[bugfix]: bound WanCausalModel num_frames_per_block override to <= 3

The constructor override only checked >= 1, bypassing the <= 3 limit the
config path enforces in CausalWanTransformer3DModel. Apply the same upper
bound with a clear error message.

.agents/skills/reseed-performance-baseline/SKILL.md

@@ -69,7 +69,7 @@ approval, then upload reviewed accepted baseline records.
 | `model_id` | Yes | Benchmark id, e.g. `wan-t2v-1.3b-2gpu`. This maps to the HF subdirectory after `sanitize(model_id)`. |
 | `gpu_type` | Yes | Exact GPU device string from the performance record, e.g. the L40S device name emitted by CI. Baselines are GPU-specific. |
 | `source_results` | Yes | One or more local paths or Buildkite artifact URLs for accepted shifted performance JSONs. Prefer normalized `normalized_perf_*.json` artifacts emitted by `compare_baseline.py`. Accept `source_result` as an alias only for a single JSON. |
-| `max_intra_batch_regression` | No | Maximum allowed regression of any source JSON against the source batch median. Default: `PERF_MAX_REGRESSION` if set, otherwise `0.05` (5%). |
+| `max_intra_batch_regression` | No | Maximum allowed regression of any source JSON against the source batch median. Default: `0.05` (5%). |
 | `intent_rationale` | Yes | One-line explanation for why the baseline shift is legitimate. This is written into provenance and should be reused in the PR. |
 
 Hardcoded defaults:
@@ -148,8 +148,7 @@ For each metric with at least two non-null source values:
 4. Stop if any source record regresses against the batch median by more than
    `max_intra_batch_regression`.
 
-Default `max_intra_batch_regression` to `PERF_MAX_REGRESSION` when set,
-otherwise `0.05`. Print a table with per-source values, batch median, and
+Default `max_intra_batch_regression` to `0.05`. Print a table with per-source values, batch median, and
 worst intra-batch regression.
 
 This check prevents uploading a mixed batch where one JSON is materially
@@ -183,7 +182,7 @@ present, that run is not a valid source for baseline reseeding.
 
 ### 2. Sync and back up existing HF records under /tmp
 
-Use `fastvideo/tests/performance/hf_store.py` helpers directly. Do **not** use
+Use `fastvideo/performance/hf_store.py` helpers directly. Do **not** use
 `compare_baseline.py` as a sync shortcut; on full main runs it can persist
 records, while this step must only fetch and back up existing history.
 
@@ -192,7 +191,7 @@ The sync command pattern is:
 ```bash
 export PERFORMANCE_TRACKING_ROOT="${PERFORMANCE_TRACKING_ROOT:-/tmp/perf-tracking}"
 export HF_REPO_ID="${HF_REPO_ID:-FastVideo/performance-tracking}"
-PYTHONPATH=fastvideo/tests/performance python -c 'from hf_store import sync_from_hf; import os; sync_from_hf(os.environ["PERFORMANCE_TRACKING_ROOT"], strict=True)'
+python -c 'from fastvideo.performance.hf_store import sync_from_hf; import os; sync_from_hf(os.environ["PERFORMANCE_TRACKING_ROOT"], strict=True)'
 ```
 
 Then back up only the sanitized model directory under `/tmp`:
@@ -200,8 +199,8 @@ Then back up only the sanitized model directory under `/tmp`:
 ```bash
 SHORT_COMMIT=$(git rev-parse --short=12 HEAD)
 TIMESTAMP=$(date -u +%Y%m%d_%H%M%S)
-MODEL_SAFE=$(PYTHONPATH=fastvideo/tests/performance python - <<'PY'
-from hf_store import sanitize
+MODEL_SAFE=$(python - <<'PY'
+from fastvideo.performance.hf_store import sanitize
 print(sanitize("<model_id>"))
 PY
 )
@@ -235,7 +234,7 @@ first baseline seed. Continue, but report that baseline history was empty.
 Load the last 5 successful records for the target:
 
 ```python
-from hf_store import load_records_for_model
+from fastvideo.performance.hf_store import load_records_for_model
 
 records = load_records_for_model(
     "/tmp/perf-tracking",
@@ -372,7 +371,7 @@ prepared records plus backup on disk.
 Use the shared storage helper so the path and repo type match CI:
 
 ```python
-from hf_store import upload_record
+from fastvideo.performance.hf_store import upload_record
 
 upload_record("<local_record_path>", record, strict=True)
 ```
@@ -460,7 +459,7 @@ directories created for this reseed. Never remove unrelated `/tmp` contents.
   intentional baseline replacement.
 - `fastvideo/tests/performance/compare_baseline.py` — normalization, rolling
   median comparison, and persistence rules.
-- `fastvideo/tests/performance/hf_store.py` — HF sync, record loading,
+- `fastvideo/performance/hf_store.py` — HF sync, record loading,
   `sanitize()`, and `upload_record()`.
 - `fastvideo/tests/performance/test_inference_performance.py` — source result
   JSON schema.

.github/workflows/ci-precommit.yml

@@ -1,7 +1,11 @@
 name: pre-commit
 
 on:
-  pull_request:
+  # pull_request_target instead of pull_request: the workflow definition and
+  # the hook config are always taken from the BASE branch, so fork /
+  # first-time-contributor PRs run immediately without a maintainer clicking
+  # "Approve and run". The PR head is checked out as data only.
+  pull_request_target:
     branches: [main]
   workflow_call:
     inputs:
@@ -15,12 +19,25 @@ permissions:
 
 jobs:
   pre-commit:
-    if: github.event_name == 'workflow_call' || github.event.pull_request.draft != true
+    if: github.event.pull_request.draft != true
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
       with:
         ref: ${{ inputs.ref || '' }}
+    # For PR events, lint the PR head — but keep the hook definitions from
+    # the base branch so an untrusted PR cannot alter what gets executed.
+    - name: Save trusted hook config
+      if: github.event_name == 'pull_request_target'
+      run: cp .pre-commit-config.yaml "$RUNNER_TEMP/trusted-pre-commit-config.yaml"
+    - uses: actions/checkout@v4
+      if: github.event_name == 'pull_request_target'
+      with:
+        ref: ${{ github.event.pull_request.head.sha }}
+        persist-credentials: false
+    - name: Restore trusted hook config
+      if: github.event_name == 'pull_request_target'
+      run: cp "$RUNNER_TEMP/trusted-pre-commit-config.yaml" .pre-commit-config.yaml
     - uses: actions/setup-python@v5
       with:
         python-version: "3.12"

docs/contributing/performance_benchmarks.md

@@ -112,10 +112,11 @@ per-metric policy with direction, percent threshold, absolute threshold, and a
 while it runs so pipeline stage execution times are available in
 `generate_video(...).logging_info`. Stage logs use pipeline-unique keys such as
 `prompt_encoding_stage` so duplicate stage classes do not collide. For
-`PipelineStage` entries, the extractor maps the `stage_class` field:
-`TextEncodingStage` maps to `text_encoder_time_s`, `DenoisingStage` and
-`DmdDenoisingStage` map to `dit_time_s`, and `DecodingStage` maps to
-`vae_decode_time_s`, with a fallback for older logs that used the class name as
+`PipelineStage` entries, shared component stage bases emit a stable
+`component_metric`: text encoding stages map to `text_encoder_time_s`,
+denoising stages and subclasses map to `dit_time_s`, and decoding stages map to
+`vae_decode_time_s`. The extractor falls back to known `stage_class` names for
+older logs that do not include `component_metric` or that used the class name as
 the stage key. Generator-side timings such as `PostDecodeFrameProcessStage`,
 `VideoSaveStage`, and `AudioMuxStage` are intentionally ignored. If a pipeline
 does not report one of the mapped stages, that component metric is stored as
@@ -432,5 +433,5 @@ pipelines that did not report a mapped component stage.
 
 **Component timing is `null`** — the generated result did not include a mapped
 stage in `logging_info.stages`. Check that the pipeline emits stage logging
-and that the stage name is listed in `STAGE_METRIC_MAP` in
-`test_inference_performance.py`.
+and that the stage emits `component_metric` or is covered by the legacy
+`STAGE_METRIC_MAP` fallback in `test_inference_performance.py`.

examples/train/configs/consistency_model/wan/causal_cd_t2v.yaml

@@ -0,0 +1,78 @@
+# Causal Consistency Distillation: Wan 2.1 T2V 1.3B Causal
+#
+# ODE-data-free distillation. A frozen teacher takes a single CFG Euler step;
+# the student matches an EMA copy of itself at the next timestep, all under
+# clean-history teacher forcing.
+#
+# All three roles initialize from the SAME checkpoint (the teacher-forcing
+# AR-diffusion model). Point init_from at that checkpoint for a real run.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+  ema:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+
+method:
+  _target_: fastvideo.train.methods.consistency_model.causal_cd.CausalConsistencyDistillationMethod
+  discrete_cd_N: 48
+  guidance_scale: 3.0
+  ema_decay: 0.99
+  ema_start_step: 200
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 18
+    num_height: 448
+    num_width: 832
+    num_frames: 69
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 3000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_causal_cd
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: distillation_wan_r
+    run_name: wan2.1_causal_cd_shift5
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+
+pipeline:
+  flow_shift: 5

examples/train/configs/fine_tuning/wan/dfsft_causal_t2v_framewise.yaml

@@ -0,0 +1,73 @@
+# DFSFT (Diffusion-Forcing SFT), frame-wise: Wan 2.1 T2V 1.3B Causal
+#
+# - Student: trainable causal Wan model with a block size of 1 frame
+# - Training: each frame gets its own independent noise level (frame-wise
+#   diffusion forcing), versus the chunk-wise variant that shares one noise
+#   level across num_frames_per_block frames.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    num_frames_per_block: 1
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.dfsft.DiffusionForcingSFTMethod
+  chunk_size: 1
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 18
+    num_height: 448
+    num_width: 832
+    num_frames: 69
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_causal_dfsft_framewise
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: distillation_wan_r
+    run_name: wan2.1_causal_dfsft_framewise_shift5_gauss_weight
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 50
+    sampling_steps: [40]
+    guidance_scale: 6.0
+    num_frames: 69
+
+pipeline:
+  flow_shift: 5

examples/train/configs/fine_tuning/wan/tfsft_causal_t2v.yaml

@@ -0,0 +1,72 @@
+# TFSFT (Teacher-Forcing SFT): Wan 2.1 T2V 1.3B Causal
+#
+# - Student: trainable causal Wan model
+# - Training: inhomogeneous timesteps per chunk, but the causal transformer
+#   denoises the current block while attending to *clean* history (clean_x),
+#   not its own noisy rollout.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
+  chunk_size: 3
+
+training:
+  distributed:
+    num_gpus: 8
+    sp_size: 1
+    tp_size: 1
+    hsdp_replicate_dim: 1
+    hsdp_shard_dim: 8
+
+  data:
+    data_path: data/Wan-Syn_77x448x832_600k
+    dataloader_num_workers: 4
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    seed: 1000
+    num_latent_t: 18
+    num_height: 448
+    num_width: 832
+    num_frames: 69
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 4000
+    gradient_accumulation_steps: 1
+
+  checkpoint:
+    output_dir: outputs/wan2.1_causal_tfsft
+    training_state_checkpointing_steps: 1000
+    checkpoints_total_limit: 3
+
+  tracker:
+    project_name: distillation_wan_r
+    run_name: wan2.1_causal_tfsft_shift5_gauss_weight
+
+  model:
+    enable_gradient_checkpointing_type: full
+
+callbacks:
+  grad_clip:
+    max_grad_norm: 1.0
+  validation:
+    pipeline_target: fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline
+    dataset_file: examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/validation_4.json
+    every_steps: 50
+    sampling_steps: [40]
+    guidance_scale: 6.0
+    num_frames: 69
+
+pipeline:
+  flow_shift: 5

fastvideo/models/dits/causal_wanvideo.py

@@ -437,6 +437,7 @@ class CausalWanTransformer3DModel(BaseDiT):
 
         # Causal-specific
         self.block_mask = None
+        self.teacher_forcing_block_mask = None
         self.num_frame_per_block = config.arch_config.num_frames_per_block
         assert self.num_frame_per_block <= 3
         self.independent_first_frame = False
@@ -500,6 +501,70 @@ class CausalWanTransformer3DModel(BaseDiT):
 
         return block_mask
 
+    @staticmethod
+    def _prepare_teacher_forcing_mask(
+        device: torch.device | str, num_frames: int = 21,
+        frame_seqlen: int = 1560, num_frame_per_block=1, local_attn_size=-1
+    ) -> BlockMask:
+        """Attention mask for the teacher-forcing ``[clean | noisy]`` sequence.
+
+        A noisy token attends to its own block plus the clean context of all
+        strictly previous blocks; clean tokens are block-wise causal.
+        """
+        if local_attn_size != -1:
+            raise NotImplementedError(
+                f"Teacher forcing ignores local_attn_size={local_attn_size}: "
+                "unlike the block-wise causal mask, this mask always attends "
+                "to the full clean context. Windowed teacher forcing is not "
+                "implemented; use local_attn_size=-1 for teacher-forcing "
+                "training.")
+        total_length = num_frames * frame_seqlen * 2
+        padded_length = math.ceil(total_length / 128) * 128 - total_length
+
+        clean_ends = num_frames * frame_seqlen
+        context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        noise_context_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        noise_context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        noise_noise_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        noise_noise_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+
+        attention_block_size = frame_seqlen * num_frame_per_block
+        frame_indices = torch.arange(
+            start=0, end=num_frames * frame_seqlen,
+            step=attention_block_size, device=device, dtype=torch.long
+        )
+        for start in frame_indices:
+            context_ends[start:start + attention_block_size] = start + attention_block_size
+
+        noisy_image_start_list = torch.arange(
+            num_frames * frame_seqlen, total_length,
+            step=attention_block_size, device=device, dtype=torch.long
+        )
+        noisy_image_end_list = noisy_image_start_list + attention_block_size
+        for block_index, (start, end) in enumerate(zip(noisy_image_start_list, noisy_image_end_list)):
+            noise_noise_starts[start:end] = start
+            noise_noise_ends[start:end] = end
+            noise_context_ends[start:end] = block_index * attention_block_size
+
+        def attention_mask(b, h, q_idx, kv_idx):
+            clean_mask = (q_idx < clean_ends) & (kv_idx < context_ends[q_idx])
+            c1 = (kv_idx < noise_noise_ends[q_idx]) & (kv_idx >= noise_noise_starts[q_idx])
+            c2 = (kv_idx < noise_context_ends[q_idx]) & (kv_idx >= noise_context_starts[q_idx])
+            noise_mask = (q_idx >= clean_ends) & (c1 | c2)
+            eye_mask = q_idx == kv_idx
+            return eye_mask | clean_mask | noise_mask
+
+        block_mask = create_block_mask(
+            attention_mask, B=None, H=None,
+            Q_LEN=total_length + padded_length, KV_LEN=total_length + padded_length,
+            _compile=False, device=device)
+
+        if not dist.is_initialized() or dist.get_rank() == 0:
+            print(f" cache a teacher-forcing mask with block size of {num_frame_per_block} frames")
+            print(block_mask)
+
+        return block_mask
+
     def _forward_inference(
                 self,
                 hidden_states: torch.Tensor,
@@ -628,9 +693,12 @@ class CausalWanTransformer3DModel(BaseDiT):
                 encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
                 | None = None,
                 start_frame: int = 0,
+                clean_x: torch.Tensor | None = None,
+                aug_t: torch.Tensor | None = None,
                 **kwargs) -> torch.Tensor:
 
         orig_dtype = hidden_states.dtype
+        teacher_forcing = clean_x is not None
         if not isinstance(encoder_hidden_states, torch.Tensor):
             encoder_hidden_states = encoder_hidden_states[0]
         if isinstance(encoder_hidden_states_image,
@@ -663,15 +731,26 @@ class CausalWanTransformer3DModel(BaseDiT):
         freqs_cis = (freqs_cos,
                      freqs_sin) if freqs_cos is not None else None
 
-        # Construct blockwise causal attn mask
-        if self.block_mask is None:
-            self.block_mask = self._prepare_blockwise_causal_attn_mask(
-                device=hidden_states.device,
-                num_frames=num_frames,
-                frame_seqlen=post_patch_height * post_patch_width,
-                num_frame_per_block=self.num_frame_per_block,
-                local_attn_size=self.local_attn_size
-            )
+        if teacher_forcing:
+            if self.teacher_forcing_block_mask is None:
+                self.teacher_forcing_block_mask = self._prepare_teacher_forcing_mask(
+                    device=hidden_states.device,
+                    num_frames=num_frames,
+                    frame_seqlen=post_patch_height * post_patch_width,
+                    num_frame_per_block=self.num_frame_per_block,
+                    local_attn_size=self.local_attn_size,
+                )
+            block_mask = self.teacher_forcing_block_mask
+        else:
+            if self.block_mask is None:
+                self.block_mask = self._prepare_blockwise_causal_attn_mask(
+                    device=hidden_states.device,
+                    num_frames=num_frames,
+                    frame_seqlen=post_patch_height * post_patch_width,
+                    num_frame_per_block=self.num_frame_per_block,
+                    local_attn_size=self.local_attn_size
+                )
+            block_mask = self.block_mask
 
         hidden_states = self.patch_embedding(hidden_states)
         grid_sizes = torch.stack(
@@ -679,6 +758,7 @@ class CausalWanTransformer3DModel(BaseDiT):
         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)
+        encoder_hidden_states_text = encoder_hidden_states
 
         temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
                         timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
@@ -694,18 +774,35 @@ class CausalWanTransformer3DModel(BaseDiT):
 
         assert encoder_hidden_states.dtype == orig_dtype
 
+        if teacher_forcing:
+            # Tile RoPE/modulation so clean frame i and noisy frame i share a position.
+            clean_tokens = self.patch_embedding(clean_x).flatten(2).transpose(1, 2)
+            hidden_states = torch.cat([clean_tokens, hidden_states], dim=1)
+            if aug_t is None:
+                aug_t = torch.zeros_like(timestep)
+            _, timestep_proj_clean, _, _ = self.condition_embedder(
+                aug_t.flatten(), encoder_hidden_states_text, None)
+            timestep_proj_clean = timestep_proj_clean.unflatten(
+                1, (6, self.hidden_size)).unflatten(dim=0, sizes=timestep.shape)
+            timestep_proj = torch.cat([timestep_proj_clean, timestep_proj], dim=1)
+            freqs_cis = (torch.cat([freqs_cos, freqs_cos], dim=0),
+                         torch.cat([freqs_sin, freqs_sin], dim=0))
+
         # 4. Transformer blocks
         if torch.is_grad_enabled() and self.gradient_checkpointing:
             for block in self.blocks:
                 hidden_states = self._gradient_checkpointing_func(
                     block, hidden_states, encoder_hidden_states,
                     timestep_proj, freqs_cis,
-                    block_mask=self.block_mask)
+                    block_mask=block_mask)
         else:
             for block in self.blocks:
                 hidden_states = block(hidden_states, encoder_hidden_states,
                                         timestep_proj, freqs_cis,
-                                        block_mask=self.block_mask)
+                                        block_mask=block_mask)
+
+        if teacher_forcing:
+            hidden_states = hidden_states[:, hidden_states.shape[1] // 2:]
 
         # 5. Output norm, projection & unpatchify
         temb = temb.unflatten(dim=0, sizes=timestep.shape).unsqueeze(2)

fastvideo/performance/hf_store.py

@@ -310,8 +310,12 @@ def load_records_for_model(
 # ---------------------------------------------------------------------------
 
 _NUMERIC_COLS = (
-    "latency", "throughput", "memory",
-    "text_encoder_time_s", "dit_time_s", "vae_decode_time_s",
+    "latency",
+    "throughput",
+    "memory",
+    "text_encoder_time_s",
+    "dit_time_s",
+    "vae_decode_time_s",
 )
 
 

fastvideo/performance/metric_policy.py

@@ -36,6 +36,7 @@ DEFAULT_METRIC_POLICIES: tuple[MetricPolicy, ...] = (
     MetricPolicy("vae_decode_time_s", "VAE Decode", 3, True, 0.05, 0.25),
 )
 
+
 def _optional_float(value: Any) -> float | None:
     if value is None or isinstance(value, bool):
         return None
@@ -57,9 +58,7 @@ def _optional_bool(value: Any) -> bool | None:
     return None
 
 
-def resolve_metric_policies(
-    threshold_overrides: Mapping[str, Any] | None,
-) -> tuple[MetricPolicy, ...]:
+def resolve_metric_policies(threshold_overrides: Mapping[str, Any] | None, ) -> tuple[MetricPolicy, ...]:
     """Return default metric policies with optional per-metric overrides."""
 
     if not isinstance(threshold_overrides, Mapping):
@@ -80,25 +79,15 @@ def resolve_metric_policies(
                 label=base_policy.label,
                 precision=base_policy.precision,
                 lower_is_better=base_policy.lower_is_better,
-                threshold_percent=(
-                    base_policy.threshold_percent
-                    if threshold_percent is None
-                    else threshold_percent
-                ),
-                threshold_absolute=(
-                    base_policy.threshold_absolute
-                    if threshold_absolute is None
-                    else threshold_absolute
-                ),
+                threshold_percent=(base_policy.threshold_percent if threshold_percent is None else threshold_percent),
+                threshold_absolute=(base_policy.threshold_absolute
+                                    if threshold_absolute is None else threshold_absolute),
                 gated=base_policy.gated if gated is None else gated,
-            )
-        )
+            ))
     return tuple(policies)
 
 
-def serialize_metric_thresholds(
-    policies: tuple[MetricPolicy, ...],
-) -> dict[str, dict[str, float | bool]]:
+def serialize_metric_thresholds(policies: tuple[MetricPolicy, ...], ) -> dict[str, dict[str, float | bool]]:
     return {
         policy.key: {
             "threshold_percent": policy.threshold_percent,
@@ -118,10 +107,7 @@ def regression_delta(
         return None
     absolute_delta = current - baseline if policy.lower_is_better else baseline - current
     percent_delta = absolute_delta / baseline
-    threshold_exceeded = (
-        percent_delta > policy.threshold_percent
-        and absolute_delta > policy.threshold_absolute
-    )
+    threshold_exceeded = (percent_delta > policy.threshold_percent and absolute_delta > policy.threshold_absolute)
     return MetricDelta(
         absolute=absolute_delta,
         percent=percent_delta,

fastvideo/pipelines/stages/base.py

@@ -34,6 +34,7 @@ class PipelineStage(ABC):
     composed with other stages to create a complete pipeline. Each stage is responsible
     for a specific part of the process, such as prompt encoding, latent preparation, etc.
     """
+    performance_component_metric: str | None = None
 
     def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
         """
@@ -155,6 +156,9 @@ class PipelineStage(ABC):
                 logger.info("[%s] Execution completed in %s ms", stage_name, execution_time * 1000)
                 batch.logging_info.add_stage_execution_time(stage_key, execution_time)
                 batch.logging_info.add_stage_metric(stage_key, "stage_class", stage_class_name)
+                component_metric = self.performance_component_metric
+                if component_metric is not None:
+                    batch.logging_info.add_stage_metric(stage_key, "component_metric", component_metric)
             except Exception as e:
                 torch.cuda.synchronize()
                 execution_time = time.perf_counter() - start_time

fastvideo/pipelines/stages/decoding.py

@@ -28,6 +28,7 @@ class DecodingStage(PipelineStage):
     This stage handles the decoding of latent representations into the final
     output format (e.g., pixel values).
     """
+    performance_component_metric = "vae_decode_time_s"
 
     def __init__(self, vae, pipeline=None) -> None:
         self.vae: ParallelTiledVAE = vae

fastvideo/pipelines/stages/denoising.py

@@ -51,6 +51,7 @@ class DenoisingStage(PipelineStage):
     This stage handles the iterative denoising process that transforms
     the initial noise into the final output.
     """
+    performance_component_metric = "dit_time_s"
 
     def __init__(self, transformer, scheduler, pipeline=None, transformer_2=None, vae=None) -> None:
         super().__init__()
@@ -1187,6 +1188,7 @@ class Cosmos25V2WDenoisingStage(Cosmos25DenoisingStage):
 
 class Cosmos25AutoDenoisingStage(PipelineStage):
     """Route Cosmos 2.5 denoising to T2W vs V2W/I2W."""
+    performance_component_metric = "dit_time_s"
 
     def __init__(self, transformer, scheduler) -> None:
         super().__init__()

fastvideo/pipelines/stages/text_encoding.py

@@ -24,6 +24,7 @@ class TextEncodingStage(PipelineStage):
     This stage handles the encoding of text prompts into the embedding space
     expected by the diffusion model.
     """
+    performance_component_metric = "text_encoder_time_s"
 
     def __init__(self, text_encoders, tokenizers) -> None:
         """
@@ -350,6 +351,7 @@ class Cosmos25TextEncodingStage(PipelineStage):
     Cosmos 2.5 uses Reason1 (Qwen2.5-VL) and relies on the encoder's
     `compute_text_embeddings_online()`.
     """
+    performance_component_metric = "text_encoder_time_s"
 
     def __init__(self, text_encoder) -> None:
         super().__init__()

fastvideo/tests/contract/test_ci_test_collection.py

@@ -0,0 +1,85 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Guard: every test directory must be collected by some CI lane or be on
+the explicit allowlist below.
+
+Three separate incidents on 2026-07-05 found test files that no CI lane
+ever collects (fastvideo/tests/stages/, tests/local_tests/ additions in
+PR #1509, and this sweep found seven dark directories in total): the tests
+pass review, merge, and then silently never run. This test makes going
+dark an explicit, reviewed decision instead of an accident: adding a new
+test directory fails CI until it is either wired into a lane or
+allowlisted here with a reason.
+
+Pure text analysis — no fastvideo imports, no GPU, no torch.
+"""
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parents[3]
+TESTS_ROOT = REPO_ROOT / "fastvideo" / "tests"
+
+# Files whose text constitutes "a CI lane references this directory".
+CI_SOURCES = [
+    TESTS_ROOT / "modal" / "pr_test.py",
+    TESTS_ROOT / "modal" / "ssim_test.py",
+    *sorted((REPO_ROOT / ".buildkite").rglob("*.yml")),
+    *sorted((REPO_ROOT / ".buildkite").rglob("*.sh")),
+]
+
+# Directories that intentionally have no CI lane today. Every entry needs a
+# reason; remove the entry when the directory gets wired into a lane.
+# State as found on 2026-07-05 — these SHOULD shrink over time, not grow.
+ALLOWLIST = {
+    "attention": "no lane yet — GPU attention-backend tests, run manually",
+    "audio": "no lane yet — audio encoder tests, run manually",
+    "distributed": "no lane yet — multi-GPU torchrun tests, run manually",
+    "hooks": "no lane yet — run manually",
+    "layers": "no lane yet — torchrun FSDP dispatch tests, run manually",
+    "nightly": "by design: nightly cadence, not per-PR",
+    "ops": "no lane yet — GPU op tests, run manually",
+    "modal": "CI infrastructure itself, not a test suite",
+}
+
+
+def _dirs_with_tests() -> list[str]:
+    dirs = []
+    for child in sorted(TESTS_ROOT.iterdir()):
+        if child.is_dir() and any(child.rglob("test_*.py")):
+            dirs.append(child.name)
+    return dirs
+
+
+def _ci_text() -> str:
+    return "\n".join(
+        src.read_text(errors="replace") for src in CI_SOURCES if src.exists())
+
+
+def test_every_test_directory_is_collected_or_allowlisted():
+    ci_text = _ci_text()
+    dark = [
+        name for name in _dirs_with_tests()
+        if f"tests/{name}" not in ci_text and name not in ALLOWLIST
+    ]
+    assert not dark, (
+        f"Test directories not referenced by any CI lane and not "
+        f"allowlisted: {dark}. Wire them into a lane in "
+        f"fastvideo/tests/modal/pr_test.py (or a Buildkite step), or add an "
+        f"allowlist entry with a reason in {__file__}.")
+
+
+def test_local_tests_stays_out_of_ci():
+    # tests/local_tests/ (repo root) is developer-local by design (author
+    # decision, 2026-07-05): parity scaffolds and machine-specific checks
+    # that must never gate CI. Fail if any CI source starts collecting it.
+    assert "tests/local_tests" not in _ci_text(), (
+        "tests/local_tests/ is local-only by design; remove the CI "
+        "reference or move the tests into a fastvideo/tests/ lane.")
+
+
+def test_allowlist_entries_are_still_real_directories():
+    # A stale allowlist hides regressions; entries must track reality.
+    missing = [
+        name for name in ALLOWLIST
+        if name != "modal" and not (TESTS_ROOT / name).is_dir()
+    ]
+    assert not missing, (
+        f"Allowlisted directories no longer exist — remove them: {missing}")

fastvideo/tests/performance/test_inference_performance.py

@@ -179,7 +179,11 @@ def _extract_component_times(result: dict) -> dict[str, float | None]:
             logger.debug("Skipping malformed stage '%s' data: %r", stage_name, stage_data)
             continue
         stage_class = stage_data.get("stage_class", stage_name)
-        metric_key = STAGE_METRIC_MAP.get(stage_class)
+        component_metric = stage_data.get("component_metric")
+        if isinstance(component_metric, str) and component_metric in component_times:
+            metric_key = component_metric
+        else:
+            metric_key = STAGE_METRIC_MAP.get(stage_class)
         if metric_key is None:
             logger.debug("Unmapped stage '%s' class '%s' (%.3fs)",
                          stage_name,

fastvideo/tests/performance/test_inference_performance_component_times.py

@@ -1,13 +1,20 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from fastvideo.pipelines.pipeline_batch_info import PipelineLoggingInfo
+from fastvideo.pipelines.stages.denoising import Cosmos25AutoDenoisingStage, DenoisingStage
+from fastvideo.pipelines.stages.text_encoding import Cosmos25TextEncodingStage
 from fastvideo.tests.performance.test_inference_performance import _extract_component_times
 
 
+class SubclassStyleDenoisingStage(DenoisingStage):
+    pass
+
+
 def test_extract_component_times_handles_pipeline_logging_info_object():
     logging_info = PipelineLoggingInfo()
     logging_info.add_stage_execution_time("prompt_encoding_stage", 1.25)
     logging_info.add_stage_metric("prompt_encoding_stage", "stage_class", "TextEncodingStage")
+    logging_info.add_stage_metric("prompt_encoding_stage", "component_metric", "text_encoder_time_s")
 
     assert _extract_component_times({"logging_info": logging_info}) == {
         "text_encoder_time_s": 1.25,
@@ -45,6 +52,35 @@ def test_extract_component_times_uses_stage_class_for_pipeline_stage_keys():
     }
 
 
+def test_denoising_stage_subclasses_inherit_component_metric():
+    assert SubclassStyleDenoisingStage.performance_component_metric == "dit_time_s"
+
+
+def test_cosmos25_direct_pipeline_stages_define_component_metrics():
+    assert Cosmos25TextEncodingStage.performance_component_metric == "text_encoder_time_s"
+    assert Cosmos25AutoDenoisingStage.performance_component_metric == "dit_time_s"
+
+
+def test_extract_component_times_uses_component_metric_for_stage_subclasses():
+    result = {
+        "logging_info": {
+            "stages": {
+                "denoising_stage": {
+                    "execution_time": 4.2,
+                    "stage_class": "CosmosDenoisingStage",
+                    "component_metric": "dit_time_s",
+                },
+            },
+        },
+    }
+
+    assert _extract_component_times(result) == {
+        "text_encoder_time_s": None,
+        "dit_time_s": 4.2,
+        "vae_decode_time_s": None,
+    }
+
+
 def test_extract_component_times_keeps_legacy_class_name_keys():
     # Backward-compatibility check for logs produced before pipeline-unique
     # stage keys carried a separate stage_class field.

fastvideo/tests/train/fixtures/wan_causal_t2v_causal_cd_min.yaml

@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+# Minimum config to run a single training step of
+# CausalConsistencyDistillationMethod on WanCausalModel for the
+# per-method smoke test. Uses the real Wan 2.1 1.3B checkpoint for both
+# the trainable student and the frozen teacher (AR Euler-step target),
+# with tiny synthetic latents.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+  teacher:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+  ema:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: false
+
+method:
+  _target_: fastvideo.train.methods.consistency_model.causal_cd.CausalConsistencyDistillationMethod
+  discrete_cd_N: 12
+  guidance_scale: 3.0
+  ema_decay: 0.95
+
+training:
+  dit_precision: bf16
+
+  distributed:
+    num_gpus: 1
+    sp_size: 1
+    tp_size: 1
+
+  data:
+    seed: 42
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    num_latent_t: 6
+    num_height: 64
+    num_width: 64
+    num_frames: 21
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 1
+    gradient_accumulation_steps: 1
+
+pipeline: {}

fastvideo/tests/train/fixtures/wan_causal_t2v_dfsft_framewise_min.yaml

@@ -0,0 +1,46 @@
+# Minimum config to run a single training step of
+# DiffusionForcingSFTMethod on a frame-wise WanCausalModel
+# (num_frames_per_block=1, chunk_size=1) for the per-method smoke
+# test.  Uses the real Wan 2.1 1.3B checkpoint with tiny synthetic
+# latents.
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+    num_frames_per_block: 1
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.dfsft.DiffusionForcingSFTMethod
+  chunk_size: 1
+
+training:
+  dit_precision: bf16
+
+  distributed:
+    num_gpus: 1
+    sp_size: 1
+    tp_size: 1
+
+  data:
+    seed: 42
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    num_latent_t: 6
+    num_height: 64
+    num_width: 64
+    num_frames: 21
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 1
+    gradient_accumulation_steps: 1
+
+pipeline: {}

fastvideo/tests/train/fixtures/wan_causal_t2v_tfsft_min.yaml

@@ -0,0 +1,45 @@
+# SPDX-License-Identifier: Apache-2.0
+# Minimum config to run a single training step of
+# TeacherForcingSFTMethod on WanCausalModel for the per-method smoke
+# test. Identical to wan_causal_t2v_dfsft_min.yaml except the method,
+# which feeds clean history to the causal transformer (teacher forcing).
+
+models:
+  student:
+    _target_: fastvideo.train.models.wan.WanCausalModel
+    init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+    trainable: true
+
+method:
+  _target_: fastvideo.train.methods.fine_tuning.tfsft.TeacherForcingSFTMethod
+  chunk_size: 3
+
+training:
+  dit_precision: bf16
+
+  distributed:
+    num_gpus: 1
+    sp_size: 1
+    tp_size: 1
+
+  data:
+    seed: 42
+    train_batch_size: 1
+    training_cfg_rate: 0.0
+    num_latent_t: 6
+    num_height: 64
+    num_width: 64
+    num_frames: 21
+
+  optimizer:
+    learning_rate: 2.0e-6
+    betas: [0.9, 0.999]
+    weight_decay: 0.01
+    lr_scheduler: constant
+    lr_warmup_steps: 0
+
+  loop:
+    max_train_steps: 1
+    gradient_accumulation_steps: 1
+
+pipeline: {}

fastvideo/tests/train/methods/test_wan_causal_cd.py

@@ -0,0 +1,147 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Per-method GPU smoke test: ``WanCausalModel`` + ``CausalConsistencyDistillationMethod``.
+
+Mirrors ``test_wan_causal_dfsft.py``. Causal consistency distillation
+bootstraps a consistency MSE between the student's ``x0`` at ``t`` and an EMA
+copy of the student at ``t_next``, where ``t_next`` is produced online by a
+single CFG Euler step of a frozen teacher (all under clean-history teacher
+forcing). This test exercises the full step: finite loss, nonzero student
+gradients, frozen teacher, and a post-step EMA update.
+"""
+
+from __future__ import annotations
+
+import os
+
+os.environ.setdefault("MASTER_ADDR", "localhost")
+os.environ.setdefault("MASTER_PORT", "29519")
+
+from pathlib import Path
+
+import pytest
+import torch
+
+from fastvideo.train.methods.consistency_model.causal_cd import (
+    CausalConsistencyDistillationMethod, )
+from fastvideo.train.models.wan import WanCausalModel
+from fastvideo.train.utils.config import load_run_config
+
+
+_FIXTURE = str(
+    Path(__file__).resolve().parent.parent / "fixtures"
+    / "wan_causal_t2v_causal_cd_min.yaml")
+
+
+def _build_synthetic_batch(
+    device: torch.device,
+    dtype: torch.dtype,
+) -> dict[str, torch.Tensor]:
+    batch_size = 1
+    return {
+        "text_embedding":
+        torch.randn(batch_size, 16, 4096, device=device, dtype=dtype),
+        "text_attention_mask":
+        torch.ones(batch_size, 16, device=device),
+        "vae_latent":
+        torch.randn(batch_size, 16, 6, 8, 8, device=device, dtype=dtype),
+    }
+
+
+@pytest.mark.usefixtures("distributed_setup")
+def test_wan_causal_cd_single_train_step(
+        monkeypatch: pytest.MonkeyPatch) -> None:
+    if not torch.cuda.is_available():
+        pytest.skip("requires CUDA")
+
+    cfg = load_run_config(_FIXTURE)
+
+    device = torch.device("cuda:0")
+    dtype = torch.bfloat16
+
+    monkeypatch.setattr(
+        "fastvideo.train.utils.dataloader."
+        "build_parquet_t2v_train_dataloader",
+        lambda *args, **kwargs: None,
+    )
+
+    student = WanCausalModel(
+        init_from=cfg.models["student"]["init_from"],
+        training_config=cfg.training,
+        trainable=True,
+    )
+    student.transformer = student.transformer.to(device=device, dtype=dtype)
+
+    teacher = WanCausalModel(
+        init_from=cfg.models["teacher"]["init_from"],
+        training_config=cfg.training,
+        trainable=False,
+    )
+    teacher.transformer = teacher.transformer.to(device=device, dtype=dtype)
+
+    ema = WanCausalModel(
+        init_from=cfg.models["ema"]["init_from"],
+        training_config=cfg.training,
+        trainable=False,
+    )
+    ema.transformer = ema.transformer.to(device=device, dtype=dtype)
+
+    method = CausalConsistencyDistillationMethod(
+        cfg=cfg,
+        role_models={"student": student, "teacher": teacher, "ema": ema},
+    )
+    method.on_train_start()
+
+    batch = _build_synthetic_batch(device, dtype)
+    loss_map, outputs, _metrics = method.single_train_step(batch, iteration=0)
+
+    loss = loss_map["total_loss"]
+    assert torch.is_tensor(loss), "total_loss must be a torch.Tensor"
+    assert torch.isfinite(loss).item(), (
+        f"total_loss is not finite: {loss.item()}")
+
+    method.backward(loss_map, outputs, grad_accum_rounds=1)
+
+    blocks = student.transformer.blocks
+    assert blocks is not None and len(blocks) > 0
+    layer0 = blocks[0]
+    trainable = [p for p in layer0.parameters() if p.requires_grad]
+    assert len(trainable) > 0, "student layer 0 has no trainable parameters"
+    for i, p in enumerate(trainable):
+        assert p.grad is not None, f"student layer 0 param[{i}] has None grad"
+        assert torch.isfinite(p.grad).all().item(), (
+            f"student layer 0 param[{i}] grad contains NaN/Inf")
+    assert any(p.grad.detach().float().norm().item() > 0.0 for p in trainable), (
+        "all student layer-0 grads are exactly zero; consistency loss "
+        "did not reach the first transformer block")
+
+    # Teacher must stay frozen.
+    assert all(not p.requires_grad for p in teacher.transformer.parameters()), (
+        "teacher must be frozen for Causal-CD")
+
+    # The EMA model and student start from the same checkpoint, so the first
+    # parameter must match before any update. FSDP fully_shard params are
+    # DTensors; compare the local shards (torch.equal is unsupported on
+    # DTensor).
+    def _local(p: torch.Tensor) -> torch.Tensor:
+        return p.to_local() if hasattr(p, "to_local") else p
+
+    ema_param = next(ema.transformer.parameters())
+    student_param = next(student.transformer.parameters())
+    assert torch.equal(_local(ema_param), _local(student_param)), (
+        "EMA model should start identical to the student (same checkpoint)")
+
+    # The EMA update must move EMA toward the student. Apply a visibly large
+    # perturbation so the bf16 lerp is well above rounding noise (the real
+    # optimizer step at lr=2e-6 would be sub-ULP in bf16).
+    with torch.no_grad():
+        student_param.add_(1.0)
+    before = _local(ema_param).detach().float().clone()
+    method._update_ema()
+    after = _local(ema_param).detach().float()
+    assert not torch.equal(before, after), (
+        "EMA weights did not move after _update_ema")
+    # EMA = decay*ema + (1-decay)*student moves ~ (1-decay) of the gap.
+    expected = before + (1.0 - method._ema_decay) * (
+        _local(student_param).detach().float() - before)
+    assert torch.allclose(after, expected, atol=1e-2), (
+        "EMA update did not follow the expected lerp")

fastvideo/tests/train/methods/test_wan_causal_dfsft_framewise.py

@@ -0,0 +1,109 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Per-method GPU smoke test: frame-wise ``WanCausalModel`` + ``DiffusionForcingSFTMethod``.
+
+Mirrors ``test_wan_causal_dfsft.py`` but with a block size of 1 frame
+(``num_frames_per_block=1`` on the model, ``chunk_size=1`` on the method),
+so each frame gets its own independent noise level. The test asserts the
+override took effect and runs one train step: forward, finite loss, and
+nonzero gradients reaching the first transformer block.
+"""
+
+from __future__ import annotations
+
+import os
+
+os.environ.setdefault("MASTER_ADDR", "localhost")
+os.environ.setdefault("MASTER_PORT", "29520")
+
+from pathlib import Path
+
+import pytest
+import torch
+
+from fastvideo.train.methods.fine_tuning.dfsft import (
+    DiffusionForcingSFTMethod, )
+from fastvideo.train.models.wan import WanCausalModel
+from fastvideo.train.utils.config import load_run_config
+
+
+_FIXTURE = str(
+    Path(__file__).resolve().parent.parent / "fixtures"
+    / "wan_causal_t2v_dfsft_framewise_min.yaml")
+
+
+def _build_synthetic_batch(
+    device: torch.device,
+    dtype: torch.dtype,
+) -> dict[str, torch.Tensor]:
+    batch_size = 1
+    return {
+        "text_embedding":
+        torch.randn(batch_size, 16, 4096, device=device, dtype=dtype),
+        "text_attention_mask":
+        torch.ones(batch_size, 16, device=device),
+        "vae_latent":
+        torch.randn(batch_size, 16, 6, 8, 8, device=device, dtype=dtype),
+    }
+
+
+@pytest.mark.usefixtures("distributed_setup")
+def test_wan_causal_dfsft_framewise_single_train_step(
+        monkeypatch: pytest.MonkeyPatch) -> None:
+    if not torch.cuda.is_available():
+        pytest.skip("requires CUDA")
+
+    cfg = load_run_config(_FIXTURE)
+
+    device = torch.device("cuda:0")
+    dtype = torch.bfloat16
+
+    monkeypatch.setattr(
+        "fastvideo.train.utils.dataloader."
+        "build_parquet_t2v_train_dataloader",
+        lambda *args, **kwargs: None,
+    )
+
+    student_cfg = cfg.models["student"]
+    model = WanCausalModel(
+        init_from=student_cfg["init_from"],
+        training_config=cfg.training,
+        trainable=True,
+        num_frames_per_block=student_cfg.get("num_frames_per_block"),
+    )
+    assert model.transformer.num_frame_per_block == 1, (
+        "frame-wise override did not reach the transformer")
+    model.transformer = model.transformer.to(device=device, dtype=dtype)
+
+    method = DiffusionForcingSFTMethod(
+        cfg=cfg,
+        role_models={"student": model},
+    )
+    method.on_train_start()
+
+    batch = _build_synthetic_batch(device, dtype)
+    loss_map, outputs, _metrics = method.single_train_step(batch, iteration=0)
+
+    loss = loss_map["total_loss"]
+    assert torch.is_tensor(loss), "total_loss must be a torch.Tensor"
+    assert torch.isfinite(loss).item(), (
+        f"total_loss is not finite: {loss.item()}")
+
+    method.backward(loss_map, outputs, grad_accum_rounds=1)
+
+    blocks = getattr(model.transformer, "blocks", None)
+    assert blocks is not None and len(blocks) > 0
+    layer0 = blocks[0]
+
+    trainable = [p for p in layer0.parameters() if p.requires_grad]
+    assert len(trainable) > 0, "layer 0 has no trainable parameters"
+
+    for i, p in enumerate(trainable):
+        assert p.grad is not None, f"layer 0 param[{i}] has None grad"
+        assert torch.isfinite(p.grad).all().item(), (
+            f"layer 0 param[{i}] grad contains NaN/Inf")
+
+    any_nonzero = any(
+        p.grad.detach().float().norm().item() > 0.0 for p in trainable)
+    assert any_nonzero, (
+        "all layer-0 grads are exactly zero; backward did not "
+        "reach the first transformer block")

fastvideo/tests/train/methods/test_wan_causal_tfsft.py

@@ -0,0 +1,113 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Per-method GPU smoke test: ``WanCausalModel`` + ``TeacherForcingSFTMethod``.
+
+Mirrors ``test_wan_causal_dfsft.py``. Teacher forcing concatenates a clean
+context copy of every frame inside the causal transformer (``clean_x``) and
+denoises the current block while attending to *clean* history. This test
+exercises the ``clean_x`` path end-to-end: forward, finite loss, and nonzero
+gradients reaching the first transformer block.
+"""
+
+from __future__ import annotations
+
+import os
+
+os.environ.setdefault("MASTER_ADDR", "localhost")
+os.environ.setdefault("MASTER_PORT", "29518")
+
+from pathlib import Path
+
+import pytest
+import torch
+
+from fastvideo.train.methods.fine_tuning.tfsft import (
+    TeacherForcingSFTMethod, )
+from fastvideo.train.models.wan import WanCausalModel
+from fastvideo.train.utils.config import load_run_config
+
+
+_FIXTURE = str(
+    Path(__file__).resolve().parent.parent / "fixtures"
+    / "wan_causal_t2v_tfsft_min.yaml")
+
+
+def _build_synthetic_batch(
+    device: torch.device,
+    dtype: torch.dtype,
+) -> dict[str, torch.Tensor]:
+    batch_size = 1
+    return {
+        "text_embedding":
+        torch.randn(batch_size, 16, 4096, device=device, dtype=dtype),
+        "text_attention_mask":
+        torch.ones(batch_size, 16, device=device),
+        "vae_latent":
+        torch.randn(batch_size, 16, 6, 8, 8, device=device, dtype=dtype),
+    }
+
+
+@pytest.mark.usefixtures("distributed_setup")
+def test_wan_causal_tfsft_single_train_step(
+        monkeypatch: pytest.MonkeyPatch) -> None:
+    if not torch.cuda.is_available():
+        pytest.skip("requires CUDA")
+
+    cfg = load_run_config(_FIXTURE)
+
+    device = torch.device("cuda:0")
+    dtype = torch.bfloat16
+
+    monkeypatch.setattr(
+        "fastvideo.train.utils.dataloader."
+        "build_parquet_t2v_train_dataloader",
+        lambda *args, **kwargs: None,
+    )
+
+    model = WanCausalModel(
+        init_from=cfg.models["student"]["init_from"],
+        training_config=cfg.training,
+        trainable=True,
+    )
+    model.transformer = model.transformer.to(device=device, dtype=dtype)
+
+    method = TeacherForcingSFTMethod(
+        cfg=cfg,
+        role_models={"student": model},
+    )
+    method.on_train_start()
+
+    batch = _build_synthetic_batch(device, dtype)
+    loss_map, outputs, _metrics = method.single_train_step(batch, iteration=0)
+
+    loss = loss_map["total_loss"]
+    assert torch.is_tensor(loss), "total_loss must be a torch.Tensor"
+    assert torch.isfinite(loss).item(), (
+        f"total_loss is not finite: {loss.item()}")
+
+    method.backward(loss_map, outputs, grad_accum_rounds=1)
+
+    blocks = getattr(model.transformer, "blocks", None)
+    assert blocks is not None and len(blocks) > 0, (
+        "CausalWanTransformer is expected to expose ``.blocks``")
+    layer0 = blocks[0]
+
+    trainable = [p for p in layer0.parameters() if p.requires_grad]
+    assert len(trainable) > 0, "layer 0 has no trainable parameters"
+
+    for i, p in enumerate(trainable):
+        assert p.grad is not None, f"layer 0 param[{i}] has None grad"
+        assert torch.isfinite(p.grad).all().item(), (
+            f"layer 0 param[{i}] grad contains NaN/Inf")
+
+    any_nonzero = any(
+        p.grad.detach().float().norm().item() > 0.0 for p in trainable)
+    assert any_nonzero, (
+        "all layer-0 grads are exactly zero; backward did not "
+        "reach the first transformer block")
+
+    # Teacher forcing must build its own (concatenated) attention mask and
+    # must not have constructed the diffusion-forcing mask.
+    assert model.transformer.teacher_forcing_block_mask is not None, (
+        "teacher-forcing mask was not constructed")
+    assert model.transformer.block_mask is None, (
+        "diffusion-forcing mask should not be built on the TF path")

fastvideo/train/callbacks/validation.py

@@ -456,11 +456,16 @@ class ValidationCallback(Callback):
             None,
         )
 
+        loaded_modules: dict[str, Any] = {"transformer": transformer}
+        # Distillation methods build the flow-match scheduler their few-step DMD
+        # sampler needs; inject it so the pipeline doesn't fall back to UniPC.
+        method_scheduler = getattr(self.method, "_sf_scheduler", None)
+        if method_scheduler is not None:
+            loaded_modules["scheduler"] = method_scheduler
+
         kwargs: dict[str, Any] = {
             "inference_mode": True,
-            "loaded_modules": {
-                "transformer": transformer,
-            },
+            "loaded_modules": loaded_modules,
             "tp_size": tc.distributed.tp_size,
             "sp_size": tc.distributed.sp_size,
             "num_gpus": tc.distributed.num_gpus,
@@ -477,12 +482,6 @@ class ValidationCallback(Callback):
             **kwargs,
         )
 
-        scheduler = self._pipeline.get_module("scheduler")
-        if (scheduler is not None and type(scheduler).__name__ == "SelfForcingFlowMatchScheduler"):
-            scheduler.sigma_min = 0.0
-            scheduler.extra_one_step = True
-            scheduler.set_timesteps(num_inference_steps=1000, training=True)
-
         self._pipeline_key = key
         return self._pipeline
 

fastvideo/train/methods/__init__.py

@@ -9,6 +9,8 @@ __all__ = [
     "KDMethod",
     "SelfForcingMethod",
     "DiffusionForcingSFTMethod",
+    "TeacherForcingSFTMethod",
+    "CausalConsistencyDistillationMethod",
 ]
 
 
@@ -28,4 +30,11 @@ def __getattr__(name: str) -> object:
     if name == "DiffusionForcingSFTMethod":
         from fastvideo.train.methods.fine_tuning.dfsft import DiffusionForcingSFTMethod
         return DiffusionForcingSFTMethod
+    if name == "TeacherForcingSFTMethod":
+        from fastvideo.train.methods.fine_tuning.tfsft import TeacherForcingSFTMethod
+        return TeacherForcingSFTMethod
+    if name == "CausalConsistencyDistillationMethod":
+        from fastvideo.train.methods.consistency_model.causal_cd import (
+            CausalConsistencyDistillationMethod, )
+        return CausalConsistencyDistillationMethod
     raise AttributeError(name)

fastvideo/train/methods/consistency_model/__init__.py

@@ -1,3 +1,22 @@
 # SPDX-License-Identifier: Apache-2.0
 
-__all__: list[str] = []
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from fastvideo.train.methods.consistency_model.causal_cd import (
+        CausalConsistencyDistillationMethod, )
+
+__all__ = [
+    "CausalConsistencyDistillationMethod",
+]
+
+
+def __getattr__(name: str) -> object:
+    if name == "CausalConsistencyDistillationMethod":
+        from fastvideo.train.methods.consistency_model.causal_cd import (
+            CausalConsistencyDistillationMethod, )
+
+        return CausalConsistencyDistillationMethod
+    raise AttributeError(name)

fastvideo/train/methods/consistency_model/causal_cd.py

@@ -0,0 +1,237 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Causal consistency distillation method (algorithm layer)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+
+from fastvideo.models.schedulers.scheduling_self_forcing_flow_match import (SelfForcingFlowMatchScheduler)
+from fastvideo.train.methods.base import LogScalar, TrainingMethod
+from fastvideo.train.models.base import ModelBase
+from fastvideo.train.utils.checkpoint import _FullModelState
+from fastvideo.train.utils.optimizer import build_optimizer_and_scheduler
+
+
+class CausalConsistencyDistillationMethod(TrainingMethod):
+
+    def __init__(
+        self,
+        *,
+        cfg: Any,
+        role_models: dict[str, ModelBase],
+    ) -> None:
+        super().__init__(cfg=cfg, role_models=role_models)
+
+        for role in ("student", "teacher", "ema"):
+            if role not in role_models:
+                raise ValueError(f"Causal-CD requires role {role!r} "
+                                 "(student trainable; teacher + ema frozen, "
+                                 "both initialized from the student's "
+                                 "checkpoint)")
+        if not self.student._trainable:
+            raise ValueError("Causal-CD requires student to be trainable")
+        self.teacher = role_models["teacher"]
+        self.ema_model = role_models["ema"]
+
+        self._attn_kind = self._infer_attn_kind()
+        self._guidance_scale = float(self.method_config.get("guidance_scale", 3.0))
+        self._discrete_cd_n = int(self.method_config.get("discrete_cd_N", 48))
+        if self._discrete_cd_n < 2:
+            raise ValueError("method.discrete_cd_N must be >= 2")
+        self._ema_decay = float(self.method_config.get("ema_decay", 0.99))
+        self._ema_start_step = int(self.method_config.get("ema_start_step", 200))
+        shift = getattr(self.training_config.pipeline_config, "flow_shift", None)
+        self._flow_shift = float(shift) if shift else 5.0
+
+        self.student.init_preprocessors(self.training_config)
+        self._sf_scheduler = SelfForcingFlowMatchScheduler(
+            num_inference_steps=self._discrete_cd_n,
+            num_train_timesteps=int(self.student.num_train_timesteps),
+            shift=self._flow_shift,
+            sigma_min=0.0,
+            sigma_max=1.0,
+            extra_one_step=True,
+            training=False,
+        )
+        self._init_optimizers_and_schedulers()
+
+    # ------------------------------------------------------------------
+
+    @property
+    def _optimizer_dict(self) -> dict[str, Any]:
+        return {"student": self._student_optimizer}
+
+    @property
+    def _lr_scheduler_dict(self) -> dict[str, Any]:
+        return {"student": self._student_lr_scheduler}
+
+    def get_optimizers(self, iteration: int) -> list[torch.optim.Optimizer]:
+        del iteration
+        return [self._student_optimizer]
+
+    def get_lr_schedulers(self, iteration: int) -> list[Any]:
+        del iteration
+        return [self._student_lr_scheduler]
+
+    def checkpoint_state(self) -> dict[str, Any]:
+        # The EMA role is frozen (so the base class skips it) but mutated by
+        # _update_ema every step; without persisting it a resume reloads the
+        # EMA from init_from and the consistency target snaps back to the
+        # base checkpoint. Mirrors DiffusionNFT's frozen "old" role.
+        states = super().checkpoint_state()
+        states["roles.ema.transformer"] = _FullModelState(self.ema_model.transformer)
+        return states
+
+    # ------------------------------------------------------------------
+
+    def single_train_step(
+        self,
+        batch: dict[str, Any],
+        iteration: int,
+    ) -> tuple[dict[str, torch.Tensor], dict[str, Any], dict[str, LogScalar]]:
+        del iteration
+        training_batch = self.student.prepare_batch(
+            batch,
+            generator=self.cuda_generator,
+            latents_source="data",
+        )
+        clean_latents = training_batch.latents
+        if not torch.is_tensor(clean_latents) or clean_latents.ndim != 5:
+            raise ValueError("Causal-CD expects [B, T, C, H, W] latents")
+
+        batch_size, num_latents = int(clean_latents.shape[0]), int(clean_latents.shape[1])
+        device = clean_latents.device
+
+        sigmas = self._sf_scheduler.sigmas.to(device)
+        timesteps = self._sf_scheduler.timesteps.to(device)
+        idx = torch.randint(0, self._discrete_cd_n - 1, (1, ), generator=self.cuda_generator, device=device).squeeze(0)
+        t, t_next = timesteps[idx], timesteps[idx + 1]
+        sigma_t, sigma_t_next = sigmas[idx], sigmas[idx + 1]
+        t_pf = t * torch.ones(batch_size, num_latents, device=device)
+        t_next_pf = t_next * torch.ones(batch_size, num_latents, device=device)
+
+        noise = torch.randn(
+            clean_latents.shape,
+            generator=self.cuda_generator,
+            device=device,
+            dtype=clean_latents.dtype,
+        )
+        latent_t = (1.0 - sigma_t) * clean_latents + sigma_t * noise
+
+        # Set before any forward: predict_noise feeds batch.timesteps into
+        # set_forward_context (VSA sparsity gating), so the teacher CFG
+        # passes below must not see the stale timesteps from prepare_batch.
+        training_batch.timesteps = t_pf
+
+        with torch.no_grad():
+            v_cond = self._predict_flow(self.teacher,
+                                        latent_t,
+                                        t_pf,
+                                        training_batch,
+                                        conditional=True,
+                                        clean_x=clean_latents)
+            v_uncond = self._predict_flow(self.teacher,
+                                          latent_t,
+                                          t_pf,
+                                          training_batch,
+                                          conditional=False,
+                                          clean_x=clean_latents)
+            v_pred = v_uncond + self._guidance_scale * (v_cond - v_uncond)
+            dt = ((t - t_next) / float(self.student.num_train_timesteps))
+            latent_t_next = latent_t - dt * v_pred
+
+        flow_student = self._predict_flow(self.student,
+                                          latent_t,
+                                          t_pf,
+                                          training_batch,
+                                          conditional=True,
+                                          clean_x=clean_latents)
+        x0_t = latent_t - sigma_t * flow_student
+
+        with torch.no_grad():
+            flow_ema = self._predict_flow(self.ema_model,
+                                          latent_t_next,
+                                          t_next_pf,
+                                          training_batch,
+                                          conditional=True,
+                                          clean_x=clean_latents)
+            x0_t_next = latent_t_next - sigma_t_next * flow_ema
+
+        loss = F.mse_loss(x0_t.float(), x0_t_next.float())
+
+        loss_map = {"total_loss": loss, "causal_cd_loss": loss}
+        attn_metadata = (training_batch.attn_metadata_vsa if self._attn_kind == "vsa" else training_batch.attn_metadata)
+        outputs: dict[str, Any] = {"_fv_backward": (t_pf, attn_metadata)}
+        metrics: dict[str, LogScalar] = {}
+        return loss_map, outputs, metrics
+
+    # ------------------------------------------------------------------
+
+    def backward(
+        self,
+        loss_map: dict[str, torch.Tensor],
+        outputs: dict[str, Any],
+        *,
+        grad_accum_rounds: int = 1,
+    ) -> None:
+        grad_accum_rounds = max(1, int(grad_accum_rounds))
+        ctx = outputs.get("_fv_backward")
+        if ctx is None:
+            super().backward(loss_map, outputs, grad_accum_rounds=grad_accum_rounds)
+            return
+        self.student.backward(loss_map["total_loss"], ctx, grad_accum_rounds=grad_accum_rounds)
+
+    def optimizers_schedulers_step(self, iteration: int) -> None:
+        super().optimizers_schedulers_step(iteration)
+        if iteration >= self._ema_start_step:
+            self._update_ema()
+
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+
+    def _predict_flow(
+        self,
+        model: ModelBase,
+        latents: torch.Tensor,
+        timestep: torch.Tensor,
+        batch: Any,
+        *,
+        conditional: bool,
+        clean_x: torch.Tensor,
+    ) -> torch.Tensor:
+        return model.predict_noise(latents,
+                                   timestep,
+                                   batch,
+                                   conditional=conditional,
+                                   cfg_uncond=None,
+                                   attn_kind=self._attn_kind,
+                                   clean_x=clean_x)
+
+    @torch.no_grad()
+    def _update_ema(self) -> None:
+        decay = self._ema_decay
+        for ema_p, p in zip(self.ema_model.transformer.parameters(), self.student.transformer.parameters(),
+                            strict=True):
+            ema_p.mul_(decay).add_(p.detach().to(ema_p.dtype), alpha=1.0 - decay)
+
+    def _init_optimizers_and_schedulers(self) -> None:
+        tc = self.training_config
+        student_lr = float(tc.optimizer.learning_rate)
+        if student_lr <= 0.0:
+            raise ValueError("training.learning_rate must be > 0 for causal-cd")
+        student_params = [p for p in self.student.transformer.parameters() if p.requires_grad]
+        (
+            self._student_optimizer,
+            self._student_lr_scheduler,
+        ) = build_optimizer_and_scheduler(
+            params=student_params,
+            optimizer_config=tc.optimizer,
+            loop_config=tc.loop,
+            learning_rate=student_lr,
+            betas=tc.optimizer.betas,
+            scheduler_name=str(tc.optimizer.lr_scheduler),
+        )

fastvideo/train/methods/fine_tuning/__init__.py

@@ -7,10 +7,12 @@ from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from fastvideo.train.methods.fine_tuning.dfsft import DiffusionForcingSFTMethod
     from fastvideo.train.methods.fine_tuning.finetune import FineTuneMethod
+    from fastvideo.train.methods.fine_tuning.tfsft import TeacherForcingSFTMethod
 
 __all__ = [
     "DiffusionForcingSFTMethod",
     "FineTuneMethod",
+    "TeacherForcingSFTMethod",
 ]
 
 
@@ -25,4 +27,9 @@ def __getattr__(name: str) -> object:
         from fastvideo.train.methods.fine_tuning.finetune import FineTuneMethod
 
         return FineTuneMethod
+    if name == "TeacherForcingSFTMethod":
+        from fastvideo.train.methods.fine_tuning.tfsft import (
+            TeacherForcingSFTMethod, )
+
+        return TeacherForcingSFTMethod
     raise AttributeError(name)

fastvideo/train/methods/fine_tuning/dfsft.py

@@ -135,12 +135,11 @@ class DiffusionForcingSFTMethod(TrainingMethod):
             t_inhom.flatten(),
         )
 
-        pred = self.student.predict_noise(
+        pred = self._predict_noise(
             noisy_latents,
             t_inhom,
             training_batch,
-            conditional=True,
-            attn_kind=self._attn_kind,
+            clean_latents,
         )
 
         if bool(self.training_config.model.precondition_outputs):
@@ -178,6 +177,23 @@ class DiffusionForcingSFTMethod(TrainingMethod):
         metrics: dict[str, LogScalar] = {}
         return loss_map, outputs, metrics
 
+    def _predict_noise(
+        self,
+        noisy_latents: torch.Tensor,
+        timestep: torch.Tensor,
+        training_batch: Any,
+        clean_latents: torch.Tensor,
+    ) -> torch.Tensor:
+        # Unused here; the teacher-forcing subclass overrides this to pass clean_x.
+        del clean_latents
+        return self.student.predict_noise(
+            noisy_latents,
+            timestep,
+            training_batch,
+            conditional=True,
+            attn_kind=self._attn_kind,
+        )
+
     # TrainingMethod override: backward
     def backward(
         self,

fastvideo/train/methods/fine_tuning/tfsft.py

@@ -0,0 +1,30 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Teacher-forcing SFT method (TFSFT; algorithm layer)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+
+from fastvideo.train.methods.fine_tuning.dfsft import (
+    DiffusionForcingSFTMethod, )
+
+
+class TeacherForcingSFTMethod(DiffusionForcingSFTMethod):
+
+    def _predict_noise(
+        self,
+        noisy_latents: torch.Tensor,
+        timestep: torch.Tensor,
+        training_batch: Any,
+        clean_latents: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.student.predict_noise(
+            noisy_latents,
+            timestep,
+            training_batch,
+            conditional=True,
+            attn_kind=self._attn_kind,
+            clean_x=clean_latents,
+        )

fastvideo/train/methods/rl/diffusion_nft.py

@@ -10,12 +10,6 @@ from typing import Any
 
 import torch
 import torch.distributed as dist
-from torch.distributed.checkpoint.state_dict import (
-    StateDictOptions,
-    get_model_state_dict,
-    set_model_state_dict,
-)
-from torch.distributed.checkpoint.stateful import Stateful
 from tqdm.auto import tqdm
 
 from fastvideo.dataset.parquet_dataset_map_style import (
@@ -37,6 +31,7 @@ from fastvideo.train.methods.rl.common import (
     validation_caption,
     validation_shard_indices,
 )
+from fastvideo.train.utils.checkpoint import _FullModelState
 from fastvideo.train.utils.config import (
     get_optional_float,
     get_optional_int,
@@ -78,31 +73,6 @@ class _DiffusionNFTEMAState:
         self._method._ema_update_count = int(update_count)
 
 
-class _FullModelState(Stateful):
-    """DCP wrapper that saves frozen model parameters too.
-
-    The shared ``ModelWrapper`` intentionally filters to ``requires_grad``
-    parameters. DiffusionNFT's old policy is frozen but must be restored on
-    resume, so it needs full model state.
-    """
-
-    def __init__(self, model: torch.nn.Module) -> None:
-        self.model = model
-
-    def state_dict(self) -> dict[str, Any]:
-        return get_model_state_dict(self.model)  # type: ignore[no-any-return]
-
-    def load_state_dict(
-        self,
-        state_dict: dict[str, Any],
-    ) -> None:
-        set_model_state_dict(
-            self.model,
-            model_state_dict=state_dict,
-            options=StateDictOptions(strict=False),
-        )
-
-
 class DiffusionNFTMethod(TrainingMethod):
     """DiffusionNFT-style RL for diffusion models.
 

fastvideo/train/models/wan/wan.py

@@ -320,6 +320,8 @@ class WanModel(ModelBase):
         conditional: bool,
         cfg_uncond: dict[str, Any] | None = None,
         attn_kind: Literal["dense", "vsa"] = "dense",
+        clean_x: torch.Tensor | None = None,
+        aug_t: torch.Tensor | None = None,
     ) -> torch.Tensor:
         device_type = self.device.type
         dtype = self._get_training_dtype()
@@ -347,7 +349,11 @@ class WanModel(ModelBase):
                 current_timestep=batch.timesteps,
                 attn_metadata=attn_metadata,
         ):
-            input_kwargs = (self._build_distill_input_kwargs(noisy_latents, timestep, text_dict))
+            input_kwargs = (self._build_distill_input_kwargs(noisy_latents,
+                                                             timestep,
+                                                             text_dict,
+                                                             clean_x=clean_x,
+                                                             aug_t=aug_t))
             transformer = self._get_transformer(timestep)
             pred_noise = transformer(**input_kwargs).permute(0, 2, 1, 3, 4)
         return pred_noise
@@ -530,17 +536,24 @@ class WanModel(ModelBase):
         noise_input: torch.Tensor,
         timestep: torch.Tensor,
         text_dict: dict[str, torch.Tensor] | None,
+        clean_x: torch.Tensor | None = None,
+        aug_t: torch.Tensor | None = None,
     ) -> dict[str, Any]:
         if text_dict is None:
             raise ValueError("text_dict cannot be None for "
                              "Wan distillation")
-        return {
+        kwargs: dict[str, Any] = {
             "hidden_states": noise_input.permute(0, 2, 1, 3, 4),
             "encoder_hidden_states": text_dict["encoder_hidden_states"],
             "encoder_attention_mask": text_dict["encoder_attention_mask"],
             "timestep": timestep,
             "return_dict": False,
         }
+        if clean_x is not None:
+            # Teacher forcing: clean context latents (+ optional aug timestep).
+            kwargs["clean_x"] = clean_x.permute(0, 2, 1, 3, 4)
+            kwargs["aug_t"] = aug_t
+        return kwargs
 
     def _get_transformer(self, timestep: torch.Tensor) -> torch.nn.Module:
         return self.transformer

fastvideo/train/models/wan/wan_causal.py

@@ -49,6 +49,7 @@ class WanCausalModel(WanModel, CausalModelBase):
         transformer_override_safetensor: str
         | None = None,
         lora: LoraConfig | dict[str, Any] | None = None,
+        num_frames_per_block: int | None = None,
     ) -> None:
         super().__init__(
             init_from=init_from,
@@ -62,6 +63,16 @@ class WanCausalModel(WanModel, CausalModelBase):
         )
         self._streaming_caches: (dict[tuple[int, str], _StreamingCaches]) = {}
 
+        if num_frames_per_block is not None:
+            num_frames_per_block = int(num_frames_per_block)
+            if not 1 <= num_frames_per_block <= 3:
+                # Same bound as CausalWanTransformer3DModel's config path
+                # (assert num_frame_per_block <= 3); this override must not
+                # bypass it.
+                raise ValueError("num_frames_per_block must be between 1 and 3, "
+                                 f"got {num_frames_per_block}")
+            self.transformer.num_frame_per_block = num_frames_per_block
+
     # --- CausalModelBase override: clear_caches ---
     def clear_caches(
         self,

fastvideo/train/utils/checkpoint.py

@@ -15,6 +15,13 @@ import numpy as np
 import torch
 import torch.distributed as dist
 import torch.distributed.checkpoint as dcp
+from torch.distributed.checkpoint.state_dict import (
+    StateDictOptions,
+    get_model_state_dict,
+    set_model_state_dict,
+)
+from torch.distributed.checkpoint.stateful import Stateful
+
 from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
@@ -131,6 +138,32 @@ class _RoleModuleContainer(torch.nn.Module):
             self.add_module(name, module)
 
 
+class _FullModelState(Stateful):
+    """DCP wrapper that saves frozen model parameters too.
+
+    The shared ``ModelWrapper`` intentionally filters to ``requires_grad``
+    parameters. Frozen-but-mutated roles (e.g. DiffusionNFT's old policy,
+    causal-CD's EMA target) must still be restored on resume, so they need
+    full model state.
+    """
+
+    def __init__(self, model: torch.nn.Module) -> None:
+        self.model = model
+
+    def state_dict(self) -> dict[str, Any]:
+        return get_model_state_dict(self.model)  # type: ignore[no-any-return]
+
+    def load_state_dict(
+        self,
+        state_dict: dict[str, Any],
+    ) -> None:
+        set_model_state_dict(
+            self.model,
+            model_state_dict=state_dict,
+            options=StateDictOptions(strict=False),
+        )
+
+
 class _CallbackStateWrapper:
     """Wraps a CallbackDict for DCP save/load."""