feat: add support for teacache

inference.py

@@ -153,6 +153,17 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help="Enable group offload."
     )
+    parser.add_argument(
+        "--enable_teacache",
+        action="store_true",
+        help="Enable teacache to speed up inference."
+    )
+    parser.add_argument(
+        "--rel_l1_thresh",
+        type=float,
+        default=0.05,
+        help="Relative L1 threshold for teacache."
+    )
     return parser.parse_args()
 
 def load_pipeline(args: argparse.Namespace, accelerator: Accelerator, weight_dtype: torch.dtype) -> OmniGen2Pipeline:
@@ -161,6 +172,7 @@ def load_pipeline(args: argparse.Namespace, accelerator: Accelerator, weight_dty
         torch_dtype=weight_dtype,
         trust_remote_code=True,
     )
+
     if args.transformer_path:
         print(f"Transformer weights loaded from {args.transformer_path}")
         pipeline.transformer = OmniGen2Transformer2DModel.from_pretrained(
@@ -178,6 +190,10 @@ def load_pipeline(args: argparse.Namespace, accelerator: Accelerator, weight_dty
         print(f"LoRA weights loaded from {args.transformer_lora_path}")
         pipeline.load_lora_weights(args.transformer_lora_path)
 
+    if args.enable_teacache:
+        pipeline.transformer.enable_teacache = True
+        pipeline.transformer.rel_l1_thresh = args.rel_l1_thresh
+
     if args.scheduler == "dpmsolver++":
         from omnigen2.schedulers.scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
         scheduler = DPMSolverMultistepScheduler(

omnigen2/models/transformers/transformer_omnigen2.py

@@ -2,6 +2,8 @@
 import itertools
 from typing import Any, Dict, List, Optional, Tuple, Union
 
+import numpy as np
+
 import torch
 import torch.nn as nn
 
@@ -20,6 +22,7 @@
 from .block_lumina2 import LuminaLayerNormContinuous, LuminaRMSNormZero, LuminaFeedForward, Lumina2CombinedTimestepCaptionEmbedding
 
 from ...utils.import_utils import is_triton_available, is_flash_attn_available
+from ...utils.teacache_util import TeaCacheParams
 
 if is_triton_available():
     from ...ops.triton.layer_norm import RMSNorm
@@ -28,7 +31,6 @@
 
 logger = logging.get_logger(__name__)
 
-
 class OmniGen2TransformerBlock(nn.Module):
     """
     Transformer block for OmniGen2 model.
@@ -342,6 +344,14 @@ def __init__(
 
         self.initialize_weights()
 
+        # TeaCache settings
+        self.enable_teacache = False
+        self.rel_l1_thresh = 0.05
+        self.teacache_params = TeaCacheParams()
+
+        coefficients = [-5.48259225, 11.48772289, -4.47407401, 2.47730926, -0.03316487]
+        self.rescale_func = np.poly1d(coefficients)
+
     def initialize_weights(self) -> None:
         """
         Initialize the weights of the model.
@@ -589,13 +599,46 @@ def forward(
 
         hidden_states = joint_hidden_states
 
-        for layer_idx, layer in enumerate(self.layers):
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                hidden_states = self._gradient_checkpointing_func(
-                    layer, hidden_states, attention_mask, rotary_emb, temb
+        if self.enable_teacache:
+            teacache_hidden_states = hidden_states.clone()
+            teacache_temb = temb.clone()
+            modulated_inp, _, _, _ = self.layers[0].norm1(teacache_hidden_states, teacache_temb)
+            if self.teacache_params.is_first_or_last_step:
+                should_calc = True
+                self.teacache_params.accumulated_rel_l1_distance = 0
+            else:
+                self.teacache_params.accumulated_rel_l1_distance += self.rescale_func(
+                    ((modulated_inp - self.teacache_params.previous_modulated_inp).abs().mean() \
+                        / self.teacache_params.previous_modulated_inp.abs().mean()).cpu().item()
                 )
+                if self.teacache_params.accumulated_rel_l1_distance < self.rel_l1_thresh:
+                    should_calc = False
+                else:
+                    should_calc = True
+                    self.teacache_params.accumulated_rel_l1_distance = 0
+            self.teacache_params.previous_modulated_inp = modulated_inp
+
+        if self.enable_teacache:
+            if not should_calc:
+                hidden_states += self.teacache_params.previous_residual
             else:
-                hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb)
+                ori_hidden_states = hidden_states.clone()
+                for layer_idx, layer in enumerate(self.layers):
+                    if torch.is_grad_enabled() and self.gradient_checkpointing:
+                        hidden_states = self._gradient_checkpointing_func(
+                            layer, hidden_states, attention_mask, rotary_emb, temb
+                        )
+                    else:
+                        hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb)
+                self.teacache_params.previous_residual = hidden_states - ori_hidden_states
+        else:
+            for layer_idx, layer in enumerate(self.layers):
+                if torch.is_grad_enabled() and self.gradient_checkpointing:
+                    hidden_states = self._gradient_checkpointing_func(
+                        layer, hidden_states, attention_mask, rotary_emb, temb
+                    )
+                else:
+                    hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb)
 
         # 4. Output norm & projection
         hidden_states = self.norm_out(hidden_states, temb)
@@ -614,4 +657,4 @@ def forward(
 
         if not return_dict:
             return output
-        return Transformer2DModelOutput(sample=output)
+        return Transformer2DModelOutput(sample=output)

omnigen2/pipelines/omnigen2/pipeline_omnigen2.py

@@ -46,8 +46,12 @@
 from diffusers.utils import BaseOutput
 
 from omnigen2.pipelines.image_processor import OmniGen2ImageProcessor
+
+from omnigen2.utils.teacache_util import TeaCacheParams
+
 from ..lora_pipeline import OmniGen2LoraLoaderMixin
 
+
 if is_torch_xla_available():
     import torch_xla.core.xla_model as xm
 
@@ -632,8 +636,19 @@ def processing(
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
         self._num_timesteps = len(timesteps)
 
+        # Use different TeaCacheParams for different conditions
+        if self.transformer.enable_teacache:
+            teacache_params = TeaCacheParams()
+            teacache_params_uncond = TeaCacheParams()
+            teacache_params_ref = TeaCacheParams()
+
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
+
+                if self.transformer.enable_teacache:
+                    teacache_params.is_first_or_last_step = i == 0 or i == len(timesteps) - 1
+                    self.transformer.teacache_params = teacache_params
+
                 model_pred = self.predict(
                     t=t,
                     latents=latents,
@@ -646,6 +661,11 @@ def processing(
                 image_guidance_scale = self.image_guidance_scale if self.cfg_range[0] <= i / len(timesteps) <= self.cfg_range[1] else 1.0
 
                 if text_guidance_scale > 1.0 and image_guidance_scale > 1.0:
+
+                    if self.transformer.enable_teacache:
+                        teacache_params_ref.is_first_or_last_step = i == 0 or i == len(timesteps) - 1
+                        self.transformer.teacache_params = teacache_params_ref
+
                     model_pred_ref = self.predict(
                         t=t,
                         latents=latents,
@@ -656,6 +676,11 @@ def processing(
                     )
 
                     if image_guidance_scale != 1:
+
+                        if self.transformer.enable_teacache:
+                            teacache_params_uncond.is_first_or_last_step = i == 0 or i == len(timesteps) - 1
+                            self.transformer.teacache_params = teacache_params_uncond
+
                         model_pred_uncond = self.predict(
                             t=t,
                             latents=latents,
@@ -670,6 +695,11 @@ def processing(
                     model_pred = model_pred_uncond + image_guidance_scale * (model_pred_ref - model_pred_uncond) + \
                     text_guidance_scale * (model_pred - model_pred_ref)
                 elif text_guidance_scale > 1.0:
+
+                    if self.transformer.enable_teacache:
+                        teacache_params_uncond.is_first_or_last_step = i == 0 or i == len(timesteps) - 1
+                        self.transformer.teacache_params = teacache_params_uncond
+
                     model_pred_uncond = self.predict(
                         t=t,
                         latents=latents,

omnigen2/utils/teacache_util.py

@@ -0,0 +1,43 @@
+"""
+Utility for TeaCache
+
+Copyright 2025 BAAI, The OmniGen2 Team and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+
+@dataclass
+class TeaCacheParams:
+    """
+    TeaCache parameters for `OmniGen2Transformer2DModel`
+    See https://github.com/ali-vilab/TeaCache/ for a more comprehensive understanding
+
+    Args:
+        previous_residual (Optional[torch.Tensor]):
+            The tensor difference between the output and the input of the transformer layers from the previous timestep.
+        previous_modulated_inp (Optional[torch.Tensor]):
+            The modulated input from the previous timestep used to indicate the change of the transformer layer's output.
+        accumulated_rel_l1_distance (float):
+            The accumulated relative L1 distance.
+        is_first_or_last_step (bool):
+            Whether the current timestep is the first or last step.
+    """
+    previous_residual: Optional[torch.Tensor] = None
+    previous_modulated_inp: Optional[torch.Tensor] = None
+    accumulated_rel_l1_distance: float = 0
+    is_first_or_last_step: bool = False