# Copyright 2026 The AnyFlow Team, NVIDIA Corp., and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # This file is the FAR causal sibling of `transformer_anyflow.py`. Shared submodules are duplicated # via `# Copied from` so `make fix-copies` keeps both files in sync; this keeps each transformer # variant readable in isolation. The FAR architecture comes from FAR # (arXiv:2503.19325); the dual-timestep flow-map embedding is AnyFlow's contribution # (arXiv:2605.13724). import math from dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.attention.flex_attention import BlockMask, create_block_mask from ...configuration_utils import ConfigMixin, register_to_config from ...loaders import FromOriginalModelMixin, PeftAdapterMixin from ...utils import BaseOutput, apply_lora_scale, logging from ...utils.torch_utils import maybe_adjust_dtype_for_device from ..attention import AttentionModuleMixin, FeedForward from ..attention_dispatch import dispatch_attention_fn from ..embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps, get_1d_rotary_pos_embed from ..modeling_outputs import Transformer2DModelOutput from ..modeling_utils import ModelMixin from ..normalization import FP32LayerNorm, RMSNorm logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.models.transformers.transformer_anyflow.apply_rotary_emb def apply_rotary_emb(hidden_states: torch.Tensor, freqs: torch.Tensor): # MPS / NPU backends do not support complex128 / float64; fall back to float32 on those devices. rotary_dtype = maybe_adjust_dtype_for_device(torch.float64, hidden_states.device) x_rotated = torch.view_as_complex(hidden_states.to(rotary_dtype).unflatten(3, (-1, 2))) x_out = torch.view_as_real(x_rotated * freqs).flatten(3, 4) return x_out.type_as(hidden_states) @dataclass class AnyFlowFARTransformerOutput(BaseOutput): """ Output dataclass for ``AnyFlowFARTransformer3DModel``'s causal forward paths. Args: sample (`torch.Tensor` or `None`): Predicted denoising target for the autoregressive chunk. ``None`` for the cache-prefill path, which only writes the KV cache and produces no usable sample. kv_cache (`list[dict[str, torch.Tensor]]`, *optional*): Per-block KV cache state used by subsequent autoregressive steps. """ sample: Optional[torch.Tensor] = None kv_cache: Optional[List[Dict[str, torch.Tensor]]] = None class AnyFlowCausalAttnProcessor: """ Causal self-attention processor for AnyFlow FAR. Routes through :func:`~diffusers.models.attention_dispatch.dispatch_attention_fn` with the ``flex`` backend and a precomputed :class:`~torch.nn.attention.flex_attention.BlockMask`. Supports KV-cache prefill (cache-write step) and autoregressive read (cache-read step). Requires the ``flex`` attention backend — the ``BlockMask`` produced by :meth:`AnyFlowFARTransformer3DModel.build_attention_mask` is consumed only by the flex backend. A clear :class:`ValueError` is raised if a non-flex backend is configured via ``_attention_backend``. """ _attention_backend = "flex" _parallel_config = None _SUPPORTED_BACKENDS = ("flex", "_native_flex") def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): raise ImportError( "AnyFlowCausalAttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0 or higher." ) def __call__( self, attn, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[Any] = None, rotary_emb: Optional[Dict[str, torch.Tensor]] = None, kv_cache: Optional[Dict[str, torch.Tensor]] = None, kv_cache_flag: Optional[Dict[str, Any]] = None, ) -> torch.Tensor: if self._attention_backend not in self._SUPPORTED_BACKENDS: raise ValueError( f"AnyFlowCausalAttnProcessor requires the 'flex' attention backend " f"(got {self._attention_backend!r}). FAR causal generation builds a " f"flex_attention.BlockMask which is only consumed by the flex backend in " f"`dispatch_attention_fn`." ) if encoder_hidden_states is None: encoder_hidden_states = hidden_states target_dtype = hidden_states.dtype # Effective compute dtype query = attn.to_q(hidden_states) key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) if attn.norm_q is not None: query = attn.norm_q(query) if attn.norm_k is not None: key = attn.norm_k(key) # norm_q and norm_k upcast query and key to FP32 due to the use of RMSNorm, so cast them back to the effective # compute dtype. query = query.to(target_dtype) key = key.to(target_dtype) # Layout (B, H, L, D) is required by KV-cache slicing and rotary application. query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2) key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2) value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2) if kv_cache is not None: if kv_cache_flag["is_cache_step"]: kv_cache["compressed_cache"][0, :, :, : kv_cache_flag["num_compressed_tokens"], :] = key[ :, :, : kv_cache_flag["num_compressed_tokens"] ] kv_cache["compressed_cache"][1, :, :, : kv_cache_flag["num_compressed_tokens"], :] = value[ :, :, : kv_cache_flag["num_compressed_tokens"] ] kv_cache["full_cache"][0, :, :, : kv_cache_flag["num_full_tokens"], :] = key[ :, :, kv_cache_flag["num_compressed_tokens"] : ] kv_cache["full_cache"][1, :, :, : kv_cache_flag["num_full_tokens"], :] = value[ :, :, kv_cache_flag["num_compressed_tokens"] : ] else: key = torch.cat( [ kv_cache["compressed_cache"][0, :, :, : kv_cache_flag["num_cached_compressed_tokens"], :], kv_cache["full_cache"][0, :, :, : kv_cache_flag["num_cached_full_tokens"], :], key, ], dim=2, ) value = torch.cat( [ kv_cache["compressed_cache"][1, :, :, : kv_cache_flag["num_cached_compressed_tokens"], :], kv_cache["full_cache"][1, :, :, : kv_cache_flag["num_cached_full_tokens"], :], value, ], dim=2, ) if rotary_emb is not None: query = apply_rotary_emb(query, rotary_emb["query"]) key = apply_rotary_emb(key, rotary_emb["key"]) # BlockMask block-size is 128 — pad seq_len to a multiple of 128. Tiny dummy components may # have head_dim < 16; flex_attention requires head_dim >= 16, so right-pad q/k/v on the head # dim with zeros and override `scale` so the result matches the original head_dim. seq_len = query.shape[2] head_dim = query.shape[3] padded_length = int(math.ceil(seq_len / 128.0) * 128.0 - seq_len) if padded_length > 0: pad_shape = [query.shape[0], query.shape[1], padded_length, head_dim] query = torch.cat([query, torch.zeros(pad_shape, device=query.device, dtype=query.dtype)], dim=2) key = torch.cat([key, torch.zeros(pad_shape, device=key.device, dtype=key.dtype)], dim=2) value = torch.cat([value, torch.zeros(pad_shape, device=value.device, dtype=value.dtype)], dim=2) head_pad = max(0, 16 - head_dim) scale = 1.0 / (head_dim**0.5) if head_pad > 0 else None if head_pad > 0: query = F.pad(query, (0, head_pad)) key = F.pad(key, (0, head_pad)) value = F.pad(value, (0, head_pad)) # `dispatch_attention_fn` expects (B, L, H, D); the flex backend permutes back to # (B, H, L, D) internally before calling flex_attention — same kernel call as the bare # flex_attention path, same numerics. Verified against # `attention_dispatch._native_flex_attention`. hidden_states = dispatch_attention_fn( query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2), attn_mask=attention_mask, dropout_p=0.0, is_causal=False, scale=scale, backend=self._attention_backend, parallel_config=self._parallel_config, ) # `dispatch_attention_fn` returns (B, L, H, D). Trim head pad on the last axis, then trim # seq pad on dim=1, then fold heads back into the channel dim. if head_pad > 0: hidden_states = hidden_states[..., :head_dim] if padded_length > 0: hidden_states = hidden_states[:, :seq_len, :, :] hidden_states = hidden_states.flatten(2, 3) hidden_states = hidden_states.type_as(query) hidden_states = attn.to_out[0](hidden_states) hidden_states = attn.to_out[1](hidden_states) return hidden_states # Copied from diffusers.models.transformers.transformer_anyflow.AnyFlowAttnProcessor class AnyFlowAttnProcessor: """ Bidirectional self-attention processor for AnyFlow. Routes through :func:`~diffusers.models.attention_dispatch.dispatch_attention_fn` so any SDPA-compatible backend is supported (SDPA, flash-attn, xformers, flex, …). FAR causal generation lives in :class:`~diffusers.models.transformers.transformer_anyflow_far.AnyFlowCausalAttnProcessor`. """ _attention_backend = None _parallel_config = None def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): raise ImportError( "AnyFlowAttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0 or higher." ) def __call__( self, attn: "AnyFlowAttention", hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[Any] = None, rotary_emb: Optional[Dict[str, torch.Tensor]] = None, ) -> torch.Tensor: if encoder_hidden_states is None: encoder_hidden_states = hidden_states query = attn.to_q(hidden_states) key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) if attn.norm_q is not None: query = attn.norm_q(query) if attn.norm_k is not None: key = attn.norm_k(key) # Layout (B, H, L, D) for rotary application; transposed to (B, L, H, D) before dispatch. query = query.unflatten(2, (attn.heads, -1)).transpose(1, 2) key = key.unflatten(2, (attn.heads, -1)).transpose(1, 2) value = value.unflatten(2, (attn.heads, -1)).transpose(1, 2) if rotary_emb is not None: query = apply_rotary_emb(query, rotary_emb["query"]) key = apply_rotary_emb(key, rotary_emb["key"]) hidden_states = dispatch_attention_fn( query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2), attn_mask=attention_mask, dropout_p=0.0, is_causal=False, backend=self._attention_backend, parallel_config=self._parallel_config, ) hidden_states = hidden_states.flatten(2, 3) hidden_states = hidden_states.type_as(query) hidden_states = attn.to_out[0](hidden_states) hidden_states = attn.to_out[1](hidden_states) return hidden_states # Copied from diffusers.models.transformers.transformer_anyflow.AnyFlowCrossAttnProcessor class AnyFlowCrossAttnProcessor: """ Cross-attention processor for AnyFlow. Always uses the dispatched SDPA-compatible backend; no rotary embedding or KV cache is applied to the text→video cross-attention path. """ _attention_backend = None _parallel_config = None def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): raise ImportError( "AnyFlowCrossAttnProcessor requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0 or higher." ) def __call__( self, attn: "AnyFlowAttention", hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: query = attn.to_q(hidden_states) key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) if attn.norm_q is not None: query = attn.norm_q(query) if attn.norm_k is not None: key = attn.norm_k(key) # (B, L, H, D) layout for dispatch_attention_fn. query = query.unflatten(2, (attn.heads, -1)) key = key.unflatten(2, (attn.heads, -1)) value = value.unflatten(2, (attn.heads, -1)) hidden_states = dispatch_attention_fn( query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False, backend=self._attention_backend, parallel_config=self._parallel_config, ) hidden_states = hidden_states.flatten(2, 3) hidden_states = hidden_states.type_as(query) hidden_states = attn.to_out[0](hidden_states) hidden_states = attn.to_out[1](hidden_states) return hidden_states # Copied from diffusers.models.transformers.transformer_anyflow.AnyFlowAttention with AnyFlowAttnProcessor->AnyFlowCausalAttnProcessor class AnyFlowAttention(torch.nn.Module, AttentionModuleMixin): """ Attention module used by :class:`AnyFlowTransformerBlock`. Layout matches the legacy :class:`~diffusers.models.attention_processor.Attention` so existing AnyFlow checkpoints load bit-exactly into this class. """ _default_processor_cls = AnyFlowCausalAttnProcessor _available_processors = [AnyFlowCausalAttnProcessor, AnyFlowCrossAttnProcessor] def __init__( self, dim: int, heads: int, dim_head: int, eps: float = 1e-6, processor: Optional[Any] = None, ): super().__init__() self.heads = heads self.inner_dim = heads * dim_head self.to_q = torch.nn.Linear(dim, self.inner_dim, bias=True) self.to_k = torch.nn.Linear(dim, self.inner_dim, bias=True) self.to_v = torch.nn.Linear(dim, self.inner_dim, bias=True) self.to_out = torch.nn.ModuleList( [ torch.nn.Linear(self.inner_dim, dim, bias=True), torch.nn.Dropout(0.0), ] ) # ``rms_norm_across_heads`` per-axis: normalize Q and K across the entire ``heads * dim_head`` # channel axis. We use diffusers' RMSNorm (rather than ``torch.nn.RMSNorm``) so the numerics # match the legacy Attention class that produced the released checkpoints. self.norm_q = RMSNorm(self.inner_dim, eps=eps) self.norm_k = RMSNorm(self.inner_dim, eps=eps) self.set_processor(processor if processor is not None else self._default_processor_cls()) def forward(self, hidden_states: torch.Tensor, **kwargs) -> torch.Tensor: return self.processor(self, hidden_states, **kwargs) # Copied from diffusers.models.transformers.transformer_anyflow.AnyFlowImageEmbedding class AnyFlowImageEmbedding(torch.nn.Module): def __init__(self, in_features: int, out_features: int): super().__init__() self.norm1 = FP32LayerNorm(in_features) self.ff = FeedForward(in_features, out_features, mult=1, activation_fn="gelu") self.norm2 = FP32LayerNorm(out_features) def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor: hidden_states = self.norm1(encoder_hidden_states_image) hidden_states = self.ff(hidden_states) hidden_states = self.norm2(hidden_states) return hidden_states class AnyFlowDualTimestepTextImageEmbeddingCausal(nn.Module): """Causal variant of :class:`AnyFlowDualTimestepTextImageEmbedding`. Splits the per-frame timestep stream into a full-resolution suffix (length ``far_cfg["num_full_frames"]``) and a FAR-compressed prefix, expanding each segment by its own ``token_per_frame`` factor so the assembled time embedding aligns with the chunk-mixed token sequence. Optionally concatenates a ``clean_timestep`` embedding for the training rollout. """ def __init__( self, dim: int, gate_value: float, deltatime_type: str, time_freq_dim: int, time_proj_dim: int, text_embed_dim: int, image_embed_dim: Optional[int] = None, ): super().__init__() self.timesteps_proj = Timesteps(num_channels=time_freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0) self.time_embedder = TimestepEmbedding(in_channels=time_freq_dim, time_embed_dim=dim) self.delta_embedder = TimestepEmbedding(in_channels=time_freq_dim, time_embed_dim=dim) self.act_fn = nn.SiLU() self.time_proj = nn.Linear(dim, time_proj_dim) self.text_embedder = PixArtAlphaTextProjection(text_embed_dim, dim, act_fn="gelu_tanh") self.image_embedder = None if image_embed_dim is not None: self.image_embedder = AnyFlowImageEmbedding(image_embed_dim, dim) self.register_buffer("delta_emb_gate", torch.tensor([gate_value], dtype=torch.float32), persistent=False) self.deltatime_type = deltatime_type # Copied from diffusers.models.transformers.transformer_anyflow.AnyFlowDualTimestepTextImageEmbedding.forward_timestep def forward_timestep( self, timestep: torch.Tensor, delta_timestep: torch.Tensor, encoder_hidden_states, token_per_frame ): batch_size, num_frames = timestep.shape timestep = timestep.reshape(-1) delta_timestep = delta_timestep.reshape(-1) timestep = self.timesteps_proj(timestep) time_embedder_dtype = next(iter(self.time_embedder.parameters())).dtype if timestep.dtype != time_embedder_dtype and time_embedder_dtype != torch.int8: timestep = timestep.to(time_embedder_dtype) temb = self.time_embedder(timestep).type_as(encoder_hidden_states) delta_timestep = self.timesteps_proj(delta_timestep) delta_embedder_dtype = next(iter(self.delta_embedder.parameters())).dtype if delta_timestep.dtype != delta_embedder_dtype and delta_embedder_dtype != torch.int8: delta_timestep = delta_timestep.to(delta_embedder_dtype) delta_emb = self.delta_embedder(delta_timestep).type_as(encoder_hidden_states) gate = self.delta_emb_gate.to(delta_embedder_dtype) rt_emb = (1 - gate) * temb + gate * delta_emb timestep_proj = self.time_proj(self.act_fn(rt_emb)) rt_emb = rt_emb.unflatten(0, (batch_size, num_frames)).repeat_interleave(token_per_frame, dim=1) timestep_proj = timestep_proj.unflatten(0, (batch_size, num_frames)).repeat_interleave(token_per_frame, dim=1) return rt_emb, timestep_proj def forward( self, timestep: torch.Tensor, r_timestep: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_hidden_states_image: Optional[torch.Tensor] = None, far_cfg=None, clean_timestep=None, ): if self.deltatime_type == "r": delta_timestep = r_timestep elif self.deltatime_type == "t-r": delta_timestep = timestep - r_timestep else: raise NotImplementedError full_frame_timestep, full_frame_timestep_proj = self.forward_timestep( timestep[:, -far_cfg["num_full_frames"] :], delta_timestep[:, -far_cfg["num_full_frames"] :], encoder_hidden_states, far_cfg["full_token_per_frame"], ) compressed_frame_timestep, compressed_frame_timestep_proj = self.forward_timestep( timestep[:, : -far_cfg["num_full_frames"]], delta_timestep[:, : -far_cfg["num_full_frames"]], encoder_hidden_states, far_cfg["compressed_token_per_frame"], ) if clean_timestep is not None: clean_timestep, clean_timestep_proj = self.forward_timestep( clean_timestep, clean_timestep, encoder_hidden_states, far_cfg["full_token_per_frame"] ) timestep = torch.cat([compressed_frame_timestep, full_frame_timestep, clean_timestep], dim=1) timestep_proj = torch.cat( [compressed_frame_timestep_proj, full_frame_timestep_proj, clean_timestep_proj], dim=1 ) else: timestep = torch.cat([compressed_frame_timestep, full_frame_timestep], dim=1) timestep_proj = torch.cat([compressed_frame_timestep_proj, full_frame_timestep_proj], dim=1) encoder_hidden_states = self.text_embedder(encoder_hidden_states) if encoder_hidden_states_image is not None: encoder_hidden_states_image = self.image_embedder(encoder_hidden_states_image) return timestep, timestep_proj, encoder_hidden_states, encoder_hidden_states_image # Copied from diffusers.models.transformers.transformer_anyflow.AnyFlowTransformerBlock class AnyFlowTransformerBlock(nn.Module): """AnyFlow transformer block. The self-attention processor is chosen at construction by ``is_causal``: the bidirectional transformer passes ``is_causal=False`` (the default), the FAR causal transformer passes ``is_causal=True``. The forward pass is identical in both modes — only the processor differs, so all causal-specific machinery (BlockMask, KV cache) lives inside the processor. """ def __init__( self, dim: int, ffn_dim: int, num_heads: int, cross_attn_norm: bool = False, eps: float = 1e-6, is_causal: bool = False, ): super().__init__() self.is_causal = is_causal # 1. Self-attention. The causal processor lives in the FAR sibling module; lazy-import to # avoid a circular import at module load time. if is_causal: from .transformer_anyflow_far import AnyFlowCausalAttnProcessor self_attn_processor = AnyFlowCausalAttnProcessor() else: self_attn_processor = AnyFlowAttnProcessor() self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False) self.attn1 = AnyFlowAttention( dim=dim, heads=num_heads, dim_head=dim // num_heads, eps=eps, processor=self_attn_processor, ) # 2. Cross-attention self.attn2 = AnyFlowAttention( dim=dim, heads=num_heads, dim_head=dim // num_heads, eps=eps, processor=AnyFlowCrossAttnProcessor(), ) self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity() # 3. Feed-forward self.ffn = FeedForward(dim, inner_dim=ffn_dim, activation_fn="gelu-approximate") self.norm3 = FP32LayerNorm(dim, eps, elementwise_affine=False) self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, temb: torch.Tensor, rotary_emb: torch.Tensor, attention_mask: torch.Tensor, kv_cache=None, kv_cache_flag=None, ) -> torch.Tensor: shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = ( self.scale_shift_table + temb.float() ).chunk(6, dim=2) shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = ( shift_msa.squeeze(2), scale_msa.squeeze(2), gate_msa.squeeze(2), c_shift_msa.squeeze(2), c_scale_msa.squeeze(2), c_gate_msa.squeeze(2), ) # noqa: E501 # 1. Self-attention norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(hidden_states) attn1_kwargs = { "hidden_states": norm_hidden_states, "rotary_emb": rotary_emb, "attention_mask": attention_mask, } # KV cache kwargs are only consumed by the FAR causal processor; the bidi processor # doesn't accept them, so we forward them only when they're actually populated. if kv_cache is not None: attn1_kwargs["kv_cache"] = kv_cache attn1_kwargs["kv_cache_flag"] = kv_cache_flag attn_output = self.attn1(**attn1_kwargs) hidden_states = (hidden_states.float() + attn_output * gate_msa).type_as(hidden_states) # 2. Cross-attention norm_hidden_states = self.norm2(hidden_states.float()).type_as(hidden_states) attn_output = self.attn2(hidden_states=norm_hidden_states, encoder_hidden_states=encoder_hidden_states) hidden_states = hidden_states + attn_output # 3. Feed-forward norm_hidden_states = (self.norm3(hidden_states.float()) * (1 + c_scale_msa) + c_shift_msa).type_as( hidden_states ) ff_output = self.ffn(norm_hidden_states) hidden_states = (hidden_states.float() + ff_output.float() * c_gate_msa).type_as(hidden_states) return hidden_states class AnyFlowCausalRotaryPosEmbed(nn.Module): """ Rotary positional embedding for the FAR causal transformer. Produces position frequencies for both the full-resolution noisy chunk(s) and the FAR-compressed context chunk(s); the compressed branch downscales the per-axis frequency table via complex average pooling so the compressed grid stays aligned with the full grid. """ def __init__( self, attention_head_dim: int, patch_size: Tuple[int, int, int], compressed_patch_size: Tuple[int, int, int], max_seq_len: int, theta: float = 10000.0, ): super().__init__() self.attention_head_dim = attention_head_dim self.patch_size = patch_size self.compressed_patch_size = compressed_patch_size self.max_seq_len = max_seq_len self.theta = theta # Frequency table is lazily built per-device in ``_build_freqs``: MPS / NPU don't support # complex128, so we downcast to complex64 there. self._freqs_cache: Optional[Tuple[Any, torch.Tensor]] = None # Copied from diffusers.models.transformers.transformer_anyflow.AnyFlowRotaryPosEmbed._build_freqs def _build_freqs(self, device: torch.device) -> torch.Tensor: # Skip the cache read/write inside torch.compile: mutating ``self._freqs_cache`` between calls # becomes a Dynamo guard and forces recompilation on the second invocation. is_compiling = torch.compiler.is_compiling() cache_key = (device.type, str(device)) if not is_compiling and self._freqs_cache is not None and self._freqs_cache[0] == cache_key: return self._freqs_cache[1] freqs_dtype = maybe_adjust_dtype_for_device(torch.float64, device) h_dim = w_dim = 2 * (self.attention_head_dim // 6) t_dim = self.attention_head_dim - h_dim - w_dim freqs_list = [] for dim in (t_dim, h_dim, w_dim): f = get_1d_rotary_pos_embed( dim, self.max_seq_len, self.theta, use_real=False, repeat_interleave_real=False, freqs_dtype=freqs_dtype, ) freqs_list.append(f.to(device)) freqs = torch.cat(freqs_list, dim=1) if not is_compiling: self._freqs_cache = (cache_key, freqs) return freqs def avg_pool_complex(self, freq: torch.Tensor, kernel_size: int, stride: int): real = freq.real # [B, C, L], float real = real.transpose(0, 1).unsqueeze(0) imag = freq.imag # [B, C, L], float imag = imag.transpose(0, 1).unsqueeze(0) pr = F.avg_pool1d(real, kernel_size, stride) pi = F.avg_pool1d(imag, kernel_size, stride) pr = pr.squeeze(0).transpose(0, 1) pi = pi.squeeze(0).transpose(0, 1) norm = torch.sqrt(pr**2 + pi**2) pr_unit = pr / norm pi_unit = pi / norm return torch.complex(pr_unit, pi_unit) def _forward_compressed_frame(self, num_frames, height, width, device): ppf, pph, ppw = num_frames, height, width # Tiny dummy components (e.g. height=16/width=16 with compressed_patch_size=(1,4,4) and # an upstream VAE stride of 8) can produce 0-element grids; the .view(0, k, 1, -1) reshape # below would be ambiguous. Real ckpts use 60x104 latents and never hit this path. freqs_full = self._build_freqs(device) if min(ppf, pph, ppw) <= 0: freq_channels = self.attention_head_dim // 2 return torch.empty((ppf, pph, ppw, freq_channels), dtype=freqs_full.dtype, device=device) downscale = [self.compressed_patch_size[i] // self.patch_size[i] for i in range(len(self.patch_size))] freqs = freqs_full.split_with_sizes( [ self.attention_head_dim // 2 - 2 * (self.attention_head_dim // 6), self.attention_head_dim // 6, self.attention_head_dim // 6, ], dim=1, ) freqs_f = self.avg_pool_complex(freqs[0], kernel_size=downscale[0], stride=downscale[0]) freqs_h = self.avg_pool_complex(freqs[1], kernel_size=downscale[1], stride=downscale[1]) freqs_w = self.avg_pool_complex(freqs[2], kernel_size=downscale[2], stride=downscale[2]) freqs_f = freqs_f[:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1) freqs_h = freqs_h[:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1) freqs_w = freqs_w[:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1) freqs = torch.cat([freqs_f, freqs_h, freqs_w], dim=-1) return freqs # Copied from diffusers.models.transformers.transformer_anyflow.AnyFlowRotaryPosEmbed._forward_full_frame def _forward_full_frame(self, num_frames, height, width, device) -> torch.Tensor: ppf, pph, ppw = num_frames, height, width freqs_full = self._build_freqs(device) if min(ppf, pph, ppw) <= 0: freq_channels = self.attention_head_dim // 2 return torch.empty((ppf, pph, ppw, freq_channels), dtype=freqs_full.dtype, device=device) freqs = freqs_full.split_with_sizes( [ self.attention_head_dim // 2 - 2 * (self.attention_head_dim // 6), self.attention_head_dim // 6, self.attention_head_dim // 6, ], dim=1, ) freqs_f = freqs[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1) freqs_h = freqs[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1) freqs_w = freqs[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1) freqs = torch.cat([freqs_f, freqs_h, freqs_w], dim=-1) return freqs def forward(self, far_cfg, device, clean_hidden_states=None): full_frame_freqs = self._forward_full_frame( num_frames=far_cfg["total_frames"], height=far_cfg["full_frame_shape"][0], width=far_cfg["full_frame_shape"][1], device=device, ) compressed_frame_freqs = self._forward_compressed_frame( num_frames=far_cfg["total_frames"], height=far_cfg["compressed_frame_shape"][0], width=far_cfg["compressed_frame_shape"][1], device=device, ) compressed_frame_freqs, full_frame_freqs = ( compressed_frame_freqs[: far_cfg["num_compressed_frames"]], full_frame_freqs[far_cfg["num_compressed_frames"] :], ) compressed_frame_freqs = compressed_frame_freqs.flatten(start_dim=0, end_dim=2) full_frame_freqs = full_frame_freqs.flatten(start_dim=0, end_dim=2) if clean_hidden_states is not None: freqs = torch.cat([compressed_frame_freqs, full_frame_freqs, full_frame_freqs], dim=0) else: freqs = torch.cat([compressed_frame_freqs, full_frame_freqs], dim=0) freqs = freqs[None, None, ...] return {"query": freqs, "key": freqs} def _build_anyflow_far_causal_block_mask( chunk_partition: List[int], height: int, width: int, patch_size: Tuple[int, int, int], compressed_patch_size: Tuple[int, int, int], full_chunk_limit: int, *, mode: str = "train", has_clean_context: bool = False, device: Optional[torch.device] = None, ) -> BlockMask: r"""Build the causal :class:`~torch.nn.attention.flex_attention.BlockMask` for the FAR transformer. Provided as a standalone function so callers can construct the mask *outside* the transformer's compiled region, which is required to wrap the forward in ``torch.compile(fullgraph=True)`` (``flex_attention.create_block_mask`` itself uses ``_compile=False`` internally and breaks the graph when invoked inside the compiled scope). Two modes are exposed, mirroring the FAR forward paths that actually consume a mask. The autoregressive ``_forward_inference`` path attends through the KV cache and does not use a full BlockMask, so it has no corresponding mode here. Args: chunk_partition: per-chunk frame counts; must sum to the number of latent frames. height, width: latent spatial dimensions. patch_size, compressed_patch_size, full_chunk_limit: must match the transformer config. mode: ``"train"`` (strict ``>`` comparison against ``full_chunk_limit``, matches :meth:`AnyFlowFARTransformer3DModel._forward_train`) or ``"cache"`` (``>=`` comparison via the ``full_chunk_limit - 1`` offset used by :meth:`AnyFlowFARTransformer3DModel._forward_cache`). has_clean_context: ``True`` when ``clean_hidden_states`` is being threaded through the transformer (training V2V/I2V). device: device for the resulting BlockMask. Defaults to CPU. """ if mode not in {"train", "cache"}: raise ValueError(f"Unknown mode {mode!r}; expected 'train' or 'cache'.") full_token_per_frame = (height // patch_size[1]) * (width // patch_size[2]) compressed_token_per_frame = (height // compressed_patch_size[1]) * (width // compressed_patch_size[2]) # `cache` uses `full_chunk_limit - 1` (an effective `>= full_chunk_limit` comparison); `train` uses a strict `>`. total_chunks = len(chunk_partition) threshold = full_chunk_limit - 1 if mode == "cache" else full_chunk_limit if total_chunks > threshold: num_full_chunk = threshold num_compressed_chunk = total_chunks - threshold else: num_full_chunk, num_compressed_chunk = total_chunks, 0 far_cfg = { "num_full_chunk": num_full_chunk, "num_compressed_chunk": num_compressed_chunk, "num_full_frames": sum(chunk_partition[num_compressed_chunk:]), "num_compressed_frames": sum(chunk_partition[:num_compressed_chunk]), "full_token_per_frame": full_token_per_frame, "compressed_token_per_frame": compressed_token_per_frame, "chunk_partition": chunk_partition, } return _build_far_block_mask_from_far_cfg(far_cfg, has_clean=has_clean_context, device=device) def _build_far_block_mask_from_far_cfg(far_cfg, has_clean, device): """Internal: build a BlockMask given an already-computed ``far_cfg`` dict. Factored out of :class:`AnyFlowFARTransformer3DModel` so it can be shared between :func:`_build_anyflow_far_causal_block_mask` (the user-facing entry point) and the in-forward fallback path used when no pre-built ``attention_mask`` is passed. """ chunk_partition = far_cfg["chunk_partition"] noise_seq_len = clean_seq_len = far_cfg["num_full_frames"] * far_cfg["full_token_per_frame"] context_seq_len = far_cfg["num_compressed_frames"] * far_cfg["compressed_token_per_frame"] noise_start = context_seq_len noise_end = noise_start + noise_seq_len clean_start = context_seq_len + noise_seq_len clean_end = clean_start + clean_seq_len if has_clean: real_seq_len = context_seq_len + noise_seq_len + clean_seq_len else: real_seq_len = context_seq_len + noise_seq_len padded_seq_len = int(math.ceil(real_seq_len / 128.0) * 128.0) context_chunk_partition, noise_chunk_partition = ( chunk_partition[: far_cfg["num_compressed_chunk"]], chunk_partition[far_cfg["num_compressed_chunk"] :], ) if len(context_chunk_partition) != 0: context_frame_idx = torch.cat( [ torch.ones(chunk_len * far_cfg["compressed_token_per_frame"], device=device) * chunk_idx for chunk_idx, chunk_len in enumerate(context_chunk_partition) ] ) else: context_frame_idx = None if has_clean: noise_frame_idx = clean_frame_idx = torch.cat( [ torch.ones(chunk_len * far_cfg["full_token_per_frame"], device=device) * (chunk_idx + len(context_chunk_partition)) for chunk_idx, chunk_len in enumerate(noise_chunk_partition) ] ) pad_frame_idx = torch.zeros(padded_seq_len - real_seq_len, device=device) if len(context_chunk_partition) != 0: frame_idx = torch.cat([context_frame_idx, noise_frame_idx, clean_frame_idx, pad_frame_idx], dim=0) else: frame_idx = torch.cat([noise_frame_idx, clean_frame_idx, pad_frame_idx], dim=0) def mask_mod(b, h, q_idx, kv_idx): # 1) is padding is_padding = (q_idx >= real_seq_len) | (kv_idx >= real_seq_len) # 2) chunk causal base = frame_idx[q_idx] >= frame_idx[kv_idx] # 3) interval mask q_is_noise = (q_idx >= noise_start) & (q_idx < noise_end) q_is_clean = (q_idx >= clean_start) & (q_idx < clean_end) k_is_noise = (kv_idx >= noise_start) & (kv_idx < noise_end) k_is_clean = (kv_idx >= clean_start) & (kv_idx < clean_end) # 4) clean -> noise: disallowed is_clean_to_noise = q_is_clean & k_is_noise # 5) noise -> noise: only same frame same_frame_idx = frame_idx[q_idx] == frame_idx[kv_idx] noise_to_noise = q_is_noise & k_is_noise noise_to_clean = q_is_noise & k_is_clean noise_to_noise_allow = noise_to_noise & same_frame_idx noise_to_noise_mask = (~noise_to_noise) | noise_to_noise_allow noise_to_clean_same = noise_to_clean & same_frame_idx noise_to_clean_disallow = noise_to_clean_same allowed = base & ~is_padding & ~is_clean_to_noise & noise_to_noise_mask & ~noise_to_clean_disallow return allowed else: noise_frame_idx = torch.cat( [ torch.ones(chunk_len * far_cfg["full_token_per_frame"], device=device) * (chunk_idx + len(context_chunk_partition)) for chunk_idx, chunk_len in enumerate(noise_chunk_partition) ] ) pad_frame_idx = torch.zeros(padded_seq_len - real_seq_len, device=device) if len(context_chunk_partition) != 0: frame_idx = torch.cat([context_frame_idx, noise_frame_idx, pad_frame_idx], dim=0) else: frame_idx = torch.cat([noise_frame_idx, pad_frame_idx], dim=0) def mask_mod(b, h, q_idx, kv_idx): is_padding = (q_idx >= real_seq_len) | (kv_idx >= real_seq_len) base = frame_idx[q_idx] >= frame_idx[kv_idx] return base & ~is_padding return create_block_mask( mask_mod, B=None, H=None, Q_LEN=padded_seq_len, KV_LEN=padded_seq_len, device=device, _compile=False, ) class AnyFlowFARTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin): r""" Causal (FAR) 3D Transformer for AnyFlow flow-map sampling with chunk-wise autoregressive generation. Extends the v0.35.1 Wan2.1 backbone with: * **FAR causal block-mask** via :func:`torch.nn.attention.flex_attention`, supporting chunk-wise autoregressive generation ([FAR](https://huggingface.co/papers/2503.19325)). * **Compressed-frame patch embedding** ``far_patch_embedding`` for context (already-generated) frames, initialized from ``patch_embedding`` via trilinear interpolation so a freshly constructed model is already at a reasonable starting point even before LoRA fine-tuning. * **Dual-timestep flow-map embedding** for any-step sampling (same as ``AnyFlowTransformer3DModel``). Use ``AnyFlowTransformer3DModel`` instead for plain bidirectional T2V — that variant skips the FAR causal masking and ``far_patch_embedding`` and is ~5–10% smaller. Args: patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`): 3D patch dimensions for full-resolution chunks. compressed_patch_size (`Tuple[int]`, defaults to `(1, 4, 4)`): Larger patch dimensions for the FAR-compressed (context) chunks. full_chunk_limit (`int`, defaults to `3`): Maximum number of full-resolution chunks before earlier chunks are demoted to compressed FAR context. The released checkpoints use ``3``. num_attention_heads (`int`, defaults to `40`): Number of attention heads. attention_head_dim (`int`, defaults to `128`): The number of channels in each head. in_channels (`int`, defaults to `16`): The number of channels in the input latent. out_channels (`int`, defaults to `16`): The number of channels in the output latent. text_dim (`int`, defaults to `4096`): Input dimension for text embeddings (UMT5). freq_dim (`int`, defaults to `256`): Dimension for sinusoidal time embeddings. ffn_dim (`int`, defaults to `13824`): Intermediate dimension in feed-forward network. num_layers (`int`, defaults to `40`): Number of transformer blocks. cross_attn_norm (`bool`, defaults to `True`): Enable cross-attention normalization. eps (`float`, defaults to `1e-6`): Epsilon for normalization layers. image_dim (`Optional[int]`, *optional*, defaults to `None`): Image embedding dimension for I2V conditioning. rope_max_seq_len (`int`, defaults to `1024`): Maximum sequence length used to precompute rotary position frequencies. gate_value (`float`, defaults to `0.25`): Mixing gate between source-timestep and delta-timestep embeddings. deltatime_type (`str`, defaults to `'r'`): Either ``"r"`` (delta is the target timestep) or ``"t-r"`` (delta is the absolute interval). chunk_partition (`Tuple[int, ...]`, defaults to `(1, 3, 3, 3, 3, 3, 3, 2)`): Default per-chunk frame counts used by the pipeline. The released NVIDIA AnyFlow-FAR checkpoints target ``num_frames=81`` (21 latent frames at VAE temporal stride 4) split as ``1 + 3*6 + 2``. A different ``num_frames`` requires a matching ``chunk_partition`` override passed to :meth:`AnyFlowFARPipeline.__call__` (and likewise to :meth:`forward`). """ _supports_gradient_checkpointing = True _skip_layerwise_casting_patterns = ["patch_embedding", "far_patch_embedding", "condition_embedder", "norm"] _no_split_modules = ["AnyFlowTransformerBlock"] _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"] _repeated_blocks = ["AnyFlowTransformerBlock"] @register_to_config def __init__( self, patch_size: Tuple[int] = (1, 2, 2), compressed_patch_size: Tuple[int] = (1, 4, 4), full_chunk_limit: int = 3, num_attention_heads: int = 40, attention_head_dim: int = 128, in_channels: int = 16, out_channels: int = 16, text_dim: int = 4096, freq_dim: int = 256, ffn_dim: int = 13824, num_layers: int = 40, cross_attn_norm: bool = True, eps: float = 1e-6, image_dim: Optional[int] = None, rope_max_seq_len: int = 1024, gate_value: float = 0.25, deltatime_type: str = "r", chunk_partition: Tuple[int, ...] = (1, 3, 3, 3, 3, 3, 3, 2), ) -> None: super().__init__() inner_dim = num_attention_heads * attention_head_dim out_channels = out_channels or in_channels # 1. Patch & position embedding (full + FAR-compressed branches). self.rope = AnyFlowCausalRotaryPosEmbed( attention_head_dim, patch_size, compressed_patch_size, rope_max_seq_len ) self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size) self.far_patch_embedding = nn.Conv3d( in_channels, inner_dim, kernel_size=compressed_patch_size, stride=compressed_patch_size ) # Warm-start the compressed branch from the full-resolution branch by trilinear interpolation. This # matches FAR-Dev's `setup_far_model()` initialization. State-dict loading will overwrite these # weights for trained checkpoints; the warm-start only matters when constructing a fresh model. original_weight = self.patch_embedding.weight.data.view(-1, 1, *patch_size) new_weight = F.interpolate(original_weight, size=compressed_patch_size, mode="trilinear", align_corners=False) new_weight = new_weight.view(inner_dim, in_channels, *compressed_patch_size) with torch.no_grad(): self.far_patch_embedding.weight.copy_(new_weight) self.far_patch_embedding.bias.copy_(self.patch_embedding.bias) # 2. Condition embedding (always dual-timestep for AnyFlow distilled checkpoints). self.condition_embedder = AnyFlowDualTimestepTextImageEmbeddingCausal( dim=inner_dim, gate_value=gate_value, deltatime_type=deltatime_type, time_freq_dim=freq_dim, time_proj_dim=inner_dim * 6, text_embed_dim=text_dim, image_embed_dim=image_dim, ) # 3. Transformer blocks (causal self-attn processor) self.blocks = nn.ModuleList( [ AnyFlowTransformerBlock(inner_dim, ffn_dim, num_attention_heads, cross_attn_norm, eps, is_causal=True) for _ in range(num_layers) ] ) # 4. Output norm & projection self.norm_out = FP32LayerNorm(inner_dim, eps, elementwise_affine=False) self.proj_out = nn.Linear(inner_dim, out_channels * math.prod(patch_size)) self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5) self.gradient_checkpointing = False @apply_lora_scale("attention_kwargs") def forward( self, hidden_states: torch.Tensor, timestep: torch.Tensor, r_timestep: torch.Tensor, encoder_hidden_states: torch.Tensor, chunk_partition: List[int], encoder_hidden_states_image: Optional[torch.Tensor] = None, clean_hidden_states: Optional[torch.Tensor] = None, clean_timestep: Optional[torch.Tensor] = None, kv_cache: Optional[List[Dict[str, torch.Tensor]]] = None, kv_cache_flag: Optional[Dict[str, Any]] = None, attention_mask: Optional[BlockMask] = None, attention_kwargs: Optional[Dict[str, Any]] = None, return_dict: bool = True, ) -> Union[Transformer2DModelOutput, AnyFlowFARTransformerOutput, Tuple]: """ FAR causal forward pass. Dispatches to one of three internal paths: * ``kv_cache is None`` → causal training rollout (returns :class:`Transformer2DModelOutput`). * ``kv_cache is not None`` and ``kv_cache_flag["is_cache_step"]`` → cache-prefill (returns :class:`AnyFlowFARTransformerOutput` with ``sample=None``). * Otherwise → autoregressive inference step (returns :class:`AnyFlowFARTransformerOutput`). Args: hidden_states (`torch.Tensor`): Latent input of shape ``(B, F, C, H, W)``. timestep (`torch.Tensor`): Source (noisier) flow-map timestep `t`. r_timestep (`torch.Tensor`): Target (cleaner) flow-map timestep `r`. encoder_hidden_states (`torch.Tensor`): UMT5 text embeddings. chunk_partition (`List[int]`): Per-chunk frame counts; total must match the number of latent frames in ``hidden_states``. encoder_hidden_states_image (`torch.Tensor`, *optional*): I2V image embedding; concatenated before text tokens when provided. clean_hidden_states (`torch.Tensor`, *optional*): Clean (noise-free) conditioning frames used by the training rollout. clean_timestep (`torch.Tensor`, *optional*): Timesteps for the clean conditioning frames in the training rollout. kv_cache (`List[Dict[str, torch.Tensor]]`, *optional*): Per-block KV cache for autoregressive inference. `None` selects the training path. kv_cache_flag (`Dict[str, Any]`, *optional*): KV-cache metadata (e.g. ``is_cache_step`` flag and token counts). attention_mask (`BlockMask`, *optional*): Pre-built causal mask, typically constructed via :meth:`build_attention_mask`. Consumed by the train and KV-cache prefill paths; the autoregressive inference path attends through the KV cache and does not use a full mask. When ``None``, the train / cache paths build the mask internally; that fallback is not compile-safe (the underlying ``flex_attention.create_block_mask`` breaks the graph under ``fullgraph=True``), so pass a pre-built mask whenever wrapping ``forward`` in ``torch.compile``. attention_kwargs (`dict`, *optional*): Forwarded to the attention processors. return_dict (`bool`, *optional*, defaults to `True`): If `False`, returns positional tuples instead of an output dataclass. Returns: [`~models.transformer_2d.Transformer2DModelOutput`], [`AnyFlowFARTransformerOutput`] or `tuple`: When `return_dict` is `False`, a plain `tuple` is returned. Otherwise, the causal training rollout (`kv_cache is None`) returns a [`~models.transformer_2d.Transformer2DModelOutput`], while the cache-prefill and autoregressive inference paths return an [`AnyFlowFARTransformerOutput`]. """ # `attention_kwargs` is consumed by the @apply_lora_scale decorator on this method; # it does not need to thread through to the inner _forward_* paths. common = { "hidden_states": hidden_states, "chunk_partition": chunk_partition, "timestep": timestep, "r_timestep": r_timestep, "encoder_hidden_states": encoder_hidden_states, "encoder_hidden_states_image": encoder_hidden_states_image, "return_dict": return_dict, } if kv_cache is not None: common["kv_cache"] = kv_cache common["kv_cache_flag"] = kv_cache_flag if kv_cache_flag is not None and kv_cache_flag.get("is_cache_step"): return self._forward_cache( clean_hidden_states=clean_hidden_states, clean_timestep=clean_timestep, attention_mask=attention_mask, **common, ) return self._forward_inference(**common) return self._forward_train( clean_hidden_states=clean_hidden_states, clean_timestep=clean_timestep, attention_mask=attention_mask, **common, ) def _unpack_latent_sequence(self, latents, num_frames, height, width, patch_size): batch_size, num_patches, channels = latents.shape height, width = height // patch_size, width // patch_size latents = latents.view( batch_size * num_frames, height, width, patch_size, patch_size, channels // (patch_size * patch_size) ) latents = latents.permute(0, 5, 1, 3, 2, 4) latents = latents.reshape( batch_size, num_frames, channels // (patch_size * patch_size), height * patch_size, width * patch_size ) return latents def _forward_far_patchify(self, hidden_states, far_cfg, clean_hidden_states=None): full_hidden_states, compressed_hidden_states = ( hidden_states[:, :, far_cfg["num_compressed_frames"] :], hidden_states[:, :, : far_cfg["num_compressed_frames"]], ) # noqa: E501 patchified_full_hidden_states = ( self.patch_embedding(full_hidden_states).flatten(start_dim=2, end_dim=4).transpose(1, 2) ) if clean_hidden_states is not None: clean_hidden_states = ( self.patch_embedding(clean_hidden_states).flatten(start_dim=2, end_dim=4).transpose(1, 2) ) patchified_full_hidden_states = torch.cat([patchified_full_hidden_states, clean_hidden_states], dim=1) if far_cfg["num_compressed_frames"] > 0: patchified_compressed_hidden_states = ( self.far_patch_embedding(compressed_hidden_states).flatten(start_dim=2, end_dim=4).transpose(1, 2) ) hidden_states = torch.cat([patchified_compressed_hidden_states, patchified_full_hidden_states], dim=1) else: hidden_states = patchified_full_hidden_states return hidden_states def _forward_far_patchify_inference(self, hidden_states): hidden_states = self.patch_embedding(hidden_states).flatten(start_dim=2, end_dim=4).transpose(1, 2) return hidden_states def build_attention_mask( self, *, chunk_partition: List[int], height: int, width: int, has_clean_context: bool = False, device: Optional[torch.device] = None, mode: str = "train", ) -> BlockMask: r"""Pre-build the causal :class:`~torch.nn.attention.flex_attention.BlockMask` outside ``forward``. Pass the result via :meth:`forward`'s ``attention_mask`` kwarg to make the whole transformer compatible with ``torch.compile(fullgraph=True)``. Without a pre-built mask, ``forward`` falls back to constructing it internally — that path uses ``flex_attention.create_block_mask(_compile=False)`` and breaks the compile graph. Args: chunk_partition: per-chunk frame counts (must sum to the number of latent frames). height, width: latent spatial dimensions. has_clean_context: ``True`` when ``clean_hidden_states`` will be threaded through :meth:`forward` (training V2V/I2V); only this presence flag affects the mask layout. device: device for the resulting :class:`BlockMask`. The mask is not auto-moved by ``device_map="auto"``; build it on the same device the transformer's inputs will live on. mode: ``"train"`` (matches :meth:`_forward_train`) or ``"cache"`` (matches :meth:`_forward_cache`). The autoregressive ``_forward_inference`` path attends through the KV cache and has no mode here. Returns: :class:`~torch.nn.attention.flex_attention.BlockMask`: causal mask spanning the FAR layout, padded to a multiple of 128 along the sequence dimension (the BlockMask block-size requirement). Raises: ValueError: if ``mode`` is neither ``"train"`` nor ``"cache"``. """ return _build_anyflow_far_causal_block_mask( chunk_partition=chunk_partition, height=height, width=width, patch_size=self.config.patch_size, compressed_patch_size=self.config.compressed_patch_size, full_chunk_limit=self.config.full_chunk_limit, mode=mode, has_clean_context=has_clean_context, device=device, ) def _forward_inference( self, hidden_states: torch.Tensor, chunk_partition, timestep: torch.LongTensor, r_timestep: torch.LongTensor, encoder_hidden_states: torch.Tensor, encoder_hidden_states_image: Optional[torch.Tensor] = None, return_dict: bool = True, kv_cache=None, kv_cache_flag=None, ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: hidden_states = hidden_states.permute(0, 2, 1, 3, 4) batch_size, num_channels, num_frames, height, width = hidden_states.shape full_token_per_frame = (height // self.config.patch_size[1]) * (width // self.config.patch_size[2]) compressed_token_per_frame = (height // self.config.compressed_patch_size[1]) * ( width // self.config.compressed_patch_size[2] ) total_chunks = 1 + kv_cache_flag["num_cached_chunks"] if total_chunks >= self.config.full_chunk_limit: num_full_chunk, num_compressed_chunk = ( self.config.full_chunk_limit, total_chunks - self.config.full_chunk_limit, ) else: num_full_chunk, num_compressed_chunk = total_chunks, 0 kv_cache_flag["num_cached_full_tokens"] = ( sum(chunk_partition[num_compressed_chunk : num_compressed_chunk + (num_full_chunk - 1)]) * full_token_per_frame ) # noqa: E501 kv_cache_flag["num_cached_compressed_tokens"] = ( sum(chunk_partition[:num_compressed_chunk]) * compressed_token_per_frame ) far_cfg = { "total_frames": sum(chunk_partition), "num_full_frames": sum(chunk_partition[num_compressed_chunk:]), "num_compressed_frames": sum(chunk_partition[:num_compressed_chunk]), "full_frame_shape": (height // self.config.patch_size[1], width // self.config.patch_size[2]), "compressed_frame_shape": ( height // self.config.compressed_patch_size[1], width // self.config.compressed_patch_size[2], ), "full_token_per_frame": full_token_per_frame, "compressed_token_per_frame": compressed_token_per_frame, } attention_mask = None hidden_states = self._forward_far_patchify_inference(hidden_states) rotary_emb = self.rope(far_cfg=far_cfg, device=hidden_states.device) rotary_emb["query"] = rotary_emb["query"][:, :, -hidden_states.shape[1] :] temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder( timestep, r_timestep, encoder_hidden_states, encoder_hidden_states_image, far_cfg=far_cfg, # noqa: E501 ) timestep_proj = timestep_proj.unflatten(2, (6, -1)) if encoder_hidden_states_image is not None: encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1) # 4. Transformer blocks for index_block, block in enumerate(self.blocks): if torch.is_grad_enabled() and self.gradient_checkpointing: hidden_states = self._gradient_checkpointing_func( block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, attention_mask, kv_cache[index_block], kv_cache_flag, ) else: hidden_states = block( hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, attention_mask, kv_cache[index_block], kv_cache_flag, ) # 5. Output norm, projection & unpatchify shift, scale = (self.scale_shift_table + temb.unsqueeze(2)).chunk(2, dim=2) shift, scale = shift.squeeze(2), scale.squeeze(2) # Move the shift and scale tensors to the same device as hidden_states. # When using multi-GPU inference via accelerate these will be on the # first device rather than the last device, which hidden_states ends up # on. shift = shift.to(hidden_states.device) scale = scale.to(hidden_states.device) hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states) output = self.proj_out(hidden_states) output = self._unpack_latent_sequence( output, num_frames=chunk_partition[-1], height=height, width=width, patch_size=self.config.patch_size[1] ) if not return_dict: return output, kv_cache return AnyFlowFARTransformerOutput(sample=output, kv_cache=kv_cache) def _forward_cache( self, hidden_states: torch.Tensor, chunk_partition, timestep: torch.LongTensor, r_timestep: torch.LongTensor, encoder_hidden_states: torch.Tensor, encoder_hidden_states_image: Optional[torch.Tensor] = None, attention_mask: Optional[BlockMask] = None, return_dict: bool = True, clean_hidden_states=None, clean_timestep=None, kv_cache=None, kv_cache_flag=None, ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: hidden_states = hidden_states.permute(0, 2, 1, 3, 4) if clean_hidden_states is not None: clean_hidden_states = clean_hidden_states.permute(0, 2, 1, 3, 4) batch_size, num_channels, num_frames, height, width = hidden_states.shape full_token_per_frame = (height // self.config.patch_size[1]) * (width // self.config.patch_size[2]) compressed_token_per_frame = (height // self.config.compressed_patch_size[1]) * ( width // self.config.compressed_patch_size[2] ) total_chunks = len(chunk_partition) full_chunk_limit = self.config.full_chunk_limit - 1 if total_chunks > full_chunk_limit: num_full_chunk, num_compressed_chunk = full_chunk_limit, total_chunks - full_chunk_limit else: num_full_chunk, num_compressed_chunk = total_chunks, 0 far_cfg = { "total_frames": sum(chunk_partition), "num_full_chunk": num_full_chunk, "num_full_frames": sum(chunk_partition[num_compressed_chunk:]), "num_compressed_chunk": num_compressed_chunk, "num_compressed_frames": sum(chunk_partition[:num_compressed_chunk]), "full_frame_shape": (height // self.config.patch_size[1], width // self.config.patch_size[2]), "compressed_frame_shape": ( height // self.config.compressed_patch_size[1], width // self.config.compressed_patch_size[2], ), "full_token_per_frame": full_token_per_frame, "compressed_token_per_frame": compressed_token_per_frame, "chunk_partition": chunk_partition, } kv_cache_flag["num_full_tokens"] = far_cfg["num_full_frames"] * far_cfg["full_token_per_frame"] kv_cache_flag["num_compressed_tokens"] = ( far_cfg["num_compressed_frames"] * far_cfg["compressed_token_per_frame"] ) if attention_mask is None: attention_mask = _build_far_block_mask_from_far_cfg( far_cfg, has_clean=clean_hidden_states is not None, device=hidden_states.device ) rotary_emb = self.rope(far_cfg=far_cfg, clean_hidden_states=clean_hidden_states, device=hidden_states.device) hidden_states = self._forward_far_patchify( hidden_states, far_cfg=far_cfg, clean_hidden_states=clean_hidden_states ) temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder( timestep, r_timestep, encoder_hidden_states, encoder_hidden_states_image, far_cfg=far_cfg, clean_timestep=clean_timestep, ) timestep_proj = timestep_proj.unflatten(2, (6, -1)) if encoder_hidden_states_image is not None: encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1) # 4. Transformer blocks for index_block, block in enumerate(self.blocks): if torch.is_grad_enabled() and self.gradient_checkpointing: hidden_states = self._gradient_checkpointing_func( block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, attention_mask, kv_cache[index_block], kv_cache_flag, ) else: hidden_states = block( hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, attention_mask, kv_cache[index_block], kv_cache_flag, ) if not return_dict: return None, kv_cache return AnyFlowFARTransformerOutput(sample=None, kv_cache=kv_cache) def _forward_train( self, hidden_states: torch.Tensor, chunk_partition, timestep: torch.LongTensor, r_timestep: torch.LongTensor, encoder_hidden_states: torch.Tensor, encoder_hidden_states_image: Optional[torch.Tensor] = None, attention_mask: Optional[BlockMask] = None, return_dict: bool = True, clean_hidden_states=None, clean_timestep=None, ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: hidden_states = hidden_states.permute(0, 2, 1, 3, 4) if clean_hidden_states is not None: clean_hidden_states = clean_hidden_states.permute(0, 2, 1, 3, 4) batch_size, num_channels, num_frames, height, width = hidden_states.shape full_token_per_frame = (height // self.config.patch_size[1]) * (width // self.config.patch_size[2]) compressed_token_per_frame = (height // self.config.compressed_patch_size[1]) * ( width // self.config.compressed_patch_size[2] ) total_chunks = len(chunk_partition) if total_chunks > self.config.full_chunk_limit: num_full_chunk, num_compressed_chunk = ( self.config.full_chunk_limit, total_chunks - self.config.full_chunk_limit, ) else: num_full_chunk, num_compressed_chunk = total_chunks, 0 far_cfg = { "total_frames": sum(chunk_partition), "num_full_chunk": num_full_chunk, "num_full_frames": sum(chunk_partition[num_compressed_chunk:]), "num_compressed_chunk": num_compressed_chunk, "num_compressed_frames": sum(chunk_partition[:num_compressed_chunk]), "full_frame_shape": (height // self.config.patch_size[1], width // self.config.patch_size[2]), "compressed_frame_shape": ( height // self.config.compressed_patch_size[1], width // self.config.compressed_patch_size[2], ), "full_token_per_frame": full_token_per_frame, "compressed_token_per_frame": compressed_token_per_frame, "chunk_partition": chunk_partition, } if attention_mask is None: # Fallback for callers that don't pre-build an attention mask (e.g. training scripts). This will introduce # a graph break, which will cause an error if `torch.compile(fullgraph=True)` is used. In this case, # pre-build the mask using `build_attention_mask` and pass it via the `attention_mask` argument. attention_mask = _build_far_block_mask_from_far_cfg( far_cfg, has_clean=clean_hidden_states is not None, device=hidden_states.device ) rotary_emb = self.rope(far_cfg=far_cfg, clean_hidden_states=clean_hidden_states, device=hidden_states.device) hidden_states = self._forward_far_patchify( hidden_states, far_cfg=far_cfg, clean_hidden_states=clean_hidden_states ) temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder( timestep, r_timestep, encoder_hidden_states, encoder_hidden_states_image, far_cfg=far_cfg, clean_timestep=clean_timestep, ) timestep_proj = timestep_proj.unflatten(2, (6, -1)) if encoder_hidden_states_image is not None: encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1) # 4. Transformer blocks for index_block, block in enumerate(self.blocks): if torch.is_grad_enabled() and self.gradient_checkpointing: hidden_states = self._gradient_checkpointing_func( block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, attention_mask, ) else: hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb, attention_mask) # 5. Output norm, projection & unpatchify shift, scale = (self.scale_shift_table + temb.unsqueeze(2)).chunk(2, dim=2) shift, scale = shift.squeeze(2), scale.squeeze(2) # Move the shift and scale tensors to the same device as hidden_states. # When using multi-GPU inference via accelerate these will be on the # first device rather than the last device, which hidden_states ends up # on. shift = shift.to(hidden_states.device) scale = scale.to(hidden_states.device) hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states) if clean_hidden_states is not None: hidden_states = hidden_states[ :, : -(far_cfg["num_full_frames"] * far_cfg["full_token_per_frame"]) ] # remove clean copy output = self.proj_out( hidden_states[:, far_cfg["num_compressed_frames"] * far_cfg["compressed_token_per_frame"] :] ) # remove far context output = self._unpack_latent_sequence( output, num_frames=far_cfg["num_full_frames"], height=height, width=width, patch_size=self.config.patch_size[1], ) # noqa: E501 if not return_dict: return (output,) return Transformer2DModelOutput(sample=output)