model2 = weight_sum_model2 / total_params_model2
-    logger.info("Model 2 weight sum: %s", weight_sum_model2)
-    logger.info("Model 2 weight mean: %s", weight_mean_model2)
-
-    weight_sum_diff = abs(weight_sum_model1 - weight_sum_model2)
-    logger.info("Weight sum difference: %s", weight_sum_diff)
-    weight_mean_diff = abs(weight_mean_model1 - weight_mean_model2)
-    logger.info("Weight mean difference: %s", weight_mean_diff)
-
-    # Set both models to eval mode
-    model1 = model1.eval()
-    model2 = model2.eval()
-
-    # Create identical inputs for both models
-    batch_size = 1
-    text_seq_len = 120
-    seq_len = math.ceil((104 * 60) /
-                            (2 * 2) *
-                            21)
-
-    # Video latents [B, C, T, H, W]
-    hidden_states = torch.randn(batch_size,
-                                21,
-                                16,
-                                60,
-                                104,
-                                device=device,
-                                generator=torch.Generator("cuda").manual_seed(1024),
-                                dtype=precision)
-
-    logger.info("Hidden states sum: %s, Hidden states shape: %s, Hidden states dtype: %s", hidden_states.float().sum(), hidden_states.shape, hidden_states.dtype)
-
-    # Text embeddings [B, L, D] (including global token)
-    # encoder_hidden_states = torch.randn(batch_size,
-    #                                     text_seq_len + 1,
-    #                                     4096,
-    #                                     device=device,
-    #                                     dtype=precision)
-    encoder_hidden_states = torch.load("../sf_cond_prompt_embeds.pt").to(device, dtype=precision)
-
-    logger.info("Encoder hidden states sum: %s, Encoder hidden states shape: %s, Encoder hidden states dtype: %s", encoder_hidden_states.float().sum(), encoder_hidden_states.shape, encoder_hidden_states.dtype)
-
-    # Timestep
-    timestep = torch.tensor([995.7627], device=device, dtype=precision)
-
-    forward_batch = ForwardBatch(
-        data_type="dummy",
-    )
-
-    # with torch.amp.autocast('cuda', dtype=precision):
-    output1 = model1(
-        x=hidden_states.permute(0, 2, 1, 3, 4),
-        context=encoder_hidden_states,
-        t=timestep,
-        seq_len=seq_len,
-    ).permute(0, 2, 1, 3, 4)
-    with set_forward_context(
-            current_timestep=0,
-            attn_metadata=None,
-            forward_batch=forward_batch,
-    ):
-        output2 = model2(hidden_states=hidden_states.permute(0, 2, 1, 3, 4),
-                            encoder_hidden_states=encoder_hidden_states,
-                            timestep=timestep).permute(0, 2, 1, 3, 4)
-
-    # Check if outputs have the same shape
-    assert output1.shape == output2.shape, f"Output shapes don't match: {output1.shape} vs {output2.shape}"
-    assert output1.dtype == output2.dtype, f"Output dtype don't match: {output1.dtype} vs {output2.dtype}"
-
-    # Check if outputs are similar (allowing for small numerical differences)
-    max_diff = torch.max(torch.abs(output1 - output2))
-    mean_diff = torch.mean(torch.abs(output1 - output2))
-    logger.info("Max Diff: %s", max_diff.item())
-    logger.info("Mean Diff: %s", mean_diff.item())
-    logger.info("Output 1 sum: %s", output1.float().sum())
-    logger.info("Output 2 sum: %s", output2.float().sum())
-    assert max_diff < 1e-4, f"Maximum difference between outputs: {max_diff.item()}"
-    # mean diff
-    assert mean_diff < 1e-4, f"Mean difference between outputs: {mean_diff.item()}"

fastvideo/tests/transformers/test_train_ori_causal_wanvideo.py

@@ -1,144 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import os
-import math
-
-import numpy as np
-import pytest
-import torch
-from fastvideo.wan.modules.causal_model import CausalWanModel
-
-from fastvideo.configs.pipelines import PipelineConfig
-from fastvideo.forward_context import set_forward_context
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.utils import maybe_download_model
-from fastvideo.configs.models.dits import WanVideoConfig
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-
-logger = init_logger(__name__)
-
-os.environ["MASTER_ADDR"] = "localhost"
-os.environ["MASTER_PORT"] = "29503"
-
-BASE_MODEL_PATH = "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH = maybe_download_model(BASE_MODEL_PATH,
-                                  local_dir=os.path.join(
-                                      'data', BASE_MODEL_PATH))
-TRANSFORMER_PATH = os.path.join(MODEL_PATH, "transformer")
-
-
-@pytest.mark.usefixtures("distributed_setup")
-def test_train_ori_causal_wan_transformer():
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    precision = torch.bfloat16
-    precision_str = "bf16"
-    args = FastVideoArgs(model_path=TRANSFORMER_PATH,
-                         dit_cpu_offload=True,
-                         pipeline_config=PipelineConfig(dit_config=WanVideoConfig(), dit_precision=precision_str))
-    args.device = device
-
-    loader = TransformerLoader()
-    model2 = loader.load(TRANSFORMER_PATH, args).to(dtype=precision)
-
-    model1 = CausalWanModel.from_pretrained(
-        "/mnt/weka/home/hao.zhang/wei/Self-Forcing/wan_models/Wan2.1-T2V-1.3B", device=device,
-        torch_dtype=precision).to(device, dtype=precision).requires_grad_(False)
-    causal_state_dict = torch.load("/mnt/weka/home/hao.zhang/wei/Self-Forcing/checkpoints/self_forcing_dmd.pt")["generator_ema"]
-    new_state_dict = {}
-    for k, v in causal_state_dict.items():
-        if k.startswith("model."):
-            new_state_dict[k.replace("model.", "")] = v
-    causal_state_dict = new_state_dict
-    model1.load_state_dict(causal_state_dict)
-
-    model1.num_frame_per_block = 3
-    model2.num_frame_per_block = 3
-
-    total_params = sum(p.numel() for p in model1.parameters())
-    # Calculate weight sum for model1 (converting to float64 to avoid overflow)
-    weight_sum_model1 = sum(
-        p.to(torch.float64).sum().item() for p in model1.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model1 = weight_sum_model1 / total_params
-    logger.info("Model 1 weight sum: %s", weight_sum_model1)
-    logger.info("Model 1 weight mean: %s", weight_mean_model1)
-
-    # Calculate weight sum for model2 (converting to float64 to avoid overflow)
-    total_params_model2 = sum(p.numel() for p in model2.parameters())
-    weight_sum_model2 = sum(
-        p.to(torch.float64).sum().item() for p in model2.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model2 = weight_sum_model2 / total_params_model2
-    logger.info("Model 2 weight sum: %s", weight_sum_model2)
-    logger.info("Model 2 weight mean: %s", weight_mean_model2)
-
-    weight_sum_diff = abs(weight_sum_model1 - weight_sum_model2)
-    logger.info("Weight sum difference: %s", weight_sum_diff)
-    weight_mean_diff = abs(weight_mean_model1 - weight_mean_model2)
-    logger.info("Weight mean difference: %s", weight_mean_diff)
-
-    # Set both models to eval mode
-    model1 = model1.eval()
-    model2 = model2.eval()
-
-    # Create identical inputs for both models
-    batch_size = 1
-    text_seq_len = 30
-    seq_len = math.ceil((160 * 90) /
-                            (2 * 2) *
-                            21)
-
-    # Video latents [B, C, T, H, W]
-    hidden_states = torch.randn(batch_size,
-                                16,
-                                21,
-                                160,
-                                90,
-                                device=device,
-                                dtype=precision)
-
-    # Text embeddings [B, L, D] (including global token)
-    encoder_hidden_states = torch.randn(batch_size,
-                                        text_seq_len + 1,
-                                        4096,
-                                        device=device,
-                                        dtype=precision)
-
-    # Timestep
-    timestep = torch.randint(0, 1000, (batch_size, 21), device=device, dtype=torch.long)
-    logger.info("timestep: %s", timestep)
-
-    forward_batch = ForwardBatch(
-        data_type="dummy",
-    )
-
-    # with torch.amp.autocast('cuda', dtype=precision):
-    output1 = model1(
-        x=hidden_states,
-        context=encoder_hidden_states,
-        t=timestep,
-        seq_len=seq_len,
-    )
-    with set_forward_context(
-            current_timestep=0,
-            attn_metadata=None,
-            forward_batch=forward_batch,
-    ):
-        output2 = model2(hidden_states=hidden_states,
-                            encoder_hidden_states=encoder_hidden_states,
-                            timestep=timestep)
-
-    # Check if outputs have the same shape
-    assert output1.shape == output2.shape, f"Output shapes don't match: {output1.shape} vs {output2.shape}"
-    assert output1.dtype == output2.dtype, f"Output dtype don't match: {output1.dtype} vs {output2.dtype}"
-
-    # Check if outputs are similar (allowing for small numerical differences)
-    max_diff = torch.max(torch.abs(output1 - output2))
-    mean_diff = torch.mean(torch.abs(output1 - output2))
-    logger.info("Max Diff: %s", max_diff.item())
-    logger.info("Mean Diff: %s", mean_diff.item())
-    assert max_diff < 1e-4, f"Maximum difference between outputs: {max_diff.item()}"
-    # mean diff
-    assert mean_diff < 1e-4, f"Mean difference between outputs: {mean_diff.item()}"

fastvideo/tests/workflow/test_components_parquet_saver.py

@@ -1,66 +0,0 @@
-from pathlib import Path
-
-import numpy as np
-import pyarrow as pa
-import pyarrow.parquet as pq
-import torch
-
-from fastvideo.workflow.preprocess.components import ParquetDatasetSaver
-from fastvideo.pipelines.pipeline_batch_info import PreprocessBatch
-
-
-def _simple_record_creator(batch: PreprocessBatch) -> list[dict]:
-    # batch.latents will be converted to numpy by the saver before this call
-    assert isinstance(batch.latents, np.ndarray)
-    num = len(batch.video_file_name)
-    records = []
-    for i in range(num):
-        arr = batch.latents[i]
-        records.append({
-            "id": batch.video_file_name[i],
-            "data_bytes": arr.tobytes(),
-            "data_shape": list(arr.shape),
-        })
-    return records
-
-
-def test_parquet_dataset_saver_flush_and_last(tmp_path: Path):
-    # Schema for the simple record creator
-    schema = pa.schema([
-        pa.field("id", pa.string()),
-        pa.field("data_bytes", pa.binary()),
-        pa.field("data_shape", pa.list_(pa.int64())),
-    ])
-
-    B = 5
-    # Build a minimal PreprocessBatch
-    batch = PreprocessBatch(
-        data_type="video",
-        latents=torch.randn(B, 2),
-        prompt_embeds=[torch.randn(B, 1, 1)],
-        # Attention mask should be integer dtype in real pipelines
-        prompt_attention_mask=[torch.ones(B, 1, dtype=torch.int64)],
-    )
-    batch.video_file_name = [f"vid_{i}" for i in range(B)]
-
-    saver = ParquetDatasetSaver(
-        flush_frequency=10,  # higher than B to avoid auto-flush
-        samples_per_file=3,
-        schema=schema,
-        record_creator=_simple_record_creator,
-    )
-
-    out_dir = tmp_path / "saver_out"
-    saver.save_and_write_parquet_batch(batch, str(out_dir))
-    # First flush: should write one full file (3 rows), keep 2 in buffer
-    saver.flush_tables()
-    files = sorted(out_dir.rglob("*.parquet"))
-    assert len(files) == 1
-    assert pq.read_table(str(files[0])).num_rows == 3
-
-    # Final flush: write remainder 2 rows
-    saver.flush_tables(write_remainder=True)
-    files2 = sorted(out_dir.rglob("*.parquet"))
-    assert len(files2) == 2
-    total = sum(pq.read_table(str(f)).num_rows for f in files2)
-    assert total == 5

fastvideo/training/distillation_pipeline.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 import copy
 import gc
-import json
 import os
 import time
 from abc import abstractmethod
@@ -12,7 +11,6 @@ from typing import Any
 import imageio
 import numpy as np
 import torch
-import torch.distributed as dist
 import torch.nn.functional as F
 import torchvision
 from einops import rearrange
@@ -38,11 +36,9 @@ from fastvideo.training.activation_checkpoint import (
     apply_activation_checkpointing)
 from fastvideo.training.training_pipeline import TrainingPipeline
 from fastvideo.training.training_utils import (
-    EMA_FSDP, clip_grad_norm_while_handling_failing_dtensor_cases,
-    get_scheduler, load_distillation_checkpoint, save_distillation_checkpoint,
-    shift_timestep)
-from fastvideo.utils import (is_vsa_available, maybe_download_model,
-                             set_random_seed, verify_model_config_and_directory)
+    clip_grad_norm_while_handling_failing_dtensor_cases, get_scheduler,
+    load_distillation_checkpoint, save_distillation_checkpoint, shift_timestep)
+from fastvideo.utils import is_vsa_available, set_random_seed
 
 import wandb  # isort: skip
 
@@ -91,27 +87,9 @@ class DistillationPipeline(TrainingPipeline):
         self.noise_scheduler = FlowMatchEulerDiscreteScheduler(
             shift=self.timestep_shift)
 
-        if training_args.real_score_model_path:
-            logger.info(
-                f"Loading real score transformer from: {training_args.real_score_model_path}"
-            )
-            self.real_score_transformer = self.load_module_from_path(
-                training_args.real_score_model_path, "transformer",
-                training_args)
-        else:
-            self.real_score_transformer = self.get_module(
-                "real_score_transformer")
-
-        if training_args.fake_score_model_path:
-            logger.info(
-                f"Loading fake score transformer from: {training_args.fake_score_model_path}"
-            )
-            self.fake_score_transformer = self.load_module_from_path(
-                training_args.fake_score_model_path, "transformer",
-                training_args)
-        else:
-            self.fake_score_transformer = self.get_module(
-                "fake_score_transformer")
+        # self.transformer is the generator model
+        self.real_score_transformer = self.get_module("real_score_transformer")
+        self.fake_score_transformer = self.get_module("fake_score_transformer")
 
         self.real_score_transformer.requires_grad_(False)
         self.real_score_transformer.eval()
@@ -138,13 +116,10 @@ class DistillationPipeline(TrainingPipeline):
         if fake_score_lr == 0.0:
             fake_score_lr = training_args.learning_rate
 
-        betas_str = training_args.fake_score_betas
-        betas = tuple(float(x.strip()) for x in betas_str.split(","))
-
         self.fake_score_optimizer = torch.optim.AdamW(
             fake_score_params,
             lr=fake_score_lr,
-            betas=betas,
+            betas=(0.9, 0.999),
             weight_decay=training_args.weight_decay,
             eps=1e-8,
         )
@@ -172,19 +147,8 @@ class DistillationPipeline(TrainingPipeline):
             self.training_args.pipeline_config.dmd_denoising_steps,
             dtype=torch.long,
             device=get_local_torch_device())
-
-        if training_args.warp_denoising_step:  # Warp the denoising step according to the scheduler time shift
-            timesteps = torch.cat((self.noise_scheduler.timesteps.cpu(),
-                                   torch.tensor([0],
-                                                dtype=torch.float32))).cuda()
-            self.denoising_step_list = timesteps[1000 -
-                                                 self.denoising_step_list]
-            logger.info("Warping denoising_step_list")
-
-        self.denoising_step_list = self.denoising_step_list.to(
-            get_local_torch_device())
-        logger.info("Distillation generator model to %s denoising steps: %s",
-                    len(self.denoising_step_list), self.denoising_step_list)
+        logger.info("Distillation generator model to %s denoising steps",
+                    len(self.denoising_step_list))
         self.num_train_timestep = self.noise_scheduler.num_train_timesteps
 
         self.min_timestep = int(self.training_args.min_timestep_ratio *
@@ -194,82 +158,6 @@ class DistillationPipeline(TrainingPipeline):
 
         self.real_score_guidance_scale = self.training_args.real_score_guidance_scale
 
-        self.generator_ema = None
-        if (self.training_args.ema_decay
-                is not None) and (self.training_args.ema_decay > 0.0):
-            self.generator_ema = EMA_FSDP(self.transformer,
-                                          decay=self.training_args.ema_decay)
-            logger.info(
-                f"Initialized generator EMA with decay={self.training_args.ema_decay}"
-            )
-        else:
-            logger.info("Generator EMA disabled (ema_decay <= 0.0)")
-
-    def load_module_from_path(self, model_path: str, module_type: str,
-                              training_args: "TrainingArgs"):
-        """
-        Load a module from a specific path using the same loading logic as the pipeline.
-        
-        Args:
-            model_path: Path to the model
-            module_type: Type of module to load (e.g., "transformer")
-            training_args: Training arguments
-            
-        Returns:
-            The loaded module
-        """
-        logger.info(f"Loading {module_type} from custom path: {model_path}")
-        # Set flag to prevent custom weight loading for teacher/critic models
-        training_args._loading_teacher_critic_model = True
-
-        try:
-            from fastvideo.models.loader.component_loader import (
-                PipelineComponentLoader)
-
-            # Download the model if it's a Hugging Face model ID
-            local_model_path = maybe_download_model(model_path)
-            logger.info(f"Model downloaded/found at: {local_model_path}")
-            config = verify_model_config_and_directory(local_model_path)
-
-            if module_type not in config:
-                if hasattr(self, '_extra_config_module_map'
-                           ) and module_type in self._extra_config_module_map:
-                    extra_module = self._extra_config_module_map[module_type]
-                    if extra_module in config:
-                        module_type = extra_module
-                        logger.info(f"Using {extra_module} for {module_type}")
-                    else:
-                        raise ValueError(
-                            f"Module {module_type} not found in config at {local_model_path}"
-                        )
-                else:
-                    raise ValueError(
-                        f"Module {module_type} not found in config at {local_model_path}"
-                    )
-
-            module_info = config[module_type]
-            if module_info is None:
-                raise ValueError(
-                    f"Module {module_type} has null value in config at {local_model_path}"
-                )
-
-            transformers_or_diffusers, architecture = module_info
-            component_path = os.path.join(local_model_path, module_type)
-            module = PipelineComponentLoader.load_module(
-                module_name=module_type,
-                component_model_path=component_path,
-                transformers_or_diffusers=transformers_or_diffusers,
-                fastvideo_args=training_args,
-            )
-
-            logger.info(
-                f"Successfully loaded {module_type} from {component_path}")
-            return module
-        finally:
-            # Always clean up the flag
-            if hasattr(training_args, '_loading_teacher_critic_model'):
-                delattr(training_args, '_loading_teacher_critic_model')
-
     @abstractmethod
     def initialize_validation_pipeline(self, training_args: TrainingArgs):
         """Initialize validation pipeline - must be implemented by subclasses."""
@@ -286,110 +174,6 @@ class DistillationPipeline(TrainingPipeline):
 
         return training_batch
 
-    def apply_ema_to_model(self, model):
-        """Apply EMA weights to the model for validation or inference."""
-        if self.generator_ema is not None:
-            with self.generator_ema.apply_to_model(model):
-                return model
-        return model
-
-    def get_ema_model_copy(self):
-        """Get a copy of the model with EMA weights applied."""
-        if self.generator_ema is not None:
-            ema_model = copy.deepcopy(self.transformer)
-            self.generator_ema.copy_to_unwrapped(ema_model)
-            return ema_model
-        return None
-
-    def is_ema_ready(self, current_step: int = None):
-        """Check if EMA is ready for use (after ema_start_step)."""
-        if current_step is None:
-            current_step = getattr(self, 'current_trainstep', 0)
-        return (self.generator_ema is not None
-                and current_step >= self.training_args.ema_start_step)
-
-    def save_ema_weights(self, output_dir: str, step: int):
-        """Save EMA weights separately for inference purposes."""
-        if self.generator_ema is None:
-            logger.warning("Cannot save EMA weights: EMA not initialized")
-            return
-
-        if not self.is_ema_ready():
-            logger.warning(
-                "Cannot save EMA weights: EMA not ready yet (step < ema_start_step)"
-            )
-            return
-
-        try:
-            ema_model = self.get_ema_model_copy()
-            if ema_model is None:
-                logger.warning("Failed to create EMA model copy")
-                return
-
-            ema_save_dir = os.path.join(output_dir, f"ema_checkpoint-{step}")
-            os.makedirs(ema_save_dir, exist_ok=True)
-
-            # save as diffusers format
-            from safetensors.torch import save_file
-
-            from fastvideo.training.training_utils import (
-                custom_to_hf_state_dict, gather_state_dict_on_cpu_rank0)
-            cpu_state = gather_state_dict_on_cpu_rank0(ema_model, device=None)
-
-            if self.global_rank == 0:
-                weight_path = os.path.join(
-                    ema_save_dir, "diffusion_pytorch_model.safetensors")
-                diffusers_state_dict = custom_to_hf_state_dict(
-                    cpu_state, ema_model.reverse_param_names_mapping)
-                save_file(diffusers_state_dict, weight_path)
-
-                config_dict = ema_model.hf_config
-                if "dtype" in config_dict:
-                    del config_dict["dtype"]
-                config_path = os.path.join(ema_save_dir, "config.json")
-                with open(config_path, "w") as f:
-                    json.dump(config_dict, f, indent=4)
-
-                logger.info(f"EMA weights saved to {weight_path}")
-
-            del ema_model
-
-        except Exception as e:
-            logger.error(f"Failed to save EMA weights: {str(e)}")
-
-    def get_ema_stats(self):
-        """Get EMA statistics for monitoring."""
-        if self.generator_ema is None:
-            return {
-                "ema_enabled": False,
-                "ema_decay": None,
-                "ema_start_step": self.training_args.ema_start_step,
-                "ema_ready": False,
-                "ema_step": self.current_trainstep,
-            }
-
-        return {
-            "ema_enabled": True,
-            "ema_decay": self.training_args.ema_decay,
-            "ema_start_step": self.training_args.ema_start_step,
-            "ema_ready": self.is_ema_ready(),
-            "ema_step": self.current_trainstep,
-        }
-
-    def reset_ema(self):
-        """Reset EMA to current model weights."""
-        if self.generator_ema is not None:
-            logger.info("Resetting EMA to current model weights")
-            self.generator_ema.update(self.transformer)
-            # Force update to current weights by setting decay to 0 temporarily
-            original_decay = self.generator_ema.decay
-            self.generator_ema.decay = 0.0
-            self.generator_ema.update(self.transformer)
-            self.generator_ema.decay = original_decay
-            logger.info("EMA reset completed")
-        else:
-            logger.warning("Cannot reset EMA: EMA not initialized")
-
     def _build_distill_input_kwargs(
             self, noise_input: torch.Tensor, timestep: torch.Tensor,
             text_dict: dict[str, torch.Tensor] | None,
@@ -546,7 +330,6 @@ class DistillationPipeline(TrainingPipeline):
     def _dmd_forward(self, generator_pred_video: torch.Tensor,
                      training_batch: TrainingBatch) -> torch.Tensor:
         """Compute DMD (Diffusion Model Distillation) loss."""
-        original_latent = generator_pred_video
         with torch.no_grad():
             timestep = torch.randint(0,
                                      self.num_train_timestep, [1],
@@ -571,7 +354,7 @@ class DistillationPipeline(TrainingPipeline):
 
             noisy_latent = self.noise_scheduler.add_noise(
                 generator_pred_video.flatten(0, 1), noise.flatten(0, 1),
-                timestep).detach().unflatten(0, (1, generator_pred_video.shape[1]))
+                timestep).unflatten(0, (1, generator_pred_video.shape[1]))
 
             # fake_score_transformer forward
             training_batch = self._build_distill_input_kwargs(
@@ -620,24 +403,24 @@ class DistillationPipeline(TrainingPipeline):
                 pred_real_video_uncond) * self.real_score_guidance_scale
 
             grad = (faker_score_pred_video - real_score_pred_video) / torch.abs(
-                original_latent - real_score_pred_video).mean()
+                generator_pred_video - real_score_pred_video).mean()
             grad = torch.nan_to_num(grad)
 
         dmd_loss = 0.5 * F.mse_loss(
-            original_latent.float(),
-            (original_latent.float() - grad.float()).detach())
+            generator_pred_video.float(),
+            (generator_pred_video.float() - grad.float()).detach())
 
         training_batch.dmd_latent_vis_dict.update({
             "training_batch_dmd_fwd_clean_latent":
             training_batch.latents,
             "generator_pred_video":
-            original_latent.detach(),
+            generator_pred_video,
             "real_score_pred_video":
-            real_score_pred_video.detach(),
+            real_score_pred_video,
             "faker_score_pred_video":
-            faker_score_pred_video.detach(),
+            faker_score_pred_video,
             "dmd_timestep":
-            timestep.detach(),
+            timestep,
         })
 
         return dmd_loss
@@ -734,12 +517,12 @@ class DistillationPipeline(TrainingPipeline):
             "encoder_hidden_states": self.negative_prompt_embeds,
             "encoder_attention_mask": self.negative_prompt_attention_mask,
         }
-        training_batch.unconditional_dict = unconditional_dict
 
         training_batch.dmd_latent_vis_dict = {}
         training_batch.fake_score_latent_vis_dict = {}
 
         training_batch.conditional_dict = conditional_dict
+        training_batch.unconditional_dict = unconditional_dict
         training_batch.raw_latent_shape = training_batch.latents.shape
         training_batch.latents = training_batch.latents.permute(0, 2, 1, 3, 4)
         self.video_latent_shape = training_batch.latents.shape
@@ -802,15 +585,8 @@ class DistillationPipeline(TrainingPipeline):
                     (dmd_loss / gradient_accumulation_steps).backward()
                 total_dmd_loss += dmd_loss.detach().item()
             self._clip_model_grad_norm_(batch_gen, self.transformer)
-            for param in self.transformer.parameters():
-                # check if the gradient is not None and not zero
-                assert param.grad is not None and param.grad.abs().sum() > 0
             self.optimizer.step()
             self.optimizer.zero_grad(set_to_none=True)
-
-            if self.generator_ema is not None:
-                self.generator_ema.update(self.transformer)
-
             avg_dmd_loss = torch.tensor(total_dmd_loss /
                                         gradient_accumulation_steps,
                                         device=self.device)
@@ -834,9 +610,6 @@ class DistillationPipeline(TrainingPipeline):
             fake_score_latent_vis_dict.update(
                 batch_fake.fake_score_latent_vis_dict)
         self._clip_model_grad_norm_(batch_fake, self.fake_score_transformer)
-        for param in self.fake_score_transformer.parameters():
-            # check if the gradient is not None and not zero
-            assert param.grad is not None and param.grad.abs().sum() > 0
         self.fake_score_optimizer.step()
         self.fake_score_lr_scheduler.step()
         self.lr_scheduler.step()
@@ -864,8 +637,7 @@ class DistillationPipeline(TrainingPipeline):
             self.transformer, self.fake_score_transformer, self.global_rank,
             self.training_args.resume_from_checkpoint, self.optimizer,
             self.fake_score_optimizer, self.train_dataloader, self.lr_scheduler,
-            self.fake_score_lr_scheduler, self.noise_random_generator,
-            self.generator_ema)
+            self.fake_score_lr_scheduler, self.noise_random_generator)
 
         if resumed_step > 0:
             self.init_steps = resumed_step
@@ -896,14 +668,6 @@ class DistillationPipeline(TrainingPipeline):
             sum(p.numel()
                 for p in self.fake_score_transformer.parameters()) / 1e9)
 
-        if self.generator_ema is not None:
-            logger.info("  Generator EMA enabled with decay: %s",
-                        self.training_args.ema_decay)
-            logger.info("  Generator EMA start step: %s",
-                        self.training_args.ema_start_step)
-        else:
-            logger.info("  Generator EMA disabled")
-
     @torch.no_grad()
     def _log_validation(self, transformer, training_args, global_step) -> None:
         training_args.inference_mode = True
@@ -935,18 +699,6 @@ class DistillationPipeline(TrainingPipeline):
 
         transformer.eval()
 
-        # Optionally use EMA model for validation if available and ready
-        use_ema_for_validation = (self.training_args.use_ema
-                                  and self.is_ema_ready(global_step))
-        if use_ema_for_validation:
-            logger.info("Using EMA model for validation")
-            validation_transformer = self.transformer
-            ema_context = self.generator_ema.apply_to_model(
-                validation_transformer)
-        else:
-            validation_transformer = transformer
-            ema_context = None
-
         validation_steps = training_args.validation_sampling_steps.split(",")
         validation_steps = [int(step) for step in validation_steps]
         validation_steps = [step for step in validation_steps if step > 0]
@@ -962,98 +714,50 @@ class DistillationPipeline(TrainingPipeline):
             step_videos: list[np.ndarray] = []
             step_captions: list[str] = []
 
-            if ema_context is not None:
-                with ema_context:
-                    for validation_batch in validation_dataloader:
-                        batch = self._prepare_validation_batch(
-                            sampling_param, training_args, validation_batch,
-                            num_inference_steps)
+            for validation_batch in validation_dataloader:
+                batch = self._prepare_validation_batch(sampling_param,
+                                                       training_args,
+                                                       validation_batch,
+                                                       num_inference_steps)
 
-                        negative_prompt = batch.negative_prompt
-                        batch_negative = ForwardBatch(
-                            data_type="video",
-                            prompt=negative_prompt,
-                            prompt_embeds=[],
-                            prompt_attention_mask=[],
-                        )
-                        result_batch = self.validation_pipeline.prompt_encoding_stage(  # type: ignore
-                            batch_negative, training_args)
-                        self.negative_prompt_embeds, self.negative_prompt_attention_mask = result_batch.prompt_embeds[
-                            0], result_batch.prompt_attention_mask[0]
+                negative_prompt = batch.negative_prompt
+                batch_negative = ForwardBatch(
+                    data_type="video",
+                    prompt=negative_prompt,
+                    prompt_embeds=[],
+                    prompt_attention_mask=[],
+                )
+                result_batch = self.validation_pipeline.prompt_encoding_stage(  # type: ignore
+                    batch_negative, training_args)
+                self.negative_prompt_embeds, self.negative_prompt_attention_mask = result_batch.prompt_embeds[
+                    0], result_batch.prompt_attention_mask[0]
 
-                        logger.info(
-                            "rank: %s: rank_in_sp_group: %s, batch.prompt: %s",
+                logger.info("rank: %s: rank_in_sp_group: %s, batch.prompt: %s",
                             self.global_rank,
                             self.rank_in_sp_group,
                             batch.prompt,
                             local_main_process_only=False)
 
-                        assert batch.prompt is not None and isinstance(
-                            batch.prompt, str)
-                        step_captions.append(batch.prompt)
+                assert batch.prompt is not None and isinstance(
+                    batch.prompt, str)
+                step_captions.append(batch.prompt)
 
-                        # Run validation inference
-                        with torch.no_grad():
-                            output_batch = self.validation_pipeline.forward(
-                                batch, training_args)
-                        samples = output_batch.output
-                        if self.rank_in_sp_group != 0:
-                            continue
+                # Run validation inference
+                with torch.no_grad():
+                    output_batch = self.validation_pipeline.forward(
+                        batch, training_args)
+                samples = output_batch.output
+                if self.rank_in_sp_group != 0:
+                    continue
 
-                        # Process outputs
-                        video = rearrange(samples, "b c t h w -> t b c h w")
-                        frames = []
-                        for x in video:
-                            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))
-                        step_videos.append(frames)
-            else:
-                # Use original transformer without EMA
-                for validation_batch in validation_dataloader:
-                    batch = self._prepare_validation_batch(
-                        sampling_param, training_args, validation_batch,
-                        num_inference_steps)
-
-                    negative_prompt = batch.negative_prompt
-                    batch_negative = ForwardBatch(
-                        data_type="video",
-                        prompt=negative_prompt,
-                        prompt_embeds=[],
-                        prompt_attention_mask=[],
-                    )
-                    result_batch = self.validation_pipeline.prompt_encoding_stage(  # type: ignore
-                        batch_negative, training_args)
-                    self.negative_prompt_embeds, self.negative_prompt_attention_mask = result_batch.prompt_embeds[
-                        0], result_batch.prompt_attention_mask[0]
-
-                    logger.info(
-                        "rank: %s: rank_in_sp_group: %s, batch.prompt: %s",
-                        self.global_rank,
-                        self.rank_in_sp_group,
-                        batch.prompt,
-                        local_main_process_only=False)
-
-                    assert batch.prompt is not None and isinstance(
-                        batch.prompt, str)
-                    step_captions.append(batch.prompt)
-
-                    # Run validation inference
-                    with torch.no_grad():
-                        output_batch = self.validation_pipeline.forward(
-                            batch, training_args)
-                    samples = output_batch.output
-                    if self.rank_in_sp_group != 0:
-                        continue
-
-                    # Process outputs
-                    video = rearrange(samples, "b c t h w -> t b c h w")
-                    frames = []
-                    for x in video:
-                        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))
-                    step_videos.append(frames)
+                # Process outputs
+                video = rearrange(samples, "b c t h w -> t b c h w")
+                frames = []
+                for x in video:
+                    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))
+                step_videos.append(frames)
 
             # Log validation results for this step
             world_group = get_world_group()
@@ -1130,16 +834,16 @@ class DistillationPipeline(TrainingPipeline):
                                                         latents.dtype)
                 else:
                     latents += self.vae.shift_factor
-                with torch.autocast("cuda", dtype=torch.bfloat16):
-                    video = self.vae.decode(latents)
-                video = (video / 2 + 0.5).clamp(0, 1)
-                video = video.cpu().float()
-                video = video.permute(0, 2, 1, 3, 4)
-                video = (video * 255).numpy().astype(np.uint8)
-                wandb_loss_dict[latent_key] = wandb.Video(
-                    video, fps=24, format="mp4")  # change to 16 for Wan2.1
-                # Clean up references
-                del video, latents
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                video = self.vae.decode(latents)
+            video = (video / 2 + 0.5).clamp(0, 1)
+            video = video.cpu().float()
+            video = video.permute(0, 2, 1, 3, 4)
+            video = (video * 255).numpy().astype(np.uint8)
+            wandb_loss_dict[latent_key] = wandb.Video(
+                video, fps=24, format="mp4")  # change to 16 for Wan2.1
+            # Clean up references
+            del video, latents
 
         # Process DMD training data if available - use decode_stage instead of self.vae.decode
         if 'generator_pred_video' in dmd_latents_vis_dict:
@@ -1200,10 +904,6 @@ class DistillationPipeline(TrainingPipeline):
             device="cpu").manual_seed(self.seed)
         logger.info("Initialized random seeds with seed: %s", seed)
 
-        # Initialize current_trainstep for EMA ready checks
-        #TODO: check if needed
-        self.current_trainstep = self.init_steps
-
         # Resume from checkpoint if specified (this will restore random states)
         if self.training_args.resume_from_checkpoint:
             self._resume_from_checkpoint()
@@ -1247,14 +947,6 @@ class DistillationPipeline(TrainingPipeline):
             self.current_trainstep = step
             training_batch.current_vsa_sparsity = current_vsa_sparsity
 
-            if (step >= self.training_args.ema_start_step) and \
-                    (self.generator_ema is None) and (self.training_args.ema_decay > 0):
-                self.generator_ema = EMA_FSDP(
-                    self.transformer, decay=self.training_args.ema_decay)
-                logger.info(
-                    f"Created generator EMA at step {step} with decay={self.training_args.ema_decay}"
-                )
-
             with torch.autocast("cuda", dtype=torch.bfloat16):
                 training_batch = self.train_one_step(training_batch)
 
@@ -1268,19 +960,11 @@ class DistillationPipeline(TrainingPipeline):
             avg_step_time = sum(step_times) / len(step_times)
 
             progress_bar.set_postfix({
-                "total_loss":
-                f"{total_loss:.4f}",
-                "generator_loss":
-                f"{generator_loss:.4f}",
-                "fake_score_loss":
-                f"{fake_score_loss:.4f}",
-                "step_time":
-                f"{step_time:.2f}s",
-                "grad_norm":
-                grad_norm,
-                "ema":
-                "✓" if (self.generator_ema is not None and self.is_ema_ready())
-                else "✗",
+                "total_loss": f"{total_loss:.4f}",
+                "generator_loss": f"{generator_loss:.4f}",
+                "fake_score_loss": f"{fake_score_loss:.4f}",
+                "step_time": f"{step_time:.2f}s",
+                "grad_norm": grad_norm,
             })
             progress_bar.update(1)
 
@@ -1308,15 +992,6 @@ class DistillationPipeline(TrainingPipeline):
                 if use_vsa:
                     log_data["VSA_train_sparsity"] = current_vsa_sparsity
 
-                if self.generator_ema is not None:
-                    log_data["ema_enabled"] = True
-                    log_data["ema_decay"] = self.training_args.ema_decay
-                else:
-                    log_data["ema_enabled"] = False
-
-                ema_stats = self.get_ema_stats()
-                log_data.update(ema_stats)
-
                 if training_batch.dmd_latent_vis_dict:
                     dmd_additional_logs = {
                         "generator_timestep":
@@ -1348,8 +1023,7 @@ class DistillationPipeline(TrainingPipeline):
                     self.global_rank, self.training_args.output_dir, step,
                     self.optimizer, self.fake_score_optimizer,
                     self.train_dataloader, self.lr_scheduler,
-                    self.fake_score_lr_scheduler, self.noise_random_generator,
-                    self.generator_ema)
+                    self.fake_score_lr_scheduler, self.noise_random_generator)
 
                 if self.transformer:
                     self.transformer.train()
@@ -1366,11 +1040,7 @@ class DistillationPipeline(TrainingPipeline):
                                              self.global_rank,
                                              self.training_args.output_dir,
                                              f"{step}_weight_only",
-                                             only_save_generator_weight=True,
-                                             generator_ema=self.generator_ema)
-
-                if self.training_args.use_ema and self.is_ema_ready():
-                    self.save_ema_weights(self.training_args.output_dir, step)
+                                             only_save_generator_weight=True)
 
             if self.training_args.log_validation and step % self.training_args.validation_steps == 0:
                 if self.training_args.log_visualization:
@@ -1390,11 +1060,7 @@ class DistillationPipeline(TrainingPipeline):
             self.training_args.output_dir, self.training_args.max_train_steps,
             self.optimizer, self.fake_score_optimizer, self.train_dataloader,
             self.lr_scheduler, self.fake_score_lr_scheduler,
-            self.noise_random_generator, self.generator_ema)
-
-        if self.training_args.use_ema and self.is_ema_ready():
-            self.save_ema_weights(self.training_args.output_dir,
-                                  self.training_args.max_train_steps)
+            self.noise_random_generator)
 
         if get_sp_group():
-            cleanup_dist_env_and_memory()
+            cleanup_dist_env_and_memory()

fastvideo/training/ode_causal_pipeline.py

@@ -3,22 +3,18 @@ import sys
 from copy import deepcopy
 from typing import cast
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 
-import wandb
-from fastvideo.dataset.dataloader.schema import (
-    pyarrow_schema_ode_trajectory_text_only)
+from fastvideo.dataset.dataloader.schema import pyarrow_schema_ode_trajectory
 from fastvideo.distributed import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs, TrainingArgs
 from fastvideo.forward_context import set_forward_context
 from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_self_forcing_flow_match import (
-    SelfForcingFlowMatchScheduler)
+from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
+    FlowMatchEulerDiscreteScheduler)
 from fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline import (
     WanCausalDMDPipeline)
-from fastvideo.pipelines.pipeline_batch_info import TrainingBatch
 from fastvideo.training.training_pipeline import TrainingPipeline
 from fastvideo.training.training_utils import (
     clip_grad_norm_while_handling_failing_dtensor_cases)
@@ -40,15 +36,11 @@ class ODEInitTrainingPipeline(TrainingPipeline):
 
     def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
         # Match the preprocess/generation scheduler for consistent stepping
-        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=1000,
-                                                training=True)
+        self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(
+            shift=fastvideo_args.pipeline_config.flow_shift)
 
     def set_schemas(self):
-        self.train_dataset_schema = pyarrow_schema_ode_trajectory_text_only
+        self.train_dataset_schema = pyarrow_schema_ode_trajectory
 
     def initialize_training_pipeline(self, training_args: TrainingArgs):
         super().initialize_training_pipeline(training_args)
@@ -59,16 +51,14 @@ class ODEInitTrainingPipeline(TrainingPipeline):
 
         self.timestep_shift = self.training_args.pipeline_config.flow_shift
         assert self.timestep_shift == 5.0, "flow_shift must be 5.0"
-        self.noise_scheduler = SelfForcingFlowMatchScheduler(
-            shift=self.timestep_shift, sigma_min=0.0, extra_one_step=True)
-        self.noise_scheduler.set_timesteps(num_inference_steps=1000,
-                                           training=True)
+        self.noise_scheduler = FlowMatchEulerDiscreteScheduler(
+            shift=self.timestep_shift)
 
         # logger.info(f"ARG dmd_denoising_steps: {training_args.pipeline_config.dmd_denoising_steps}")
         logger.info(
             f"ARG dmd_denoising_steps: {self.training_args.pipeline_config.dmd_denoising_steps}"
         )
-        self.dmd_denoising_steps = torch.tensor([1000, 750, 500, 250],
+        self.dmd_denoising_steps = torch.tensor([1000, 750, 500, 0],
                                                 dtype=torch.long,
                                                 device=get_local_torch_device())
         # self.dmd_denoising_steps = torch.tensor([1000, 750, 500, 250], dtype=torch.long, device=get_local_torch_device())
@@ -81,9 +71,9 @@ class ODEInitTrainingPipeline(TrainingPipeline):
                                                  self.dmd_denoising_steps]
             logger.info(
                 f"warped self.dmd_denoising_steps: {self.dmd_denoising_steps}")
-            # assert False, "warp_denoising_step must be false"
+            assert False, "warp_denoising_step must be false"
         else:
-            assert False, "warp_denoising_step must be true"
+            # assert False, "warp_denoising_step must be true"
             logger.info("not warped")
         self.dmd_denoising_steps = self.dmd_denoising_steps.to(
             get_local_torch_device())
@@ -108,8 +98,7 @@ class ODEInitTrainingPipeline(TrainingPipeline):
         args_copy.inference_mode = True
         # Warm start validation with current transformer
         self.validation_pipeline = WanCausalDMDPipeline.from_pretrained(
-            # training_args.model_path,
-            "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers",
+            training_args.model_path,
             args=args_copy,  # type: ignore
             inference_mode=True,
             loaded_modules={
@@ -122,75 +111,55 @@ class ODEInitTrainingPipeline(TrainingPipeline):
             dit_cpu_offload=True)
 
     def _get_next_batch(self, training_batch):  # type: ignore[override]
-        # batch = next(self.train_loader_iter, None)  # type: ignore
-        # if batch is None:
-        #     self.current_epoch += 1
-        #     logger.info("Starting epoch %s", self.current_epoch)
-        #     self.train_loader_iter = iter(self.train_dataloader)
-        #     batch = next(self.train_loader_iter)
+        batch = next(self.train_loader_iter, None)  # type: ignore
+        if batch is None:
+            self.current_epoch += 1
+            logger.info("Starting epoch %s", self.current_epoch)
+            self.train_loader_iter = iter(self.train_dataloader)
+            batch = next(self.train_loader_iter)
 
-        # # Required fields from parquet (ODE trajectory schema)
-        # encoder_hidden_states = batch['text_embedding']
-        # encoder_attention_mask = batch['text_attention_mask']
-        # infos = batch['info_list']
+        # Required fields from parquet (ODE trajectory schema)
+        encoder_hidden_states = batch['text_embedding']
+        encoder_attention_mask = batch['text_attention_mask']
+        infos = batch['info_list']
 
-        # # Trajectory tensors may include a leading singleton batch dim per row
-        # trajectory_latents = batch['trajectory_latents']
-        # if trajectory_latents.dim() == 7:
-        #     # [B, 1, S, C, T, H, W] -> [B, S, C, T, H, W]
-        #     trajectory_latents = trajectory_latents[:, 0]
-        # elif trajectory_latents.dim() == 6:
-        #     # already [B, S, C, T, H, W]
-        #     pass
-        # else:
-        #     raise ValueError(
-        #         f"Unexpected trajectory_latents dim: {trajectory_latents.dim()}"
-        #     )
+        # Trajectory tensors may include a leading singleton batch dim per row
+        trajectory_latents = batch['trajectory_latents']
+        if trajectory_latents.dim() == 7:
+            # [B, 1, S, C, T, H, W] -> [B, S, C, T, H, W]
+            trajectory_latents = trajectory_latents[:, 0]
+        elif trajectory_latents.dim() == 6:
+            # already [B, S, C, T, H, W]
+            pass
+        else:
+            raise ValueError(
+                f"Unexpected trajectory_latents dim: {trajectory_latents.dim()}"
+            )
 
-        # trajectory_timesteps = batch['trajectory_timesteps']
-        # if trajectory_timesteps.dim() == 3:
-        #     # [B, 1, S] -> [B, S]
-        #     trajectory_timesteps = trajectory_timesteps[:, 0]
-        # elif trajectory_timesteps.dim() == 2:
-        #     # [B, S]
-        #     pass
-        # else:
-        #     raise ValueError(
-        #         f"Unexpected trajectory_timesteps dim: {trajectory_timesteps.dim()}"
-        #     )
-        # # [B, S, C, T, H, W] -> [B, S, T, C, H, W] to match self-forcing
-        # trajectory_latents = trajectory_latents.permute(0, 1, 3, 2, 4, 5)
+        trajectory_timesteps = batch['trajectory_timesteps']
+        if trajectory_timesteps.dim() == 3:
+            # [B, 1, S] -> [B, S]
+            trajectory_timesteps = trajectory_timesteps[:, 0]
+        elif trajectory_timesteps.dim() == 2:
+            # [B, S]
+            pass
+        else:
+            raise ValueError(
+                f"Unexpected trajectory_timesteps dim: {trajectory_timesteps.dim()}"
+            )
+        # [B, S, C, T, H, W] -> [B, S, T, C, H, W] to match self-forcing
+        trajectory_latents = trajectory_latents.permute(0, 1, 3, 2, 4, 5)
 
-        # # Move to device
+        # Move to device
         device = get_local_torch_device()
-        # training_batch.encoder_hidden_states = encoder_hidden_states.to(
-        #     device, dtype=torch.bfloat16)
-        # training_batch.encoder_attention_mask = encoder_attention_mask.to(
-        #     device, dtype=torch.bfloat16)
-        # training_batch.infos = infos
+        training_batch.encoder_hidden_states = encoder_hidden_states.to(
+            device, dtype=torch.bfloat16)
+        training_batch.encoder_attention_mask = encoder_attention_mask.to(
+            device, dtype=torch.bfloat16)
+        training_batch.infos = infos
 
-        # return training_batch, trajectory_latents.to(
-        #     device, dtype=torch.bfloat16), trajectory_timesteps.to(device)
-
-        ## TEMP
-        path = "/mnt/weka/home/hao.zhang/wei/FastVideo/data/ode_vidprom_1_fv/00000.pt"
-        b = torch.load(path)
-        for k, v in b.items():
-            logger.info(f"b[{k}]: {type(v)}")
-            if isinstance(v, torch.Tensor):
-                logger.info(f"b[{k}]: {v.shape}")
-            else:
-                logger.info(f"b[{k}]: {v}")
-        training_batch.encoder_hidden_states = b["text_embedding"][0].unsqueeze(
-            0).to(device, dtype=torch.bfloat16)
-        trajectory_latents = b["ode_latent"].to(device, dtype=torch.bfloat16)
-        logger.info(f"trajectory_latents: {trajectory_latents.shape}")
-        logger.info(
-            f"encoder_hidden_states: {training_batch.encoder_hidden_states.shape}"
-        )
-
-        return training_batch, trajectory_latents.to(device,
-                                                     dtype=torch.bfloat16), None
+        return training_batch, trajectory_latents.to(
+            device, dtype=torch.bfloat16), trajectory_timesteps.to(device)
 
     def _get_timestep(self,
                       min_timestep: int,
@@ -222,9 +191,7 @@ class ODEInitTrainingPipeline(TrainingPipeline):
         self, traj_latents: torch.Tensor, traj_timesteps: torch.Tensor,
         encoder_hidden_states: torch.Tensor,
         encoder_attention_mask: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, dict[str,
-                                                              torch.Tensor]]:
-        latent_vis_dict = {}
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         device = get_local_torch_device()
         target_latent = traj_latents[:, -1]
 
@@ -244,23 +211,18 @@ class ODEInitTrainingPipeline(TrainingPipeline):
             # self._cached_closest_idx_per_dmd = distances_ks.argmin(dim=1).to(torch.long).cpu()  # [K]
             self._cached_closest_idx_per_dmd = torch.tensor(
                 [0, 12, 24, 36], dtype=torch.long).cpu()
-            logger.info(
-                f"self._cached_closest_idx_per_dmd: {self._cached_closest_idx_per_dmd}"
-            )
-            logger.info(
-                f"corresponding timesteps: {self.noise_scheduler.timesteps[self._cached_closest_idx_per_dmd]}"
-            )
+            # logger.info(f"self._cached_closest_idx_per_dmd: {self._cached_closest_idx_per_dmd}")
+            # logger.info(f"corresponding timesteps: {self.noise_scheduler.timesteps[self._cached_closest_idx_per_dmd]}")
 
         # logger.info(f"traj_latents: {traj_latents.shape}")
         # Select the K indexes from traj_latents using self._cached_closest_idx_per_dmd
         # traj_latents: [B, S, C, T, H, W], self._cached_closest_idx_per_dmd: [K]
         # Output: [B, K, C, T, H, W]
-        # relevant_traj_latents = torch.index_select(
-        #     traj_latents,
-        #     dim=1,
-        #     index=self._cached_closest_idx_per_dmd.to(traj_latents.device))
-        relevant_traj_latents = traj_latents
-        logger.info(f"relevant_traj_latents sum: {relevant_traj_latents.float().sum().item()}, relevant_traj_latents: {relevant_traj_latents.shape}")
+        relevant_traj_latents = torch.index_select(
+            traj_latents,
+            dim=1,
+            index=self._cached_closest_idx_per_dmd.to(traj_latents.device))
+        # logger.info(f"relevant_traj_latents: {relevant_traj_latents.shape}")
         # assert relevant_traj_latents.shape[0] == 1
 
         indexes = self._get_timestep(  # [B, num_frames]
@@ -270,9 +232,8 @@ class ODEInitTrainingPipeline(TrainingPipeline):
             num_frames,
             3,
             uniform_timestep=False)
-        logger.info(f"indexes: {indexes.shape}")
-        logger.info(f"indexes: {indexes}")
         # noisy_input = relevant_traj_latents[indexes]
+        # logger.info(f"indexes: {indexes.shape}")
         noisy_input = torch.gather(
             relevant_traj_latents,
             dim=1,
@@ -303,51 +264,34 @@ class ODEInitTrainingPipeline(TrainingPipeline):
         # logger.info(f"timestep: {timestep}")
 
         # Prepare inputs for transformer
-        latent_vis_dict["noisy_input"] = noisy_input.permute(
-            0, 2, 1, 3, 4).detach().clone().cpu()
-        latent_vis_dict["x0"] = target_latent.permute(0, 2, 1, 3,
-                                                      4).detach().clone().cpu()
-
-        logger.info("noisy input sum: %s, noisy input shape: %s, noisy input dtype: %s", noisy_input.float().sum().item(), noisy_input.shape, noisy_input.dtype)
-        logger.info("encoder hidden states sum: %s, encoder hidden states shape: %s, encoder hidden states dtype: %s", encoder_hidden_states.float().sum().item(), encoder_hidden_states.shape, encoder_hidden_states.dtype)
-        logger.info("timestep sum: %s, timestep shape: %s, timestep dtype: %s", timestep.float().sum().item(), timestep.shape, timestep.dtype)
-        logger.info("model dtype set: %s. model weight sum: %s", set(p.dtype for p in self.transformer.parameters()), sum(p.float().sum().item() for p in self.transformer.parameters()))
-
-        # model_dtype = next(self.transformer.parameters()).dtype
-        model_dtype = torch.bfloat16
         input_kwargs = {
             "hidden_states": noisy_input.permute(0, 2, 1, 3, 4),
             "encoder_hidden_states": encoder_hidden_states,
-            "timestep": timestep,
+            "timestep": timestep.to(device, dtype=torch.bfloat16),
+            "encoder_attention_mask": encoder_attention_mask,
             "return_dict": False,
-            "scheduler": self.modules["scheduler"]
         }
         # Predict noise and step the scheduler to obtain next latent
         with set_forward_context(current_timestep=timestep,
                                  attn_metadata=None,
                                  forward_batch=None):
-            pred_video = self.transformer(**input_kwargs)
+            noise_pred = self.transformer(**input_kwargs).permute(0, 2, 1, 3, 4)
             # logger.info(f"noise_pred: {noise_pred.shape}")
-        # logger.info("noise pred sum: %s, noise pred shape: %s, noise pred dtype: %s", noise_pred.float().sum().item(), noise_pred.shape, noise_pred.dtype)
-        # if isinstance(noise_pred, (tuple, list)):
-        #     noise_pred = noise_pred[0]
+        if isinstance(noise_pred, (tuple, list)):
+            noise_pred = noise_pred[0]
 
-        # from fastvideo.models.utils import pred_noise_to_pred_video
-        # pred_video = pred_noise_to_pred_video(
-        #     pred_noise=noise_pred.flatten(0, 1),
-        #     noise_input_latent=noisy_input.flatten(0, 1),
-        #     timestep=timestep.to(dtype=model_dtype).flatten(0, 1),
-        #     scheduler=self.modules["scheduler"]).unflatten(
-        #         0, noise_pred.shape[:2])
-        logger.info("pred video sum: %s, pred video shape: %s, pred video dtype: %s", pred_video.float().sum().item(), pred_video.shape, pred_video.dtype)
-
-        latent_vis_dict["pred_video"] = pred_video.permute(
-            0, 2, 1, 3, 4).detach().clone().cpu()
+        from fastvideo.models.utils import pred_noise_to_pred_video
+        noise_pred = pred_noise_to_pred_video(
+            pred_noise=noise_pred.flatten(0, 1),
+            noise_input_latent=noisy_input.flatten(0, 1),
+            timestep=timestep.flatten(0, 1),
+            scheduler=self.modules["scheduler"]).unflatten(
+                0, noise_pred.shape[:2])
 
         # noisy_input = pred_noise_to_pred_video(noise_pred, noisy_input, t, self.modules["scheduler"])
         # next_latent_pred = self.modules["scheduler"].step(
         #     noise_pred, t, current_latents, return_dict=False)[0]
-        return pred_video, target_latent, timestep, latent_vis_dict
+        return noise_pred, target_latent, timestep
 
     def train_one_step(self, training_batch):  # type: ignore[override]
         self.transformer.train()
@@ -386,10 +330,8 @@ class ODEInitTrainingPipeline(TrainingPipeline):
             #     t = t.long()
 
             # Forward to predict next latent by stepping scheduler with predicted noise
-            noise_pred, target_latent, t, latent_vis_dict = self._step_predict_next_latent(
-                traj_latents, training_batch.current_timestep, text_embeds, text_attention_mask)
-
-            training_batch.latent_vis_dict.update(latent_vis_dict)
+            noise_pred, target_latent, t = self._step_predict_next_latent(
+                traj_latents, traj_timesteps, text_embeds, text_attention_mask)
 
             mask = t != 0
 
@@ -403,28 +345,16 @@ class ODEInitTrainingPipeline(TrainingPipeline):
                                      attn_metadata=None,
                                      forward_batch=None):
                 loss.backward()
-
-            logger.info("Loss sum: %s, Loss dtype: %s", loss.float().sum().item(), loss.dtype)
-            assert self.transformer.blocks[0].to_q.weight.dtype == torch.float32
-            logger.info("blocks[0].to_q param sum before backprop: %s", self.transformer.blocks[0].to_q.weight.float().sum().item())
-            logger.info("blocks[0].to_q param grad sum: %s", self.transformer.blocks[0].to_q.weight.grad.float().sum().item())
-            logger.info("Transformer grad dtype: %s", set(p.grad.dtype for p in self.transformer.parameters()))
-            logger.info("Transformer param dtype before backprop: %s", set(p.dtype for p in self.transformer.parameters()))
             avg_loss = loss.detach().clone()
             training_batch.total_loss += avg_loss.item()
 
         # Clip grad and step optimizers
-        # grad_norm = clip_grad_norm_while_handling_failing_dtensor_cases(
-        #     [p for p in self.transformer.parameters() if p.requires_grad],
-        #     args.max_grad_norm if args.max_grad_norm is not None else 0.0)
-        grad_norm = None
+        grad_norm = clip_grad_norm_while_handling_failing_dtensor_cases(
+            [p for p in self.transformer.parameters() if p.requires_grad],
+            args.max_grad_norm if args.max_grad_norm is not None else 0.0)
 
         self.optimizer.step()
-        logger.info("Transformer param dtype after backprop: %s", set(p.dtype for p in self.transformer.parameters()))
-        logger.info("blocks[0].to_q param param after backprop sum: %s", self.transformer.blocks[0].to_q.weight.float().sum().item())
-
-        assert False
-        # self.lr_scheduler.step()
+        self.lr_scheduler.step()
 
         if grad_norm is None:
             grad_value = 0.0
@@ -439,36 +369,6 @@ class ODEInitTrainingPipeline(TrainingPipeline):
         training_batch.grad_norm = grad_value
         return training_batch
 
-    def visualize_intermediate_latents(self, training_batch: TrainingBatch,
-                                       training_args: TrainingArgs, step: int):
-        """Add visualization data to wandb logging and save frames to disk."""
-        wandb_loss_dict = {}
-        latents_vis_dict = training_batch.latent_vis_dict
-        latent_log_keys = ['noisy_input', 'x0', 'pred_video']
-        for latent_key in latent_log_keys:
-            assert latent_key in latents_vis_dict and latents_vis_dict[
-                latent_key] is not None
-            latent = latents_vis_dict[latent_key]
-            pixel_latent = self.validation_pipeline.decoding_stage.decode(
-                latent, training_args)
-
-            video = pixel_latent.cpu().float()
-            video = video.permute(0, 2, 1, 3, 4)
-            video = (video * 255).numpy().astype(np.uint8)
-            wandb_loss_dict[latent_key] = wandb.Video(
-                video, fps=16, format="mp4")  # change to 16 for Wan2.1
-            # Clean up references
-            del video, pixel_latent, latent
-
-        # Log to wandb
-        if self.global_rank == 0:
-            wandb.log(wandb_loss_dict, step=step)
-
-        # dmd_latents_vis_dict = training_batch.dmd_latent_vis_dict
-        # fake_score_latents_vis_dict = training_batch.fake_score_latent_vis_dict
-        # fake_score_log_keys = ['generator_pred_video']
-        # dmd_log_keys = ['faker_score_pred_video', 'real_score_pred_video']
-
 
 def main(args) -> None:
     logger.info("Starting ODE-init training pipeline...")

fastvideo/training/training_pipeline.py

@@ -22,7 +22,6 @@ from tqdm.auto import tqdm
 import fastvideo.envs as envs
 from fastvideo.attention.backends.video_sparse_attn import (
     VideoSparseAttentionMetadataBuilder)
-# from fastvideo.attention.backends.vmoba import VideoMobaAttentionMetadataBuilder
 from fastvideo.configs.sample import SamplingParam
 from fastvideo.dataset import build_parquet_map_style_dataloader
 from fastvideo.dataset.dataloader.schema import pyarrow_schema_t2v
@@ -42,15 +41,11 @@ from fastvideo.training.training_utils import (
     compute_density_for_timestep_sampling, get_scheduler, get_sigmas,
     load_checkpoint, normalize_dit_input, save_checkpoint,
     shard_latents_across_sp)
-# from fastvideo.utils import (is_vmoba_available, is_vsa_available,
-#                              set_random_seed, shallow_asdict)
-from fastvideo.utils import (is_vsa_available,
-                             set_random_seed, shallow_asdict)
+from fastvideo.utils import is_vsa_available, set_random_seed, shallow_asdict
 
 import wandb  # isort: skip
 
 vsa_available = is_vsa_available()
-# vmoba_available = is_vmoba_available()
 
 logger = init_logger(__name__)
 
@@ -122,14 +117,10 @@ class TrainingPipeline(LoRAPipeline, ABC):
         params_to_optimize = self.transformer.parameters()
         params_to_optimize = list(
             filter(lambda p: p.requires_grad, params_to_optimize))
-        # Parse betas from string format "beta1,beta2"
-        betas_str = training_args.betas
-        betas = tuple(float(x.strip()) for x in betas_str.split(","))
-        
         self.optimizer = torch.optim.AdamW(
             params_to_optimize,
             lr=training_args.learning_rate,
-            betas=betas,
+            betas=(0.9, 0.999),
             weight_decay=training_args.weight_decay,
             eps=1e-8,
         )
@@ -281,20 +272,6 @@ class TrainingPipeline(LoRAPipeline, ABC):
                 patch_size=patch_size,
                 VSA_sparsity=current_vsa_sparsity,
                 device=get_local_torch_device())
-        # elif vmoba_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VMOBA_ATTN":
-        #     moba_params = self.training_args.moba_config.copy()
-        #     moba_params.update({
-        #         "current_timestep":
-        #         training_batch.timesteps,
-        #         "raw_latent_shape":
-        #         training_batch.raw_latent_shape[2:5],
-        #         "patch_size":
-        #         self.training_args.pipeline_config.dit_config.patch_size,
-        #         "device":
-        #         get_local_torch_device(),
-        #     })
-        #     training_batch.attn_metadata = VideoMobaAttentionMetadataBuilder(
-        #     ).build(**moba_params)
         else:
             training_batch.attn_metadata = None
 
@@ -319,7 +296,6 @@ class TrainingPipeline(LoRAPipeline, ABC):
 
     def _transformer_forward_and_compute_loss(
             self, training_batch: TrainingBatch) -> TrainingBatch:
-        # if vsa_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VIDEO_SPARSE_ATTN" or vmoba_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VMOBA_ATTN":
         if vsa_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VIDEO_SPARSE_ATTN":
             assert training_batch.attn_metadata is not None
         else:
@@ -486,9 +462,6 @@ class TrainingPipeline(LoRAPipeline, ABC):
                 current_decay_times = min(step // vsa_decay_interval_steps,
                                           vsa_sparsity // vsa_decay_rate)
                 current_vsa_sparsity = current_decay_times * vsa_decay_rate
-            # elif vmoba_available:
-            #     # TODO: add vmoba sparsity scheduling here
-            #     current_vsa_sparsity = 0.0
             else:
                 current_vsa_sparsity = 0.0
 
@@ -530,10 +503,6 @@ class TrainingPipeline(LoRAPipeline, ABC):
                 self.transformer.train()
                 self.sp_group.barrier()
             if self.training_args.log_validation and step % self.training_args.validation_steps == 0:
-                if self.training_args.log_visualization:
-                    self.visualize_intermediate_latents(training_batch,
-                                                        self.training_args,
-                                                        step)
                 self._log_validation(self.transformer, self.training_args, step)
                 gpu_memory_usage = torch.cuda.memory_allocated() / 1024**2
                 trainable_params = round(
@@ -735,8 +704,3 @@ class TrainingPipeline(LoRAPipeline, ABC):
         # Re-enable gradients for training
         training_args.inference_mode = False
         transformer.train()
-    
-    def visualize_intermediate_latents(self, training_batch: TrainingBatch,
-                                       training_args: TrainingArgs, step: int):
-        """Add visualization data to wandb logging and save frames to disk."""
-        raise NotImplementedError("Visualize intermediate latents is not implemented for training pipeline")

fastvideo/training/training_utils.py

@@ -202,7 +202,6 @@ def save_distillation_checkpoint(generator_transformer,
                                  generator_scheduler=None,
                                  fake_score_scheduler=None,
                                  noise_generator=None,
-                                 generator_ema=None,
                                  only_save_generator_weight=False) -> None:
     """
     Save distillation checkpoint with both generator and fake_score models.
@@ -234,8 +233,6 @@ def save_distillation_checkpoint(generator_transformer,
         if generator_scheduler is not None:
             generator_states["scheduler"] = SchedulerWrapper(
                 generator_scheduler)
-        if generator_ema is not None:
-            generator_states["ema"] = generator_ema.state_dict()
 
         generator_dcp_dir = os.path.join(save_dir, "distributed_checkpoint",
                                          "generator")
@@ -409,8 +406,7 @@ def load_distillation_checkpoint(generator_transformer,
                                  dataloader=None,
                                  generator_scheduler=None,
                                  fake_score_scheduler=None,
-                                 noise_generator=None,
-                                 generator_ema=None) -> int:
+                                 noise_generator=None) -> int:
     """
     Load distillation checkpoint with both generator and fake_score models.
     Returns the step number from which training should resume.
@@ -464,18 +460,6 @@ def load_distillation_checkpoint(generator_transformer,
         end_time - begin_time,
         local_main_process_only=False)
 
-    # Load EMA state if available and generator_ema is provided
-    if generator_ema is not None:
-        try:
-            ema_state = generator_states.get("ema")
-            if ema_state is not None:
-                generator_ema.load_state_dict(ema_state)
-                logger.info("rank: %s, generator EMA state loaded successfully", rank)
-            else:
-                logger.info("rank: %s, no EMA state found in checkpoint", rank)
-        except Exception as e:
-            logger.warning("rank: %s, failed to load EMA state: %s", rank, str(e))
-
     # Load critic distributed checkpoint
     critic_dcp_dir = os.path.join(checkpoint_path, "distributed_checkpoint",
                                   "critic")
@@ -1298,156 +1282,3 @@ def get_scheduler(
                          num_warmup_steps=num_warmup_steps,
                          num_training_steps=num_training_steps,
                          last_epoch=last_epoch)
-
-
-class EMA_FSDP:
-    """
-    FSDP2-friendly EMA with two modes:
-      - mode="local_shard" (default): maintain float32 CPU EMA of local parameter shards on every rank.
-        Provides a context manager to temporarily swap EMA weights into the live model for teacher forward.
-      - mode="rank0_full": maintain a consolidated float32 CPU EMA of full parameters on rank 0 only
-        using gather_state_dict_on_cpu_rank0(). Useful for checkpoint export; not for teacher forward.
-
-    Usage (local_shard for CM teacher):
-      ema = EMA_FSDP(model, decay=0.999, mode="local_shard")
-      for step in ...:
-          ema.update(model)
-      with ema.apply_to_model(model):
-          with torch.no_grad():
-              y_teacher = model(...)
-
-    Usage (rank0_full for export):
-      ema = EMA_FSDP(model, decay=0.999, mode="rank0_full")
-      ema.update(model)
-      ema.state_dict()  # on rank 0
-    """
-    def __init__(self, module, decay: float = 0.999, mode: str = "local_shard"):
-        self.decay = float(decay)
-        self.mode = mode
-        self.shadow: dict[str, torch.Tensor] = {}
-        self.rank = dist.get_rank() if dist.is_initialized() else 0
-        if self.mode not in {"local_shard", "rank0_full"}:
-            raise ValueError(f"Unsupported EMA_FSDP mode: {self.mode}")
-        self._init_shadow(module)
-
-    @staticmethod
-    def _to_local_tensor(t: torch.Tensor) -> torch.Tensor:
-        # DTensor-aware to_local fetch; fall back to raw tensor
-        try:
-            from torch.distributed.tensor import DTensor  # type: ignore
-            if isinstance(t, DTensor):
-                return t.to_local()
-        except Exception:
-            pass
-        return t
-
-    @torch.no_grad()
-    def _init_shadow(self, module):
-        if self.mode == "rank0_full":
-            cpu_state = gather_state_dict_on_cpu_rank0(module, device=None)
-            if self.rank == 0:
-                self.shadow = {k: v.detach().clone().float().cpu() for k, v in cpu_state.items()}
-            else:
-                self.shadow = {}
-            return
-
-        # local_shard: maintain EMA of local shards for requires_grad params
-        self.shadow = {}
-        for name, p in module.named_parameters():
-            if not p.requires_grad:
-                continue
-            local = self._to_local_tensor(p.detach())
-            self.shadow[name] = local.clone().float().cpu()
-
-    @torch.no_grad()
-    def update(self, module):
-        d = self.decay
-        if self.mode == "rank0_full":
-            if self.rank != 0:
-                return
-            cpu_state = gather_state_dict_on_cpu_rank0(module, device=None)
-            for n, v in cpu_state.items():
-                v_cpu = v.detach().float().cpu()
-                if n not in self.shadow:
-                    self.shadow[n] = v_cpu.clone()
-                else:
-                    self.shadow[n].mul_(d).add_(v_cpu, alpha=1.0 - d)
-            return
-
-        # local_shard: update local shard EMA on every rank
-        for name, p in module.named_parameters():
-            if not p.requires_grad:
-                continue
-            local = self._to_local_tensor(p.detach())
-            v_cpu = local.float().cpu()
-            if name not in self.shadow:
-                self.shadow[name] = v_cpu.clone()
-            else:
-                self.shadow[name].mul_(d).add_(v_cpu, alpha=1.0 - d)
-
-    def state_dict(self) -> dict[str, torch.Tensor]:
-        if self.mode == "rank0_full":
-            return {k: v.clone() for k, v in self.shadow.items()} if self.rank == 0 else {}
-        return {k: v.clone() for k, v in self.shadow.items()}
-
-    def load_state_dict(self, sd: dict[str, torch.Tensor]):
-        self.shadow = {k: v.clone() for k, v in sd.items()}
-
-    @torch.no_grad()
-    def copy_to_unwrapped(self, module) -> None:
-        """
-        Copy EMA weights into a non-sharded (unwrapped) module. Intended for export/eval.
-        For mode="rank0_full", only rank 0 has the full EMA state.
-        """
-        if self.mode == "rank0_full" and self.rank != 0:
-            return
-        name_to_param = dict(module.named_parameters())
-        for n, w in self.shadow.items():
-            if n in name_to_param:
-                p = name_to_param[n]
-                p.data.copy_(w.to(dtype=p.dtype, device=p.device))
-
-    class _ApplyEMACtx:
-        def __init__(self, ema: "EMA_FSDP", module):
-            self.ema = ema
-            self.module = module
-            self.saved: dict[str, torch.Tensor] = {}
-
-        def __enter__(self):
-            if self.ema.mode != "local_shard":
-                raise RuntimeError("EMA apply_to_model is only supported for mode='local_shard'")
-            with torch.no_grad():
-                for name, p in self.module.named_parameters():
-                    if not p.requires_grad:
-                        continue
-                    # Save local shard
-                    p_local = EMA_FSDP._to_local_tensor(p.detach())
-                    if p_local.numel() == 0:
-                        # Nothing to swap on this rank for this param
-                        continue
-                    self.saved[name] = p_local.clone().to(device=p_local.device, dtype=p_local.dtype)
-                    if name in self.ema.shadow:
-                        ema_cpu = self.ema.shadow[name]
-                        if ema_cpu.numel() != p_local.numel():
-                            # Shard shape mismatch (e.g., empty shard here), skip
-                            continue
-                        # Copy EMA shard into local param shard
-                        p_local.copy_(ema_cpu.to(dtype=p_local.dtype, device=p_local.device))
-            return self.module
-
-        def __exit__(self, exc_type, exc, tb):
-            with torch.no_grad():
-                for name, p in self.module.named_parameters():
-                    if name in self.saved:
-                        p_local = EMA_FSDP._to_local_tensor(p.detach())
-                        if p_local.numel() == 0:
-                            continue
-                        saved_local = self.saved[name]
-                        if saved_local.numel() != p_local.numel():
-                            continue
-                        p_local.copy_(saved_local)
-            self.saved.clear()
-            return False
-
-    def apply_to_model(self, module):
-        return EMA_FSDP._ApplyEMACtx(self, module)

fastvideo/training/wan_self_forcing_distillation_pipeline.py

@@ -1,72 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import sys
-from copy import deepcopy
-
-from fastvideo.fastvideo_args import FastVideoArgs, TrainingArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
-    FlowMatchEulerDiscreteScheduler)
-from fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline import WanCausalDMDPipeline
-from fastvideo.training.self_forcing_distillation_pipeline import SelfForcingDistillationPipeline
-from fastvideo.utils import is_vsa_available
-
-vsa_available = is_vsa_available()
-
-logger = init_logger(__name__)
-
-
-class WanSelfForcingDistillationPipeline(SelfForcingDistillationPipeline):
-    """
-    A self-forcing distillation pipeline for Wan that uses the self-forcing methodology
-    with DMD for video generation.
-    """
-    _required_config_modules = [
-        "scheduler", "transformer", "vae", "real_score_transformer",
-        "fake_score_transformer"
-    ]
-
-    def create_training_stages(self, training_args: TrainingArgs):
-        """
-        May be used in future refactors.
-        """
-        pass
-
-    def initialize_validation_pipeline(self, training_args: TrainingArgs):
-        logger.info("Initializing validation pipeline...")
-        args_copy = deepcopy(training_args)
-
-        args_copy.inference_mode = True
-        validation_pipeline = WanCausalDMDPipeline.from_pretrained(
-            training_args.model_path,
-            args=args_copy,  # type: ignore
-            inference_mode=True,
-            loaded_modules={"transformer": self.get_module("transformer")},
-            tp_size=training_args.tp_size,
-            sp_size=training_args.sp_size,
-            num_gpus=training_args.num_gpus,
-            pin_cpu_memory=training_args.pin_cpu_memory,
-            dit_cpu_offload=True)
-
-        self.validation_pipeline = validation_pipeline
-
-
-def main(args) -> None:
-    logger.info("Starting Wan self-forcing distillation pipeline...")
-
-    pipeline = WanSelfForcingDistillationPipeline.from_pretrained(
-        args.pretrained_model_name_or_path, args=args)
-
-    args = pipeline.training_args
-    pipeline.train()
-    logger.info("Wan self-forcing distillation pipeline completed")
-
-
-if __name__ == "__main__":
-    argv = sys.argv
-    from fastvideo.fastvideo_args import TrainingArgs
-    from fastvideo.utils import FlexibleArgumentParser
-    parser = FlexibleArgumentParser()
-    parser = TrainingArgs.add_cli_args(parser)
-    parser = FastVideoArgs.add_cli_args(parser)
-    args = parser.parse_args()
-    main(args) 

fastvideo/utils.py

@@ -27,14 +27,10 @@ from typing import Any, TypeVar, cast
 
 import cloudpickle
 import filelock
-import imageio
-import numpy as np
 import torch
-import torchvision
 import yaml
 from diffusers.loaders.lora_base import (
     _best_guess_weight_name)  # watch out for potetential removal from diffusers
-from einops import rearrange
 from huggingface_hub import snapshot_download
 from remote_pdb import RemotePdb
 from torch.distributed.fsdp import MixedPrecisionPolicy
@@ -822,17 +818,6 @@ def is_vsa_available() -> bool:
     return importlib.util.find_spec("vsa") is not None
 
 
-@lru_cache(maxsize=1)
-def is_vmoba_available() -> bool:
-    if importlib.util.find_spec("csrc.attn.vmoba_attn.vmoba") is None:
-        return False
-    try:
-        import flash_attn
-        return flash_attn.__version__ >= "2.7.4"
-    except Exception:
-        return False
-
-
 # adapted from: https://github.com/Wan-Video/Wan2.2/blob/main/wan/utils/utils.py
 def masks_like(tensor,
                zero=False,
@@ -896,8 +881,7 @@ def best_output_size(w, h, dw, dh, expected_area):
         return ow2, oh2
 
 
-def save_decoded_latents_as_video(decoded_latents: list[torch.Tensor],
-                                  output_path: str, fps: int):
+def save_decoded_latents_as_video(decoded_latents: list[torch.Tensor], output_path: str, fps: int):
     # Process outputs
     videos = rearrange(decoded_latents, "b c t h w -> t b c h w")
     frames = []

fastvideo/wan/README.md

@@ -1,2 +0,0 @@
-Code in this folder is modified from https://github.com/Wan-Video/Wan2.1
-Apache-2.0 License 

fastvideo/wan/__init__.py

@@ -1,3 +0,0 @@
-from . import configs, distributed, modules
-from .image2video import WanI2V
-from .text2video import WanT2V