Add model selection

.gitignore

@@ -40,6 +40,9 @@ dist/
 *.egg
 eggs/
 .eggs/
+node_modules/
+.vite
+vite.config.js.timestamp-*.mjs
 
 # MkDocs documentation
 site/

fastvideo/configs/models/dits/__init__.py

@@ -7,9 +7,11 @@ from fastvideo.configs.models.dits.ltx2 import LTX2VideoConfig
 from fastvideo.configs.models.dits.stepvideo import StepVideoConfig
 from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
 from fastvideo.configs.models.dits.hyworld import HYWorldConfig
+from fastvideo.configs.models.dits.wangamevideo import WanGameVideoConfig
 
 __all__ = [
     "HunyuanVideoConfig", "HunyuanVideo15Config", "WanVideoConfig",
     "StepVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig",
-    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig"
+    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig",
+    "WanGameVideoConfig"
 ]

fastvideo/configs/models/dits/wangamevideo.py

@@ -0,0 +1,122 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+def is_blocks(n: str, m) -> bool:
+    return "blocks" in n and str.isdigit(n.split(".")[-1])
+
+
+@dataclass
+class WanGameVideoArchConfig(DiTArchConfig):
+    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_blocks])
+
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            r"^patch_embedding\.(.*)$":
+            r"patch_embedding.proj.\1",
+            r"^condition_embedder\.text_embedder\.linear_1\.(.*)$":
+            r"condition_embedder.text_embedder.fc_in.\1",
+            r"^condition_embedder\.text_embedder\.linear_2\.(.*)$":
+            r"condition_embedder.text_embedder.fc_out.\1",
+            r"^condition_embedder\.time_embedder\.linear_1\.(.*)$":
+            r"condition_embedder.time_embedder.mlp.fc_in.\1",
+            r"^condition_embedder\.time_embedder\.linear_2\.(.*)$":
+            r"condition_embedder.time_embedder.mlp.fc_out.\1",
+            r"^condition_embedder\.time_proj\.(.*)$":
+            r"condition_embedder.time_modulation.linear.\1",
+            r"^condition_embedder\.image_embedder\.ff\.net\.0\.proj\.(.*)$":
+            r"condition_embedder.image_embedder.ff.fc_in.\1",
+            r"^condition_embedder\.image_embedder\.ff\.net\.2\.(.*)$":
+            r"condition_embedder.image_embedder.ff.fc_out.\1",
+            r"^blocks\.(\d+)\.attn1\.to_q\.(.*)$":
+            r"blocks.\1.to_q.\2",
+            r"^blocks\.(\d+)\.attn1\.to_k\.(.*)$":
+            r"blocks.\1.to_k.\2",
+            r"^blocks\.(\d+)\.attn1\.to_v\.(.*)$":
+            r"blocks.\1.to_v.\2",
+            r"^blocks\.(\d+)\.attn1\.to_out\.0\.(.*)$":
+            r"blocks.\1.to_out.\2",
+            r"^blocks\.(\d+)\.attn1\.norm_q\.(.*)$":
+            r"blocks.\1.norm_q.\2",
+            r"^blocks\.(\d+)\.attn1\.norm_k\.(.*)$":
+            r"blocks.\1.norm_k.\2",
+            r"^blocks\.(\d+)\.attn2\.to_out\.0\.(.*)$":
+            r"blocks.\1.attn2.to_out.\2",
+            r"^blocks\.(\d+)\.ffn\.net\.0\.proj\.(.*)$":
+            r"blocks.\1.ffn.fc_in.\2",
+            r"^blocks\.(\d+)\.ffn\.net\.2\.(.*)$":
+            r"blocks.\1.ffn.fc_out.\2",
+            r"^blocks\.(\d+)\.norm2\.(.*)$":
+            r"blocks.\1.self_attn_residual_norm.norm.\2",
+        })
+
+    # Reverse mapping for saving checkpoints: custom -> hf
+    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+
+    # Some LoRA adapters use the original official layer names instead of hf layer names,
+    # so apply this before the param_names_mapping
+    lora_param_names_mapping: dict = field(
+        default_factory=lambda: {
+            r"^blocks\.(\d+)\.self_attn\.q\.(.*)$": r"blocks.\1.attn1.to_q.\2",
+            r"^blocks\.(\d+)\.self_attn\.k\.(.*)$": r"blocks.\1.attn1.to_k.\2",
+            r"^blocks\.(\d+)\.self_attn\.v\.(.*)$": r"blocks.\1.attn1.to_v.\2",
+            r"^blocks\.(\d+)\.self_attn\.o\.(.*)$":
+            r"blocks.\1.attn1.to_out.0.\2",
+            r"^blocks\.(\d+)\.cross_attn\.q\.(.*)$": r"blocks.\1.attn2.to_q.\2",
+            r"^blocks\.(\d+)\.cross_attn\.k\.(.*)$": r"blocks.\1.attn2.to_k.\2",
+            r"^blocks\.(\d+)\.cross_attn\.v\.(.*)$": r"blocks.\1.attn2.to_v.\2",
+            r"^blocks\.(\d+)\.cross_attn\.o\.(.*)$":
+            r"blocks.\1.attn2.to_out.0.\2",
+            r"^blocks\.(\d+)\.ffn\.0\.(.*)$": r"blocks.\1.ffn.fc_in.\2",
+            r"^blocks\.(\d+)\.ffn\.2\.(.*)$": r"blocks.\1.ffn.fc_out.\2",
+        })
+
+    patch_size: tuple[int, int, int] = (1, 2, 2)
+    text_len = 512
+    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
+    qk_norm: str = "rms_norm_across_heads"
+    eps: float = 1e-6
+    image_dim: int | None = None
+    added_kv_proj_dim: int | None = None
+    rope_max_seq_len: int = 1024
+    pos_embed_seq_len: int | None = None
+    exclude_lora_layers: list[str] = field(default_factory=lambda: ["embedder"])
+
+    # Wan MoE
+    boundary_ratio: float | None = None
+
+    # Causal Wan
+    local_attn_size: int = 6  # Window size for temporal local attention (-1 indicates global attention)
+    sink_size: int = 0  # Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache
+    num_frames_per_block: int = 3
+    sliding_window_num_frames: int = 21
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.out_channels = self.out_channels or self.in_channels
+        self.hidden_size = self.num_attention_heads * self.attention_head_dim
+        self.num_channels_latents = self.out_channels
+
+
+@dataclass
+class WanGameVideoConfig(DiTConfig):
+    arch_config: DiTArchConfig = field(default_factory=WanGameVideoArchConfig)
+
+    prefix: str = "WanGame"
+
+
+@dataclass
+class WanLingBotVideoConfig(DiTConfig):
+    arch_config: DiTArchConfig = field(default_factory=WanGameVideoArchConfig)
+
+    prefix: str = "WanLingBot"

fastvideo/configs/pipelines/registry.py

@@ -23,7 +23,7 @@ from fastvideo.configs.pipelines.wan import (
     Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config, Wan2_2_TI2V_5B_Config,
     WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig, WanT2V720PConfig,
     SelfForcingWanT2V480PConfig, WANV2VConfig, SelfForcingWan2_2_T2V480PConfig,
-    MatrixGameI2V480PConfig)
+    MatrixGameI2V480PConfig, WanGameI2V480PConfig)
 # isort: on
 from fastvideo.logger import init_logger
 from fastvideo.utils import (maybe_download_model_index,
@@ -42,6 +42,7 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorldConfig,
     "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
     "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": WanI2V480PConfig,
+    "weizhou03/Wan2.1-Game-Fun-1.3B-InP-Diffusers": WanGameI2V480PConfig,
     "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers": WANV2VConfig,
     "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V480PConfig,
     "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V720PConfig,
@@ -93,6 +94,8 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "hyworld" in id.lower(),
     "matrixgame":
     lambda id: "matrix-game" in id.lower() or "matrixgame" in id.lower(),
+    "wangameactionimagetovideo":
+    lambda id: "wangameactionimagetovideo" in id.lower(),
     "wanpipeline":
     lambda id: "wanpipeline" in id.lower(),
     "wanimagetovideo":
@@ -124,6 +127,7 @@ PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
     "hunyuan":
     HunyuanConfig,  # Base Hunyuan config as fallback for any Hunyuan variant
     "matrixgame": MatrixGameI2V480PConfig,
+    "wangameactionimagetovideo": WanGameI2V480PConfig,
     "hunyuan15":
     Hunyuan15T2V480PConfig,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
     "hyworld":

fastvideo/configs/pipelines/wan.py

@@ -7,6 +7,7 @@ import torch
 from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
 from fastvideo.configs.models.dits import WanVideoConfig
 from fastvideo.configs.models.dits.matrixgame import MatrixGameWanVideoConfig
+from fastvideo.configs.models.dits.wangamevideo import WanGameVideoConfig
 from fastvideo.configs.models.encoders import (BaseEncoderOutput,
                                                CLIPVisionConfig, T5Config,
                                                WAN2_1ControlCLIPVisionConfig)
@@ -217,3 +218,23 @@ class MatrixGameI2V480PConfig(WanI2V480PConfig):
     context_noise: int = 0
     num_frames_per_block: int = 3
     # sliding_window_num_frames: int = 15
+
+
+# =============================================
+# ============= WanGame ======================
+# =============================================
+@dataclass
+class WanGameI2V480PConfig(WanI2V480PConfig):
+    """Configuration for WanGame image-to-video pipeline."""
+    dit_config: DiTConfig = field(default_factory=WanGameVideoConfig)
+
+    image_encoder_config: EncoderConfig = field(
+        default_factory=WAN2_1ControlCLIPVisionConfig)
+
+    is_causal: bool = True
+    flow_shift: float | None = 5.0
+    dmd_denoising_steps: list[int] | None = field(
+        default_factory=lambda: [1000, 666, 333])
+    warp_denoising_step: bool = True
+    context_noise: int = 0
+    num_frames_per_block: int = 3

fastvideo/configs/sample/registry.py

@@ -92,6 +92,9 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     "KyleShao/Cosmos-Predict2.5-2B-Diffusers":
     Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
 
+    # WanGame models
+    "weizhou03/Wan2.1-Game-Fun-1.3B-InP-Diffusers": MatrixGame2_SamplingParam,
+
     # MatrixGame2.0 models
     "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGame2_SamplingParam,
     "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGame2_SamplingParam,
@@ -130,6 +133,8 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "wandmdpipeline" in id.lower(),
     "wancausaldmdpipeline":
     lambda id: "wancausaldmdpipeline" in id.lower(),
+    "wangame":
+    lambda id: "wangame" in id.lower(),
     "matrixgame":
     lambda id: "matrixgame" in id.lower() or "matrix-game" in id.lower(),
     "turbodiffusion":
@@ -157,6 +162,7 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     "wandmdpipeline": FastWanT2V480P_SamplingParam,
     "wancausaldmdpipeline": SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
     "stepvideo": StepVideoT2VSamplingParam,
+    "wangame": MatrixGame2_SamplingParam,
     "matrixgame": MatrixGame2_SamplingParam,
     "turbodiffusion":
     TurboDiffusionT2V_1_3B_SamplingParam,  # Default to T2V for fallback

fastvideo/entrypoints/streaming_generator.py

@@ -171,7 +171,7 @@ class StreamingVideoGenerator(VideoGenerator):
 
     def step(
             self, keyboard_cond: torch.Tensor,
-            mouse_cond: torch.Tensor) -> tuple[list[np.ndarray], Future | None]:
+            mouse_cond: torch.Tensor) -> tuple[list[np.ndarray], Future | None, dict | None]:
         if self.batch is None:
             raise RuntimeError("Call reset() before step()")
 
@@ -195,7 +195,10 @@ class StreamingVideoGenerator(VideoGenerator):
                 # Returns Future for block file, or None if no block_dir
                 block_future = self.writer.add_frames(frames)
 
-        return frames, block_future
+        # Extract stage timings if available
+        stage_timings = getattr(output_batch, 'stage_timings', None)
+
+        return frames, block_future, stage_timings
 
     async def step_async(
             self, keyboard_cond: torch.Tensor,

fastvideo/models/dits/matrixgame/action_module.py

@@ -496,14 +496,14 @@ class ActionModule(nn.Module):
                         else:
                             local_end_index = kv_cache_keyboard["local_end_index"].item() + current_end - kv_cache_keyboard["global_end_index"].item()
                             local_start_index = local_end_index - num_new_tokens
-                        assert k.shape[0] == S # BS == 1 or the cache should not be saved/ load method should be modified
-                        kv_cache_keyboard["k"][:, local_start_index:local_end_index] = k[:1]
-                        kv_cache_keyboard["v"][:, local_start_index:local_end_index] = v[:1]
+                        assert k.shape[0] % S == 0  # BS >= 1
+                        kv_cache_keyboard["k"][:, local_start_index:local_end_index] = k[::S]
+                        kv_cache_keyboard["v"][:, local_start_index:local_end_index] = v[::S]
 
                         attn = self.keyboard_attn_layer(
                             q,
-                            kv_cache_keyboard["k"][:, max(0, local_end_index - max_attention_size):local_end_index].repeat(S, 1, 1, 1),
-                            kv_cache_keyboard["v"][:, max(0, local_end_index - max_attention_size):local_end_index].repeat(S, 1, 1, 1),
+                            kv_cache_keyboard["k"][:, max(0, local_end_index - max_attention_size):local_end_index].repeat_interleave(S, dim=0),
+                            kv_cache_keyboard["v"][:, max(0, local_end_index - max_attention_size):local_end_index].repeat_interleave(S, dim=0),
                         )
 
                         kv_cache_keyboard["global_end_index"].fill_(current_end)

fastvideo/models/dits/matrixgame/causal_model.py

@@ -14,7 +14,7 @@ from torch.nn.attention.flex_attention import BlockMask
 from fastvideo.forward_context import get_forward_context
 
 flex_attention = torch.compile(
-    flex_attention, dynamic=False, mode="max-autotune-no-cudagraphs")
+    flex_attention, dynamic=True, mode="max-autotune-no-cudagraphs")
 
 from fastvideo.attention import LocalAttention
 from fastvideo.configs.models.dits.matrixgame import MatrixGameWanVideoConfig
@@ -465,6 +465,7 @@ _DEFAULT_MATRIXGAME_CONFIG = MatrixGameWanVideoConfig()
 
 class CausalMatrixGameWanModel(BaseDiT):
     supports_action_input = True
+    _concatenates_image_latent = True  # Model handles image_latent internally via forward_context
 
     _fsdp_shard_conditions = _DEFAULT_MATRIXGAME_CONFIG._fsdp_shard_conditions
     _compile_conditions = _DEFAULT_MATRIXGAME_CONFIG._compile_conditions

fastvideo/models/dits/wangame/__init__.py

@@ -0,0 +1,12 @@
+from .model import WanGameActionTransformer3DModel
+from .causal_model import (CausalWanGameTransformer3DModel,
+                           CausalWanTransformer3DModel)
+from .hyworld_action_module import WanGameActionTimeImageEmbedding, WanGameActionSelfAttention
+
+__all__ = [
+    "WanGameActionTransformer3DModel",
+    "CausalWanTransformer3DModel",
+    "CausalWanGameTransformer3DModel",
+    "WanGameActionTimeImageEmbedding",
+    "WanGameActionSelfAttention",
+]

fastvideo/models/dits/wangame/causal_model.py

@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: Apache-2.0
+from typing import Any
+
+import torch
+
+from fastvideo.configs.models.dits.wangamevideo import WanGameVideoConfig
+from fastvideo.models.dits.matrixgame.causal_model import CausalMatrixGameWanModel
+
+_DEFAULT_WANGAME_CAUSAL_CONFIG = WanGameVideoConfig()
+
+
+class CausalWanTransformer3DModel(CausalMatrixGameWanModel):
+    supports_action_input = False
+
+    _fsdp_shard_conditions = _DEFAULT_WANGAME_CAUSAL_CONFIG._fsdp_shard_conditions
+    _compile_conditions = _DEFAULT_WANGAME_CAUSAL_CONFIG._compile_conditions
+    _supported_attention_backends = _DEFAULT_WANGAME_CAUSAL_CONFIG._supported_attention_backends
+    param_names_mapping = _DEFAULT_WANGAME_CAUSAL_CONFIG.param_names_mapping
+    reverse_param_names_mapping = _DEFAULT_WANGAME_CAUSAL_CONFIG.reverse_param_names_mapping
+    lora_param_names_mapping = _DEFAULT_WANGAME_CAUSAL_CONFIG.lora_param_names_mapping
+
+    def __init__(self,
+                 config: WanGameVideoConfig,
+                 hf_config: dict[str, Any],
+                 **kwargs) -> None:
+        super().__init__(config=config, hf_config=hf_config, **kwargs)
+
+    def _normalize_action_inputs(
+        self,
+        mouse_cond: torch.Tensor | None,
+        keyboard_cond: torch.Tensor | None,
+    ) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+        # drop
+        if len(getattr(self, "action_config", {})) == 0:
+            return None, None
+        return mouse_cond, keyboard_cond
+
+    def _forward_inference(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+        timestep: torch.LongTensor,
+        encoder_hidden_states_image: torch.Tensor | list[torch.Tensor] | None = None,
+        mouse_cond: torch.Tensor | None = None,
+        keyboard_cond: torch.Tensor | None = None,
+        kv_cache: dict | None = None,
+        kv_cache_mouse: dict | None = None,
+        kv_cache_keyboard: dict | None = None,
+        crossattn_cache: dict | None = None,
+        current_start: int = 0,
+        cache_start: int = 0,
+        start_frame: int = 0,
+        **kwargs,
+    ) -> torch.Tensor:
+        mouse_cond, keyboard_cond = self._normalize_action_inputs(
+            mouse_cond, keyboard_cond)
+        return super()._forward_inference(
+            hidden_states=hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            timestep=timestep,
+            encoder_hidden_states_image=encoder_hidden_states_image,
+            mouse_cond=mouse_cond,
+            keyboard_cond=keyboard_cond,
+            kv_cache=kv_cache,
+            kv_cache_mouse=kv_cache_mouse,
+            kv_cache_keyboard=kv_cache_keyboard,
+            crossattn_cache=crossattn_cache,
+            current_start=current_start,
+            cache_start=cache_start,
+            start_frame=start_frame,
+            **kwargs,
+        )
+
+    def _forward_train(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+        timestep: torch.LongTensor,
+        encoder_hidden_states_image: torch.Tensor | list[torch.Tensor] | None = None,
+        mouse_cond: torch.Tensor | None = None,
+        keyboard_cond: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        mouse_cond, keyboard_cond = self._normalize_action_inputs(
+            mouse_cond, keyboard_cond)
+        return super()._forward_train(
+            hidden_states=hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            timestep=timestep,
+            encoder_hidden_states_image=encoder_hidden_states_image,
+            mouse_cond=mouse_cond,
+            keyboard_cond=keyboard_cond,
+            **kwargs,
+        )
+
+
+class CausalWanGameTransformer3DModel(CausalWanTransformer3DModel):
+    pass

fastvideo/models/dits/wangame/hyworld_action_module.py

@@ -0,0 +1,289 @@
+import math
+
+import torch
+import torch.nn as nn
+
+from fastvideo.layers.visual_embedding import TimestepEmbedder, ModulateProjection, timestep_embedding
+from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.attention import DistributedAttention
+from fastvideo.forward_context import set_forward_context
+from fastvideo.models.dits.wanvideo import WanImageEmbedding
+
+from fastvideo.models.dits.hyworld.camera_rope import prope_qkv
+from fastvideo.layers.rotary_embedding import _apply_rotary_emb
+from fastvideo.layers.mlp import MLP
+
+class WanGameActionTimeImageEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        time_freq_dim: int,
+        image_embed_dim: int | None = None,
+    ):
+        super().__init__()
+
+        self.time_freq_dim = time_freq_dim
+        self.time_embedder = TimestepEmbedder(
+            dim, frequency_embedding_size=time_freq_dim, act_layer="silu")
+        self.time_modulation = ModulateProjection(dim,
+                                                  factor=6,
+                                                  act_layer="silu")
+        
+        self.image_embedder = None
+        if image_embed_dim is not None:
+            self.image_embedder = WanImageEmbedding(image_embed_dim, dim)
+
+        self.action_embedder = MLP(
+            time_freq_dim,
+            dim,
+            dim,
+            bias=True,
+            act_type="silu"
+        )
+        # Initialize fc_in with kaiming_uniform (same as nn.Linear default)
+        nn.init.kaiming_uniform_(self.action_embedder.fc_in.weight, a=math.sqrt(5))
+        # Initialize fc_out with zeros for residual-like behavior
+        nn.init.zeros_(self.action_embedder.fc_out.weight)
+        if self.action_embedder.fc_out.bias is not None:
+            nn.init.zeros_(self.action_embedder.fc_out.bias)
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        action: torch.Tensor,
+        encoder_hidden_states: torch.Tensor, # Kept for interface compatibility
+        encoder_hidden_states_image: torch.Tensor | None = None,
+        timestep_seq_len: int | None = None,
+    ):
+        """
+        Args:
+            timestep: [B] diffusion timesteps (one per batch sample)
+            action: [B, T] action labels (one per frame per batch sample)
+        
+        Returns:
+            temb: [B*T, dim] combined timestep + action embedding
+            timestep_proj: [B*T, 6*dim] modulation projection
+        """
+        # timestep: [B] -> temb: [B, dim]
+        temb = self.time_embedder(timestep, timestep_seq_len)
+        
+        # Handle action embedding for batch > 1
+        # action shape: [B, T] where B=batch_size, T=num_frames
+        batch_size = action.shape[0]
+        num_frames = action.shape[1]
+        
+        # Compute action embeddings: [B, T] -> [B*T] -> [B*T, dim]
+        action_flat = action.flatten()  # [B*T]
+        action_emb = timestep_embedding(action_flat, self.time_freq_dim)
+        action_embedder_dtype = next(iter(self.action_embedder.parameters())).dtype
+        if (
+            action_emb.dtype != action_embedder_dtype
+            and action_embedder_dtype != torch.int8
+        ):
+            action_emb = action_emb.to(action_embedder_dtype)
+        action_emb = self.action_embedder(action_emb).type_as(temb)  # [B*T, dim]
+        
+        # Expand temb to match action_emb: [B, dim] -> [B, T, dim] -> [B*T, dim]
+        # Each batch's temb is repeated for all its frames
+        temb_expanded = temb.unsqueeze(1).expand(-1, num_frames, -1)  # [B, T, dim]
+        temb_expanded = temb_expanded.reshape(batch_size * num_frames, -1)  # [B*T, dim]
+        
+        # Add action embedding to expanded temb
+        temb = temb_expanded + action_emb  # [B*T, dim]
+
+        timestep_proj = self.time_modulation(temb)  # [B*T, 6*dim]
+
+        # MatrixGame does not use text embeddings, so we ignore encoder_hidden_states
+        
+        if encoder_hidden_states_image is not None:
+            assert self.image_embedder is not None
+            encoder_hidden_states_image = self.image_embedder(
+                encoder_hidden_states_image)
+
+        encoder_hidden_states = torch.zeros((batch_size, 0, temb.shape[-1]),
+                                            device=temb.device,
+                                            dtype=temb.dtype)
+
+        return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image
+
+class WanGameActionSelfAttention(nn.Module):
+    """
+    Self-attention module with support for:
+    - Standard RoPE-based attention
+    - Camera PRoPE-based attention (when viewmats and Ks are provided)
+    - KV caching for autoregressive generation
+    """
+
+    def __init__(self,
+                 dim: int,
+                 num_heads: int,
+                 local_attn_size: int = -1,
+                 sink_size: int = 0,
+                 qk_norm=True,
+                 eps=1e-6) -> None:
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.local_attn_size = local_attn_size
+        self.sink_size = sink_size
+        self.qk_norm = qk_norm
+        self.eps = eps
+        self.max_attention_size = 32760 if local_attn_size == -1 else local_attn_size * 1560
+
+        # Scaled dot product attention (using DistributedAttention for SP support)
+        self.attn = DistributedAttention(
+            num_heads=num_heads,
+            head_size=self.head_dim,
+            softmax_scale=None,
+            causal=False,
+            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
+                                          AttentionBackendEnum.TORCH_SDPA))
+
+    def forward(self,
+                q: torch.Tensor,
+                k: torch.Tensor,
+                v: torch.Tensor,
+                freqs_cis: tuple[torch.Tensor, torch.Tensor],
+                kv_cache: dict | None = None,
+                current_start: int = 0,
+                cache_start: int | None = None,
+                viewmats: torch.Tensor | None = None,
+                Ks: torch.Tensor | None = None,
+                is_cache: bool = False,
+                attention_mask: torch.Tensor | None = None):
+        """
+        Forward pass with camera PRoPE attention combining standard RoPE and projective positional encoding.
+        
+        Args:
+            q, k, v: Query, key, value tensors [B, L, num_heads, head_dim]
+            freqs_cis: RoPE frequency cos/sin tensors
+            kv_cache: KV cache dict (may have None values for training)
+            current_start: Current position for KV cache
+            cache_start: Cache start position
+            viewmats: Camera view matrices for PRoPE [B, cameras, 4, 4]
+            Ks: Camera intrinsics for PRoPE [B, cameras, 3, 3]
+            is_cache: Whether to store to KV cache (for inference)
+            attention_mask: Attention mask [B, L] (1 = attend, 0 = mask)
+        """
+        if cache_start is None:
+            cache_start = current_start
+
+        # Apply RoPE manually
+        cos, sin = freqs_cis
+        query_rope = _apply_rotary_emb(q, cos, sin, is_neox_style=False).type_as(v)
+        key_rope = _apply_rotary_emb(k, cos, sin, is_neox_style=False).type_as(v)
+        value_rope = v
+
+        # # DEBUG: Check camera matrices
+        # if self.training and torch.distributed.get_rank() == 0:
+        #     vm_info = f"viewmats={viewmats.shape if viewmats is not None else None}"
+        #     ks_info = f"Ks={Ks.shape if Ks is not None else None}"
+        #     vm_nonzero = (viewmats != 0).sum().item() if viewmats is not None else 0
+        #     ks_nonzero = (Ks != 0).sum().item() if Ks is not None else 0
+        #     print(f"[DEBUG] PRoPE input: {vm_info} nonzero={vm_nonzero}, {ks_info} nonzero={ks_nonzero}", flush=True)
+        
+        # Get PRoPE transformed q, k, v
+        query_prope, key_prope, value_prope, apply_fn_o = prope_qkv(
+            q.transpose(1, 2),  # [B, num_heads, L, head_dim]
+            k.transpose(1, 2),
+            v.transpose(1, 2),
+            viewmats=viewmats,
+            Ks=Ks,
+            patches_x=40,  # hardcoded for now
+            patches_y=22,
+        )
+        # PRoPE returns [B, num_heads, L, head_dim], convert to [B, L, num_heads, head_dim]
+        query_prope = query_prope.transpose(1, 2)
+        key_prope = key_prope.transpose(1, 2)
+        value_prope = value_prope.transpose(1, 2)
+        
+        # # DEBUG: Check prope_qkv output
+        # if self.training and torch.distributed.get_rank() == 0:
+        #     q_nz = (query_prope != 0).sum().item()
+        #     k_nz = (key_prope != 0).sum().item()
+        #     v_nz = (value_prope != 0).sum().item()
+        #     print(f"[DEBUG] prope_qkv output: q_nonzero={q_nz}, k_nonzero={k_nz}, v_nonzero={v_nz}", flush=True)
+
+        # KV cache handling
+        if kv_cache is not None:
+            cache_key = kv_cache.get("k", None)
+            cache_value = kv_cache.get("v", None)
+
+            if cache_value is not None and not is_cache:
+                cache_key_rope, cache_key_prope = cache_key.chunk(2, dim=-1)
+                cache_value_rope, cache_value_prope = cache_value.chunk(2, dim=-1)
+
+                key_rope = torch.cat([cache_key_rope, key_rope], dim=1)
+                value_rope = torch.cat([cache_value_rope, value_rope], dim=1)
+                key_prope = torch.cat([cache_key_prope, key_prope], dim=1)
+                value_prope = torch.cat([cache_value_prope, value_prope], dim=1)
+
+            if is_cache:
+                # Store to cache (update input dict directly)
+                kv_cache["k"] = torch.cat([key_rope, key_prope], dim=-1)
+                kv_cache["v"] = torch.cat([value_rope, value_prope], dim=-1)
+
+        # Concatenate rope and prope paths (matching original)
+        query_all = torch.cat([query_rope, query_prope], dim=0)
+        key_all = torch.cat([key_rope, key_prope], dim=0)
+        value_all = torch.cat([value_rope, value_prope], dim=0)
+
+        # Check if Q and KV have different sequence lengths (KV cache mode)
+        # In this case, use LocalAttention (supports different Q/KV lengths)
+        if query_all.shape[1] != key_all.shape[1]:
+            # KV cache mode: Q has new tokens only, KV has cached + new tokens
+            # Use LocalAttention which supports different Q/KV lengths
+            # LocalAttention will use the appropriate backend (SageAttn, FlashAttn, etc.)
+            if not hasattr(self, '_kv_cache_attn'):
+                from fastvideo.attention import LocalAttention
+                self._kv_cache_attn = LocalAttention(
+                    num_heads=self.num_heads,
+                    head_size=self.head_dim,
+                    causal=False,
+                    supported_attention_backends=(AttentionBackendEnum.SAGE_ATTN,
+                                                  AttentionBackendEnum.FLASH_ATTN,
+                                                  AttentionBackendEnum.TORCH_SDPA)
+                )
+            hidden_states_all = self._kv_cache_attn(query_all, key_all, value_all)
+        else:
+            # Same sequence length: use DistributedAttention (supports SP)
+            # Create default attention mask if not provided
+            # NOTE: query_all has shape [2*B, L, ...] (rope+prope concatenated), so mask needs 2*B
+            if attention_mask is None:
+                batch_size, seq_len = q.shape[0], q.shape[1]
+                attention_mask = torch.ones(batch_size * 2, seq_len, device=q.device, dtype=q.dtype)
+        
+            from fastvideo.attention.backends.sdpa import SDPAMetadataBuilder
+            attn_metadata_builder = SDPAMetadataBuilder
+            if q.dtype != torch.float32:
+                try:
+                    from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
+                    attn_metadata_builder = FlashAttnMetadataBuilder
+                except ImportError:
+                    pass  # fall back to SDPA
+            attn_metadata = attn_metadata_builder().build(
+                current_timestep=0,
+                attn_mask=attention_mask,
+            )
+            with set_forward_context(current_timestep=0, attn_metadata=attn_metadata):
+                hidden_states_all, _ = self.attn(query_all, key_all, value_all, attention_mask=attention_mask)
+
+        hidden_states_rope, hidden_states_prope = hidden_states_all.chunk(2, dim=0)
+        
+        # # DEBUG: Check attention output and apply_fn_o
+        # if self.training and torch.distributed.get_rank() == 0:
+        #     attn_all_nz = (hidden_states_all != 0).sum().item()
+        #     rope_nz = (hidden_states_rope != 0).sum().item()
+        #     prope_before = (hidden_states_prope != 0).sum().item()
+        #     print(f"[DEBUG] attn output: all_nonzero={attn_all_nz}, rope_nonzero={rope_nz}, prope_before_apply={prope_before}", flush=True)
+        
+        hidden_states_prope = apply_fn_o(hidden_states_prope.transpose(1, 2)).transpose(1, 2)
+        
+        # # DEBUG: Check after apply_fn_o
+        # if self.training and torch.distributed.get_rank() == 0:
+        #     prope_after = (hidden_states_prope != 0).sum().item()
+        #     print(f"[DEBUG] prope_after_apply_fn_o={prope_after}", flush=True)
+
+        return hidden_states_rope, hidden_states_prope

fastvideo/models/dits/wangame/model.py

@@ -0,0 +1,427 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from fastvideo.configs.models.dits.wangamevideo import WanGameVideoConfig
+from fastvideo.distributed.parallel_state import get_sp_world_size
+from fastvideo.layers.layernorm import (FP32LayerNorm, LayerNormScaleShift,
+                                        RMSNorm, ScaleResidual,
+                                        ScaleResidualLayerNormScaleShift)
+from fastvideo.layers.linear import ReplicatedLinear
+from fastvideo.layers.mlp import MLP
+from fastvideo.layers.rotary_embedding import (_apply_rotary_emb,
+                                               get_rotary_pos_embed)
+from fastvideo.layers.visual_embedding import PatchEmbed
+from fastvideo.logger import init_logger
+from fastvideo.models.dits.base import BaseDiT
+from fastvideo.models.dits.wanvideo import WanI2VCrossAttention
+from fastvideo.platforms import AttentionBackendEnum, current_platform
+
+# Import ActionModule
+from fastvideo.models.dits.wangame.hyworld_action_module import WanGameActionTimeImageEmbedding, WanGameActionSelfAttention
+
+logger = init_logger(__name__)
+
+
+class WanGameCrossAttention(WanI2VCrossAttention):
+    def forward(self, x, context, context_lens=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            context(Tensor): Shape [B, L2, C]
+            context_lens(Tensor): Shape [B]
+        """
+        context_img = context
+        b, n, d = x.size(0), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
+        k_img = self.norm_added_k(self.add_k_proj(context_img)[0]).view(
+            b, -1, n, d)
+        v_img = self.add_v_proj(context_img)[0].view(b, -1, n, d)
+        img_x = self.attn(q, k_img, v_img)
+
+        # output
+        x = img_x.flatten(2)
+        x, _ = self.to_out(x)
+        return x
+
+class WanGameActionTransformerBlock(nn.Module):
+    """
+    Transformer block for WAN Action model with support for:
+    - Self-attention with RoPE and camera PRoPE
+    - Cross-attention with text/image context
+    - Feed-forward network with AdaLN modulation
+    """
+
+    def __init__(self,
+                 dim: int,
+                 ffn_dim: int,
+                 num_heads: int,
+                 local_attn_size: int = -1,
+                 sink_size: int = 0,
+                 qk_norm: str = "rms_norm_across_heads",
+                 cross_attn_norm: bool = False,
+                 eps: float = 1e-6,
+                 added_kv_proj_dim: int | None = None,
+                 supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        # 1. Self-attention
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+        self.to_q = ReplicatedLinear(dim, dim, bias=True)
+        self.to_k = ReplicatedLinear(dim, dim, bias=True)
+        self.to_v = ReplicatedLinear(dim, dim, bias=True)
+        self.to_out = ReplicatedLinear(dim, dim, bias=True)
+        
+        self.attn1 = WanGameActionSelfAttention(
+            dim,
+            num_heads,
+            local_attn_size=local_attn_size,
+            sink_size=sink_size,
+            qk_norm=qk_norm,
+            eps=eps)
+        
+        self.hidden_dim = dim
+        self.num_attention_heads = num_heads
+        self.local_attn_size = local_attn_size
+        dim_head = dim // num_heads
+
+        if qk_norm == "rms_norm":
+            self.norm_q = RMSNorm(dim_head, eps=eps)
+            self.norm_k = RMSNorm(dim_head, eps=eps)
+        elif qk_norm == "rms_norm_across_heads":
+            self.norm_q = RMSNorm(dim, eps=eps)
+            self.norm_k = RMSNorm(dim, eps=eps)
+        else:
+            raise ValueError(f"QK Norm type {qk_norm} not supported")
+
+        assert cross_attn_norm is True
+        self.self_attn_residual_norm = ScaleResidualLayerNormScaleShift(
+            dim,
+            norm_type="layer",
+            eps=eps,
+            elementwise_affine=True,
+            compute_dtype=torch.float32)
+
+        # 2. Cross-attention (I2V only for now)
+        self.attn2 = WanGameCrossAttention(dim,
+                                          num_heads,
+                                          qk_norm=qk_norm,
+                                          eps=eps)
+        # norm3 for FFN input 
+        self.norm3 = LayerNormScaleShift(dim, norm_type="layer", eps=eps,
+                                         elementwise_affine=False)
+
+        # 3. Feed-forward
+        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
+        self.mlp_residual = ScaleResidual()
+
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+
+        # PRoPE output projection (initialized via add_discrete_action_parameters on the model)
+        self.to_out_prope = ReplicatedLinear(dim, dim, bias=True)
+        nn.init.zeros_(self.to_out_prope.weight)
+        if self.to_out_prope.bias is not None:
+            nn.init.zeros_(self.to_out_prope.bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        freqs_cis: tuple[torch.Tensor, torch.Tensor],
+        kv_cache: dict | None = None,
+        crossattn_cache: dict | None = None,
+        current_start: int = 0,
+        cache_start: int | None = None,
+        viewmats: torch.Tensor | None = None,
+        Ks: torch.Tensor | None = None,
+        is_cache: bool = False,
+    ) -> torch.Tensor:
+        if hidden_states.dim() == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        num_frames = temb.shape[1]
+        frame_seqlen = hidden_states.shape[1] // num_frames
+        bs, seq_length, _ = hidden_states.shape
+        orig_dtype = hidden_states.dtype
+
+        # Cast temb to float32 for scale/shift computation
+        e = self.scale_shift_table + temb.float()
+        assert e.shape == (bs, num_frames, 6, self.hidden_dim)
+        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(6, dim=2)
+
+        # 1. Self-attention
+        norm_hidden_states = (self.norm1(hidden_states.float()).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
+            (1 + scale_msa) + shift_msa).to(orig_dtype).flatten(1, 2)
+        
+        query, _ = self.to_q(norm_hidden_states)
+        key, _ = self.to_k(norm_hidden_states)
+        value, _ = self.to_v(norm_hidden_states)
+
+        if self.norm_q is not None:
+            query = self.norm_q.forward_native(query)
+        if self.norm_k is not None:
+            key = self.norm_k.forward_native(key)
+
+        query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+        key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+        value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+
+        # Self-attention with camera PRoPE
+        attn_output_rope, attn_output_prope = self.attn1(
+            query, key, value, freqs_cis,
+            kv_cache, current_start, cache_start, viewmats, Ks,
+            is_cache=is_cache
+        )
+        # Combine rope and prope outputs
+        attn_output_rope = attn_output_rope.flatten(2)
+        attn_output_rope, _ = self.to_out(attn_output_rope)
+        attn_output_prope = attn_output_prope.flatten(2)
+        
+        # # DEBUG: Check if prope input is zero
+        # if self.training and torch.distributed.get_rank() == 0:
+        #     prope_nonzero = (attn_output_prope != 0).sum().item()
+        #     prope_total = attn_output_prope.numel()
+        #     if prope_nonzero == 0:
+        #         print(f"[DEBUG] to_out_prope INPUT is ALL ZEROS! shape={attn_output_prope.shape}", flush=True)
+        
+        attn_output_prope, _ = self.to_out_prope(attn_output_prope)
+        attn_output = attn_output_rope.squeeze(1) + attn_output_prope.squeeze(1)
+
+        # Self-attention residual + norm in float32
+        null_shift = null_scale = torch.zeros(1, device=hidden_states.device, dtype=torch.float32)
+        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
+            hidden_states.float(), attn_output.float(), gate_msa, null_shift, null_scale)
+        hidden_states = hidden_states.type_as(attn_output)
+        norm_hidden_states = norm_hidden_states.type_as(attn_output)
+
+        # 2. Cross-attention
+        attn_output = self.attn2(norm_hidden_states.to(orig_dtype),
+                                 context=encoder_hidden_states,
+                                 context_lens=None)
+        # Cross-attention residual in bfloat16
+        hidden_states = hidden_states + attn_output
+        
+        # norm3 for FFN input in float32
+        norm_hidden_states = self.norm3(
+            hidden_states.float(), c_shift_msa, c_scale_msa
+        ).type_as(hidden_states)
+
+        # 3. Feed-forward
+        ff_output = self.ffn(norm_hidden_states.to(orig_dtype))
+        hidden_states = self.mlp_residual(hidden_states.float(), ff_output.float(), c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)  # Cast back to original dtype
+
+        return hidden_states
+
+class WanGameActionTransformer3DModel(BaseDiT):
+    """
+    WAN Action Transformer 3D Model for video generation with action conditioning.
+
+    Extends the base WAN video model with:
+    - Action embedding support for controllable generation
+    - camera PRoPE attention for 3D-aware generation
+    - KV caching for autoregressive inference
+    """
+    _uses_discrete_action = True  # Uses single action tensor [B, T] instead of keyboard/mouse KV caches
+    _kv_cache_head_dim_multiplier = 2  # PRoPE stores cat([rope, prope], dim=-1) in KV cache
+
+    _fsdp_shard_conditions = WanGameVideoConfig()._fsdp_shard_conditions
+    _compile_conditions = WanGameVideoConfig()._compile_conditions
+    _supported_attention_backends = WanGameVideoConfig()._supported_attention_backends
+    param_names_mapping = WanGameVideoConfig().param_names_mapping
+    reverse_param_names_mapping = WanGameVideoConfig().reverse_param_names_mapping
+    lora_param_names_mapping = WanGameVideoConfig().lora_param_names_mapping
+
+    def __init__(self, config: WanGameVideoConfig, hf_config: dict[str, Any]) -> None:
+        super().__init__(config=config, hf_config=hf_config)
+
+        inner_dim = config.num_attention_heads * config.attention_head_dim
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_dim = config.attention_head_dim
+        self.in_channels = config.in_channels
+        self.out_channels = config.out_channels
+        self.num_channels_latents = config.num_channels_latents
+        self.patch_size = config.patch_size
+        self.local_attn_size = config.local_attn_size
+        self.inner_dim = inner_dim
+
+        # 1. Patch & position embedding
+        self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
+                                          embed_dim=inner_dim,
+                                          patch_size=config.patch_size,
+                                          flatten=False)
+
+        # 2. Condition embeddings (with action support)
+        self.condition_embedder = WanGameActionTimeImageEmbedding(
+            dim=inner_dim,
+            time_freq_dim=config.freq_dim,
+            image_embed_dim=config.image_dim,
+        )
+
+        # 3. Transformer blocks
+        self.blocks = nn.ModuleList([
+            WanGameActionTransformerBlock(
+                inner_dim,
+                config.ffn_dim,
+                config.num_attention_heads,
+                config.local_attn_size,
+                config.sink_size,
+                config.qk_norm,
+                config.cross_attn_norm,
+                config.eps,
+                config.added_kv_proj_dim,
+                supported_attention_backends=self._supported_attention_backends,
+                prefix=f"{config.prefix}.blocks.{i}")
+            for i in range(config.num_layers)
+        ])
+
+        # 4. Output norm & projection
+        self.norm_out = LayerNormScaleShift(inner_dim,
+                                            norm_type="layer",
+                                            eps=config.eps,
+                                            elementwise_affine=False,
+                                            dtype=torch.float32)
+        self.proj_out = nn.Linear(
+            inner_dim, config.out_channels * math.prod(config.patch_size))
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)
+
+        self.gradient_checkpointing = False
+
+        # Causal-specific
+        self.num_frame_per_block = config.arch_config.num_frames_per_block
+        assert self.num_frame_per_block <= 3
+
+        self.__post_init__()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+        timestep: torch.LongTensor,
+        encoder_hidden_states_image: torch.Tensor | list[torch.Tensor],
+        guidance=None,
+        action: torch.Tensor | None = None,
+        viewmats: torch.Tensor | None = None,
+        Ks: torch.Tensor | None = None,
+        kv_cache: list[dict] | None = None,
+        crossattn_cache: list[dict] | None = None,
+        current_start: int = 0,
+        cache_start: int = 0,
+        start_frame: int = 0,
+        is_cache: bool = False,
+        **kwargs
+    ) -> torch.Tensor:
+        """
+        Forward pass for both training and inference with KV caching.
+        
+        Args:
+            hidden_states: Video latents [B, C, T, H, W]
+            encoder_hidden_states: Text embeddings [B, L, D]
+            timestep: Timestep tensor
+            encoder_hidden_states_image: Optional image embeddings
+            action: Action tensor [B, T] for per-frame conditioning
+            viewmats: Camera view matrices for PRoPE [B, T, 4, 4]
+            Ks: Camera intrinsics for PRoPE [B, T, 3, 3]
+            kv_cache: KV cache for autoregressive inference (list of dicts per layer)
+            crossattn_cache: Cross-attention cache for inference
+            current_start: Current position for KV cache
+            cache_start: Cache start position
+            start_frame: RoPE offset for new frames in autoregressive mode
+            is_cache: If True, populate KV cache and return early (cache-only mode)
+        """
+        orig_dtype = hidden_states.dtype
+        # if not isinstance(encoder_hidden_states, torch.Tensor):
+        #     encoder_hidden_states = encoder_hidden_states[0]
+        if isinstance(encoder_hidden_states_image, list) and len(encoder_hidden_states_image) > 0:
+            encoder_hidden_states_image = encoder_hidden_states_image[0]
+        # else:
+        #     encoder_hidden_states_image = None
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p_t, p_h, p_w = self.patch_size
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p_h
+        post_patch_width = width // p_w
+
+        # Get rotary embeddings
+        d = self.hidden_size // self.num_attention_heads
+        rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
+        freqs_cos, freqs_sin = get_rotary_pos_embed(
+            (post_patch_num_frames * get_sp_world_size(), post_patch_height, post_patch_width),
+            self.hidden_size,
+            self.num_attention_heads,
+            rope_dim_list,
+            dtype=torch.float32 if current_platform.is_mps() else torch.float64,
+            rope_theta=10000,
+            start_frame=start_frame
+        )
+        freqs_cos = freqs_cos.to(hidden_states.device)
+        freqs_sin = freqs_sin.to(hidden_states.device)
+        freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
+
+        hidden_states = self.patch_embedding(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+
+        if timestep.dim() == 2:
+            # condition_embedder expects [B] timestep, not [B*T].
+            # All frames share the same timestep, so take first column.
+            timestep = timestep[:, 0]
+
+        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
+            timestep, action, encoder_hidden_states, encoder_hidden_states_image=encoder_hidden_states_image)
+        
+        # condition_embedder returns:
+        # - temb: [B*T, dim] where T = post_patch_num_frames
+        # - timestep_proj: [B*T, 6*dim]
+        # Reshape to [B, T, 6, dim] for transformer blocks
+        timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size))  # [B*T, 6, dim]
+        timestep_proj = timestep_proj.view(batch_size, post_patch_num_frames, 6, self.hidden_size)  # [B, T, 6, dim]
+
+        encoder_hidden_states = encoder_hidden_states_image
+
+        # Transformer blocks
+        for block_idx, 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, freqs_cis,
+                    kv_cache[block_idx] if kv_cache else None,
+                    crossattn_cache[block_idx] if crossattn_cache else None,
+                    current_start, cache_start,
+                    viewmats, Ks, is_cache)
+            else:
+                hidden_states = block(
+                    hidden_states, encoder_hidden_states, timestep_proj, freqs_cis,
+                    kv_cache[block_idx] if kv_cache else None,
+                    crossattn_cache[block_idx] if crossattn_cache else None,
+                    current_start, cache_start,
+                    viewmats, Ks, is_cache)
+
+        # If cache-only mode, return early
+        if is_cache:
+            return kv_cache
+
+        # Output norm, projection & unpatchify
+        # temb is [B*T, dim], reshape to [B, T, 1, dim]
+        temb = temb.view(batch_size, post_patch_num_frames, -1).unsqueeze(2)  # [B, T, 1, dim]
+        
+        shift, scale = (self.scale_shift_table.unsqueeze(1) + temb).chunk(2, dim=2)
+        hidden_states = self.norm_out(hidden_states, shift, scale)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+
+        return output

fastvideo/models/registry.py

@@ -44,6 +44,8 @@ _IMAGE_TO_VIDEO_DIT_MODELS = {
     "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
     "MatrixGameWanModel": ("dits", "matrixgame", "MatrixGameWanModel"),
     "CausalMatrixGameWanModel": ("dits", "matrixgame", "CausalMatrixGameWanModel"),
+    "WanGameActionTransformer3DModel": ("dits", "wangame", "WanGameActionTransformer3DModel"),
+    "CausalWanGameTransformer3DModel": ("dits", "wangame", "CausalWanGameTransformer3DModel"),
 }
 
 _TEXT_ENCODER_MODELS = {

fastvideo/pipelines/basic/matrixgame/matrixgame_causal_dmd_pipeline.py

@@ -96,28 +96,47 @@ class MatrixGameCausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
         self._vae_cache = None
 
     def streaming_step(self, keyboard_action, mouse_action) -> ForwardBatch:
+        import time
         denoiser = self._stage_name_mapping["denoising_stage"]
         ctx = denoiser._streaming_ctx
         assert ctx is not None, "streaming_ctx must be set"
 
         start_idx = ctx.start_index
+
+        # Time DiT forward pass
+        torch.cuda.synchronize()
+        t0 = time.perf_counter()
         batch = denoiser.streaming_step(keyboard_action, mouse_action)
+        torch.cuda.synchronize()
+        dit_time_ms = (time.perf_counter() - t0) * 1000
+
         end_idx = ctx.start_index
 
         # Decode only the new generated block
+        vae_time_ms = 0.0
         if end_idx > start_idx:
             current_latents = batch.latents[:, :, start_idx:end_idx, :, :]
             args = ctx.fastvideo_args
             decoder = self._stage_name_mapping["decoding_stage"]
+
+            # Time VAE decode
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
             decoded_frames, self._vae_cache = decoder.streaming_decode(
                 current_latents,
                 args,
                 cache=self._vae_cache,
                 is_first_chunk=(start_idx == 0))
+            torch.cuda.synchronize()
+            vae_time_ms = (time.perf_counter() - t0) * 1000
+
             batch.output = decoded_frames
         else:
             batch.output = None
 
+        # Store timings in batch for caller to access
+        batch.stage_timings = {"dit_ms": dit_time_ms, "vae_ms": vae_time_ms}
+
         return batch
 
     def streaming_clear(self) -> None:

fastvideo/pipelines/basic/wan/wangame_causal_dmd_pipeline.py

@@ -0,0 +1,122 @@
+# SPDX-License-Identifier: Apache-2.0
+"""WanGame causal DMD pipeline implementation."""
+
+from fastvideo.fastvideo_args import FastVideoArgs
+import torch
+from fastvideo.logger import init_logger
+from fastvideo.pipelines import ComposedPipelineBase, ForwardBatch, LoRAPipeline
+
+from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
+                                        InputValidationStage,
+                                        LatentPreparationStage,
+                                        TextEncodingStage,
+                                        MatrixGameCausalDenoisingStage)
+from fastvideo.pipelines.stages.image_encoding import (
+    MatrixGameImageEncodingStage, MatrixGameImageVAEEncodingStage)
+
+logger = init_logger(__name__)
+
+
+class WanGameCausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
+    _required_config_modules = [
+        "vae", "transformer", "scheduler", "image_encoder", "image_processor"
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        if (self.get_module("text_encoder", None) is not None
+                and self.get_module("tokenizer", None) is not None):
+            self.add_stage(stage_name="prompt_encoding_stage",
+                           stage=TextEncodingStage(
+                               text_encoders=[self.get_module("text_encoder")],
+                               tokenizers=[self.get_module("tokenizer")],
+                           ))
+
+        if (self.get_module("image_encoder", None) is not None
+                and self.get_module("image_processor", None) is not None):
+            self.add_stage(
+                stage_name="image_encoding_stage",
+                stage=MatrixGameImageEncodingStage(
+                    image_encoder=self.get_module("image_encoder"),
+                    image_processor=self.get_module("image_processor"),
+                ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer", None)))
+
+        self.add_stage(
+            stage_name="image_latent_preparation_stage",
+            stage=MatrixGameImageVAEEncodingStage(vae=self.get_module("vae")))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=MatrixGameCausalDenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           transformer_2=self.get_module("transformer_2", None),
+                           scheduler=self.get_module("scheduler"),
+                           pipeline=self,
+                           vae=self.get_module("vae")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+        logger.info("WanGameCausalDMDPipeline initialized")
+
+    @torch.no_grad()
+    def streaming_reset(self, batch: ForwardBatch,
+                        fastvideo_args: FastVideoArgs):
+        if not self.post_init_called:
+            self.post_init()
+
+        stages_to_run = [
+            "input_validation_stage", "prompt_encoding_stage",
+            "image_encoding_stage", "conditioning_stage",
+            "latent_preparation_stage", "image_latent_preparation_stage"
+        ]
+
+        for stage_name in stages_to_run:
+            if stage_name in self._stage_name_mapping:
+                batch = self._stage_name_mapping[stage_name].forward(
+                    batch, fastvideo_args)
+
+        denoiser = self._stage_name_mapping["denoising_stage"]
+        denoiser.streaming_reset(batch, fastvideo_args)
+        self._vae_cache = None
+
+    def streaming_step(self, keyboard_action, mouse_action) -> ForwardBatch:
+        denoiser = self._stage_name_mapping["denoising_stage"]
+        ctx = denoiser._streaming_ctx
+        assert ctx is not None, "streaming_ctx must be set"
+
+        start_idx = ctx.start_index
+        batch = denoiser.streaming_step(keyboard_action, mouse_action)
+        end_idx = ctx.start_index
+
+        if end_idx > start_idx:
+            current_latents = batch.latents[:, :, start_idx:end_idx, :, :]
+            args = ctx.fastvideo_args
+            decoder = self._stage_name_mapping["decoding_stage"]
+            decoded_frames, self._vae_cache = decoder.streaming_decode(
+                current_latents,
+                args,
+                cache=self._vae_cache,
+                is_first_chunk=(start_idx == 0))
+            batch.output = decoded_frames
+        else:
+            batch.output = None
+
+        return batch
+
+    def streaming_clear(self) -> None:
+        denoiser = self._stage_name_mapping.get("denoising_stage")
+        if denoiser is not None and hasattr(denoiser, "streaming_clear"):
+            denoiser.streaming_clear()
+        self._vae_cache = None
+
+EntryClass = [WanGameCausalDMDPipeline]

fastvideo/pipelines/basic/wan/wangame_i2v_pipeline.py

@@ -0,0 +1,78 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Wan video diffusion pipeline implementation.
+
+This module contains an implementation of the Wan video diffusion pipeline
+using the modular pipeline architecture.
+"""
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.lora_pipeline import LoRAPipeline
+
+# isort: off
+from fastvideo.pipelines.stages import (
+    ImageEncodingStage, ConditioningStage, DecodingStage, DenoisingStage,
+    ImageVAEEncodingStage, InputValidationStage, LatentPreparationStage,
+    TimestepPreparationStage)
+# isort: on
+from fastvideo.models.schedulers.scheduling_flow_unipc_multistep import (
+    FlowUniPCMultistepScheduler)
+
+logger = init_logger(__name__)
+
+
+class WanGameActionImageToVideoPipeline(LoRAPipeline, ComposedPipelineBase):
+
+    _required_config_modules = [
+        "vae", "transformer", "scheduler", \
+        "image_encoder", "image_processor"
+    ]
+
+    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
+        self.modules["scheduler"] = FlowUniPCMultistepScheduler(
+            shift=fastvideo_args.pipeline_config.flow_shift)
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        """Set up pipeline stages with proper dependency injection."""
+
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        self.add_stage(
+            stage_name="image_encoding_stage",
+            stage=ImageEncodingStage(
+                image_encoder=self.get_module("image_encoder"),
+                image_processor=self.get_module("image_processor"),
+            ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="timestep_preparation_stage",
+                       stage=TimestepPreparationStage(
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer")))
+
+        self.add_stage(stage_name="image_latent_preparation_stage",
+                       stage=ImageVAEEncodingStage(vae=self.get_module("vae")))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=DenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+
+class WanLingBotImageToVideoPipeline(WanGameActionImageToVideoPipeline):
+    pass
+
+
+EntryClass = [WanGameActionImageToVideoPipeline, WanLingBotImageToVideoPipeline]

fastvideo/pipelines/pipeline_registry.py

@@ -33,6 +33,8 @@ _PIPELINE_NAME_TO_ARCHITECTURE_NAME: dict[str, str] = {
     "Cosmos2_5Pipeline": "cosmos",
     "MatrixGamePipeline": "matrixgame",
     "MatrixGameCausalDMDPipeline": "matrixgame",
+    "WanGameActionImageToVideoPipeline": "wan",
+    "WanGameCausalDMDPipeline": "wan",
     "LongCatPipeline": "longcat",
     "LongCatImageToVideoPipeline": "longcat",
     "LongCatVideoContinuationPipeline": "longcat",

fastvideo/pipelines/stages/image_encoding.py

@@ -845,7 +845,18 @@ class MatrixGameImageVAEEncodingStage(ImageVAEEncodingStage):
             num_frames = batch.num_frames if isinstance(
                 batch.num_frames, int) else batch.num_frames[0]
 
+        def _gpu_mem(label=""):
+            a = torch.cuda.memory_allocated() / 1024**3
+            r = torch.cuda.memory_reserved() / 1024**3
+            logger.info("VAE stage [%s]: alloc=%.2fGiB, reserved=%.2fGiB", label, a, r)
+
+        _gpu_mem("before vae.to(GPU)")
         self.vae = self.vae.to(get_local_torch_device())
+        _gpu_mem("after vae.to(GPU)")
+        logger.info("VAE use_feature_cache=%s, use_tiling=%s, use_temporal_tiling=%s",
+                     getattr(self.vae, 'use_feature_cache', 'N/A'),
+                     getattr(self.vae, 'use_tiling', 'N/A'),
+                     getattr(self.vae, 'use_temporal_tiling', 'N/A'))
 
         # Process single image for I2V (latent dimensions computed but not used directly)
 
@@ -906,15 +917,23 @@ class MatrixGameImageVAEEncodingStage(ImageVAEEncodingStage):
         vae_autocast_enabled = (
             vae_dtype != torch.float32) and not fastvideo_args.disable_autocast
 
-        # Encode Image
-        with torch.autocast(device_type="cuda",
-                            dtype=vae_dtype,
-                            enabled=vae_autocast_enabled):
+        _gpu_mem("before encode")
+        logger.info("video_condition shape=%s dtype=%s device=%s",
+                     video_condition.shape, video_condition.dtype,
+                     video_condition.device)
+
+        # Encode Image (no_grad avoids autograd graph that would retain all
+        # intermediate activations across the feature-cache loop iterations)
+        with torch.no_grad(), torch.autocast(
+                device_type="cuda", dtype=vae_dtype,
+                enabled=vae_autocast_enabled):
             if fastvideo_args.pipeline_config.vae_tiling:
                 self.vae.enable_tiling()
             if not vae_autocast_enabled:
                 video_condition = video_condition.to(vae_dtype)
+            _gpu_mem("right before vae.encode()")
             encoder_output = self.vae.encode(video_condition)
+            _gpu_mem("after vae.encode()")
 
         # MatrixGame uses deterministic VAE encode for the first-frame conditioning.
         # Sampling would inject random noise into the cond_concat tensor and destroy the action guidance.

fastvideo/pipelines/stages/matrixgame_denoising.py

@@ -313,32 +313,43 @@ class MatrixGameCausalDenoisingStage(DenoisingStage):
         attention_head_dim = getattr(
             self.transformer, 'attention_head_dim',
             self.transformer.hidden_size // num_attention_heads)
+        # WanGame PRoPE stores cat([rope, prope], dim=-1) in cache
+        head_dim_mult = getattr(self.transformer,
+                                '_kv_cache_head_dim_multiplier', 1)
+        attention_head_dim = attention_head_dim * head_dim_mult
         if self.local_attn_size != -1:
             kv_cache_size = self.local_attn_size * self.frame_seq_length
         else:
             kv_cache_size = self.frame_seq_length * self.sliding_window_num_frames
 
+        # WanGame uses None-initialized cache (concat-based); MatrixGame uses
+        # zero-tensor cache with index tracking.
+        use_none_init = head_dim_mult > 1
+
         for _ in range(self.num_transformer_blocks):
-            kv_cache.append({
-                "k":
-                torch.zeros([
-                    batch_size, kv_cache_size, num_attention_heads,
-                    attention_head_dim
-                ],
-                            dtype=dtype,
-                            device=device),
-                "v":
-                torch.zeros([
-                    batch_size, kv_cache_size, num_attention_heads,
-                    attention_head_dim
-                ],
-                            dtype=dtype,
-                            device=device),
-                "global_end_index":
-                torch.tensor([0], dtype=torch.long, device=device),
-                "local_end_index":
-                torch.tensor([0], dtype=torch.long, device=device),
-            })
+            if use_none_init:
+                kv_cache.append({"k": None, "v": None})
+            else:
+                kv_cache.append({
+                    "k":
+                    torch.zeros([
+                        batch_size, kv_cache_size, num_attention_heads,
+                        attention_head_dim
+                    ],
+                                dtype=dtype,
+                                device=device),
+                    "v":
+                    torch.zeros([
+                        batch_size, kv_cache_size, num_attention_heads,
+                        attention_head_dim
+                    ],
+                                dtype=dtype,
+                                device=device),
+                    "global_end_index":
+                    torch.tensor([0], dtype=torch.long, device=device),
+                    "local_end_index":
+                    torch.tensor([0], dtype=torch.long, device=device),
+                })
 
         return kv_cache
 
@@ -429,6 +440,227 @@ class MatrixGameCausalDenoisingStage(DenoisingStage):
             })
         return crossattn_cache
 
+    def _denoise_one_step(
+        self,
+        current_latents: torch.Tensor,
+        noise_latents_btchw: torch.Tensor,
+        batch: ForwardBatch,
+        start_index: int,
+        current_num_frames: int,
+        timestep: torch.Tensor,
+        step_idx: int,
+        next_timestep: torch.Tensor | None,
+        ctx: BlockProcessingContext,
+        action_kwargs: dict[str, Any],
+        noise_generator: Callable[[tuple, torch.dtype, int], torch.Tensor]
+        | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Run one denoising step. Returns (updated_latents, updated_noise_latents_btchw)."""
+        prompt_embeds = batch.prompt_embeds
+        t_cur = timestep
+
+        if ctx.boundary_timestep is not None and t_cur < ctx.boundary_timestep:
+            current_model = self.transformer_2 if self.transformer_2 is not None else self.transformer
+        else:
+            current_model = self.transformer
+
+        noise_latents = noise_latents_btchw.clone()
+        latent_model_input = current_latents.to(ctx.target_dtype)
+
+        # For models that don't handle image_latent internally (e.g., WanGame),
+        # concatenate image conditioning along the channel dimension.
+        # MatrixGame handles this inside its forward() via forward_context.
+        if (batch.image_latent is not None
+                and not getattr(current_model, '_concatenates_image_latent',
+                                False)):
+            image_cond = batch.image_latent.to(ctx.target_dtype)
+            end = start_index + current_num_frames
+            if image_cond.shape[2] >= end:
+                image_cond = image_cond[:, :, start_index:end]
+            elif image_cond.shape[2] > start_index:
+                image_cond = image_cond[:, :, start_index:]
+                pad_frames = current_num_frames - image_cond.shape[2]
+                if pad_frames > 0:
+                    pad = torch.zeros(image_cond.shape[0],
+                                      image_cond.shape[1],
+                                      pad_frames,
+                                      image_cond.shape[3],
+                                      image_cond.shape[4],
+                                      device=image_cond.device,
+                                      dtype=image_cond.dtype)
+                    image_cond = torch.cat([image_cond, pad], dim=2)
+            else:
+                image_cond = torch.zeros(image_cond.shape[0],
+                                         image_cond.shape[1],
+                                         current_num_frames,
+                                         image_cond.shape[3],
+                                         image_cond.shape[4],
+                                         device=image_cond.device,
+                                         dtype=image_cond.dtype)
+            latent_model_input = torch.cat([latent_model_input, image_cond],
+                                           dim=1)
+
+        independent_first_frame = getattr(self.transformer,
+                                          'independent_first_frame', False)
+        if batch.image_latent is not None and independent_first_frame and start_index == 0:
+            latent_model_input = torch.cat([
+                latent_model_input,
+                batch.image_latent.to(ctx.target_dtype)
+            ],
+                                           dim=2)
+
+        # t_expand needs to be [batch * frames] to match flattened pred_noise/noise_latents
+        t_expand = t_cur.repeat(latent_model_input.shape[0] *
+                                current_num_frames)
+
+        # Build attention metadata if VSA is available
+        if vsa_available and self.attn_backend == VideoSparseAttentionBackend:
+            self.attn_metadata_builder_cls = self.attn_backend.get_builder_cls(
+            )
+            if self.attn_metadata_builder_cls is not None:
+                self.attn_metadata_builder = self.attn_metadata_builder_cls(
+                )
+                h, w = current_latents.shape[-2:]
+                attn_metadata = self.attn_metadata_builder.build(
+                    current_timestep=step_idx,
+                    raw_latent_shape=(current_num_frames, h, w),
+                    patch_size=ctx.fastvideo_args.pipeline_config.
+                    dit_config.patch_size,
+                    STA_param=batch.STA_param,
+                    VSA_sparsity=ctx.fastvideo_args.VSA_sparsity,
+                    device=get_local_torch_device(),
+                )
+                assert attn_metadata is not None, "attn_metadata cannot be None"
+            else:
+                attn_metadata = None
+        else:
+            attn_metadata = None
+
+        with torch.autocast(device_type="cuda",
+                            dtype=ctx.target_dtype,
+                            enabled=ctx.autocast_enabled), \
+            set_forward_context(current_timestep=step_idx,
+                                attn_metadata=attn_metadata,
+                                forward_batch=batch):
+            # Expand timestep to per-frame format [batch, num_frames] for causal model
+            t_expanded_noise = t_cur * torch.ones(
+                (latent_model_input.shape[0], current_num_frames),
+                device=latent_model_input.device,
+                dtype=torch.long)
+
+            model_kwargs = {
+                "kv_cache": ctx.get_kv_cache(t_cur),
+                "crossattn_cache": ctx.crossattn_cache,
+                "current_start": start_index * self.frame_seq_length,
+                "start_frame": start_index,
+            }
+
+            if self.use_action_module and current_model == self.transformer:
+                model_kwargs.update({
+                    "kv_cache_mouse":
+                    ctx.kv_cache_mouse,
+                    "kv_cache_keyboard":
+                    ctx.kv_cache_keyboard,
+                })
+                model_kwargs.update(action_kwargs)
+
+            # For models with discrete action embedding (WanGame-style):
+            # convert keyboard one-hot to scalar action = trans_label * 9
+            # and provide identity camera matrices for PRoPE
+            if getattr(current_model, '_uses_discrete_action', False):
+                dev = latent_model_input.device
+                b = latent_model_input.shape[0]
+                vae_ratio = 4
+                start_pixel = (0 if start_index == 0 else 1 + vae_ratio *
+                               (start_index - 1))
+                if (batch.keyboard_cond is not None
+                        and start_pixel < batch.keyboard_cond.shape[1]):
+                    kbd = batch.keyboard_cond[:, start_pixel, :].to(dev)
+                    has_key = kbd.sum(dim=-1) > 0  # [B]
+                    trans_label = (kbd.argmax(dim=-1) + 1) * has_key.long()
+                else:
+                    trans_label = torch.zeros(b, device=dev, dtype=torch.long)
+                action_value = trans_label * 9  # composite: trans * 9 + rot
+                model_kwargs["action"] = action_value.unsqueeze(1).expand(
+                    -1, current_num_frames).to(dev)
+                # Identity viewmats and default normalized intrinsics for PRoPE
+                model_kwargs["viewmats"] = torch.eye(
+                    4, device=dev, dtype=ctx.target_dtype
+                ).unsqueeze(0).unsqueeze(0).expand(
+                    b, current_num_frames, -1, -1).contiguous()
+                default_K = torch.tensor(
+                    [[0.5052, 0.0, 0.5],
+                     [0.0, 0.8979, 0.5],
+                     [0.0, 0.0, 1.0]],
+                    device=dev, dtype=ctx.target_dtype)
+                model_kwargs["Ks"] = default_K.unsqueeze(0).unsqueeze(
+                    0).expand(b, current_num_frames, -1, -1).contiguous()
+
+            pred_noise_btchw = current_model(
+                latent_model_input,
+                prompt_embeds,
+                t_expanded_noise,
+                **ctx.image_kwargs,
+                **ctx.pos_cond_kwargs,
+                **model_kwargs,
+            ).permute(0, 2, 1, 3, 4)
+
+        if ctx.boundary_timestep is not None and t_cur >= ctx.boundary_timestep:
+            pred_video_btchw = pred_noise_to_x_bound(
+                pred_noise=pred_noise_btchw.flatten(0, 1),
+                noise_input_latent=noise_latents.flatten(0, 1),
+                timestep=t_expand,
+                boundary_timestep=torch.ones_like(t_expand) *
+                ctx.boundary_timestep,
+                scheduler=self.scheduler).unflatten(
+                    0, pred_noise_btchw.shape[:2])
+        else:
+            pred_video_btchw = pred_noise_to_pred_video(
+                pred_noise=pred_noise_btchw.flatten(0, 1),
+                noise_input_latent=noise_latents.flatten(0, 1),
+                timestep=t_expand,
+                scheduler=self.scheduler).unflatten(
+                    0, pred_noise_btchw.shape[:2])
+
+        if next_timestep is not None:
+            next_t = next_timestep * torch.ones(
+                [1], dtype=torch.long, device=pred_video_btchw.device)
+
+            # Use custom noise generator if provided (for streaming), else generate
+            if noise_generator is not None:
+                noise = noise_generator(pred_video_btchw.shape,
+                                        pred_video_btchw.dtype, step_idx)
+            else:
+                noise = torch.randn(
+                    pred_video_btchw.shape,
+                    dtype=pred_video_btchw.dtype,
+                    generator=(batch.generator[0] if isinstance(
+                        batch.generator, list) else batch.generator)).to(
+                            pred_video_btchw.device)
+
+            noise_btchw = noise
+            if ctx.boundary_timestep is not None and ctx.high_noise_timesteps is not None and step_idx < len(
+                    ctx.high_noise_timesteps) - 1:
+                noise_latents_btchw = self.scheduler.add_noise_high(
+                    pred_video_btchw.flatten(0, 1),
+                    noise_btchw.flatten(0, 1), next_t,
+                    torch.ones_like(next_t) *
+                    ctx.boundary_timestep).unflatten(
+                        0, pred_video_btchw.shape[:2])
+            elif ctx.boundary_timestep is not None and ctx.high_noise_timesteps is not None and step_idx == len(
+                    ctx.high_noise_timesteps) - 1:
+                noise_latents_btchw = pred_video_btchw
+            else:
+                noise_latents_btchw = self.scheduler.add_noise(
+                    pred_video_btchw.flatten(0,
+                                             1), noise_btchw.flatten(0, 1),
+                    next_t).unflatten(0, pred_video_btchw.shape[:2])
+            current_latents = noise_latents_btchw.permute(0, 2, 1, 3, 4)
+        else:
+            current_latents = pred_video_btchw.permute(0, 2, 1, 3, 4)
+
+        return current_latents, noise_latents_btchw
+
     def _process_single_block(
         self,
         current_latents: torch.Tensor,
@@ -442,144 +674,23 @@ class MatrixGameCausalDenoisingStage(DenoisingStage):
         | None = None,
         progress_bar: Any | None = None,
     ) -> torch.Tensor:
-        prompt_embeds = batch.prompt_embeds
         noise_latents_btchw = current_latents.permute(0, 2, 1, 3, 4)
 
         for i, t_cur in enumerate(timesteps):
-            if ctx.boundary_timestep is not None and t_cur < ctx.boundary_timestep:
-                current_model = self.transformer_2 if self.transformer_2 is not None else self.transformer
-            else:
-                current_model = self.transformer
-
-            noise_latents = noise_latents_btchw.clone()
-            latent_model_input = current_latents.to(ctx.target_dtype)
-
-            independent_first_frame = getattr(self.transformer,
-                                              'independent_first_frame', False)
-            if batch.image_latent is not None and independent_first_frame and start_index == 0:
-                latent_model_input = torch.cat([
-                    latent_model_input,
-                    batch.image_latent.to(ctx.target_dtype)
-                ],
-                                               dim=2)
-
-            # t_expand needs to be [batch * frames] to match flattened pred_noise/noise_latents
-            t_expand = t_cur.repeat(latent_model_input.shape[0] *
-                                    current_num_frames)
-
-            # Build attention metadata if VSA is available
-            if vsa_available and self.attn_backend == VideoSparseAttentionBackend:
-                self.attn_metadata_builder_cls = self.attn_backend.get_builder_cls(
-                )
-                if self.attn_metadata_builder_cls is not None:
-                    self.attn_metadata_builder = self.attn_metadata_builder_cls(
-                    )
-                    h, w = current_latents.shape[-2:]
-                    attn_metadata = self.attn_metadata_builder.build(
-                        current_timestep=i,
-                        raw_latent_shape=(current_num_frames, h, w),
-                        patch_size=ctx.fastvideo_args.pipeline_config.
-                        dit_config.patch_size,
-                        STA_param=batch.STA_param,
-                        VSA_sparsity=ctx.fastvideo_args.VSA_sparsity,
-                        device=get_local_torch_device(),
-                    )
-                    assert attn_metadata is not None, "attn_metadata cannot be None"
-                else:
-                    attn_metadata = None
-            else:
-                attn_metadata = None
-
-            with torch.autocast(device_type="cuda",
-                                dtype=ctx.target_dtype,
-                                enabled=ctx.autocast_enabled), \
-                set_forward_context(current_timestep=i,
-                                    attn_metadata=attn_metadata,
-                                    forward_batch=batch):
-                # Expand timestep to per-frame format [batch, num_frames] for causal model
-                t_expanded_noise = t_cur * torch.ones(
-                    (latent_model_input.shape[0], current_num_frames),
-                    device=latent_model_input.device,
-                    dtype=torch.long)
-
-                model_kwargs = {
-                    "kv_cache": ctx.get_kv_cache(t_cur),
-                    "crossattn_cache": ctx.crossattn_cache,
-                    "current_start": start_index * self.frame_seq_length,
-                    "start_frame": start_index,
-                }
-
-                if self.use_action_module and current_model == self.transformer:
-                    model_kwargs.update({
-                        "kv_cache_mouse":
-                        ctx.kv_cache_mouse,
-                        "kv_cache_keyboard":
-                        ctx.kv_cache_keyboard,
-                    })
-                    model_kwargs.update(action_kwargs)
-
-                pred_noise_btchw = current_model(
-                    latent_model_input,
-                    prompt_embeds,
-                    t_expanded_noise,
-                    **ctx.image_kwargs,
-                    **ctx.pos_cond_kwargs,
-                    **model_kwargs,
-                ).permute(0, 2, 1, 3, 4)
-
-            if ctx.boundary_timestep is not None and t_cur >= ctx.boundary_timestep:
-                pred_video_btchw = pred_noise_to_x_bound(
-                    pred_noise=pred_noise_btchw.flatten(0, 1),
-                    noise_input_latent=noise_latents.flatten(0, 1),
-                    timestep=t_expand,
-                    boundary_timestep=torch.ones_like(t_expand) *
-                    ctx.boundary_timestep,
-                    scheduler=self.scheduler).unflatten(
-                        0, pred_noise_btchw.shape[:2])
-            else:
-                pred_video_btchw = pred_noise_to_pred_video(
-                    pred_noise=pred_noise_btchw.flatten(0, 1),
-                    noise_input_latent=noise_latents.flatten(0, 1),
-                    timestep=t_expand,
-                    scheduler=self.scheduler).unflatten(
-                        0, pred_noise_btchw.shape[:2])
-
-            if i < len(timesteps) - 1:
-                next_timestep = timesteps[i + 1] * torch.ones(
-                    [1], dtype=torch.long, device=pred_video_btchw.device)
-
-                # Use custom noise generator if provided (for streaming), else generate
-                if noise_generator is not None:
-                    noise = noise_generator(pred_video_btchw.shape,
-                                            pred_video_btchw.dtype, i)
-                else:
-                    noise = torch.randn(
-                        pred_video_btchw.shape,
-                        dtype=pred_video_btchw.dtype,
-                        generator=(batch.generator[0] if isinstance(
-                            batch.generator, list) else batch.generator)).to(
-                                pred_video_btchw.device)
-
-                noise_btchw = noise
-                if ctx.boundary_timestep is not None and ctx.high_noise_timesteps is not None and i < len(
-                        ctx.high_noise_timesteps) - 1:
-                    noise_latents_btchw = self.scheduler.add_noise_high(
-                        pred_video_btchw.flatten(0, 1),
-                        noise_btchw.flatten(0, 1), next_timestep,
-                        torch.ones_like(next_timestep) *
-                        ctx.boundary_timestep).unflatten(
-                            0, pred_video_btchw.shape[:2])
-                elif ctx.boundary_timestep is not None and ctx.high_noise_timesteps is not None and i == len(
-                        ctx.high_noise_timesteps) - 1:
-                    noise_latents_btchw = pred_video_btchw
-                else:
-                    noise_latents_btchw = self.scheduler.add_noise(
-                        pred_video_btchw.flatten(0,
-                                                 1), noise_btchw.flatten(0, 1),
-                        next_timestep).unflatten(0, pred_video_btchw.shape[:2])
-                current_latents = noise_latents_btchw.permute(0, 2, 1, 3, 4)
-            else:
-                current_latents = pred_video_btchw.permute(0, 2, 1, 3, 4)
+            next_timestep = timesteps[i + 1] if i < len(timesteps) - 1 else None
+            current_latents, noise_latents_btchw = self._denoise_one_step(
+                current_latents=current_latents,
+                noise_latents_btchw=noise_latents_btchw,
+                batch=batch,
+                start_index=start_index,
+                current_num_frames=current_num_frames,
+                timestep=t_cur,
+                step_idx=i,
+                next_timestep=next_timestep,
+                ctx=ctx,
+                action_kwargs=action_kwargs,
+                noise_generator=noise_generator,
+            )
 
             if progress_bar is not None:
                 progress_bar.update()
@@ -605,6 +716,36 @@ class MatrixGameCausalDenoisingStage(DenoisingStage):
                                dtype=torch.long) * int(context_noise)
         context_bcthw = current_latents.to(ctx.target_dtype)
 
+        # Channel concatenation for models that don't handle it internally
+        if (batch.image_latent is not None
+                and not getattr(self.transformer, '_concatenates_image_latent',
+                                False)):
+            image_cond = batch.image_latent.to(ctx.target_dtype)
+            end = start_index + current_num_frames
+            if image_cond.shape[2] >= end:
+                image_cond = image_cond[:, :, start_index:end]
+            elif image_cond.shape[2] > start_index:
+                image_cond = image_cond[:, :, start_index:]
+                pad_frames = current_num_frames - image_cond.shape[2]
+                if pad_frames > 0:
+                    pad = torch.zeros(image_cond.shape[0],
+                                      image_cond.shape[1],
+                                      pad_frames,
+                                      image_cond.shape[3],
+                                      image_cond.shape[4],
+                                      device=image_cond.device,
+                                      dtype=image_cond.dtype)
+                    image_cond = torch.cat([image_cond, pad], dim=2)
+            else:
+                image_cond = torch.zeros(image_cond.shape[0],
+                                         image_cond.shape[1],
+                                         current_num_frames,
+                                         image_cond.shape[3],
+                                         image_cond.shape[4],
+                                         device=image_cond.device,
+                                         dtype=image_cond.dtype)
+            context_bcthw = torch.cat([context_bcthw, image_cond], dim=1)
+
         with torch.autocast(device_type="cuda",
                             dtype=ctx.target_dtype,
                             enabled=ctx.autocast_enabled), \
@@ -628,6 +769,37 @@ class MatrixGameCausalDenoisingStage(DenoisingStage):
                 })
                 context_model_kwargs.update(action_kwargs)
 
+            # Discrete action and camera matrices for WanGame-style models
+            if getattr(self.transformer, '_uses_discrete_action', False):
+                dev = latents_device
+                b = context_bcthw.shape[0]
+                vae_ratio = 4
+                start_pixel = (0 if start_index == 0 else 1 + vae_ratio *
+                               (start_index - 1))
+                if (batch.keyboard_cond is not None
+                        and start_pixel < batch.keyboard_cond.shape[1]):
+                    kbd = batch.keyboard_cond[:, start_pixel, :].to(dev)
+                    has_key = kbd.sum(dim=-1) > 0
+                    trans_label = (kbd.argmax(dim=-1) + 1) * has_key.long()
+                else:
+                    trans_label = torch.zeros(b, device=dev, dtype=torch.long)
+                action_value = trans_label * 9
+                context_model_kwargs["action"] = (
+                    action_value.unsqueeze(1).expand(
+                        -1, current_num_frames).to(dev))
+                context_model_kwargs["viewmats"] = torch.eye(
+                    4, device=dev, dtype=ctx.target_dtype
+                ).unsqueeze(0).unsqueeze(0).expand(
+                    b, current_num_frames, -1, -1).contiguous()
+                default_K = torch.tensor(
+                    [[0.5052, 0.0, 0.5],
+                     [0.0, 0.8979, 0.5],
+                     [0.0, 0.0, 1.0]],
+                    device=dev, dtype=ctx.target_dtype)
+                context_model_kwargs["Ks"] = default_K.unsqueeze(
+                    0).unsqueeze(0).expand(
+                        b, current_num_frames, -1, -1).contiguous()
+
             if ctx.boundary_timestep is not None and self.transformer_2 is not None:
                 self.transformer_2(
                     context_bcthw,
@@ -641,6 +813,10 @@ class MatrixGameCausalDenoisingStage(DenoisingStage):
                     **ctx.pos_cond_kwargs,
                 )
 
+            # WanGame uses is_cache=True to store KV without prepending old cache
+            if getattr(self.transformer, '_uses_discrete_action', False):
+                context_model_kwargs["is_cache"] = True
+
             self.transformer(
                 context_bcthw,
                 prompt_embeds,

fastvideo/pipelines/stages/multi_user_engine.py

@@ -0,0 +1,695 @@
+"""Multi-user engine implementing ORCA-style batching for streaming generation.
+
+Groups users at the same (block_idx, denoising_step) and batches their forward
+passes together, sharing model weights while maintaining per-user KV caches.
+"""
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass, field
+from typing import Any, Iterator
+
+import torch
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.pipelines.stages.matrixgame_denoising import (
+    BlockProcessingContext,
+    MatrixGameCausalDenoisingStage,
+)
+
+logger = init_logger(__name__)
+
+
+@dataclass
+class CompletedResult:
+    """Result for a user whose block generation finished."""
+    user_id: str
+    output_batch: ForwardBatch
+
+
+@dataclass
+class UserSession:
+    """Per-user state for multi-user streaming."""
+    user_id: str
+    ctx: BlockProcessingContext
+    vae_cache: list[torch.Tensor | None] | None
+    batch: ForwardBatch
+    fastvideo_args: FastVideoArgs
+
+    # Denoising state (set when a step is in progress)
+    denoising_step: int = -1  # -1 means no pending work
+    current_latents: torch.Tensor | None = None
+    noise_latents_btchw: torch.Tensor | None = None
+    action_kwargs: dict[str, Any] = field(default_factory=dict)
+    block_start_index: int = 0
+    block_num_frames: int = 0
+    dit_elapsed_ms: float = 0.0  # accumulated DiT time across denoising steps
+
+
+class MultiUserEngine:
+    """ORCA-style scheduler that batches multiple users on a single GPU."""
+
+    def __init__(self, pipeline, disable_batching: bool = False):
+        self.pipeline = pipeline
+        self.disable_batching = disable_batching
+        if not pipeline.post_init_called:
+            pipeline.post_init()
+        self.denoiser: MatrixGameCausalDenoisingStage = (
+            pipeline._stage_name_mapping["denoising_stage"]
+        )
+        self.decoder = pipeline._stage_name_mapping["decoding_stage"]
+        self.users: dict[str, UserSession] = {}
+
+    def add_user(
+        self,
+        user_id: str,
+        batch: ForwardBatch,
+        fastvideo_args: FastVideoArgs,
+    ) -> None:
+        """Initialize a new user session (runs preprocessing + streaming_reset)."""
+        def _gpu_mem():
+            a = torch.cuda.memory_allocated() / 1024**3
+            r = torch.cuda.memory_reserved() / 1024**3
+            return f"alloc={a:.2f}GiB, reserved={r:.2f}GiB"
+
+        logger.info("add_user(%s) start: %s", user_id, _gpu_mem())
+        logger.info("add_user(%s) memory summary:\n%s",
+                     user_id, torch.cuda.memory_summary())
+
+        if user_id in self.users:
+            logger.warning("User %s already exists, removing first", user_id)
+            self.remove_user(user_id)
+
+        # Free any existing streaming state FIRST to make room on GPU.
+        # After warmup, the denoiser holds KV caches + noise pool.
+        import gc
+        logger.info("add_user(%s) denoiser._streaming_initialized=%s, ctx=%s",
+                     user_id, self.denoiser._streaming_initialized,
+                     self.denoiser._streaming_ctx is not None)
+        if self.denoiser._streaming_initialized:
+            ctx = self.denoiser._streaming_ctx
+            if ctx is not None:
+                # Explicitly delete large tensors to break any reference cycles
+                ctx.kv_cache1 = None
+                ctx.kv_cache2 = None
+                ctx.kv_cache_mouse = None
+                ctx.kv_cache_keyboard = None
+                ctx.crossattn_cache = None
+                ctx.noise_pool = None
+                if ctx.batch is not None:
+                    ctx.batch.latents = None
+                    ctx.batch.image_latent = None
+                    ctx.batch.prompt_embeds = None
+                ctx.batch = None
+            self.denoiser._streaming_ctx = None
+            self.denoiser._streaming_initialized = False
+            logger.info("add_user(%s) after ctx cleanup: %s", user_id, _gpu_mem())
+            gc.collect()
+            torch.cuda.empty_cache()
+            logger.info("add_user(%s) after gc+empty_cache: %s", user_id, _gpu_mem())
+            logger.info("add_user(%s) post-cleanup summary:\n%s",
+                         user_id, torch.cuda.memory_summary())
+
+        # Run preprocessing stages
+        if not self.pipeline.post_init_called:
+            self.pipeline.post_init()
+
+        # Ensure VAE temporal tiling is enabled to avoid OOM when encoding
+        # the full video_condition [1,3,num_frames,H,W] through 3D conv layers.
+        # Enable on both the pipeline's VAE and the stage's VAE reference.
+        vae = self.pipeline.get_module("vae", None)
+        if vae is not None:
+            vae.enable_tiling(use_temporal_tiling=True)
+            logger.info("Pipeline VAE tiling: use_tiling=%s, temporal=%s, min_frames=%s, id=%s",
+                        vae.use_tiling, vae.use_temporal_tiling,
+                        getattr(vae, 'tile_sample_min_num_frames', 'N/A'), id(vae))
+        img_stage = self.pipeline._stage_name_mapping.get("image_latent_preparation_stage")
+        if img_stage is not None and hasattr(img_stage, 'vae'):
+            img_stage.vae.enable_tiling(use_temporal_tiling=True)
+            logger.info("Stage VAE tiling: use_tiling=%s, temporal=%s, min_frames=%s, id=%s",
+                        img_stage.vae.use_tiling, img_stage.vae.use_temporal_tiling,
+                        getattr(img_stage.vae, 'tile_sample_min_num_frames', 'N/A'),
+                        id(img_stage.vae))
+
+        stages_to_run = [
+            "input_validation_stage", "prompt_encoding_stage",
+            "image_encoding_stage", "conditioning_stage",
+            "latent_preparation_stage", "image_latent_preparation_stage",
+        ]
+        for stage_name in stages_to_run:
+            if stage_name in self.pipeline._stage_name_mapping:
+                batch = self.pipeline._stage_name_mapping[stage_name].forward(
+                    batch, fastvideo_args)
+                logger.info("add_user(%s) after %s: %s",
+                            user_id, stage_name, _gpu_mem())
+
+        # Initialize denoiser state for this user (creates KV caches etc.)
+        # We call streaming_reset to set up the context, then steal it
+        logger.info("add_user(%s) before streaming_reset: %s", user_id, _gpu_mem())
+        self.denoiser.streaming_reset(batch, fastvideo_args)
+        ctx = self.denoiser._streaming_ctx
+        assert ctx is not None
+        # Detach from denoiser so it doesn't interfere with other users
+        self.denoiser._streaming_ctx = None
+        self.denoiser._streaming_initialized = False
+
+        self.users[user_id] = UserSession(
+            user_id=user_id,
+            ctx=ctx,
+            vae_cache=None,
+            batch=batch,
+            fastvideo_args=fastvideo_args,
+        )
+        logger.info("Added user %s (total: %d)", user_id, len(self.users))
+
+    def remove_user(self, user_id: str) -> None:
+        """Remove a user session and free GPU memory."""
+        session = self.users.pop(user_id, None)
+        if session is not None:
+            # Let GC handle tensor cleanup
+            session.ctx = None  # type: ignore
+            session.vae_cache = None
+            session.current_latents = None
+            session.noise_latents_btchw = None
+            logger.info("Removed user %s (remaining: %d)",
+                        user_id, len(self.users))
+
+    def submit_step(
+        self,
+        user_id: str,
+        keyboard_action: torch.Tensor | None,
+        mouse_action: torch.Tensor | None,
+    ) -> None:
+        """Queue a block generation request for a user."""
+        session = self.users.get(user_id)
+        if session is None:
+            raise KeyError(f"User {user_id} not found")
+
+        ctx = session.ctx
+        if ctx.block_idx >= len(ctx.block_sizes):
+            logger.warning("User %s has no more blocks to generate", user_id)
+            return
+
+        batch = session.batch
+        latents = batch.latents
+        assert latents is not None
+
+        current_num_frames = ctx.block_sizes[ctx.block_idx]
+        start_index = ctx.start_index
+
+        current_latents = latents[:, :, start_index:start_index +
+                                  current_num_frames, :, :]
+
+        # Update batch with new actions
+        if keyboard_action is not None or mouse_action is not None:
+            vae_ratio = 4
+            start_frame = 0 if start_index == 0 else 1 + vae_ratio * (
+                start_index - 1)
+
+            if keyboard_action is not None:
+                n = keyboard_action.shape[1]
+                batch.keyboard_cond[:, start_frame:start_frame +
+                                    n] = keyboard_action.to(
+                                        batch.keyboard_cond.device)
+            if mouse_action is not None:
+                n = mouse_action.shape[1]
+                batch.mouse_cond[:, start_frame:start_frame +
+                                 n] = mouse_action.to(batch.mouse_cond.device)
+
+        action_kwargs = self.denoiser._prepare_action_kwargs(
+            batch, start_index, current_num_frames)
+
+        # Set up denoising state
+        session.denoising_step = 0
+        session.dit_elapsed_ms = 0.0
+        session.current_latents = current_latents
+        session.noise_latents_btchw = current_latents.permute(0, 2, 1, 3, 4)
+        session.action_kwargs = action_kwargs
+        session.block_start_index = start_index
+        session.block_num_frames = current_num_frames
+
+    def has_pending_work(self) -> bool:
+        """Check if any user has pending denoising work."""
+        return any(s.denoising_step >= 0 for s in self.users.values())
+
+    def run_iteration(self) -> Iterator[CompletedResult]:
+        """Run one denoising step for all ready users (ORCA iteration-level scheduling).
+
+        Groups users by (block_idx, denoising_step) and batches compatible groups.
+        Yields completed results immediately after each user's VAE decode finishes,
+        so responses can be sent without waiting for all users in the batch.
+        """
+        # Group users with pending work by (block_idx, denoising_step)
+        groups: dict[tuple[int, int], list[UserSession]] = {}
+        for session in self.users.values():
+            if session.denoising_step < 0:
+                continue
+            key = (session.ctx.block_idx, session.denoising_step)
+            groups.setdefault(key, []).append(session)
+
+        for (block_idx, step_idx), sessions in groups.items():
+            timesteps = sessions[0].ctx.timesteps
+            t_cur = timesteps[step_idx]
+            next_timestep = timesteps[step_idx + 1] if step_idx < len(timesteps) - 1 else None
+
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+            user_ids = [s.user_id[:8] for s in sessions]
+            if len(sessions) == 1 or self.disable_batching:
+                # Process each user individually
+                for s in sessions:
+                    logger.info("ORCA step: block=%d step=%d single user=%s",
+                                block_idx, step_idx, s.user_id[:8])
+                    self._run_single_user_step(s, t_cur, step_idx, next_timestep)
+            else:
+                # Multiple users at same state - batch them
+                logger.info("ORCA step: block=%d step=%d BATCHED %d users=%s",
+                            block_idx, step_idx, len(sessions), user_ids)
+                self._run_batched_step(sessions, t_cur, step_idx, next_timestep)
+
+            torch.cuda.synchronize()
+            step_ms = (time.perf_counter() - t0) * 1000
+
+            # Advance step counters and finalize completed users
+            for s in sessions:
+                s.dit_elapsed_ms += step_ms
+                s.denoising_step += 1
+                if s.denoising_step >= len(timesteps):
+                    # Denoising complete — VAE decode and yield immediately
+                    result = self._finalize_block(s)
+                    if result is not None:
+                        yield result
+
+    def _run_single_user_step(
+        self,
+        session: UserSession,
+        timestep: torch.Tensor,
+        step_idx: int,
+        next_timestep: torch.Tensor | None,
+    ) -> None:
+        """Run one denoising step for a single user (no batching)."""
+        noise_gen = self._make_noise_generator(session)
+
+        current_latents, noise_latents_btchw = self.denoiser._denoise_one_step(
+            current_latents=session.current_latents,
+            noise_latents_btchw=session.noise_latents_btchw,
+            batch=session.batch,
+            start_index=session.block_start_index,
+            current_num_frames=session.block_num_frames,
+            timestep=timestep,
+            step_idx=step_idx,
+            next_timestep=next_timestep,
+            ctx=session.ctx,
+            action_kwargs=session.action_kwargs,
+            noise_generator=noise_gen,
+        )
+        session.current_latents = current_latents
+        session.noise_latents_btchw = noise_latents_btchw
+
+    def _run_batched_step(
+        self,
+        sessions: list[UserSession],
+        timestep: torch.Tensor,
+        step_idx: int,
+        next_timestep: torch.Tensor | None,
+    ) -> None:
+        """Run one denoising step for multiple users batched together."""
+        n = len(sessions)
+
+        # Concatenate along batch dimension
+        batched_latents = torch.cat(
+            [s.current_latents for s in sessions], dim=0)
+        batched_noise = torch.cat(
+            [s.noise_latents_btchw for s in sessions], dim=0)
+
+        # Build a merged ForwardBatch with concatenated per-user tensors
+        merged_batch = self._merge_batches(sessions)
+
+        # Build merged context with concatenated KV caches
+        merged_ctx = self._merge_contexts(sessions, merged_batch)
+
+        # Merge action kwargs
+        merged_action_kwargs = self._merge_action_kwargs(sessions)
+
+        # Create noise generator that concatenates per-user noise
+        device = batched_latents.device
+
+        def batched_noise_gen(shape, dtype, si):
+            noises = []
+            for s in sessions:
+                gen = self._make_noise_generator(s)
+                if gen is not None:
+                    per_user_shape = (1,) + shape[1:]
+                    noises.append(gen(per_user_shape, dtype, si))
+                else:
+                    noises.append(torch.randn(
+                        (1,) + shape[1:], dtype=dtype).to(device))
+            return torch.cat(noises, dim=0)
+
+        current_latents, noise_latents_btchw = self.denoiser._denoise_one_step(
+            current_latents=batched_latents,
+            noise_latents_btchw=batched_noise,
+            batch=merged_batch,
+            start_index=sessions[0].block_start_index,
+            current_num_frames=sessions[0].block_num_frames,
+            timestep=timestep,
+            step_idx=step_idx,
+            next_timestep=next_timestep,
+            ctx=merged_ctx,
+            action_kwargs=merged_action_kwargs,
+            noise_generator=batched_noise_gen,
+        )
+
+        # Unbatch results back to per-user tensors
+        latents_list = current_latents.split(1, dim=0)
+        noise_list = noise_latents_btchw.split(1, dim=0)
+
+        # Restore per-user KV caches from the batched caches
+        self._split_contexts(merged_ctx, sessions)
+
+        for i, s in enumerate(sessions):
+            s.current_latents = latents_list[i]
+            s.noise_latents_btchw = noise_list[i]
+
+    def _merge_batches(self, sessions: list[UserSession]) -> ForwardBatch:
+        """Create a merged ForwardBatch with concatenated user tensors."""
+        ref = sessions[0].batch
+        # prompt_embeds and image_embeds are shared (same game)
+        # latents, keyboard_cond, mouse_cond differ per user
+        merged = ForwardBatch.__new__(ForwardBatch)
+        merged.__dict__.update(ref.__dict__)
+
+        # image_latent: cat along batch dim if present
+        if ref.image_latent is not None:
+            merged.image_latent = torch.cat(
+                [s.batch.image_latent for s in sessions], dim=0)
+
+        return merged
+
+    def _merge_contexts(
+        self, sessions: list[UserSession],
+        merged_batch: ForwardBatch | None = None,
+    ) -> BlockProcessingContext:
+        """Create a merged BlockProcessingContext with concatenated KV caches."""
+        ref = sessions[0].ctx
+        n = len(sessions)
+
+        # Concatenate KV caches along batch dimension
+        merged_kv1 = self._cat_kv_caches([s.ctx.kv_cache1 for s in sessions])
+        merged_kv2 = None
+        if ref.kv_cache2 is not None:
+            merged_kv2 = self._cat_kv_caches(
+                [s.ctx.kv_cache2 for s in sessions])
+
+        merged_crossattn = self._cat_crossattn_caches(
+            [s.ctx.crossattn_cache for s in sessions])
+
+        merged_kv_mouse = None
+        merged_kv_keyboard = None
+        if ref.kv_cache_mouse is not None:
+            merged_kv_mouse = self._cat_action_kv_caches(
+                [s.ctx.kv_cache_mouse for s in sessions],
+                is_mouse=True)
+        if ref.kv_cache_keyboard is not None:
+            merged_kv_keyboard = self._cat_kv_caches(
+                [s.ctx.kv_cache_keyboard for s in sessions])
+
+        # image_kwargs: cat image embeds
+        merged_image_kwargs = dict(ref.image_kwargs)
+        if "encoder_hidden_states_image" in ref.image_kwargs:
+            embeds = ref.image_kwargs["encoder_hidden_states_image"]
+            if isinstance(embeds, list) and len(embeds) > 0 and torch.is_tensor(embeds[0]):
+                # Cat each tensor in the list across users
+                merged_embeds = []
+                for idx in range(len(embeds)):
+                    merged_embeds.append(torch.cat(
+                        [s.ctx.image_kwargs["encoder_hidden_states_image"][idx]
+                         for s in sessions], dim=0))
+                merged_image_kwargs["encoder_hidden_states_image"] = merged_embeds
+
+        if merged_batch is None:
+            merged_batch = self._merge_batches(sessions)
+
+        return BlockProcessingContext(
+            batch=merged_batch,
+            block_idx=ref.block_idx,
+            start_index=ref.start_index,
+            kv_cache1=merged_kv1,
+            kv_cache2=merged_kv2,
+            kv_cache_mouse=merged_kv_mouse,
+            kv_cache_keyboard=merged_kv_keyboard,
+            crossattn_cache=merged_crossattn,
+            timesteps=ref.timesteps,
+            block_sizes=ref.block_sizes,
+            noise_pool=None,  # noise handled per-user
+            fastvideo_args=ref.fastvideo_args,
+            target_dtype=ref.target_dtype,
+            autocast_enabled=ref.autocast_enabled,
+            boundary_timestep=ref.boundary_timestep,
+            high_noise_timesteps=ref.high_noise_timesteps,
+            context_noise=ref.context_noise,
+            image_kwargs=merged_image_kwargs,
+            pos_cond_kwargs=ref.pos_cond_kwargs,
+        )
+
+    def _cat_kv_caches(
+        self, caches_list: list[list[dict[str, Any]]]
+    ) -> list[dict[str, Any]]:
+        """Concatenate KV caches along batch dimension."""
+        num_layers = len(caches_list[0])
+        merged = []
+        for layer_idx in range(num_layers):
+            merged.append({
+                "k": torch.cat(
+                    [c[layer_idx]["k"] for c in caches_list], dim=0),
+                "v": torch.cat(
+                    [c[layer_idx]["v"] for c in caches_list], dim=0),
+                "global_end_index": caches_list[0][layer_idx]["global_end_index"],
+                "local_end_index": caches_list[0][layer_idx]["local_end_index"],
+            })
+        return merged
+
+    def _cat_action_kv_caches(
+        self,
+        caches_list: list[list[dict[str, Any]]],
+        is_mouse: bool = False,
+    ) -> list[dict[str, Any]]:
+        """Concatenate action KV caches.
+
+        Mouse caches have shape [B*frame_seq_length, ...] so we need
+        to concatenate correctly.
+        """
+        num_layers = len(caches_list[0])
+        merged = []
+        for layer_idx in range(num_layers):
+            if is_mouse:
+                # Mouse: [B*F, cache_size, heads, dim] -> [N*F, cache_size, heads, dim]
+                # Each user has [1*F, ...], so simple cat along dim 0 works
+                merged.append({
+                    "k": torch.cat(
+                        [c[layer_idx]["k"] for c in caches_list], dim=0),
+                    "v": torch.cat(
+                        [c[layer_idx]["v"] for c in caches_list], dim=0),
+                    "global_end_index": caches_list[0][layer_idx]["global_end_index"],
+                    "local_end_index": caches_list[0][layer_idx]["local_end_index"],
+                })
+            else:
+                merged.append({
+                    "k": torch.cat(
+                        [c[layer_idx]["k"] for c in caches_list], dim=0),
+                    "v": torch.cat(
+                        [c[layer_idx]["v"] for c in caches_list], dim=0),
+                    "global_end_index": caches_list[0][layer_idx]["global_end_index"],
+                    "local_end_index": caches_list[0][layer_idx]["local_end_index"],
+                })
+        return merged
+
+    def _cat_crossattn_caches(
+        self, caches_list: list[list[dict[str, Any]]]
+    ) -> list[dict[str, Any]]:
+        """Concatenate cross-attention caches along batch dimension."""
+        num_layers = len(caches_list[0])
+        merged = []
+        for layer_idx in range(num_layers):
+            merged.append({
+                "k": torch.cat(
+                    [c[layer_idx]["k"] for c in caches_list], dim=0),
+                "v": torch.cat(
+                    [c[layer_idx]["v"] for c in caches_list], dim=0),
+                "is_init": caches_list[0][layer_idx]["is_init"],
+            })
+        return merged
+
+    def _split_contexts(
+        self,
+        merged_ctx: BlockProcessingContext,
+        sessions: list[UserSession],
+    ) -> None:
+        """Split merged KV caches back into per-user caches after forward pass."""
+        n = len(sessions)
+
+        self._split_kv_caches(merged_ctx.kv_cache1,
+                              [s.ctx.kv_cache1 for s in sessions])
+        if merged_ctx.kv_cache2 is not None:
+            self._split_kv_caches(merged_ctx.kv_cache2,
+                                  [s.ctx.kv_cache2 for s in sessions])
+
+        self._split_crossattn_caches(merged_ctx.crossattn_cache,
+                                     [s.ctx.crossattn_cache for s in sessions])
+
+        if merged_ctx.kv_cache_mouse is not None:
+            frame_seq_len = self.denoiser.frame_seq_length
+            self._split_action_kv_caches(
+                merged_ctx.kv_cache_mouse,
+                [s.ctx.kv_cache_mouse for s in sessions],
+                is_mouse=True, frame_seq_length=frame_seq_len)
+        if merged_ctx.kv_cache_keyboard is not None:
+            self._split_kv_caches(merged_ctx.kv_cache_keyboard,
+                                  [s.ctx.kv_cache_keyboard for s in sessions])
+
+    def _split_kv_caches(
+        self,
+        merged: list[dict[str, Any]],
+        per_user: list[list[dict[str, Any]]],
+    ) -> None:
+        """Copy data from merged KV cache back to per-user caches."""
+        n = len(per_user)
+        for layer_idx in range(len(merged)):
+            k_split = merged[layer_idx]["k"].split(1, dim=0)
+            v_split = merged[layer_idx]["v"].split(1, dim=0)
+            for i in range(n):
+                per_user[i][layer_idx]["k"].copy_(k_split[i])
+                per_user[i][layer_idx]["v"].copy_(v_split[i])
+                per_user[i][layer_idx]["global_end_index"].copy_(
+                    merged[layer_idx]["global_end_index"])
+                per_user[i][layer_idx]["local_end_index"].copy_(
+                    merged[layer_idx]["local_end_index"])
+
+    def _split_action_kv_caches(
+        self,
+        merged: list[dict[str, Any]],
+        per_user: list[list[dict[str, Any]]],
+        is_mouse: bool = False,
+        frame_seq_length: int = 1,
+    ) -> None:
+        """Split action KV caches back to per-user."""
+        n = len(per_user)
+        for layer_idx in range(len(merged)):
+            if is_mouse:
+                # [N*F, ...] -> N chunks of [F, ...]
+                k_split = merged[layer_idx]["k"].split(frame_seq_length, dim=0)
+                v_split = merged[layer_idx]["v"].split(frame_seq_length, dim=0)
+            else:
+                k_split = merged[layer_idx]["k"].split(1, dim=0)
+                v_split = merged[layer_idx]["v"].split(1, dim=0)
+            for i in range(n):
+                per_user[i][layer_idx]["k"].copy_(k_split[i])
+                per_user[i][layer_idx]["v"].copy_(v_split[i])
+                per_user[i][layer_idx]["global_end_index"].copy_(
+                    merged[layer_idx]["global_end_index"])
+                per_user[i][layer_idx]["local_end_index"].copy_(
+                    merged[layer_idx]["local_end_index"])
+
+    def _split_crossattn_caches(
+        self,
+        merged: list[dict[str, Any]],
+        per_user: list[list[dict[str, Any]]],
+    ) -> None:
+        """Split cross-attention caches back to per-user."""
+        n = len(per_user)
+        for layer_idx in range(len(merged)):
+            k_split = merged[layer_idx]["k"].split(1, dim=0)
+            v_split = merged[layer_idx]["v"].split(1, dim=0)
+            for i in range(n):
+                per_user[i][layer_idx]["k"].copy_(k_split[i])
+                per_user[i][layer_idx]["v"].copy_(v_split[i])
+                per_user[i][layer_idx]["is_init"] = merged[layer_idx]["is_init"]
+
+    def _merge_action_kwargs(
+        self, sessions: list[UserSession]
+    ) -> dict[str, Any]:
+        """Merge action kwargs by concatenating tensors along batch dim."""
+        ref = sessions[0].action_kwargs
+        if not ref:
+            return {}
+
+        merged: dict[str, Any] = {}
+        for key in ref:
+            vals = [s.action_kwargs[key] for s in sessions]
+            if torch.is_tensor(vals[0]):
+                merged[key] = torch.cat(vals, dim=0)
+            else:
+                # Scalars (like num_frame_per_block) - use ref value
+                merged[key] = vals[0]
+        return merged
+
+    def _make_noise_generator(self, session: UserSession):
+        """Create a noise generator using the user's pre-allocated noise pool."""
+        ctx = session.ctx
+        latents = session.batch.latents
+
+        def noise_gen(shape, dtype, step_idx):
+            if ctx.noise_pool is not None and step_idx < len(ctx.noise_pool):
+                return ctx.noise_pool[step_idx][:, :shape[1], :, :, :].to(
+                    latents.device)
+            else:
+                return torch.randn(shape, dtype=dtype).to(latents.device)
+
+        return noise_gen
+
+    def _finalize_block(self, session: UserSession) -> CompletedResult | None:
+        """Finalize a completed block: update context cache + VAE decode."""
+        ctx = session.ctx
+        batch = session.batch
+        latents = batch.latents
+        start_index = session.block_start_index
+        current_num_frames = session.block_num_frames
+
+        # Write denoised latents back
+        latents[:, :, start_index:start_index +
+                current_num_frames, :, :] = session.current_latents
+
+        # Update KV caches with clean context
+        self.denoiser._update_context_cache(
+            current_latents=session.current_latents,
+            batch=batch,
+            start_index=start_index,
+            current_num_frames=current_num_frames,
+            ctx=ctx,
+            action_kwargs=session.action_kwargs,
+            context_noise=ctx.context_noise,
+        )
+
+        # Advance streaming state
+        old_start = ctx.start_index
+        ctx.start_index += current_num_frames
+        ctx.block_idx += 1
+
+        # VAE decode
+        torch.cuda.synchronize()
+        t0 = time.perf_counter()
+
+        current_latents_for_vae = latents[:, :, start_index:start_index +
+                                          current_num_frames, :, :]
+        decoded_frames, session.vae_cache = self.decoder.streaming_decode(
+            current_latents_for_vae,
+            session.fastvideo_args,
+            cache=session.vae_cache,
+            is_first_chunk=(start_index == 0),
+        )
+        torch.cuda.synchronize()
+        vae_ms = (time.perf_counter() - t0) * 1000
+
+        batch.output = decoded_frames
+        batch.stage_timings = {"dit_ms": session.dit_elapsed_ms, "vae_ms": vae_ms}
+
+        # Reset denoising state
+        session.denoising_step = -1
+        session.current_latents = None
+        session.noise_latents_btchw = None
+
+        return CompletedResult(user_id=session.user_id, output_batch=batch)
+

fastvideo/worker/gpu_worker.py

@@ -75,6 +75,10 @@ class Worker:
 
         self.pipeline = build_pipeline(self.fastvideo_args)
 
+    def get_pipeline(self):
+        """Return the loaded pipeline instance."""
+        return self.pipeline
+
     def execute_forward(self, forward_batch: ForwardBatch,
                         fastvideo_args: FastVideoArgs) -> ForwardBatch:
         output_batch = self.pipeline.forward(forward_batch, self.fastvideo_args)

fastvideo/worker/multiproc_executor.py

@@ -39,13 +39,17 @@ logger = init_logger(__name__)
 class StreamingTaskType(str, Enum):
     """
     Enumeration for different streaming task types.
-    
+
     Inherits from str to allow string comparison for backward compatibility.
     """
     RESET = "reset"
     STEP = "step"
     CLEAR = "clear"
     EXIT = "exit"
+    # Multi-user task types
+    USER_JOIN = "user_join"
+    USER_STEP = "user_step"
+    USER_LEAVE = "user_leave"
 
 
 @dataclass
@@ -58,6 +62,8 @@ class StreamingTask:
     # For RESET tasks:
     batch: ForwardBatch | None = None
     fastvideo_args: FastVideoArgs | None = None
+    # For multi-user tasks:
+    user_id: str | None = None
 
 
 @dataclass
@@ -66,6 +72,8 @@ class StreamingResult:
     task_type: StreamingTaskType
     output_batch: ForwardBatch | None = None
     error: Exception | None = None
+    # For multi-user results:
+    user_id: str | None = None
 
 
 class MultiprocExecutor(Executor):
@@ -183,6 +191,13 @@ class MultiprocExecutor(Executor):
         self.collective_rpc("start_streaming_queue_loop")
         self._streaming_enabled = True
 
+    def enable_multi_user_streaming(self) -> None:
+        if self._streaming_enabled:
+            return
+
+        self.collective_rpc("start_multi_user_streaming_loop")
+        self._streaming_enabled = True
+
     def disable_streaming(self) -> None:
         if not self._streaming_enabled:
             return
@@ -225,6 +240,42 @@ class MultiprocExecutor(Executor):
             self._streaming_input_queue.put(
                 StreamingTask(task_type=StreamingTaskType.CLEAR))
 
+    def submit_user_join(self, user_id: str, forward_batch: ForwardBatch,
+                         fastvideo_args: FastVideoArgs) -> None:
+        if not self._streaming_enabled:
+            self.enable_streaming()
+
+        self._streaming_input_queue.put(
+            StreamingTask(
+                task_type=StreamingTaskType.USER_JOIN,
+                user_id=user_id,
+                batch=forward_batch,
+                fastvideo_args=fastvideo_args,
+            ))
+
+    def submit_user_step(self, user_id: str,
+                         keyboard_action: torch.Tensor | None,
+                         mouse_action: torch.Tensor | None) -> None:
+        if not self._streaming_enabled:
+            raise RuntimeError(
+                "Streaming mode not enabled. Call enable_streaming() first.")
+
+        self._streaming_input_queue.put(
+            StreamingTask(
+                task_type=StreamingTaskType.USER_STEP,
+                user_id=user_id,
+                keyboard_action=keyboard_action,
+                mouse_action=mouse_action,
+            ))
+
+    def submit_user_leave(self, user_id: str) -> None:
+        if self._streaming_enabled and self._streaming_input_queue is not None:
+            self._streaming_input_queue.put(
+                StreamingTask(
+                    task_type=StreamingTaskType.USER_LEAVE,
+                    user_id=user_id,
+                ))
+
     def get_result(self,
                    timeout: float | None = None) -> StreamingResult | None:
         if not self._streaming_enabled or self._streaming_output_queue is None:
@@ -642,6 +693,11 @@ class WorkerMultiprocProc:
                             {"status": "streaming_queue_loop_started"})
                         self.streaming_queue_loop()
                         continue
+                    if method == "start_multi_user_streaming_loop":
+                        self.pipe.send(
+                            {"status": "multi_user_streaming_loop_started"})
+                        self.multi_user_streaming_loop()
+                        continue
                     if method == 'execute_forward':
                         forward_batch = kwargs['forward_batch']
                         fastvideo_args = kwargs['fastvideo_args']
@@ -715,11 +771,126 @@ class WorkerMultiprocProc:
                     self.worker.execute_streaming_clear()
                     self.streaming_output_queue.put(
                         StreamingResult(task_type=StreamingTaskType.CLEAR))
+                elif task.task_type in (StreamingTaskType.USER_JOIN,
+                                        StreamingTaskType.USER_STEP,
+                                        StreamingTaskType.USER_LEAVE):
+                    # Switch to multi-user mode
+                    self.multi_user_streaming_loop(first_task=task)
+                    break
             except Exception as e:
                 logger.error("Worker %d queue loop error: %s", self.rank, e)
                 self.streaming_output_queue.put(
                     StreamingResult(task_type=StreamingTaskType.STEP, error=e))
 
+    def multi_user_streaming_loop(
+        self, first_task: StreamingTask | None = None
+    ) -> None:
+        """ORCA-style multi-user streaming loop.
+
+        Processes USER_JOIN/USER_STEP/USER_LEAVE tasks and batches
+        compatible users together for each denoising step.
+        """
+        from fastvideo.pipelines.stages.multi_user_engine import MultiUserEngine
+
+        if self.streaming_input_queue is None or self.streaming_output_queue is None:
+            logger.error("Worker %d: streaming queues not initialized",
+                         self.rank)
+            return
+
+        pipeline = self.worker.get_pipeline()
+        try:
+            from ui.world_model.server.config import DISABLE_BATCHING
+        except ImportError:
+            DISABLE_BATCHING = False
+        engine = MultiUserEngine(pipeline, disable_batching=DISABLE_BATCHING)
+        logger.info("Worker %d: multi-user streaming loop started", self.rank)
+
+        def handle_task(task: StreamingTask) -> bool:
+            """Handle a single task. Returns False if should exit."""
+            if task.task_type == StreamingTaskType.EXIT:
+                return False
+            elif task.task_type == StreamingTaskType.USER_JOIN:
+                try:
+                    engine.add_user(
+                        task.user_id, task.batch, task.fastvideo_args)
+                    self.streaming_output_queue.put(
+                        StreamingResult(
+                            task_type=StreamingTaskType.USER_JOIN,
+                            user_id=task.user_id))
+                except Exception as e:
+                    logger.error("Worker %d user_join error for %s: %s",
+                                 self.rank, task.user_id, e)
+                    self.streaming_output_queue.put(
+                        StreamingResult(
+                            task_type=StreamingTaskType.USER_JOIN,
+                            user_id=task.user_id, error=e))
+            elif task.task_type == StreamingTaskType.USER_STEP:
+                try:
+                    engine.submit_step(
+                        task.user_id, task.keyboard_action, task.mouse_action)
+                except Exception as e:
+                    logger.error("Worker %d user_step submit error for %s: %s",
+                                 self.rank, task.user_id, e)
+                    self.streaming_output_queue.put(
+                        StreamingResult(
+                            task_type=StreamingTaskType.USER_STEP,
+                            user_id=task.user_id, error=e))
+            elif task.task_type == StreamingTaskType.USER_LEAVE:
+                engine.remove_user(task.user_id)
+                self.streaming_output_queue.put(
+                    StreamingResult(
+                        task_type=StreamingTaskType.USER_LEAVE,
+                        user_id=task.user_id))
+            return True
+
+        # Handle the first task that triggered the switch
+        if first_task is not None:
+            if not handle_task(first_task):
+                return
+
+        while True:
+            # 1. Drain input queue (non-blocking) for new requests
+            while True:
+                try:
+                    task = self.streaming_input_queue.get_nowait()
+                    if not handle_task(task):
+                        return
+                except queue.Empty:
+                    break
+
+            # 2. Run one ORCA iteration
+            if engine.has_pending_work():
+                try:
+                    completed = engine.run_iteration()
+                    for result in completed:
+                        self.streaming_output_queue.put(
+                            StreamingResult(
+                                task_type=StreamingTaskType.USER_STEP,
+                                user_id=result.user_id,
+                                output_batch=result.output_batch))
+                except Exception as e:
+                    logger.error("Worker %d ORCA iteration error: %s",
+                                 self.rank, e, exc_info=True)
+                    # Reset all pending users to prevent infinite error loop
+                    for uid, sess in engine.users.items():
+                        if sess.denoising_step >= 0:
+                            sess.denoising_step = -1
+                            sess.current_latents = None
+                            sess.noise_latents_btchw = None
+                            self.streaming_output_queue.put(
+                                StreamingResult(
+                                    task_type=StreamingTaskType.USER_STEP,
+                                    user_id=uid, error=e))
+
+            # 3. If no work, block on queue to avoid busy-waiting
+            if not engine.has_pending_work():
+                try:
+                    task = self.streaming_input_queue.get(timeout=0.1)
+                    if not handle_task(task):
+                        return
+                except queue.Empty:
+                    continue
+
     @staticmethod
     def setup_proc_title_and_log_prefix() -> None:
         dp_size = get_dp_group().world_size

ui/world_model/client/index.html

@@ -0,0 +1,12 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>World Model Interface</title>
+</head>
+<body>
+  <div id="app"></div>
+  <script type="module" src="/src/main.js"></script>
+</body>
+</html>

ui/world_model/client/package.json

@@ -0,0 +1,16 @@
+{
+  "name": "wm-interface-frontend",
+  "private": true,
+  "version": "0.0.1",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview"
+  },
+  "devDependencies": {
+    "@sveltejs/vite-plugin-svelte": "^3.0.1",
+    "svelte": "^4.2.8",
+    "vite": "^5.0.11"
+  }
+}

ui/world_model/client/public/logo.svg

@@ -0,0 +1,18 @@
+<svg width="252" height="105" viewBox="0 0 252 105" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M89.4843 55.5457H101.361L87.7028 101H74.638L89.4843 55.5457Z" fill="#356CFF"/>
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+<path d="M8.12646 55.5451L14.065 100.999L15.2527 100.999L9.31417 55.5451L8.12646 55.5451Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
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+<path d="M15.2524 48.2724L30.0988 1H33.6619L18.8156 48.2724H15.2524Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M8.12646 48.2724L22.9728 1H24.1605L9.31417 48.2724H8.12646Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
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+<path d="M85.3271 55.5457H67.5116L87 12.7363L44.3513 68.2729H58.6038L43.1636 101L85.3271 55.5457Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.18771" stroke-miterlimit="16"/>
+</svg>

ui/world_model/client/src/main.js

@@ -0,0 +1,8 @@
+import App from './App.svelte'
+import './style.css'
+
+const app = new App({
+  target: document.getElementById('app')
+})
+
+export default app

ui/world_model/client/src/style.css

@@ -0,0 +1,20 @@
+* {
+  margin: 0;
+  padding: 0;
+  box-sizing: border-box;
+}
+
+body {
+  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+  background: #1a1a1a;
+  color: white;
+  min-height: 100vh;
+  overflow-y: auto;
+}
+
+#app {
+  width: 100%;
+  min-height: 100vh;
+  display: flex;
+  flex-direction: column;
+}

ui/world_model/client/vite.config.js

@@ -0,0 +1,18 @@
+import { defineConfig } from 'vite'
+import { svelte } from '@sveltejs/vite-plugin-svelte'
+
+export default defineConfig({
+  plugins: [svelte()],
+  server: {
+    port: 5173,
+    proxy: {
+      '/ws': {
+        target: 'http://localhost:8000',
+        ws: true,
+      },
+      '/status': {
+        target: 'http://localhost:8000',
+      },
+    },
+  }
+})

ui/world_model/server/config.py

@@ -0,0 +1,61 @@
+from pathlib import Path
+
+# Repo root: ui/world_model/server/config.py -> go up 3 levels
+_REPO_ROOT = Path(__file__).resolve().parents[3]
+
+# Model registry - add new models here
+MODEL_REGISTRY = {
+    "matrix-game-2.0-base": {
+        "name": "Matrix-Game 2.0 Base",
+        "model_path": "FastVideo/Matrix-Game-2.0-Base-Diffusers",
+        "keyboard_dim": 4,
+        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/universal/0000.png",
+    },
+    "wangame-1.3b-mc-w-only-still-7k": {
+        "name": "WANGame 1.3B MC (W-Only, 7k)",
+        "model_path": "weizhou03/Wan2.1-Game-Fun-1.3B-InP-Diffusers",
+        "init_weights_from_safetensors": str(_REPO_ROOT / "checkpoints" / "wangame-1.3b-mc-w-only-still-7k" / "checkpoint-7000" / "transformer"),
+        "override_transformer_cls_name": "WanGameActionTransformer3DModel",
+        "override_pipeline_cls_name": "WanGameCausalDMDPipeline",
+        "keyboard_dim": 4,
+        #"image_url": "/server-assets/mc.png",
+        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/universal/0000.png",
+        # "image_path": str(Path(__file__).resolve().parent / "mc.png"),
+    },
+}
+
+DEFAULT_MODEL_ID = "matrix-game-2.0-base"
+
+# Active model configuration (set by server or user selection)
+MODEL_CONFIG = MODEL_REGISTRY[DEFAULT_MODEL_ID]
+
+# Keyboard mappings (WASD)
+KEYBOARD_MAP = {
+    "w": [1, 0, 0, 0],
+    "s": [0, 1, 0, 0],
+    "a": [0, 0, 1, 0],
+    "d": [0, 0, 0, 1],
+}
+
+# Camera mappings (Arrow keys)
+CAM_VALUE = 0.1
+CAMERA_MAP = {
+    "ArrowUp": [CAM_VALUE, 0],
+    "ArrowDown": [-CAM_VALUE, 0],
+    "ArrowLeft": [0, -CAM_VALUE],
+    "ArrowRight": [0, CAM_VALUE],
+}
+
+# Generation limits
+MAX_BLOCKS = 50
+SESSION_TIMEOUT_SECONDS = 900
+MAX_USERS_PER_GPU = 16
+DISABLE_BATCHING = False  # Process users sequentially (batch_size=1) instead of batching
+
+# Frame settings
+NUM_FRAMES = 597
+FRAME_HEIGHT = 352
+FRAME_WIDTH = 640
+NUM_INFERENCE_STEPS = 4
+JPEG_QUALITY = 85
+BATCH_SIZE = 12

ui/world_model/server/main.py

@@ -0,0 +1,262 @@
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from contextlib import asynccontextmanager
+import asyncio
+import base64
+import cv2
+import os
+import time
+import uuid
+
+from config import (
+    KEYBOARD_MAP, CAMERA_MAP, MAX_BLOCKS, JPEG_QUALITY, BATCH_SIZE,
+    SESSION_TIMEOUT_SECONDS, MODEL_CONFIG, MODEL_REGISTRY, DEFAULT_MODEL_ID
+)
+from gpu_pool import GPUPool, GPUSlot, get_available_gpus
+
+# Global GPU pool
+gpu_pool: GPUPool = None
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Application lifespan manager."""
+    global gpu_pool
+
+    print("Starting server...")
+
+    # Get available GPUs
+    gpu_ids = get_available_gpus()
+    print(f"Detected GPUs: {gpu_ids}")
+
+    # Initialize GPU pool (spawns subprocess per GPU)
+    gpu_pool = GPUPool(gpu_ids)
+    await gpu_pool.initialize()
+
+    print("Server ready")
+    yield
+
+    print("Shutting down server...")
+    await gpu_pool.shutdown()
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/status")
+async def get_status():
+    """Get the current status of the GPU pool."""
+    if gpu_pool is None:
+        return {"error": "GPU pool not initialized"}
+    return gpu_pool.get_status()
+
+
+@app.get("/models")
+async def get_models():
+    """Get available models and the currently active model."""
+    models = []
+    for model_id, config in MODEL_REGISTRY.items():
+        models.append({
+            "id": model_id,
+            "name": config["name"],
+        })
+    return {
+        "models": models,
+        "default_model_id": DEFAULT_MODEL_ID,
+    }
+
+
+def encode_frames(frames: list) -> list[str]:
+    """Encode frames to base64 JPEG."""
+    encoded = []
+    for frame in frames:
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        _, buffer = cv2.imencode('.jpg', frame_rgb, [cv2.IMWRITE_JPEG_QUALITY, JPEG_QUALITY])
+        encoded.append(base64.b64encode(buffer.tobytes()).decode("utf-8"))
+    return encoded
+
+
+async def send_frames(websocket: WebSocket, frames: list):
+    """Encode and send frames in batches."""
+    if not frames:
+        return
+    encoded_frames = encode_frames(frames)
+    for i in range(0, len(encoded_frames), BATCH_SIZE):
+        batch = encoded_frames[i:i + BATCH_SIZE]
+        await websocket.send_json({"type": "frame_batch", "frames": batch})
+
+
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    await websocket.accept()
+
+    client_id = str(uuid.uuid4())
+    print(f"Client {client_id[:8]} connected")
+
+    # Send initial queue status
+    status = gpu_pool.get_status()
+    await websocket.send_json({
+        "type": "queue_status",
+        "position": status["queue_size"] + 1 if status["available_gpus"] == 0 else 0,
+        "total_gpus": status["total_gpus"],
+        "available_gpus": status["available_gpus"]
+    })
+
+    gpu_id = None
+    slot: GPUSlot = None
+    timeout_task: asyncio.Task = None
+
+    async def session_timeout():
+        """Close the session after timeout."""
+        await asyncio.sleep(SESSION_TIMEOUT_SECONDS)
+        print(f"[GPU {gpu_id}] Session timeout for client {client_id[:8]}")
+        try:
+            await websocket.send_json({
+                "type": "session_timeout",
+                "message": f"Session expired after {SESSION_TIMEOUT_SECONDS} seconds"
+            })
+            await websocket.close(code=1000, reason="Session timeout")
+        except Exception:
+            pass  # WebSocket may already be closed
+
+    try:
+        # Wait for model selection from client
+        model_id = DEFAULT_MODEL_ID
+        model_config = MODEL_CONFIG
+        try:
+            init_data = await asyncio.wait_for(websocket.receive_json(), timeout=5.0)
+            if init_data.get("type") == "select_model":
+                model_id = init_data.get("model_id", DEFAULT_MODEL_ID)
+                if model_id in MODEL_REGISTRY:
+                    model_config = MODEL_REGISTRY[model_id]
+                    print(f"Client {client_id[:8]} selected model: {model_id}")
+        except asyncio.TimeoutError:
+            pass  # Use default model config
+
+        # Acquire a GPU slot (may wait in queue, may share with other users)
+        gpu_id, slot = await gpu_pool.acquire(client_id, websocket)
+
+        # Start session timeout
+        timeout_task = asyncio.create_task(session_timeout())
+
+        # Join the multi-user engine on this GPU (triggers reload if model differs)
+        await slot.join_user(client_id, model_id=model_id)
+
+        # Notify client they're connected to a GPU
+        await websocket.send_json({
+            "type": "gpu_assigned",
+            "gpu_id": gpu_id,
+            "session_timeout": SESSION_TIMEOUT_SECONDS,
+            "image_url": model_config.get("image_url")
+        })
+
+        # Generate and send initial frame
+        block_count = 0
+        await websocket.send_json({"type": "block_count", "count": block_count, "max": MAX_BLOCKS})
+
+        try:
+            frames, timings = await slot.user_step(
+                client_id, [0, 0, 0, 0], [0, 0])
+            block_count = 1
+            await websocket.send_json({"type": "block_count", "count": block_count, "max": MAX_BLOCKS})
+            await send_frames(websocket, frames)
+            if timings:
+                print(f"[GPU {gpu_id}] Initial ({client_id[:8]}): model={timings.get('model_step_ms', 0):.0f}ms")
+        except Exception as e:
+            print(f"[GPU {gpu_id}] Initial frame error for {client_id[:8]}: {e}")
+
+        # Main event loop
+        while True:
+            data = await websocket.receive_json()
+            message_type = data.get("type", "key")
+
+            if message_type == "reset":
+                print(f"[GPU {gpu_id}] Reset requested by client {client_id[:8]}")
+                await websocket.send_json({"type": "reset_started"})
+
+                try:
+                    # Leave and rejoin to reset user state
+                    await slot.leave_user(client_id)
+                    await slot.join_user(client_id, model_id=model_id)
+
+                    frames, timings = await slot.user_step(
+                        client_id, [0, 0, 0, 0], [0, 0])
+                    block_count = 1
+                    await websocket.send_json({"type": "block_count", "count": block_count, "max": MAX_BLOCKS})
+                    await send_frames(websocket, frames)
+                    await websocket.send_json({"type": "reset_complete"})
+                    print(f"[GPU {gpu_id}] Reset complete for {client_id[:8]}")
+                except Exception as e:
+                    print(f"[GPU {gpu_id}] Reset error for {client_id[:8]}: {e}")
+                    await websocket.send_json({"type": "reset_complete"})
+                continue
+
+            # Handle key press
+            key = data.get("key")
+            if key and block_count < MAX_BLOCKS:
+                if key in CAMERA_MAP:
+                    keyboard_vector = [0, 0, 0, 0]
+                    mouse_vector = CAMERA_MAP[key]
+                else:
+                    keyboard_vector = KEYBOARD_MAP.get(key, [0, 0, 0, 0])
+                    mouse_vector = [0, 0]
+
+                print(f"[GPU {gpu_id}] Key '{key}' from {client_id[:8]}, generating block {block_count + 1}...")
+
+                t_start = time.time()
+                frames, timings = await slot.user_step(
+                    client_id, keyboard_vector, mouse_vector)
+                t_generation = time.time() - t_start
+
+                block_count += 1
+                await websocket.send_json({"type": "block_count", "count": block_count, "max": MAX_BLOCKS})
+
+                if frames:
+                    t_encode_start = time.time()
+                    await send_frames(websocket, frames)
+                    t_encode = time.time() - t_encode_start
+
+                    dit_ms = timings.get('dit_ms', 0)
+                    vae_ms = timings.get('vae_ms', 0)
+                    print(f"[GPU {gpu_id}] ({client_id[:8]}) DiT: {dit_ms:.0f}ms, VAE: {vae_ms:.0f}ms, Encode+Send: {t_encode*1000:.0f}ms, Total: {t_generation*1000:.0f}ms")
+
+    except WebSocketDisconnect:
+        print(f"Client {client_id[:8]} disconnected")
+    except Exception as e:
+        print(f"Client {client_id[:8]} error: {e}")
+        import traceback
+        traceback.print_exc()
+    finally:
+        # Cancel the timeout task if still running
+        if timeout_task and not timeout_task.done():
+            timeout_task.cancel()
+            try:
+                await timeout_task
+            except asyncio.CancelledError:
+                pass
+        if client_id:
+            await gpu_pool.release(client_id)
+
+
+# Serve server-side assets (images, etc.)
+server_dir = os.path.dirname(os.path.abspath(__file__))
+app.mount("/server-assets", StaticFiles(directory=server_dir), name="server-assets")
+
+# Serve built frontend (must be after API/WebSocket routes)
+static_dir = os.path.join(server_dir, "..", "client", "dist")
+if os.path.isdir(static_dir):
+    app.mount("/", StaticFiles(directory=static_dir, html=True), name="static")
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)

ui/world_model/server/mock_server.py

@@ -0,0 +1,332 @@
+"""
+Mock server to test latency perception.
+Generates frames with a moving object based on keyboard input.
+Configurable latency to find the acceptable threshold.
+"""
+
+import asyncio
+import base64
+import io
+import time
+import uuid
+from contextlib import asynccontextmanager
+
+import numpy as np
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from PIL import Image, ImageDraw
+import os
+
+# Configuration
+LATENCY_MS = 200  # Simulated model latency - adjust this to test
+FRAME_WIDTH = 640
+FRAME_HEIGHT = 352
+NUM_FRAMES = 12  # Frames per block
+FPS = 24
+BATCH_SIZE = 4
+JPEG_QUALITY = 85
+SESSION_TIMEOUT_SECONDS = 90
+MAX_BLOCKS = 50
+
+# Mock game state
+class GameState:
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.x = FRAME_WIDTH // 2
+        self.y = FRAME_HEIGHT // 2
+        self.size = 40
+        self.color = (100, 200, 255)  # Light blue
+        self.trail = []  # Previous positions for motion blur effect
+
+    def update(self, keyboard_vector, mouse_vector):
+        """Store the movement direction for this block."""
+        self.keyboard_vector = keyboard_vector
+        self.mouse_vector = mouse_vector
+
+        # Arrow keys for camera (just change color as visual feedback)
+        if mouse_vector[0] != 0 or mouse_vector[1] != 0:
+            r = int(100 + mouse_vector[0] * 50) % 256
+            g = int(200 + mouse_vector[1] * 50) % 256
+            self.color = (r, g, 255)
+
+    def render_frame(self, frame_idx: int) -> np.ndarray:
+        """Render a single frame with incremental movement."""
+        speed_per_frame = 8
+
+        # Apply movement for this frame
+        if hasattr(self, 'keyboard_vector'):
+            if self.keyboard_vector[0]:  # W - up
+                self.y -= speed_per_frame
+            if self.keyboard_vector[1]:  # A - left
+                self.x -= speed_per_frame
+            if self.keyboard_vector[2]:  # S - down
+                self.y += speed_per_frame
+            if self.keyboard_vector[3]:  # D - right
+                self.x += speed_per_frame
+
+        # Wrap around screen
+        self.x = self.x % FRAME_WIDTH
+        self.y = self.y % FRAME_HEIGHT
+
+        # Update trail
+        self.trail.append((self.x, self.y))
+        if len(self.trail) > 5:
+            self.trail.pop(0)
+        """Render a single frame."""
+        # Create frame with gradient background
+        img = Image.new('RGB', (FRAME_WIDTH, FRAME_HEIGHT), (20, 20, 30))
+        draw = ImageDraw.Draw(img)
+
+        # Draw grid for visual reference
+        for x in range(0, FRAME_WIDTH, 40):
+            draw.line([(x, 0), (x, FRAME_HEIGHT)], fill=(40, 40, 50), width=1)
+        for y in range(0, FRAME_HEIGHT, 40):
+            draw.line([(0, y), (FRAME_WIDTH, y)], fill=(40, 40, 50), width=1)
+
+        # Interpolate position for smooth animation within block
+        t = frame_idx / NUM_FRAMES
+
+        # Draw trail (motion blur effect)
+        for i, (tx, ty) in enumerate(self.trail):
+            alpha = (i + 1) / len(self.trail) * 0.3
+            trail_size = int(self.size * (0.5 + alpha * 0.5))
+            trail_color = tuple(int(c * alpha) for c in self.color)
+            draw.ellipse(
+                [tx - trail_size//2, ty - trail_size//2,
+                 tx + trail_size//2, ty + trail_size//2],
+                fill=trail_color
+            )
+
+        # Draw main object (circle with glow)
+        # Outer glow
+        for r in range(3, 0, -1):
+            glow_size = self.size + r * 8
+            glow_alpha = 0.2 / r
+            glow_color = tuple(int(c * glow_alpha) for c in self.color)
+            draw.ellipse(
+                [self.x - glow_size//2, self.y - glow_size//2,
+                 self.x + glow_size//2, self.y + glow_size//2],
+                fill=glow_color
+            )
+
+        # Main circle
+        draw.ellipse(
+            [self.x - self.size//2, self.y - self.size//2,
+             self.x + self.size//2, self.y + self.size//2],
+            fill=self.color
+        )
+
+        # Inner highlight
+        highlight_size = self.size // 3
+        highlight_offset = self.size // 6
+        draw.ellipse(
+            [self.x - highlight_offset - highlight_size//2,
+             self.y - highlight_offset - highlight_size//2,
+             self.x - highlight_offset + highlight_size//2,
+             self.y - highlight_offset + highlight_size//2],
+            fill=(255, 255, 255)
+        )
+
+        # Add latency indicator text
+        draw.text((10, 10), f"Latency: {LATENCY_MS}ms", fill=(150, 150, 150))
+        draw.text((10, 30), f"Frame: {frame_idx + 1}/{NUM_FRAMES}", fill=(100, 100, 100))
+
+        return np.array(img)
+
+
+def encode_frame(frame: np.ndarray) -> str:
+    """Encode frame to base64 JPEG."""
+    img = Image.fromarray(frame)
+    buffer = io.BytesIO()
+    img.save(buffer, format='JPEG', quality=JPEG_QUALITY)
+    return base64.b64encode(buffer.getvalue()).decode('utf-8')
+
+
+def generate_frames(game_state: GameState) -> list[str]:
+    """Generate and encode a block of frames."""
+    encoded = []
+    for i in range(NUM_FRAMES):
+        frame = game_state.render_frame(i)
+        encoded.append(encode_frame(frame))
+    return encoded
+
+
+# Keyboard/camera mappings (same as real server)
+KEYBOARD_MAP = {
+    'w': [1, 0, 0, 0],
+    'a': [0, 1, 0, 0],
+    's': [0, 0, 1, 0],
+    'd': [0, 0, 0, 1],
+}
+
+CAMERA_MAP = {
+    'ArrowUp': [0, -1],
+    'ArrowDown': [0, 1],
+    'ArrowLeft': [-1, 0],
+    'ArrowRight': [1, 0],
+}
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    print(f"Mock server starting with {LATENCY_MS}ms simulated latency...")
+    yield
+    print("Mock server shutting down...")
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+async def send_frames(websocket: WebSocket, frames: list[str]):
+    """Send frames in batches."""
+    for i in range(0, len(frames), BATCH_SIZE):
+        batch = frames[i:i + BATCH_SIZE]
+        await websocket.send_json({"type": "frame_batch", "frames": batch})
+
+
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    await websocket.accept()
+
+    client_id = str(uuid.uuid4())
+    print(f"Client {client_id[:8]} connected")
+
+    game_state = GameState()
+    block_count = 0
+    timeout_task = None
+
+    async def session_timeout():
+        await asyncio.sleep(SESSION_TIMEOUT_SECONDS)
+        try:
+            await websocket.send_json({
+                "type": "session_timeout",
+                "message": f"Session expired after {SESSION_TIMEOUT_SECONDS} seconds"
+            })
+            await websocket.close()
+        except Exception:
+            pass
+
+    try:
+        # Send initial status
+        await websocket.send_json({
+            "type": "queue_status",
+            "position": 0,
+            "total_gpus": 1,
+            "available_gpus": 1
+        })
+
+        # Simulate GPU assignment delay
+        await asyncio.sleep(0.1)
+
+        # Start timeout
+        timeout_task = asyncio.create_task(session_timeout())
+
+        # Send GPU assigned
+        await websocket.send_json({
+            "type": "gpu_assigned",
+            "gpu_id": 0,
+            "session_timeout": SESSION_TIMEOUT_SECONDS,
+            "image_url": None  # No initial image for mock
+        })
+
+        # Generate initial frame
+        await websocket.send_json({"type": "block_count", "count": 0, "max": MAX_BLOCKS})
+
+        # Simulate latency
+        await asyncio.sleep(LATENCY_MS / 1000)
+
+        frames = generate_frames(game_state)
+        block_count = 1
+        await websocket.send_json({"type": "block_count", "count": block_count, "max": MAX_BLOCKS})
+        await send_frames(websocket, frames)
+
+        print(f"[Mock] Initial frames sent")
+
+        # Main loop
+        while True:
+            data = await websocket.receive_json()
+            message_type = data.get("type", "key")
+
+            if message_type == "reset":
+                print(f"[Mock] Reset requested")
+                await websocket.send_json({"type": "reset_started"})
+
+                game_state.reset()
+                await asyncio.sleep(LATENCY_MS / 1000)
+
+                frames = generate_frames(game_state)
+                block_count = 1
+                await websocket.send_json({"type": "block_count", "count": block_count, "max": MAX_BLOCKS})
+                await send_frames(websocket, frames)
+                await websocket.send_json({"type": "reset_complete"})
+                continue
+
+            key = data.get("key")
+            if key and block_count < MAX_BLOCKS:
+                if key in CAMERA_MAP:
+                    keyboard_vector = [0, 0, 0, 0]
+                    mouse_vector = CAMERA_MAP[key]
+                else:
+                    keyboard_vector = KEYBOARD_MAP.get(key, [0, 0, 0, 0])
+                    mouse_vector = [0, 0]
+
+                print(f"[Mock] Key '{key}' pressed, generating block {block_count + 1}...")
+
+                t_start = time.time()
+
+                # Simulate model latency
+                await asyncio.sleep(LATENCY_MS / 1000)
+
+                # Update game state and generate frames
+                game_state.update(keyboard_vector, mouse_vector)
+                frames = generate_frames(game_state)
+
+                t_total = (time.time() - t_start) * 1000
+
+                block_count += 1
+                await websocket.send_json({"type": "block_count", "count": block_count, "max": MAX_BLOCKS})
+                await send_frames(websocket, frames)
+
+                print(f"[Mock] Block generated in {t_total:.0f}ms (target: {LATENCY_MS}ms)")
+
+    except WebSocketDisconnect:
+        print(f"Client {client_id[:8]} disconnected")
+    except Exception as e:
+        print(f"Client {client_id[:8]} error: {e}")
+        import traceback
+        traceback.print_exc()
+    finally:
+        if timeout_task and not timeout_task.done():
+            timeout_task.cancel()
+
+
+# Serve static files
+static_dir = os.path.join(os.path.dirname(__file__), "..", "client", "dist")
+if os.path.isdir(static_dir):
+    app.mount("/", StaticFiles(directory=static_dir, html=True), name="static")
+
+
+if __name__ == "__main__":
+    import argparse
+    import uvicorn
+
+    parser = argparse.ArgumentParser(description="Mock server for latency testing")
+    parser.add_argument("--latency", type=int, default=200, help="Simulated latency in ms")
+    parser.add_argument("--port", type=int, default=8001, help="Server port")
+    args = parser.parse_args()
+
+    LATENCY_MS = args.latency
+    print(f"Starting mock server with {LATENCY_MS}ms latency on port {args.port}")
+
+    uvicorn.run(app, host="0.0.0.0", port=args.port)

ui/world_model/server/test_batching.py

@@ -0,0 +1,149 @@
+"""Test ORCA batching: synchronized vs random keypresses."""
+import argparse
+import asyncio
+import json
+import random
+import time
+import websockets
+
+SERVER_URL = "ws://localhost:8000/ws"
+KEYS = ["w", "a", "s", "d"]
+# Think time range for random mode (ms)
+MIN_THINK_MS = 200
+MAX_THINK_MS = 1500
+
+
+async def connect_and_wait(client_id, ws):
+    """Drain messages until initial frame_batch is received."""
+    while True:
+        msg = json.loads(await ws.recv())
+        if msg["type"] == "frame_batch":
+            print(f"  Client {client_id}: got initial frame")
+            return
+
+
+async def synced_client(client_id: int, barrier: asyncio.Barrier, results: list,
+                        num_rounds: int):
+    """Synchronized keypresses — all clients press at the same time."""
+    async with websockets.connect(
+        SERVER_URL, max_size=50_000_000, ping_interval=None,
+    ) as ws:
+        await connect_and_wait(client_id, ws)
+
+        for round_idx in range(num_rounds):
+            await barrier.wait()
+
+            t0 = time.perf_counter()
+            await ws.send(json.dumps({"type": "key", "key": "w"}))
+
+            while True:
+                msg = json.loads(await ws.recv())
+                if msg["type"] == "frame_batch":
+                    break
+
+            elapsed_ms = (time.perf_counter() - t0) * 1000
+            results.append(elapsed_ms)
+            print(f"  Client {client_id} round {round_idx}: {elapsed_ms:.0f}ms")
+
+
+async def random_client(client_id: int, start_event: asyncio.Event, results: list,
+                        num_rounds: int):
+    """Random keypresses with think time between presses."""
+    async with websockets.connect(
+        SERVER_URL, max_size=50_000_000, ping_interval=None,
+    ) as ws:
+        await connect_and_wait(client_id, ws)
+        start_event.set()
+
+        # Small stagger so clients don't all start at once
+        await asyncio.sleep(random.uniform(0, 1.0))
+
+        for _ in range(num_rounds):
+            think_ms = random.uniform(MIN_THINK_MS, MAX_THINK_MS)
+            await asyncio.sleep(think_ms / 1000)
+
+            t0 = time.perf_counter()
+            await ws.send(json.dumps({"type": "key", "key": random.choice(KEYS)}))
+
+            while True:
+                msg = json.loads(await ws.recv())
+                if msg["type"] == "frame_batch":
+                    break
+
+            elapsed_ms = (time.perf_counter() - t0) * 1000
+            results.append(elapsed_ms)
+
+        print(f"  Client {client_id}: done")
+
+
+def print_stats(times: list[float], label: str):
+    """Print summary statistics and histogram."""
+    times.sort()
+    n = len(times)
+    print(f"\n--- {label} ({n} keypresses) ---")
+    print(f"  Min:    {times[0]:.0f}ms")
+    print(f"  p25:    {times[n//4]:.0f}ms")
+    print(f"  Median: {times[n//2]:.0f}ms")
+    print(f"  p75:    {times[3*n//4]:.0f}ms")
+    print(f"  p95:    {times[int(n*0.95)]:.0f}ms")
+    print(f"  Max:    {times[-1]:.0f}ms")
+    print(f"  Avg:    {sum(times)/n:.0f}ms")
+
+    buckets = [0] * 10
+    labels = ["<1s", "<1.5s", "<2s", "<2.5s", "<3s",
+              "<3.5s", "<4s", "<4.5s", "<5s", "5s+"]
+    for ms in times:
+        idx = min(int(ms / 500), 9)
+        buckets[idx] += 1
+    print(f"\n  Distribution:")
+    for lbl, count in zip(labels, buckets):
+        if count > 0:
+            bar = "#" * count
+            pct = count / n * 100
+            print(f"    {lbl:>6s}: {bar} ({count}, {pct:.0f}%)")
+
+
+async def run_synced(num_clients: int, num_rounds: int):
+    print(f"=== SYNCHRONIZED MODE ({num_clients} clients, {num_rounds} rounds) ===")
+    barrier = asyncio.Barrier(num_clients)
+    results = []
+    tasks = [synced_client(i, barrier, results, num_rounds) for i in range(num_clients)]
+    await asyncio.gather(*tasks)
+    print_stats(results, "Synchronized (worst case)")
+
+
+async def run_random(num_clients: int, num_rounds: int):
+    print(f"=== RANDOM MODE ({num_clients} clients, {num_rounds} presses each) ===")
+    print(f"    Think time: {MIN_THINK_MS}-{MAX_THINK_MS}ms")
+    results = []
+    events = [asyncio.Event() for _ in range(num_clients)]
+    tasks = [random_client(i, events[i], results, num_rounds) for i in range(num_clients)]
+    await asyncio.gather(*tasks)
+    print_stats(results, "Random (realistic)")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Test ORCA multi-user batching")
+    parser.add_argument("mode", nargs="?", default="both",
+                        choices=["synced", "random", "both"])
+    parser.add_argument("-c", "--clients", type=int, default=4,
+                        help="Number of concurrent clients (default: 4)")
+    parser.add_argument("-r", "--rounds", type=int, default=10,
+                        help="Number of rounds per client (default: 10)")
+    return parser.parse_args()
+
+
+async def main():
+    args = parse_args()
+    print(f"Connecting {args.clients} clients to {SERVER_URL}...\n")
+
+    if args.mode in ("synced", "both"):
+        await run_synced(args.clients, args.rounds)
+    if args.mode in ("random", "both"):
+        if args.mode == "both":
+            print("\n" + "=" * 60 + "\n")
+        await run_random(args.clients, args.rounds)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())