[Feat] support TeaCache for Flux2 klein

vllm_omni/diffusion/cache/teacache/backend.py

@@ -48,7 +48,29 @@ def forward_alias(self, *args, **kwargs):
     )
 
 
-CUSTOM_TEACACHE_ENABLERS = {"BagelPipeline": enable_bagel_teacache}
+def enable_flux2_klein_teacache(pipeline: Any, config: DiffusionCacheConfig) -> None:
+    """
+    Enable TeaCache for Flux2 Klein model.
+    """
+    teacache_config = TeaCacheConfig(
+        transformer_type="Flux2Klein",
+        rel_l1_thresh=config.rel_l1_thresh,
+        coefficients=config.coefficients,
+    )
+    transformer = pipeline.transformer
+
+    apply_teacache_hook(transformer, teacache_config)
+
+    logger.info(
+        f"TeaCache applied with rel_l1_thresh={teacache_config.rel_l1_thresh}, "
+        f"transformer_class={teacache_config.transformer_type}"
+    )
+
+
+CUSTOM_TEACACHE_ENABLERS = {
+    "BagelPipeline": enable_bagel_teacache,
+    "Flux2KleinPipeline": enable_flux2_klein_teacache,
+}
 
 
 class TeaCacheBackend(CacheBackend):

vllm_omni/diffusion/cache/teacache/config.py

@@ -15,6 +15,15 @@
         -3.82021401e00,
         2.64230861e-01,
     ],
+    # Flux2 Klein transformer coefficients
+    # Same as FLUX.1 (similar dual-stream architecture)
+    "Flux2Klein": [
+        4.98651651e02,
+        -2.83781631e02,
+        5.58554382e01,
+        -3.82021401e00,
+        2.64230861e-01,
+    ],
     # Qwen-Image transformer coefficients from ComfyUI-TeaCache
     # Tuned specifically for Qwen's dual-stream transformer architecture
     # Used for all Qwen-Image Family pipelines, in general

vllm_omni/diffusion/cache/teacache/extractors.py

@@ -566,6 +566,149 @@ def postprocess(h):
     )
 
 
+def extract_flux2_klein_context(
+    module,
+    hidden_states: torch.Tensor,
+    encoder_hidden_states: torch.Tensor = None,
+    timestep: torch.LongTensor = None,
+    img_ids: torch.Tensor = None,
+    txt_ids: torch.Tensor = None,
+    guidance: torch.Tensor = None,
+    joint_attention_kwargs: dict[str, Any] | None = None,
+    **kwargs: Any,
+) -> CacheContext:
+    """
+    Extract cache context for Flux2Klein model.
+
+    Only caches transformer_blocks output. single_transformer_blocks is always executed.
+
+    Args:
+        module: Flux2Transformer2DModel instance
+        hidden_states: Input image hidden states tensor
+        encoder_hidden_states: Input text hidden states tensor
+        timestep: Current diffusion timestep
+        img_ids: Image position IDs for RoPE
+        txt_ids: Text position IDs for RoPE
+        guidance: Optional guidance scale for CFG
+        joint_attention_kwargs: Additional attention kwargs
+
+    Returns:
+        CacheContext with all information needed for generic caching
+    """
+    from diffusers.models.modeling_outputs import Transformer2DModelOutput
+
+    if not hasattr(module, "transformer_blocks") or len(module.transformer_blocks) == 0:
+        raise ValueError("Module must have transformer_blocks")
+
+    dtype = hidden_states.dtype
+
+    num_txt_tokens = encoder_hidden_states.shape[1]
+
+    timestep = timestep.to(dtype=dtype) * 1000
+    if guidance is not None:
+        guidance = guidance.to(dtype=dtype) * 1000
+
+    temb = module.time_guidance_embed(timestep, guidance)
+
+    double_stream_mod_img = module.double_stream_modulation_img(temb)
+    double_stream_mod_txt = module.double_stream_modulation_txt(temb)
+    single_stream_mod = module.single_stream_modulation(temb)[0]
+
+    hidden_states = module.x_embedder(hidden_states)
+    encoder_hidden_states = module.context_embedder(encoder_hidden_states)
+
+    if img_ids.ndim == 3:
+        img_ids = img_ids[0]
+    if txt_ids.ndim == 3:
+        txt_ids = txt_ids[0]
+
+    image_rotary_emb = module.pos_embed(img_ids)
+    text_rotary_emb = module.pos_embed(txt_ids)
+    concat_rotary_emb = (
+        torch.cat([text_rotary_emb[0], image_rotary_emb[0]], dim=0),
+        torch.cat([text_rotary_emb[1], image_rotary_emb[1]], dim=0),
+    )
+
+    block = module.transformer_blocks[0]
+
+    norm_hidden_states = block.norm1(hidden_states)
+    norm_hidden_states = (1 + double_stream_mod_img[0][0]) * norm_hidden_states + double_stream_mod_img[0][1]
+
+    modulated_input = norm_hidden_states
+
+    def run_flux2_transformer_blocks():
+        h = hidden_states
+        c = encoder_hidden_states
+        for block in module.transformer_blocks:
+            c, h = block(
+                hidden_states=h,
+                encoder_hidden_states=c,
+                temb_mod_params_img=double_stream_mod_img,
+                temb_mod_params_txt=double_stream_mod_txt,
+                image_rotary_emb=concat_rotary_emb,
+                joint_attention_kwargs=joint_attention_kwargs,
+            )
+        return (h, c)
+
+    def run_flux2_single_transformer_blocks(c, h):
+        h_concat = torch.cat([c, h], dim=1)
+        for block in module.single_transformer_blocks:
+            h_concat = block(
+                hidden_states=h_concat,
+                encoder_hidden_states=None,
+                temb_mod_params=single_stream_mod,
+                image_rotary_emb=concat_rotary_emb,
+                joint_attention_kwargs=joint_attention_kwargs,
+            )
+        return h_concat[:, num_txt_tokens:, ...]
+
+    def run_flux2_full_transformer_with_single(ori_h, ori_c):
+        h = ori_h
+        c = ori_c
+        for block in module.transformer_blocks:
+            c, h = block(
+                hidden_states=h,
+                encoder_hidden_states=c,
+                temb_mod_params_img=double_stream_mod_img,
+                temb_mod_params_txt=double_stream_mod_txt,
+                image_rotary_emb=concat_rotary_emb,
+                joint_attention_kwargs=joint_attention_kwargs,
+            )
+        h_concat = torch.cat([c, h], dim=1)
+        for block in module.single_transformer_blocks:
+            h_concat = block(
+                hidden_states=h_concat,
+                encoder_hidden_states=None,
+                temb_mod_params=single_stream_mod,
+                image_rotary_emb=concat_rotary_emb,
+                joint_attention_kwargs=joint_attention_kwargs,
+            )
+        final_hidden_states = h_concat[:, num_txt_tokens:, ...]
+        return final_hidden_states, c
+
+    return_dict = kwargs.get("return_dict", True)
+
+    def postprocess(h):
+        h = module.norm_out(h, temb)
+        h = module.proj_out(h)
+        if not return_dict:
+            return (h,)
+        return Transformer2DModelOutput(sample=h)
+
+    return CacheContext(
+        modulated_input=modulated_input,
+        hidden_states=hidden_states,
+        encoder_hidden_states=encoder_hidden_states,
+        temb=temb,
+        run_transformer_blocks=run_flux2_transformer_blocks,
+        postprocess=postprocess,
+        extra_states={
+            "run_flux2_single_transformer_blocks": run_flux2_single_transformer_blocks,
+            "run_flux2_full_transformer_with_single": run_flux2_full_transformer_with_single,
+        },
+    )
+
+
 # Registry for model-specific extractors
 # Key: Transformer class name
 # Value: extractor function with signature (module, *args, **kwargs) -> CacheContext
@@ -576,6 +719,7 @@ def postprocess(h):
     "QwenImageTransformer2DModel": extract_qwen_context,
     "Bagel": extract_bagel_context,
     "ZImageTransformer2DModel": extract_zimage_context,
+    "Flux2Klein": extract_flux2_klein_context,
     # Future models:
     # "FluxTransformer2DModel": extract_flux_context,
     # "CogVideoXTransformer3DModel": extract_cogvideox_context,

vllm_omni/diffusion/cache/teacache/hook.py

@@ -157,20 +157,26 @@ def new_forward(self, module: torch.nn.Module, *args: Any, **kwargs: Any) -> Any
                 ctx.encoder_hidden_states.clone() if ctx.encoder_hidden_states is not None else None
             )
 
-            # Run transformer blocks using model-specific callable
-            outputs = ctx.run_transformer_blocks()
-
-            # Update context with outputs
-            ctx.hidden_states = outputs[0]
-            if len(outputs) > 1 and ctx.encoder_hidden_states is not None:
-                ctx.encoder_hidden_states = outputs[1]
-
-            # Cache residuals for next timestep
-            state.previous_residual = (ctx.hidden_states - ori_hidden_states).detach()
-            if ori_encoder_hidden_states is not None:
-                state.previous_residual_encoder = (ctx.encoder_hidden_states - ori_encoder_hidden_states).detach()
-
-            output = ctx.hidden_states
+            # Handle models with additional blocks (e.g., Flux2 single_transformer_blocks)
+            if getattr(ctx, "extra_states", None) and "run_flux2_full_transformer_with_single" in ctx.extra_states:
+                run_full = ctx.extra_states["run_flux2_full_transformer_with_single"]
+                ctx.hidden_states, ctx.encoder_hidden_states = run_full(ori_hidden_states, ori_encoder_hidden_states)
+                output = ctx.hidden_states
+                state.previous_residual = (ctx.hidden_states - ori_hidden_states).detach()
+            else:
+                # Run transformer blocks using model-specific callable
+                outputs = ctx.run_transformer_blocks()
+                # Update context with outputs
+                ctx.hidden_states = outputs[0]
+                if len(outputs) > 1 and ctx.encoder_hidden_states is not None:
+                    ctx.encoder_hidden_states = outputs[1]
+
+                output = ctx.hidden_states
+
+                # Cache residuals for next timestep
+                state.previous_residual = (ctx.hidden_states - ori_hidden_states).detach()
+                if ori_encoder_hidden_states is not None:
+                    state.previous_residual_encoder = (ctx.encoder_hidden_states - ori_encoder_hidden_states).detach()
 
         # Update state
         state.previous_modulated_input = ctx.modulated_input.detach()